Reviewerly/exHarmony huggingface dataset:
The efficacy of health experts’ communication in inducing support for COVID-19 measures and effect on trustworthiness: A survey in Hong Kong

To control the spread of COVID-19, governments may implement freedoms-infringing health measures. Therefore, citizens' support for these measures is important. This study investigates: (1) whether health experts' communication induces support for COVID-19 measures, and (2) whether health experts' agreeing or disagreeing with government directives affects their trustworthiness. A cross-sectional online questionnaire was completed by 1072 adults in the Hong Kong general population between May 26 and June 3, 2021. Three COVID-19 measures were examined: contact-tracing mobile application, restriction-testing, and ban of public assembly. For each, participants were randomly assigned to three groups to view, respectively: vignettes with a neutral government announcement only; vignettes with a government announcement and a health expert's quote supporting the government's decision; and vignettes with a government announcement and a health expert's quote disagreeing with the government's decision. The result shows that positive health experts' communication increased the support for banning public assembly; no effects were found for the support for contact-tracing mobile applications and restriction testing. Participants who only viewed health experts disagreeing with the government had higher trust in health experts relative to participants who viewed health experts agreeing with the government at least once. The results render doubtful the strategy that health experts can be involved for garnering support for unpopular health measures without jeopardizing public trust in them.
Adaptive cooperative exploration for reinforcement learning from imperfect demonstrations

In reinforcement learning, exploration is an important way to learn new skills, but it is usually inefficient when faced with huge state-action space or sparse extrinsic rewards. Generally, expert demonstrations can assist the policy learning by leading the agent to imitate or explore these data. However, the demonstrations are often imperfect due to the data collection noise or immature expert. To this end, we propose a novel adaptive cooperative exploration method that can effectively alleviate the issues of imperfect demonstrations and improve the policy learning with them. Specifically, we propose a cooperative learning module to encourage two agents to explore diversely with it and then fuse the learned policies. Meanwhile, the adaptive self-supervised exploration method is presented to dynamically explore the demonstrations considering the environmental feedback. Therefore, the proposed method can achieve effective utilization of the imperfect demonstrations for policy learning. Experimental results demonstrate the effectiveness of the proposed method on MuJoCo benchmark.
Images Reconstruction from Functional Magnetic Resonance Imaging Patterns Based on the Improved Deep Generative Multiview Model

Reconstructing visual stimulus images from the brain activity signals is an important research task in the field of brain decoding. Many methods of reconstructing visual stimulus images mainly focus on how to use deep learning to classify the brain activities measured by functional magnetic resonance imaging or identify visual stimulus images. Accurate reconstruction of visual stimulus images by using deep learning still remains challenging. This paper proposes an improved deep generative multiview model to further promote the accuracy of reconstructing visual stimulus images. Firstly, an encoder based on residual-in-residual dense blocks is designed to fit the deep and multiview visual features of human natural state, and extract the features of visual stimulus images. Secondly, the structure of original decoder is extended to a deeper network in the deep generative multiview model, which makes the features obtained by each deconvolution layer more distinguishable. Finally, we configure the parameters of the optimizer and compare the performance of various optimizers under different parameter values, and then the one with the best performance is chosen and adopted to the whole model. The performance evaluations conducted on two publicly available datasets demonstrate that the improved model has more accurate reconstruction effectiveness than the original deep generative multiview model.
Reply to Fildani, A.; Hessler, A.M. Comment on “Gerbaudo et al. Are We Ready for a Sustainable Development? A Survey among Young Geoscientists in Italy. Sustainability 2022, 14, 7621”

We very much appreciate and thank Andrea Fildani and Angela Hessler for their comment [...]
Computational Model of Recommender System Intervention

A recommender system is an information selection system that offers preferences to users and enhances their decision-making. This system is commonly implemented in human-computer-interaction (HCI) intervention because of its information filtering and personalization. However, its success rate in decision-making intervention is considered low and the rationale for this is associated with users’ psychological reactance which is causing unsuccessful recommender system interventions. This paper employs a computational model to depict factors that lead to recommender system rejection by users and how these factors can be enhanced to achieve successful recommender system interventions. The study made use of design science research methodology by executing a computational analysis based on an agent-based simulation approach for the model development and implementation. A total of sixteen model concepts were identified and formalized which were implemented in a Matlab environment using three major case conditions as suggested in previous studies. The result of the study provides an explicit comprehension on interplaying of recommender system that generate psychological reactance which is of great importance to recommender system developers and designers to depict how successful recommender system interventions can be achieved without users experiencing reactance and rejection on the system.
FUSION: Measuring Binary Function Similarity with Code-Specific Embedding and Order-Sensitive GNN

