Reviewerly/exHarmony huggingface dataset:
Joint-task learning to improve perceptually-aware super-resolution of aerial images

Deep neural networks have become very popular for solving many problems in computer vision. Super-resolution (SR) is a particularly challenging task since new information must be created at increased resolution, possibly leading to visual artefacts or incoherent texture. In the context of remote sensing, this image restoration technique has great potential for synthesizing high-resolution (HR) data from low-resolution (LR) images. While there are multiple methods that enhance the perceptual quality of SR images, most of them fail to recover detailed information from aerial imagery. One of the main reasons for that is the difficulty in defining a ‘good-looking’ image from the perspective of the machine. In this work, we propose an end-to-end training procedure that unifies networks related to different tasks: an SR module based on generative adversarial networks (GANs) and a semantic segmentation module. Our claim is that by including a classification loss when estimating the HR image, the GAN generator produces more coherent structures and textural information, synthesizing, therefore, more realistic images according to perception-based scores. Our experimental results show that the proposed method is capable of improving perceptual outputs of deep-learning oriented networks with a small training overhead, surpassing multiple state-of-the-art super-resolution methods. Our code is available at http://github.com/elitonfilho/SegSR.
Evaluasi Penerapan Aspek 5M Dalam Penggunaan Tracer di Unit Penyimpanan Rekam Medis RSIA Husada Bunda Malang

Tracer is an outguide when medical record documents are borrowed or taken by health workers in health services. Tracers at RSIA Husada Bunda is still not appropriate, seen in terms of the design of the marker slip which does not include the destination poly and the use of tracers is still done manually. This study aims to evaluate the use of tracers based on 5M aspects in the medical record unit of RSIA Husada Bunda. This research was conducted using a descriptive qualitative method with instruments in the form of observations and interviews conducted to the head of medical records and medical records officers in the storage section. The results of this study show that in the Man aspect there are discrepancies related to HR criteria in the medical records unit. In the Material aspect, the tracer uses semihard materials, while the use of the tracer refers to a special method, namely the SOP. The application of the tracer in terms of Machine still uses a written manual. In supporting all activities in the medical records unit, it is supported by Money (budget funds) and some are allocated for training activities and seminars. Overall, the use of tracers in the medical records unit of RSIA Husada Bunda has been relatively effective but still needs a stage of improvement in several aspects. Improvements related to the use of tracers can be in the form of updates to tracer materials, redesign of marker slips, and maximizing the use of machines in operational activities using tracers.
Revolutionising dual language learning for young learners with the RILCA app: a mobile-assisted approach

Abstract The rising popularity of mobile technologies has led to an increase in mobile-assisted language learning, in which students can independently learn any language at any time they choose. However, research into the efficacy of such studies for dual-language learning has been restricted, notably in regard to a researcher-created mobile learning application (app). This paper reports on a study that examined the dual-language development of 146 young Thai pupils from three different primary schools who participated in a fifteen-week language instruction experiment through the use of the RILCA app. They were assigned to either an experimental group (mobile-supported class) with RILCA-based activity or a control group (conventional class) that performed traditional activity. At the conclusion of the experiment, a dual-language vocabulary, sentence, and grammatical achievement test was conducted to see whether this mobile app increased the course's learning outcomes. The findings of the vocabulary, sentence, and grammatical comprehension tests reveal that the experimental group outperformed the control group. Implications for deploying the RILCA app to boost dual language learning in elementary schools are also discussed.
Robustness of Deep Learning Models for Vision Tasks

In recent years, artificial intelligence technologies in vision tasks have gradually begun to be applied to the physical world, proving they are vulnerable to adversarial attacks. Thus, the importance of improving robustness against adversarial attacks has emerged as an urgent issue in vision tasks. This article aims to provide a historical summary of the evolution of adversarial attacks and defense methods on CNN-based models and also introduces studies focusing on brain-inspired models that mimic the visual cortex, which is resistant to adversarial attacks. As the origination of CNN models was in the application of physiological findings related to the visual cortex of the time, new physiological studies related to the visual cortex provide an opportunity to create more robust models against adversarial attacks. The authors hope this review will promote interest and progress in artificially intelligent security by improving the robustness of deep learning models for vision tasks.
Earth observation approach for targeting stratiform deposit of manganese in central India

