Reviewerly/exHarmony huggingface dataset:
An evaluation of recent neural sequence tagging models in Turkish named entity recognition

Named entity recognition (NER) is an extensively studied task that extracts and classifies named entities in a text. NER is crucial not only in downstream language processing applications such as relation extraction and question answering but also in large scale big data operations such as real-time analysis of online digital media content. Recent research efforts on Turkish, a less studied language with morphologically rich nature, have demonstrated the effectiveness of neural architectures on well-formed texts and yielded state-of-the art results by formulating the task as a sequence tagging problem. In this work, we empirically investigate the use of recent neural architectures (Bidirectional long short-term memory (BiLSTM) and Transformer-based networks) proposed for Turkish NER tagging in the same setting. Our results demonstrate that transformer-based networks which can model long-range context overcome the limitations of BiLSTM networks where different input features at the character, subword, and word levels are utilized. We also propose a transformer-based network with a conditional random field (CRF) layer that leads to the state-of-the-art result (95.95% f-measure) on a common dataset. Our study contributes to the literature that quantifies the impact of transfer learning on processing morphologically rich languages.
How to shape academic freedom in the digital age? Are the retractions of opinionated papers a prelude to “cancel culture” in academia?

When academics’ opinions, which are published in academic journals as letters to the editor or commentaries, are retracted based on sensitivities and objections that are raised for example on social media, there needs to be a reflection on what this might represent. On one hand, an opinion is precisely that, i.e., a subjective and biased view about an issue. Those views might even be radical, unpopular, or insensitive, but ultimately approved by editors for publication nonetheless. To maintain a truly sustainable scholarly discourse, the best academic way to counter such opinions is by allowing disagreeing voices to express themselves, also as letters to the editor or commentaries. Pressure-induced retractions of opinions not only stifle academic debate, they send the message that opinions need to be moderated and standardized to meet a publishing market that is being increasingly driven by legal parameters, political correctness, as well as business and commercial values rather than academic ones. In an environment of restrictive academic freedom, what emerges is an academia in which the way things are said, tone, and the sensitivity of those that might be affected are given greater weight than the message itself. By cherry-picking parts of the message that detractors or critics might disagree with, the original message may be drowned out by the noise of the objectors. The struggle of academics to liberally voice their opinions in the scholarly publishing realm, and to preserve those opinions, has never been more acute in this age of misinformation and radicalism fueled by polarized social and mass media. Is the politicization and/or commercialization of academia, alongside the retraction of opinions, stifling open and healthy academic debate, or expressing itself as the retraction of opinions, and does this represent a distinct form of “cancel culture” in academia and academic publishing?
Redefining threat appraisals of organizational insiders and exploring the moderating role of fear in cyberattack protection motivation

Increasingly sophisticated cyberattacks often systematically target organizational insiders. Their motivation for self-protection has therefore an important role in cybersecurity of organizations. Protection motivation studies in information security literature are largely based on the protection motivation theory (PMT) without proper adaptation to the organizational context. Additionally, only few studies consider the role of fear in protection motivation although PMT itself is based on fear appeals. This paper aims to revise PMT to better fit the organizational context of organizational insiders. A survey was conducted among academics (N = 255) at six Slovenian universities to reexamine threat appraisals of organizational insiders, and the mediating and moderating roles of fear of cyberattacks in protection motivation. CB-SEM analysis of survey data supports the distinction between appraisals of threats to the individual and to the organization. It also supports differentiating between perceived threats and fear of cyberattacks. Although we did not find support for the mediating role of fear of cyberattacks, perceived threats may mediate the association between perceived severity and vulnerability, and protection motivation. Only perceived vulnerability of the individual and perceived severity of consequences for the organization affect perceived threats. Perceived threats and measure efficacy influence protection motivation. Fear of cyberattacks dampens the positive relationship between self-efficacy and protection motivation. Self-efficacy influences protection motivation only when fear of cyberattacks is low. Interventions aiming to increase protection motivation need to focus on raising the perceived vulnerability of individuals, emphasizing the consequences for the organization, and increasing the efficacy of self-protective measures. Interventions aiming to improve self-efficacy may be effective only when there is low fear of cyberattacks and can be avoided when high fear of cyberattacks is expected.
HCMSL: Hybrid Cross-modal Similarity Learning for Cross-modal Retrieval

