Reviewerly/exHarmony huggingface dataset:
CIFDM: Continual and Interactive Feature Distillation for Multi-Label Stream Learning

Multi-label learning algorithms have attracted more and more attention as of recent. This is mainly because real-world data is generally associated with multiple and non-exclusive labels, which could correspond to different objects, scenes, actions, and attributes. In this paper, we consider the following challenging multi-label stream scenario: the new labels emerge continuously in the changing environments, and are assigned to the previous data. In this setting, data mining solutions must be able to learn the new concepts and avoid catastrophic forgetting simultaneously. We propose a novel continual and interactive feature distillation-based learning framework (CIFDM), to effectively classify instances with novel labels. We utilize the knowledge from the previous tasks to learn new knowledge to solve the current task. Then, the system compresses historical and novel knowledge and preserves it while waiting for new emerging tasks. CIFDM consists of three components: 1) a knowledge bank that stores the existing feature-level compressed knowledge, and predicts the observed labels so far; 2) a pioneer module that aims to learn and predict new emerged labels based on knowledge bank.; 3) an interactive knowledge compression function which is used to compress and transfer the new knowledge to the bank, and then apply the current compressed knowledge to initialize the label embedding of the pioneer for the next task.
Lexical Simplification System to Improve Web Accessibility

People with intellectual, language and learning disabilities face accessibility barriers when reading texts with complex words.Following accessibility guidelines, complex words can be identified, and easy synonyms and definitions can be provided for them as reading aids.To offer support to these reading aids, a lexical simplification system for Spanish has been developed and is presented in this article.The system covers the complex word identification (CWI) task and offers replacement candidates with the substitute generation and selection (SG/SS) task.These tasks have followed machine learning techniques and contextual embeddings using Easy Reading and Plain Language resources, such as dictionaries and corpora.Additionally, due to the polysemy present in the language, the system provides definitions for complex words, which are disambiguated by a rule-based method supported by a state-of-the-art embedding resource.This system is integrated into a web system that provides an easy way to improve the readability and comprehension of Spanish texts.The results obtained are satisfactory; in the CWI task, better results were obtained than with other systems that used the same dataset.The SG/SS task results are comparable to similar works in the English language and provide a solid starting point to improve this task for the Spanish language.Finally, the results of the disambiguation process evaluation were good when evaluated by a linguistic expert.These findings represent an additional advancement in the lexical simplification of texts in Spanish and in a generic domain using easy-to-read resources, among others, to provide systematic support to compliance with accessibility guidelines.
Distributed unscented Kalman filtering for nonlinear systems: A mixed event‐triggered strategy

Abstract In this article, the problem of distributed state estimation is addressed for nonlinear systems based on the unscented Kalman filter (UKF) framework. The local estimates are obtained from distributed UKFs with stochastic event‐triggered schedules. The consensus of estimated states is achieved by employing the covariance intersection fusion method, while a novel event‐triggered strategy for the node‐to‐node communication is developed. Furthermore, the boundedness and convergence of the covariance matrix are analyzed. Compared with the existing works on distributed Kalman filtering, a new nonlinear global observability condition is proposed to relax the constraint that the system is necessarily observable by each sensor, and the relationship between the convergence and the event‐triggering parameters is revealed. Finally, simulations demonstrate the results in this article.
Vaccination System using Blockchain Technology: A Prototype Development

Vaccination is foreseen to becoming a compulsory medical requirement enforced by the government to the citizen. It is as an effective way to control an effective immunity herd in a community. Blockchain is an emerging technology that introduces a trusted community with its immutable characteristic and decentralized ledger. Thus, Blockchain system is anticipated to be a future system to create a trusted system that can be used in creating a safe vaccination system. This project proposes to design a vaccination system using blockchain as the data storage infrastructure to connect the healthcare provider's vaccination record with one another. To protect the patient data, the system implements permissioned blockchain which restricts anyone without authorization to participating in the network. The design implements three network participant (node) which validates and add the data into the blockchain. This fulfills the minimum requirement for a blockchain network to be Byzantine Fault Tolerant (BFT). The system was developed successfully as a prototype for further enhancement in the future and to investigate or mitigate the issues and challenges in Blockchain.
Health Monitoring Systems for the Elderly During COVID-19 Pandemic: Measurement Requirements and Challenges

