Reviewerly/exHarmony huggingface dataset:
Cognitive mechanisms of perspective-taking across adulthood: An eye-tracking study using the director task.

Perspective-taking plays an important role in daily life, allowing consideration of other people's perspectives and viewpoints. This study used a large sample of 265 community-based participants (aged 20-86 years) to examine changes in perspective-taking abilities-a component of "Theory of Mind"-across adulthood, and how these changes may relate to individual differences in executive functions at different ages. Participants completed a referential-communication task (the "Director" task) while behavioral responses and eye movements were recorded, along with four measures of executive functions (inhibitory control, working memory, cognitive flexibility, and planning). Results revealed a quadratic fit of age in egocentric errors; performance on the task plateaued between 20 to ~37 years old but showed a substantial decline from ~38 years onward (i.e., increased egocentric errors). A similar pattern was established in eye-movement measures, demonstrating that advancing age led to a decrease in efficient attention orientation to a target. In other words, older adults were more distracted by a hidden competitor object (egocentric interference) and were therefore delayed in orienting their attention to the correct target object. Mediation analyses revealed that executive functions partially mediated the effect of age on perspective-taking abilities. Importantly, however, the relationship between age and egocentric bias in task performance remained significant when controlling for changes in executive functions, indicating a decline in social cognition abilities with advancing age that was independent of age-related declines in more domain-general abilities, such as executive functions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Automatic Semantic Modeling for Structural Data Source with the Prior Knowledge from Knowledge Base

A critical step in sharing semantic content online is to map the structural data source to a public domain ontology. This problem is denoted as the Relational-To-Ontology Mapping Problem (Rel2Onto). A huge effort and expertise are required for manually modeling the semantics of data. Therefore, an automatic approach for learning the semantics of a data source is desirable. Most of the existing work studies the semantic annotation of source attributes. However, although critical, the research for automatically inferring the relationships between attributes is very limited. In this paper, we propose a novel method for semantically annotating structured data sources using machine learning, graph matching and modified frequent subgraph mining to amend the candidate model. In our work, Knowledge graph is used as prior knowledge. Our evaluation shows that our approach outperforms two state-of-the-art solutions in tricky cases where only a few semantic models are known.
Effect of spatial distortions in head-mounted displays on visually induced motion sickness

Incomplete optical distortion correction in VR HMDs leads to spatial dynamic distortion, which is a potential cause of VIMS. A perception experiment is designed for the investigation with three spatial distortion levels, with the subjective SSQ, five-scale VIMS level rating, and objective postural instability adopted as the evaluation metrics. The results show that the factor of spatial distortion level has a significant effect on all metrics increments ( p &lt;0.05). As the spatial distortion level drops off, the increments of VIMS symptoms decrease. The study highlights the importance of perfect spatial distortion correction in VR HMDs for eliminating the potential VIMS aggravation effect.
Design and key technology of the energy consumption management system for the liquid cooling data center

Abstract In view of the serious problem of energy consumption waste in the application process of liquid cooling data center, a new energy consumption management system of liquid cooling data center is constructed in this research. Energy consumption predictor, resource controller and resource configurator are used to monitor and manage energy consumption and optimize resource allocation of liquid cooling data center. The S3C2440A microprocessor with the internal core of ARM920T is adopted as the core controller of the energy consumption data collection system, and the energy consumption sampling circuit is designed with the voltage transformer and the current transformer, and attenuation network is adopted to prevent frequency aliasing in data sampling. Particle swarm optimization algorithm is used to identify the parameters in the model estimator, and the resource coordinator is used to solve the power consumption and the performance models. When using multiple physical servers to simulate the data center environment, the experimental structure shows that the system in the research can control the overall energy consumption of the server within 260 W, and the prediction error of the model estimator is kept lower than 2.4%.
Investigation of microseismic signal denoising using an improved wavelet adaptive thresholding method

There are high- and low-frequency noise signals in a microseismic signal that can lead to the distortion and submersion of an effective waveform. At present, effectively removing high- and low-frequency noise without losing the effective signal of local waveform spikes remains a challenge. This work addresses this issue with an improved wavelet adaptive thresholding method. Because a denoised signal conceptually approximates the minimum error, a dynamic selection model is established for the optimal threshold. On this basis, an adaptive correction factor aj is proposed to reflect the noise intensity, which uses the 1/2 power of the ratio of the median absolute value to the amplitude of the monitoring data to reflect the noise intensity of the wavelet detail signal and corrects the size of the denoising scale. Finally, the performance of the improved method is quantitatively evaluated in terms of the denoising quality and efficiency using the signal-to-noise ratio, root-mean-square error, sample entropy and running time.
Distributed multi-UAV cooperation for path planning by an NTVPSO-ADE algorithm