The stratiform manganese deposits, hosted within the Sausar Group of rocks, in Madhya Pradesh state of India, has undergone polyphase deformation and metamorphism. The region is an enterprising geosite to understand the lithological and tectonic control on manganese mineralization. We have carried out a conjugate remote sensing based analysis using Visible, Near Infrared and Short Wave Infrared bands (VIS-NIR-SWIR) of optical imagery from multiple sensors supplemented with analysis of L-band microwave data over the Balaghat district of Madhya Pradesh to delineate prospective areas for manganese exploration, supported by the lab and field-based evidence. The enhanced image products (i.e.band ratios, principal components) and spectral anomaly map derived from Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) and Sentinel-2B multispectral bands are used to delineate the lithological contrast of the host rock and indicate key locales of mineralization. The geological structures interpreted using the merged product of Advanced Land Observation Satellite Phased Array L-band SAR (ALOS PALSAR −1), Landsat 8 PC bands (2 & 3) and the processed Horizontal Gradient Magnitude (HGM) from advanced satellite gravity model (EIGEN-6C4), illustrates the structural control on the mineralization. The petrological details derived from the host rock samples shows microstructures like micro-folding, foliations, quartz grain elongation etc., thus representing the microscale deformations within the rock units occurring in the area.The lab-based spectral signatures of manganese-bearing host rock was resampled to image spectra and spectral anomaly map using Constrained Energy Minimization (CEM), which shows good correlation with the existing mine locations. The information from remote sensing and lab analysis has been integrated thematically using the knowledge-based Weighted Sum Method (WSM) with existing mine locations as the training dataset. The derived mineral prospect map indicates several new areas of manganese prospectivity which can serve as a guide for detailed future explorations in the region .
Adversarial composite prediction of normal video dynamics for anomaly detection

Dynamic modeling of normal videos is the key problem for unsupervised video anomaly detection. In this paper, we propose a novel adversarial composite prediction framework for unsupervised video anomaly detection, namely ACP-VAD. It consists of an adversarial composite prediction (ACP) module and a foreground motion metric learning (FMML) module. Given a video sequence, ACP module aims to learn reasonable normal video dynamics by using a generator for the sequential and composite predicting of video frames, and two discriminators for the frame-level and sequence-level adversarial learning. Moreover, to learn a more compact representation of normal video dynamics and improve the detection sensibility of motion anomalies, FMML module utilizes the sophisticated metric learning scheme over the latent space distributions of large foreground motion amplitude sequence, real normal sequence as well as real normal sequence containing previously sequential or composite prediction frames. Through these two modules, our framework can learn reasonable and compact spatial-temporal dynamics for normal videos, and the final anomaly detection task can be solved by computing the error between the predicted frame and its corresponding real frame. Comprehensive experimental results on UCSD Ped2, Avenue, and ShanghaiTech datasets demonstrate the effectiveness of both ACP module and FMML module, and our final ACP-VAD framework achieves impressive performance, surpassing most of the state-of-the-art methods.
Motion parameters estimation of manoeuvring weak target with multiple motion stages based on range symmetry transform and short‐time fractional Fourier transform

Abstract With the advancement of manoeuvrability and the increasement of coherent processing interval (CPI), the observed target might be of multiple motion stages. Therefore, most existing long‐time coherent integration (LTCI) algorithms based on the assumption of single‐motion stage is not proper any more. To address this problem, this letter proposes an effective motion parameters estimation algorithm, which is the key step of LTCI, for the manoeuvring weak target with multiple motion stages based on range symmetry transform and short‐time fractional Fourier transform (RST‐STFrFT). The effectiveness of the proposed algorithm is verified by signal‐level simulations.
BTDM: A Bi-Directional Translating Decoding Model-Based Relational Triple Extraction

The goal of relational triple extraction is to extract knowledge-rich relational triples from unstructured text. Although the previous methods obtain considerable performance, there are still some problems, such as error propagation, the overlapping triple problem, and suboptimal subject–object alignment. To address the shortcomings above, in this paper, we decompose this task into three subtasks from a fresh perspective: entity extraction, subject–object alignment and relation judgement, as well as propose a novel bi-directional translating decoding model (BTDM). Specifically, a bidirectional translating decoding structure is designed to perform entity extraction and subject–object alignment, which decodes entity pairs from both forward and backward extraction. The bidirectional structure effectively mitigates the error propagation problem and aligns the subject–object pairs. The translating decoding approach handles the overlapping triple problem. Finally, a (entity pair, relation) bipartite graph is designed to achieve practical relationship judgement. Experiments show that our model outperforms previous methods and achieves state-of-the-art performance on NYT and WebNLG. We achieved F1-scores of 92.7% and 93.8% on the two datasets. Meanwhile, in various complementary experiments on complex scenarios, our model demonstrates consistent performance gain in various complex scenarios.
Deep-Learning-Based Precision Visual Tracking