The COVID-19 pandemic is disproportionally impacting senior citizens around the world. Indeed, the higher percentage of death is among people 60 years and older. In Canada, 50% of the total fatalities are due to multiple COVID-19 outbreaks in senior care facilities. In the wake of such heart-breaking reality, healthcare officials, industry, and academics are seeking to develop innovative methods and policies to protect the elderly in Canadian long-term senior care homes. Consequently, there is a growing interest in intelligent health monitoring systems that incorporate multiple connected smart devices (e.g., wearable, actuators, sensors, etc.) to keep track of elderly residents in senior homes continuously. Many experts suggest that such systems provide cost-effective solutions and superior health monitoring services that could save lives during pandemics. This article covers the measurement requirements of these systems, the strength and weaknesses of existing methods, and the open research challenges.
A Unified Channel Estimation Framework for Stationary and Non-Stationary Fading Environments

Channel estimation is crucial to modern wireless systems and becomes more and more challenging with the growth of user throughput in sub-6 GHz multiple input multiple output configuration. Plenty of literature spends great efforts in improving the estimation accuracy, while the interpolation schemes are overlooked. To deal with this challenge, we exploit the super-resolution image recovery scheme to model the non-linear interpolation mechanisms. Moreover, in order to extend the estimation scheme into the non-stationary environment which is especially attractive in the coming 6G, we utilize the recurrent network structure to approximate the non-linear channel statistic correlation to model the non-stationary behavior which is difficult to accomplish in the theoretical way. To make it more practical, we offline generate numerical channel coefficients according to the statistical channel models to train the neural networks and directly apply them in different environments. As shown in this paper, the proposed unified super-resolution based channel estimation scheme can outperform the conventional approaches in both stationary and non-stationary scenarios, which we believe can significantly change the current channel estimation method in the near future.
Node Co-occurrence based Graph Neural Networks for Knowledge Graph Link Prediction

We introduce a novel embedding model, named NoGE, which aims to integrate co-occurrence among entities and relations into graph neural networks to improve knowledge graph completion (i.e., link prediction). Given a knowledge graph, NoGE constructs a single graph considering entities and relations as individual nodes. NoGE then computes weights for edges among nodes based on the co-occurrence of entities and relations. Next, NoGE proposes Dual Quaternion Graph Neural Networks (DualQGNN) and utilizes DualQGNN to update vector representations for entity and relation nodes. NoGE then adopts a score function to produce the triple scores. Comprehensive experimental results show that NoGE obtains state-of-the-art results on three new and difficult benchmark datasets CoDEx for knowledge graph completion.
Nonlinear Higher-Order Label Spreading

Label spreading is a general technique for semi-supervised learning with point cloud or network data, which can be interpreted as a diffusion of labels on a graph. While there are many variants of label spreading, nearly all of them are linear models, where the incoming information to a node is a weighted sum of information from neighboring nodes. Here, we add nonlinearity to label spreading via nonlinear functions involving higher-order network structure, namely triangles in the graph. For a broad class of nonlinear functions, we prove convergence of our nonlinear higher-order label spreading algorithm to the global solution of an interpretable semi-supervised loss function. We demonstrate the efficiency and efficacy of our approach on a variety of point cloud and network datasets, where the nonlinear higher-order model outperforms classical label spreading, hypergraph clustering, and graph neural networks.
Towards a Live Feedback Training System: Interchangeability of Orbbec Persee and Microsoft Kinect for Exercise Monitoring

Many existing motion sensing applications in research, entertainment and exercise monitoring are based on the Microsoft Kinect and its skeleton tracking functionality. With the Kinect’s development and production halted, researchers and system designers are in need of a suitable replacement. We investigated the interchangeability of the discontinued Kinect v2 and the all-in-one, image-based motion tracking system Orbbec Persee for the use in an exercise monitoring system prototype called ILSE. Nine functional training exercises were performed by six healthy subjects in front of both systems simultaneously. Comparing the systems’ internal tracking states from ’not tracked’ to ‘tracked’ showed that the Persee system is more confident during motion sequences, while the Kinect is more confident for hip and trunk joint positions. Assessing the skeleton tracking robustness, the Persee’s tracking of body segment lengths was more consistent. Furthermore, we used both skeleton datasets as input for the ILSE exercise monitoring including posture recognition and repetition-counting. Persee data from exercises with lateral movement and in uncovered full-body frontal view provided the same results as Kinect data. The Persee further preferred tracking of quasi-static lower limb motions and tight-fitting clothes. With these limitations in mind, we find that the Orbbec Persee is a suitable replacement for the Microsoft Kinect for motion sensing within the ILSE exercise monitoring system.
Convolutional neural network based dictionary learning to create hash codes for content-based image retrieval

