Reviewerly/exHarmony huggingface dataset:
Noise2Void Denoising of PET Images

Qualitative and quantitative interpretation of PET images is often a challenging task due to high levels of noise in the images. While deep learning architectures based on convolutional neural networks have produced unprecedented accuracy at denoising PET images, most existing approaches require large training datasets with corrupt and clean image pairs, which are often unavailable for many clinical applications. The Noise2Noise technique obviates the need for clean target images but instead introduces the requirement for two noise realizations for each corrupt input. In this paper, we present a denoising technique for PET based on the Noise2Void paradigm, which requires only a single noisy image for training thus ensuring wider applicability and adoptability. During the training phase, a single noisy PET image serves as both the input and the target. The method was validated on simulation data based on the BrainWeb digital phantom. Our results show that it generates comparable performance at the training and validation stages for varying noise levels. Furthermore, its performance remains robust even when the validation inputs have different count levels than the training inputs.
A systematic evaluation of coding strategies for sparse binary images

Inpainting-based compression represents images in terms of a sparse subset of its pixel data. Storing the carefully optimised positions of known data creates a lossless compression problem on sparse and often scattered binary images. This central issue is crucial for the performance of such codecs. Since it has only received little attention in the literature, we conduct the first systematic investigation of this problem so far. To this end, we first review and compare a wide range of existing methods from image compression and general purpose coding in terms of their coding efficiency and runtime. Afterwards, an ablation study enables us to identify and isolate the most useful components of existing methods. With context mixing, we combine those ingredients into new codecs that offer either better compression ratios or a more favourable trade-off between speed and performance.
Determinants concerning Legendre symbols

The evaluations of determinants with Legendre symbol entries have close relation with combinatorics and character sums over finite fields.Recently, Sun [9] posed some conjectures on this topic.In this paper, we prove some conjectures of Sun and also study some variants.For example, we show the following result:Let p = a 2 + 4b 2 be a prime with a, b integers and a ≡ 1 (mod 4).Then for the determinant, the number S(1, p)/a is an integral square, which confirms a conjecture posed by Cohen, Sun and Vsemirnov.
Inter-Modality Fusion Based Attention for Zero-Shot Cross-Modal Retrieval

Zero-shot cross-modal retrieval (ZS-CMR) performs the task of cross-modal retrieval where the classes of test categories have a different scope than the training categories. It borrows the intuition from zero-shot learning which targets to transfer the knowledge inferred during the training phase for seen classes to the testing phase for unseen classes. It mimics the real-world scenario where new object categories are continuously populating the multi-media data corpus. Unlike existing ZS-CMR approaches which use generative adversarial networks (GANs) to generate more data, we propose Inter-Modality Fusion based Attention (IMFA) and a framework ZS_INN_FUSE (Zero-Shot cross-modal retrieval using INNer product with image-text FUSEd). It exploits the rich semantics of textual data as guidance to infer additional knowledge during the training phase. This is achieved by generating attention weights through the fusion of image and text modalities to focus on the important regions in an image. We carefully create a zero-shot split based on the large-scale MS-COCO and Flickr30k datasets to perform experiments. The results show that our method achieves improvement over the ZS-CMR baseline and self-attention mechanism, demonstrating the effectiveness of inter-modality fusion in a zero-shot scenario.
Natural Language Processing Techniques to Reveal Human-Computer Interaction for Development Research Topics

Designing systems for/with marginalized populations requires innovation and the integration of sophisticated domain knowledge with emergent technologies and trends. Researchers need to be cognizant of existing research trends when aspiring to design interventions to build on current and emergent needs. Traditional manual mechanisms for revealing developments in a field, such as systematic literature reviews (SLRs), cannot meet this challenge because they are time and effort intensive and the domain itself is dynamic and ever expanding. This compromises the efficacy of SLRs in keeping up with the growing academic literature. A number of emergent technologies and modern methods exist that could be harnessed to make it possible to monitor the field more effectively and efficiently. In this paper, we propose the use of natural language processing (NLP), an AI-powered text analysis technique that operates efficiently and requires limited human intervention. To investigate the use and usefulness of NLP for identifying research themes, we applied Latent Dirichlet Allocation (LDA), a topic modelling technique that uses a probabilistic model to find the co-occurrence patterns of terms that correspond to semantic topics. We applied it to a collection of 176 articles published in the Human-Computer Interaction for Development (HCI4D) field. We demonstrate the usefulness of the LDA method by comparing the findings of the LDA analysis to those of a manual analysis carried out by researchers. While NLP techniques may not be able to replace SLRs at this stage, we share some insights on how NLP techniques can complement SLRs to offset investigator bias and save time and effort in revealing emerging domain-related themes.
Priors comparison in Bayesian Models of risk factor of Malaysian coronary artery disease male patients

