Reviewerly/exHarmony huggingface dataset:
Implementing CRIS interfaces with RIS Synergy: Challenges and opportunities of a multidisciplinary bottom-up approach

This paper introduces the RIS Synergy project, an Austrian digitisation project that aims to synergise CRIS Systems across a diverse research landscape. The project brings together various Austrian research institutions and funders to collaborate on the development and implementation of open, standardised interfaces and shared services for research information services. In this paper, we present how the project's bottom up approach proves to provide an effective method to manage system interoperability and information exchange in a multidisciplinary consortium. Furthermore, we offer an insight into RIS Synergy's approach to the conception of an Austrian research portal.
Skip-Concatenated Image Super-Resolution Network for Mobile Devices

Single-image super-resolution technology has been widely studied in various applications to improve the quality and resolution of degraded images acquired from noise-sensitive low-resolution sensors.As most studies on single-image super-resolution focused on the development of deep learning networks operating on high-performance GPUs, this study proposed an efficient and lightweight super-resolution network that enables real-time performance on mobile devices.To replace the relatively slow element-wise addition layer on mobile devices, we introduced a skip connection layer by directly concatenating a low-resolution input image with an intermediate feature map.In addition, we introduced weighted clipping to reduce the quantization errors commonly encountered during float-to-int8 model conversion.Moreover, a reparameterization method was selectively applied without increasing the cost in terms of inference time and number of parameters.Based on the contributions, the proposed network has been recognized as the best solution in Mobile AI & AIM 2022 Real-Time Single-Image Super-Resolution Challenge with PSNR of 30.03 dB and NPU runtime of 19.20 ms.
Texture Brush for Fashion Inspiration Transfer: A Generative Adversarial Network With Heatmap-Guided Semantic Disentanglement

Automatically accomplishing intelligent fashion design with certain ‘inspiration’ images can greatly facilitate a designer’s design process, as well as allow users to interactively participate in the process. In this research, we propose a generative adversarial network with heatmap-guided semantic disentanglement (HSD-GAN) to perform an ‘intelligent’ design with ‘inspiration’ transfer. Our model aims to learn how to integrate the feature representations, from the styles of both source fashion items and target fashion items, in an unsupervised manner. Specifically, a semantic disentanglement attention-based encoder is proposed to capture the most discriminative regions of different input fashion items and disentangle the features into two key factors: attribute and texture. A generator is then developed to synthesize mixed-style fashion items by utilizing the two factors. In addition, a heatmap-based patch loss is introduced to evaluate the visual-semantic matching degree between the texture of the generated fashion items and the input texture information. Extensive experimental results show that our proposed HSD-GAN consistently achieves superior performance, compared to other state-of-the-art methods.
PV Power Prediction and Investigation for Movable Solar PV Module

In this paper, A moving solar panel’s power output, and solar irradiation output are compared to a fixed solar panel experimentally. The solar module’s performance was first measured while it was in a fixed position and then dual axis solar tracker is employed to rotate the solar module to track the sun in two axes while the appropriate measurements were taken. solar modules using solar trackers will increase the output of annual solar irradiation that incident on the module surface compared to a fixed solar module at an optimum tilt angle. A moving solar module produces higher power output than a fixed module. Thus, it produces higher power output which means it utilizes the most of available solar radiation effectively.
RA360SR: A Real-time Acceleration-adaptive 360-degree Video Super-resolution System

With the development of virtual reality headsets, panoramic videos have started to gain increased popularity, and 360-degree video streaming technologies and devices have achieved significant success in the market. However, the video resolution provided by existing mainstream panoramic video cameras is not high enough to give users a viewing experience close to that of a conventional display, and the network bandwidth is another bottleneck for high-resolution 360-degree video streaming. Researchers have introduced numerous approaches to resolve these issues, including viewport prediction and regional super-resolution. Nevertheless, existing methods can-not ensure that users always have high-resolution viewport content because of the inevitable prediction error and the heavy server-side pre-process consumption. In this paper, we present RA360SR, a real-time acceleration-adaptive 360-degree video super-resolution system. We develop a dual-camera system with Unity3D post-processing to implement the real-time super-resolution model processing. Additionally, to obtain a more stable frame rate, we propose an acceleration-adaptive approach to switch the super-resolution model processing status based on the acceleration of users' head movements. Our results show that RA360SR can deliver a sharper video viewing experience for users while providing an acceptable frame rate.
Chilean and Spanish teachers’ beliefs and disposition when teaching writing in the pandemic: a contrastive study

