Reviewerly/exHarmony huggingface dataset:
Experiences of Patients and Therapists Testing a Virtual Reality Exposure App for Symptoms of Claustrophobia: Mixed Methods Study

The effectiveness of virtual reality exposure (VRE) in the treatment of anxiety disorders is well established. Several psychological mechanisms of VRE have been identified, whereby both emotional processing and the sense of presence play a key role. However, there are only few studies that contribute to our knowledge of examples of implementation in the case of VRE for claustrophobia based on patients' experiences and the perspective of therapists.This study asks for key elements of a VRE app that are necessary for effective exposure for people with claustrophobic symptoms.A mixed methods design was applied in which patients (n=15) and therapeutic experts (n=15) tested a VRE intervention of an elevator ride at 5 intensity levels. Intensity was varied by elevator size, duration of the elevator ride, and presence of virtual humans. Quantitative measures examined self-reported presence with the Igroup Presence Questionnaire (IPQ) ranging from 0 to 6 and 15 Likert-scaled evaluation items that had been developed for the purpose of this study, ranging from 1 to 5. In both measures, higher scores indicate higher levels of presence or agreement. Think-aloud protocols of the patients and semistructured interviews posttreatment of all participants were conducted to gain in-depth perspectives on emotional processes.The intervention induced a feeling of presence in patients and experts, posttreatment scores showed a high IPQ presence score (mean 3.84, SD 0.88), with its subscores IPQ spatial presence (mean 4.53, SD 1.06), IPQ involvement (mean 3.83, SD 1.22), and IPQ experienced realism (mean 2.75, SD 1.02). Patients preferred a setting in the presence of a therapist (mean 4.13, SD 0.83) more than the experts did (mean 3.33, SD 1.54). Think-aloud protocols of the patients revealed that presence and anxiety both were achieved. Qualitative interviews of patients and experts uncovered 8 topics: feelings and emotions, personal story, telepresence, potential therapeutic effects, barriers, conditions and requirements, future prospects, and realization. The intensity levels were felt to appropriately increase in challenge, with ambivalent results regarding the final level. Virtual humans contributed to feelings of fear.Key elements of a VRE app for claustrophobic symptoms should include variation of intensity by adding challenging cues in order to evoke presence and anxiety. Virtual humans are a suitable possibility to make the intervention realistic and to provide a sense of closeness; however, some of the fears might then be related to symptoms of social phobia or agoraphobia. Patients may need the physical presence of a therapist, though not all of them share this view. A higher degree of sophistication in the intensity levels is needed to deliver targeted help for specific symptoms of anxiety.
A transformer-based generative adversarial network for brain tumor segmentation

Brain tumor segmentation remains a challenge in medical image segmentation tasks. With the application of transformer in various computer vision tasks, transformer blocks show the capability of learning long-distance dependency in global space, which is complementary to CNNs. In this paper, we proposed a novel transformer-based generative adversarial network to automatically segment brain tumors with multi-modalities MRI. Our architecture consists of a generator and a discriminator, which is trained in min-max game progress. The generator is based on a typical "U-shaped" encoder-decoder architecture, whose bottom layer is composed of transformer blocks with Resnet. Besides, the generator is trained with deep supervision technology. The discriminator we designed is a CNN-based network with multi-scale L1 loss, which is proved to be effective for medical semantic image segmentation. To validate the effectiveness of our method, we conducted exclusive experiments on BRATS2015 dataset, achieving comparable or better performance than previous state-of-the-art methods. On additional datasets, including BRATS2018 and BRATS2020, experimental results prove that our technique is capable of generalizing successfully.
Learning a bi-directional discriminative representation for deep clustering

Nowadays, deep clustering achieves superior performance by jointly performing representation learning and cluster assignment. Although numerous deep clustering algorithms have emerged, most of them have difficulty learning representations that fit the clustering distribution. To address this issue, we propose a bi-directional discriminative representation learning clustering (BDRC) framework in this paper. In our framework, a dual autoencoder network, a bi-directional mutual information maximization module and a self-supervised cluster prediction module are combined into a joint optimization framework. To learn more cluster-friendly representations, the bi-directional mutual information maximization module is executed on both samples and their nearest neighbors to explore the cluster relationships between samples. In order to improve the stability of the model, a self-supervised cluster prediction module is devised to predict clustering assignments to supervise the autoencoder using the KL-divergence. Moreover, the UMAP is used to find the manifold of the latent representations which can better preserve the global structure. Experiments on some benchmark datasets demonstrate the superiority of the proposed BDRC algorithm.
Public perceptions of U.S. earthquake early warning post-alert messages: Findings from focus groups and interviews

