Reviewerly/exHarmony huggingface dataset:
Developing an Incivility Dictionary for German Online Discussions – a Semi-Automated Approach Combining Human and Artificial Knowledge

Incivility in online discussions has become an important issue in political communication research. Instruments and tools for the automated analysis of uncivil content, however, are rare, especially for non-English user-generated text. In this study, we present a) an extensive dictionary (DIKI - Diktionär für Inzivilität, English: Dictionary for Incivility) to detect incivility in German-language online discussions, and b) a semi-automated two-step-approach that combines manual content analysis with automated keyword collection using a pre-trained word embedding model. We show that DIKI clearly outperforms comparable dictionaries that have been used as alternative instruments to measure incivility (e.g., the LIWC) as well as basic machine learning approaches to text classification. Further, we provide evidence that pre-trained word embeddings can fruitfully be employed in the explorative phase of creating dictionaries. Still, the manual evaluation of DIKI confirms that detecting complex and context-dependent forms of incivility remains challenging and constant update would be needed to maintain performance. Finally, the detailed documentation of the developing and evaluation process of DIKI may serve as a guideline for further research. We therefore provide DIKI as a freely available instrument that also will be applicable in a web interface for drag-and-drop data analysis (diki.limitedminds.org).
Saliency Detection from Subitizing Processing

Most of the saliency methods are evaluated for their ability to generate saliency maps, and not for their functionality in a complete vision pipeline, for instance, image classification or salient object subitizing. In this work, we introduce saliency subitizing as the weak supervision. This task is inspired by the ability of people to quickly and accurately identify the number of items within the subitizing range (e.g., 1 to 4 different types of things). This means that the subitizing information will tell us the number of featured objects in a given image. To this end, we propose a saliency subitizing process (SSP) as a first approximation to learn saliency detection, without the need for any unsupervised methods or some random seeds. We conduct extensive experiments on two benchmark datasets (Toronto and SID4VAM). The experimental results show that our method outperforms other weakly supervised methods and even performs comparable to some fully supervised methods as a first approximation.
A method for identifying and repairing holes on the surface of unorganized point cloud

During scanning, reflection and occlusion of objects, produce holes in the acquired point clouds, causing problems to functions such as map building and target detection. In this paper, a new method for identifying and repairing such holes is proposed. First, the density and the 2D projection points of the point cloud are calculated. Based on the Euclidean distance, the hole boundary points of the original point cloud are obtained. Following, a novel preprocessing segmentation algorithm is used to obtain the boundary points of holes. Next, even if the boundary points are not fully identified, the total 2D repairing points are obtained by the proposed algorithm. Finally, a new hole repairing model, based on fuzzy inference system, is proposed, integrating geometry and curvature information of boundary points, while it can fast and accurately estimate the height coordinates of the repairing points. The experimental results show that the proposed algorithm is efficient.
Span-based single-stage joint entity-relation extraction model

Extracting entities and relations from the unstructured text has attracted increasing attention in recent years. The existing work has achieved considerable results, yet it is difficult to solve entity overlap and exposure bias. To address cascading errors, exposure bias, and entity overlap in existing entity relation extraction approaches, we propose a joint entity relation extraction model (SMHS) based on a span-level multi-head selection mechanism, transforming entity relation extraction into a span-level multi-head selection problem. Our model uses span-tagger and span-embedding to construct span semantic vectors, utilizes LSTM and multi-head self-attention mechanism for span feature extraction, multi-head selection mechanism for span-level relation decoding, and introduces span classification task for multi-task learning to decode out the relation triad in a single-stage. Experiments on the classic English dataset NYT and the publicly available Chinese relationship extraction dataset DuIE 2.0 show that this method achieves better results than the baseline method, which verifies the effectiveness of this method. Source code and data are published here(https://github.com/Beno-waxgourd/NLP.git).
THE ROLE OF NATIONAL AND RELIGIOUS TOLERANCE IN ENSURING YOUTH MORAL SECURITY

The role of national and religious tolerance in ensuring the moral security of young people and the mechanisms of its provision are scientifically studied in the article. At the same time, to increase the qualities of national and religious tolerance among young people, to cultivate feelings of tolerance, to further strengthen education and moral, religious and ideological propaganda among young people, and in this process to increase the role of self-government bodies, civil society institutions, youth-social institutions-society continuous maintenance of society's stability based on the formation of a system of relations is highlighted.
IMGCAT: An approach to dismantle the anonymity of a source camera using correlative features and an integrated 1D convolutional neural network

