Reviewerly/exHarmony huggingface dataset:
Development of a N-type GM-PHD filter for multiple target, multiple type visual tracking

We propose a new framework that extends the standard Probability Hypothesis Density (PHD) filter for multiple targets having N⩾2 different types based on Random Finite Set theory, taking into account not only background clutter, but also confusions among detections of different target types, which are in general different in character from background clutter. Under Gaussianity and linearity assumptions, our framework extends the existing Gaussian mixture (GM) implementation of the standard PHD filter to create a N-type GM-PHD filter. The methodology is applied to real video sequences by integrating object detectors’ information into this filter for two scenarios. For both cases, Munkres’s variant of the Hungarian assignment algorithm is used to associate tracked target identities between frames. This approach is evaluated and compared to both raw detection and independent GM-PHD filters using the Optimal Sub-pattern Assignment metric and discrimination rate. This shows the improved performance of our strategy on real video sequences.
Online learning for effort reduction in interactive neural machine translation

Neural machine translation systems require large amounts of training data and resources. Even with this, the quality of the translations may be insufficient for some users or domains. In such cases, the output of the system must be revised by a human agent. This can be done in a post-editing stage or following an interactive machine translation protocol. We explore the incremental update of neural machine translation systems during the post-editing or interactive translation processes. Such modifications aim to incorporate the new knowledge, from the edited sentences, into the translation system. Updates to the model are performed on-the-fly, as sentences are corrected, via online learning techniques. In addition, we implement a novel interactive, adaptive system, able to react to single-character interactions. This system greatly reduces the human effort required for obtaining high-quality translations. In order to stress our proposals, we conduct exhaustive experiments varying the amount and type of data available for training. Results show that online learning effectively achieves the objective of reducing the human effort required during the post-editing or the interactive machine translation stages. Moreover, these adaptive systems also perform well in scenarios with scarce resources. We show that a neural machine translation system can be rapidly adapted to a specific domain, exclusively by means of online learning techniques.
Hierarchical Edge Computing: A Novel Multi-Source Multi-Dimensional Data Anomaly Detection Scheme for Industrial Internet of Things

Every year, many people around the world die because of mining accidents. Industrial Internet of Things (IIoT) can be employed to sense public safety hazards and provide early warning of accidents, thereby ensuring safe operations at underground mining, personnel positioning, and specific items supervision and emergency response. Real-time data anomaly detection can predict the probability of occurrence of the abnormal event. However, massive heterogeneous monitoring data, poor wireless environment and data spatio-temporal association have posed a serious challenge to data anomaly detection for underground mining. Existing methods are mostly concerned about single data or processing at cloud platform, with little regard for the time and space association. Focus on the accuracy and timeliness of data anomaly detection, a novel multi-source multi-dimensional data anomaly detection scheme based on hierarchical edge computing model is presented in this paper. Firstly, a hierarchical edge computing model is proposed to realize load balance and low-latency data processing at the sensor end and base-station end. Then a single-source data anomaly detection algorithm is designed based on fuzzy theory, which can comprehensively analyze the anomaly detection results of multiple consecutive moments. Finally, a multi-source data anomaly detection algorithm executed at the base-station end is designed to consider the sensing data associated attributes of time and space. Experimental results reveal that the proposed scheme has higher detection accuracy and lower processing delay compared with traditional solutions.
Delayed dynamical systems: networks, chimeras and reservoir computing

We present a systematic approach to reveal the correspondence between time delay dynamics and networks of coupled oscillators. After early demonstrations of the usefulness of spatio-temporal representations of time-delay system dynamics, extensive research on optoelectronic feedback loops has revealed their immense potential for realizing complex system dynamics such as chimeras in rings of coupled oscillators and applications to reservoir computing. Delayed dynamical systems have been enriched in recent years through the application of digital signal processing techniques. Very recently, we have showed that one can significantly extend the capabilities and implement networks with arbitrary topologies through the use of field programmable gate arrays (FPGAs). This architecture allows the design of appropriate filters and multiple time delays which greatly extend the possibilities for exploring synchronization patterns in arbitrary topological networks. This has enabled us to explore complex dynamics on networks with nodes that can be perfectly identical, introduce parameter heterogeneities and multiple time delays, as well as change network topologies to control the formation and evolution of patterns of synchrony.
The electronic interface for quantum processors

