Reviewerly/exHarmony huggingface dataset:
Information Extraction from Electronic Medical Records Using Multitask Recurrent Neural Network with Contextual Word Embedding

Clinical named entity recognition is an essential task for humans to analyze large-scale electronic medical records efficiently. Traditional rule-based solutions need considerable human effort to build rules and dictionaries; machine learning-based solutions need laborious feature engineering. For the moment, deep learning solutions like Long Short-term Memory with Conditional Random Field (LSTM–CRF) achieved considerable performance in many datasets. In this paper, we developed a multitask attention-based bidirectional LSTM–CRF (Att-biLSTM–CRF) model with pretrained Embeddings from Language Models (ELMo) in order to achieve better performance. In the multitask system, an additional task named entity discovery was designed to enhance the model’s perception of unknown entities. Experiments were conducted on the 2010 Informatics for Integrating Biology &amp; the Bedside/Veterans Affairs (I2B2/VA) dataset. Experimental results show that our model outperforms the state-of-the-art solution both on the single model and ensemble model. Our work proposes an approach to improve the recall in the clinical named entity recognition task based on the multitask mechanism.
Taxonomy of bio-inspired optimization algorithms

Bio-Inspired optimization algorithms are inspired from principles of natural biological evolution and distributed collective of a living organism such as (insects, animal, â€¦. etc.) for obtaining the optimal possible solutions for hard and complex optimization problems. In computer science Bio-Inspired optimization algorithms have been broadly used because of their exhibits extremely diverse, robust, dynamic, complex and fascinating phenomenon as compared to other existing classical techniques.This paper presents an overview study on the taxonomy of bio-inspired optimization algorithms according to the biological field that are inspired from and the areas where these algorithms have been successfully applied
A Multiple Diversity-Driven Brain Storm Optimization Algorithm With Adaptive Parameters

Brain storm optimization (BSO) is a swarm intelligence optimization algorithm which is proven to have practical values in various fields. During these years, many modifications have been facilitated to effectively improve BSO's search performance. So far, these modifications focus on improving the solution quality by applying different clustering methods and learning strategies, in which the population diversity is often neglected. However, in recent studies, population diversity plays a more significant role in designing optimization algorithm. A population that maintains its diversity in a high level can easily obtain better solutions than the one with low level of diversity. Therefore, this paper proposes a control method that evaluates the population diversity of BSO to improve its performance. Two diversity measures, which are known as distance-based diversity and fitness-based diversity, are implemented to realize the adaptation of algorithm parameters. The new algorithm is called multiple diversity-driven BSO (MDBSO). Its performance is verified by CEC2017 benchmark function suit and a neuron model training task. The results demonstrate the effectiveness and efficiency of MDBSO.
The Exploration/Exploitation Tradeoff in Whale Optimization Algorithm

The whale optimization algorithm(WOA) is a novel meta-heuristic evolutionary algorithm inspired by the behavior of whales predation.An important factor to the success of WOA is the balancing between exploration and exploitation.In the WOA, the distance control parameter a is the main factor to find an appropriate balance between exploration and exploitation.In the standard WOA, the distance control parameter a is optimized by linear control strategy (LCS), but the process of whales predation is not simply linear process.To address the issue, this paper proposed a nonlinear control strategy based on arcsine function (NCS-Arcsin) to optimize WOA.The NCS-Arcsin is applied to adjust distance control parameter a.The NCS-Arcsin is considered to accurately describe the process of whales predation.Experiments on twelve well-known benchmark functions show the NCS-Arcsin can significantly improve the exploration and exploitation capabilities of WOA.In addition, the performance of proposed NCS-Arcsin is compared with LCS and other have been proposed NCS.The experimental results show that the optimization effect of NCS-Arcsin is stronger than that of LCS and other NCS.The NCSs based on the arcsine function is the best NCS, which can significantly improve the optimize performance of WOA.
Enhanced Feature Representation in Detection for Optical Remote Sensing Images

In recent years, deep learning has led to a remarkable breakthrough in object detection in remote sensing images. In practice, two-stage detectors perform well regarding detection accuracy but are slow. On the other hand, one-stage detectors integrate the detection pipeline of two-stage detectors to simplify the detection process, and are faster, but with lower detection accuracy. Enhancing the capability of feature representation may be a way to improve the detection accuracy of one-stage detectors. For this goal, this paper proposes a novel one-stage detector with enhanced capability of feature representation. The enhanced capability benefits from two proposed structures: dual top-down module and dense-connected inception module. The former efficiently utilizes multi-scale features from multiple layers of the backbone network. The latter both widens and deepens the network to enhance the ability of feature representation with limited extra computational cost. To evaluate the effectiveness of proposed structures, we conducted experiments on horizontal bounding box detection tasks on the challenging DOTA dataset and gained 73.49% mean Average Precision (mAP), achieving state-of-the-art performance. Furthermore, our method ran significantly faster than the best public two-stage detector on the DOTA dataset.
Spontaneous Conversational Speech Synthesis from Found Data