With the proliferation of smartphones, digital data collection has become trivial. The ability to analyze images has increased, but source authentication has stagnated. Editing and tampering of images has become more common with advancements in signal processing technology. Recent developments have introduced the use of seam carving (insertion and deletion) techniques to disguise the identity of the camera, specifically in the child pornography market. In this article, we focus on the available features in the image based on PRNU (photo response nonuniformity). The forced-seam sculpting technique is a well-known method to create occlusion for camera attribution by injecting seams into each 50 × 50 pixel block. To counter this, we perform camera identification using a 1D CNN integrated with feature extractions on 20 × 20 pixel blocks. We achieve state-of-the-art performance for our proposed IMGCAT (image categorization) in three-class classification over the baselines (original, seam removed, seam inserted). Based on our experimental findings, our model is robust when dealing with blind facts related to the questionable camera.
On the Importance of Spatio-Temporal Learning for Video Quality Assessment

Video quality assessment (VQA) has sparked a lot of interest in the computer vision community, as it plays a critical role in services that provide customers with high quality video content. Due to the lack of high quality reference videos and the difficulties in collecting subjective evaluations, assessing video quality is a challenging and still unsolved problem. Moreover, most of the public research efforts focus only on user-generated content (UGC), making it unclear if reliable solutions can be adopted for assessing the quality of production-related videos. The goal of this work is to assess the importance of spatial and temporal learning for production-related VQA. In particular, it assesses state-of-the-art UGC video quality assessment perspectives on LIVE-APV dataset, demonstrating the importance of learning contextual characteristics from each video frame, as well as capturing temporal correlations between them.
ThermalSynth: A Novel Approach for Generating Synthetic Thermal Human Scenarios

In this paper, we propose ThermalSynth, a novel approach for creating synthetic thermal images by mixing 3D characters generated using the Unity game engine with real thermal backgrounds. We use a shader based on the Stefan-Boltzmann law [18] to approximate the appearance in the thermal domain of the synthetic characters. Additionally, we provide a post-processing pipeline to better blend the high-fidelity synthetic data with the lower-resolution real thermal surveillance one. The proposed approach is used to create a dataset for people falling into water near a harbor front. Diverse scenarios of such falls are generated with an ample amount of data to enable the use of deep learning algorithms. To demonstrate the effectiveness of the generated data, we train two standard deep neural networks (AlexNet and ResNet-18) on our synthetic thermal dataset using a supervised learning approach. We test our system on small datasets containing real video footage of actual falls. We observe that training these simple classification networks yields an accuracy of 98.70% at a sensitivity of 100% on the real-world voluntary fall dataset. The code for Ther-malSynth and the dataset is publically available at https://github.com/NeeluMadan/Thermal-Synth.
Test-time Adaptation vs. Training-time Generalization: A Case Study in Human Instance Segmentation using Keypoints Estimation

We consider the problem of improving the human instance segmentation mask quality for a given test image using keypoints estimation. We compare two alternative approaches. The first approach is a test-time adaptation (TTA) method, where we allow test-time modification of the segmentation network's weights using a single unlabeled test image. In this approach, we do not assume test-time access to the labeled source dataset. More specifically, our TTA method consists of using the keypoint estimates as pseudo labels and backpropagating them to adjust the backbone weights. The second approach is a training-time generalization (TTG) method, where we permit offline access to the labeled source dataset but not the test-time modification of weights. Furthermore, we do not assume the availability of any images from or knowledge about the target domain. Our TTG method consists of augmenting the backbone features with those generated by the keypoints head and feeding the aggregate vector to the mask head. Through a comprehensive set of ablations, we evaluate both approaches and identify several factors limiting the TTA gains. In particular, we show that in the absence of a significant domain shift, TTA may hurt and TTG show only a small gain in performance, whereas for a large domain shift, TTA gains are smaller and dependent on the heuristics used, while TTG gains are larger and robust to architectural choices.
MetaMax: Improved Open-Set Deep Neural Networks via Weibull Calibration

Open-set recognition refers to the problem in which classes that were not seen during training appear at inference time. This requires the ability to identify instances of novel classes while maintaining discriminative capability for closed-set classification. OpenMax was the first deep neural network-based approach to address open-set recognition by calibrating the predictive scores of a standard closed-set classification network. In this paper we present MetaMax, a more effective post-processing technique that improves upon contemporary methods by directly modeling class activation vectors. MetaMax removes the need for computing class mean activation vectors (MAVs) and distances between a query image and a class MAV as required in OpenMax. Experimental results show that MetaMax outperforms OpenMax and is comparable in performance to other state-of-the-art approaches.
Meet-in-the-middle: Multi-scale upsampling and matching for cross-resolution face recognition

