Reviewerly/exHarmony huggingface dataset:
Reservoir computing with solitons

Abstract Reservoir computing is a promising framework that facilitates the approach to physical neuromorphic hardware by enabling a given nonlinear physical system to act as a computing platform. In this work, we exploit this paradigm to propose a versatile and robust soliton-based computing system using a discrete soliton chain as a reservoir. By taking advantage of its tunable governing dynamics, we show that sufficiently strong nonlinear dynamics allows our soliton-based solution to perform accurate regression and classification tasks of non-linear separable datasets. At a conceptual level, the results presented pave a way for the physical realization of novel hardware solutions and have the potential to inspire future research on soliton-based computing using various physical platforms, leveraging its ubiquity across multiple fields of science, from nonlinear optical media to quantum systems.
Dropout technique for image classification based on extreme learning machine

An extreme learning machine is a feed-forward neural network is a feature learning method used in image classification efficiently because of the faster learning rate, speed, good generalization ability, ease of implementation, and efficiency in classifications. This paper presents a new model for the classification of images by using an extreme learning machine classifier which works in two stages using deep learning techniques. A new framework is built by using the dropout technique of CNN for classifying images with less training time and also no need to over train neurons. The proposed model has a high accuracy of 98% using 1000 images. The proposed paper accomplishes the best results in image classification compare with other related methods on image classification.
Activity Recognition for Ambient Assisted Living with Videos, Inertial Units and Ambient Sensors

Worldwide demographic projections point to a progressively older population. This fact has fostered research on Ambient Assisted Living, which includes developments on smart homes and social robots. To endow such environments with truly autonomous behaviours, algorithms must extract semantically meaningful information from whichever sensor data is available. Human activity recognition is one of the most active fields of research within this context. Proposed approaches vary according to the input modality and the environments considered. Different from others, this paper addresses the problem of recognising heterogeneous activities of daily living centred in home environments considering simultaneously data from videos, wearable IMUs and ambient sensors. For this, two contributions are presented. The first is the creation of the Heriot-Watt University/University of Sao Paulo (HWU-USP) activities dataset, which was recorded at the Robotic Assisted Living Testbed at Heriot-Watt University. This dataset differs from other multimodal datasets due to the fact that it consists of daily living activities with either periodical patterns or long-term dependencies, which are captured in a very rich and heterogeneous sensing environment. In particular, this dataset combines data from a humanoid robot’s RGBD (RGB + depth) camera, with inertial sensors from wearable devices, and ambient sensors from a smart home. The second contribution is the proposal of a Deep Learning (DL) framework, which provides multimodal activity recognition based on videos, inertial sensors and ambient sensors from the smart home, on their own or fused to each other. The classification DL framework has also validated on our dataset and on the University of Texas at Dallas Multimodal Human Activities Dataset (UTD-MHAD), a widely used benchmark for activity recognition based on videos and inertial sensors, providing a comparative analysis between the results on the two datasets considered. Results demonstrate that the introduction of data from ambient sensors expressively improved the accuracy results.
FINANCIAL INCLUSION, INSTITUTIONAL QUALITY AND FINANCIAL DEVELOPMENT: EMPIRICAL EVIDENCE FROM OIC COUNTRIES

This unique study examines the moderation effect of institutional quality (IQ) on the relationship between financial inclusion (FI) and financial development (FD) of 45 Organization of Islamic Cooperation (OIC) countries. For empirical analysis, panel data are used for the period 2000–2016. We use the Arellano–Bond generalized method of moments (GMM) and two-stage least-squares (2SLS) method in our estimations to draw multidimensional results. The empirical results confirm the significant positive relationship between FI, IQ and FD. Interestingly, we find that IQ moderates FI and has a significant positive impact on FD. Our findings are robust to alternative econometric specifications of FI, IQ and FD. Therefore, policymakers must sensibly understand the pivotal role of FI and IQ in establishing sustainable future development of OIC countries.
Handling Vanishing Gradient Problem Using Artificial Derivative

Sigmoid function and ReLU are commonly used activation functions in neural networks (NN). However, sigmoid function is vulnerable to the vanishing gradient problem, while ReLU has a special vanishing gradient problem that is called dying ReLU problem. Though many studies provided methods to alleviate this problem, there has not been an efficient feasible solution. Hence, we proposed a method replacing the original derivative function with an artificial derivative in a pertinent way. Our method optimized gradients of activation functions without varying activation functions nor introducing extra layers. Our investigations demonstrated that the method can effectively alleviate the vanishing gradient problem for both ReLU and sigmoid function with few computational cost.
Variational Bayes for High-Dimensional Linear Regression With Sparse Priors

