Reviewerly/exHarmony huggingface dataset:
Limited Memory Measurement Noise Adaptive Random Weighted Filtering Algorithm

A new adaptive random weighted filtering algorithm is proposed. It is based on online estimation of limited memory measurement noise to overcome the problem of low filtering precision caused by arithmetic average estimation of measurement noise and its covariance matrix in the existing Kalman filtering algorithm of limited memory online estimation of measurement noise. This method establishes the stochastic weighting theory to estimate the measurement noise online and its covariance by adaptive adj usting the weights of measurement noise statistics. The weight of measurement noise statistics is used to suppress the influence of measurement noise on state estimation and improve the accuracy of filter estimation. Through simulations and analysis, the superiority of the proposed adaptive random weighted filtering algorithm based on online estimation of limited memory measurement noise algorithm is proved.
Personal Health Mention Identification from Tweets Using Convolutional Neural Network

The past decade witnesses the unprecedent growth of social media users worldwide. People express health related outcomes, information, and views on social media platforms. This provides many opportunities to utilize the data source for health monitoring and surveillance, and digital epidemiology in real time. Personal health mention (PHM) is among one of the critical tasks for such purpose. It tries to identify whether a person's health condition is mentioned in a sentence. However, social media texts contain noises, many creative and novel phrases, sarcastic Emoji expressions, and misspellings. This poses challenges to detect PHM from social media text. This paper explores the PHM identification task for six diseases from twitter using convolutional neural network (CNN). Specifically, word embeddings are used to encode the twitter text. Then they are fed into CNN structure to train the classifier for PHM identification. We also explore how the performance of different methods are affected by data imbalance issue and training sample size.
Evolutionary Design of Long Short Term Memory (LSTM) Ensemble

Long Short Term Memory (LSTM) based Recurrent Neural Network is an effective deep learning model with a wide variety of applications. Despite numerous recent success, using a single LSTM network cannot achieve reliable performance on complicated machine learning tasks. An important approach to improve the performance is to learn an ensemble of LSTMs rather than learning a single LSTM. The performance of the ensemble depends on the accuracy and diversity of individual LSTMs. This paper proposes a new evolutionary multi-objective algorithm to automatically design a collection of diverse and accurate LSTMs based on Non-dominated Sort Genetic Algorithm II. We also propose a method to select suitable LSTMs from an evolved non-dominated set of LSTMs to form the ensemble. The obtained results on multiple benchmark classification tasks show that the proposed algorithm can evolve an ensemble of LSTMs, which significantly outperform many state-of-the-art models.
Challenges of Stream Learning for Predictive Maintenance in the Railway Sector

Smart trains nowadays are equipped with sensors that generate an abundance of data during operation. Such data may, directly or indirectly, reflect the health state of the trains. Thus, it is of interest to analyze these data in a timely manner, preferably on-the-fly as they are being generated, to make maintenance operations more proactive and efficient. This paper provides a brief overview of predictive maintenance and stream learning, with the primary goal of leveraging stream learning in order to enhance maintenance operations in the railway sector. We justify the applicability and promising benefits of stream learning via the example of a real-world railway dataset of the train doors.
Steganography in animated emoji using self-reference

Abstract Animated emoji is a kind of GIF image, which is widely used in online social networks (OSN) for its efficiency in transmitting vivid and personalized information. Aiming at realizing covert communication in animated emoji, this paper proposes an improved steganography framework in animated emoji. We propose a self-reference algorithm to improve the steganography security. Meanwhile, the relations between adjacent frames of the cover GIF image are considered to further improve the distortion function. After that we embed the secret message into the GIF image using the popular framework of Syndrome Trellis Coding (STC). Experimental results show that the proposed method can provide better security performances than state-of-the-art works.
Near Infrared Face Recognition using End to End Light Convolution Neural Network

Face Recognition in the visible spectrum is a widely used biometric tool. However, it is often influenced by environmental lighting that reduces its performance drastically. So as to solve the effects of illumination, face images are captured in the near-infrared (NIR) spectrum. While several deep learning models are available to solve the problem of face recognition in the visible spectrum, only a few models exist in the NIR spectrum. This paper proposes an end to end light CNN architecture that performs face recognition in the NIR spectrum. The proposed architecture has given higher accuracies for CASIA NIR VIS 2.0 (98.16%) and Oulu CASIA (99.26%) which are considered to be the largest and the most challenging publicly available datasets. Further, it has produced high accuracies for other publicly available datasets namely PolyU (97.90%), CBSR (98.22%) and HITSZ (99.72%).
3D Dense Geometry-Guided Facial Expression Synthesis by Adversarial Learning

