Reviewerly/exHarmony huggingface dataset:
End-to-End Single Shot Detector Using Graph-Based Learnable Duplicate Removal

Non-Maximum Suppression (NMS) is widely used to remove duplicates in object detection. In strong disagreement with the deep learning paradigm, NMS often remains as the only heuristic step. Learning NMS methods have been proposed that are either designed for Faster-RCNN or rely on separate networks. In contrast, learning NMS for SSD models is not well investigated. In this paper, we show that even a very simple rescoring network can be trained end-to-end with an underlying SSD model to solve the duplicate removal problem efficiently. For this, detection scores and boxes are refined from image features by modeling relations between detections in a Graph Neural Network (GNN). Our approach is applicable to the large number of object proposals in SSD using a pre-filtering head. It can easily be employed in arbitrary SSD-like models with weight-shared box predictor. Experiments on MS-COCO and KITTI show that our method improves accuracy compared with other duplicate removal methods at significantly lower inference time.
FastSiam: Resource-Efficient Self-supervised Learning on a Single GPU

Self-supervised pretraining has shown impressive performance in recent years, matching or even outperforming ImageNet weights on a broad range of downstream tasks. Unfortunately, existing methods require massive amounts of computing power with large batch sizes and batch norm statistics synchronized across multiple GPUs. This effectively excludes substantial parts of the computer vision community from the benefits of self-supervised learning who do not have access to extensive computing resources. To address that, we develop FastSiam with the aim of matching ImageNet weights given as little computing power as possible. We find that a core weakness of previous methods like SimSiam is that they compute the training target based on a single augmented crop (or “view”), leading to target instability. We show that by using multiple views per image instead of one, the training target can be stabilized, allowing for faster convergence and substantially reduced runtime. We evaluate FastSiam on multiple challenging downstream tasks including object detection, instance segmentation and keypoint detection and find that it matches ImageNet weights after 25 epochs of pretraining on a single GPU with a batch size of only 32.
Modern Approaches for Concepts and Relations Extraction for Ontology Learning

This paper describes the state of the art in the natural language processing area of information retrieval. In particular, this paper considers effective approaches to extracting concepts and relationships between concepts from natural language texts to automate the construction of subject ontologies. These approaches use various machine learning methods to understand the text. The practical value of the work is improving the quality of highlighting key terms from a raw text in Russian, as well as extracting semantic binary relationships between the selected terms of the subject area. The degree of quality is measured by appropriate metrics. The scientific novelty of this work is the using of a bidirectional model with a transformer architecture with the ability to encode relationships between pairs of entities to extract relationships between concepts, as well as using a faster approach based on the attention mechanism to extract directly the concepts themselves from natural language text to build subject ontologies.
Improving Unsupervised Label Propagation for Pose Tracking and Video Object Segmentation

Label propagation is a challenging task in computer vision with many applications. One approach is to learn representations of visual correspondence. In this paper, we study recent works on label propagation based on correspondence, carefully evaluate the effect of various aspects of their implementation, and improve upon various details. Our pipeline assembled from these best practices outperforms the previous state of the art in terms of PCK $$_{0.1}$$ on the JHMDB dataset by $$6.5\%$$ . We also propose a novel joint framework for tracking and keypoint propagation, which in contrast to the core pipeline is applicable to tracking small objects and obtains results that substantially exceed the performance of the core pipeline. Finally, for VOS, we extend the core pipeline to a fully unsupervised one by initializing the first frame with the self-attention layer from DINO. Our pipeline for VOS runs online and can handle static objects. It outperforms unsupervised frameworks with these characteristics.
Designing a VR Application for Typhoon Preparedness Training in a Classroom

In the Disaster Readiness and Risk Reduction Senior High School course under the Philippine K-12 curriculum, there is little hands-on application of lessons learned for disaster preparedness. There are existing virtual reality applications that have been designed to train disaster preparedness. However, issues related to user experience and the missed potential of VR in hands-on training were found. Most were also not designed for the classroom. In this paper, a design for a VR application that would address said issues and be intended for Senior High School students and teachers is proposed. This is meant to supplement the classroom experience. Features such as the usage of virtual hands, movement options, and recording of the user’s activity for future viewing, among others, are incorporated.
Augmentation Learning for Semi-Supervised Classification