Abstract Coronary artery disease (CAD) continues to be one of the leading causes of morbidity and mortality globally. Of particular relevance for this issue is that major efforts should be focused on understanding the risk factor involved. In this study, three types of Bayesian models, each with different prior distribution were considered to identify associated risk factors in CAD among Malaysian male patients presenting with ST-Elevation Myocardial Infarction (STEMI) and to obtain a feasible model to fit the data. The results of the three models were compared to find the best model. A total of 7180 STEMI male patients from the National Cardiovascular Disease Database-Acute Coronary Syndrome (NCVD-ACS) registry year 2006-2013 were analysed. Univariate and multivariate analyses for the three models were performed using one of the Bayesian Markov Chain Monte Carlo (MCMC) simulation approach known as Gibbs sampling. Models’ performances are evaluated through overall model fit. Bayesian model C which used both Beta and Dirichlet prior distributions, consisted of six significant variables namely diabetes mellitus, family history of cardiovascular disease, chronic lung disease, renal disease, Killip class and age group was considered as the best model. The same set of variables that were observed to be significant in the Bayesian model C was also found to be significant in models A and B which used single prior distribution, respectively. Model C has a better fit than models A and B as the deviance value produced was the smallest. This study showed that posterior estimation was mostly influenced by the existing prior knowledge. Though applying the non-informative prior which were both Beta and Dirichlet distribution priors, model C can minimise uncertainty in making effective clinical decisions and provides better parameters estimates of the posterior distribution.
Evaluating Different Selection Criteria for Phase Type Survival Tree Construction

Due to its interpretability and intuitiveness, survival tree based analysis is a powerful Artificial Intelligence method for modelling longitudinal survival data, its relationship with covariates and the interrelationship between covariates. Furthermore, it is being increasingly used for a range of applications including clustering, prognostication and classification. Phase type survival tree methods have been demonstrated to have important applications, including clustering patients into clinically meaningful groups, patient pathway prognostication and forecasting bed requirements. In this article, we critically investigate and assess several selection information criteria with regards to their suitability and limitations when used as splitting criteria in phase type survival tree construction. As shown in Table 12, the results of this analysis are compared and discussed. Furthermore, a text mining approach is utilised to further assess correlations, which have been extracted from hospital data, between the three underlying diseases and the two different types of population groups, namely age and gender groups. Its aim is to provide further investigative tools. In fact, due to its ability to analyse large volumes of textual data, text mining can provide a useful approach to this research area.
Real‐time CVSA decals recognition system using deep convolutional neural network architectures

The Commercial Vehicle Safety Alliance (CVSA) aims to achieve uniformity, compatibility and reciprocity of commercial motor vehicle inspections and enforcement by certified inspectors dedicated to driver and vehicle safety. Commercial vehicles that pass a CVSA inspection are eligible for a decal representing a commitment to safety. In this work, we propose a two-step automatic CVSA decal recognition system using deep convolutional neural network architectures. The first step localizes a vehicle's windshield and the CVSA decal within, and classifies the decal colour. The CVSA decal is cropped and used as input to the second stage, which localizes and classifies a digit and the corner-cut of a CVSA decal. With the corner cut, colour, and digit, the system can determine the decal's date of issue. We use as our baseline the MobileDet architecture, customizing the backbone to our tasks. Our first custom architecture is larger than the baseline because it needs more representational power to detect small decals within an image. The second architecture is much smaller because digit and corner-cut recognition is a simpler task. Our custom architectures reduce processing time and exceed accuracies relative to pre-trained architectures. We implemented our models on different edge hardware accelerators (e.g. the Google Coral, Nvidia Jetsons, and Intel NCS) and compared the performance when processing a real-time video stream. Our system can predict frames at 173.31 FPS on an NVIDIA Jetson AGX Xavier with 98.5% mean average precision @ 0.5 IoU.
A No-Reference CNN-Based Super-Resolution Method for KOMPSAT-3 Using Adaptive Image Quality Modification