Manipulating facial expressions is a challenging task due to fine-grained shape changes produced by facial muscles and the lack of input-output pairs for supervised learning. Unlike previous methods using Generative Adversarial Networks (GAN), which rely on cycle-consistency loss or sparse geometry (landmarks) loss for expression synthesis, we propose a novel GAN framework to exploit 3D dense (depth and surface normals) information for expression manipulation. However, a large-scale dataset containing RGB images with expression annotations and their corresponding depth maps is not available. To this end, we propose to use an off-the-shelf state-of-the-art 3D reconstruction model to estimate the depth and create a large-scale RGB-Depth dataset after a manual data clean-up process. We utilise this dataset to minimise the novel depth consistency loss via adversarial learning (note we do not have ground truth depth maps for generated face images) and the depth categorical loss of synthetic data on the discriminator. In addition, to improve the generalisation and lower the bias of the depth parameters, we propose to use a novel confidence regulariser on the discriminator side of the framework. We extensively performed both quantitative and qualitative evaluations on two publicly available challenging facial expression benchmarks: AffectNet and RaFD. Our experiments demonstrate that the proposed method outperforms the competitive baseline and existing arts by a large margin.
Speaker Identification and Its Application to Social Network Construction for Chinese Novels

Character is one of the three elements of a novel, and conversation is an important way to describe characters. The personality, emotion, and interpersonal relationships of characters are reflected in conversations. Thus, extracting conversations, speakers and other conversation elements from novels are crucial for character analysis and content understanding. We start with Jin Yong's novels, annotate the largest corpus for speaker identification with 9721 quotes, propose a machine learning-based speaker identification method, and design feature templates showing a good performance. For the application of speaker identification, we construct the social network of characters in Jin Yong's novels based on dialogue chain, which lays the foundation for the analysis of the relationship between characters in long text novels.
Simulating quantum computations with Tutte polynomials

We establish a classical heuristic algorithm for exactly computing quantum probability amplitudes. Our algorithm is based on mapping output probability amplitudes of quantum circuits to evaluations of the Tutte polynomial of graphic matroids. The algorithm evaluates the Tutte polynomial recursively using the deletion-contraction property while attempting to exploit structural properties of the matroid. We consider several variations of our algorithm and present experimental results comparing their performance on two classes of random quantum circuits. Further, we obtain an explicit form for Clifford circuit amplitudes in terms of matroid invariants and an alternative efficient classical algorithm for computing the output probability amplitudes of Clifford circuits.
When do additional quantum noises affect controller performance?

The introduction of quantum noise in a system is necessary for realizing it as physical quantum system. In general, quantum noise can be detrimental to the performance of a coherent quantum feedback control system. This paper gives a necessary and sufficient condition for when a quantum system corresponding to a given Linear Time Invariant (LTI) controller can be made physically realizable in the presence of both direct feedthrough quantum vacuum noise and additional quantum vacuum noise such that the additional quantum noise has no effect on the controller output. Furthermore, an example is presented to illustrate our result.
Hybrid FCM learning algorithm based on particle swarm optimization and gradient descent algorithm

The learning algorithm of fuzzy cognitive map(FCM) is mainly based on expert intervention or effective historical data to learn the weight value in weight matrix W. With the increase of the number of nodes, the number of weights need to be learned will also increase square, the performance of the learning algorithm will decline sharply. Therefore, it is an important task to design a high-performance learning algorithm which depends on expert knowledge as little as possible. A single learning algorithm has some limitations, such as easy local optimization, slow learning rate and so on. Among them, particle swarm optimization algorithm has strong global optimization ability, which can quickly optimize the weight matrix to a certain accuracy, but the follow-up optimization time is too long. For the gradient descent algorithm, when the initial value is chosen properly, its can approach the global optimal solution quickly. In this paper, we design particle swarm optimization (PSO) and gradient descent algorithm (GDA) to learn FCM respectively. According to the characteristics of the two algorithms, a hybrid algorithm(HA) is proposed: first, the PSO algorithm is used to optimize weight matrix to a certain accuracy, then the gradient descent method is added, which makes up for the disadvantage that the error of single PSO algorithm decreases slowly in the later stage. Experimental results show that the hybrid algorithm proposed in this paper has a significant improvement in both accuracy and speed.
From One-hot Encoding to Privacy-preserving Synthetic Electronic Health Records Embedding

