Reviewerly/exHarmony huggingface dataset:
Towards Egocentric Compositional Action Anticipation with Adaptive Semantic Debiasing

Predicting the unknown from the first-person perspective is expected as a necessary step towards machine intelligence, which is essential for practical applications including autonomous driving and robotics. As a human-level task, egocentric action anticipation aims at predicting an unknown action seconds before it is performed from the first-person viewpoint. While egocentric actions are usually provided as verb-noun pairs, predicting the unknown action may be trapped in insufficient training data for all possible combinations. Therefore, it is crucial for intelligent systems to use limited known verb-noun pairs to predict new combinations of actions that have never appeared, which is known as compositional generalization. In this paper, we are the first to explore the egocentric compositional action anticipation problem, which is more in line with real-world settings but neglected by existing studies. While prediction results are prone to suffer from semantic bias considering the distinct difference between training and test distributions, we further introduce a general and flexible adaptive semantic debiasing (ASD) framework that is compatible with different deep neural networks. To capture and mitigate semantic bias, we can imagine one counterfactual situation where no visual representations have been observed and only semantic patterns of observation are used to predict the next action. Instead of the traditional counterfactual analysis scheme that reduces semantic bias in a mindless way, we devise a novel counterfactual analysis scheme to adaptively amplify or penalize the effect of semantic experience by considering the discrepancy both among categories and among examples. We also demonstrate that the traditional counterfactual analysis scheme is a special case of the devised adaptive counterfactual analysis scheme. We conduct experiments on three large-scale egocentric video datasets. Experimental results verify the superiority and effectiveness of our proposed solution.
A meta-heuristic optimization approach for optimizing cross-pollination using UAVs

ABSTRACT Pollination using Unmanned Aerial Vehicles (UAVs) has emerged as a promising solution to the current pollination crisis. The dwindling number of natural pollinators forces the production of cutting-edge pollination technologies. This work proposes a module to optimize path planning for UAVs to travel in a minimum time. This study suggests a novel approach to maximize cross-pollination and minimize travel time with a highly efficient meta-heuristic optimization algorithm. This paper briefly describes a process we previously developed for flower insights that includes flower gender and gene identification and classification. With an insight into flowers, the proposed algorithm aims to achieve efficient and accurate pollination while minimizing energy consumption and convergence time. The Versatile Flower Pollination Algorithm’s (VFPA) approach is superior because it significantly reduces the amount of computing required while maintaining almost optimal performance. The proposed algorithm was successfully implemented to compute the distance between the male and female flowers and transfer nectar with a difference in the nectar value. The proposed approach shows promise for addressing the pollination crisis and reducing the reliance on traditional methods.
Sentiment Analysis of Students’ Feedback on E-Learning Using a Hybrid Fuzzy Model

It is crucial to analyze opinions about the significant shift in education systems around the world, because of the widespread use of e-learning, to gain insight into the state of education today. A particular focus should be placed on the feedback from students regarding the profound changes they experience when using e-learning. In this paper, we propose a model that combines fuzzy logic with bidirectional long short-term memory (BiLSTM) for the sentiment analysis of students’ textual feedback on e-learning. We obtained this feedback from students’ tweets expressing their opinions about e-learning. There were some ambiguous characteristics in terms of the writing style and language used in the collected feedback. It was written informally and not in adherence to standardized Arabic language writing rules by using the Saudi dialects. The proposed model benefits from the capabilities of the deep neural network BiLSTM to learn and also from the ability of fuzzy logic to handle uncertainties. The proposed models were evaluated using the appropriate evaluation metrics: accuracy, F1-score, precision, and recall. The results showed the effectiveness of our proposed model and that it worked well for analyzing opinions obtained from Arabic texts written in Saudi dialects. The proposed model outperformed the compared models by obtaining an accuracy of 86% and an F1-score of 85%.
Telehealth strategies in the care of people with chronic kidney disease: integrative review

