Reviewerly/exHarmony huggingface dataset:
Sinhala Fingerspelling Sign Language Recognition with Computer Vision

Computer vision based sign language translation is usually based on using thousands of images or video sequences for model training. This is not an issue in the case of widely used languages such as American Sign Language. However, in case of languages with low resources such as Sinhala Sign Language, it’s challenging to use similar methods for developing translators since there are no known data sets available for such studies.In this study we have contributed a new dataset and developed a sign language translation method for the Sinhala Fingerspelling Alphabet. Our approach for recognizing fingerspelling signs involve decoupling pose classification from pose estimation and using postural synergies to reduce the dimensionality of features. As shown by our experiments, our method can achieve an average accuracy of over 87%. The size of the data set used is less than 12% of the size of data sets used in methods which have comparable accuracy. We have made the source code and the dataset publicly available.
A Prufer-leaf Coding Genetic Algorithm For Bayesian Network Structure Learning

All Bayesian network is a probabilistic graph model which is used to describe the causal distribution among variables and has been widely used in many fields. While Bayesian network structure learning is the core of Bayesian network research, it is also an NP-hard problem. This paper draws on the idea of Prufer encoding/decoding, combines genetic algorithm and mutual information theory, and proposes a genetic algorithm based on improved Prufer-Leaf coding to solve the learning optimization problem of Bayesian network structures. Finally, the superiority of the algorithm in learning Bayesian network structure is verified through experiments.
Research on Medical Text Classification based on BioBERT-GRU-Attention

The growing sophistication of deep learning technology has driven advances in automated processing of medical texts. Applying deep learning technology to medical text classification can reduce the degree of manual intervention and improve the classification accuracy of medical texts. Therefore, this paper designs and proposes BGA and B2GA models based on BioBERT. The models use BioBERT as the word embedding layer, and use bidirectional GRU (BiGRU) and multi-head self-attention mechanism to extract feature vectors. Experimental verification: In the medical field, the classification effect of the BioBERT model is more accurate than the general BERT, and the accuracy rate is increased by about 38%; For datasets with fewer entries, BiGRU is more suitable than Bidirectional LSTM (BiLSTM); The BGA model proposed in this paper has an accuracy of more than 72% for automatic classification of medical texts, and the proposed B2GA model has an accuracy of more than 70% for automatic classification of medical texts. Compared with the BioBERT (proposed by Korea University) and Bio-ClinicalBERT model, they have a better text classification effect.
Constrained Visual Servoing of Quadrotors Based on Model Predictive Control

One of the main issues of visual servoing schemes occurs when the target objects leave out the field of view (FOV) of the camera, which causes failure or poor performance of the controller. Solving this problem can be a challenge due to traditional controllers cannot include system's constraints. This work presents model predictive control (MPC) for constrained image-based visual servoing (IBVS) applied in quadrotors, considering constraints in FOV and restrictions in the control actions. To handle with image constraints the MPC considers: (1) the target objects stay only in camera's FOV and this work converts these restrictions in state constraints, (2) merge image instantaneous kinematics and the dynamic of commercial quadrotors (Mavic Pro 2) in a general mathematical model in order to satisfy the bounded control actions and image constraints. Due to commercial quadrotors allow velocities like control inputs, this work considers the reduced dynamic model in general velocities space and it was identified using Dynamic Mode Decomposition with control (DMDc) algorithm. This work uses Webots to evaluate the performance of the proposed controller. Finally the controller is compared with a classical IBVS scheme in order to verify the efficacy of the proposed controller and systematically evaluate the performance considering the system constraints.
Patient Health Representation Learning via Correlational Sparse Prior of Medical Features

Exploiting the correlations between medical features is essential to the success of healthcare data analysis. However, most existing methods are either suffering large estimation variance for data insufficiency or inflexible in terms of demanding task-specific medical knowledge. In this paper, we propose a novel patient health representation learning framework dubbed <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SAFARI</monospace> . <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SAFARI</monospace> learns a compact representation by imposing a clinical-fact-inspired task-agnostic correlational sparsity prior to the correlations of medical feature pairs. Specifically, we learn the compact representation by solving the bi-level optimization problem, which involves solving the high-level inter-group correlations and the nested lower-level intra-group correlations. We leverage the Laplacian kernel as a robust metric for feature grouping and graph neural networks for solving the bi-level optimization problem following the optimal value reformulation paradigm. Experiments on five datasets of various inputs and tasks demonstrate the efficacy of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SAFARI</monospace> . The discovered findings are also consistent with our insights and medical literature, which can provide valuable clinical explanations.
Sparsity-Regularized Geometric Mean Metric Learning for Kinship Verification