Categorical Encoding, typically one-hot encoding, plays a central role when we learn Machine Learning models. This classic approach is the most prevalent strategy due to its simplicity. However, as the number of categories grows large and sparse, it becomes infeasible to train since it creates high-dimensional vectors, which is also at the risk of revealing private information and breaking its underlying structure. We here propose to utilize data intermediate representation learning (embedding) to overcome such limitations. Instead of representing data with a one-hot vector of many cardinalities, an embedding serves as a lower-dimensional dense vector in which each cell can contain any number, capturing the latent hierarchical structures of the features in the meantime. It can also be assumed that sharing embedding is safer than releasing raw one-hot encoded data, as the presence of a particular feature is represented by the value of 1, otherwise 0. With the assist of Generative Adversarial Network further alleviates sensitive information leakage issue by creating synthetic data for modeling. Our result suggests that even embedded features may more or less pose privacy flaws, deploying GAN will make a wider variety of medical datasets available by retaining its relative utility while preserving data privacy, which has been identified as a promising method for medical machine learning and prediction.
A Bayesian Mixture Model Approach to Anomaly Detection with Application to Wind Tunnel Experiments

Large-scale sensor systems, such as those found in modern wind tunnels, require continual monitoring to validate their instruments’ quality as corrupted data will increase experimental downtime, loss of budgetary resources, and worse, lead to inconclusive scientific and engineering results. Wind tunnel monitoring methodologies such as Principal Component Analysis and a Robust Bayesian scale-mixture Regression have been examined for anomaly detection. However, anomalous observations can heavily impact the parameter estimation and the error rates of these methodologies. We propose a Bayesian mixture model of signals that decompose the anomalous and non-anomalous sensor readings into two components using non-local heavy-tailed Cauchy component to characterize the anomalous sensor readings. This paper compares two system monitoring methodologies: (1) a method based on a Principal Component Analysis technique, and (2) a Bayesian, heavy-tailed posterior distribution with our proposed Modified Cauchy-Net method. We emphasize the need for compartmentalizing anomalous sensor readings from reliable readings through the simulation and wind tunnel experiments.
Crowd Density Analysis and Suspicious Activity Detection

There is a growing need for smart surveillance systems in public and private places to logically differentiate between normal and abnormal behaviour. This is not just important for the convenience of the people but also for their security. Understanding a video footage and classifying an activity as normal or suspicious especially in densely packed regions is possible and has been demonstrated in this paper. The proposed system makes use of the YOLOv3 algorithm for object detection. The COCO dataset, which is a large-scale object detection, segmentation and captioning dataset, has been used for training this model. The whole framework is made up of two parts. First the features are computed from the image. Then based on the detected features, the classifier makes a prediction. Depending on the object detected, the algorithm classifies a frame as suspicious or normal. Crowd density has been calculated by detecting the number of people in a frame and suspicion detection has been performed by analysing a frame for suspicious objects like isolated bags, knives and guns.
Lightweight Deep Neural Network-based Real-Time Pose Estimation on Embedded Systems

This paper proposes a novel real-time pose estimation system on embedded devices for a driver and a front passenger. The main goal of the proposed system is to operate in real time with limited hardware resources while preserving the high accuracy. The proposed system is divided into an object detection and a pose estimation. In the object detection, we eliminate the redundant and inaccurate bounding boxes by considering the characteristics of the target image domain. In the pose estimation, a single-person pose estimation with a lightweight deep learning model has been proposed and knowledge distillation has been adopted to maximize the performance while maintaining the high speed. In the experimental results, the proposed pose estimation has up to 9 % of the accuracy and the 9 times less computation compared to the previous methods. The operation speed is 195 frame per second on NVIDIA Jetson TX2.
Implementation of Violence Detection System using Soft Computing Approach

Numerous techniques have been evolved for the detection of violence in human beings. Prior detection of human action can help to prevent and control suspicious and criminal activities. The offline video processing system has been used for post-action analysis. We address the violence detection trouble of humans in real-time visual surveillance such as punching, fighting. The present research work proposes a novel framework that processes real-time video data received from fixed cameras installed area of interest under surveillance. To determine the security level, we developed a new algorithm based on the decision-making classifier to recognize the violent situation in real time. In the view of human violence detection, the proposed work is simple and unique. The transition effects observed during violence detection are deliberated in detail. It has wide applications in the area of visual indexing, biometrics, telehealth, and human–computer interaction.
PSO-assisted Lyapunov control design for quantum systems