Objective: to evaluate the evidence about telehealth strategies in caring for people with chronic kidney disease. Method: integrative literature review. The search for primary studies was carried out in six databases: PubMed/MEDLINE, Web of Science, EMBASE, CINAHL, LILACS, and Scopus. The sample consisted of 48 articles published between 2000 and 2021. The telehealth strategy was applied by a multidisciplinary team of doctors, nurses, pharmacists, nutritionis, and social workers. The type of study, country, strategy applied, setting, population, and professional were extracted from the articles. The studies were selected by reading the title and abstract (phase 1) and then reading them in full (phase 2), categorizing them by telehealth strategy. The results were summarized descriptively and the studies were classified according to their level of evidence. Results: the home was the most representative in dialysis and conservative treatment. Six categories of telehealth strategies were identified: remote monitoring devices, teleconsultation, digital platforms, apps, multimodality strategies, and telephone contact. Conclusion: using these strategies for the care of people with chronic kidney disease presents different forms and implementations, being feasible for the renal population at any stage of the disease and applicable by different health professionals with an emphasis on the home environment. The evidence shows that telehealth favors lower cost, accessibility to remote locations, and better monitoring of dialysis with positive resul in symptom control, risk reduction, and patient training.
Service robot anthropomorphism on consumer usage intention: curvilinear and linear effect

Purpose This research proposes and examines a theoretical model grounded in anthropomorphism theory considering the curvilinear and linear relationships between service robot anthropomorphism and consumer usage intention and explores the mediating effect of perceived risk. Design/methodology/approach To examine the developed model, two complementary studies are designed. In Study 1, multi-time data of 511 participants show that service robot anthropomorphism inverts U-shaped (curvilinear) relationship on consumer usage intention and perceived risk mediates this curvilinear relationship. In Study 2, multi-source data of 460 volunteers are used to confirm the findings of Study 1 and examine that consumer empathy moderates the complex nonlinear effect of service robot anthropomorphism on perceived risk, and the indirect curvilinear effect of service robot anthropomorphism on consumer usage intention through perceived risk. Findings This research provides preliminary and yet important findings on how service robot anthropomorphism most likely is positively associated with consumer usage intention, i.e. the positively influence mechanism of service robot anthropomorphism on consumer usage intention. Originality/value This research provides preliminary and yet important findings on how service robot anthropomorphism most likely is positively associated with consumer usage intention, i.e. the positively influence mechanism of service robot anthropomorphism on consumer usage intention.
Horizontal Association Modeling: Deep Relation Modeling

The rapid development of internet finance has caused increasing concern in online payment fraud due to its great threat. Online payment fraud detection, a challenge faced by online service, plays an important role in rapidly evolving e-commerce. At present, most platforms use rule systems or machine learning based technologies to detect fraud. It is usually taken for granted that the occurrence of unauthorized behaviors is necessary for fraud detection in online payment services. Behavior-based methods are recognized as promising methods for online payment fraud detection. However, building high-performance behavior models for fraud detection faces several huge challenges, e.g., ex-ante anti-fraud and new fraud attacks. To this end, we have designed two horizontal association modeling solutions: $$\bullet $$ We strive to design an ex-ante anti-fraud method that can work before unauthorized behaviors occur. The feasibility of our solution is supported by the cooperation of a characteristic and a finding in online payment fraud scenarios: The well-recognized characteristic is that online payment frauds are mostly caused by account compromise. Our finding is that account theft is indeed predictable based on users’ high-risk behaviors, without relying on the behaviors of thieves. Accordingly, we propose an account risk prediction scheme to realize the ex-ante fraud detection. It takes in an account’s historical transaction sequence, and outputs its risk score. The risk score is then used as an early evidence of whether a new transaction is fraudulent or not, before the occurrence of the new transaction. We examine our method on a real-world B2C transaction dataset from a commercial bank. Experimental results show that the ex-ante detection method can prevent more than $$80\%$$ of the fraudulent transactions before they actually occur. When the proposed method is combined with an interim detection to form a real-time anti-fraud system, it can detect more than $$94\%$$ of fraudulent transactions while maintaining a very low false alarm rate (less than $$0.1\%$$ ). $$\bullet $$ We pursue an adaptive learning approach to detect fraudulent online payment transactions with automatic sliding time windows. Accordingly, we make efforts on optimizing the setting of windows and improving the adaptability. We design an intelligent window, called learning automatic window (LAW). It utilizes the learning automata to learn the proper parameters of time windows and adjust them dynamically and regularly according to the variation and oscillation of fraudulent transaction patterns. By the experiments over a real-world dataset of the online payment service from a commercial bank, we validate the gain of LAW in terms of detection effectiveness and robustness. To the best of our knowledge, this is the first work to make a sliding time window for fraud detection capable of learning its proper size in changing situations.
Biosensor-Based Multimodal Deep Human Locomotion Decoding via Internet of Healthcare Things