Kinship verification through face images is a challenging research problem in biometrics. In this paper, we propose a sparsity-regularized geometric mean metric learning (SGMML) method to improve the well-known geometric mean metric learning (GMML) method and apply it to kinship verification task. Unlike GMML method that utilizes a linear map with fixed dimension, our SGMML method is capable of automatically learning the best projection dimension by employing the sparsity constraints on Mahalabios metric matrix. The proposed SGMML can effectively tackle the over-fitting problem and the data mixing up problem in the projected space. We conduct experiments on two benchmark kinship verification datasets, and experimental results demonstrate the effectiveness of our SGMML approach in kinship verification.
Evaluation Ranking is More Important for NAS

Search space, searching method, and candidate evaluation scheme are critical to the success of Neural Architecture Search (NAS), especially the evaluation strategy. An effective and efficient neural architecture performance evaluator could successfully save computing costs and search time while guiding the NAS process to the optimal solution as fast as possible. Most existing NAS algorithms attempt to compute the absolute accuracy of the candidate architecture, which is almost impossible to achieve and meaningless to the final performance improvement. In this paper, we propose ERNAS, a novel neural architecture performance evaluation approach that optimizes the ranking of the candidate architecture performance, rather than the absolute accuracy itself. With the help of ERNAS, many existing NAS methods could achieve better performance without further evaluation. The experimental results demonstrate that ERNAS can be trained effectively enough with extremely limited training data (423 neural architectures randomly sampled form NAS-Bench-101, which is only 0.1% of the entire search space). The accuracy of the neural architecture search result produced by ERNAS is greater than that of the SOTA methods.
Cold-Start Sequential Recommendation with Collaborative Boosted Meta Transitional Learning

As one of the essential tasks of sequential recom-mendation, it is of vital importance and challenge to capture user preferences exactly according to user behaviors. Actually, a large number of cold-start users have only few behavior records in actual scenarios, which brings the learning of user behavior patterns into dilemma. However, many existing sequential meth-ods can not give a full play of their advantages and achieve desired effect for cold-start users due to the limited interaction data. Despite many works have been proposed to ameliorate the predictive effect in cold-start scene, most of them rely on auxiliary information and little works absorbed in mining from user behavior data purely. In this paper, we propose a novel method dubbed Collaborative boosted Meta Transitional Learning (Co-MetaTL) to make use of user historical behaviors more efficiently in cold-start sequential recommendation. Specifically, based on the framework of meta transitional learning, the proposed Co-MetaTL mines the collaborative neighbor relevance and utilize it to enhance the learning process of dynamic pattern in user preferences. We perform comparative experiments on three real-world datasets and the results demonstrate that our proposed method is superior than other methods and bring in at least 7.29 % improvement compared with the state-of-the-art in se-quential cold-start recommendation.
Achieving Information Super-resolution for Sentinel-2 NDVI Through Gaussian Process Regression

Super-resolution is used to recover high resolution images from low resolution images. We use this concept in a slightly different context to achieve higher quality knowledge from low resolution satellite images. The technique involves transfer learning from high to low resolution images using a Gaussian Process Regression model. We use high resolution drone images to train the model. This technique is applied in three case studies to verify the consistency of results in case of NDVI computation. However, the same technique can be applied to obtain for other application of satellite images in plant vigor assessment.
Assessment of Test Items Quality and Adaptive Testing on the Rasch Model

In this paper the algorithm for adaptive testing of students’ knowledge in distance learning and an assessment of its effectiveness in the educational process has been proposed. The results of the study are based on the achievements of modern testing theory IRT. The aim of the study was to build an adaptive testing algorithm that allows you to objectively assess students’ knowledge and assess the quality of test items. To achieve this goal, a mathematical model of modern testing theory IRT, namely the Rasch model, was used. The effectiveness of the proposed algorithm for the objective assessment of students’ knowledge has been experimentally shown. The correspondence of the empirical data to the Rasch model was assessed based on the Pearson’s chi-squared test. The quality of test items was provided based on the Rasch model.
Group-$k$ Consistent Measurement Set Maximization for Robust Outlier Detection