In May 2019, a false earthquake alert message was sent to the city of Ridgecrest, CA, in the U.S.A., an area that just 10 months prior had experienced a significant series of earthquake events. The false alert was followed by a post-alert message, indicating that the message was cancelled and under investigation. This event, the first of its kind in the U.S.A., provided an opportunity to learn about public perceptions of the post-alert message, including what individuals understood about the threat and their safety, and what actions they should take as a result. We conducted individual interviews with 40 persons in the Ridgecrest community, followed by a series of focus groups in Southern California to discuss post-alert messages, and to learn about information people most needed following false earthquake alerts. We found that individuals with and without prior earthquake experience expressed confusion about content describing the investigatory actions of the organization and had a largely negative response toward content that complimented those who took action in response to the initial earthquake early warning. While current post-alert messages are intended to reinforce the good intentions of the organization and the protective actions taken by message receivers, the message issued was perceived by members of the public to be largely ineffective in achieving either objective because it did not provide the information they desired most—an explicit statement about their safety. Instead, message receivers need information that primarily affirms their current level of safety so they can return to normal functioning.
Inversion study of soil organic matter content based on reflectance spectroscopy and the improved hybrid extreme learning machine

The continuous exploitation of mining resources and a number of other factors have led to serious soil pollution problems. Soil organic matter (SOM) can be used as an important measure of soil fertility and ecological environment quality in mining areas. The traditional method of measuring SOM content by chemical methods is costly and time-consuming, so it is important to develop a rapid, accurate and economical method to measure SOM content in reclaimed mines for the ecological restoration and the evaluation of the ecological reclamation effect of mines. In this paper, the visible-near infrared spectral data of soil were obtained using an open pit mine as the study area. The three pre-processing methods of multiple scattering correction, continuous removal and Savitzky-Golay smoothing were compared. Then the two-band ratio spectral index RI was constructed to obtain the sensitive band combinations. To address the problem that the HHO-TELM hybrid model tended to converge prematurely, the GHO-TELM model was proposed in this paper. GHO-TELM introduced a gene learning strategy in HHO-TELM. The crossover, mutation and selection mechanisms were used to cultivate high-quality examples, enhance the quality of individuals and prevent premature convergence of the model. GHO-TELM was compared with several machine learning models. The experimental results showed that GHO-TELM obtained the best performance indexes, with correlation coefficient (r) of 0.9586, and relative percentage deviation (RPD) of 3.0823, indicating that the model had a good prediction effect.
Analyzing Malicious Activities and Detecting Adversarial Behavior in Cryptocurrency based Permissionless Blockchains: An Ethereum Usecase

Different malicious activities occur in cryptocurrency-based permissionless blockchains such as Ethereum and Bitcoin. Some activities are due to the exploitation of vulnerabilities which are present in the blockchain infrastructure, some activities target its users through social engineering techniques, while some activities use it to facilitate different malicious activities. Since cryptocurrency-based permissionless blockchains provide pseudonymity to its users, bad actors prefer to carry out transactions related to malicious activities on them. Towards this, we aim at automatically flagging blockchain accounts as suspects that indulge in malicious activities, thus reducing the unintended support that cryptocurrency-based permissionless blockchains provide to malicious actors. We first use the cosine similarity (CS) metrics to study the similarities between the feature vector of accounts associated with different malicious activities and find that most of the malicious activities associated with the Ethereum blockchain behave similarly. We then use the K-Means clustering algorithm to check if accounts associated with similar malicious activities cluster together. We also study the effect of bias on the performance of a machine learning (ML) algorithm, due to the number of accounts associated with malicious activity. We then compare the different state-of-the-art models and identify that Neural Networks (NNs) are resistant to bias associated with a malicious activity and are also robust against adversarial attacks. The previously used ML algorithms for identifying malicious accounts also show bias towards an over-represented malicious activity.
Fully automatic tumor segmentation of breast ultrasound images with deep learning

