Reviewerly/exHarmony huggingface dataset:
SAR Image Compression Using Discretized Gaussian Adaptive Model and Generalized Subtractive Normalization

Synthetic aperture radar (SAR) image compression plays an important role in the manipulation of images. However, existing optical compression methods cannot properly handle SAR compression due to the absence of feature learning and representation for SAR images. In this letter, we propose an end-to-end trainable model to effectively fit the feature distribution and reduce information dependencies toward SAR image compression. To better parameterize the distribution of latent codes, a discretized Gaussian adaptive model is designed to achieve a flexible entropy process. To further remove the remaining redundancies, generalized subtractive normalization is introduced to reduce the statistical dependencies in SAR images. Extensive experiments show that the proposed compression method outperforms the traditional compression methods and learning-based algorithms on both the ICEYE and Sandia datasets.
A Reinforcement Learning Based Two-Stage Model for Emotion Cause Pair Extraction

Recently, many efforts have been devoted to promoting the Emotion-Cause Pair Extraction (ECPE) task, as jointly extracting emotions and their causes is considered more helpful than only identifying the emotions in many applications. Among the existing efforts, end-to-end approaches are getting popular as the main trend, while others like pipeline models have been overlooked due to their potential issues of cascading errors. Nevertheless, the advantages of the pipeline models, such as logically dividing a complicated task into multiple easier subtasks, are underestimated and not well exploited. Moreover, the existing end-to-end approaches fail to capture the implicit co-occurrence or exclusion patterns between multiple pairs of emotions and causes since they are extracted independently. In view of these limitations, we propose a novel two-stage model to address the ECPE task and incorporate reinforcement learning (RL) to tackle the cascading error issue. In particular, our two-stage model first detects emotion clauses and then recognizes cause clauses for each detected emotion clause sequentially. By representing the error of each decision as an explicit reward, our model clearly knows how the error at each stage affects the final performance, hence the model can adjust itself for better performance. Furthermore, the sequential prediction enables our model to use the results achieved in the previous stages as auxiliary information in the subsequent stages. Extensive experiments on the benchmark dataset demonstrate the effectiveness of our proposed two-stage model, and the ablation comparison shows the promising effect of reducing cascading errors by incorporating RL.
Adversarially Robust Continual Learning

Recent approaches in continual learning (CL) have focused on extracting various types of features from multi-task datasets to prevent catastrophic forgetting - without formally evaluating the quality, robustness and usefulness of these features. Recently, it has been shown that adversarial robustness can be understood by decomposing learned features into robust and non-robust types. The robust features have been used to build robust datasets and have been shown to increase adversarial robustness significantly. There has not been any assessment on using such robust features in CL frameworks to enhance the robustness of CL models against adversarial attacks. Current CL algorithms use standard features - a mixture of robust and non-robust features - and result in models vulnerable to both natural and adversarial noise. This paper presents an empirical study to demonstrate the importance of robust features in the context of class incremental learning (CIL). We adopted the publicly available CIFAR10 dataset for our CIL experiments. We used CIFAR10-Corrupted dataset to evaluate the robustness of the standard, robust and non-robust models against various types of noise including bright-ness, contrast, Gaussian noise and more. To test these models against adversarially attacked input, we created a new dataset using the project gradient descent (PGD) and fast gradient sign (FGSM) algorithm. Our experiments demonstrate that a set of models trained on the standard (a mixture of both robust and non-robust) features obtained a higher accuracy compared to the models trained either using robust features or non-robust features. However, the models trained using standard and non-robust features performed poorly in noisy and adversarial conditions as compared to the model trained using robust features. The model trained using non-robust features performed the worst in noisy conditions and under adversarial attacks. Our study underlines the significance of using robust features in CIL.
Cloud databases

As database vendors are increasingly moving towards the cloud data service, i.e., databases as a service (DBaaS), cloud databases have become prevalent. Compared with the early cloud-hosted databases, the new generation of cloud databases, also known as cloud-native databases, seek for higher elasticity and lower cost by developing new techniques, e.g., compute-storage disaggregation and the log is the database. To better harness the power of these cloud databases, it is important to study and compare the pros and cons of their key techniques. In this tutorial, we offer a comprehensive survey of cloud-native databases. Based on various system architectures, we introduce a taxonomy for the state-of-the-art cloud-native OLTP databases and OLAP databases, respectively. We then take a deep dive into their key techniques regarding storage management, transaction processing, analytical processing, data replication, serverless computing, database recovery, and security. Finally, we discuss the research challenges and opportunities.
Pull &amp; Push: Leveraging Differential Knowledge Distillation for Efficient Unsupervised Anomaly Detection and Localization

