Reviewerly/exHarmony huggingface dataset:
The Impact of Gender on the Use of Augmented Reality and Virtual Reality in Students with ASD

Today’s education is in the process of transformation as a consequence of the inclusion of educational technology in learning spaces. Among the most prominent technologies is augmented and virtual reality. These emerging technologies have reflected great educational potential in different contexts. The study's objective is focused on knowing the influence of gender in the application of augmented and virtual reality in students with ASD. They have been considered in various dimensions such as motivation, attention, communication, autonomy, and learning outcomes. A quantitative research methodology has been used through a descriptive and correlational non-experimental design to achieve this objective. 46 Spanish students with ASD participated in the study. The data were collected through a questionnaire. The results reveal differences in motivation, attention, and communication based on gender. However, in terms of autonomy and in learning outcomes, gender does not suppose any conditioner. The work culminates with various theoretical and practical implications.
Enhancing customer satisfaction with chatbots: The influence of communication styles and consumer attachment anxiety

Chatbots are increasingly occupying the online retailing landscape, and the volume of consumer-chatbot service interactions is exploding. Even so, it still remains unclear how chatbots should communicate with consumers to ensure positive customer service experiences and, in particular, to improve their satisfaction. A fundamental decision in this regard is the choice of a communication style, specifically, whether a social-oriented or a task-oriented communication style should be best used for chatbots. In this paper, we investigate how using a social-oriented versus task-oriented communication style can improve customer satisfaction. Two experimental studies reveal that using a social-oriented communication style boosts customer satisfaction. Warmth perception of the chatbot mediates this effect, while consumer attachment anxiety moderates these effects. Our results indicate that social-oriented communication style can be beneficial in enhancing service satisfaction for highly anxiously attached customers, but it does not work for the lowly anxiously attached. This study provides theoretical and practical implications about how to implement chatbots in service encounters.
PCEE-BERT: Accelerating BERT Inference via Patient and Confident Early Exiting

BERT and other pre-trained language models (PLMs) are ubiquitous in modern NLP.Even though PLMs are the state-of-the-art (SOTA) models for almost every NLP task (Qiu et al., 2020), the significant latency during inference prohibits wider industrial usage.In this work, we propose Patient and Confident Early Exiting BERT (PCEE-BERT), an off-the-shelf sample-dependent early exiting method that can work with different PLMs and can also work along with popular model compression methods.With a multi-exit BERT as the backbone model, PCEE-BERT will make the early exiting decision if enough numbers (patience parameter) of consecutive intermediate layers are confident about their predictions.The entropy value measures the confidence level of an intermediate layer's prediction.Experiments on the GLUE benchmark demonstrate that our method outperforms previous SOTA early exiting methods.Ablation studies show that: (a) our method performs consistently well on other PLMs, such as ALBERT and TinyBERT; (b) PCEE-BERT can achieve different speed-up ratios by adjusting the patience parameter and the confidence threshold.The code for PCEE-BERT can be found at https://github.
Learn from Relation Information: Towards Prototype Representation Rectification for Few-Shot Relation Extraction

Few-shot Relation Extraction refers to fast adaptation to novel relation classes with few samples through training on the known relation classes.Most existing methods focus on implicitly introducing relation information (i.e., relation label or relation description) to constrain the prototype representation learning, such as contrastive learning, graphs, and specifically designed attentions, which may bring useless and even harmful parameters.Besides, these approaches are limited in handing outlier samples far away from the class center due to the weakly implicit constraint.In this paper, we propose an effective and parameter-less Prototype Rectification Method (PRM) to promote few-shot relation extraction, where we utilize a prototype rectification module to rectify original prototypes explicitly by the relation information.Specifically, PRM is composed of two gate mechanisms.One gate decides how much of the original prototype remains, and another one updates the remained prototype with relation information.In doing so, better and stabler global relation information can be captured for guiding prototype representations, and thus PRM can robustly deal with outliers.Moreover, we also extend PRM to both noneof-the-above (NOTA) and domain adaptation scenarios.Experimental results on FewRel 1.0 and 2.0 datasets demonstrate the effectiveness of our proposed method, which achieves stateof-the-art performance.
Towards communication-efficient vertical federated learning training via cache-enabled local updates