Synthesising spontaneous speech is a difficult task due to disfluencies, high variability and syntactic conventions different from those of written language. Using found data, as opposed to lab-rec ...
PSO-based growing echo state network

Reservoir computing (RC), with the idea of using a large randomly and sparsely connected recurrent layer, has turned out to be an efficient paradigm for training recurrent neural networks. Echo state network (ESN) is one of typical representatives of RC, whose good performance mainly depends on the selection of right parameters. In this paper, we introduce an approach to pre-train a growing ESN with multiple sub-reservoirs by optimizing singular values, based on particle swarm optimization (PSO) and singular value decomposition (SVD). During the network growing, the sub-reservoirs are added to the network one by one, which leads to a quasi-diagonal reservoir weight matrix. For each sub-reservoir, before being added to the network, the singular values are optimized by PSO, then the sub-reservoir weight matrix is designed by SVD with the optimized singular values. In order to evaluate the performance of sub-reservoirs during optimization, a novel fitness function is derived based on some concepts in vector space. Finally, simulation results, on both benchmark and real-world data sets, show the superior performance of the proposed method.
Robust countermeasure against detector control attack in a practical quantum key distribution system

In real-life implementations of quantum key distribution (QKD), the physical systems with unwanted imperfections would be exploited by an eavesdropper.Based on imperfections in the detectors, detector control attacks have been successfully launched on several QKD systems, and attracted widespread concerns.Here, we propose a robust countermeasure against these attacks just by introducing a variable attenuator in front of the detector.This countermeasure is not only effective against the attacks with blinding light, but also robust against the attacks without blinding light which are more concealed and threatening.Different from previous technical improvements, the single photon detector in our countermeasure model is treated as a blackbox, and the eavesdropper can be detected by statistics of the detection and error rates of the QKD system.Besides theoretical proof, the countermeasure is also supported by an experimental demonstration.Our countermeasure is general in sense that it is independent of the technical details of the detector, and can be easily applied to the existing QKD systems.
Network Traffic Prediction Using Variational Mode Decomposition and Multi- Reservoirs Echo State Network

The network traffic prediction is significant for the network load pre-warning and network congestion control. But the nonlinearity and nonstationarity of the actual network traffic data would reduce the prediction accuracy. In this paper, an optimized network traffic prediction method using variational mode decomposition (VMD) and multi-reservoirs echo state network (ESN) is presented. VMD method has advantages of reducing the signal transmission errors, removing the mode aliasing, and decreasing the degree of endpoint effects. However, VMD needs to preset the number of modes and the iterative factor, which are mainly decided by subjective experiences. In order to solve this, an optimized VMD method is proposed, and then a multi-reservoirs echo state network based prediction model is constructed. The main works are as follows: First, VMD is used to decompose the original network traffic data into several subsets; then, multiple subreservoirs are built after the phase space reconstruction (PSR) of each data subset; finally, the training set is used to train the prediction model. Moreover, in the training process, an improved fruit fly optimization algorithm (IFOA) is proposed combined with the levy's flight function and the cloud generator, which is used to optimize some model parameters. Compared with several prediction models, the proposed VMD-IFOA-ESN has better predictive stability and convergence performance. Three WIDE backbone network traffic data sets with different time intervals verify the effectiveness of the proposed prediction model.
Visual Measurement of Water Level under Complex Illumination Conditions

Image-based water level measurement is a visual-sensing technique which automatically inspects the reading of the water line via image processing instead of the human eye. It can be realized easily on an existing video surveillance system and has advantages like low cost, non-contact, as well as results that are verifiable. It has the potential to be widely used in flood and waterlogging monitoring, while facing the challenge that water-line detection under complex natural or artificial illumination conditions is quite difficult in field applications. To handle this problem, a method is proposed assuming that the water line is generally located on the row with the largest local change of gray or edge features in the image of the water gauge. The water line is determined by coarse-to-fine detection of the position of the maximum mean difference (MMD) of the horizontal projections of gray and edge images. Image-based flow-level measurement systems were developed at two measurement sites. In situ comparative experiments were conducted with the float-type stage gauge and other image-based methods. The results show that the fusion of gray and edge features can overcome the shortcomings of single feature methods under complex illumination conditions such as dim light, glares, shadows and artificial night lighting. A coarse-to-fine strategy utilizes the periodicity of the surface pattern distribution of the standard bicolor water gauge, which improves the reliability of water-line detection. The resolution and accuracy of water-level measurement are 1 mm and 1 cm, respectively. In particular, the MMD value is efficient at identifying extremely unfavorable conditions and reducing gross errors.
Light Scattering from Solid-State Quantum Emitters: Beyond the Atomic Picture