Multiple Internet of Healthcare Things (IoHT)-based devices have been utilized as sensing methodologies for human locomotion decoding to aid in applications related to e-healthcare. Different measurement conditions affect the daily routine monitoring, including the sensor type, wearing style, data retrieval method, and processing model. Currently, several models are present in this domain that include a variety of techniques for pre-processing, descriptor extraction, and reduction, along with the classification of data captured from multiple sensors. However, such models consisting of multiple subject-based data using different techniques may degrade the accuracy rate of locomotion decoding. Therefore, this study proposes a deep neural network model that not only applies the state-of-the-art Quaternion-based filtration technique for motion and ambient data along with background subtraction and skeleton modeling for video-based data, but also learns important descriptors from novel graph-based representations and Gaussian Markov random-field mechanisms. Due to the non-linear nature of data, these descriptors are further utilized to extract the codebook via the Gaussian mixture regression model. Furthermore, the codebook is provided to the recurrent neural network to classify the activities for the locomotion-decoding system. We show the validity of the proposed model across two publicly available data sampling strategies, namely, the HWU-USP and LARa datasets. The proposed model is significantly improved over previous systems, as it achieved 82.22% and 82.50% for the HWU-USP and LARa datasets, respectively. The proposed IoHT-based locomotion-decoding model is useful for unobtrusive human activity recognition over extended periods in e-healthcare facilities.
DVST: Deformable Voxel Set Transformer for 3D Object Detection from Point Clouds

The use of a transformer backbone in LiDAR point-cloud-based models for 3D object detection has recently gained significant interest. The larger receptive field of the transformer backbone improves its representation capability but also results in excessive attention being given to background regions. To solve this problem, we propose a novel approach called deformable voxel set attention, which we utilized to create a deformable voxel set transformer (DVST) backbone for 3D object detection from point clouds. The DVST aims to efficaciously integrate the flexible receptive field of the deformable mechanism and the powerful context modeling capability of the transformer. Specifically, we introduce the deformable mechanism into voxel-based set attention to selectively transfer candidate keys and values of foreground queries to important regions. An offset generation module was designed to learn the offsets of the foreground queries. Furthermore, a globally responsive convolutional feed-forward network with residual connection is presented to capture global feature interactions in hidden space. We verified the validity of the DVST on the KITTI and Waymo open datasets by constructing single-stage and two-stage models. The findings indicated that the DVST enhanced the average precision of the baseline model while preserving computational efficiency, achieving a performance comparable to state-of-the-art methods.
Effects of real‐time VR clinical practice on reducing the stigma toward dementia among students of occupational therapy: A randomized controlled trial

Abstract Aim This study aimed to examine the effects of real‐time online clinical practice using real‐time virtual reality (VR) compared with 2D PC screening on reducing stigma toward dementia, and to investigate the feasibility of online clinical practice using VR. Methods A single‐center, open‐label, randomized controlled trial was conducted. Occupational therapy students were randomized to view occupational therapy evaluation screens for dementia patients using a VR headset or 2D monitor. The Attitudes Toward Dementia Scale (ADS), the Dementia Knowledge Scale (DKS), and Images of the Elderly with Dementia (IED) were assessed before and after the intervention. The level of clinical practice satisfaction and the System Usability Scale (SUS) were also assessed. Results The number of subjects in the intervention and control groups was 10 and 9, respectively. In ADS scores and IED, the main effect was shown in both groups and did not show interactions. In DKS scores, the main effect and interaction were not shown. The VR headset tended to be more usable than the 2D monitor in terms of usability. Satisfaction ratings indicated the characteristics of a realistic clinical experience through real‐time VR viewing. Conclusion Real‐time VR and 2D online clinical practice could reduce the stigma toward dementia, but there were no significant differences between the types. The real‐time VR experience was more similar to actual clinical practice than a 2D PC screening due to the sense of immersion, but issues in blinding and lack of audio and video quality were found.
Variational inference for semiparametric Bayesian novelty detection in large datasets