In this paper, we aim to address the large domain gap between high-resolution face images, e.g., from professional portrait photography, and low-quality surveillance images, e.g., from security cameras. Establishing an identity match between disparate sources like this is a classical surveillance face identification scenario, which continues to be a challenging problem for modern face recognition techniques. To that end, we propose a method that combines face super-resolution, resolution matching, and multi-scale template accumulation to reliably recognize faces from long-range surveillance footage, including from low quality sources. The proposed approach does not require training or fine-tuning on the target dataset of real surveillance images. Extensive experiments show that our proposed method is able to outperform even existing methods fine-tuned to the SCFace dataset.
Camouflaged Adversarial Patch Attack on Object Detector

Adversarial attacks have received great attentions for their capacity to distract state-of-the-art neural networks by modifying objects in physical domain. Patch-based attack especially have got much attention for its optimization effectiveness and feasible adaptation to any objects to attack neural network-based object detectors. However, despite their strong attack performance, generated patches are strongly perceptible for humans, violating the fundamental assumption of adversarial examples. In this paper, we propose a camouflaged adversarial patch optimization method using military camouflage assessment metrics for naturalistic patch attacks. We also investigate camouflaged attack loss functions, applications of various camouflaged patches on army tank images, and validate the proposed approach with extensive experiments attacking Yolov5 detection model. Our methods produce more natural and realistic looking camouflaged patches while achieving competitive performance.
High-precision projection moiré measurement method based on virtual reality bridging

Abstract The projection moiré method is a high-precision optical topography measurement method. The existing moiré models calibrate the phase-height mapping relationship by setting and moving the reference plane, which introduces many system errors and reduces the usability and scanning scalability of the methods. This paper proposes a moiré model based on virtual reality bridging and the corresponding scanning method. First, we use the imaging parameters of the real moiré system and the derived moiré phase correspondence to build a virtual moiré model matching the real system. Then we use a variety of phase optimization algorithms to improve the coincidence between the virtual and the reality. In addition, we propose a method to calculate the pose between views to facilitate the reconstruction of the corresponding virtual moiré model during scanning measurement. Finally, we use the virtual moiré model and scanning method to conduct fixed position and scanning measurements. The results show that the proposed virtual moiré model has high accuracy and robustness, and the scanning method can achieve high-precision full-field three-dimensional shape measurement.
Learning asymmetric embedding for attributed networks via convolutional neural network

Recently network embedding has gained increasing attention due to its advantages in facilitating network computation tasks such as link prediction, node classification and node clustering. The objective of network embedding is to represent network nodes in a low-dimensional vector space while retaining as much information as possible from the original network including structural, relational, and semantic information. However, asymmetric nature of directed networks poses many challenges as how to best preserve edge directions during the embedding process. Here, we propose a novel deep asymmetric attributed network embedding model based on the convolutional graph neural network, called AAGCN. The main idea is to maximally preserve the asymmetric proximity and asymmetric similarity of directed attributed networks. AAGCN introduces two neighbourhood feature aggregation schemes to separately aggregate the features of a node with the features of its in- and out- neighbours. Then, it learns two embedding vectors for each node, one source embedding vector and one target embedding vector. The final representations are the results of concatenating source and target embedding vectors. We test the performance of AAGCN on four real-world networks for network reconstruction, link prediction, node classification and visualization downstream tasks and investigate the impact of hyperparameters of the proposed method on the performance of the tasks. The experimental results show the superiority of AAGCN against state-of-the-art embedding methods.
Quantum Robust Optimal Control for Linear Complex Quantum Systems With Uncertainties

The aim of this note is to study the quantum robust optimal control problem for a class of linear quantum systems with uncertainties. It is shown that such problem can be converted into a mixed linear quadratic Gaussian and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H^{\infty }$</tex-math></inline-formula> quantum control problem. A physical model described by complex quantum stochastic differential equations (CQSDEs) is presented for the quantum system with uncertainties. The quantum system described by CQSDEs is called a complex quantum system in terms of annihilation and creation operators (Maalouf and Petersen, 2011). For an uncertain quantum system, we build a connection with a scaled linear system without uncertainty. The desired quantum robust optimal control results are derived based on the connection. We provide one numerical procedure for quantum robust optimal controller synthesis and then provide an example. The resulting controller presents robustness and optimal performance.
Cascaded Deep Video Deblurring Using Temporal Sharpness Prior and Non-Local Spatial-Temporal Similarity