Binary code similarity measurement is a popular research area in binary analysis with the recent development of deep learning-based models. Current state-of-the-art methods often use the pre-trained language model (PTLM) to embed instructions into basic blocks as representations of nodes within a control flow graph (CFG). These methods will then use the graph neural network (GNN) to embed the whole CFG and measure the binary similarities between these code embeddings. However, these methods almost directly treat the assembly code as a natural language text and ignore its code-specific features when training PTLM. Moreover, They barely consider the direction of edges in the CFG or consider it less efficient. The weaknesses of the above approaches may limit the performances of previous methods. In this paper, we propose a novel method called function similarity using code-specific PPTs and order-sensitive GNN (FUSION). Since the similarity of binary codes is a symmetric/asymmetric problem, we were guided by the ideas of symmetry and asymmetry in our research. They measure the binary function similarity with two code-specific PTLM training strategies and an order-sensitive GNN, which, respectively, alleviate the aforementioned weaknesses. FUSION outperforms the state-of-the-art binary similarity methods by up to 5.4% in accuracy, and performs significantly better.
Enhancing user interaction with context-awareness in cultural spaces

Abstract The emergence of the Internet of Things has fueled a proliferation of smart things in many fields, including cultural spaces. Context-awareness addresses the production of large volumes of context by analyzing raw data and adding a meaning to them. Middleware systems have emerged, which perform context modelling and reasoning, supporting context-aware applications. The services provided by such applications can be personalized, automated and adapted to the current situation, thus enhancing the user interaction with the devices and the digital environment. In this work, a context-aware middleware system is presented, based on a hybrid reasoning schema, which combines multiple techniques to efficiently address each problem. The proposed middleware system is evaluated in a cultural space, where scenarios were designed and tested, using a mixture of real and artificial data. The experiments measured the accuracy, performance in terms of reaction time and scalability and the interactivity enhancement, achieved by the proposed middleware.
The development of an automatic speech recognition model using interview data from long-term care for older adults

In long-term care (LTC) for older adults, interviews are used to collect client perspectives that are often recorded and transcribed verbatim, which is a time-consuming, tedious task. Automatic speech recognition (ASR) could provide a solution; however, current ASR systems are not effective for certain demographic groups. This study aims to show how data from specific groups, such as older adults or people with accents, can be used to develop an effective ASR.An initial ASR model was developed using the Mozilla Common Voice dataset. Audio and transcript data (34 h) from interviews with residents, family, and care professionals on quality of care were used. Interview data were continuously processed to reduce the word error rate (WER).Due to background noise and mispronunciations, an initial ASR model had a WER of 48.3% on interview data. After finetuning using interview data, the average WER was reduced to 24.3%. When tested on speech data from the interviews, a median WER of 22.1% was achieved, with residents displaying the highest WER (22.7%). The resulting ASR model was at least 6 times faster than manual transcription.The current method decreased the WER substantially, verifying its efficacy. Moreover, using local transcription of audio can be beneficial to the privacy of participants.The current study shows that interview data from LTC for older adults can be effectively used to improve an ASR model. While the model output does still contain some errors, researchers reported that it saved much time during transcription.
Comprehensive Analysis of VM Migration Trends in Cloud Data Centers

Background: Virtualization adequately maintains increasing requirements for storage, networking, servers, and computing in exhaustive cloud data centers (CDC)s. Virtualization assists in gaining different objectives like dedicated server sustenance, fault tolerance, comprehensive service availability, and load balancing, by virtual machine (VM) migration. The VM migration process continuously requires CPU cycles, communication bandwidth, memory, and processing power. Therefore, it detrimentally prevails over the performance of dynamic applications and cannot be completely neglected in the synchronous large-scale CDC, explicitly when service level agreement (SLA) and analytical trade goals are to be defined. Introduction: Live VM migration is intermittently adopted as it grants the operational service even when the migration is executed. Currently, power competence has been identified as the primary design requirement for the current CDC model. It grows from a single server to numerous data centres and clouds, which consume an extensive amount of electricity. Consequently, appropriate energy management techniques are especially important for CDCs. Method: This review paper delineates the need for energy efficiency in the CDC, the systematic mapping of VM migration methods, and research pertinent to it. After that, an analysis of VM migration techniques, the category of VM migration, duplication, and context-based VM migration is presented along with its relative analysis. Results: The various VM migration techniques were compared on the basis of various performance measures. The techniques based on duplication and context-based VM migration methods provide an average reduction in migration time of up to 38.47%, data transfer rate of up to 51.4%, migration downtime of up to 36.33%, network traffic rate of up to 44% and reduced application efficiency overhead up to 14.27%. Conclusion: The study aids in analyzing threats and research challenges related to VM migration techniques which ultimately help in exploring future research directions that would help aspiring cloud professionals.
Emotional Talking Faces