Quantum computers can potentially provide an unprecedented speed-up with respect to traditional computers. However, a significant increase in the number of quantum bits (qubits) and their performance is required to demonstrate such quantum supremacy. While scaling up the underlying quantum processor is extremely challenging, building the electronics required to interface such large-scale processor is just as relevant and arduous. This paper discusses the challenges in designing a scalable electronic interface for quantum processors. To that end, we discuss the requirements dictated by different qubit technologies and present existing implementations of the electronic interface. The limitations in scaling up such state-of-the-art implementations are analyzed, and possible solutions to overcome those hurdles are reviewed. The benefits offered by operating the electronic interface at cryogenic temperatures in close proximity to the low-temperature qubits are discussed. Although several significant challenges must still be faced by researchers in the field of cryogenic control for quantum processors, a cryogenic electronic interface appears the viable solution to enable large-scale quantum computers able to address world-changing computational problems.
Dynamic Structure Embedded Online Multiple-Output Regression for Streaming Data

Online multiple-output regression is an important machine learning technique for modeling, predicting, and compressing multi-dimensional correlated data streams. In this paper, we propose a novel online multiple-output regression method, called MORES, for streaming data. MORES can dynamically learn the structure of the regression coefficients to facilitate the model's continuous refinement. Considering that limited expressive ability of regression models often leading to residual errors being dependent, MORES intends to dynamically learn and leverage the structure of the residual errors to improve the prediction accuracy. Moreover, we introduce three modified covariance matrices to extract necessary information from all the seen data for training, and set different weights on samples so as to track the data streams' evolving characteristics. Furthermore, an efficient algorithm is designed to optimize the proposed objective function, and an efficient online eigenvalue decomposition algorithm is developed for the modified covariance matrix. Finally, we analyze the convergence of MORES in certain ideal condition. Experiments on two synthetic datasets and three real-world datasets validate the effectiveness and efficiency of MORES. In addition, MORES can process at least 2,000 instances per second (including training and testing) on the three real-world datasets, more than 12 times faster than the state-of-the-art online learning algorithm.
Pair-Linking for Collective Entity Disambiguation: Two Could Be Better Than All

Collective entity disambiguation, or collective entity linking aims to jointly resolve multiple mentions by linking them to their associated entities in a knowledge base. Previous works are primarily based on the underlying assumption that entities within the same document are highly related. However, the extent to which these entities are actually connected in reality is rarely studied and therefore raises interesting research questions. For the first time, this paper shows that the semantic relationships between mentioned entities within a document are in fact less dense than expected. This could be attributed to several reasons such as noise, data sparsity, and knowledge base incompleteness. As a remedy, we introduce MINTREE, a new tree-based objective for the problem of entity disambiguation. The key intuition behind MINTREE is the concept of coherence relaxation which utilizes the weight of a minimum spanning tree to measure the coherence between entities. Based on this new objective, we design Pair-Linking, a novel iterative solution for the MINTREE optimization problem. The idea of Pair-Linking is simple: instead of considering all the given mentions, Pair-Linking iteratively selects a pair with the highest confidence at each step for decision making. Via extensive experiments on eight benchmark datasets, we show that our approach is not only more accurate but also surprisingly faster than many state-of-the-art collective linking algorithms.
Prospects and challenges for clean energy in European Islands.The TILOS paradigm

The current paper reflects on the challenges faced by European islands with regards to electricity supply, focusing on an island area of major interest, i.e. the Aegean Sea region. In this context, the main problems related to the use of imported oil and the associated high electricity production costs are discussed, together with the prospects of increased exploitation of Renewable Energy Sources (RES). For the latter accomplishment, the main elements of an integrated energy solution are presented, comprising mature RES technologies and more novel aspects, corresponding to energy storage, Demand Side Management and intelligent Energy Management on the basis of advanced forecasting techniques. Moreover, insight and preliminary operational evidence are provided with regards to the application of such an integrated solution on the island of Tilos, in Greece, where the development and test-operation of a pilot smart microgrid and Hybrid Power Station were recently completed, in the context of the TILOS Horizon 2020 Program.
Data-Knowledge-Based Fuzzy Neural Network for Nonlinear System Identification

