Reviewerly/exHarmony huggingface dataset:
Binarized graph neural network

Recently, there have been some breakthroughs in graph analysis by applying the graph neural networks (GNNs) following a neighborhood aggregation scheme, which demonstrate outstanding performance in many tasks. However, we observe that the parameters of the network and the embedding of nodes are represented in real-valued matrices in existing GNN-based graph embedding approaches which may limit the efficiency and scalability of these models. It is well-known that binary vector is usually much more space and time efficient than the real-valued vector. This motivates us to develop a binarized graph neural network to learn the binary representations of the nodes with binary network parameters following the GNN-based paradigm. Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches to binarize the model parameters and learn the compact embedding. Extensive experiments indicate that the proposed binarized graph neural network, namely BGN, is orders of magnitude more efficient in terms of both time and space while matching the state-of-the-art performance.
Securing Medical Forensic System Using Hyperledger Based Private Blockchain

Forensic pathology applies medical skills and knowledge to a criminal inquiry that recognizes or develops the information regarding the accidental or unlawful death of an innocent person or the victims of significant physical injury. It is a legal process which collects, examines, analyzes and reports the evidence. Hyperledger Fabric is a permissioned and private blockchain structure which includes the present estimation of the element's property and the historical backdrop of transactions. In forensic department, every piece of evidence should be properly identified, collected, preserved, analyzed and finally admitted to the proper authority. But in reality assembling, evaluating, storing evidence is not secured enough. So in this paper, we are focused on creating a blockchain based medical forensic system using Hyperledger. The system can track any unauthorized access and modification by retrieving the Historian Record and Asset Registries and thus guarantees the impartiality of evidence, secrecy and validity of transactions.
Graph Computing for Financial Crime and Fraud Detection: Trends, Challenges and Outlook

The rise of digital payments has caused consequential changes in the financial crime landscape. As a result, traditional fraud detection approaches such as rule-based systems have largely become ineffective. Artificial intelligence (AI) and machine learning solutions using graph computing principles have gained significant interest in recent years. Graph-based techniques provide unique solution opportunities for financial crime detection. However, implementing such solutions at industrial-scale in real-time financial transaction processing systems has brought numerous application challenges to light. In this paper, we discuss the implementation difficulties current and next-generation graph solutions face. Furthermore, financial crime and digital payments trends indicate emerging challenges in the continued effectiveness of the detection techniques. We analyze the threat landscape and argue that it provides key insights for developing graph-based solutions.
A Technical Framework for Selection of Autonomous UAV Navigation Technologies and Sensors

The autonomous navigation of an Unmanned Aerial Vehicle (UAV) relies heavily on the navigation sensors. The UAV’s level of autonomy depends upon the various navigation systems, such as state measurement, mapping, and obstacle avoidance. Selecting the correct components is a critical part of the design process. However, this can be a particularly difficult task, especially for novices as there are several technologies and components available on the market, each with their own individual advantages and disadvantages. For example, satellite-based navigation components should be avoided when designing indoor UAVs. Incorporating them in the design brings no added value to the final product and will simply lead to increased cost and power consumption. Another issue is the number of vendors on the market, each trying to sell their hardware solutions which often incorporate similar technologies. The aim of this paper is to serve as a guide, proposing various methods to support the selection of fit-for-purpose technologies and components whilst avoiding system layout conflicts. The paper presents a study of the various navigation technologies and supports engineers in the selection of specific hardware solutions based on given requirements. The selection methods are based on easy-to-follow flow charts. A comparison of the various hardware components specifications is also included as part of this work.
Named Entity Recognition Using BERT with Whole World Masking in Cybersecurity Domain

Nowadays the amount of cybersecurity data grows quickly on the Internet, however most of them are textual unstructured data, which is hard for security analysis to understand in time and is not suitable for automated security systems to directly use. The automated and real-time switching of cybersecurity information from unstructured text sources to structured representations can help the cyber threat intelligence analysis know the cyber situation better. Named Entity Recognition (NER) is able to convert unstructured data into structured data. Recently, a language representation model named Bidirectional Encoder Representations from Transformers (BERT) has achieved great improvements among different NLP tasks. In this paper, we apply BERT and its improved version BERT with whole world masking (BERT <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">wwm</sub> ) to the NER task for cybersecurity. We combine the BERT model with the BiLSTM-CRF architecture, and the experiment reveals that our method achieves greater performance on the precision, recall, and F1 score compared with the state-of-the-art model whether on the overall entity or single entity.
Enhancing Graph Neural Networks by a High-quality Aggregation of Beneficial Information

