Reviewerly/exHarmony huggingface dataset:
Qualitative analysis of hotspots and intrusive memories after viewing an aversive film highlights their sensory and spatial features

Intrusive memories of trauma are recurrent distressing sensory-perceptual impressions of the traumatic event that enter consciousness spontaneously and unwanted. They often contain the worst moment/s ('hotspots') of the trauma memory and have primarily been studied in clinical populations after real trauma. Intrusive memories can also be studied using analogue trauma as an 'experimental psychology model'. Little is known about the features of analogue trauma hotspots. Here we report an ancillary analysis of data from a randomized controlled trial. Seventy non-clinical participants viewed a trauma film containing COVID-19 related footage. Features of hotspots/intrusive memories of the film were explored using linguistic analysis and qualitative content coding. Participants reported on average five hotspots (M = 9.5 words/hotspot). Akin to hotspots soon after real trauma, analogue hotspots/intrusions primarily contained words related to space. Most contained sensory features, yet few cognitions and emotions. Results indicate that features of analogue trauma hotspots mirror those of hotspots soon after real trauma, speaking to the clinical validity of this 'experimental psychology model'.ClinicalTrials.gov ID: NCT04608097, registered on 29/10/2020.
Fabricate It or Render It? Digital Fabrication vs. Virtual Reality for Creating Objects Instantly

In the technical human-computer interaction (HCI) community, two research fields that gained significant popularity in the last decade are digital fabrication and augmented/virtual reality (AR/VR). Although the two fields deal with different technical challenges, both aim for a single end goal: creating "objects" instantly – either by fabricating them physically or rendering them virtually. In this panel, we will discuss the pros and cons of both approaches, discuss which one may prevail in the future, and what opportunities exist for closer collaboration between researchers from the two research fields.
Unsupervised clustering of Roman potsherds via Variational Autoencoders

In this paper we propose an artificial intelligence imaging solution to support archaeologists in the classification task of Roman commonware potsherds. Usually, each potsherd is represented by its sectional profile as a two dimensional black–white image and printed in archaeological books related to specific archaeological excavations. The partiality and handcrafted variance of the fragments make their matching a challenging problem: we propose to pair similar profiles via the unsupervised hierarchical clustering of non-linear features learned in the latent space of a deep convolutional Variational Autoencoder (VAE) network. Our contribution also include the creation of a ROman COmmonware POTtery (ROCOPOT) database, with more than 4000 potsherds profiles extracted from 25 Roman pottery corpora, and a MATLAB GUI software for the easy inspection of shape similarities. Results are commented both from a mathematical and archaeological perspective so as to unlock new research directions in both communities.
Modified Harris Hawks Optimization Algorithm with Exploration Factor and Random Walk Strategy

One of the most popular population-based metaheuristic algorithms is Harris hawks optimization (HHO), which imitates the hunting mechanisms of Harris hawks in nature. Although HHO can obtain optimal solutions for specific problems, it stagnates in local optima solutions. In this paper, an improved Harris hawks optimization named ERHHO is proposed for solving global optimization problems. Firstly, we introduce tent chaotic map in the initialization stage to improve the diversity of the initialization population. Secondly, an exploration factor is proposed to optimize parameters for improving the ability of exploration. Finally, a random walk strategy is proposed to enhance the exploitation capability of HHO further and help search agent jump out the local optimal. Results from systematic experiments conducted on 23 benchmark functions and the CEC2017 test functions demonstrated that the proposed method can provide a more reliable solution than other well-known algorithms.
Field Evidence of the Effects of Privacy, Data Transparency, and Pro-social Appeals on COVID-19 App Attractiveness

COVID-19 exposure-notification apps have struggled to gain adoption. Existing literature posits as potential causes of this low adoption: privacy concerns, insufficient data transparency, and the type of appeal – collective- vs. individual-good – used to frame the app. As policy guidance suggests using tailored advertising to evaluate the effects of these factors, we present the first field study of COVID-19 contact tracing apps with a randomized, control trial of 14 different advertisements for CovidDefense, Louisiana's COVID-19 exposure-notification app. We find that all three hypothesized factors – privacy, data transparency, and appeals framing – relate to app adoption, even when controlling for age, gender, and community density. Our results offer (1) the first field evidence supporting the use of collective-good appeals, (2) nuanced findings regarding the efficacy of data and privacy transparency, the effects of which are moderated by appeal framing and potential users' demographics, and (3) field-evidence-based guidance for future efforts to encourage pro-social health technology adoption.
Exploration of university students’ lived experiences of using smartphones for English language learning