By using a quadratic Lyapunov function with un-known parameters and a particle swarm optimization (PSO) algorithm, this paper proposes a new Lyapunov control scheme for quantum systems. This approach can achieve high-probability population transfer to a given target state. For the case when the target state is an eigenstate of the internal Hamiltonian, we introduce a virtual control law into the system model and design corresponding control law. The stability of the system under the action of the Lyapunov control law is analyzed via the LaSalle invariance principle. For the case when the target state is a superposition state, we design a control law by performing a unitary transformation for the quantum system model under consideration. To achieve desired state transfer, we further introduce a PSO algorithm to search for the unknown parameters contained in the control law. Numerical results on a five-level quantum system and a three-qubit system are presented to demonstrate the effectiveness of the proposed approaches.
Reinforcement Learning for Playing WrapSlide

The toroidal puzzle game WrapSlide shares a lot of characteristics with the famous Rubik’s cube in that it consists of a grid of tiles that has to be slid in different directions to get to a solved state. WrapSlide is a mobile game that may be played with a $$4\times 4, 6\times 6$$ or $$8\times 8$$ grid with either two, three or four colours in the grid. The objective of the game is to group the tiles of the same colour into the four quadrants of the grid. In this paper, a reinforcement learning approach towards playing the game WrapSlide is implemented in order to ascertain whether a reinforcement learning agent is capable of solving the puzzle in a near-optimal number of moves.
Graph-Based Supervised Clustering in Vector Space

Neural Networks are able to cluster data sets and our goal was to figure out how well the neural network clustered. The MNIST data set was ran through a neural network and the distances were extracted from both the feature and output layer. Five different distances were used on both layers. K-means clustering was used assess the clustering performance of each layer in the neural network. Results conveyed that the feature layer was not as proficient at clustering when compared to the output layer. The type of distance did not make a significant difference for clustering. These conclusions can be derived from qualitative observation of the cluster graphs. By observing the clustering performance of the different layers in the CNN, we are able to gain insight on the neural network.
Corporate Bodies: Access Points and Authority Control

The concept of authorship is central to how libraries organize their collections. But libraries do not only collect resources created by individuals, they also collect documents issued by organizations. Library catalogers use the concept of a “corporate body” to treat organizations as authors for the purpose of making their documents discoverable to users. This essay looks at the key features of establishing authorized access points (AAPs) and applying authority control for corporate bodies. It examines how practices with regard to corporate bodies have changed over time and considers the changes catalogers might expect to see in the future.
Generic Convoying Functionality for Autonomous Vehicles in Unstructured Outdoor Environments

Autonomously following a leading vehicle is a major step towards fully autonomous vehicles. The contribution of this work consists in the development, implementation, and validation of two following modes: 1) Exact following: accurate compliance with the reference path. 2) Flexible following: tolerate deviation from the reference path in order to avoid obstacles. The proposed method can easily be integrated into existing frameworks for autonomous vehicles. Therefore our approach is flexible enough to be applied to a large variety of different vehicles. To demonstrate the feasibility of our approach an experimental validation is carried out on two autonomous vehicles with major differences in kinematics, weight, and size: A cross-country wheelchair and an off-road truck. Both exact and flexible following have been successfully demonstrated in unstructured outdoor environments.
Learning chordal extensions

A highly influential ingredient of many techniques designed to exploit sparsity in numerical optimization is the so-called chordal extension of a graph representation of the optimization problem. The definitive relation between chordal extension and the performance of the optimization algorithm that uses the extension is not a mathematically understood task. For this reason, we follow the current research trend of looking at Combinatorial Optimization tasks by using a Machine Learning lens, and we devise a framework for learning elimination rules yielding high-quality chordal extensions. As a first building block of the learning framework, we propose an imitation learning scheme that mimics the elimination ordering provided by an expert rule. Results show that our imitation learning approach is effective in learning two classical elimination rules: the minimum degree and minimum fill-in heuristics, using simple Graph Neural Network models with only a handful of parameters. Moreover, the learned policies display remarkable generalization performance, across both graphs of larger size, and graphs from a different distribution.