Reviewerly/exHarmony huggingface dataset:
Introduction

This book proposes a novel paradigm for more flexible, adaptive, and user-centric interaction between humans and artificial systems. The proposed approach for this development mainly builds on the theory of Computing with Words and Perceptions, and automated reasoning by analogies. This introductory chapter provides an overview of the motivation that leads to the necessity of developing this research effort, followed by the general objectives that this book tries to reach and the methods followed over the whole duration of this research effort. A summary of the works published by the author in the context of this research project concludes the chapter, giving an anticipation of the content of the rest of the manuscript.
Phenotropic Interaction in Virtual Assistants

Virtual assistants serve as popular conversation-based interfaces, enabling hands-free interactions and pervasive control of home automation systems. To enhance customization, virtual assistants often offer the ability to program customized behaviors. These skills are based on IF This Then That (IFTTT) rules, which dictate the execution of tasks based on observed events or commands. However, virtual assistants often struggle with accuracy in understanding and retrieving information in these personalized rule scenarios where specific wording must be replicated exactly. This chapter explores techniques to improve virtual assistant interactions by implementing phenotropic interaction design principles. By applying techniques from previous chapters, this chapter demonstrates an automated approach to extending IFTTT rules and applying them in similar situations. This technique aims to create a more natural interaction between users and virtual assistants by enabling reasoning and understanding of queries beyond exact matching. The chapter introduces the Flexible Virtual Assistant prototype, which embodies these enhancements and demonstrates the potential of phenotropic human–computer interaction based on a conversational interface. This prototype is used as the basis for a user experiment that highlights the benefits of phenotropic design principles in enhancing naturalness, robustness, and user satisfaction with an interactive system.
A survey of path planning of industrial robots based on rapidly exploring random trees

Path planning is an essential part of robot intelligence. In this paper, we summarize the characteristics of path planning of industrial robots. And owing to the probabilistic completeness, we review the rapidly-exploring random tree (RRT) algorithm which is widely used in the path planning of industrial robots. Aiming at the shortcomings of the RRT algorithm, this paper investigates the RRT algorithm for path planning of industrial robots in order to improve its intelligence. Finally, the future development direction of the RRT algorithm for path planning of industrial robots is proposed. The study results have particularly guided significance for the development of the path planning of industrial robots and the applicability and practicability of the RRT algorithm.
Cognitive and Perceptual Computing

This chapter focuses on the essential components of phenotropic interaction, a bio-inspired and human-centered approach to diverse system interaction, as outlined in the phenotropic interaction framework. To produce a more natural interactive experience for humans, it is fundamental to build this on fundamental aspects of human conversation, such as handling the imprecision of language, understanding semantic information exchange, reasoning, and providing explanations. To reach these goals, the application of cognitive and perceptual computing techniques is proposed. This chapter introduces the general methods from cognitive computing and perceptual computing that form the building blocks of phenotropic interaction. It covers conversation theory, frameworks for handling word meaning, automated reasoning, trust building, improvement over time based on user feedback, interactive machine learning, and explainability. By incorporating these methods, one can argue that phenotropic interaction can effectively satisfy its design principles. The chapter concludes with a summary underscoring the significance of the presented methods for the phenotropic interaction framework. Overall, this chapter provides insights into the practical application of cognitive computing and perceptual computing techniques, highlighting their role in achieving successful phenotropic interaction and advancing the field of human–system interaction.
BTDNet: A Multi-Modal Approach for Brain Tumor Radiogenomic Classification

Brain tumors pose significant health challenges worldwide, with glioblastoma being one of the most aggressive forms. The accurate determination of the O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status is crucial for personalized treatment strategies. However, traditional methods are labor-intensive and time-consuming. This paper proposes a novel multi-modal approach, BTDNet, that leverages multi-parametric MRI scans, including FLAIR, T1w, T1wCE, and T2 3D volumes, to predict the MGMT promoter methylation status. BTDNet’s main contribution involves addressing two main challenges: the variable volume lengths (i.e., each volume consists of a different number of slices) and the volume-level annotations (i.e., the whole 3D volume is annotated and not the independent slices that it consists of). BTDNet consists of four components: (i) data augmentation (which performs geometric transformations, convex combinations of data pairs, and test-time data augmentation); (ii) 3D analysis (which performs global analysis through a CNN-RNN); (iii) routing (which contains a mask layer that handles variable input feature lengths); and (iv) modality fusion (which effectively enhances data representation, reduces ambiguities, and mitigates data scarcity). The proposed method outperformed state-of-the-art methods in the RSNA-ASNR-MICCAI BraTS 2021 Challenge by at least 3.3% in terms of the F1 score, offering a promising avenue for enhancing brain tumor diagnosis and treatment.
Systematic iterative design of interactive devices for animals