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Pose Refinement Graph Convolutional Network for Skeleton-Based Action Recognition

With the advances in capturing 2D or 3D skeleton data, skeleton-based action recognition has received an increasing interest over the last years. As skeleton data is commonly represented by graphs, graph convolutional networks have been proposed for this task. While current graph convolutional networks accurately recognize actions, they are too expensive for robotics applications where limited computational resources are available. In this letter, we therefore propose a highly efficient graph convolutional network that addresses the limitations of previous works. This is achieved by a parallel structure that gradually fuses motion and spatial information and by reducing the temporal resolution as early as possible. Furthermore, we explicitly address the issue that human poses can contain errors. To this end, the network first refines the poses before they are further processed to recognize the action. We therefore call the network Pose Refinement Graph Convolutional Network. Compared to other graph convolutional networks, our network requires 86%--93% less parameters and reduces the floating point operations by 89%--96% while achieving a comparable accuracy. It therefore provides a much better trade-off between accuracy, memory footprint and processing time, which makes it suitable for robotics applications.
A Novel Semi-Supervised Learning Approach to Pedestrian Reidentification

One of the important Internet-of-Things applications is to use image and video to realize automatic people monitoring, surveillance, tracking, and reidentification (Re-ID). Despite some recent advances, pedestrian Re-ID remains a challenging task. Existing algorithms based on fully supervised learning for it usually requires numerous labeled image and video data, while often ignoring the problem of data imbalance. This work proposes a method based on unlabeled samples generated by cycle generative adversarial networks. For a newly generated unlabeled sample, it learns its pseudorelationship between unlabeled samples and labeled ones in a low-dimensional space by using a self-paced learning approach. Then, these unlabeled ones having pseudo-relationship with labeled ones are added in a training set to better mine discriminative information between positive and negative samples, which is in turn used to learn a more effective metric. We name this method as a semi-supervised learning approach based on the built pseudopairwise relations between labeled data and unlabeled one. It can greatly enhance the performance of pedestrian Re-ID in case of insufficient labeled images. By using only about 10% labeled images in a given database, the proposed method obtains higher accuracy than state-of-the-art supervised learning methods using all labeled ones, e.g., deep-learning ones, thus greatly advancing the field of pedestrian Re-ID.
A 3D attention networks for classification of white blood cells from microscopy hyperspectral images

Peripheral blood smear analysis is a key process for hematologists to reflect condition of human immune system and one of the earliest clinical applications that benefit from automatic computer-aided analysis. Changes in the ratio of white blood cell (WBC) types are associated with the determination of blood diseases, and therefore accurate classification assures accurate therapy. In this study, we applied deep convolution networks on microscopy hyperspectral images for WBC classification. We proposed a 3D convolutional networks named deep hyper that enables learning spectral and spatial features by itself to make fully use of three dimensional hyperspectral data for white blood cell classification. In order to enhance the representative power of the model in an efficient manner, we integrated a 3D attention module with the last block of the model to put more emphasis on important features. The overall classification accuracy of deep hyper can achieve 96% compared to 90% of the machine learning based method and the attention module allows deep hyper to achieve the best performance of 97.72%. Furthermore, we explored the correlation between spectral features and WBC classification performances to present that hyperspectral characteristics play an important role in classifying specific type of WBC. These findings demonstrate that combing microscopy hyperspectral image with deep convolution networks is beneficial for blood smear analysis especially white blood cell classification.
Adaptive pruning-based optimization of parameterized quantum circuits

Variational hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices. While past studies have developed powerful and expressive ansatze, their near-term applications have been limited by the difficulty of optimizing in the vast parameter space. In this work, we propose a heuristic optimization strategy for such ansatze used in variational quantum algorithms, which we call "Parameter-Efficient Circuit Training" (PECT). Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms, in which each iteration of the algorithm activates and optimizes a subset of the total parameter set. To update the parameter subset between iterations, we adapt the dynamic sparse reparameterization scheme by Mostafa et al. (arXiv:1902.05967). We demonstrate PECT for the Variational Quantum Eigensolver, in which we benchmark unitary coupled-cluster ansatze including UCCSD and k-UpCCGSD, as well as the low-depth circuit ansatz (LDCA), to estimate ground state energies of molecular systems. We additionally use a layerwise variant of PECT to optimize a hardware-efficient circuit for the Sycamore processor to estimate the ground state energy densities of the one-dimensional Fermi-Hubbard model. From our numerical data, we find that PECT can enable optimizations of certain ansatze that were previously difficult to converge and more generally can improve the performance of variational algorithms by reducing the optimization runtime and/or the depth of circuits that encode the solution candidate(s).
XBlock-EOS: Extracting and exploring blockchain data from EOSIO