AbstractAbstractThis study aimed to understand the nature of learning English in the digital age from students' personal 'learning lives'. Drawing on hermeneutic phenomenology's principles, this study conducted conversational interviews and obtained reflective reports from students over 5 months. The findings reveal that students' English learning experiences are sociocultural processes involving a complex interplay between learner agency, other agents, the cultural practices using smartphones, and the wider context. Furthermore, learning English using smartphones facilitates an approach to it or Computer Assisted Language Learning that embraces 'anytime' and 'anywhere' learning and involves a seamless integration of personal, social, formal, and informal learnings. Therefore, we propose smartphone-assisted English language learning (SAELL), addressing smartphone use for learning English, based on qualitative data and recent trends in the field. Future research should include rich qualitative evidence to support the SAELL framework in practice in multiple empirical settings. Empirical studies are mostly applied within university settings; however, the practical implications have more relevance to students, teachers, educators, and policymakers who may be involved in shaping novel smartphone learning spaces for formal and informal contexts.Keywords: Smartphoneslived experiencesmobile-assisted language learning AcknowledgementsWe would like to express our gratitude to the journal reviewers for considering the manuscript.Disclosure statementThe manuscript contains anonymised data and abides by the British Educational Research Association's guidelines.The authors confirm there are no conflicts of interest.Additional informationNotes on contributorsYujuan LuoYujuan Luo is a Doctoral Researcher at Brunel University London. Her research interests include mobile learning, second language acquisition and collaborative language learning.Mike WattsMike Watts is Professor of Education and Director of Internationalisation at the Department of Education, Brunel University London. He has been teaching and writing about science education for many (many) years.
Threshold estimation based on local minima for nucleus and cytoplasm segmentation

Image segmentation is the process of partitioning an image into separate objects or regions. It is an essential step in image processing to segment the regions of interest for further processing. We propose a method for segmenting the nuclei and cytoplasms from white blood cells (WBCs).Initially, the method computes an initial value based on the minimum and maximum values of the input image. Then, a histogram of the input image is computed and approximated to obtain function values. The method searches for the first local maximum and local minimum from the approximated function values in the order of increasing of knots sequence. We approximate the required threshold from the first local minimum and the computed initial value based on defined conditions. The threshold is applied to the input image to binarize it, and then post-processing is performed to obtain the final segmented nucleus. We segment the whole WBC before segmenting the cytoplasm depending on the complexity of the objects in the image. For WBCs that are well separated from red blood cells (RBCs), n thresholds are generated and then produce n thresholded images. Then, a standard Otsu method is used to binarize the average of the produced images. Morphological operations are applied on the binarized image, and then a single-pixel point from the segmented nucleus is used to segment the WBC. For images in which RBCs touch the WBCs, we segment the whole WBC using SLIC and watershed methods. The cytoplasm is obtained by subtracting the segmented nucleus from the segmented WBC.The method is tested on two different public data sets and the results are compared to the state of art methods. The performance analysis shows that the proposed method segments the nucleus and cytoplasm well.We propose a method for nucleus and cytoplasm segmentation based on the local minima of the approximated function values from the image's histogram. The method has demonstrated its utility in segmenting nuclei, WBCs, and cytoplasm, and the results are satisfactory.
An Action Recognition Technology for Badminton Players Using Deep Learning

With the fast development of sports in recent years, the number of people participating in various sports has increased day by day. Because of the advantages of fewer constraints on the field and ease of learning, badminton became one of the most popular sports among them. Numerous works have been done specifically for the recognition of action of badminton players to improve and popularize it, but the traditional badminton player’s badminton action recognition algorithm employs the method of manually constructing a topology map to model the action sequence contained in multiple video frames. Besides, it learns each video frame in a targeted manner to reflect data changes, which is prone to high computational cost, low network generalization, and catastrophic forgetting. In response to the above problems, this paper proposes a deep learning-based action recognition technology for badminton players, which re-encodes the human hitting action sequence data with multirelational characteristics into relational triples, and learns by decoupling based on long short-term memory network. At the same time, this paper designs and completes a set of badminton action recognition schemes based on acceleration and angular velocity signals. Experimental results show that the proposed method achieves 63%, 84%, and 92%, respectively, recognition accuracy on multiple benchmark datasets, which improves the accuracy of human hitting action recognition. As a result, the evaluation will be useful in future work to improve the structure of current deep learning models for higher results in badminton action recognition.
Advanced calibration of mortality prediction on cardiovascular disease using feature-based artificial neural network