Vertical federated learning (VFL) is an emerging paradigm that allows different parties (e.g., organizations or enterprises) to collaboratively build machine learning models with privacy protection. In the training phase, VFL only exchanges the intermediate statistics, i.e., forward activations and backward derivatives, across parties to compute model gradients. Nevertheless, due to its geo-distributed nature, VFL training usually suffers from the low WAN bandwidth. In this paper, we introduce CELU-VFL, a novel and efficient VFL training framework that exploits the local update technique to reduce the cross-party communication rounds. CELU-VFL caches the stale statistics and reuses them to estimate model gradients without exchanging the ad hoc statistics. Significant techniques are proposed to improve the convergence performance. First, to handle the stochastic variance problem, we propose a uniform sampling strategy to fairly choose the stale statistics for local updates. Second, to harness the errors brought by the staleness, we devise an instance weighting mechanism that measures the reliability of the estimated gradients. Theoretical analysis proves that CELU-VFL achieves a similar sub-linear convergence rate as vanilla VFL training but requires much fewer communication rounds. Empirical results on both public and real-world workloads validate that CELU-VFL can be up to six times faster than the existing works.
Creation of Immersive Resources Based on Virtual Reality for Dissemination and Teaching in Chemical Engineering

Chemical Engineering is a complex discipline that is mainly applied to the industrial context, which makes it difficult to approach real equipment and systems in the classroom to students. Nevertheless, Information and Communication Technologies (ICTs) are particularly useful to encourage active and autonomous learning, normally associated with deeper levels of engagement and understanding of the subjects taught. In this way, several studies have demonstrated that immersion has the potential to increase learning experiences and improve creativity and engagement, Virtual Reality (VR) being a remarkable example. In this context, we created and shared with students two immersive resources based on VR: (a) a laboratory 360° video tour, which was used to disseminate the Chemical Engineering Degree in an Open Door Days organized for high school students; and (b) an experiential learning tool integrated with Moodle, which was available previously to face-to-face practical lessons of the subject Separation Operations. In both cases, the feedback from the participant students was positive. High school students increased their interest in Chemical Engineering Degree after viewing the 360° video, meanwhile undergraduate students found the immersive tool useful in the subsequent performance of real practical activities. From the perspective of professors, creation of these resources required purchase of equipment, time and effort, but they were highly valued as a tool for disseminating and supporting teaching, being an initial starting point for the creation of more enhanced VR-based materials.
Extreme learning machine tuning by original sine cosine algorithm

Extreme learning machine (ELM) is a revolutionary approach for training single-hidden layer feedforward neural networks that combines both high performance and rapid learning speed. Because the input weights and hidden neurons biases are randomly initialized and stay fixed during the process of learning, and the output weights are analytically calculated. ELM produces high generalization capability with a huge number of hidden neurons. The sine cosine method was presented in this study for tuning the input weights and hidden biases. The suggested method is named SCA-ELM, and it selects the input weights and hidden biases using SCA while determining the output weights using the Moore-Penrose (MP) generalized inverse, The aim is to improve the original extreme learning machine algorithm.The suggested methodologies were evaluated on several benchmark classification data sets, and compared with other recent state-of-art algorithms. Simulations reveal that the suggested method outperforms the other alternatives in the comparative analysis.
Multi-View HRRP Generation With Aspect-Directed Attention GAN

where he is currently pursuing the Ph.D. degree in the School of Information and Electronics.His current research interests include high resolution radar systems, radar image generation, and radar automatic target recognition.
S-Swin Transformer: simplified Swin Transformer model for offline handwritten Chinese character recognition

The Transformer shows good prospects in computer vision. However, the Swin Transformer model has the disadvantage of a large number of parameters and high computational effort. To effectively solve these problems of the model, a simplified Swin Transformer (S-Swin Transformer) model was proposed in this article for handwritten Chinese character recognition. The model simplifies the initial four hierarchical stages into three hierarchical stages. In addition, the new model increases the size of the window in the window attention; the number of patches in the window is larger; and the perceptual field of the window is increased. As the network model deepens, the size of patches becomes larger, and the perceived range of each patch increases. Meanwhile, the purpose of shifting the window's attention is to enhance the information interaction between the window and the window. Experimental results show that the verification accuracy improves slightly as the window becomes larger. The best validation accuracy of the simplified Swin Transformer model on the dataset reached 95.70%. The number of parameters is only 8.69 million, and FLOPs are 2.90G, which greatly reduces the number of parameters and computation of the model and proves the correctness and validity of the proposed model.
CateNorm: Categorical Normalization for Robust Medical Image Segmentation