This paper presents a method for the robust selection of measurements in a simultaneous localization and mapping (SLAM) framework. Existing methods check consistency or compatibility on a pairwise basis, however many measurement types are not sufficiently constrained in a pairwise scenario to determine if either measurement is inconsistent with the other. This paper presents group- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> consistency maximization ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{G}k\text{CM}$</tex> ) that estimates the largest set of measurements that is internally group- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> consistent. Solving for the largest set of group- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> consistent measurements can be formulated as an instance of the maximum clique problem on generalized graphs and can be solved by adapting current methods. This paper evaluates the performance of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{G}k\text{CM}$</tex> using simulated data and compares it to pairwise consistency maximization (PCM) presented in previous work.
Health Parameters Estimation of Turbofan Engine Based on Improved UKF Method

In view of the shortcomings of the standard Unscented Kalman Filter (UKF), such as slow convergence speed, low precision and easy divergence, the state estimation problem is transformed into an unconstrained optimization problem, and an iterative Unscented Kalman Filter based on the optimization method is adopted. The Levenberg-Marquart nonlinear optimization method is used to improve the measurement update equation of the filtering process. Moreover, the numerical simulation of health parameters estimation on a certain turbofan engine is carried out. Simulation results show that the established parameters estimation model can accurately estimate the changes of health parameters in different performance degradation modes, and the convergence speed and stability are obviously better than the standard UKF method. In addition, the improved algorithm was successfully deployed on the NVIDIA Jetson Xavier embedded platform to complete the online estimation, which indicates that the proposed method has good real-time performance.
3D Point Cloud Semantic Segmentation System

With the continuous progress of science and technology, artificial intelligence and big data are also constantly evolving. In artificial intelligence, the 2D image recognition of deep learning is relatively mature, but the 3D point cloud image is still in development. Because the data of 3D point cloud is larger than that of 2D image, and the scanning method is different, which affects the point cloud image to generate different data types. For various reasons, the development of 3D point clouds still requires us to make more efforts to study how to improve them. With the continuous expansion of the data size, the computing performance becomes more important, and in deep learning, the convolutional layer occupies a very important position. If the performance can be improved in the convolutional layer, it will definitely bring good benefits to artificial intelligence, so this paper will propose the use of efficient reuse, depthwise separable convolution, quantization, and Winograd algorithm to accelerate the operation and reduce the weight. Finally, according to the digital integrated circuit design process based on the standard cell library, the convolution layer operation in the neural network is realized.
Digging into Radiance Grid for Real-Time View Synthesis with Detail Preservation

Neural Radiance Fields (NeRF) [31] series are impressive in representing scenes and synthesizing high-quality novel views. However, most previous works fail to preserve texture details and suffer from slow training speed. A recent method SNeRG [11] demonstrates that baking a trained NeRF as a Sparse Neural Radiance Grid enables real-time view synthesis with slight scarification of rendering quality. In this paper, we dig into the Radiance Grid representation and present a set of improvements, which together result in boosted performance in terms of both speed and quality. First, we propose an HieRarchical Sparse Radiance Grid (HrSRG) representation that has higher voxel resolution for informative spaces and fewer voxels for other spaces. HrSRG leverages a hierarchical voxel grid building process inspired by [30, 55], and can describe a scene at high resolution without excessive memory footprint. Furthermore, we show that directly optimizing the voxel grid leads to surprisingly good texture details in rendered images. This direct optimization is memory-friendly and requires multiple orders of magnitude less time than conventional NeRFs as it only involves a tiny MLP. Finally, we find that a critical factor that prevents fine details restoration is the misaligned 2D pixels among images caused by camera pose errors. We propose to use the perceptual loss to add tolerance to misalignments, leading to the improved visual quality of rendered images.
Curriculum Adversarial Training for Robust Reinforcement Learning

Reinforcement learning with adversarial training is currently a key method for improving the robustness of DRL. However, in adversarial training, especially for unstable or disturbance-sensitive systems, the adversary always learns the policy significantly faster than the DRL agent and thus easily generates powerful perturbations. The agent cannot effectively adapt to the overly powerful adversary, which leads to unstable training and even failure to learn the robust policy. In this work, we propose a novel adversarial training method, called Curriculum Adversarial Training, inspired by the idea of curriculum learning. The method dynamically adjusts the strength of the adversary through natural curriculum learning for progressive adversarial training. Thus, the DRL system considers to reasonable learning rules, and the agent faces a suitable learning process. Furthermore, we adopt an advanced action space perturbation method with a most attractive ability as the adversary during training. The proposed method is compared with popular baseline methods through MuJoCo tasks. Experimental results show that our method can improve the robustness of the policy significantly and adapt to uncertain environment effectively.
A Novel Network Security Situation Awareness Model for Advanced Persistent Threat