Abstract The numerous systems designed to facilitate animals’ use of computers often are specific to the animals involved, their unique context, and the applications – enrichment among them. Hence, several development methods have arisen in parallel, largely transposed from the human-computer interaction (HCI) domain. In light of that prior work, the paper presents a step-by-step guide for iteratively designing and constructing interactive computers for animals, informed by the rich history of HCI yet applying animal-centred principles, to enrich animal-computer interaction. For each stage in the iterative design (requirements, ideation, prototyping, and testing), the author reflects on real-world experience of building interactive devices for various animals. The paper concludes with overarching considerations vital for future practice of developing interactive computers for animals. Thus, it serves as a valuable reference and information source for researchers designing novel computer systems for animals.
Fusion of Attention-Based Convolution Neural Network and HOG Features for Static Sign Language Recognition

The deaf and hearing-impaired community expresses their emotions, communicates with society, and enhances the interaction between humans and computers using sign language gestures. This work presents a strategy for efficient feature extraction that uses a combination of two different methods that are the convolutional block attention module (CBAM)-based convolutional neural network (CNN) and standard handcrafted histogram of oriented gradients (HOG) feature descriptor. The proposed framework aims to enhance accuracy by extracting meaningful features and resolving issues like rotation, similar hand orientation, etc. The HOG feature extraction technique provides a compact feature representation that signifies meaningful information about sign gestures. The CBAM attention module is incorporated into the structure of CNN to enhance feature learning using spatial and channel attention mechanisms. Then, the final feature vector is formed by concatenating these features. This feature vector is provided to the classification layers to predict static sign gestures. The proposed approach is validated on two publicly available static Massey American Sign Language (ASL) and Indian Sign Language (ISL) databases. The model’s performance is evaluated using precision, recall, F1-score, and accuracy. Our proposed methodology achieved 99.22% and 99.79% accuracy for the ASL and ISL datasets. The acquired results signify the efficiency of the feature fusion and attention mechanism. Our network performed better in accuracy compared to the earlier studies.
Any-to-One Non-Parallel Voice Conversion System Using an Autoregressive Conversion Model and LPCNet Vocoder

We present an any-to-one voice conversion (VC) system, using an autoregressive model and LPCNet vocoder, aimed at enhancing the converted speech in terms of naturalness, intelligibility, and speaker similarity. As the name implies, non-parallel any-to-one voice conversion does not require paired source and target speeches and can be employed for arbitrary speech conversion tasks. Recent advancements in neural-based vocoders, such as WaveNet, have improved the efficiency of speech synthesis. However, in practice, we find that the trajectory of some generated waveforms is not consistently smooth, leading to occasional voice errors. To address this issue, we propose to use an autoregressive (AR) conversion model along with the high-fidelity LPCNet vocoder. This combination not only solves the problems of waveform fluidity but also produces more natural and clear speech, with the added capability of real-time speech generation. To precisely represent the linguistic content of a given utterance, we use speaker-independent PPG features (SI-PPG) computed from an automatic speech recognition (ASR) model trained on a multi-speaker corpus. Next, a conversion model maps the SI-PPG to the acoustic representations used as input features for the LPCNet. The proposed autoregressive structure enables our system to produce the following prediction step outputs from the acoustic features predicted in the previous step. We evaluate the effectiveness of our system by performing any-to-one conversion pairs between native English speakers. Experimental results show that the proposed method outperforms state-of-the-art systems, producing higher speech quality and greater speaker similarity.
Refactoring BZIP2 on the new‐generation sunway supercomputer

Abstract High‐performance computing is progressively assuming a fundamental role in advancing scientific research and engineering domains. However, the ever‐expanding scales of scientific simulations pose challenges for efficient data I/O and storage. The data compression technology has garnered significant attention as a solution to reduce data transmission and storage costs while enhancing performance. In particular, the BZIP2 lossless compression algorithm has been widely used due to its exceptional compression ratio, moderate compression speed, high reliability, and open‐source nature. This paper focuses on the design and realization of a parallelized BZIP2 algorithm tailored for deployment on the New‐Generation Sunway supercomputing platform. By leveraging the unique cache patterns of the New‐Generation Sunway processor, we propose the highly tuned multi‐threading and multi‐node implementations of the BZIP2 applications for different scenarios. Moreover, we also propose the efficient BZIP2 libraries based on the management processing element and computing processing element which support the commonly used high‐level (de)compression interfaces. The test results indicate that the our multi‐threading implementation achieves maximum speedup of 23.09 (8.57) in decompression(compression) compared to the sequential implementation. Furthermore, the multi‐node implementation achieves 50.81% (26.35%) parallel efficiency and peak performance of 16.6 GB/s (52.8 GB/s) for compression(decompression) when scaling up to 2048 processes.
A Survey of Deep Active Learning for Foundation Models