In this paper, we target the “precision visual tracking” problem, which is the precise tracking of a target point on a moving object. Compared with the abundant efforts in object-level tracking, which aims at accurately predicting the bounding box (or the mask) of the object, much less attention has been drawn to precision visual tracking. To this end, we present a template tracking framework that consists of two parts, template matching and template updating. For template matching, we trained a deep architecture to directly estimate the projective transformation that deforms the template to the search image. For template updating, we came up with a systematic strategy to update the initial and new templates. To avoid drift build-ups, we incorporate a fast-running dense correspondence matching module into the template update step. The proposed method was extensively tested on both synthetic and real data. To generate the synthetic data, we created a dataset of 480 image sequences. Each image sequence comes with the trajectory ground truth of a target point moving across all the frames. We compared the proposed method with six comparative approaches on this dataset. The tracking accuracy achieved by the proposed method was as follows: around 44% of the tracking errors were less than one pixel, about 78% of them were less than three pixels, and only about 14% of them exceeded five pixels. The proposed method is fast, running at 14 frames-per-second (fps) on [Formula: see text] image sequences on a workstation equipped with a Nvidia RTX 2080Ti graphic card. Qualitative results also show that the proposed method is applicable to real-world image sequences. The related code, pre-trained models, and the test data will be made publicly available at https://github.com/XM-Peng/Precision-Visual-Tracking/ .
Deepfake Speech Recognition and Detection

Deepfake technology, especially deep voice, which has been derived from artificial intelligence in recent years, is potentially harmful, and the public is not yet wary. However, many speech synthesis models measure the degree of true restitution by Mean Opinion Rating (MOS), a subjective assessment of naturalness and quality of speech by human subjects, but in future it will be difficult to distinguish the interlocutor’s identity through the screen. For this reason, this study addresses the threat posed by this new technology by combining representational learning and 0transfer learning in two sub-systems: a recognition system and a voice print system. The recognition system is responsible for the detection of which voice is a fake voice generated by speech conversion or speech synthesis techniques, while the acoustic system is responsible for the verification of the speaker’s identity through acoustic features. In the speech recognition system, we use the representation learning method and the transfer classification method. We use X-vector data for training, and then fine-tune the model using four types of marker data to learn the representation vectors of real and fake voice, and use support vector machine to classify real and fake voice in the back-end to reduce the negative effect of the new technique.
Non-episodic Variational Weight Imprinting for Few-Shot Learning

Few-shot learning methods enable models to recognize novel classes from one or only a few examples. Most of the existing methods use episodic training which require multiple training models and/or variable memory requirements. We propose a non-episodic model, which combines a $$\beta$$ -variational auto encoder ( $$\beta$$ -VAE) with the cosine similarity classifier (CSC). It is trained only once with fixed memory requirement and can be used for all test settings. We have also created our own printed character dataset with style attributes that includes six Indic scripts to analyze the effect of $$\beta$$ -VAE’s disentangled content on few-shot classification accuracy. In our model, the encoder of $$\beta$$ -VAE generates both the content and style distribution where only the content is used by the CSC. The network is trained in an end-to-end fashion which enables the encoder to disentangle the content features needed for classification from style attributes. After the training phase, the weights of novel classes are generated by normalizing the encoder’s content representation and used for testing. In case of Omniglot, we got 98.91% classification accuracy for 5-way 1-shot which is on par/better than the state-of-the art episodic methods. Our ablation study shows that $$\beta$$ -VAE with CSC network outperforms CSC network on Indic script dataset. Our proposed model which is trained only once with fixed memory requirement gives comparable results with episodic and non-episodic methods as the encoder feeds the disentangled content features to the classifier. In a few settings, it also outperforms the episodic methods.
Intelligent generation of combat simulation scenarios based on UML diagram recognition

