Reviewerly/exHarmony huggingface dataset:
Recent Research on AI in Games

Games tend to have the properties of vast state space and high complexity, making them excellent benchmarks for evaluating various techniques, including AI ones. Techniques utilized in games capable of making them more attractive, immersive, smarter etc. can all be considered to be certain forms of game AI. Considering there are few reviews on the more recent work in the game AI field from the perspective of essential applications, in this paper, we make a systematic review of typical research from 2018 on three application fields of game AI: believable agents in non-player characters research, game level generation in procedural content generation, and player profiling in player modeling. We also provide a timeline of game AI history to give the readers a clearer picture of the game AI field. Moreover, general game AI and hybrid intelligence for games are discussed.
Low-Complexity Chase Decoding of Reed-Solomon Codes Using Module

The interpolation based algebraic soft decoding yields a high decoding performance for Reed-Solomon (RS) codes with a polynomial-time complexity. Its computationally expensive interpolation can be facilitated using the module structure. The desired Gröbner basis can be achieved by reducing the basis of a module. This paper proposes the low-complexity Chase (LCC) decoding algorithm using this module basis reduction (BR) interpolation technique, namely the LCC-BR algorithm. By identifying η unreliable symbols, 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">η</sup> decoding test-vectors will be formulated. The LCC-BR algorithm first constructs a common basis which will be shared by the decoding of all test-vectors. This eliminates the redundant computation in decoding each test-vector, resulting in a lower decoding complexity and latency. This paper further proposes the progressive LCC-BR algorithm that decodes the test-vectors sequentially and terminates once the maximum-likelihood decision decoding outcome is reached. Exploiting the difference between the adjacent test-vectors, this progressive decoding is realized without any additional memory cost. Complexity analysis shows that the LCC-BR algorithm yields a lower complexity and latency, especially for high rate codes, which will be validated by the numerical results.
A consensus-based square-root cubature Kalman filter for manoeuvring target tracking in sensor networks

In this paper, a novel distributed tracking method is proposed for the problem of manoeuvring target tracking in sensor networks. Firstly, an adaptive adjustment tracking model is established by extended state observer (ESO) theory. Then, the consensus-based square-root cubature Kalman filter (SCKF) algorithm is proposed in order to improve the global accuracy and stability. In addition, the integrated model could reduce the influence of measurement noise. Finally, simulation is performed to verify the effectiveness of the scheme, whereby comparison results show that the estimation accuracy of the method proposed is higher than that of the traditional ESO and SCKF.
Communication-aware Distributed Gaussian Process Regression Algorithms for Real-time Machine Learning

We propose a communication-aware Gaussian process regression algorithm that allows a network of robots to collaboratively learn about a common latent function in real time using streaming data. We quantify the improvement that inter-robot communication brings on the transient performance of the learning algorithm. Simulations are performed to validate the proposed algorithm.
A novel word similarity measure method for IoT-enabled Healthcare applications

With the development of the Internet of Things (IoT), Natural Language Processing(NLP) has become a key part of IoT applications in Healthcare. NLP is bringing a revolutionary shift to Healthcare, powered by rapid progress of NLP analytics techniques and increasing availability of Healthcare data. Therefore, using NLP solution for IoT enable Healthcare application is an urgent and valuable task. Word similarity measurement is the basis of semantic analysis, which can be applied to translation and disambiguation of medical terms, prescription analysis, medical question and answer systems, diagnostic assistance, etc. Previous similarity measures have mainly focused on either knowledge-graph-based or word-embedding-based methods, which suffer from two problems: (1) word-embedding-based methods have difficulty discriminating words with approximately the same surrounding context; and (2) knowledge-graph-based methods do not contain multiexpression words or named entities and cannot generally converge for large-scale and updated words. To solve these two problems, this paper proposes a novel method that combines knowledge-graph-based and word-embedding-based similarity measures via word entropy. An experiment is conducted on five public datasets (R&G, M&C, WS353, WS353-Sim and SimLex). The experimental results show that the proposed method achieves significant improvements over other word similarity measures in terms of the correlation coefficient.
Error Bounds and Guidelines for Privacy Calibration in Differentially Private Kalman Filtering