Coherent scattering of light by a single quantum emitter is a fundamental process at the heart of many proposed quantum technologies. Unlike atomic systems, solid-state emitters couple to their host lattice by phonons. Using a quantum dot in an optical nanocavity, we resolve these interactions in both time and frequency domains, going beyond the atomic picture to develop a comprehensive model of light scattering from solid-state emitters. We find that even in the presence of a cavity, phonon coupling leads to a sideband that is completely insensitive to excitation conditions, and to a non-monotonic relationship between laser detuning and coherent fraction, both major deviations from atom-like behaviour.
A broad class of discrete-time hypercomplex-valued Hopfield neural networks

In this paper, we address the stability of a broad class of discrete-time hypercomplex-valued Hopfield-type neural networks. To ensure the neural networks belonging to this class always settle down at a stationary state, we introduce novel hypercomplex number systems referred to as real-part associative hypercomplex number systems. Real-part associative hypercomplex number systems generalize the well-known Cayley–Dickson algebras and real Clifford algebras and include the systems of real numbers, complex numbers, dual numbers, hyperbolic numbers, quaternions, tessarines, and octonions as particular instances. Apart from the novel hypercomplex number systems, we introduce a family of hypercomplex-valued activation functions called B-projection functions. Broadly speaking, a B-projection function projects the activation potential onto the set of all possible states of a hypercomplex-valued neuron. Using the theory presented in this paper, we confirm the stability analysis of several discrete-time hypercomplex-valued Hopfield-type neural networks from the literature. Moreover, we introduce and provide the stability analysis of a general class of Hopfield-type neural networks on Cayley–Dickson algebras.
A Preliminary Study on Data Augmentation of Deep Learning for Image Classification

Deep learning models have a large number of free parameters that need to be calculated by effective training of the models on a great deal of training data to improve their generalization performance. However, data obtaining and labeling is expensive in practice. Data augmentation is one of the methods to alleviate this problem. In this paper, we conduct a preliminary study on how four variables (augmentation method, augmentation rate, size of basic dataset per label, and method combination) can affect the accuracy of deep learning for image classification. The study provides some guidelines: (1) altering the geometry of the images is not always better than those just lighting and color. (2) 2-3 times augmentation rate is good enough for training. (3) the combination of two geometry methods degrade the performance, while combinations with at least one photometric method, will improve the performance, especially when one method is a photometric method and another is a geometry method. (4) the sequence of methods in combination has little effect on the performance.
Sampling‐based hierarchical motion planning for a reconfigurable wheel‐on‐leg planetary analogue exploration rover

Abstract Reconfigurable mobile planetary rovers are versatile platforms that may safely traverse cluttered environments by morphing their physical geometry. Planning paths for these adaptive robots is challenging due to their many degrees of freedom, and the need to consider potentially continuous platform reconfiguration along the length of the path. We propose a novel hierarchical structure for asymptotically optimal (AO) sampling‐based planners and specifically apply it to the state‐of‐the‐art Fast Marching Tree (FMT*) AO planner. Our algorithm assumes a decomposition of the full configuration space into multiple subspaces, and begins by rapidly finding a set of paths through one such subspace. This set of solutions is used to generate a biased sampling distribution, which is then explored to find a solution in the full configuration space. This technique provides a novel way to incorporate prior knowledge of subspaces to efficiently bias search within existing AO sampling‐based planners. Importantly, probabilistic completeness and asymptotic optimality are preserved. Experimental results in simulation are provided that benchmark the algorithm against state‐of‐the‐art sampling‐based planners without the hierarchical variation. Additional experimental results performed with a physical wheel‐on‐leg platform demonstrate application to planetary rover mobility and showcase how constraints such as actuator failures and sensor pointing may be easily incorporated into the planning problem. In minimizing an energy objective that combines an approximation of the mechanical work required for platform locomotion with that required for reconfiguration, the planner produces intuitive behaviors where the robot dynamically adjusts its footprint, varies its height, and clambers over obstacles using legged locomotion. These results illustrate the generality of the planner in exploiting the platform's mechanical ability to fluidly transition between various physical geometric configurations, and wheeled/legged locomotion modes, without the need for predefined configurations.
Covert Channels in the MQTT-Based Internet of Things