In recent years, research on increasing the spatial resolution and enhancing the quality of satellite images using the deep learning-based super-resolution (SR) method has been actively conducted. In a remote sensing field, conventional SR methods required high-quality satellite images as the ground truth. However, in most cases, high-quality satellite images are difficult to acquire because many image distortions occur owing to various imaging conditions. To address this problem, we propose an adaptive image quality modification method to improve SR image quality for the KOrea Multi-Purpose Satellite-3 (KOMPSAT-3). The KOMPSAT-3 is a high performance optical satellite, which provides 0.7-m ground sampling distance (GSD) panchromatic and 2.8-m GSD multi-spectral images for various applications. We proposed an SR method with a scale factor of 2 for the panchromatic and pan-sharpened images of KOMPSAT-3. The proposed SR method presents a degradation model that generates a low-quality image for training, and a method for improving the quality of the raw satellite image. The proposed degradation model for low-resolution input image generation is based on Gaussian noise and blur kernel. In addition, top-hat and bottom-hat transformation is applied to the original satellite image to generate an enhanced satellite image with improved edge sharpness or image clarity. Using this enhanced satellite image as the ground truth, an SR network is then trained. The performance of the proposed method was evaluated by comparing it with other SR methods in multiple ways, such as edge extraction, visual inspection, qualitative analysis, and the performance of object detection. Experimental results show that the proposed SR method achieves improved reconstruction results and perceptual quality compared to conventional SR methods.
Deep feature enhancing and selecting network for weakly supervised temporal action localization

Weakly supervised temporal action localization is a challenging computer vision problem that uses only video-level labels and lacks the supervision of temporal annotations. In this task, the majority of existing methods usually identify the most discriminative snippets and ignore other relevant snippets. To address this problem, we propose a deep feature enhancing and selecting network. It generates multiple masks for both capturing more complete temporal interval of actions and keeping its high classification accuracy. After that, we further propose a novel selection strategy to balance the influence of multiple masks and improve the model performance. In the experiments, we evaluate the proposed method on the THUMOS'14 and ActivityNet datasets, and the results show the effectiveness of our approach for weakly supervised temporal action localization.
FREPD: A Robust Federated Learning Framework on Variational Autoencoder

Federated learning is an ideal solution to the limitation of not preserving the users’ privacy information in edge computing. In federated learning, the cloud aggregates local model updates from the devices to generate a global model. To protect devices’ privacy, the cloud is designed to have no visibility into how these updates are generated, making detecting and defending malicious model updates a challenging task. Unlike existing works that struggle to tolerate adversarial attacks, the paper manages to exclude malicious updates from the global model’s aggregation. This paper focuses on Byzantine attack and backdoor attack in the federated learning setting. We propose a federated learning framework, which we call Federated Reconstruction Error Probability Distribution (FREPD). FREPD uses a VAE model to compute updates’ reconstruction errors. Updates with higher reconstruction errors than the average reconstruction error are deemed as malicious updates and removed. Meanwhile, we apply the Kolmogorov-Smirnov test to choose a proper probability distribution function and tune its parameters to fit the distribution of reconstruction errors from observed benign updates. We then use the distribution function to estimate the probability that an unseen reconstruction error belongs to the benign reconstruction error distribution. Based on the probability, we classify the model updates as benign or malicious. Only benign updates are used to aggregate the global model. FREPD is tested with extensive experiments on independent and identically distributed (IID) and non-IID federated benchmarks, showing a competitive performance over existing aggregation methods under Byzantine attack and backdoor attack.
Direct binarization a quality-and-time efficient binarization strategy

Most of the best known binarization algorithms have grayscale conversion as a pre-processing step, before applying the binarization strategy itself. Many algorithms produce equally good or even better quality images if fed with only one component of the image, instead of its gray-scale/luminance equivalent. The time-gain here is obtained in avoiding the several floating-point calculations in converting a RGB-color image into grayscale. More than 60 binarization algorithms were tested using "real-world" images.
The Influence of Bit-Depth Reduction on Correlation of Texture Features with a Patient’s Age

As we age, irreversible changes occur in the human spine. This process is affected not only by aging but also by diseases, lifestyle and genetics. The relationship between a patient’s age and bone condition can be valuable diagnostic information. This is made possible by texture analysis, which provides information about the structure and properties of imaged tissue; however, it depends on many factors, such as image format or image noise. The aim of this work was to test whether the analyzed textures are also sensitive to image quantization. To achieve this, 183 cervical spine radiographs were analyzed to extract features that correlate with patients’ age. The research made it possible to distinguish features that are or are not sensitive to bit-depth reduction. Moreover, significantly lower correlation values of texture features with age were observed for X-rays of female patients than for those of males.
Features as Keypoints and How Fuzzy Transforms Retrieve Them