Recently, much attention has been paid to segmenting subtle unknown defect regions by knowledge distillation in an unsupervised setting. Most previous studies concentrated on guiding the student network to learn the same representations on the normality, neglecting the different behaviors of the abnormality. This leads to a high probability of false detection of subtle defects. To address such an issue, we propose to push representations on abnormal areas of the teacher and student network as far as possible while pulling representations on normal areas as close as possible. Based on this idea, we design an efficient teacher-student model for anomaly detection and localization, which maximizes pixel-wise discrepancies for anomalous regions approximated by data augmentation and simultaneously minimizes discrepancies for pixel-wise normal regions between these two networks. The explicit differential knowledge distillation enlarges the margin between normal representations and abnormal ones in favour of discriminating them. Then, the appropriate small student network is not only efficient, but more importantly, helps inhibit the generalization ability of anomalous patterns when learning normal patterns, facilitating the precise decision boundary. The experimental results on the MVTec AD, Fashion-MNIST, and CIFAR-10 datasets demonstrate that our proposed method achieves better performance than current state-of-the-art (SOTA) approaches. Especially, For the MVTec AD dataset with high resolution images, we achieve 98.1 AUROC% and 93.6 AUPRO% in anomaly localization, outperforming knowledge distillation based SOTA methods by 1.1 AUROC% and 1.5 AUPRO% with a lightweight model.
Self Supervised Learning for Multiple Object Tracking in 3D Point Clouds

Multiple object tracking in 3D point clouds has applications in mobile robots and autonomous driving. This is a challenging problem due to the sparse nature of the point clouds and the added difficulty of annotation in 3D for supervised learning. To overcome these challenges, we propose a neural network architecture that learns effective object features and their affinities in a self supervised fashion for multiple object tracking in 3D point clouds captured with LiDAR sensors. For self supervision, we use two approaches. First, we generate two augmented LiDAR frames from a single real frame by applying translation, rotation and cutout to the objects. Second, we synthesize a LiDAR frame using CAD models or primitive geometric shapes and then apply the above three augmentations to them. Hence, the ground truth object locations and associations are known in both frames for self supervision. This removes the need to annotate object associations in real data, and additionally the need for training data collection and annotation for object detection in synthetic data. To the best of our knowledge, this is the first self supervised multiple object tracking method for 3D data. Our model achieves state of the art results.
Weakly-Supervised Stitching Network for Real-World Panoramic Image Generation

Recently, there has been growing attention on an end-to-end deep learning-based stitching model. However, the most challenging point in deep learning-based stitching is to obtain pairs of input images with a narrow field of view and ground truth images with a wide field of view captured from real-world scenes. To overcome this difficulty, we develop a weakly-supervised learning mechanism to train the stitching model without requiring genuine ground truth images. In addition, we propose a stitching model that takes multiple real-world fisheye images as inputs and creates a 360 $$^{\circ }$$ output image in an equirectangular projection format. In particular, our model consists of color consistency corrections, warping, and blending, and is trained by perceptual and SSIM losses. The effectiveness of the proposed algorithm is verified on two real-world stitching datasets.
Extensions to Generalized Annotated Logic and an Equivalent Neural Architecture

While deep neural networks have led to major advances in image recognition, language translation, data mining, and game playing, there are well-known limits to the paradigm such as lack of explainability, difficulty of incorporating prior knowledge, and modularity. Neuro symbolic hybrid systems have recently emerged as a straightforward way to extend deep neural networks by incorporating ideas from symbolic reasoning such as computational logic. In this paper, we propose a list desirable criteria for neuro symbolic systems and examine how some of the existing approaches address these criteria. We then propose an extension to generalized annotated logic that allows for the creation of an equivalent neural architecture comprising an alternate neuro symbolic hybrid. However, unlike previous approaches that rely on continuous optimization for the training process, our framework is designed as a binarized neural network that uses discrete optimization. We provide proofs of correctness and discuss several of the challenges that must be overcome to realize this framework in an implemented system.
Mitigating Sovereign Data Exchange Challenges: A Mapping to Apply Privacy- and Authenticity-Enhancing Technologies