• Developed a novel neural network for CVD mortality prediction with clinical data. • Proposed a more time-efficient feature-based artificial neural network (ANN). • Investigated feature representation to improve ANN for practical guideline. • Boosted calibration of neural networks and enabled model to make less mistakes. • Made model updates in clinical context more accessible, flexible and reliable. Cardiovascular (CVD) is the leading cause of death worldwide and a significant public health concern. Therefore, its mortality prediction is crucial to many existing treatment guidelines. Medical claims data can be used to accurately foresee the health outcomes of patients contracting to a variety of diseases. Many machine learning algorithms, especially deep learning artificial neural networks, can predict mortality rate among patients with CVD using clinical data. Calibration of probabilistic prediction is essential for precise medical interventions as it indicates how well a model’s output matches the probability of the event. However, deep learning neural networks are poorly calibrated. Through experiments, we observe that feature representation is an important factor influencing calibration. This paper proposes a novel feature-based deep learning neural network framework to predict the mortality rate among patients with CVD. Our focus is to present a comprehensive study to achieve advanced performance calibration of mortality prediction on CVD in leveraging deep learning architecture and feature representations. Our study demonstrates that the proposed feature-based neural network framework integrated with Principal Component Analysis or Autoencoders significantly reduces training time and boosts calibration, making model updates in clinical context more flexible and decision-making in medical prevention more reliable.
Multilingual Text-To-Speech Training Using Cross Language Voice Conversion And Self-Supervised Learning Of Speech Representations

State of the art text-to-speech (TTS) models can generate high fidelity monolingual speech, but it is still challenging to synthesize multilingual speech from the same speaker. One major hurdle is for training data. It’s hard to find speakers who have native proficiency in several languages. One way of mitigating this issue is by generating polyglot corpus through voice conversion. In this paper, we train such multilingual TTS system through a novel cross-lingual voice conversion model trained with speaker-invariant features extracted from a speech representation model which is pre-trained with 53 languages through self-supervised learning [1]. To further improve the speaker identity shift, we also adopt a speaker similarity loss term during training. We then use this model to convert multilingual multi-speaker speech data to the voice of the target speaker. Through augmenting data from 4 other languages, we train a multilingual TTS system for a native monolingual English speaker which speaks 5 languages(English, French, German, Italian and Spanish). Our system achieves improved mean opinion score (MOS) compared with the baseline of multi-speaker system for all languages, specifically: 3.74 vs 3.62 for Spanish, 3.11 vs 2.71 for German, 3.47 vs 2.84 for Italian, and 2.72 vs 2.41 for French.
A Curve Approximation Approach Using Bio-inspired Polar Coordinate Bald Eagle Search Algorithm

Abstract In this paper, a curve approximation approach using bio-inspired polar coordinate bald eagle search algorithm (PBES) is proposed. PBES algorithm is inspired by the spiral mechanism of bald eagle during predation. By introducing polar coordinate, the spiral predation process of the bald eagle will become more intuitive, which is more conducive for the algorithm to polar coordinate optimization problems. The initialization stage of PBES algorithm is modified to make the distribution of initialized individuals more uniform and some parameters are introduced to strengthen the exploration and exploitation capabilities of algorithm. The performance of the PBES algorithm is tested in three aspects: polar coordinate transcendental equation, curve approximation and robotic manipulator. The experimental results show that the PBES algorithm is superior to the well-known metaheuristic algorithms as it is effectively applicable for curve approximation problem.
A Novel Transformer-Based Attention Network for Image Dehazing

Image dehazing is challenging due to the problem of ill-posed parameter estimation. Numerous prior-based and learning-based methods have achieved great success. However, most learning-based methods use the changes and connections between scale and depth in convolutional neural networks for feature extraction. Although the performance is greatly improved compared with the prior-based methods, the performance in extracting detailed information is inferior. In this paper, we proposed an image dehazing model built with a convolutional neural network and Transformer, called Transformer for image dehazing (TID). First, we propose a Transformer-based channel attention module (TCAM), using a spatial attention module as its supplement. These two modules form an attention module that enhances channel and spatial features. Second, we use a multiscale parallel residual network as the backbone, which can extract feature information of different scales to achieve feature fusion. We experimented on the RESIDE dataset, and then conducted extensive comparisons and ablation studies with state-of-the-art methods. Experimental results show that our proposed method effectively improves the quality of the restored image, and it is also better than the existing attention modules in performance.
Temporal Early Exiting for Streaming Speech Commands Recognition