Lip synchronization and talking face generation have gained a specific interest from the research community with the advent and need of digital communication in different fields. Prior works propose several elegant solutions to this problem. However, they often fail to create realistic-looking videos that account for people's expressions and emotions. To mitigate this, we build a talking face generation framework conditioned on a categorical emotion to generate videos with appropriate expressions, making them more real-looking and convincing. With a broad range of six emotions i.e., anger, disgust, fear, happiness, neutral, and sad, we show that our model generalizes across identities, emotions, and languages.
<i>Beat Saber</i> as Virtual Reality Exercising in 360 Degrees: A Moderated Mediation Model of VR Playable Angles on Physiological and Psychological Outcomes

Physical inactivity is a global problem, and active video games (AVGs) have been demonstrated as effective at motivating players to be more physically active. Virtual-reality AVGs (VRAVGs) further encourage engagement with exerting gameplay by challenging players to move in a full 360-degree range of motion. As a unique feature of VRAVGs, we examined the influence of using multiple playable angles in Beat Saber on players' physical activity and psychological outcomes. A between-subjects experiment randomly assigned participants (N = 240) to one of the three playable angles (single fixed angle, 90-degree, and 360-degree modes). Informed by the interactivity-as-demand model, a moderated mediation model of playable angles on enjoyment was supported. Players perceive higher cognitive and exertional demands in both multi-angle modes than those in single-angle mode, which leads to greater perceived difficulty and further enhances the enjoyment for players with a higher need for cognition, but negatively affects the enjoyment for players with a lower need for cognition. Multi-angle modes also led to greater motivation for future play and subjective vitality through greater cognitive demands, and more physical activity via higher exertional demands. We conclude that, in VR exercise, multiple playable angles are a unique feature that positively influences physical activity outcomes.
Statistical Picking of Multivariate Waveforms

In this paper, we propose a new approach based on the fitting of a generalized linear regression model in order to detect points of change in the variance of a multivariate-covariance Gaussian variable, where the variance function is piecewise constant. By applying this new approach to multivariate waveforms, our method provides simultaneous detection of change points in functional time series. The proposed approach can be used as a new picking algorithm in order to automatically identify the arrival times of P- and S-waves in different seismograms that are recording the same seismic event. A seismogram is a record of ground motion at a measuring station as a function of time, and it typically records motions along three orthogonal axes (X, Y, and Z), with the Z-axis being perpendicular to the Earth's surface and the X- and Y-axes being parallel to the surface and generally oriented in North-South and East-West directions, respectively. The proposed method was tested on a dataset of simulated waveforms in order to capture changes in the performance according to the waveform characteristics. In an application to real seismic data, our results demonstrated the ability of the multivariate algorithm to pick the arrival times in quite noisy waveforms coming from seismic events with low magnitudes.
MAGNet: A Camouflaged Object Detection Network Simulating the Observation Effect of a Magnifier

In recent years, protecting important objects by simulating animal camouflage has been widely employed in many fields. Therefore, camouflaged object detection (COD) technology has emerged. COD is more difficult to achieve than traditional object detection techniques due to the high degree of fusion of objects camouflaged with the background. In this paper, we strive to more accurately and efficiently identify camouflaged objects. Inspired by the use of magnifiers to search for hidden objects in pictures, we propose a COD network that simulates the observation effect of a magnifier called the MAGnifier Network (MAGNet). Specifically, our MAGNet contains two parallel modules: the ergodic magnification module (EMM) and the attention focus module (AFM). The EMM is designed to mimic the process of a magnifier enlarging an image, and AFM is used to simulate the observation process in which human attention is highly focused on a particular region. The two sets of output camouflaged object maps were merged to simulate the observation of an object by a magnifier. In addition, a weighted key point area perception loss function, which is more applicable to COD, was designed based on two modules to give greater attention to the camouflaged object. Extensive experiments demonstrate that compared with 19 cutting-edge detection models, MAGNet can achieve the best comprehensive effect on eight evaluation metrics in the public COD dataset. Additionally, compared to other COD methods, MAGNet has lower computational complexity and faster segmentation. We also validated the model's generalization ability on a military camouflaged object dataset constructed in-house. Finally, we experimentally explored some extended applications of COD.
Enhancing Semantic-Consistent Features and Transforming Discriminative Features for Generalized Zero-Shot Classifications