Many nonlinear dynamical systems are usually lack of abundant datasets since the data acquiring process is time consuming. It is difficult to utilize the incomplete datasets to build an effective data-driven model to improve the industry productivity. To overcome this problem, a data-knowledge-based fuzzy neural network (DK-FNN) was developed in this article. Compared with the existing methods, the proposed DK-FNN consists of the following obvious advantages. First, through the multilayered connectionist structure, this proposed DK-FNN could not only make full use of the data from the current scene, but also use the existing knowledge from the source scene to improve the learning performance. Second, an integrated-form transfer learning (ITL) method was developed to improve the learning performance of DK-FNN. This first reported ITL method was able to integrate the internal information from the datasets in the source scene and the knowledge from the current scene to offset the data shortage in the learning process. Third, a mutual attraction strategy (MAS) was designed to balance the difference of data distributions to reduce the identification errors of DK-FNN. Then, the proposed DK-FNN was able to satisfy the nonlinear dynamical systems. Finally, the effectiveness and the merit of DK-FNN were validated by applying it to several practical systems.
Dynamic multi-client searchable symmetric encryption with support for boolean queries

With the rapid growth of cloud computing, an increasing amount of data is being outsourced to cloud servers, in the meantime, how to search data securely and efficiently has got an unprecedented concern. Searchable symmetric encryption (SSE) that enables keyword-based searches over encrypted data provides an efficient way to this problem. However, the majority of existing SSE schemes focus on single keyword searches in the single-client setting, which limits their wide application in cloud computing. In this paper, we propose a Dynamic Multi-client SSE (DMSSE) scheme with support for boolean queries, by incorporating a client’s authorization information into search tokens and indexes. Our scheme allows a data owner to authorize multiple clients to perform boolean queries over an encrypted database, and limits a client’s search ability to legitimate keywords. Compared with existing MSSE schemes, our DMSSE scheme has the following merits: 1) Non-interactivity. After the grant of search permission, the clients can perform queries on their own without the help of the data owner. 2) Dynamic. The data owner can efficiently update a client’s search permission without affecting other clients. Experimental evaluations conducted on a real data set demonstrate that our DMSSE scheme is practical for use in a large-scale encrypted database.
Efficient Deep Learning Approaches for Health Informatics

The need of data analytics in health informatics for better decision making is a challenging domain for the past decade. This stimulates more interest of researchers for the design of data driven models on the basis of machine learning such as deep learning models for health informatics. Deep learning is an emerging machine learning technique with various applications for health care monitoring such as medical imaging, bioinformatics, pervasive sensing and public health care, etc. There exist various deep learning approaches with their own pros and cons for health informatics to address multiple challenges in medical data processing such as high dimensional, heterogeneous, incomplete, unstructured biomedical, temporally dependent and irregular data, and so on. Deep learning techniques have their own added characteristics suited for health informatics such as enhanced performance, end-to-end learning embedded with features learning, executing complex and multimodal data, etc. Deep learning has proven its performance in multiple domains and showed progressive improvements, which in turn provide more opportunities to design new models for health informatics. Although the existing deep learning approaches designed for health informatics addressed multiple challenges, processed and analyzed complex data, new medical issues and kinds of data are arising every day in the health care domain. Hence, there is a need for in-depth exploration of deep learning approaches to address various challenges related to the health care features. Thus this book chapter deals with detailed analysis of various deep learning approaches in health informatics. A comparative analysis shows the pros and cons of each model and their applications in the health care domain. Also the work explores the challenges and limitations existing in the deep learning models and highlights the new kinds of data which have to be identified to connect with health care information work flow and also for clinical decision making.
On Stabilizing Generative Adversarial Training With Noise

We present a novel method and analysis to train generative adversarial networks (GAN) in a stable manner. As shown in recent analysis, training is often undermined by the probability distribution of the data being zero on neighborhoods of the data space. We notice that the distributions of real and generated data should match even when they undergo the same filtering. Therefore, to address the limited support problem we propose to train GANs by using different filtered versions of the real and generated data distributions. In this way, filtering does not prevent the exact matching of the data distribution, while helping training by extending the support of both distributions. As filtering we consider adding samples from an arbitrary distribution to the data, which corresponds to a convolution of the data distribution with the arbitrary one. We also propose to learn the generation of these samples so as to challenge the discriminator in the adversarial training. We show that our approach results in a stable and well-behaved training of even the original minimax GAN formulation. Moreover, our technique can be incorporated in most modern GAN formulations and leads to a consistent improvement on several common datasets.
Concept Mover’s Distance: measuring concept engagement via word embeddings in texts