Breast ultrasound (BUS) imaging is one of the most prevalent approaches for the detection of breast cancers. Tumor segmentation of BUS images can facilitate doctors in localizing tumors and is a necessary step for computer-aided diagnosis systems. While the majority of clinical BUS scans are normal ones without tumors, segmentation approaches such as U-Net often predict mass regions for these images. Such false-positive problem becomes serious if a fully automatic artificial intelligence system is used for routine screening.In this study, we proposed a novel model which is more suitable for routine BUS screening. The model contains a classification branch that determines whether the image is normal or with tumors, and a segmentation branch that outlines tumors. Two branches share the same encoder network. We also built a new dataset that contains 1600 BUS images from 625 patients for training and a testing dataset with 130 images from 120 patients for testing. The dataset is the largest one with pixel-wise masks manually segmented by experienced radiologists. Our code is available at https://github.com/szhangNJU/BUS_segmentation.The area under the receiver operating characteristic curve (AUC) for classifying images into normal/abnormal categories was 0.991. The dice similarity coefficient (DSC) for segmentation of mass regions was 0.898, better than the state-of-the-art models. Testing on an external dataset gave a similar performance, demonstrating a good transferability of our model. Moreover, we simulated the use of the model in actual clinic practice by processing videos recorded during BUS scans; the model gave very low false-positive predictions on normal images without sacrificing sensitivities for images with tumors.Our model achieved better segmentation performance than the state-of-the-art models and showed a good transferability on an external test set. The proposed deep learning architecture holds potential for use in fully automatic BUS health screening.
Strategies to reduce visual attention changes while learning and training in extended reality environments

Abstract Computer-Based training (CBT) is a widely used strategy in interactive learning and skills training to provide the trainee with information while in training. Extended Reality (XR) is a set of technologies used in the fourth industrial revolution for this purpose. However, an important XR limitation is the cognitive overload in trainees due to continuous changes in attention. The effects of the changes in attention on cognitive load have been studied in environments such as printed material and desktop PC’s. However, such effects are not yet fully known on XR platforms. This study’s aim was to identify strategies to reduce the changes in attention in instructional/educational materials, which use extended reality to present information to trainees. The presented information can guide the use of combined strategies to reduce the cognitive overload generated by XR display platforms. Therefore, an extensive literature review was carried out. From a set of 1946 articles, 53 studies were selected. The selected studies evaluated the cognitive load in instructional materials that used XR as a visualization platform. The results showed three strategy groups: those associated with the spatial integration of information, those associated with the use of visual features and those associated with the content segmentation. Effects of this strategies in the user performance and user cognitive load are discussed.
A Comprehensive Study of Security Aspects in Blockchain

Knowledge is power, and in this digital age, knowledge is represented by data, making it one of the most valuable assets. With rapidly evolving technology, there are challenges that directly or indirectly threaten the integrity of data, such as cybercrime, privacy concerns, theft, malware, and viruses. The development of Blockchain Technology has helped in the mitigation of some of these problems by safeguarding online data resources. In this chapter, we introduce the concept of blockchain, discuss its structure and features, and understand its operation. The main focus of this chapter is to observe the vulnerabilities of this technology and scrutinize several attacks exploiting them to understand their outcomes. We go over a few security improvements in an attempt to protect from attacks and alleviate the existing threats. In addition, we explore its application and implementation in various fields. We conclude by discussing the major challenges this technology is facing at present and may encounter in the future.
PyBanshee version (1.0): A Python implementation of the MATLAB toolbox BANSHEE for Non-Parametric Bayesian Networks with updated features

<h2>Abstract</h2> In this paper we discuss PyBanshee, which is a Python-based open-source implementation of the MATLAB toolbox BANSHEE. PyBanshee constitutes the first fully open-source package to quantify, visualize and validate Non-Parametric Bayesian Networks (NPBNs). The architecture of PyBanshee is heavily based on its MATLAB predecessor. It presents the full implementation of existing tools and introduces new modules. Specifically, PyBanshee allows for: (i) choosing fully parametric one-dimensional margins, (ii) choosing different sample sizes for the model-validation tests based on the Hellinger distance, (iii) drawing user-defined sample sizes of the NPBN, (iv) sample-based conditioning sampling (similarly to the closed-source proprietary package UNINET by LightTwist Software) and (v) visualizing the comparison between the histograms of the unconditional and conditional marginal distributions. New detailed examples demonstrating new features are provided.
Deep Multi-Scale Features Fusion for Effective Violence Detection and Control Charts Visualization