The purpose of cross-modal retrieval is to find the relationship between different modal samples and to retrieve other modal samples with similar semantics by using a certain modal sample. As the data of different modalities presents heterogeneous low-level feature and semantic-related high-level features, the main problem of cross-modal retrieval is how to measure the similarity between different modalities. In this article, we present a novel cross-modal retrieval method, named Hybrid Cross-Modal Similarity Learning model (HCMSL for short). It aims to capture sufficient semantic information from both labeled and unlabeled cross-modal pairs and intra-modal pairs with same classification label. Specifically, a coupled deep fully connected networks are used to map cross-modal feature representations into a common subspace. Weight-sharing strategy is utilized between two branches of networks to diminish cross-modal heterogeneity. Furthermore, two Siamese CNN models are employed to learn intra-modal similarity from samples of same modality. Comprehensive experiments on real datasets clearly demonstrate that our proposed technique achieves substantial improvements over the state-of-the-art cross-modal retrieval techniques.
RoI Tanh-polar transformer network for face parsing in the wild

Face parsing aims to predict pixel-wise labels for facial components of a target face in an image. Existing approaches usually crop the target face from the input image with respect to a bounding box calculated during pre-processing, and thus can only parse inner facial Regions of Interest (RoIs). Peripheral regions like hair are ignored and nearby faces that are partially included in the bounding box can cause distractions. Moreover, these methods are only trained and evaluated on near-frontal portrait images and thus their performance for in-the-wild cases has been unexplored. To address these issues, this paper makes three contributions. First, we introduce iBugMask dataset for face parsing in the wild, which consists of 21,866 training images and 1000 testing images. The training images are obtained by augmenting an existing dataset with large face poses. The testing images are manually annotated with 11 facial regions and there are large variations in sizes, poses, expressions and background. Second, we propose RoI Tanh-polar transform that warps the whole image to a Tanh-polar representation with a fixed ratio between the face area and the context, guided by the target bounding box. The new representation contains all information in the original image, and allows for rotation equivariance in the convolutional neural networks (CNNs). Third, we propose a hybrid residual representation learning block, coined HybridBlock, that contains convolutional layers in both the Tanh-polar space and the Tanh-Cartesian space, allowing for receptive fields of different shapes in CNNs. Through extensive experiments, we show that the proposed method improves the state-of-the-art for face parsing in the wild and does not require facial landmarks for alignment.
Deep learning with self-supervision and uncertainty regularization to count fish in underwater images

Effective conservation actions require effective population monitoring. However, accurately counting animals in the wild to inform conservation decision-making is difficult. Monitoring populations through image sampling has made data collection cheaper, wide-reaching and less intrusive but created a need to process and analyse this data efficiently. Counting animals from such data is challenging, particularly when densely packed in noisy images. Attempting this manually is slow and expensive, while traditional computer vision methods are limited in their generalisability. Deep learning is the state-of-the-art method for many computer vision tasks, but it has yet to be properly explored to count animals. To this end, we employ deep learning, with a density-based regression approach, to count fish in low-resolution sonar images. We introduce a large dataset of sonar videos, deployed to record wild Lebranche mullet schools (Mugil liza), with a subset of 500 labelled images. We utilise abundant unlabelled data in a self-supervised task to improve the supervised counting task. For the first time in this context, by introducing uncertainty quantification, we improve model training and provide an accompanying measure of prediction uncertainty for more informed biological decision-making. Finally, we demonstrate the generalisability of our proposed counting framework through testing it on a recent benchmark dataset of high-resolution annotated underwater images from varying habitats (DeepFish). From experiments on both contrasting datasets, we demonstrate our network outperforms the few other deep learning models implemented for solving this task. By providing an open-source framework along with training data, our study puts forth an efficient deep learning template for crowd counting aquatic animals thereby contributing effective methods to assess natural populations from the ever-increasing visual data.
Quantum Power Flow

This letter is a proof of concept for quantum power flow (QPF) algorithms which underpin various unprecedentedly efficient power system analytics exploiting quantum computing. Our contributions are three-fold: 1) Establish a quantum-state-based fast decoupled model empowered by Hermitian and constant Jacobian matrices; 2) Devise an enhanced Harrow-Hassidim-Lloyd (HHL) algorithm to solve the fast decoupled QPF; 3) Further improve the HHL efficiency by parameterizing quantum phase estimation and reciprocal rotation only at the beginning stage. Promising test results validate the accuracy and efficacy of QPF and demonstrate QPF's enormous potential in the era of quantum computing.
GazeEMD: Detecting Visual Intention in Gaze-Based Human-Robot Interaction