Multi-UAV cooperative trajectory planning faces issues such as model establishment difficulty and a considerable amount of computation. This work proposes a distributed hybrid particle swarm optimization (PSO) and differential evolution (DE) technique. A nonlinear time-varying PSO (NTVPSO) is developed to update the velocity and position of the PSO, aiming at the difficult balance of exploration and exploitation search for PSO. An adaptive DE (ADE) is proposed in order to improve the information sharing between particles and the algorithm's convergence speed. In order to realize the collaborative planning of multiple UAVs, a distributed method is used to solve the single UAV trajectory planning task by using NTVPSO-ADE. The trace is fed back to the PSO velocity update formula to guide the direction of particle movement. The simulation results show that the algorithm is highly competitive in terms of path optimality and can be used as a viable alternative to solve multi-UAV cooperative trajectory planning problems.
Effective Presentation Attack Detection Driven by Face Related Task

The robustness and generalization ability of Presentation Attack Detection (PAD) methods is critical to ensure the security of Face Recognition Systems (FRSs). However, in a real scenario, Presentation Attacks (PAs) are various and it is hard to predict the Presentation Attack Instrument (PAI) species that will be used by the attacker. Existing PAD methods are highly dependent on the limited training set and cannot generalize well to unknown PAI species. Unlike this specific PAD task, other face related tasks trained by huge amount of real faces (e.g. face recognition and attribute editing) can be effectively adopted into different application scenarios. Inspired by this, we propose to trade position of PAD and face related work in a face system and apply the free acquired prior knowledge from face related tasks to solve face PAD, so as to improve the generalization ability in detecting PAs. The proposed method, first introduces task specific features from other face related task, then, we design a Cross-Modal Adapter using a Graph Attention Network (GAT) to re-map such features to adapt to PAD task. Finally, face PAD is achieved by using the hierarchical features from a CNN-based PA detector and the re-mapped features. The experimental results show that the proposed method can achieve significant improvements in the complicated and hybrid datasets, when compared with the state-of-the-art methods. In particular, when training on the datasets OULU-NPU, CASIA-FASD, and Idiap Replay-Attack, we obtain HTER (Half Total Error Rate) of 5.48% for the testing dataset MSU-MFSD, outperforming the baseline by 7.39%. The source code is available at https://github.com/WentianZhang-ML/FRT-PAD .
End-to-End Feature Decontaminated Network for UAV Tracking

Object feature pollution is one of the burning issues in vision-based UAV tracking, commonly caused by occlusion, fast motion, and illumination variation. Due to the contaminated information in the polluted object features, most trackers fail to precisely estimate the object location and scale. To address the above disturbing issue, this work proposes a novel end-to-end feature decontaminated network for efficient and effective UAV tracking, i.e., FDNT. FDNT mainly includes two modules: a decontaminated downsampling network and a decontaminated upsampling network. The former reduces the interference information of the feature pollution and enhanced the expression of the object location information with two asymmetric convolution branches. The latter restores the object scale information with the super-resolution technology-based low-to-high encoder, achieving a further decontamination effect. Moreover, a novel pooling distance loss is carefully developed to assist the decontaminated downsampling network in concentrating on the critical regions with the object information. Exhaustive experiments on three well-known benchmarks validate the effectiveness of FDNT, especially on the sequences with feature pollution. In addition, real-world tests show the efficiency of FDNT with 31.4 frames per second. The code and demo videos are available at https://github.com/vision4robotics/FDNT.
Into the Vœrtex: Case study of a stereoscopic abstract animation installation

In Max Hattler’s stereoscopic installation Vœrtex two-dimensional abstract animation is turned into immersive images with strong three-dimensional depth. Utilizing a two-screen, semi-Cave Automatic Virtual Environment, Vœrtex creates a unique viewing experience for the viewer, a hybrid space situated halfway between immersion-in-abstraction and the real world of the gallery space, allowing the viewer to be in two spaces simultaneously. This article discusses the technical set-up, production processes and stereoscopic techniques, including binocular colour rivalry and depth displacements employed in Vœrtex. This is followed by a discussion of Vœrtex in relation to the concept of lucidity and an audience study into viewers’ perception of the work. The hybrid set-up of Vœrtex , combining both stereoscopic and interactive visual texts, allows for a sense of creative inspiration and inquiry into the dreaming mind, and participants can actively choose to explore different aspects of their perception by oscillating between two divergent states: an immersive dream sensation and a sense of being anchored in the real world.
Content Adaptive Latents and Decoder for Neural Image Compression