Abstract National and international researches have shown how students and teachers face the teaching of writing. Nevertheless, the pandemic has created new needs of pedagogical research on topics like the teaching of writing in distance learning processes. The research aims to compare pedagogical experiences and practices a group of Chilean and Spanish teachers had during distance learning in the COVID-19 pandemic times. This is a qualitative study that approaches from a digital ethnographic perspective, and it is circumscribed by the socio-critical paradigm. 20 Chilean and 20 Spanish primary school teachers volunteered to participate. They all confronted distance learning after the pandemic started in March 2020. A semi structured interview was conducted to gather data. The obtained information has been processed with the qualitative data analysis software NVivo 12. The answers of the teachers indicate a general change in the pedagogical practices after the pandemic, particularly in the teaching of writing. However, on the one hand, Chilean teachers are especially concerned about the students, on the other hand Spanish teachers are about training. The need of further training in distance teaching of writing can be detected in both contexts.
Few-Shot Object Detection by Knowledge Distillation Using Bag-of-Visual-Words Representations

While fine-tuning based methods for few-shot object detection have achieved remarkable progress, a crucial challenge that has not been addressed well is the potential class-specific overfitting on base classes and sample-specific overfitting on novel classes. In this work we design a novel knowledge distillation framework to guide the learning of the object detector and thereby restrain the overfitting in both the pre-training stage on base classes and fine-tuning stage on novel classes. To be specific, we first present a novel Position-Aware Bag-of-Visual-Words model for learning a representative bag of visual words (BoVW) from a limited size of image set, which is used to encode general images based on the similarities between the learned visual words and an image. Then we perform knowledge distillation based on the fact that an image should have consistent BoVW representations in two different feature spaces. To this end, we pre-learn a feature space independently from the object detection, and encode images using BoVW in this space. The obtained BoVW representation for an image can be considered as distilled knowledge to guide the learning of object detector: the extracted features by the object detector for the same image are expected to derive the consistent BoVW representations with the distilled knowledge. Extensive experiments validate the effectiveness of our method and demonstrate the superiority over other state-of-the-art methods.
Privacy-Preserving Face Recognition with Learnable Privacy Budgets in Frequency Domain

Face recognition technology has been used in many fields due to its high recognition accuracy, including the face unlocking of mobile devices, community access control systems, and city surveillance. As the current high accuracy is guaranteed by very deep network structures, facial images often need to be transmitted to third-party servers with high computational power for inference. However, facial images visually reveal the user's identity information. In this process, both untrusted service providers and malicious users can significantly increase the risk of a personal privacy breach. Current privacy-preserving approaches to face recognition are often accompanied by many side effects, such as a significant increase in inference time or a noticeable decrease in recognition accuracy. This paper proposes a privacy-preserving face recognition method using differential privacy in the frequency domain. Due to the utilization of differential privacy, it offers a guarantee of privacy in theory. Meanwhile, the loss of accuracy is very slight. This method first converts the original image to the frequency domain and removes the direct component termed DC. Then a privacy budget allocation method can be learned based on the loss of the back-end face recognition network within the differential privacy framework. Finally, it adds the corresponding noise to the frequency domain features. Our method performs very well with several classical face recognition test sets according to the extensive experiments. Code will be available at https://github.com/Tencent/TFace/tree/master/recognition/tasks/dctdp .
Combining Context-Awareness and Data Analytics in Support of Drone Technology

Drones performing an autonomous mission need to adapt to frequent changes in their environment. In other words, they have to be context-aware. Most current context-aware systems are designed to distinguish between situations that have been pre-defined in terms of anticipated situation types and corresponding desired behavior types. This only partially benefits drone technology because many types of drone missions can be characterized by situations that are hard to predict at design time. We suggest combining context-awareness and data analytics for a better situation coverage. This could be achieved by using performance data (generated at real-time) as training data for supervised machine learning – it would allow relating situations to appropriate behaviors that a drone could follow. The conceptual ideas are presented in this position paper while validation is left for future work.
Learning Games for Defending Advanced Persistent Threats in Cyber Systems

A cyber system may face to multiple attackers from diverse adversaries, who usually employ sophisticated techniques to both continuously steal sensitive data and avoid being detected by defense strategies. This continuous process is typically involved in an advanced persistent threat (APT). Since the game theory is an ideal mathematical model for investigating continuous decision making of competing players, it is broadly used to research the interaction between defenders and APT attackers. Although many researchers are now using the game theory to defend against APT attacks, most of the existing solutions are limited to single-defender, single-attacker scenarios. In the real world, threats by multiple attackers are not uncommon and multiple defenders can be put in place. Therefore, to overcome the limitation of the existing solutions, we develop a multiagent deep reinforcement learning (MADRL) method with a novel sampling approach. The MADRL method allows defenders to create strategies on the fly and share their experience with other defenders. To develop this method, we create a multidefender, multiattacker game model and analyze the equilibrium of this model. The results of a series of experiments demonstrate that, with MADRL, defenders can quickly learn efficient strategies against attackers.
Rethinking and Improving Feature Pyramids for One-Stage Referring Expression Comprehension