In gaze-based Human-Robot Interaction (HRI), it is important to determine human visual intention for interacting with robots. One typical HRI interaction scenario is that a human selects an object by gaze and a robotic manipulator will pick up the object. In this work, we propose an approach, GazeEMD, that can be used to detect whether a human is looking at an object for HRI application. We use Earth Mover’s Distance (EMD) to measure the similarity between the hypothetical gazes at objects and the actual gazes. Then, the similarity score is used to determine if the human visual intention is on the object. We compare our approach with a fixation-based method and HitScan with a run length in the scenario of selecting daily objects by gaze. Our experimental results indicate that the GazeEMD approach has higher accuracy and is more robust to noises than the other approaches. Hence, the users can lessen cognitive load by using our approach in the real-world HRI scenario.
3D MRI Reconstruction Based on 2D Generative Adversarial Network Super-Resolution

The diagnosis of brain pathologies usually involves imaging to analyze the condition of the brain. Magnetic resonance imaging (MRI) technology is widely used in brain disorder diagnosis. The image quality of MRI depends on the magnetostatic field strength and scanning time. Scanners with lower field strengths have the disadvantages of a low resolution and high imaging cost, and scanning takes a long time. The traditional super-resolution reconstruction method based on MRI generally states an optimization problem in terms of prior information. It solves the problem using an iterative approach with a large time cost. Many methods based on deep learning have emerged to replace traditional methods. MRI super-resolution technology based on deep learning can effectively improve MRI resolution through a three-dimensional convolutional neural network; however, the training costs are relatively high. In this paper, we propose the use of two-dimensional super-resolution technology for the super-resolution reconstruction of MRI images. In the first reconstruction, we choose a scale factor of 2 and simulate half the volume of MRI slices as input. We utilize a receiving field block enhanced super-resolution generative adversarial network (RFB-ESRGAN), which is superior to other super-resolution technologies in terms of texture and frequency information. We then rebuild the super-resolution reconstructed slices in the MRI. In the second reconstruction, the image after the first reconstruction is composed of only half of the slices, and there are still missing values. In our previous work, we adopted the traditional interpolation method, and there was still a gap in the visual effect of the reconstructed images. Therefore, we propose a noise-based super-resolution network (nESRGAN). The noise addition to the network can provide additional texture restoration possibilities. We use nESRGAN to further restore MRI resolution and high-frequency information. Finally, we achieve the 3D reconstruction of brain MRI images through two super-resolution reconstructions. Our proposed method is superior to 3D super-resolution technology based on deep learning in terms of perception range and image quality evaluation standards.
Depthwise Spatio-Temporal STFT Convolutional Neural Networks for Human Action Recognition

Conventional 3D convolutional neural networks (CNNs) are computationally expensive, memory intensive, prone to overfitting, and most importantly, there is a need to improve their feature learning capabilities. To address these issues, we propose spatio-temporal short-term Fourier transform (STFT) blocks, a new class of convolutional blocks that can serve as an alternative to the 3D convolutional layer and its variants in 3D CNNs. An STFT block consists of non-trainable convolution layers that capture spatially and/or temporally local Fourier information using an STFT kernel at multiple low frequency points, followed by a set of trainable linear weights for learning channel correlations. The STFT blocks significantly reduce the space-time complexity in 3D CNNs. In general, they use 3.5 to 4.5 times less parameters and 1.5 to 1.8 times less computational costs when compared to the state-of-the-art methods. Furthermore, their feature learning capabilities are significantly better than the conventional 3D convolutional layer and its variants. Our extensive evaluation on seven action recognition datasets, including Something 2 v1 and v2, Jester, Diving-48, Kinetics-400, UCF 101, and HMDB 51, demonstrate that STFT blocks based 3D CNNs achieve on par or even better performance compared to the state-of-the-art methods.
Tumor detection using deep learning method in automated breast ultrasound

Accurate and efficient breast tumor detection of automated breast ultrasound (ABUS) plays a significant role for clinical diagnosis. However, reviewing thousands of ABUS slices is extremely a time-consuming task. Besides, speckle, shadowing, non-uniform contrast of certain structures and high variability of the lesion echogenicity, make tumor detection in ABUS images difficult and prone to false positive (FP) regions. To improve the tumor detection rate and reduce FP regions, a Bayesian YOLOv4 network based on Monte Carlo dropout (MC-Drop) is proposed. Firstly, some tricks in YOLOv4 network are adjusted to make it more suitable for ABUS tumor detection. Secondly, MC-Drop is applied to introduce uncertainty into the detection network and the Bayesian YOLOv4 network is constructed, which effectively reduces accidental FP regions and improves the detection rate for hard tumor region. This method was tested on 68 volumes, including 21,624 slices (1683 tumor slices and 19,941 normal slices). It obtains a promising result with sensitivity of 0.88 and FPs/S at 0.19. Experimental results demonstrate that our Bayesian YOLOv4 outperforms the original YOLOv4 network and some other mainstream object detectors.
Deep Learning Based Modulation Recognition With Multi-Cue Fusion