The study of automated video surveillance systems study using computer vision techniques is a hot research topic and has been deployed in many real-world CCTV environments. The main focus of the current systems is higher accuracy, while the assistance of surveillance experts in effective data analysis and instant decision making using efficient computer vision algorithms need researchers' attentions. In this research, to the best of our knowledge, we are the first to introduce a process control technique: control charts for surveillance video data analysis. The control charts concept is merged with a novel deep learning-based violence detection framework. Different from the existing methods, the proposed technique considers the importance of spatial information, as well as temporal representations of the input video data, to detect human violence. The spatial information are fused with the temporal dimension of the deep learning model using a multi-scale strategy to ensure that the temporal information are properly assisted by the spatial representations at multi-levels. The proposed frameworks' results are kept in the history-maintaining module of the control charts to validate the level of risks involved in the live input surveillance video. The detailed experimental results over the existing datasets and the real-world video data demonstrate that the proposed approach is a prominent solution towards automated surveillance with the pre- and post-analyses of violent events.
Graph Neural Pre-training for Recommendation with Side Information

Leveraging the side information associated with entities (i.e., users and items) to enhance recommendation systems has been widely recognized as an essential modeling dimension. Most of the existing approaches address this task by the integration-based scheme , which incorporates the entity side information by combining the recommendation objective with an extra side information-aware objective. Despite the growing progress made by the existing integration-based approaches, they are largely limited by the potential conflicts between the two objectives. Moreover, the heterogeneous side information among entities is still under-explored in these systems. In this article, we propose a novel pre-training scheme to leverage the entity side information by pre-training entity embeddings using the multi-graph neural network. Instead of jointly training with two objectives, our pre-training scheme first pre-trains two representation models under the entity multi/single relational graphs constructed by their side information and then fine-tunes their embeddings under an existing general representation-based recommendation model. Our proposed multi-graph and single-graph neural networks can generate within-entity knowledge-encapsulated embeddings, while capturing the heterogeneity from the entity side information simultaneously, thereby improving the performance of the underlying recommendation model. An extensive evaluation of our pre-training scheme fine-tuned under four general representation-based recommender models, namely, MF, NCF, NGCF, and LightGCN, shows that effectively pre-training embeddings with both the user’s and item’s side information can significantly improve these original models in terms of both effectiveness and stability.
A reliable sentiment analysis for classification of tweets in social networks

In modern society, the use of social networks is more than ever and they have become the most popular medium for daily communications. Twitter is a social network where users are able to share their daily emotions and opinions with tweets. Sentiment analysis is a method to identify these emotions and determine whether a text is positive, negative, or neutral. In this article, we apply four widely used data mining classifiers, namely K-nearest neighbor, decision tree, support vector machine, and naive Bayes, to analyze the sentiment of the tweets. The analysis is performed on two datasets: first, a dataset with two classes (positive and negative) and then a three-class dataset (positive, negative and neutral). Furthermore, we utilize two ensemble methods to decrease variance and bias of the learning algorithms and subsequently increase the reliability. Also, we have divided the dataset into two parts: training set and testing set with different percentages of data to show the best train-test split ratio. Our results show that support vector machine demonstrates better outcomes compared to other algorithms, showing an improvement of 3.53% on dataset with two-class data and 7.41% on dataset with three-class data in accuracy rate compared to other algorithms. The experiments show that the accuracy of single classifiers slightly outperforms that of ensemble methods; however, they propose more reliable learning models. Results also demonstrate that using 50% of the dataset as training data has almost the same results as 70%, while using tenfold cross-validation can reach better results.
A hybrid optimization algorithm using BiLSTM structure for sentiment analysis

Sentiment analysis can assist consumers in providing clear and objective sentiment recommendations based on large amounts of data, and it is helpful in overcoming unclear human flaws in subjective assessments. Existing sentiment analysis methods, on the other hand, must be enhanced in terms of robustness and accuracy. To improve marketing strategies based on product reviews, a reliable mechanism for forecasting sentiment polarity should be implemented. This paper proposes a new approach for sentiment analysis called Taylor–Harris Hawks Optimization driven long short-term memory (THHO- BiLSTM). By incorporating Taylor series in HHO, Taylor–HHO is formed, which aids in improving the BiLSTM classifier's performance by picking optimal weights in the hidden layers. The proposed method was evaluated using Amazon product reviews and reviews from the Taboada corpus benchmark datasets, yielding findings with 96.93% and 93% accuracy, respectively. When compared to existing approaches, the suggested model exceeds them in terms of accuracy. The proposed approach helps manufacturers improve their products based on user feedback.