Harmful repercussions from sharing sensitive or personal data can hamper institutions' willingness to engage in data exchange. Thus, institutions consider Authenticity-Enhancing Technologies (AETs) and Privacy-Enhancing Technologies (PETs) to engage in Sovereign Data Exchange (SDE), i.e., sharing data with third parties without compromising their own or their users' data sovereignty. However, these technologies are often technically complex, which impedes their adoption. To support practitioners select PETs and AETs for SDE use cases and highlight SDE challenges researchers and practitioners should address, this study empirically constructs a challenge-oriented technology mapping. First, we compile challenges of SDE by conducting a systematic literature review and expert interviews. Second, we map PETs and AETs to the SDE challenges and identify which technologies can mitigate which challenges. We validate the mapping through investigator triangulation. Although the most critical challenge concerns data usage and access control, we find that the majority of PETs and AETs focus on data processing issues.
Application of Ghost-DeblurGAN to Fiducial Marker Detection

Feature extraction or localization based on the fiducial marker could fail due to motion blur in real-world robotic applications. To solve this problem, a lightweight generative adversarial network, named Ghost-DeblurGAN, for real-time motion deblurring is developed in this paper. Furthermore, on account that there is no existing deblurring benchmark for such task, a new large-scale dataset, YorkTag, is proposed that provides pairs of sharp/blurred images containing fiducial markers. With the proposed model trained and tested on YorkTag, it is demonstrated that when applied along with fiducial marker systems to motion-blurred images, Ghost-DeblurGAN improves the marker detection significantly. The datasets and codes used in this paper are available at: https://github.com/York-SDCNLab/Ghost-DeblurGAN.
Conceptual hanger for displaced concepts: a framework for information processes variability

In this paper, at a qualitative level, we mean the concept as an abstraction based on the characteristics of the perceived reality. In analytic activity, a concept is considered to be some label imposed on a phenomenon, which forces us to connect individual observations and perform generalizations. For convenience, it is identified with the name given to observations and events. Each concept is associated with some kind of its properties. A variable is often understood as a measured concept as a property that is associated with the concept and changes when measured. If the property does not change, then it is considered a constant. We proceed from the fact that concepts should have a mechanism for change. Under these assumptions, a two-parameter commutative diagram is constructed that connects possible worlds and property transformations. The resulting semantic map sets the framework for their variation. Thus, the regulation of the variability of the associated structure of variable concepts is achieved, which determines the possible relationships of the displaced concepts.
BoostTree and BoostForest for Ensemble Learning

Bootstrap aggregating (Bagging) and boosting are two popular ensemble learning approaches, which combine multiple base learners to generate a composite model for more accurate and more reliable performance. They have been widely used in biology, engineering, healthcare, etc. This article proposes BoostForest, which is an ensemble learning approach using BoostTree as base learners and can be used for both classification and regression. BoostTree constructs a tree model by gradient boosting. It increases the randomness (diversity) by drawing the cut-points randomly at node splitting. BoostForest further increases the randomness by bootstrapping the training data in constructing different BoostTrees. BoostForest generally outperformed four classical ensemble learning approaches (Random Forest, Extra-Trees, XGBoost and LightGBM) on 35 classification and regression datasets. Remarkably, BoostForest tunes its parameters by simply sampling them randomly from a parameter pool, which can be easily specified, and its ensemble learning framework can also be used to combine many other base learners.
EMG-based HCI Using CNN-LSTM Neural Network for Dynamic Hand Gestures Recognition

Human-computer interaction(HCI) has a broad range of applications. Many HCI systems are based on bio-signal analysis and classification. The surface electromyographic(sEMG) signal is one of the most used signals that are formed by muscle activation although the details are rather complex. The applications of sEMG signals are referred to as myoelectric control since the dominant use of this signal is to activate a device even if (as the term control may imply) feedback is not always used in the process. With the development of deep neural networks, various deep learning architectures are used for sEMG-based gesture recognition with many researchers having reported good performance. Nevertheless, challenges remain in accurately recognizing sEMG patterns generated by gestures produced by the hand or the upper arm. For instance, one of the difficulties in hand gesture recognition is the influence of limb positions. Several papers have shown that the accuracy of gesture classification decreases when the limb position changes even if the gesture remains the same. Prior work by our team has shown that dynamic gesture recognition is in principle more reliable in detecting human intent, which is often the underlying idea of gesture recognition. In this paper, a Convolutional Neural Network (CNN) with Long Short-Term Memory or LSTM (CNN-LSTM) is proposed to classify five common dynamic gestures. Each dynamic gesture would be performed in five different limb positions as well. The trained neural network model with high recognition performances is then used to enable a human subject to control a robotic arm.
A Novel Machine Learning Approach to Predict Sales of an Item in E-commerce