Recently, a number of new Semi-Supervised Learning methods have emerged. As the accuracy for ImageNet and similar datasets increased over time, the performance on tasks beyond the classification of natural images is yet to be explored. Most Semi-Supervised Learning methods rely on a carefully manually designed data augmentation pipeline that is not transferable for learning on images of other domains. In this work, we propose a Semi-Supervised Learning method that automatically selects the most effective data augmentation policy for a particular dataset. We build upon the Fixmatch method and extend it with meta-learning of augmentations. The augmentation is learned in additional training before the classification training and makes use of bi-level optimization, to optimize the augmentation policy and maximize accuracy. We evaluate our approach on two domain-specific datasets, containing satellite images and hand-drawn sketches, and obtain state-of-the-art results. We further investigate in an ablation the different parameters relevant for learning augmentation policies and show how policy learning can be used to adapt augmentations to datasets beyond ImageNet.
AspectSol: A Solidity Aspect-Oriented Programming Tool with Applications in Runtime Verification

Aspect-oriented programming tools aim to provide increased code modularity by enabling programming of cross-cutting concerns separate from the main body of code. Since the inception of runtime verification, aspect-oriented programming has regularly been touted as a perfect accompanying tool, by allowing for non-invasive monitoring instrumentation techniques. In this paper we present, AspectSol, which enables aspect-oriented programming for smart contracts written in Solidity, and then discuss the design space for pointcuts and aspects in this context. We present and evaluate practical runtime verification uses and applications of the tool.
Breast imaging and deep learning: past, present, and future

Since 2017, there has been steadily growing interest and work on the application of deep learning techniques to breast MRI analysis, and convolutional neural network–based methods are now the state of the art. Deep learning has markedly improved breast MRI segmentation algorithms. Deep learning has also been applied to breast MRI lesion detection, lesion classification, cancer risk assessment, molecular subtype prediction, and treatment response (e.g., prediction of nodal status and pathologic complete response). This initial work is encouraging but remains mostly preliminary and mostly based on relatively small, single-institution datasets containing fewer than 1000 cases. Although many technical and policy-related hurdles remain to be overcome before breast imaging AI algorithms can be introduced in the clinic, we expect that this subfield of medical imaging AI will continue to develop in exciting ways over the next decade.
Multiple attentional aggregation network for handwritten Dongba character recognition

Handwritten Dongba character recognition (HDCR) is a challenging task due to different recognition granularity and it plays an important role in history and philology. Recently, deep learning-based methods are adopted and outperform traditional approaches. However, they do not well explore the multi-granularity information, leading to many highly similar Dongba characters cannot be recognized accurately. In this paper, a Multiple Attentional Aggregation Network (MAAN) is proposed for HDCR. Specifically, a novel Hybrid Attentional Mapping Unit (HAMU) is designed for multi-scale and richer feature expression. The hybrid attention mechanism in HAMU can enhance the discrimination between important and redundant features. Besides, a novel Spatial Attentional Aggregation Unit (SAAU) is adopted to aggregate multi-scale features of different depths. In this way, more details from large scale features and global semantic information from small scale features will be adequately aggregated. Experiments are conducted on our constructed novel Dongba character image dataset and validate the effectiveness and superiority of our methods and the extensive experimental results demonstrate that our proposed MAAN outperforms state-of-the-art algorithms.
Deep survival forests with feature screening

Deep forest has recently received much attention, mainly from the machine learning community, because it has fewer hyper-parameters, better performance, and is easier to construct than deep neural networks. This approach, as well as its variations, are commonly used for image classification, face recognition, music classification, and other related tasks. Its potential in survival analysis, on the other hand, has yet to be determined. In this paper, we propose a deep survival forests framework for modeling right-censored data with high and ultra-high dimensional covariates. We construct the proposed framework by merging the cascade survival forest structure with a feature screening mechanism. Experimental and statistical analysis results on well-known ultra-high and high-dimensional datasets show that the proposed methodology outperforms popular machine learning-based methods such as deep neural survival networks, random survival forests and their several variants in terms of prediction accuracy.
Classifying news articles in multiple languages: leveraging context aware models

Despite the recent advances in text classification and the performance improvement yielded by Transformers models, the absence or inaccessibility of an adequate dataset to train a text classifier motivates the choice for alternative routes. In this study, the need to detect specific topics in the news and to discard irrelevant content encouraged the development of an article tagging pipeline which assesses the similarity between a user-defined dictionary of topic-specific keywords and news article keywords. The innovation of the paper stands in the exploitation of two BERT-based algorithms to retrieve article keywords and to embed them, which previous studies have shown to outperform state of the art solutions for keywords extraction and semantic textual similarity. In a nutshell, the pipeline computes the semantic similarity between the sentence embeddings generated from topic-specific keywords and those produced from news article keywords extracted with the KeyBERT algorithm, finally classifying each article according to a previously defined topic. The results are supported by sound coherence and diversity metrics computed, by aggregating each article by their first tag, which attests to the semantic validity of the pipeline outputs.
Overall Dynamic Optimization of Metal Mine Technical Indicators Considering Spatial Distribution of Ore Grade Using AADE