Limited-vocabulary speech commands recognition is the task of classifying a short utterance as one of several speech commands, for which neural networks obtain state-of-the-art results. In particular, recurrent neural networks represent a common approach for streaming commands recognition systems. In this paper, we explore resource-efficient methods to short-circuit such systems in the time domain when the model is confident in its prediction. We propose applying a frame-level labeling objective to further improve the efficiency–accuracy trade-off. On two datasets in limited-vocabulary commands recognition, our best method achieves an average time savings of 45% of the utterance without reducing the absolute accuracy by more than 0.6 points. We show that the per-instance savings depend on the length of the unique prefix in the phonemes across a dataset.
APuML: An Efficient Approach to Detect Mobile Phishing Webpages using Machine Learning

Nowadays, the growth of mobile phones users has gained a significant increase because of the features offered by them in abundant amounts. These devices are being used rapidly for accessing the web and many online services. However, the security mechanisms that are available in smartphones are not yet mature. Therefore, smartphones are vulnerable to various types of attacks, such as phishing. The browsers on smartphones are very trivial and the smartphones security abilities have been lessened, to match the smartphone's capabilities. Therefore, detection of the malicious website is different from the previously known technique, which is used on the desktop. Many anti-phishing techniques for mobile devices have been developed but still, there is a lack of a full-fledged solution. Therefore, this paper presents an efficient approach to detect malicious mobile webpages. The proposed approach APuML (Anti Phishing using Machine Learning) extracts all the static and site popularity features from the given URL to create a feature vector. An appropriate machine learning classification algorithm is then applied on the feature set to obtain the result and update the database accordingly. In our approach, the Random Forest classifier outperforms over other classifiers and achieved detection accuracy of 93.85%. We have also created an endpoint application for the users to interact with our system using his/her mobile devices. Moreover, the proposed approach can identify drive-by downloads attack, zero-day attack and clickjacking attack with high accuracy.
Stability of Solutions to Systems of Nonlinear Differential Equations with Discontinuous Right-Hand Sides: Applications to Hopfield Artificial Neural Networks

In this paper, we study the stability of solutions to systems of differential equations with discontinuous right-hand sides. We have investigated nonlinear and linear equations. Stability sufficient conditions for linear equations are expressed as a logarithmic norm for coefficients of systems of equations. Stability sufficient conditions for nonlinear equations are expressed as the logarithmic norm of the Jacobian of the right-hand side of the system of equations. Sufficient conditions for the stability of solutions of systems of differential equations expressed in terms of logarithmic norms of the right-hand sides of equations (for systems of linear equations) and the Jacobian of right-hand sides (for nonlinear equations) have the following advantages: (1) in investigating stability in different metrics from the same standpoints, we have obtained a set of sufficient conditions; (2) sufficient conditions are easily expressed; (3) robustness areas of systems are easily determined with respect to the variation of their parameters; (4) in case of impulse action, information on moments of impact distribution is not required; (5) a method to obtain sufficient conditions of stability is extended to other definitions of stability (in particular, to p-moment stability). The obtained sufficient conditions are used to study Hopfield neural networks with discontinuous synapses and discontinuous activation functions.
Adversarial Sample Detection for Speaker Verification by Neural Vocoders

Automatic speaker verification (ASV), one of the most important technology for biometric identification, has been widely adopted in security-critical applications. However, ASV is seriously vulnerable to recently emerged adversarial attacks, yet effective counter-measures against them are limited. In this paper, we adopt neural vocoders to spot adversarial samples for ASV. We use the neural vocoder to re-synthesize audio and find that the difference between the ASV scores for the original and re-synthesized audio is a good indicator for discrimination between genuine and adversarial samples. This effort is, to the best of our knowledge, among the first to pursue such a technical direction for detecting time-domain adversarial samples for ASV, and hence there is a lack of established baselines for comparison. Consequently, we implement the Griffin-Lim algorithm as the detection baseline. The proposed approach achieves effective detection performance that outperforms the baselines in all the settings. We also show that the neural vocoder adopted in the detection framework is dataset-independent. Our codes will be made open-source for future works to do fair comparison <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .