Reviewerly/exHarmony huggingface dataset:
TapType: Ten-finger text entry on everyday surfaces via Bayesian inference

Despite the advent of touchscreens, typing on physical keyboards remains most efficient for entering text, because users can leverage all fingers across a full-size keyboard for convenient typing. As users increasingly type on the go, text input on mobile and wearable devices has had to compromise on full-size typing. In this paper, we present TapType, a mobile text entry system for full-size typing on passive surfaces—without an actual keyboard. From the inertial sensors inside a band on either wrist, TapType decodes and relates surface taps to a traditional QWERTY keyboard layout. The key novelty of our method is to predict the most likely character sequences by fusing the finger probabilities from our Bayesian neural network classifier with the characters’ prior probabilities from an n-gram language model. In our online evaluation, participants on average typed 19 words per minute with a character error rate of 0.6% after 30 minutes of training. Expert typists thereby consistently achieved more than 25 WPM at a similar error rate. We demonstrate applications of TapType in mobile use around smartphones and tablets, as a complement to interaction in situated Mixed Reality outside visual control, and as an eyes-free mobile text input method using an audio feedback-only interface.
Trade facilitation, institutions, and sustainable economic growth: Empirical evidence from Sub‐Saharan Africa

Abstract This paper examines the direct and interactive impacts of trade facilitation and institutions on sustainable economic growth (SEG) for 41 Sub‐Saharan African economies from 2005 to 2019. The study explores the components of trade facilitation focusing on costs, documents and days (exports and imports) with the aggregated index (TFindex) and institutions, including voice and accountability, government effectiveness, regulatory quality, rule of law, political stability and violence, and control of corruption, used on GDP per capita growth. The empirical evidence relies on the two‐step dynamic‐system generalized method of moments estimator, accounting for endogeneity, simultaneity, measurement error and reverse causality concerns. The study finds that facilitating trade procedures is a viable option for achieving SEG in the Sub‐Saharan Africa region. In addition, institutions driven by government effectiveness, political stability, and regulatory quality are boosters to SEG in the region. Lastly, the interactive impacts of trade facilitation and institutions positively predict the SEG. The study suggests that achieving efficient trading procedures and a sturdy institutional framework will enhance full exploration of the growth potential in SEG.
A hyperchaotic image encryption scheme based on the triple dislocation of the Liu and Lorenz system

This paper proposes a novel color image encryption scheme of Liu Lorenz-XOR Zigzag Arnold (LL-XZA) encryption algorithm. Firstly, to prove that the three-dimensional(3D) Liu system and the four-dimensional(4D) Lorenz system are in a hyperchaotic state, we performed the visual analysis of their key parameters, such as the Lyapunov exponent, bifurcation diagram, attractor, Poincare cross-section diagram and initial value sensitivity. Then, we adopt the 3D Liu hyperchaotic system as the primary hyperchaotic mapping scheme of the color image. The 4D Lorenz hyperchaotic system is discretized by the Runge–Kutta method to generate a pseudorandom number sequence of the control parameter with stronger randomicity. Furthermore, XOR, 3D Zigzag transformation, and 3D Arnold transformation are reset to construct the encryption scheme. Finally, we explored the security performance of the proposed scheme with the RGB grayscale histogram, pixel correlation, information entropy by color image simulation. The results verified that the LL-XZA encryption algorithm can significantly improve the color image security performance and has superiorities in stable encryption effect, visual security, and robustness.
Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains

Participatory design (PD) for Artificially Intelligent (AI) systems has gained in popularity in recent years across multiple application domains, both within the private and public sectors. PD methods broadly enable stakeholders of diverse backgrounds to inform new use cases for AI and the design of AI-based technologies that directly impact people's lives. Such participation can be vital for mitigating adverse implications of AI on society that are becoming increasingly apparent and pursuing more positive impact, especially to vulnerable populations. This panel brings together researchers who have, or are, conducting participatory design of AI systems across diverse subject areas. The goal of the panel is to elucidate similarities and differences, as well as successes and challenges, in how PD methods can be applied to Artificially Intelligent systems in practical and meaningful ways. The panel serves as an opportunity for the HCI research community to collectively reflect on opportunities for PD of AI to facilitate collaboration amongst stakeholders, as well as persistent challenges to participatory AI design.
Virtual Reality and Embodiment in Multimodal Meaning Making

Immersive virtual reality (VR) technology is becoming widespread in education, yet research of VR technologies for students’ multimodal communication is an emerging area of research in writing and literacies scholarship. Likewise, the significance of new ways of embodied meaning making in VR environments is undertheorized—a gap that requires attention given the potential for broadened use of the sensorium in multimodal language and literacy learning. This classroom research investigated multimodal composition using the virtual paint program Google Tilt Brush™ with 47 elementary school students (ages 10–11 years) using a head-mounted display and motion sensors. Multimodal analysis of video, screen capture, and think-aloud data attended to sensory-motor affordances and constraints for embodiment. Modal constraints were the immateriality of the virtual text, virtual disembodiment, and somatosensory mismatch between the virtual and physical worlds. Potentials for new forms of embodied multimodal representation in VR involved extensive bodily, haptic, and locomotive movement. The findings are significant given that research of embodied cognition points to sensorimotor action as the basis for language and communication.
HMSM-Net: Hierarchical multi-scale matching network for disparity estimation of high-resolution satellite stereo images

Disparity estimation of satellite stereo images is an essential and challenging task in photogrammetry and remote sensing. Recent researches have greatly promoted the development of disparity estimation algorithms by using CNN (Convolutional Neural Networks) based deep learning techniques. However, it is still difficult to handle intractable regions that are mainly caused by occlusions, disparity discontinuities, texture-less areas, and repetitive patterns. Besides, the lack of training datasets for satellite stereo images remains another major issue that blocks the usage of CNN techniques due to the difficulty of obtaining ground-truth disparities. In this paper, we propose an end-to-end disparity learning model, termed hierarchical multi-scale matching network (HMSM-Net), for the disparity estimation of high-resolution satellite stereo images. First, multi-scale cost volumes are constructed by using pyramidal features that capture spatial information of multiple levels, which learn correspondences at multiple scales and enable HMSM-Net to be more robust in intractable regions. Second, stereo matching is executed in a hierarchical coarse-to-fine manner by applying supervision to each scale, which allows a lower scale to act as prior knowledge and guides a higher scale to attain finer matching results. Third, a refinement module that incorporates the intensity and gradient information of the input left image is designed to regress a detailed full-resolution disparity map for local structure preservation. For network training and testing, a dense stereo matching dataset is created and published by using GaoFen-7 satellite stereo images. Finally, the proposed network is evaluated on the Urban Semantic 3D and GaoFen-7 datasets. Experimental results demonstrate that HMSM-Net achieves superior accuracy compared with state-of-the-art methods, and the improvement on intractable regions is noteworthy. Additionally, results and comparisons of different methods on the GaoFen-7 dataset show that it can severs as a challenging benchmark for performance assessment of methods applied to disparity estimation of satellite stereo images. The source codes and evaluation dataset are made publicly available at https://github.com/Sheng029/HMSM-Net.
Fusion of Lineament Factor (LF) Map Analysis and Multifractal Technique for Massive Sulfide Copper Exploration: The Sahlabad Area, East Iran

Fault systems are characteristically one of the main factors controlling massive sulfide mineralization. The main objective of this study was to investigate the relationship between fault systems and host lithology with massive sulfide copper mineralization in the Sahlabad area, South Khorasan province, east of Iran. Subsequently, the rose diagram analysis, Fry analysis, lineament factor (LF) map analysis and multifractal technique were implemented for geological and geophysical data. Airborne geophysical analysis (aeromagnetometric data) was executed to determine the presence of intrusive and extrusive masses associated with structural systems. Accordingly, the relationship between the formation boundaries and the fault system was understood. Results indicate that the NW-SE fault systems are controlling the lithology of the host rock for copper mineralization in the Sahlabad area. Hence, the NW-SE fault systems are consistent with the main trend of lithological units related to massive sulfide copper mineralization in the area. Additionally, the distance of copper deposits, mines and indices in the Sahlabad area with fault systems was calculated and interpreted. Fieldwork results confirm that the NW-SE fault systems are entirely matched with several massive sulfide copper mineralizations in the area. This study demonstrates that the fusion of lineament factor (LF) map analysis and multifractal technique is a valuable and inexpensive approach for exploring massive sulfide mineralization in metallogenic provinces.
Inverse design of spontaneous parametric downconversion for generation of high-dimensional qudits

Spontaneous parametric downconversion (SPDC) in quantum optics is an invaluable resource for the realization of high-dimensional qudits with spatial modes of light. One of the main open challenges is how to directly generate a desirable qudit state in the SPDC process. This problem can be addressed through advanced computational learning methods; however, due to difficulties in modeling the SPDC process by a fully differentiable algorithm, progress has been limited. Here, we overcome these limitations and introduce a physically constrained and differentiable model, validated against experimental results for shaped pump beams and structured crystals, capable of learning the relevant interaction parameters in the process. We avoid any restrictions induced by the stochastic nature of our physical model and integrate the dynamic equations governing the evolution under the SPDC Hamiltonian. We solve the inverse problem of designing a nonlinear quantum optical system that achieves the desired quantum state of downconverted photon pairs. The desired states are defined using either the second-order correlations between different spatial modes or by specifying the required density matrix. By learning nonlinear photonic crystal structures as well as different pump shapes, we successfully show how to generate maximally entangled states. Furthermore, we simulate all-optical coherent control over the generated quantum state by actively changing the profile of the pump beam. Our work can be useful for applications such as novel designs of high-dimensional quantum key distribution and quantum information processing protocols. In addition, our method can be readily applied for controlling other degrees of freedom of light in the SPDC process, such as spectral and temporal properties, and may even be used in condensed-matter systems having a similar interaction Hamiltonian.
Joint bi-adversarial learning for unsupervised domain adaptation

An important challenge of unsupervised domain adaptation (UDA) is how to sufficiently utilize the structure and information of the data distribution, so as to exploit the source domain knowledge for a more accurate classification of the unlabeled target domain. Currently, much research work has been devoted to UDA. However, existing works have mostly considered only distribution alignment or learning domain invariant features by adversarial techniques, ignoring feature processing and intra-domain category information. To this end, we design a new cross-domain discrepancy metric, namely joint distribution for maximum mean discrepancy (JD-MMD), and propose a deep unsupervised domain adaptation learning method, namely joint bi-adversarial learning for unsupervised domain adaptation (JBL-UDA). Specifically, JD-MMD measures cross-domain divergence in terms of both discrepancy and relevance by preserving cross-domain joint distribution discrepancy, as well as their class discriminability. Then, with such divergence measure, JBL-UDA models with two learning modalities, one is founded by the bi-adversarial learning from domains and classes implicitly, while the other explicitly addresses domains and classes alignment via the JD-MMD metric. Besides, JBL-UDA explores structural prior knowledge from data classes and domains to generate class-discriminative and domain-invariant representations. Finally, extensive evaluations exhibit state-of-the-art accuracy of the proposed methodology.
Optimising Multilayer Perceptron weights and biases through a Cellular Genetic Algorithm for medical data classification

In recent years, technology in medicine has shown a significant advance due to artificial intelligence becoming a framework to make accurate medical diagnoses. Models like Multilayer Perceptrons (MLPs) can detect implicit patterns in data, allowing identifying patients conditions that cannot be seen easily. MLPs consist of biased neurons arranged in layers, connected by weighted connections. Their effectiveness depends on finding the optimal weights and biases that reduce the classification error, which is usually done by using the Back Propagation algorithm (BP). But BP has several disadvantages that could provoke the MLP not to learn. Metaheuristics are alternatives to BP that reach high-quality solutions without using many computational resources. In this work, the Cellular Genetic Algorithm (CGA) with a specially designed crossover operator called Damped Crossover (DX), is proposed to optimise weights and biases of the MLP to classify medical data. When compared against state-of-the-art algorithms, the CGA configured with DX obtained the minimal Mean Square Error value in three out of the five considered medical datasets and was the quickest algorithm with four datasets, showing a better balance between time consumed and optimisation performance. Additionally, it is competitive in enhancing classification quality, reaching the best accuracy with two datasets and the second-best accuracy with two of the remaining.
Tunable narrow-band single-channel add-drop integrated optical filter with ultrawide FSR

Abstract Free-spectral-range (FSR)-free optical filters have always been a critical challenge for photonic integrated circuits. A high-performance FSR-free filter is highly desired for communication, spectroscopy, and sensing applications. Despite significant progress in integrated optical filters, the FSR-free filter with a tunable narrow-band, high out-of-band rejection, and large fabrication tolerance has rarely been demonstrated. In this paper, we propose an exact and robust design method for add-drop filters (ADFs) with an FSR-free operation capability, a sub-nanometer optical bandwidth, and a high out-of-band rejection (OBR) ratio. The achieved filter has a 3-dB bandwidth of &lt; 0.5 nm and an OBR ratio of 21.5 dB within a large waveband of 220 nm, which to the best of our knowledge, is the largest-FSR ADF demonstrated on a silicon photonic platform. The filter exhibits large tunability of 12.3 nm with a heating efficiency of 97 pm/mW and maintains the FSR-free feature in the whole tuning process. In addition, we fabricated a series of ADFs with different periods, which all showed reliable and excellent performances.
Does corruption undermine tourism development in Africa?

While the determinants of tourism demand have been extensively investigated, the impact of corruption on tourism development is less documented. To fill the gap in the literature, this study uses various indicators of tourism development and corruption measurement to examine the influence of corruption on tourism development in 30 African countries over the period 1996–2020. The empirical evidence is based on the fixed effect and panel corrected standard errors (PCSE) estimation techniques. The results unveil that the impact of corruption on tourism development is negative and statistically significant, both for domestic and international tourists. It indicates as corruption worsens, tourism-related investments and tourism destinations are negatively affected. These results support the sand-in-the-wheel hypothesis. Additionally, macroeconomic indicators like GDP growth, foreign direct investment and infrastructural development are found to be ideal for promoting the tourism sector. However, the magnitude of the impact of the variables varies across various specifications, particularly for the sub-Saharan sample group. Based on the results, policy implications are suggested from the study. The results recommended that institutional reforms and promotion of good governances are critical in order to maximize the benefits of tourism sector.
Postdigital Learning for a Changing Higher Education

Abstract This article draws upon the authors’ previous work on the opportunities and challenges learners face as they move into, through, and out of transitional learning spaces. The authors argue that equipping students to locate themselves with/in the tensions of learning is imperative, especially in postdigital learning environments. Teaching in ways that enable students to acknowledge their ‘will to learn’ and to understand that liminal learning can prompt distress and disjunction or the ability to think differently is discussed. This article begins by examining the relationship between the postdigital and the liminal, offers an Integrated Model of Transitional Learning Spaces, and presents different types of disjunctions. It then explores how learners navigate the liminal and concludes by arguing for a need to focus on postdigital learning and liminal learning in a changing higher education landscape.
Prediction of unplanned 30-day readmission for ICU patients with heart failure

Intensive Care Unit (ICU) readmissions in patients with heart failure (HF) result in a significant risk of death and financial burden for patients and healthcare systems. Prediction of at-risk patients for readmission allows for targeted interventions that reduce morbidity and mortality.We presented a process mining/deep learning approach for the prediction of unplanned 30-day readmission of ICU patients with HF. A patient's health records can be understood as a sequence of observations called event logs; used to discover a process model. Time information was extracted using the DREAM (Decay Replay Mining) algorithm. Demographic information and severity scores upon admission were then combined with the time information and fed to a neural network (NN) model to further enhance the prediction efficiency. Additionally, several machine learning (ML) algorithms were developed to be used as the baseline models for the comparison of the results.By using the Medical Information Mart for Intensive Care III (MIMIC-III) dataset of 3411 ICU patients with HF, our proposed model yielded an area under the receiver operating characteristics (AUROC) of 0.930, 95% confidence interval of [0.898-0.960], the precision of 0.886, sensitivity of 0.805, accuracy of 0.841, and F-score of 0.800 which were far better than the results of the best baseline model and the existing literature.The proposed approach was capable of modeling the time-related variables and incorporating the medical history of patients from prior hospital visits for prediction. Thus, our approach significantly improved the outcome prediction compared to that of other ML-based models and health calculators.
A resilient Trust Management framework towards trust related attacks in the Social Internet of Things

As a huge number of heterogeneous smart things in the Social Internet of Things (SIoT) paradigm are connected to exchange services, security principles are needed to ensure safe communication in the network. From this perspective, various Trust Management models have been set forward in the literature in order to assess nodes trustworthiness and therefore to avoid malicious connections in the SIoT network. However, these models still lack practical solutions to address SIoT trust related attacks performed towards the trust evaluation process. In this regard, the present work stands for an in-depth study on the effectiveness of the trust attack management, as it is integrated into the trust model, with the aim of classifying the nodes behaviors more accurately in order to ensure secure connections in the network. For this reason, nodes behaviors analysis, based on Machine Learning (ML) algorithms, is carried out within our trust assessment process. Our objective lies in limiting interactions with not only poor service provider, but also with attacker nodes. The proposed trust method’s acquired outcome reveals that our introduced TM model demonstrates an improved efficiency in identifying malicious nodes compared to a trust classification algorithm without nodes behaviors evaluation. Experimentation conducted within a simulated dataset based on real social data through Cooja; corroborates the efficiency of our proposed solution in terms of coping with simulated trust related attacks performed by malicious nodes.