Graph Neural Networks (GNNs), such as GCN, GraphSAGE, GAT, and SGC, have achieved state-of-the-art performance on a wide range of graph-based tasks. These models all use a technique called neighborhood aggregation, in which the embedding of each node is updated by aggregating the embeddings of its neighbors. However, not all information aggregated from neighbors is beneficial. In some cases, a portion of the neighbor information may be harmful to the downstream tasks. For the high-quality aggregation of beneficial information, we propose a flexible method EGAI (Enhancing Graph neural networks by a high-quality Aggregation of beneficial Information). The core concept of this method is to filter out the redundant and harmful information by removing specific edges during each training epoch. The practical and theoretical motivations, considerations, and strategies related to this method are discussed in detail. EGAI is a general method that can be combined with many backbone models (e.g., GCN, GraphSAGE, GAT, and SGC) to enhance their performance in the node classification task. In addition, EGAI reduces the convergence speed of over-smoothing that occurs when models are deepened. Extensive experiments on three real-world networks demonstrate that EGAI indeed improves the performance for both shallow and deep GNN models, and to some extent, mitigates over-smoothing. The code is available at https://github.com/liucoo/egai.
Efficient Two-Step Networks for Temporal Action Segmentation

Due to boundary ambiguity and over-segmentation issues, identifying all the frames in long untrimmed videos is still challenging. To address these problems, we present the Efficient Two-Step Network (ETSN) with two components. The first step of ETSN is Efficient Temporal Series Pyramid Networks (ETSPNet) that capture both local and global frame-level features and provide accurate predictions of segmentation boundaries. The second step is a novel unsupervised approach called Local Burr Suppression (LBS), which significantly reduces the over-segmentation errors. Our empirical evaluations on the benchmarks including 50Salads, GTEA and Breakfast dataset demonstrate that ETSN outperforms the current state-of-the-art methods by a large margin.
Fast Adaptive Meta-Learning for Few-Shot Image Generation

Generative Adversarial Networks (GANs) are capable of effectively synthesising new realistic images and estimating the potential distribution of samples utilising adversarial learning. Nevertheless, conventional GANs require a large amount of training data samples to produce plausible results. Inspired by the capacity for humans to quickly learn new concepts from a small number of examples, several meta-learning approaches for the few-shot datasets are presented. However, most of meta-learning algorithms are designed to tackle few-shot classification and reinforcement learning tasks. Moreover, the existing meta-learning models for image generation are complex, thereby affecting the length of training time required. Fast Adaptive Meta-Learning (FAML) based on GAN and the encoder network is proposed in this study for few-shot image generation. This model demonstrates the capability to generate new realistic images from previously unseen target classes with only a small number of examples required. With 10 times faster convergence, FAML requires only one-fourth of the trainable parameters in comparison baseline models by training a simpler network with conditional feature vectors from the encoder, while increasing the number of generator iterations. The visualisation results are demonstrated in the paper. This model is able to improve few-shot image generation with the lowest FID score, highest IS, and comparable LPIPS to MNIST, Omniglot, VGG-Faces, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mini</i> ImageNet datasets. The source code is available on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/phaphuang/FAML</uri> .
An evolving neuro-fuzzy system based on uni-nullneurons with advanced interpretability capabilities

This paper proposes a hybrid architecture based on neural networks, fuzzy systems, and n-uninorms for solving pattern classification problems, termed as ENFS-Uni0 (short for evolving neuro-fuzzy system based on uni-nullneurons). The model can produce knowledge in an on-line (single-pass) and evolving learning context in a particular form of neuro-fuzzy rules representing the dependencies among input features through IF-THEN type relations. The rules antecedents are thereby realized through uni-nullneurons, which are constructed from n-uninorms, leading to the possibility to express both, AND- and OR-connections (and a mixture of these) among the single antecedent parts of a rule (and thus achieving an advanced interpretability aspect of the rules). The neurons' evolution is done through an extended version of an autonomous data partition method (ADPA). On-line interpretation of the timely evolution of rules is addressed by (i) a concept for tracking the degree of changes of the rules over data stream samples, which may indicate experts/operators how much dynamics is in the process and may be used as a structural active learning component to request operator's feedback in the case of significant changes and (ii) a concept for updating feature weights incrementally. These weights express the (possibly changing) impact degrees of features on the classification problem: features with low weights can be seen as unimportant and masked out when showing rules to an expert (→ rule length reduction). The rules' consequents are represented by certainty vectors and are recursively updated by an indicator-based recursive weighted least squares (I-RWLS) approach (one RWLS estimator per class) where the weights are given through the neuron activation levels in order to gain stable local learning. The model proposed in this paper was successfully compared to related hybrid and evolving approaches in the literature for classifying binary and multi-class patterns. The results obtained by the proposed model show an outperformance of the related works in terms of higher accuracy trend lines over time, while offering a high degree of interpretability through coherent neuro-fuzzy rules to solve the classification problems.
Adaptive Semiparametric Language Models