Blockchain-based cryptocurrencies and applications have flourished in blockchain research community. Massive data generated from diverse blockchain systems bring not only huge business values but also technological challenges in data analytics of heterogeneous blockchain data. Different from Bitcoin and Ethereum, EOSIO has richer diversity and a higher volume of blockchain data due to its unique architectural design in resource management, consensus scheme and high throughput. Despite its popularity (e.g., 89,800,000 blocks generated till November 14, 2019 since its launch on June 8, 2018), few studies have been made on data analysis of EOSIO. To fill this gap, we collect and process the up-to-date on-chain data from EOSIO. We name these well-processed EOSIO datasets as XBlock-EOS, which consists of 7 well-processed datasets: (1) Block, Transaction and Action, (2) Internal and External EOS Transfer Action, (3) Contract Information, (4) Contract Invocation, (5) Token Action, (6) Account Creation, (7) Resource Management. It is challenging to process and analyze a high volume of raw EOSIO data and establish the mapping from original raw data to the well-grained datasets since it requires substantial efforts in extracting various types of data as well as sophisticated knowledge on software engineering and data analytics. Meanwhile, we present statistics and exploration on these datasets. Moreover, we also outline the possible research opportunities based on XBlock-EOS.
Dynamic Attention Guided Multi-Trajectory Analysis for Single Object Tracking

Most of the existing single object trackers track the target in a unitary local search window, making them particularly vulnerable to challenging factors such as heavy occlusions and out-of-view movements. Despite the attempts to further incorporate global search, prevailing mechanisms that cooperate local and global search are relatively static, thus are still sub-optimal for improving tracking performance. By further studying the local and global search results, we raise a question: can we allow more dynamics for cooperating both results? In this paper, we propose to introduce more dynamics by devising a dynamic attention-guided multi-trajectory tracking strategy. In particular, we construct dynamic appearance model that contains multiple target templates, each of which provides its own attention for locating the target in the new frame. Guided by different attention, we maintain diversified tracking results for the target to build multi-trajectory tracking history, allowing more candidates to represent the true target trajectory. After spanning the whole sequence, we introduce a multi-trajectory selection network to find the best trajectory that deliver improved tracking performance. Extensive experimental results show that our proposed tracking strategy achieves compelling performance on various large-scale tracking benchmarks. The project page of this paper can be found at <uri>https://sites.google.com/view/mt-track/</uri>.
Visually meaningful image encryption based on universal embedding model

Visually meaningful image encryption (VMIE) means that a plain image can be encrypted into a visually meaningful cipher image (VMCI), which makes the secret more imperceptible than noise-like cipher images. Here, we first present a universal embedding model (UEM) and further present a new UEM-based VMIE algorithm. The plain image is pre-encrypted and then embedded into the integer wavelet subbands of the host image in a dynamic way. In order to avoid the overflow, a threshold limiting function is used to modify the range of pixel values of the host image. To adapt different types of wavelet transform subbands, a UEM is proposed to be used to embed the secret information domain into the host domain. Moreover, in the embedding process, a four-dimensional discrete chaotic system is used to ensure the security of embedding. The numbers of embedded bits in the four embedded domains can be adjusted flexibly. A traversal algorithm is designed to find the optimal numbers of embedded bits for different types of wavelet transform in order to achieve an optimal visual quality of cipher images. A matrix-bit-depth-conversion algorithm is designed where a large matrix with low bit depth can be transformed into a small matrix with high bit depth to meet the input requirements of UEM, which means that the size of the plain image is not limited to a quarter of the size of the host image. Simulation results and performance comparisons show that the proposed VMIE algorithm can achieve a better visual quality than existing VMIE algorithms.
Head2Head: Video-based Neural Head Synthesis