Referring Expression Comprehension (REC) is an important task in the vision-and-language community, since it is an essential step for many cross-modal tasks such as VQA, image retrieval and image caption. To obtain a better trade-off between speed and accuracy, existing researches usually follow a one-stage paradigm, where this task can be considered as a language-conditioned object detection task. Meanwhile, previous one-stage REC frameworks provide many different research perspectives, such as the strategies of fusion, the stage of fusion and the design of detection head. Surprisingly, these works mostly ignore the value of integrating multi-level features and even only apply single-scale features to locate the target. In this paper, we focus on rethinking and improving feature pyramids for one-stage REC. By experimental validations, we first prove that although multi-scale fusion is an effective approach for improving performance, the mature neck structures from object detection (e.g., FPN, BFN and HRFPN) have a limited impact on this task. Further, we visualize the outputs of FPN and find the underlying reason is that these coarse-grained FPN fusion strategies suffer from semantic ambiguity problem. Based on the above insights, we propose a new Language-Guided FPN (LG-FPN) method, which can dynamically allocate and select the fine-grained information by stacking language-gate and union-gate. A large number of contrastive and ablative experiments show that our LG-FPN is an effective and reliable module that can adapt to different visual backbones, fusion strategies and detection heads. Finally, our method achieves state-of-the-art performance on four referring expression datasets.
A statistical framework for labeling unlabelled data: a case study on anomaly detection in pressurization systems for high-speed railway trains

The ability to perform predictive maintenance, as one of the main asset of Industry 4.0, is known to help improve downtime, costs, control and production quality. Modern predictive maintenance programs involve machine learning techniques, within the AI umbrella, that work in a data-driven fashion. This is true in all machinery where, through intelligent sensors, it is possible to collect data to be processed to detect faults or carry out anomaly detection activities. This paper presents a system for the detection of anomalies in the railway context and, specifically, in the pressurization systems of Italian high-speed trains. The available real-world dataset is in form of unlabeled time series of fixed length of 600 samples. Hence, it is proposed a two-stage machine learning workflow where the first stage acts in an unsupervised fashion through a statistical technique validated by field experts with the aim of building a labeled dataset. In the second stage, the faced problem is conceived as a classification task in the context of a strong class imbalance problem - very likely in predictive maintenance - where are compared two feature engineering techniques. The first one considers directly the raw signals as input of a SVM algorithm. In the second, time series are subjected to an adaptive heuristic procedure of piece-wise approximation, whose output is a sequence of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{R}^{2}$</tex> vectors (slopes and intercepts). In this case, the classification task is carried out in the so-called “dissimilarity space” for pattern recognition adopting different dimensions of the representation set obtained through a clustering algorithm. The dissimilarity measure consists of an ad-hoc edit distance capable of measuring the dissimilarity between 2-dimensional sequences. In this study a k-medoids clustering procedure is adopted for balancing the dataset together with further additional techniques for solving the challenging problem of unbalanced data, offering a deep comparison related to various experimental methodologies.
Teaching with Soft Label Smoothing for Mitigating Noisy Labels in Facial Expressions

Recent studies have highlighted the problem of noisy labels in large scale in-the-wild facial expressions datasets due to the uncertainties caused by ambiguous facial expressions, low-quality facial images, and the subjectiveness of annotators. To solve the problem of noisy labels, we propose Soft Label Smoothing (SLS), which smooths out multiple high-confidence classes in the logits by assigning them a probability based on the corresponding confidence, and at the same time assigning a fixed low probability to the low-confidence classes. Specifically, we introduce what we call the Smooth Operator Framework for Teaching (SOFT), based on a mean-teacher (MT) architecture where SLS is applied over the teacher's logits. We find that the smoothed teacher's logit provides a beneficial supervision to the student via a consistency loss – at 30% noise rate, SLS leads to 15% reduction in the error rate compared with MT. Overall, SOFT beats the state of the art at mitigating noisy labels by a significant margin for both symmetric and asymmetric noise. Our code is available at https://github.com/toharl/soft .
The Impact of Black Edge Artifact on QoE of the FOV-Based Cloud VR Services