Abstract We present a language model that combines a large parametric neural network (i.e., a transformer) with a non-parametric episodic memory component in an integrated architecture. Our model uses extended short-term context by caching local hidden states—similar to transformer-XL—and global long-term memory by retrieving a set of nearest neighbor tokens at each timestep. We design a gating function to adaptively combine multiple information sources to make a prediction. This mechanism allows the model to use either local context, short-term memory, or long-term memory (or any combination of them) on an ad hoc basis depending on the context. Experiments on word-based and character-based language modeling datasets demonstrate the efficacy of our proposed method compared to strong baselines.
Integrating the Role of Computational Intelligence and Digital Signal Processing in Education: Emerging Technologies and Mathematical Tools

Rapid progress in the development of technological and computational tools has motivated substantial changes in the educational approach to the different disciplines of signal, image, and video processing. Moreover, the parallel evolution of sensor systems, data acquisition methods, and computational intelligence has emphasized the importance of signal processing and information engineering, particularly its role in integrating different scientific disciplines through the use of a common set of tools and underlying mathematics. Modern educational courses follow these trends and generally combine the teaching of fundamental computational methods of signal and system modeling with applications to selected case studies. The unifying idea is to apply similar mathematical methods for data processing in completely diverse areas. Emerging methods used in education contribute to this progress, and they provide opportunities to bring together specialists from different disciplines. New technologies facilitate real or virtual activities through excursions to remote laboratories, allowing the demonstration of robotic and speech recognition systems, for example. Participation in seminars, videoconferences, and discussions during colloquia meetings, when included in educational courses, can form further progressive and attractive teaching methods for the rapidly developing interdisciplinary area of signal processing.
Gesture Recognition Using Wearable Sensors With Bi-Long Short-Term Memory Convolutional Neural Networks

In this study, we propose a gesture recognition system that is applicable for controlling home appliances. We utilized sensors embedded inside common smart watches, such as accelerometers and gyroscopes, for alleviating the obtrusiveness to users. One-dimensional convolutional neural networks and bi-long short-term memory (1D-CNN-biLSTM) are proposed for analyzing, learning, and representing features from the sensor signals. In addition, a dataset of 18,000 gestures with 18 labels was collected from 20 subjects to verify our proposed methods. Notably, the proposed hand gesture vocabulary was found to be easy to learn for users. Moreover, it provides them with improved control over their home appliances. The results of an empirical experiment conducted on three public datasets in addition to our self-collected dataset (GesHome) indicate that the proposed 1D-CNN-biLSTM model can achieve an F1-score of 90% and outperforms previous state-of-the art methods. Moreover, a demonstrative system was employed to illustrate the efficiency of the proposed model for home appliance control in a real-world scenario.
Market Making With Signals Through Deep Reinforcement Learning

Deep reinforcement learning has recently been successfully applied to a plethora of diverse and difficult sequential decision-making tasks, ranging from the Atari games to robotic motion control.Among the foremost such tasks in quantitative finance is the problem of optimal market making.Market making is the process of simultaneously quoting limit orders on both sides of the limit order book of a security with the goal of repeatedly capturing the quoted spread while minimizing the inventory risk.Most of the existing analytical approaches to market making tend to be predicated on a set of strong, naïve assumptions, whereas current machine learning-based approaches either resort to crudely discretized quotes or fail to incorporate additional predictive signals.In this paper, we present a novel framework for market making with signals based on model-free deep reinforcement learning, addressing these shortcomings.A new state space formulation incorporating outputs from standalone signal generating units, as well as a novel action space and reward function formulation, are introduced.The framework is underpinned by both ideas from adversarial reinforcement learning and neuroevolution.Experimental results on historical data demonstrate the superior reward-to-risk performance of the proposed framework over several standard market making benchmarks.More specifically, the resulting reinforcement learning agent achieves between 20-30% higher terminal wealth than the benchmarks while being exposed to only around 60% of their inventory risks.Finally, an insight into its policy is provided for the sake of interpretability.
A distributed dynamic authorisation method for Internet+ medical &amp; healthcare data access based on consortium blockchain

Privacy protection to medical & healthcare data is one of the bottleneck problems in Internet information fusion. In order to balance data processing capability with privacy concerns, a distributed dynamic authorisation method based on blockchain is proposed to realise trustworthy data access. URLs (Universal Resource Locator) are included in the authorisation information to indicate the data access interface as well as separating patients’ privacy data from blockchain to improve data process efficiency. The contents of smart contracts are demonstrated in a case study to validate the usability of the proposed authorisation method in medical data access between different hospitals.
Image encryption algorithm for crowd data based on a new hyperchaotic system and Bernstein polynomial

A new two-dimensional chaotic system in the form of a cascade structure is designed, which is derived from the Chebyshev system and the infinite collapse system. Performance analysis including trajectory, Lyapunov exponent and approximate entropy indicate that it has a larger chaotic range, better ergodicity and more complex chaotic behaviour than those of advanced two-dimensional chaotic system recently proposed. Moreover, to protect the security of the crowd image data, the newly designed two-dimensional chaotic system is utilized to propose a visually meaningful image cryptosystem combined with singular value decomposition and Bernstein polynomial. First, the plain image is compressed by singular value decomposition, and then encrypted to the noise-like cipher image by scrambling and diffusion algorithm. Later, the steganographic image is obtained by randomly embedding the cipher image into a carrier image in spatial domain through the Bernstein polynomial-based embedding method, thereby realizing the double security of image information and image appearance. Besides, the visual quality of the steganographic image can be improved by the adjustment factor according to different carrier images during the embedding process. Ultimately, security analyses indicate that it has higher encryption efficiency (2 Mbps) and the visual quality of steganography image can reach 39 dB.
TinyDL: Edge Computing and Deep Learning Based Real-time Hand Gesture Recognition Using Wearable Sensor

Offloading data analysis to edge devices by decentralizing processing can be used to decrease bandwidth requirements, latency, and can decrease the total transmission time required in wireless devices. This can be especially useful for compact wearable devices used for health monitoring, human activity recognition, and gesture recognition, where sending raw data over wireless protocols such as Bluetooth can be both time and power consuming. By performing analysis on the wearable device, wireless radio usage can be greatly decreased, reducing a main power consumer on the device. Deep learning (DL) methods, specifically using Tensorflow (TF) and Keras were evaluated for their usage in such a case, in this example gesture recognition. A multilayer long short-term memory (LSTM) model was trained and evaluated off of data (10 gestures, 1000 trials total, balanced) from a finger-worn ring profile device that collected acceleration data, and was found to perform with accuracy from 75-95% per gesture. The attempted conversion of the model into a compressed TF Lite format, to allow for analysis on-device did not succeed, due to current incompatibilities between the different frameworks. Future work may improve the accuracy, and potentially expand the use of neural networks on wearables for health diagnostics or as input devices.
Uncertainty Guided Multi-Scale Attention Network for Raindrop Removal From a Single Image

Raindrops adhered to a glass window or camera lens appear in various blurring degrees and resolutions due to the difference in the degrees of raindrops aggregation. The removal of raindrops from a rainy image remains a challenging task because of the density and diversity of raindrops. The abundant location and blur level information are strong prior guide to the task of raindrop removal. However, existing methods use a binary mask to locate and estimate the raindrop with the value 1 (adhesion of raindrops) and 0 (no adhesion), which ignores the diversity of raindrops. Meanwhile, it is noticed that different scale versions of a rainy image have similar raindrop patterns, which makes it possible to employ such complementary information to represent raindrops. In this work, we first propose a soft mask with the value in [-1,1] indicating the blurring level of the raindrops on the background, and explore the positive effect of the blur degree attribute of raindrops on the task of raindrop removal. Secondly, we explore the multi-scale fusion representation for raindrops based on the deep features of the input multi-scale images. The framework is termed uncertainty guided multi-scale attention network (UMAN). Specifically, we construct a multi-scale pyramid structure and introduce an iterative mechanism to extract blur-level information about raindrops to guide the removal of raindrops at different scales. We further introduce the attention mechanism to fuse the input image with the blur-level information, which will highlight raindrop information and reduce the effects of redundant noise. Our proposed method is extensively evaluated on several benchmark datasets and obtains convincing results.