In recent years, neural image compression (NIC) algorithms have shown powerful coding performance. However, most of them are not adaptive to the image content. Although several content adaptive methods have been proposed by updating the encoder-side components, the adaptability of both latents and the decoder is not well exploited. In this work, we propose a new NIC framework that improves the content adaptability on both latents and the decoder. Specifically, to remove redundancy in the latents, our content adaptive channel dropping (CACD) method automatically selects the optimal quality levels for the latents spatially and drops the redundant channels. Additionally, we propose the content adaptive feature transformation (CAFT) method to improve decoder-side content adaptability by extracting the characteristic information of the image content, which is then used to transform the features in the decoder side. Experimental results demonstrate that our proposed methods with the encoder-side updating algorithm achieve the state-of-the-art performance.
Analysis of college students' consumption behavior based on campus card data

In order to further improve the intelligent management level of universities, we use big data and data mining technology to analyze the data including a university's campus card consumption records. Firstly, through the preprocessing and feature engineering of the extracted data, we analyze students' consumption level, consumption habits and other laws. Then, we cluster students into different groups with different consumption behaviors, and analyze the consumption structure and consumption behavior characteristics for the different groups in depth. Finally, by constructing a student co-occurrence network, we study the social relationships of students and find the lonely persons. The experimental results show that our clustering algorithm can assist the accurate student aid work, and the construction of the student co-occurrence network can also provide a certain reference for the psychological counseling work.
Comparison Several Regression Algorithms for Prediction of Job Satisfaction

Job satisfaction is the attitude or feelings of employees towards the pleasant or unpleasant aspects of work that in accordance with the assessment of each employee. There are several factors that affect job satisfaction, such as work environment satisfaction and employees' welfare. With obtained datasets, ‘welfare satisfaction’ and ‘environment satisfaction’ were used as independent variables with project numbers as dependent variables to examine an algorithm indicating the highest accuracy with the lowest RMSE and MAE values and the highest R-Squared values out of four algorithms, namely Linear Regression, Support Vector Regression, AdaBoost Regression, and Random Forest Regression. The results suggested that Random Forest Regression was the most accurate algorithm with MAE and RMSE values of 0.097000 and 0.185068, respectively, and R-Squared values of 0.981878.
V4Design: Intelligent Analysis and Integration of Multimedia Content for Creative Industries

Nowadays, vast amounts of multimedia content are being produced, archived, and digitized, resulting in great troves of data of interest. Examples include user-generated content, such as images, videos, text, and audio posted by users on social media and wikis, or content provided through official publishers and distributors, such as digital libraries, organizations, and online museums. This digital content can serve as a valuable source of inspiration to the creative industries, such as architecture and gaming, to produce new innovative assets or to enhance and (re-)use existing ones. However, in its current form, this content is difficult to be reused and repurposed due to the lack of appropriate solutions for its retrieval, analysis, and integration into the design process. In this article, we present V4Design, a novel framework for the automatic content analysis, linking, and seamless transformation of heterogeneous multimedia content to help architects and virtual reality game designers establish innovative value chains and end-user applications. By integrating and intelligently combining state-of-the-art technologies in computer vision, 3-D generation, text analysis, generation and semantic integration, and interlinking, V4Design provides architects and video game designers with innovative tools to draw inspiration from archive footage and documentaries, inspiring and eventually supporting the design process.
Chinese Named Entity Recognition Method Combining ALBERT and a Local Adversarial Training and Adding Attention Mechanism

For Chinese NER tasks, there is very little annotation data available. To increase the dataset, improve the accuracy of Chinese NER task, and improve the model's stability, the authors propose a method to add local adversarial training to the transfer learning model and integrate the attention mechanism. The model uses ALBERT for migration pre-training and adds perturbation factors to the output matrix of the embedding layer to constitute local adversarial training. BILSTM is used to encode the shared and private features of the task, and the attention mechanism is introduced to capture the characters that focus more on the entities. Finally, the best entity annotation is obtained by CRF. Experiments are conducted on People's Daily 2004 and Tsinghua University open-source text classification datasets. The experimental results show that compared with the SOTA model, the F1 values of the proposed method in this paper are improved by 7.32 and 7.98 in the two different datasets, respectively, proving that the accuracy of the method in this paper is improved in the Chinese domain.
ITU-T Standardization Activities Targeting Gaming Quality of Experience