We present compact and effective deep convolutional neural networks (CNNs) by exploring properties of videos for video deblurring. Motivated by the non-uniform blur property that not all the pixels of the frames are blurry, we develop a CNN to integrate a temporal sharpness prior (TSP) for removing blur in videos. The TSP exploits sharp pixels from adjacent frames to facilitate the CNN for better frame restoration. Observing that the motion field is related to latent frames instead of blurry ones in the image formation model, we develop an effective cascaded training approach to solve the proposed CNN in an end-to-end manner. As videos usually contain similar contents within and across frames, we propose a non-local similarity mining approach based on a self-attention method with the propagation of global features to constrain CNNs for frame restoration. We show that exploring the domain knowledge of videos can make CNNs more compact and efficient, where the CNN with the non-local spatial-temporal similarity is 3× smaller than the state-of-the-art methods in terms of model parameters while its performance gains are at least 1 dB higher in terms of PSNRs. Extensive experimental results show that our method performs favorably against state-of-the-art approaches on benchmarks and real-world videos.
An Autoformer-CSA Approach for Long-Term Spectrum Prediction

In this letter, we develop an Autoformer with a series channel-spatial attention module (CSAM) (Autoformer-CSA) for long-term spectrum prediction. More specifically, the CSAM ingeniously replaces 2-dimensional (2D) convolution in image attention (including channel attention and spatial attention) with 1-dimensional (1D) convolution. The CSAM replaces Autoformer’s feed-forward network. It is used to assign different concentrations to features mapped to the high-dimensional space, improving the learning ability of the Autoformer. We follow the series decomposition block and auto-correlation mechanism of the Autoformer. Experiments on a real-world dataset show that the Autoformer-CSA is superior to the state-of-the-art benchmarks.
Enhancing the Detection of Misogynistic Content in Social Media by Transferring Knowledge From Song Phrases

Misogyny is a severe social problem that affects women’s mental and physical health or even leads to femicide. This cultural problem is visible and prevalent in different communication channels, such as music and social media, confirming or inciting this behavior. Hence, the automatic detection of misogynistic content in social media using computational methods that analyze the language is of increasing interest. Most approaches follow a supervised machine learning strategy, with the main challenge of capturing the diversity and complexity of the offensive language directed at women. Therefore, the size and quality of the training data play essential roles. In this context, we designed a novel data augmentation approach that leverages song phrases to increase the models’ ability to generalize and improve their performance. In addition, this paper introduces a methodology to compile a labeled dataset with song segments conveying misogyny, which can be used to enrich different techniques in this field. The proposed approach was evaluated using English and Spanish benchmark datasets. It successfully overcomes conventional transfer learning techniques and achieves high competitiveness compared with state-of-the-art methods, outperforming them on the Spanish dataset. These encouraging results demonstrate the usefulness of the proposed approach.
Analytical method of multiqubit entanglement robustness in correlated quantum channels

In practice, the interaction between quantum system and environment will lead to the decoherence of entanglement. By quantifying the entanglement through the correlation tensor, we systematically investigate the entanglement robustness of two-qubit states under the four types of noisy channels and corresponding correlated channels. We use an analytical method to find all of the most robust and fragile states under the noisy channels. The puzzling phenomenon that the most robust and fragile states have the same entanglement under noisy channels has been investigated. Moreover, we compare the entanglement robustness of a three-qubit Greenberger-Horne-Zeilinger state with a $W$ state. Our method shows the behavior of entanglement under the noisy channels and characterizes the entanglement robustness of the multiqubit system analytically.
Skew angle estimation and alleviation based multi-layer perceptron in V-shaped read-head array BPMR systems

The demand for a massive storage device has kept rising and it has led to the emergence of advanced technology with a vast storage capacity. Achieving this idea might bring some obstacles called two-dimensional interference as well as a skew angle (SA). Array-reader-based magnetic recording can be the potential to give a better performance in the system by reading multiple tracks simultaneously. This paper investigates a V-shaped read-head array (VRA) with three read sensors performance on five levels of skew angle consideration in three different zones of hard disk drive. VRA with three read sensors is being grouped; therefore, SA can be considered as the change in this group movement. To achieve a good system performance, we propose an SA estimation method for bit-patterned media recording systems by computing a specific target by three readback sequences before estimating the SA value and finally detecting the data. Both estimation and detection processes are carried out using a neural network method known as multilayer perceptron.