Network covert channels are a part of the information hiding research area that deals with the secret transfer of information over communication networks. Covert channels can be utilized, for instance, for data leakage and stealthy malware communications. While data hiding in communication networks has been studied within the last years for several major communication protocols, currently no work is available that investigates covert channels for the publish-subscriber model. To fill this gap, we present the first comprehensive study of covert channels in a protocol utilizing the publish-subscriber model, i.e., the Message Queuing Telemetry Transport (MQTT) protocol which is widely deployed in Internet of Things (IoT) environments. In particular, we describe seven direct and six indirect covert channels and we evaluate and categorize them using the network information hiding patterns approach. Finally, in order to prove that MQTT-based covert channels are practically feasible and effective, we implement the chosen data hiding scheme and perform its experimental evaluation.
GKP Signal Processing Using Deep CNN and SVM for Tongue-Machine Interface

The tongue is one of the few organs with high mobility in the case of severe spinal cord injuries.However, most tongue-machine interfaces (TMIs) require the patient to wear obtrusive and unhygienic devices in and around the mouth.This paper aims to develop a TMI based on the glossokinetic potentials (GKPs), i.e. the electrical signals generated by the tongue when it touches the buccal walls.Ten participants were recruited for this research.The GKP patterns were classified by convolutional neural network (CNN) and support vector machine (SVM).It was observed that the CNN outperformed the SVM in individual and average scores for both raw and preprocessed datasets, reaching an accuracy of 97~99%.The CNN-based GKP processing method makes it easy to build a natural, appealing and robust TMI for the paralyzed.Being the first attempt to process GKPs with the CNN, our research offers an alternative to the traditional brain-computer interfaces (BCIs), which suffers from the instability and low signalto-noise ratio (SNR) of electroencephalography (EEG).
From Semantics to Execution: Integrating Action Planning With Reinforcement Learning for Robotic Causal Problem-Solving

Reinforcement learning is an appropriate and successful method to learn robot control. Symbolic action planning is useful to resolve causal dependencies and to break a causally complex problem down into a sequence of simpler high-level actions. A problem with the integration of both approaches is that action planning is based on discrete high-level action- and state spaces, whereas reinforcement learning is usually driven by a continuous reward function. Recent advances in model-free reinforcement learning, specifically, universal value function approximators and hindsight experience replay, have focused on goal-independent methods based on sparse rewards that are only given at the end of a rollout, and only if the goal has been fully achieved. In this article, we build on these novel methods to facilitate the integration of action planning with model-free reinforcement learning. Specifically, the article demonstrates how the reward-sparsity can serve as a bridge between the high-level and low-level state- and action spaces. As a result, we demonstrate that the integrated method is able to solve object manipulation problems that involve tool use and non-trivial causal dependencies under noisy conditions, exploiting both data and knowledge.
Light of Two Atoms in Free Space: Bunching or Antibunching?

Photon statistics divides light sources into three different categories, characterized by bunched, antibunched, or uncorrelated photon arrival times. Single atoms, ions, molecules, or solid state emitters display antibunching of photons, while classical thermal sources exhibit photon bunching. Here we demonstrate a light source in free space, where the photon statistics depends on the direction of observation, undergoing a continuous crossover between photon bunching and antibunching. We employ two trapped ions, observe their fluorescence under continuous laser light excitation, and record spatially resolved the autocorrelation function ${g}^{(2)}(\ensuremath{\tau})$ with a movable Hanbury Brown and Twiss detector. Varying the detector position we find a minimum value for antibunching, ${g}^{(2)}(0)=0.60(5)$ and a maximum of ${g}^{(2)}(0)=1.46(8)$ for bunching, demonstrating that this source radiates fundamentally different types of light alike. The observed variation of the autocorrelation function is understood in the Dicke model from which the observed maximum and minimum values can be modeled, taking independently measured experimental parameters into account.
Taggle: Combining overview and details in tabular data visualizations

Most tabular data visualization techniques focus on overviews, yet many practical analysis tasks are concerned with investigating individual items of interest. At the same time, relating an item to the rest of a potentially large table is important. In this work, we present Taggle, a tabular visualization technique for exploring and presenting large and complex tables. Taggle takes an item-centric, spreadsheet-like approach, visualizing each row in the source data individually using visual encodings for the cells. At the same time, Taggle introduces data-driven aggregation of data subsets. The aggregation strategy is complemented by interaction methods tailored to answer specific analysis questions, such as sorting based on multiple columns and rich data selection and filtering capabilities. We demonstrate Taggle by a case study conducted by a domain expert on complex genomics data analysis for the purpose of drug discovery.