For more information about the gaming activities described in this report, please contact Sebastian Möller (sebastian.moeller@tu-berlin.de).
WriArm: Leveraging Wrist Movement to Design Wrist+Arm Based Teleportation in VR

Teleportation, a widely used locomotion technique in Virtual Reality (VR), is used to move users through a virtual environment. Until recently, handheld controllers have been used for teleportation, where users use controllers to point to a location and perform an action (e.g., button press) to be instantly moved to the targeted location. Recent advancements in VR hand tracking enable users to move through and interact with the virtual world without controllers. This opens the opportunity for compelling alternatives to explore hand tracking-based teleportation techniques for more natural, intuitive and immersive interactions. Prior work mostly explores using arm movement for teleportation as an alternative to using the controller. In this paper, we design and evaluate WriArm, a VR locomotion technique that leverages both wrist and arm movement for VR teleportation. We first conduct a design study to find suitable hand gesture sets that can be mapped to teleportation activities such as activation, pointing, confirmation and cancellation for WriArm and arm-based techniques. Based on the results, we conduct a study comparing users’ performance while navigating tasks with the two techniques and three gesture sets. Results show that WriArm improves navigation efficiency by allowing users to navigate the environment quickly. We conclude with design guidelines for arm and wrist-based teleportation in VR.
Learning Invariant Representations for Equivariant Neural Networks Using Orthogonal Moments

The convolutional layers of standard convolutional neural networks (CNNs) are equivariant to translation. However, the convolution and fully-connected layers are not equivariant or invariant to other affine geometric transformations. Recently, a new class of CNNs is proposed in which the conventional layers of CNNs are replaced with equivariant convolution, pooling, and batch-normalization layers. The final classification layer in equivariant neural networks is invariant to different affine geometric transformations such as rotation, reflection and translation, and the scalar value is obtained by either eliminating the spatial dimensions of filter responses using convolution and down-sampling throughout the network or average is taken over the filter responses. In this work, we propose to integrate the orthogonal moments which gives the high-order statistics of the function as an effective means for encoding global invariance with respect to rotation, reflection and translation in fully-connected layers. As a result, the intermediate layers of the network become equivariant while the classification layer becomes invariant. The most widely used Zernike, pseudo-Zernike and orthogonal Fourier-Mellin moments are considered for this purpose. The effectiveness of the proposed work is evaluated by integrating the invariant transition and fully-connected layer in the architecture of group-equivariant CNNs (G-CNNs) on rotated MNIST and CIFAR10 datasets.
Aggregate Programming for Customized Building Management and Users Preference Implementation

Cognitive buildings are equipped with sensors and actuators to customize the indoor conditions according to users’ needs and preferences. The eLUX lab, at the Smart Campus of the University of Brescia (Italy), is the first national cognitive building where educational spaces are monitored and dashboards promote the users’ awareness. There, a fixed IoT network allows gathering data to perform analytics for prompt fault detection and fine-tuning of the conditions and possibly of the energy management. However, the users’ involvement is paramount for correct tuning and customization, as indoor conditions can be measured by a sensor (e.g., temperature, humidity, illuminance, CO2 concentration) located in a single point in the indoor space, while the parameters can strongly vary in the presence of, e.g., a fan coil or the distance from the windows and doors. To address this issue, an RTLS (Real-Time Location System) can be connected to the users’ direct and indirect feedback to support a precise definition of the indoor conditions and boost the improvement of the cognitive behaviour (i.e., automatic window opening or lighting dimming). In this chapter, we illustrate how the eLUX Lab can be enhanced to support the aggregate programming paradigm for offering resilient distributed services (exploiting an RTLS) that run on wearable devices without relying on the connection to a central server.
Single Image Defogging via Recurrent Bilateral Learning

Image defogging is an important computer vision topic for a long time. Current end-to-end defogging methods based on convolutional neural networks (CNNs) have achieved significant success. However, the inherent ambiguity between scene albedo and depth in foggy degraded images makes foggy image restoration still challenging. In this paper, we introduce a novel fog removal network based on recurrent bilateral learning (RBL) that generates detailed scene depth features and then fully leverages the scene depth to resolve this ambiguity. This network is composed of multiple sets of bilateral grid modules (BGM). In the BGM, we first extract haze-related features in low-resolution streams. Then, the scene depth information is separated from a trainable bilateral grid. Finally, it performs slicing operations for data-dependent lookup in the bilateral grid to reconstruct scene depth with high-frequency features. Moreover, we embed Conv-LSTM in the BGM to better correlate context-relevant information in recurrent structure. Experimental results demonstrate that the proposed method performs favorably against the state-of-the-art algorithms.