Generalized zero-shot learning (GZSL) aims to classify classes that do not appear during training. Recent state-of-the-art approaches rely on generative models, which use correlating semantic embeddings to synthesize unseen classes visual features; however, these approaches ignore the semantic and visual relevance, and visual features synthesized by generative models do not represent their semantics well. Although existing GZSL methods based on generative model disentanglement consider consistency between visual and semantic models, these methods consider semantic consistency only in the training phase and ignore semantic consistency in the feature synthesis and classification phases. The absence of such constraints may lead to an unrepresentative synthesized visual model with respect to semantics, and the visual and semantic features are not modally well aligned, thus causing the bias between visual and semantic features. Therefore, an approach for GZSL is proposed to enhance semantic-consistent features and discriminative features transformation (ESTD-GZSL). The proposed method can enhance semantic-consistent features at all stages of GZSL. A semantic decoder module is first added to the VAE to map synthetic and real features to the corresponding semantic embeddings. This regularization method allows synthesizing unseen classes for a more representative visual representation, and synthetic features can better represent their semantics. Then, the semantic-consistent features decomposed by the disentanglement module and the features output by the semantic decoder are transformed into enhanced semantic-consistent discriminative features and used in classification to reduce the ambiguity between categories. The experimental results show that our proposed method achieves more competitive results on four benchmark datasets (AWA2, CUB, FLO, and APY) of GZSL.
Sketch-based entropy estimation

This work presents the implementation of a tabular interpolation approach to estimate empirical Shannon entropy on programmable data plane ASICs using P4. The technique transforms the complex computations of the random projection into fast lookup over pre-computed tables in the match-action pipeline. Likewise, the interpolation heuristic further reduces the table size substantially. Thus, more tables can be accommodated, achieving higher estimation accuracy. Simulations based on real-world network traffic traces are performed to evaluate the estimation accuracy. The scheme is deployed in a Barefoot Tofino2 switch connected to the International Center for Advanced Internet Research (iCAIR) national testbed. The system can estimate the entropy of network traffic accurately at 400 Gbps throughput.
Relevancy between Objects Based on Common Sense for Semantic Segmentation

Research on image classification sparked the latest deep-learning boom. Many downstream tasks, including semantic segmentation, benefit from it. The state-of-the-art semantic segmentation models are all based on deep learning, and they sometimes make some semantic mistakes. In a semantic segmentation dataset with a small number of categories, images are often collected from a single scene, and there is a close semantic connection between any two categories. However, in the semantic segmentation dataset collected from multiple scenes, two categories may be irrelevant. The probability of objects in one category appearing next to objects in other categories is different, which is the basis of the paper. Semantic segmentation methods need to solve two problems of positioning and classification. This paper is dedicated to correcting those clearly wrong classifications that are contrary to reality. Specifically, we first calculate the relevancy between different class pairs. Then, based on this knowledge, we infer the category of a connected component according to the relationships of this connected component with its surrounding connected components and correct the obviously wrong classifications made by a deep learning semantic segmentation model. Several well-performing deep learning models are experimented on two challenging public datasets in the field of semantic image segmentation. Our proposed method improves the performance of UPerNet, OCRNet and SETR from 40.7%, 43% and 48.64% to 42.07%, 44.09% and 49.09% mean IoU on the ADE20K validation set, and the performance of PSPNet, DeepLabV3 and OCRNet from 37.26%, 37.3% and 39.5% to 38.93%, 38.95% and 40.63% mean IoU on the COCO-Stuff dataset, which shows the effectiveness of the method.
AXIOMATIZATIONS OF PEANO ARITHMETIC: A TRUTH-THEORETIC VIEW

Abstract We employ the lens provided by formal truth theory to study axiomatizations of Peano Arithmetic ${\textsf {(PA)}}$ . More specifically, let Elementary Arithmetic ${\textsf {(EA)}}$ be the fragment $\mathsf {I}\Delta _0 + \mathsf {Exp}$ of ${\textsf {PA}}$ , and let ${\textsf {CT}}^-[{\textsf {EA}}]$ be the extension of ${\textsf {EA}}$ by the commonly studied axioms of compositional truth ${\textsf {CT}}^-$ . We investigate both local and global properties of the family of first order theories of the form ${\textsf {CT}}^-[{\textsf {EA}}] +\alpha $ , where $\alpha $ is a particular way of expressing “ ${\textsf {PA}}$ is true” (using the truth predicate). Our focus is dominantly on two types of axiomatizations, namely: (1) schematic axiomatizations that are deductively equivalent to ${\textsf {PA}}$ and (2) axiomatizations that are proof-theoretically equivalent to the canonical axiomatization of ${\textsf {PA}}$ .