Batch normalization (BN) uniformly shifts and scales the activations based on the statistics of a batch of images. However, the intensity distribution of the background pixels often dominates the BN statistics because the background accounts for a large proportion of the entire image. This paper focuses on enhancing BN with the intensity distribution of foreground pixels, the one that really matters for image segmentation. We propose a new normalization strategy, named categorical normalization (CateNorm), to normalize the activations according to categorical statistics. The categorical statistics are obtained by dynamically modulating specific regions in an image that belong to the foreground. CateNorm demonstrates both precise and robust segmentation results across five public datasets obtained from different domains, covering complex and variable data distributions. It is attributable to the ability of CateNorm to capture domain-invariant information from multiple domains (institutions) of medical data. Code is available at https://github.com/lambert-x/CateNorm .
Development and Evaluation of a Motion-based VR Bicycle Simulator

Bicycle simulators are becoming an increasingly used research tool. However, due to the complex cycling dynamics, these simulators have issues of simulator sickness and perceived realism. A potential method to address these issues could be providing a motion-based tilting function. Some bicycle simulators with tilt functionality have already been presented but still lack a systematic evaluation. In this work, we present a motion-based bicycle simulator without centrifugal force simulation and the results from a user study that compared different tilt modes. N=31 participants completed a study in virtual reality with a strong and a weak tilt mode, as well as a baseline condition without movement. We discovered that weak tilting could significantly improve the cycling realism without decreasing cycling performance and simulator sickness. Furthermore, our research suggests that there is a sweet spot for a tilting function, which facilitates a balance between presence/immersion and simulator sickness.
Are Thermal Attacks a Realistic Threat? Investigating the Preconditions of Thermal Attacks in Users’ Daily Lives

Thermal attacks refer to the possibility of capturing heat traces that result from interacting with user interfaces to reveal sensitive input, such as passwords. The technical feasibility and effectiveness of thermal attacks have already been demonstrated. Yet, several preconditions have to be met for successful thermal attacks. In this paper, we investigate user awareness of thermal attacks and to which extent the attack’s preconditions are met in the users’ daily lives. We present results from an online study with 101 participants showing that users are frequently at risk of thermal attacks based on their behavior, e.g., due to leaving devices unattended, or their choice of authentication method. Further, only 7 of our 101 participants had heard of thermal attacks. Based on our results, we discuss the implications on user security, operators of public spaces, and the development of thermal attack-resistant input methods.
Investigating Wearable Fitness Applications: Data Privacy and Digital Forensics Analysis on Android

Wearable devices are becoming more and more prevalent in our daily lives as people become more curious about how well they are doing in monitoring, improving, or maintaining their health and fitness. Fitness trackers and smartwatches have become almost ubiquitous, so these devices have begun to play a critical role in forensic investigations. In this paper, the authors conducted a forensic analysis of the controlling applications for three popular fitness bands and smartwatches (i.e., Amazon Halo, Garmin Connect, and Mobvoi) on an Android smartphone device to (1) provide forensic investigators with a road-map of forensically relevant data that are stored within these applications and (2) highlight any privacy concerns that the stored data within these applications may present to the applications’ users. Our findings indicate that the three fitness applications store a wealth of user data. In particular, the Amazon Halo app stores daily, weekly, and monthly activity-related data for at least the last 13 days. The user’s Tone Analysis results were also recovered. The Garmin Connect application also records detailed user activity information, as it was possible to recover the last 15 days worth of user activity data. The Garmin Connect user’s general location was also determined via the application’s weather notification feature. Lastly, the Mobvoi application records all data points from the time the device is first used until the last time the device is used. These data points may include heart rates taken every 5 min and step counts. Our findings highlight the possibility of collecting personally identifiable information about users of these devices and apps, including their profile information, habits, location, and state of mind. These findings would be pertinent to forensic investigators in the event that these or similar applications are part of an investigation.
Selection of Lunar South Pole Landing Site Based on Constructing and Analyzing Fuzzy Cognitive Maps

The Permanently Shadowed Regions (PSRs) of the lunar south pole have never been directly sampled. To explore and discover lunar resources, the Chinese lunar south pole exploration mission is scheduled to land in direct sunlight near the PSR, where sampling and analysis will be carried out. The selection of sites for lunar landing sampling sites is one of the key steps of the mission. The main factors affecting the site selection are the distribution of PSRs, lunar surface slopes, rock distribution, light intensity, and maximum temperature. In this paper, the main factors affecting site selection are analyzed based on lunar multi-source remote sensing data. Combined with previous engineering constraints, we then propose a comprehensive multi-factor fuzzy cognition and selection model for the lunar south site selection. An analytical model based on a fuzzy cognitive map algorithm is also established. Furthermore, to make a preliminary landing area selection, we determine the evaluation index for the candidate landing areas using fuzzy reasoning. Using the proposed model and combined scoring index, we also verify and analyze the prominent impact craters at the lunar south pole. The scores of de Gerlache (88.48°S 88.34°W), Shackleton (89.67°S 129.78°E), and Amundsen (84.5°S, 82.8°E) craters are determined using fuzzy interference as 0.816, 0.814, and 0.784, respectively. Moreover, using our proposed approach, we identify feasible landing sites around the de Gerlache crater close to the PSR to facilitate discovery of water ice exposures in future missions. The proposed method is capable of evaluating alternative landing zones subject to multiple engineering constraints on the Moon or Mars based on the existing data.
Mitigating Label-Noise for Facial Expression Recognition in the Wild