This study investigates the suitability of sparse vectors in the dictionary learning (DL) method for content-based image retrieval (CBIR) tasks. Since DL usually performs the learning process in an unsupervised manner, it cannot generate robust features for the retrieval task, especially if a complex background is involved. In order to overcome this drawback, a DL approach using the feature representation power of the convolutional neural network (CNN) is proposed. The initialization values of the dictionaries in the proposed CNN based DL method are taken randomly from the middle layers of the CNN architecture. The vector of each image obtained from the CNN architecture is used as DL input. The lambda vectors produced by the DL structure are converted into binaries. In this way, DL acts as a hash code generator. The performance of the proposed framework is tested on modified COREL dataset. The results prove that it is an open-to-improvement approach and is promising.
Path-based Deep Network for Candidate Item Matching in Recommenders

The large-scale recommender system mainly consists of two stages: matching and ranking. The matching stage (also known as the retrieval step) identifies a small fraction of relevant items from billion-scale item corpus in low latency and computational cost. Item-to-item collaborative filtering (item-based CF) and embedding-based retrieval (EBR) have been long used in the industrial matching stage owing to its efficiency. However, item-based CF is hard to meet personalization, while EBR has difficulty in satisfying diversity. In this paper, we propose a novel matching architecture, Path-based Deep Network (named PDN), through incorporating both personalization and diversity to enhance matching performance. Specifically, PDN is comprised of two modules: Trigger Net and Similarity Net. PDN utilizes Trigger Net to capture the user's interest in each of his/her interacted item. Similarity Net is devised to evaluate the similarity between each interacted item and the target item based on these items' profile and CF information. The final relevance between the user and the target item is calculated by explicitly considering user's diverse interests, \ie aggregating the relevance weights of the related two-hop paths (one hop of a path corresponds to user-item interaction and the other to item-item relevance). Furthermore, we describe the architecture design of the proposed PDN in a leading real-world E-Commerce service (Mobile Taobao App). Based on offline evaluations and online A/B test, we show that PDN outperforms the existing solutions for the same task. The online results also demonstrate that PDN can retrieve more personalized and more diverse items to significantly improve user engagement. Currently, PDN system has been successfully deployed at Mobile Taobao App and handling major online traffic.
Generative Compositional Augmentations for Scene Graph Prediction

Inferring objects and their relationships from an image in the form of a scene graph is useful in many applications at the intersection of vision and language. We consider a challenging problem of compositional generalization that emerges in this task due to a long tail data distribution. Current scene graph generation models are trained on a tiny fraction of the distribution corresponding to the most frequent compositions, e.g. <cup, on, table>. However, test images might contain zero- and few-shot compositions of objects and relationships, e.g. <cup, on, surfboard>. Despite each of the object categories and the predicate (e.g. ‘on’) being frequent in the training data, the models often fail to properly understand such unseen or rare compositions. To improve generalization, it is natural to attempt increasing the diversity of the training distribution. However, in the graph domain this is non-trivial. To that end, we propose a method to synthesize rare yet plausible scene graphs by perturbing real ones. We then propose and empirically study a model based on conditional generative adversarial networks (GANs) that allows us to generate visual features of perturbed scene graphs and learn from them in a joint fashion. When evaluated on the Visual Genome dataset, our approach yields marginal, but consistent improvements in zero- and few-shot metrics. We analyze the limitations of our approach indicating promising directions for future research.
Tavis–Cummings Model with Moving Atoms

In this work, we examine a nonlinear version of the Tavis–Cummings model for two two-level atoms interacting with a single-mode field within a cavity in the context of power-law potentials. We consider the effect of the particle position that depends on the velocity and acceleration, and the coupling parameter is supposed to be time-dependent. We examine the effect of velocity and acceleration on the dynamical behavior of some quantumness measures, namely as von Neumann entropy, concurrence and Mandel parameter. We have found that the entanglement of subsystem states and the photon statistics are largely dependent on the choice of the qubit motion and power-law exponent. The obtained results present potential applications for quantum information and optics with optimal conditions.
High-Dimensional Sparse Cross-Modal Hashing with Fine-Grained Similarity Embedding