We are focused on a new fast and robust algorithm of image/signal feature extraction in the form of representative keypoints. We analyze various multi-scale representations of a one-dimensional signal in spaces with a closeness relation determined by a symmetric and positive semi-definite kernel. We show that kernels arising from generating functions of fuzzy partitions can be used in a scale space representation of a one-dimensional signal. We show that the reconstruction from the proposed multi-scale representations is of better quality than the reconstruction from MLP with almost double the number of neurons in 4 hidden layers. Finally, we propose a new algorithm of keypoints localization and description and test it on financial time series with high volatility.
Sustainability as a Driver for Vocational Education and Competitiveness

The modern world faces a series of global challenges, and sustainable development as a strategy of civilization, which is an alternative to economic growth, is called upon to answer them. In 2015, in connection with the adoption by the United Nations of the 17 goals in the field of sustainable development for the period up to 2030, special training is needed to implement innovative and breakthrough projects. Such projects are aimed at improving competitiveness through the development and implementation of the best available technologies, strengthening human capital. This problem is relevant both for the Russian Federation and the whole world. In the paper, on the basis of constructivist, systemic, and convergent approaches, proposals for improving the effectiveness of vocational education and training for sustainable development are formulated. The paper summarizes results of scientific and methodological research and presents successful practices. The results of scientometric analysis indicate a disproportion in the study of sustainable development issues associated with a significant predominance of economic research. This makes it difficult to conduct a disciplinary review of the problem and prepare appropriate educational programs. The feasibility of a practice-oriented approach to vocational education in the interests of sustainable development, based on the participation of students in the implementation of “agendas 21” at the institutional level (for an educational organization, production enterprise) is justified. The previously developed competency-based model for training a specialist in sustainable development is complemented by a number of “soft” skills.
Pose Guided Person Image Generation With Hidden P-Norm Regression

In this paper, we propose a novel approach to solve the pose guided person image generation task. We assume that the relation between pose and appearance information can be described by a simple matrix operation in hidden space. Based on this assumption, our method estimates a pose-invariant feature matrix for each identity, and uses it to predict the target appearance conditioned on the target pose. The estimation process is formulated as a p-norm regression problem in hidden space. By utilizing the differentiation of the solution of this regression problem, the parameters of the whole framework can be trained in an end-to-end manner. While most previous works are only applicable to the supervised training and single-shot generation scenario, our method can be easily adapted to unsupervised training and multi-shot generation. Extensive experiments on the challenging Market-1501 dataset show that our method yields competitive performance in all the aforementioned variant scenarios.
Research on College Mathematics Teaching Method based on Computer Modeling Thought

Abstract Mathematics exists in all aspects of human society, which plays an important role in production and life. With the application of computer technology, mathematical computing ability has been greatly developed, which can also be used to calculate more complex social problems. Therefore, solving practical problems through mathematical methods has become the most important way. Mathematical modeling is a tool to solve practical problems, which is also an important basis for computer application in various industries. Through the idea of modeling, we can solve many practical problems. Therefore, college teachers should make rational use of the idea of modeling in mathematics teaching, which has important theoretical and practical value. By cultivating students’ awareness of discovering, analyzing and solving problems, colleges can cultivate students’ observation ability, abstract ability and cooperative spirit, which will improve students’ ability of applying mathematics. Firstly, this paper analyzes the important ways of mathematical modeling. Finally, some methods are proposed.
Evaluating linear functions to symmetric monoidal categories

A number of domain specific languages, such as circuits or data-science workflows, are best expressed as diagrams of boxes connected by wires. Unfortunately, functional languages have traditionally been ill-equipped to embed this sort of languages. The Arrow abstraction is an approximation, but we argue that it does not capture the right properties.
Entity Relationship Extraction Based on Bi-LSTM and Attention Mechanism

The extraction methods based on deep learning can automatically learn sentence features without complex feature engineering. But most current methods ignore the mining of text semantics. Therefore, based on the existing research, considering that Bi-LSTM can capture the advantages of bidirectional semantic dependence and the attention mechanism can assign different weights to the semantic features of different functions, this paper combines the two to perform entity relationship extraction. Beside, in the feature extraction layer, four types of features, part-of-speech, entity recognition type, relative position and the context of entities are introduced. In order to obtain the main connection between entities, the shortest dependency path is also introduced.