Active learning (AL) is an effective sample selection approach that annotates only a subset of the training data to address the challenge of data annotation, and deep learning (DL) is data-intensive and reliant on abundant training data. Deep active learning (DeepAL) benefits from the integration of AL and DL, offering an efficient solution that balances model performance and annotation costs. The importance of DeepAL has been increasingly recognized with the emergence of large foundation models that depend heavily on substantial computational resources and extensive training data. This survey endeavors to provide a comprehensive overview of DeepAL. Specifically, we first analyze and summarize various sample query strategies, data querying considerations, model training paradigms, and real-world applications of DeepAL. In addition, we discuss the challenges that arise in the era of foundation models and propose potential directions for future AL research. The survey aims to bridge a gap in the existing literature by organizing and summarizing current approaches, offering insights into DeepAL and highlighting the necessity of developing specialized DeepAL techniques tailored to foundation models. By critically examining the current state of DeepAL, this survey contributes to a more profound understanding of the field and serves as a guide for researchers and practitioners interested in DeepAL techniques.
Forest Single-Frame Remote Sensing Image Super-Resolution Using GANs

Generative Adversarial Networks (GANs) possess remarkable fitting capabilities and play a crucial role in the field of computer vision. Super-resolution restoration is the process of converting low-resolution images into high-resolution ones, providing more detail and information. This is of paramount importance for monitoring and managing forest resources, enabling the surveillance of vegetation, wildlife, and potential disruptive factors in forest ecosystems. In this study, we propose an image super-resolution model based on Generative Adversarial Networks. We incorporate Multi-Scale Residual Blocks (MSRB) as the core feature extraction component to obtain image features at different scales, enhancing feature extraction capabilities. We introduce a novel attention mechanism, GAM Attention, which is added to the VGG network to capture more accurate feature dependencies in both spatial and channel domains. We also employ the adaptive activation function Meta ACONC and Ghost convolution to optimize training efficiency and reduce network parameters. Our model is trained on the DIV2K and LOVEDA datasets, and experimental results indicate improvements in evaluation metrics compared to SRGAN, with a PSNR increase of 0.709/2.213 dB, SSIM increase of 0.032/0.142, and LPIPS reduction of 0.03/0.013. The model performs on par with Real-ESRGAN but offers significantly improved speed. Our model efficiently restores single-frame remote sensing images of forests while achieving results comparable to state-of-the-art methods. It overcomes issues related to image distortion and texture details, producing forest remote sensing images that closely resemble high-resolution real images and align more closely with human perception. This research has significant implications on a global scale for ecological conservation, resource management, climate change research, risk management, and decision-making processes.
Tensor Networks for Interpretable and Efficient Quantum-Inspired Machine Learning

It is a critical challenge to simultaneously achieve high interpretability and high efficiency with the current schemes of deep machine learning (ML). The tensor network (TN), a well-established mathematical tool originating from quantum mechanics, has shown its unique advantages in developing efficient “white-box” ML schemes. Here, we provide a brief review of the inspiring progress in TN-based ML. On the one hand, the interpretability of TN ML can be accommodated by a solid theoretical foundation based on quantum information and many-body physics. On the other hand, high efficiency can be obtained from powerful TN representations and the advanced computational techniques developed in quantum many-body physics. Keeping pace with the rapid development of quantum computers, TNs are expected to produce novel schemes runnable on quantum hardware in the direction of “quantum artificial intelligence” in the near future.
Detection of Breast Cancer in Mammography Using Pretrained Convolutional Neural Networks with Fine-Tuning