Differential privacy has emerged as a formal framework for protecting sensitive information in control systems. One key feature is that it is immune to post-processing, which means that arbitrary post-hoc computations can be performed on privatized data without weakening differential privacy. It is therefore common to filter private data streams. To characterize this setup, in this paper we present error and entropy bounds for Kalman filtering differentially private state trajectories. We consider systems in which an output trajectory is privatized in order to protect the state trajectory that produced it. We provide bounds on a priori and a posteriori error and differential entropy of a Kalman filter which is processing the privatized output trajectories. Using the error bounds we develop, we then provide guidelines to calibrate privacy levels in order to keep filter error within pre-specified bounds. Simulation results are presented to demonstrate these developments.
Knowledge-Enhanced Relation Extraction for Chinese EMRs

The growing demand for the meaningful use of electronic medical records has led to great interest in medical entities and relation extraction technologies. Most existing methods perform relation extraction based on manually labeled documents and rarely consider incorporating knowledge graphs that include rich, valuable structured knowledge, which will cause semantic ambiguities. To address this problem, we propose a knowledge-enhanced relation extraction (KERE) model. First, we extract knowledge information from the knowledge graph to generate a knowledge-guided word embedding. Then, the lexical features are considered supplementary information for semantic understanding. Experiments on real-world datasets show that the KERE model achieves important improvements in a biomedical relation extraction task.
Navigation Functions with non-Point Destinations and Moving Obstacles

This paper formally expands the application domain of robot motion planning methods that are based on navigation functions to the case of moving obstacles. It generalizes the navigation function methodology from static sphere world environments, to dynamic ones. Specifically, it allows the obstacles' locations to be time-varying, albeit unknown, and accommodates the case where the navigation goal is not a single isolated point, but rather a spherical manifold. For such cases, the paper presents analytical bounds on the tuning parameters that guarantee the navigation function properties of the time-varying potential function, uniformly in time. Thus using the same choice of tuning parameters, the agent is ensured that at every instance in time, the artificial potential field that directs it to its destination is free of local minima. The parameter bounds naturally depend on the geometry of the agent workspace, and include conditions on how close the obstacles can approach each other, the fixed workspace boundary, and the destination sphere. The bounds presented here are conservative; their analytic determination serves mainly the purpose of theoretically guaranteeing completeness properties for the methodology in the time-varying obstacle case.
Learned Conjugate Gradient Descent Network for Massive MIMO Detection

In this work, we consider the use of model-driven deep learning (DL) techniques for signal detection in massive multiple-input multiple-output (MIMO) system. Massive MIMO promises improved spectral efficiency, coverage and reliability, compared to conventional MIMO systems. Unfortunately, these benefits usually come at the cost of significantly increased computational complexity. To address this difficulty, a learned conjugate gradient descent network, referred to as LcgNet, is presented by unfolding the iterative conjugate gradient descent (CG) detector. In the proposed network, instead of calculating the exact values of the scalar step-sizes for every problem instance, we explicitly learn their universal values. We show that the performance of the proposed network can be greatly improved by augmenting the dimensions of these step-sizes. Furthermore, due to the limited learnable parameters to be optimized, the proposed networks are easy and fast to train. Numerical results demonstrate that this approach can achieve superior performance over some state-of-the-art MIMO detectors such as the CG detector, the linear minimum mean squared error (LMMSE) detector etc., with much lower computational complexity.
Energy-Aware Path Planning for Skid-Steer Robots Operating on Hilly Terrain

This paper presents an optimized approach to planning energy-aware paths for skid-steer vehicles during elevation changes. Specifically, this work expands upon a previously presented power model by including the effect of elevation changes on the energy usage of the robot. The total power needed to travel to a goal location is then combined with an instantaneous center of rotation (ICR) kinematic model to plan energy-aware paths using a Sampling Based Model Predictive Optimization (SBMPO) algorithm. This method is demonstrated using a simulated environment with a wide range of varying scenarios representative of real-world usages. The results show that, in some hilly cases, it is more energy efficient to take a longer path when operating skid-steer robots on mixed terrain. These results are intended to improve the accuracy of energy consumption models for robotics.
Robust Text-independent Speaker recognition with Short Utterances using Gaussian Mixture Models