Recently, with the discoveries in neurobiology, high-dimensional sparse hashing has attracted increasing attention. In contrast with general hashing that generates low-dimensional hash codes, the high-dimensional sparse hashing maps inputs into a higher dimensional space and generates sparse hash codes, achieving superior performance. However, the sparse hashing has not been fully studied in hashing literature yet. For example, how to fully explore the power of sparse coding in cross-modal retrieval tasks; how to discretely solve the binary and sparse constraints so as to avoid the quantization error problem. Motivated by these issues, in this paper, we present an efficient sparse hashing method, i.e., High-dimensional Sparse Cross-modal Hashing, HSCH for short. It not only takes the high-level semantic similarity of data into consideration, but also properly exploits the low-level feature similarity. In specific, we theoretically design a fine-grained similarity with two critical fusion rules. Then we take advantage of sparse codes to embed the fine-grained similarity into the to-be-learnt hash codes. Moreover, an efficient discrete optimization algorithm is proposed to solve the binary and sparse constraints, reducing the quantization error. In light of this, it becomes much more trainable, and the learnt hash codes are more discriminative. More importantly, the retrieval complexity of HSCH is as efficient as general hash methods. Extensive experiments on three widely-used datasets demonstrate the superior performance of HSCH compared with several state-of-the-art cross-modal hashing approaches.
Integrating Domain Knowledge in Data-Driven Earth Observation With Process Convolutions

The modelling of Earth observation data is a challenging problem, typically approached by either purely mechanistic or purely data-driven methods. Mechanistic models encode the domain knowledge and physical rules governing the system. Such models, however, need the correct specification of all interactions between variables in the problem and the appropriate parameterization is a challenge in itself. On the other hand, machine learning approaches are flexible data-driven tools, able to approximate arbitrarily complex functions, but lack interpretability and struggle when data is scarce or in extrapolation regimes. In this paper, we argue that hybrid learning schemes that combine both approaches can address all these issues efficiently. We introduce Gaussian process (GP) convolution models for hybrid modelling in Earth observation (EO) problems. We specifically propose the use of a class of GP convolution models called latent force models (LFMs) for EO time series modelling, analysis and understanding. LFMs are hybrid models that incorporate physical knowledge encoded in differential equations into a multioutput GP model. LFMs can transfer information across time-series, cope with missing observations, infer explicit latent functions forcing the system, and learn parameterizations which are very helpful for system analysis and interpretability. We consider time series of soil moisture from active (ASCAT) and passive (SMOS, AMSR2) microwave satellites. We show how assuming a first order differential equation as governing equation, the model automatically estimates the e-folding time or decay rate related to soil moisture persistence and discovers latent forces related to precipitation. The proposed hybrid methodology reconciles the two main approaches in remote sensing parameter estimation by blending statistical learning and mechanistic modeling.
On the line-search gradient methods for stochastic optimization

We consider several line-search based gradient methods for stochastic optimization: a gradient and accelerated gradient methods for convex optimization and gradient method for non-convex optimization. The methods simultaneously adapt to the unknown Lipschitz constant of the gradient and variance of the stochastic approximation for the gradient. The focus of this paper is to numerically compare such methods with state-of-the-art adaptive methods which are based on a different idea of taking norm of the stochastic gradient to define the stepsize, e.g., AdaGrad and Adam.
Invisible Poison: A Blackbox Clean Label Backdoor Attack to Deep Neural Networks

This paper reports a new clean-label data poisoning backdoor attack, named Invisible Poison, which stealthily and aggressively plants a backdoor in neural networks. It converts a regular trigger to a noised trigger that can be easily concealed inside images for training NN, with the objective to plant a backdoor that can be later activated by the trigger. Compared with existing data poisoning backdoor attacks, this newfound attack has the following distinct properties. First, it is a blackbox attack, requiring zero-knowledge of the target model. Second, this attack utilizes "invisible poison" to achieve stealthiness where the trigger is disguised as `noise', and thus can easily evade human inspection. On the other hand, this noised trigger remains effective in the feature space to poison training data. Third, the attack is practical and aggressive. A backdoor can be effectively planted with a small amount of poisoned data and is robust to most data augmentation methods during training. The attack is fully tested on multiple benchmark datasets including MNIST, Cifar10, and ImageNet10, as well as application specific data sets such as Yahoo Adblocker and GTSRB. Two countermeasures, namely Supervised and Unsupervised Poison Sample Detection, are introduced to defend the attack.
Ship Detection and Segmentation using Unet

Maritime surveillance is very crucial for every nation even if it is surrounded by land. The surveillance can not only inform about the threats and illegal activities but also manage the traffic and regulate a lot of movement on water. With more and more satellites with Synthetic-Aperture Radar (SAR) and other capabilities to capture high-resolution images being launched, automation of ship detection from these images is now a necessity. The experiments are performed on satellite image datasets where various encoders and loss functions for the Unet model are evaluated based on segmentation results over the test dataset. Our research and analysis of these encoders and loss functions for the Unet model lead us to certain conclusions as to which situation would benefit from which of the loss function and encoder and their combination. The research shows that the modes are robust and can perform well even with difficult backgrounds like clouds, waves etc. Our best model archived 0.823 on F2 score at different IOU thresholds on Airbus ship detection challenge test dataset.