In this letter, a novel multi-cue fusion (MCF) network for automatic modulation recognition is proposed. To exploit the potentially non-trivial and informative contents that previous works ignored, multi-cue learning is injected into neural network design. The MCF network consists of a signal cue multi-stream (SCMS) module and a visual cue discrimination (VCD) module. The SCMS module based on Convolutional Neural Network (CNN) and Independently Recurrent Neural Network (IndRNN) is built for modeling spatial-temporal correlations from two signal cues (i.e., In-phase/Quadrature (I/Q) and amplitude-phase (A/Φ)), which aims to explore various differences and leverage the complements from multiple data-form. In VCD module, raw I/Q data is converted to constellation diagrams as the visual cue to exploit the structural information utilizing the feature extracting capability of CNN. Experimental results on RadioML2016.10a and RadioML2018 datasets achieve 97.8% and 96.1% respectively, which outperform the state-of-the-art works.
AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition

Multi-modal learning, which focuses on utilizing various modalities to improve the performance of a model, is widely used in video recognition. While traditional multi-modal learning offers excellent recognition results, its computational expense limits its impact for many real-world applications. In this paper, we propose an adaptive multi-modal learning framework, called AdaMML, that selects on-the-fly the optimal modalities for each segment conditioned on the input for efficient video recognition. Specifically, given a video segment, a multi-modal policy net-work is used to decide what modalities should be used for processing by the recognition model, with the goal of improving both accuracy and efficiency. We efficiently train the policy network jointly with the recognition model using standard back-propagation. Extensive experiments on four challenging diverse datasets demonstrate that our proposed adaptive approach yields 35% − 55% reduction in computation when compared to the traditional baseline that simply uses all the modalities irrespective of the in-put, while also achieving consistent improvements in accuracy over the state-of-the-art methods. Project page: https://rpand002.github.io/adamml.html.
Research on estimation models of the spectral characteristics of soil organic matter based on the soil particle size

Soil organic matter (SOM) is an important part of soil fertility and the main nutrient source for crop growth. The establishment of an effective SOM content estimation model can provide technical support for the improvement of saline soil and the implementation of precision agriculture. In this paper, a laboratory spectrometer was used to measure the spectral reflectance of saline soils with particle sizes of 1 mm, 0.50 mm, 0.25 mm and 0.15 mm collected from Kenli County. After spectral preprocessing and spectral transformation, the characteristic bands of the SOM spectrum were extracted by the successive projections algorithm (SPA). Finally, stepwise multiple linear regression (SMLR), principal component regression (PCR) and partial least squares regression (PLSR) were used to establish SOM content estimation models based on soil particle size. The results showed the following. (i) Soil particle size had a significant impact on soil spectral reflectance. The smaller the soil particle size was, the greater the soil spectral reflectance. (ii) The sensitive bands for SOM were mainly concentrated in the visible light region (400–760 nm). First derivative (FD) transformation can effectively improve the characteristic spectral information obtained from SOM. (iii) Among the three models established with the characteristic bands, the estimation ability of the PLSR model was better than that of the PCR and SMLR models. (iv) The FD of the original spectral reflectance of the 0.25 mm particles combined with the PLSR model gave the best estimation of the SOM content. When the soil particle size was less than 0.25 mm, the estimation results of the model were not improved. These results provide a basis for effective estimation of the SOM content and improvement of saline-alkali soil in Kenli County in the Yellow River Delta.
Virtual reality-based conflict resolution: The impact of immersive 360° video on changing view points and moral judgment in the context of violent intergroup conflict

We present an experimental study conducted in the context of the Israeli–Palestinian conflict that examined the effect of immersive 360° video on inducing a more critical perception of the ingroup’s actions in the conflict. An immersive experience of a simulated conflict scenario filmed from the outgroup’s point of view led to the judgment of the ingroup actors’ behavior as less moral and less justified compared to watching the same scenario as a two-dimensional video. This effect was not mediated through increased outgroup perspective-taking and empathy but through higher levels of hostile emotions toward the ingroup actors, which in turn were influenced by an increased sense of presence and engagement in the immersive experience. These findings provide initial evidence for the still widely unexplored potential of virtual reality as a new method for conflict resolution.