In optimizing the production of a metal mine, either the overall dynamic relations between technical indicators or the spatial distribution of the ore grade are usually considered, but few studies have considered both factors together. These two factors in combination have a greater effect on the optimization of mine production in terms of economic benefit and resource utilization than they do individually. We proposed an overall dynamic optimization model of technical indicators of metal mine production that considers the spatial distribution of the ore grade to better optimize the technical indicators and improve sustainable development of mineral resources. We incorporated an adaptive mutation strategy and adaptive control parameters into a differential evolution algorithm (AADE) in order to overcome the drawbacks of the differential evolution algorithm in solving this optimization model. The adaptive mutation strategy and adaptive control parameters were used to increase the rate of convergence and improve the search for a global maximum. To assess the performance of AADE, we used a real case and four test functions (the Sphere, Griewank, Rastrigin and Rosenbrock functions) in tests that compared AADE with a standard genetic algorithm, a standard differential evolution algorithm and the recently developed adaptive differential evolution algorithm. The results indicate that the optimization model we created is better aligned with mine production processes than current optimization models. In optimizing the technical indicators of metal mine production to maximize economic benefits, AADE performed significantly better than the other three algorithms tested in terms of convergence rate and global search ability.
A Cost-Effective Interpolation for Multi-Magnification Super-Resolution

Super-Resolution (SR) was an important research topic, and SR methods based on Convolutional Neural Network (CNN) confirmed its groundbreaking performance. However, notably implementing the CNN model into resource-limited hardware devices is a great challenge. Therefore, we present a hardware-friendly and low-cost interpolation for Multi-Magnification SR image reconstruction. We follow our previous work, which is a learning-based interpolation (LCDI) with a self-defined classifier of image texture, and extend its original x2 architecture to x3 and x4 architecture. Besides, the required pre-trained weights are reduced by the fusion scheme. Experimentally, the proposed method has only 75% lower pre-trained weights than LCDI. Compared to the related work OLM-SI (One linear learning mapping-SI), the run-time and quantity of pre-trained weights of the x2 proposed method are at least 90% lower. Compared to CNN-based SR methods, the proposed method loses a little lower performance, but the evaluation of computational cost is much lower. In conclusion, the proposed method is cost-effective and a practical solution for resource-limited hardware and device.
Learning a Unified Latent Space for NAS: Toward Leveraging Structural and Symbolic Information

Automatically designing neural architectures, i.e., NAS (Neural Architecture Search), is a promising path in machine learning. However, the main challenge for NAS algorithms is to reduce the considerable elapsed time to evaluate a proposed network. A recent strategy which attracted much attention is to use surrogate predictive models. The predictive models attempt to forecast the performance of a neural model ahead of training, exploiting only their architectural features. However, preparing the training data for predictive models is laborious and resource demanding. Thus, improving the model’s sample efficiency is of high value. For the best performance, the predictive model should be given a representative encoding of the network architecture. Still, the potential of a proper architecture encoding in pruning and filtering out the unwanted architectures is often overlooked in previous studies. Here, we discuss how to build a proper representation of network architecture that preserves explicit or implicit information inside the architecture. To perform the experiments, two standard NAS benchmarks, NASbench 101 and NASbench 201 are used. Extensive experiments on the mentioned spaces, demonstrate the effectiveness of the proposed method as compared with the state-of-the-art predictors.
High-Performance Visual Tracking Based on High-Order Pooling Network

Convolution Neural Network (CNN) features have been widely used in visual tracking due to their powerful representation. As an important component of CNN, the pooling layer plays a critical role, but the max/average/min operation only explores the first-order information, which limits the discrimination ability of the CNN features in some complex situations. In this paper, a high-order pooling layer is integrated into the VGG16 network for visual tracking. In detail, a high-order covariance pooling layer is employed to replace the last maxpooling layer to learn discrimination features and is trained on the ImageNet and CUB200-2011 data sets. In tracking stage, the multiple levels of feature maps are extracted as the appearance representation of the target. After that, the extracted CNN features are integrated into the correlation filters framework when tracking is on-the-fly. The experimental results show that the proposed algorithm achieves excellent performance in both success rate and tracking accuracy.
Asymptotically Optimal and Secure Multiwriter/Multireader Similarity Search