Abstract After being trained on a fully-labeled training set, where the observations are grouped into a certain number of known classes, novelty detection methods aim to classify the instances of an unlabeled test set while allowing for the presence of previously unseen classes. These models are valuable in many areas, ranging from social network and food adulteration analyses to biology, where an evolving population may be present. In this paper, we focus on a two-stage Bayesian semiparametric novelty detector, also known as Brand, recently introduced in the literature. Leveraging on a model-based mixture representation, Brand allows clustering the test observations into known training terms or a single novelty term. Furthermore, the novelty term is modeled with a Dirichlet Process mixture model to flexibly capture any departure from the known patterns. Brand was originally estimated using MCMC schemes, which are prohibitively costly when applied to high-dimensional data. To scale up Brand applicability to large datasets, we propose to resort to a variational Bayes approach, providing an efficient algorithm for posterior approximation. We demonstrate a significant gain in efficiency and excellent classification performance with thorough simulation studies. Finally, to showcase its applicability, we perform a novelty detection analysis using the openly-available dataset, a large collection of satellite imaging spectra, to search for novel soil types.
Nursing Undergraduates' Smartphone addiction and Academic Procrastination behavior: Serial multiple mediating effects of Self-compassion and Emotional Intelligence

Objectives The purposes of this study were to o examine the mediating effects of self-compassion and emotional intelligence on smartphone addiction and academic procrastination behavior among nursing undergraduates.&#x0D; Methods Data was obtained via an online survey conducted from May to June 2023, with senior nursing students and multi-mediating effects were analyzed using the PROCESS macro for SPSS software, utilizing bootstrap techniques.&#x0D; Results Smartphone addiction showed a negative correlation with self-compassion. Academic procrastination and sma rtphone addiction showed a positive correlation, and self-compassion and emotional intelligence each showed a negative correlation. Smartphone addiction and self-compassion showed a negative correlation. The indirect effect of smartphone on academic procrastination behavior through self-compassion had significant mediating effects(Std. estimate=-.0213, CI: -.0518~.0048). However, emotional intelligence was not a mediating effect (Std. estimate=-.0110, CI: -.0505~.0264). There was a significant indirect effect of smartphone addiction on academic procrastination behavior through self-compassion and emotional intelligence (Std. estimate=.0470, CI: .0217~.0796).&#x0D; Conclusions The findings highlight the necessity of nursing education interventions for enhancing self-compassion and emotional intelligence, ultimately improving caregiving competency in nursing undergraduates.
Approaches for Working with the Body in the Design of Electronic Music Performance Systems

This article outlines a body-centred focus in the design of performance systems for electronic music.Through a movement-led design process, I discuss the interaction of the body, controllers, data and mapping.A flat hierarchy approach to performance system design aims to support bodily autonomy in performance, challenge human-machine dualism and expand the definition of virtuosity in performance to include system virtuosity.The affordances and behaviours of the components of performance systems are discussed in terms of their cooperation.With an awareness of the non-neutrality of data, controllers, and sensors, I outline methods for making adaptive and flexible systems which respond to and respect individual bodies.This embodied and hybrid approach to system design can facilitate posthuman expression in electronic music performance.
SOLID: a novel similarity metric for mono-modal and multi-modal deformable image registration

Medical image registration is an integral part of various clinical applications including image guidance, motion tracking, therapy assessment and diagnosis. We present a robust approach for mono-modal and multi-modal medical image registration. To this end, we propose the novel shape operator based local image distance (SOLID) which estimates the similarity of images by comparing their second-order curvature information. Our similarity metric is rigorously tailored to be suitable for comparing images from different medical imaging modalities or image contrasts. A critical element of our method is the extraction of local features using higher-order shape information, enabling the accurate identification and registration of smaller structures. In order to assess the efficacy of the proposed similarity metric, we have implemented a variational image registration algorithm that relies on the principle of matching the curvature information of the given images. The performance of the proposed algorithm has been evaluated against various alternative state-of-the-art variational registration algorithms. Our experiments involve mono-modal as well as multi-modal and cross-contrast co-registration tasks in a broad variety of anatomical regions. Compared to the evaluated alternative registration methods, the results indicate a very favorable accuracy, precision and robustness of the proposed SOLID method in various highly challenging registration tasks.