Cloud virtual reality (Cloud VR) services usually introduce high latency in rendering and streaming, resulting in a mismatch between the visual and vestibular systems, causing user sickness and dizziness during the service. To solve this problem, asynchronous rendering technology is usually used to provide smooth viewing. The asynchronous solution, on the other hand, will introduce another “black edge” (BE) artifact in the service, which frequently appears at the viewport's boundary with a black area when users turn their heads. This unwanted BE artifact also has an impact on the user's quality of experience (QoE). In this paper, we investigated the impact of the BE artifact on the user's QoE of the field of view (FOV) Cloud VR gaming services. The appearance of the BE artifact during the playing period was regarded as a series of BE events and the impact of BE artifact on the users’ QoE was evaluated by accumulating the influence of the BE events during the whole playing period. More specifically, the user's QoE affected by a single BE event was first evaluated by combining the area ratio and duration of the BE artifact. Then, the QoE affected by multiple BE events was analyzed, where the cumulative influence of the previous BE events on the user's current QoE was evaluated. Finally, a unified event-based evaluation model was proposed to predict the user's time-varying QoE at any point in time. Experimental results showed that the proposed model performed exceptionally well in predicting the impact of BE artifact on the user's QoE.
An Underwater Structured Light Vision Calibration Method Considering Unknown Refractive Index Based on Aquila Optimizer

Underwater structured light vision systems have attracted the attention of researchers because of their high measurement accuracy and robustness. At present, most underwater structured light vision measurement systems consider the refractive index as a known parameter. However, the refractive index of water will change because of environmental changes such as salinity and temperature. Inaccurate refractive index will have serious influence on the measurement accuracy of the underwater structured light vision system. Therefore, an underwater structured light vision calibration method considering unknown refractive index is proposed. To avoid complex calibration process and complex solution process of a nonlinear underwater structured light vision model, a novel checkerboard usage calibration method based on Aquila Optimizer (AO) is proposed. The calibration solution process is regarded as a nonlinear optimization problem, and the optimization objective is established with the distance between the feature points intersected by the checkerboard and the laser. Finally, calibration and measurement experiments are carried out to verify the effectiveness and superiority of the proposed method.
Generalization of Forgery Detection With Meta Deepfake Detection Model

Face forgery generating algorithms that produce a range of manipulated videos/images have developed quickly.Consequently, this causes an increase in the production of fake information, making it difficult to identify.Because facial manipulation technologies raise severe concerns, face forgery detection is gaining increasing attention in the area of computer vision.In real-world applications, face forgery detection systems frequently encounter and perform poorly in unseen domains, due to poor generalization.In this paper, we propose a deepfake detection method based on meta-learning called Meta Deepfake Detection (MDD).The goal of the model is to develop a generalized model capable of directly solving new unseen domains without the need for model updates.The MDD algorithm establishes various weights for facial images from various domains.Specifically, MDD uses meta-weight learning to shift information from the source domains to the target domains with meta-optimization steps, which aims for the model to generate effective representations of the source and target domains.We build multi-domain sets using meta splitting strategy to create a meta-train set and meta-test set.Based on these sets, the model determines the gradient descent and obtains backpropagation.The inner and outer loop gradients were aggregated to update the model to enhance generalization.By introducing pair-attention loss and average-center alignment loss, the detection capabilities of the system were substantially enhanced.In addition, we used some evaluation benchmarks established from several popular deepfake datasets to compare the generalization of our proposal in several baselines and assess its effectiveness.
Explainable Supervised Domain Adaptation

Domain adaptation techniques have contributed to the success of deep learning. Leveraging knowledge from an auxiliary source domain for learning in labeled data-scarce target domain is fundamental to domain adaptation. While these techniques result in increasing accuracy, the adaptation process, particularly the knowledge leveraged from the source domain, remains unclear. This paper proposes an explainable by design supervised domain adaptation framework - XSDA-Net. We integrate a case-based reasoning mechanism into the XSDA-Net to explain the prediction of a test instance in terms of similar-looking regions in the source and target train images. We empirically demonstrate the utility of the proposed framework by curating the domain adaptation settings on datasets popularly known to exhibit part-based explainability.
Purifying Low-Light Images via Near-Infrared Enlightened Image

Cameras usually produce low-quality images under low-light conditions. Though many methods have been proposed to enhance the visibility of low-light images, they are mainly designed for illumination correction and less capable of sup-pressing the artifacts. In this paper, we propose to enhance the visibility and suppress artifacts by purifying low-light images under the guidance of the NIR enlightened image captured by using the near-infrared light as compensation. Specifically, we introduce a disentanglement framework to disentangle the structure and color components from the NIR enlightened and RGB images, respectively. Correspondingly, we introduce a new dataset with the RGB and NIR enlightened images for training and evaluation purposes. The experimental results show that our proposed method achieves promising results.