An important amount of speech is typically required for speaker identification system development and evaluation. Nowadays, robust speaker identification systems when short utterances are used remains a key consideration for automatic speaker recognition, since a lot of real world applications are able to deal with only limited duration speech data. This paper presents a new approach based on a low complexity solution based on a new feature vectors to build Gaussian Mixture Models (GMM) for speaker identification systems especially when training and testing utterance lengths are reduced. We compared our proposed system to the state-of-the-art based system in Speaker identification. Experiments on TIMIT database were conducted to demonstrate that this new feature vector can outperform the standard GMM-based system and show that there is no need for extra-data to identify the speakers.
Intelligent Block Assignment for Blockchain Based Wireless IoT Systems

In legacy blockchain based systems, each involved node has to store a complete blockchain to ensure the system security without any central authoritative controller. However, it is usually impossible for a wireless IoT node to store a complete blockchain, especially for those simple sensor nodes without sufficient storage and computing resources. In this paper, we propose a block assignment scheme for blockchain based wireless IoT systems with aim to tackle the blockchain storage problem. Specifically, we propose to maintain a complete blockchain by a set of IoT nodes in a collaborative way on the premise of ensuring that each node can check every transaction. On the other hand, we should save the storage space of IoT nodes to the greatest extent for saving more blocks so as to maximize the lifetime of IoT nodes. We formulate this optimal block assignment problem as a 0-1 mixed integer-programming problem. We propose to incorporate Chaotic optimized algorithm into Genetic algorithm to provide an efficient near-optimal solution. Compared with the brute-force and conventional Genetic algorithms, our proposed algorithm can achieve the minimum storage occupancy to store blocks. Meanwhile, the proposed algorithm has the lowest computational complexity.
Optimized deep belief network and entropy-based hybrid bounding model for incremental text categorization

Purpose This paper aims to model a technique that categorizes the texts from huge documents. The progression in internet technologies has raised the count of document accessibility, and thus the documents available online become countless. The text documents comprise of research article, journal papers, newspaper, technical reports and blogs. These large documents are useful and valuable for processing real-time applications. Also, these massive documents are used in several retrieval methods. Text classification plays a vital role in information retrieval technologies and is considered as an active field for processing massive applications. The aim of text classification is to categorize the large-sized documents into different categories on the basis of its contents. There exist numerous methods for performing text-related tasks such as profiling users, sentiment analysis and identification of spams, which is considered as a supervised learning issue and is addressed with text classifier. Design/methodology/approach At first, the input documents are pre-processed using the stop word removal and stemming technique such that the input is made effective and capable for feature extraction. In the feature extraction process, the features are extracted using the vector space model (VSM) and then, the feature selection is done for selecting the highly relevant features to perform text categorization. Once the features are selected, the text categorization is progressed using the deep belief network (DBN). The training of the DBN is performed using the proposed grasshopper crow optimization algorithm (GCOA) that is the integration of the grasshopper optimization algorithm (GOA) and Crow search algorithm (CSA). Moreover, the hybrid weight bounding model is devised using the proposed GCOA and range degree. Thus, the proposed GCOA + DBN is used for classifying the text documents. Findings The performance of the proposed technique is evaluated using accuracy, precision and recall is compared with existing techniques such as naive bayes, k-nearest neighbors, support vector machine and deep convolutional neural network (DCNN) and Stochastic Gradient-CAViaR + DCNN. Here, the proposed GCOA + DBN has improved performance with the values of 0.959, 0.959 and 0.96 for precision, recall and accuracy, respectively. Originality/value This paper proposes a technique that categorizes the texts from massive sized documents. From the findings, it can be shown that the proposed GCOA-based DBN effectively classifies the text documents.
Measurement-driven Analysis of an Edge-Assisted Object Recognition System