The traditional generation of combat simulation scenarios often requires a manual understanding of conceptual scenarios and transformation into simulation scenarios. This method has the problems of long development time and high development threshold. Conceptual scenarios are usually visual Unified Modeling Language (UML) diagrams, so we can use artificial intelligence technology to extract key semantics from them, and automatically map the extracted semantics to simulation scenarios. This method is called the intelligent generation of combat simulation scenarios. To extract the key semantics from conceptual scenarios in UML form, we propose the UML diagram recognition method based on Keypoint Region-based Convolutional Neural Network (R-CNN). This method includes three parts: image character recognizer, primitive object detector, and image semantic extractor. First, we use optical character recognition (OCR) technology to achieve image character recognition. Second, we manually annotate the primitive target-detection dataset and propose a new primitive target-detection model—Keypoint R-CNN. This model considers the direction of connecting lines and realizes the detection of symbols and connecting lines. Third, we propose a targeted combined primitive detection and primitive relationship extraction method to extract the high-level semantics of UML diagrams. Then, we carried out experiments and evaluations on the self-made dataset. Compared with other methods, the F1 score of our method is improved by about 7%, and the Jaccard coefficient is improved by about 10%. Finally, we use a case study to implement the intelligent generation process of combat simulation scenarios using the UML recognition method we proposed. This case shows the operability and efficiency of our method. Our method greatly reduces the labor cost and development threshold of combat simulation scenario generation and improves the development efficiency of the combat simulation system.
Improved binary crocodiles hunting strategy optimization for feature selection in sentiment analysis

Feature Selection (FS) for Sentiment Analysis (SA) becomes a complex problem because of the large-sized learning datasets. However, to reduce the data dimensionality, researchers have focused on FS using swarm intelligence approaches that reflect the best classification performance. Crocodiles Hunting Strategy (CHS), a novel swarm-based meta-heuristic that simulates the crocodiles’ hunting behaviour, has demonstrated excellent optimization results. Hence, in this work, two FS algorithms, i.e., Binary CHS (BCHS) and Improved BCHS (IBCHS) based on original CHS were applied for FS in the SA field. In IBCHS, the opposition-based learning technique is applied in the initialization and displacement phases to enhance the search space exploration ability of the IBCHS. The two proposed approaches were evaluated using six well-known corpora in the SA area (Semeval-2016, Semeval-2017, Sanders, Stanford, PMD, and MRD). The obtained result showed that IBCHS outperformed BCHS regarding search capability and convergence speed. The comparison results of IBCHS to several recent state-of-the-art approaches show that IBCHS surpassed other approaches in almost all used corpora. The comprehensive results reveal that the use of OBL in BCHS greatly impacts the performance of BCHS by enhancing the diversity of the population and the exploitation ability, which improves the convergence of the IBCHS.
Development and tolerability of a novel virtual- and proprioception-based car crash simulator as a new research tool in motor vehicle trauma research

Purpose: Investigations of causal theories of neck pain (NP) following motor vehicle crashes (MVC) has been difficult, as simulation is limited. Thus, we sought to evaluate tolerability to a novel virtual reality (VR)-based road collision simulator and screen for adverse reactions. Materials and Methods: Cross-sectional study. 25 healthy participants were exposed to a novel VR-based rear-end MVC with a small perturbation (0.2 g). The Simulator Sickness Questionnaire (SSQ) and Presence Questionnaire (PQ) were measured post-exposure and adverse reactions were recorded. Results: The system was well tolerated with no adverse reactions, however one participant reported NP the following day not lasting longer than 48 h. Participants reported low levels of simulator sickness (mean SSQ = 23.49 ± 21.98, range = 0.00 to 89.76; max score = 235.62), while presence (mean PQ = 91.04 ± 14.08, range = 54.00 to 112.00; max score = 133), was lower than literature recommendations. Conclusion: A VR-based road collision simulator can be safely used to explore the phenomenon of a motor vehicle crashes under controlled circumstances. Future work is needed to optimize the virtual reality environment and to investigate the effects of crash parameters.
Readability Metrics for Machine Translation in Dutch: Google vs. Azure &amp; IBM

This paper introduces a novel method to predict when a Google translation is better than other machine translations (MT) in Dutch. Instead of considering fidelity, this approach considers fluency and readability indicators for when Google ranked best. This research explores an alternative approach in the field of quality estimation. The paper contributes by publishing a dataset with sentences from English to Dutch, with human-made classifications on a best-worst scale. Logistic regression shows a correlation between T-Scan output, such as readability measurements like lemma frequencies, and when Google translation was better than Azure and IBM. The last part of the results section shows the prediction possibilities. First by logistic regression and second by a generated automated machine learning model. Respectively, they have an accuracy of 0.59 and 0.61.