We propose a method for measuring a text’s engagement with a focal concept using distributional representations of the meaning of words. More specifically, this measure relies on word mover’s distance, which uses word embeddings to determine similarities between two documents. In our approach, which we call Concept Mover’s Distance, a document is measured by the minimum distance the words in the document need to travel to arrive at the position of a “pseudo document” consisting of only words denoting a focal concept. This approach captures the prototypical structure of concepts, is fairly robust to pruning sparse terms as well as variation in text lengths within a corpus, and with pre-trained embeddings, can be used even when terms denoting concepts are absent from corpora and can be applied to bag-of-words datasets. We close by outlining some limitations of the proposed method as well as opportunities for future research.
Perceptual hashing for color image based on color opponent component and quadtree structure

In this paper, a perceptual image hashing scheme based on color opponent component (COC) and Quadtree structure is proposed. Firstly, image normalization and Gaussian low-pass filtering are applied on input image to produce a secondary image, and then COC is extracted from the secondary image. The color change information is calculated from COC as color feature and Quadtree decomposition (QD) is applied on the intensity image of secondary image to produce Quadtree structure features. Finally, color features and Quadtree structure features are concatenated and pseudo-randomly permuted to produce final hash sequence. Experimental results show that the proposed scheme is robust to common content-preserving manipulations. The receiver operating characteristics (ROC) curves show that the proposed method outperforms some state-of-the-art methods in the performances of perceptual robustness and discrimination.
BookBuddy

Digitization of education has brought a tremendous amount of online materials that are potentially useful for language learners to practice their reading skills. However, these digital materials rarely help with conversational practice, a key component of foreign language learning. Leveraging recent advances in chatbot technologies, we developed BookBuddy, a scalable virtual reading companion that can turn any reading material into an interactive conversation-based English lesson. We piloted our virtual tutor with five 6-year-old native Chinese-speaking children currently learning English. Preliminary results suggest that children enjoyed speaking English with our virtual tutoring chatbot and were highly engaged during the interaction.
A self-training method based on density peaks and an extended parameter-free local noise filter for k nearest neighbor

Self-training method is one of the relatively successful methodologies of semi-supervised classification. It can exploit both labeled data and unlabeled data to train a satisfactory supervised classifier. Mislabeling is one of the largest challenges in the self-training method and the most common technique for removing mislabeled samples is the local noise filter. However, existing local noise filters used in self-training methods confront following technical defects: parameter dependence and using only labeled data to remove mislabeled samples. To address these shortcomings, this paper proposes a novel self-training method based on density peaks and an extended parameter-free local noise filter (STDPNF). In STDPNF, the self-training method based on density peaks is redesigned to be more suitable for combination with local noise filters. Moreover, a new local noise filter based on natural neighbors is proposed to filter out mislabeled instances. Compared with existing local noise filters used in self-training methods, the one in STDPNF is parameter-free and can remove mislabeled samples by exploiting the information of both labeled data and unlabeled data. We focus on k nearest neighbor as a base classifier. In experiments, we verify the efficiency of STDPNF in improving the performance of the base classifier of k nearest neighbor and the advantage of STDPNF in having the ability to remove mislabeled instances efficiently even when labeled data are not sufficient.
Simultaneous dual-wavelength phase-shifting interferometry for surface topography measurement

To deal with vibration disturbance in interferometric measurement of surface topography, we present a simultaneous dual-wavelength phase-shifting interferometry (SDWPSI). SDWPSI collects phase-shifting interferograms of two wavelengths simultaneously with two cameras to accelerate data collection. The optical phases are retrieved from dual-wavelength interferograms with an iterative algorithm that is tolerant of phase-shifting error caused by vibration. And then the measured surface topography is reconstructed by fusing the phases of two wavelengths. Simulations and experiments verify that SDWPSI has a lower measurement noise than single-wavelength interferometry and extends the discontinuity limit to several micrometres. SDWPSI has advantages of high speed, vibration resistance, and low cost, and could be applied to in-workshop and on-machine measurement.