Label-noise constitutes a major challenge for facial expression recognition in the wild due to the ambiguity of facial expressions worsened by low-quality images. To deal with this problem, we propose a simple but effective Label-noise Robust Network (LRN) which explores the inter-class correlations for mitigating ambiguity that usually happens between morphologically similar classes. Specifically, LRN leverages a multivariate normal distribution to model such correlations at the final hidden layer of the neural network to suppress the heteroscedastic uncertainty caused by inter-class label noise. Furthermore, LRN utilizes a confidence-based label-free loss to extract compact intra-class feature representations under label noise while preserving the intrinsic inter-class relationships. Experiments on three in-the-wild facial expression datasets demonstrates the superiority of our method.
Towards Natural Language-Based Visualization Authoring

A key challenge to visualization authoring is the process of getting familiar with the complex user interfaces of authoring tools. Natural Language Interface (NLI) presents promising benefits due to its learnability and usability. However, supporting NLIs for authoring tools requires expertise in natural language processing, while existing NLIs are mostly designed for visual analytic workflow. In this paper, we propose an authoring-oriented NLI pipeline by introducing a structured representation of users' visualization editing intents, called editing actions, based on a formative study and an extensive survey on visualization construction tools. The editing actions are executable, and thus decouple natural language interpretation and visualization applications as an intermediate layer. We implement a deep learning-based NL interpreter to translate NL utterances into editing actions. The interpreter is reusable and extensible across authoring tools. The authoring tools only need to map the editing actions into tool-specific operations. To illustrate the usages of the NL interpreter, we implement an Excel chart editor and a proof-of-concept authoring tool, VisTalk. We conduct a user study with VisTalk to understand the usage patterns of NL-based authoring systems. Finally, we discuss observations on how users author charts with natural language, as well as implications for future research.
Evidential prototype-based clustering based on transfer learning

In some real clustering tasks, the data may be sparse and uncertain. Although there is usually some useful knowledge in related scenes, the data among different domains is often of great inconsistency. A new unsupervised transfer learning method is proposed in the framework of belief functions to handle the insufficiency and uncertain problems in clustering simultaneously. Firstly, under the assumption that the source and target domains have the same number of clusters, the Transfer Evidential C Means (TECM) is developed by incorporating the idea of transfer learning and evidential clustering. A novel objective function is designed to employ the cluster prototypes of the source data as references to guide the clustering process on the target. Furthermore, ETECM, as an extended version of TECM, is also introduced for the situation that the two domains have different numbers of clusters. Some experiments conducted on synthetic and real-world data sets demonstrate the advantages of TECM and ETECM.
Obstacle Avoidance-Based Autonomous Navigation of a Quadrotor System

Livestock management is an emerging area of application of the quadrotor, especially for monitoring, counting, detecting, recognizing, and tracking animals through image or video footage. The autonomous operation of the quadrotor requires the development of an obstacle avoidance scheme to avoid collisions. This research develops an obstacle avoidance-based autonomous navigation of a quadrotor suitable for outdoor applications in livestock management. A Simulink model of the UAV is developed to achieve this, and its transient and steady-state performances are measured. Two genetic algorithm-based PID controllers for the quadrotor altitude and attitude control were designed, and an obstacle avoidance algorithm was applied to ensure the autonomous navigation of the quadrotor. The simulation results show that the quadrotor flies to the desired altitude with a settling time of 6.51 s, an overshoot of 2.65%, and a steady-state error of 0.0011 m. At the same time, the attitude controller records a settling time of 0.43 s, an overshoot of 2.50%, and a zero steady-state error. The implementation of the obstacle avoidance scheme shows that the distance threshold of 1 m is sufficient for the autonomous navigation of the quadrotor. Hence, the developed method is suitable for managing livestock with the average size of an adult sheep.