In this paper, we propose a novel machine learning architecture for facial reenactment. In particular, contrary to the model-based approaches or recent frame-based methods that use Deep Convolutional Neural Networks (DCNNs) to generate individual frames, we propose a novel method that (a) exploits the special structure of facial motion (paying particular attention to mouth motion) and (b) enforces temporal consistency. We demonstrate that the proposed method can transfer facial expressions, pose and gaze of a source actor to a target video in a photo-realistic fashion more accurately than state-of-the-art methods.
Effects of the self-regulated strategy within the context of spherical video-based virtual reality on students’ learning performances in an art history class

In recent years, several researchers have introduced spherical video-based virtual reality (SVVR) into classroom teaching to help students learn different subjects. In SVVR learning, learners are typically presented with great autonomy over their learning process. Therefore, learners should engage in self-regulated strategy (SRS) learning in order to successfully study in SVVR. However, learners often struggle to self-regulate their learning. Therefore, we implemented a SRS intervention in SVVR. In this study, a SVVR environment was developed to situate students in art history contexts; moreover, the SRS was employed to guide students to improve their performance and to make reflections on their own performance. In order to evaluate the proposed approach’s effectiveness, we carried out an experiment in a university art history class. The experiment results reveal more positive effects of the SRS-based SVVR approach compared with the non-SRS-based SVVR approach in terms of the learners’ learning achievements, self-regulation, meta-cognitive awareness, as well as self-efficacy. In addition, this study specified that this approach would not have an effect on students’ cognitive load. As a result, the SRS-based SVVR approach proposed in the current study was effective in art history education.
On the design and implementation of a secure blockchain-based hybrid framework for Industrial Internet-of-Things

Industrial Internet-of-Things (IIoT) refers to the next stage in the evolution of organizations where collecting, analyzing, recording the data and controlling the entire activities of the various entities is achieved with connected machines in real time with enhanced quality and minimum production cost. Although, various phenomenal schemes for cross management activities exist in current systems, however there are still several concerns with such setups within the organizations. Further, the introduction of Internet-of-things (IoT) within the industries is increasing the scope of applications by connecting every device with the Internet. However, these IoT devices are prone to various attacks by the intruder, thus affecting the industry with lower production and high manufacturing cost, to name a few. To address these issues, Blockchain is considered as one of the best scheme which offers protection and secrecy of control systems in real time up to certain level. In this paper, we have proposed a hybrid Blockchain mechanism for providing security for a multi-national level IIoT with offices located in multiple countries. The proposed framework is validated rigorously against various security metrics over conventional mechanism. The simulation results suggest that our proposed solution leads to 94% efficiency in terms of DoS and DDoS threat, message alteration attack and authentication delay.
Blockchain-Enabled healthcare system for detection of diabetes

Blockchain has penetrated numerous domains such as, industries, government agencies, online voting, and healthcare, etc. Among these domains, healthcare is one of the trending and most important one, which consists of a control system and an Electronic Health Records (EHRs). Diabetes is one of the most rapidly growing chronic diseases that increases the death ratio across the globe. This paper presents a Blockchain-enabled diabetes disease detection framework that provides an earlier detection of this disease by using various machine learning classification algorithms and maintains the EHRs of the patients in a secure manner. Our EHRs sharing framework combines symptom-based disease prediction, Blockchain, and interplanetary file system (IPFS) in which the patient's health information are collected via wearable sensor devices. This information is then sent to EHRs manager, where an ML model is executed for further processing to collect the desired results. The results along with the physiological parameters are then stored in the Blockchain with the approval of concerned patient and his/her practitioner. It is anticipated that our proposed system will help the healthcare society in order to store, process, and share the patient health information in a secure manner.
Dynamic Graph Collaborative Filtering

Dynamic recommendation is essential for modern recommender systems to provide real-time predictions based on sequential data. In real-world scenarios, the popularity of items and interests of users change over time. Based on this assumption, many previous works focus on interaction sequences and learn evolutionary embeddings of users and items. However, we argue that sequence-based models are not able to capture collaborative information among users and items directly. Here we propose Dynamic Graph Collaborative Filtering (DGCF), a novel framework leveraging dynamic graphs to capture collaborative and sequential relations of both items and users at the same time. We propose three update mechanisms: zero-order `inheritance', first-order `propagation', and second-order `aggregation', to represent the impact on a user or item when a new interaction occurs. Based on them, we update related user and item embeddings simultaneously when interactions occur in turn, and then use the latest embeddings to make recommendations. Extensive experiments conducted on three public datasets show that DGCF significantly outperforms the state-of-the-art dynamic recommendation methods up to 30%. Our approach achieves higher performance when the dataset contains less action repetition, indicating the effectiveness of integrating dynamic collaborative information.