Privacy-preserving similarity search is a method of data retrieval from potentially untrusted hosts based on the similarity between encrypted data items. In this setting, a major concern is how to support searches when multiple users (multireader) request for searching similar items over data encrypted by multiple data owners (multiwriter). Unfortunately, previous similarity search schemes address this by enforcing users to communicate with data owners. This limitation incurs a significant communication overhead. Moreover, these schemes use deterministic algorithms to encrypt data, which not only violates the privacy of data but also complicates the proof of semantic security. In this paper, we propose an efficient and secure multiwriter/multireader similarity search scheme over encrypted data in cloud storage. In the proposed scheme, the cloud server is able to perform searches without incurring any interaction between users and data owners. Thus, we achieve asymptotically optimal communication cost. We provide rigorous proofs of data privacy in the standard model. Then, we show the proposed scheme achieves semantic security based on the data privacy. An in-depth experiment on an INRIA image dataset demonstrates the practicality of the proposed scheme.
Creation of a Game-Based Augmented Learning Experience System (GL-XP AR)

This article presents the creation of a game-based learning experiences system with augmented reality, using as a reference the IEEE Std 1589TM-2020 (ARLEM) and xAPI to get learning experiences while using a prototype of a Learning Management System (LMS) and a Learning Record Store (LRS) synchronized to an augmented reality (AR) mobile application as a game-based tool to finish a learning activit, getting as a result learning records (Triggered by each interaction in the application) that can be managed into the LMS and evaluated in future work.
Exploring Bayesian Uncertainty Modeling for Book Genre Classification

In this paper, we aim to model the Bayesian uncertainty of a model designed to solve the task of book genre classification. Model prediction confidence can judge the predictive quality and usability of predictions made from a machine learning model. This work explores two methods to ascertain model uncertainty using Monte Carlo dropouts and deep ensembling. We apply uncertainty modeling to a bidirectional LSTM model trained on the CMU book summary dataset to perform book genre classification from book summaries. We show how these techniques improve results by 14% from the best baseline model and discuss their feasibility in real-world scenarios.
Feature Refinement Network for Crowd Counting

Crowd counting is generally a pixel-wise regression task and owns the broad application prospects. In order to generate high-quality density maps, recent remarkable works mostly adopt the long skip connection or encoder-decoder architectures which aggregate features via the step-by-step upsampling operations and direct contextual information fusion. Despite the great achievements, the researchers would ignore the negative effects of feature alignment and semantic imbalance caused by the above architectures. Faced with the mentioned problem, we propose a deep architecture called Feature Refinement Network (FRNet) for crowd counting. Our FRNet is composed of two major schemes: the Inter-dimensional Refinement Module (IRM) and the Semantic Refinement Module (SRM). In particular, the IRM attempts to explore the discriminative triple attention and the transformation point offsets to jointly repair the serious spatial dislocation between adjacent features and enhance the description ability. Meanwhile, the SRM explicitly learns the semantic interdependence within features of diverse levels to relieve the semantic imbalance caused by the rough context fusion. Finally, extensive experiments are conducted on multiple crowd counting datasets, the ShanghaiTech, the UCF-QNRF, the NWPU-Crowd, the JHU-CROWD++. The results have indicated the superior performance of our FRNet against the state-of-the-art methods.
Hybrid Morphological/Linear Probability Machine

The accuracy of many (nonlinear) classification algorithms depends heavily on the selection of model parameters and kernel parameters. In order to weaken the influence of parameters on classification accuracy, this paper, using the idea of Support Vector Machine and the Wolfe dual form of convex quadratic programming, we combine minimax probability machine with Hybrid Morphological/Linear Perceptron, and propose a Hybrid Morphological/Linear Probability Machine for data classification. Experimental results show that the proposed algorithm not only weakens the influence of model parameters, but also avoids the selection of kernel function, and is effective and competitive in classification accuracy and running time.
A Manifold ADMM for Orthogonal Nonnegative Matrix Factorization with Application to Clustering

Clustering has attracted more and more attention for big data. Studies have shown that orthogonal nonnegative matrix factorization (ONMF) is a promising clustering model, it can produce better clustering results in clustering tasks, such as image classification. However, the ONMF optimization problem is challenging to solve due to the coupling problems of orthogonality and non-negative constraints. In this paper, we transform the original ONMF model into a new equivalent optimization model. We solve the model based on the Alternating Direction Method of Multipliers (ADMM) framework and use Riemann Manifold optimization method to solve the subproblem on Stiefel manifold. Numerical experiments show that our algorithm performs well in clustering, normalized residual and function values.