Traditional defense methods can only evaluate a single security element and cannot determine the threat of Advanced Persistent Threat (APT) according to multi-source data. This paper proposes a network security situation awareness (NSSA) model to get the network situation under APT attacks based on knowledge graph. Firstly, the vulnerability knowledge graph and APT attack knowledge graph are constructed using public security databases and ATT & CK (Adversarial Tactics, Techniques, and Common Knowledge), and the targeted knowledge graph APT-NSKG is obtained by combining the two using Bidirectional Encoder Representations from Transformers (BERT). Then, according to the Endsley model and the characteristics of APT, the NSSA model for APT is proposed. The model uses APT-NSKG to obtain situation elements, and then comprehensively assesses and predicts the network situation from the perspectives of network asset dimension, vulnerability dimension, security dimension and threat dimension. Finally, the effectiveness of the model is verified by the data from the U.S. Cybersecurity and Infrastructure Security Agency.
Masked or Unmasked Face Detection from Online Video using Learning Aided Pattern Recognition Method

Security and surveillance are major concerns for every organization. During the Covid-19 pandemic situation, wearing of face mask has been recommended to be essential while accessing a sanitized place. Demand for developing surveillance mechanism for pandemic compliant infrastructure has arisen. This paper discusses a computer vision technique for detection of unmasked or masked faces from video sequence and describes a method for unmasked and masked face detection from custom data set using YOLOv5. The network is trained from scratch using a custom dataset and images are labeled into two groups. The model is trained from scratch with a public dataset and is capable of detecting human faces with and without mask correctly with high rate of accuracy. The system has detected multiple occurrences of such faces. The model has also worked properly with video input.
Verifiable and Energy Efficient Medical Image Analysis with Quantised Self-attentive Deep Neural Networks

Convolutional Neural Networks have played a significant role in various medical imaging tasks like classification and segmentation. They provide state-of-the-art performance compared to classical image processing algorithms. However, the major downside of these methods is the high computational complexity, reliance on high-performance hardware like GPUs and the inherent black-box nature of the model. In this paper, we propose quantised stand-alone self-attention based models as an alternative to traditional CNNs. In the proposed class of networks, convolutional layers are replaced with stand-alone self-attention layers, and the network parameters are quantised after training. We experimentally validate the performance of our method on classification and segmentation tasks. We observe 50–80% reduction in model size, 60–80% lesser number of parameters, 40–85% fewer FLOPs and 65–80% more energy efficiency during inference on CPUs. The code will be available at https://github.com/Rakshith2597/Quantised-Self-Attentive-Deep-Neur al-Network .
LegoAI: Towards Building Reliable AI Software for Real-world Applications

Deep learning is a powerful technique for many real- world problems. However, due to its unexplainable characteristic and over-fitting issue, there remains a great challenge for building reliable system with deep learning modules. In this paper, we present the idea of LegoAI that aims to build reliable AI software with pluggable modules of different functionalities, such as ensemble for fault tolerance and anomaly detection for result validation. In particular, we have applied the idea to develop a real-world AI software for handwritten digit recognition and achieved promising results.
Event Coreference Resolution based on Convolutional Siamese network and Circle Loss

The purpose of the cross-document event coreference resolution task is to solve multi-text processing. However, cross-document coreference resolution has not been fully explored. None of the existing methods explore the situation where the predicted coreference scores are also low when similar events have a low semantic similarity. To solve such a problem, in this paper, we propose a novel model by focusing on event classes with low event semantic similarity. Specifically, by building the Siamese network framework to enhance the feature representation, we convert the event-to-node into event-to-domain. In addition, by changing the form of the problem from traditional pairwise to listwise, the distance between coreference event nodes is greatly reduced, and the common coreference event resolution is solved. Our model only employs a very small dataset to annotate information, increases the semantic distance of these event pairs in the new vector space, and greatly improves the effect of event clustering. For the cross-document coreference resolution dataset ECB+, our model achieves better results than other models that have not been fine-tuned on more datasets or language models.