Predicting the sales of the item through online comments given by the user is challenging research today. Consumer comments, user transactions, and item ratings are taken into account by the majority of existing prediction systems in order to determine which item is most likely to be predicted. But these comments may be changed over time since the interest to the item for the user may vary. Due to the fact that users' interests change over time, present sale prediction are ineffective in identifying items that are currently relevant to the consumer interest. This article presents a novel sales prediction technique that merges an incorporated selection approach for features and convolutional neural network classifier along with fuzzy logic. The empirical evaluation of the sales prediction system demonstrate that it outperforms the existing sales prediction system in the context of accuracy in predicting appropriate items for target consumers and in the time required to offer such predictions.
A Knowledge Graph Recommendation Model via High-order Feature Interaction and Intent Decomposition

Knowledge Graph(KG) contains structured attribute information which has been widely utilized for recommendations, as well as can effectively tackle the sparsity and cold start problems of collaborative filtering. In recent years, Graph Neural Networks (GNNs) serve as a novel deep learning technique that can significantly enhance recommendation performance. Unfortunately, existing KG-based GNN models are coarse-grained ignoring i)effective high-order feature interaction and fusion mechanism and ii)interpretable user latent intent decomposition. In this paper, we propose a new method named Knowledge Graph recommendation model via high-order feature Interaction and intent Decomposition(KGID), which explicitly models the fine-grained feature interaction and intent factors in KG-based GNN recommendation. Initially, high-order feature interactions are captured via the two-granularity convolutional neural networks on the item side. Next, the implicit intent factor behind the user decisions is modeled by two-level attention mechanisms. Ultimately, user representations and item representations are augmented simultaneously. We conduct experiments on three benchmark datasets to elucidate the superiority of the KGID to state-of-the-art baselines.
Immersive virtual environment for scale cognition and learning: Expert-based evaluation for balancing usability versus cognitive theories

While scale cognition and learning is a crosscutting concept that pervades science and can aid students in making connections across disciplines, students struggle to conceptualize and consider scales that go far beyond their everyday world experience. Virtual reality technology affords embodied learning experiences, which enable students to physically engage in learning activities in an environment with rich information. Scale Worlds is a virtual learning environment implemented in an immersive CAVE, which portrays scientific entities of a wide range of sizes. A user can scale themself up or down by powers of ten, in order to experience entities from an atom to the Sun. This paper reports on an expert-based usability evaluation of Scale Worlds, including three sets of A/B testing, by five usability experts. Outcomes of the usability evaluation will inform the refinement of Scale Worlds. The evaluation provides insights for usability evaluation and design in immersive virtual environments.
Manifold Learning Benefits GANs

In this paper <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code: https://qithub.com/MaxwellYaoNi/LCSAGAN., we improve Generative Adversarial Net-works by incorporating a manifold learning step into the discriminator. We consider locality-constrained linear and subspace-based manifolds <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> The coding spaces considered in this paper are loosely termed man-ifolds. In most cases they are not manifolds in the strict mathematical sense, but rather topological spaces such as varieties, or simplicial com-plexes. The word will be used only in an informal sense., and locality-constrained non-linear manifolds. In our design, the manifold learning and coding steps are intertwined with layers of the discrimina-tor, with the goal of attracting intermediate feature repre-sentations onto manifolds. We adaptively balance the dis-crepancy between feature representations and their mani-fold view, which is a trade-off between denoising on the manifold and refining the manifold. We find that locality-constrained non-linear manifolds outperform linear mani-folds due to their non-uniform density and smoothness. We also substantially outperform state-of-the-art baselines.
Education 4.0

The emergence of Education 4.0 is a result of Industry 4.0 that demands the transformation of the education sector at a different scale. Therefore, students have been challenged to adopt such new methods and technology to integrate into their course of learning. This chapter aims to develop a conceptual framework of Education 4.0 in association with students' well-being in order to contribute a new dimension of Education 4.0 to the well-being of students. Descriptive and exploratory approaches were applied to figure out whether Education 4.0 would lead to the well-being of students in the educational environment. The results outlined the various factors that lead to the positive aspect of Education 4.0 along with various challenges that are required to explore at the society level to implement Education 4.0, which is the current need of society. As we are moving towards digitalization and technology-enabled education system because of the worldwide COVID-19 pandemic, such adoption may uplift the education system.