Breast cancer is a major health concern for women, especially in Latin America where the incidence and mortality rates are high. Mammography is an essential diagnostic tool in detecting breast cancer, but interpreting mammogram images can be challenging due to their complex nature. To assist radiologists in identifying abnormalities in mammogram images, deep learning algorithms, specifically deep convolutional neural networks (DCNNs), are being employed. This chapter explores the effectiveness of several pretrained DCNN models, such as ResNet-50, ResNet152, VGG19, and EfficientB7, in classifying mammogram images. To ensure reliable results, the Mini-MIAS and CBIS-DDSM datasets, consisting of 334 and 2620 scanned film mammography images, respectively, were selected for this study. The images were categorized into binary classification and multiclassification groups based on the severity of the lesion. For both datasets, the same preprocessing approach was used to enhance image quality. This involved normalizing the images and applying contrast limited adaptive histogram equalization (CLAHE). The efficacy of the preprocessing techniques was evaluated by comparing the performance of the models on the entire dataset and just the normalized images. Four different stages were tested using images from both datasets, and the performance of each model was evaluated using five metrics, namely, accuracy, precision, recall, F1-score, and area under the ROC curve (AUC).
Multi-Sensor Data Fusion Method Based on Self-Attention Mechanism

In 3D reconstruction tasks, single-sensor data fusion based on deep learning is limited by the integrity and accuracy of the data, which reduces the accuracy and reliability of the fusion results. To address this issue, this study proposes a multi-sensor data fusion method based on a self-attention mechanism. A multi-sensor data fusion model for acquiring multi-source and multi-modal data is constructed, with the core component being a convolutional neural network with self-attention (CNN-SA), which employs CNNs to process multi-source and multi-modal data by extracting their features. Additionally, it introduces an SA mechanism to weigh and sum the features of different modalities, adaptively focusing on the importance of different modal data. This enables mutual support, complementarity, and correction among the multi-modal data. Experimental results demonstrate that the accuracy of the CNN-SA network is improved by 72.6%, surpassing the improvements of 29.9% for CNN-CBAM, 23.6% for CNN, and 11.4% for CNN-LSTM, exhibiting enhanced generalization capability, accuracy, and robustness. The proposed approach will contribute to the effectiveness of multi-sensor data fusion processing.
An optical flow-based terrain extraction framework of VHR optical satellite stereo images

Stereo vision has attracted much interest in computer vision and remote sensing. Large-scale terrain extraction from optical satellites has achieved various applications due to the continuous development of optical satellite technology in the last forty years. This study analyzes the literature sources published in the last decade on stereo vision, optical flow, and remote sensing and finds that applications of the optical flow method in very high resolution (VHR) optical satellite stereo matching are inadequate. Therefore, it proposes a comprehensive optical flow-based terrain extraction framework from optical satellite stereo images. Experiments are conducted using stereo images from four different optical satellites, and the results are compared to the pertinent algorithms of ENVI, ERDAS, and ASP software. The qualitative and quantitative analysis demonstrates that the proposed method has significantly superior matching effect and robustness with a higher accuracy level compared with other approaches, especially for mountainous areas. In addition, the root mean square errors (RMSEs) of elevation on four datasets are 1.228 m, 2.159 m, 3.150 m, and 5.763 m, indicating the feasibility of the proposed method for terrain extraction from optical satellite stereo images.
Camera Independent Motion Deblurring in Videos Using Machine Learning

In this paper, we will be looking at our efforts to find a novel solution for motion deblurring in videos. In addition, our solution has the requirement of being camera-independent. This means that the solution is fully implemented in software and is not aware of any of the characteristics of the camera. We found a solution by implementing a Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) hybrid model. Our CNN-LSTM is able to deblur video without any knowledge of the camera hardware. This allows it to be implemented on any system that allows the camera to be swapped out with any camera model with any physical characteristics.
Short digital-competence test based on DigComp2.1: Does digital competence support research competence in undergraduate students?

Abstract This article presents a ten-item short scale for measuring digital competence. The scale is based on the Digital Competence Framework for Citizens, DigComp2.1 (Carretero et al., 2017). For our surveys, we used five items from the DigCompSat study (Clifford et al., 2020) and created five new ones to address the competence areas defined by DigCom2.1. We tested the scale on a sample of 1416 students from four countries (Germany, Lithuania, Austria, UK), using the questionnaire in two languages (English, German). The scale proved to be reliable (Cronbach’s α of 0.87 and McDonald’s ω t of 0.88) and valid (construct and content validity). Using the scale, we replicated findings from previous studies on differences in digital literacy by gender, study subject, and level of study. Despite the inhomogeneous structure of items from five different competence areas (according to DigComp2.1) and of two different types (specific, general), the scale does not seem to be multifactorial. A detailed analysis of digital competence and undergraduate research in the context of the pandemic shows: digital competence seems to support research competence and may even support inclusion.