We develop an edge-assisted object recognition system with the aim of studying the system-level trade-offs between end-to-end latency and object recognition accuracy. We focus on developing techniques that optimize the transmission delay of the system and demonstrate the effect of image encoding rate and neural network size on these two performance metrics. We explore optimal trade-offs between these metrics by measuring the performance of our real time object recognition application. Our measurements reveal hitherto unknown parameter effects and sharp trade-offs, hence paving the road for optimizing this key service. Finally, we formulate two optimization problems using our measurement-based models and following a Pareto analysis we find that careful tuning of the system operation yields at least 33% better performance for real time conditions, over the standard transmission method.
A Real-time Interactive Tai Chi Learning System Based on VR and Motion Capture Technology

As a popular traditional Chinese exercise, Tai Chi is of great benefit to physical and mental health. Professional and face-to-face guidance is a traditional and effective way to learn Tai Chi, but it is restricted by factors such as site and time. Although the way to learn Tai Chi by watching videos is not limited by time or place, it is not convenient for learners to observe learning from multiple perspectives since it shows sequences of actions that have been recorded from fixed viewpoints. This paper proposes a real-time interactive Tai Chi learning system based on VR and motion capture technology, in which the learner wears the head-mounted display to follow the virtual coach in the scene, and can watch the demonstration of the virtual coach in all directions; the learner can also drive and watch his own avatar, and can operate the menu by natural interaction in the virtual environment in real time through wearable motion capture device, so that he can know the effects of his training and learning of each action. The actions of the avatar and virtual coach are also compared by using a motion similarity matching algorithm, to evaluate the quality of learning and training and guide the learner to adjust his motion in real time. With the help of our system, learners can practice Tai Chi in the same way as in the real environments and drive their avatars in virtual scenes, so as to achieve effective self-learning and evaluation.
MVCLN: Multi-View Convolutional LSTM Network for Cross-Media 3D Shape Recognition

Cross-media 3D model recognition is an important and challenging task in computer vision, which can be utilized in many applications such as landmark detection, image set classification, etc.In recent years, with the development of deep learning, many approaches have been proposed to handle the 3D model recognition problem.However, all of these methods focus on the structure information representation and the multi-view information fusion, and ignore the spatial and temporal information.So that it is not suitable for the cross-media 3D model recognition.In this paper, we utilize the sequence views to represent each 3D model and propose a novel Multi-view Convolutional LSTM Network (MVCLN), which utilizes the LSTM structure to extract temporal information and applies the convolutional operation to extract spatial information.More especially, the spatial and temporal information both are considered during the training process, which can effectively utilize the differences between the view's spatial information to improve the final performance.Meanwhile, we also introduce the classic attention model to define the weight of each view, which can reduce the redundant information of view's spatial information in the information fusion step.We evaluate the proposed method on the ModelNet40 for 3D model classification and retrieval task.We also construct a dataset utilizing the overlap categories of MV-RED, ShapenetCore and ModelNet to demonstrate the effectiveness of our approach for the cross-media 3D model recognition.Experimental results and comparisons with the state-of-the-art methods demonstrate that our framework can achieve superior performance.
Kernel methods and their derivatives: Concept and perspectives for the earth system sciences

Kernel methods are powerful machine learning techniques which use generic non-linear functions to solve complex tasks. They have a solid mathematical foundation and exhibit excellent performance in practice. However, kernel machines are still considered black-box models as the kernel feature mapping cannot be accessed directly thus making the kernels difficult to interpret. The aim of this work is to show that it is indeed possible to interpret the functions learned by various kernel methods as they can be intuitive despite their complexity. Specifically, we show that derivatives of these functions have a simple mathematical formulation, are easy to compute, and can be applied to various problems. The model function derivatives in kernel machines is proportional to the kernel function derivative and we provide the explicit analytic form of the first and second derivatives of the most common kernel functions with regard to the inputs as well as generic formulas to compute higher order derivatives. We use them to analyze the most used supervised and unsupervised kernel learning methods: Gaussian Processes for regression, Support Vector Machines for classification, Kernel Entropy Component Analysis for density estimation, and the Hilbert-Schmidt Independence Criterion for estimating the dependency between random variables. For all cases we expressed the derivative of the learned function as a linear combination of the kernel function derivative. Moreover we provide intuitive explanations through illustrative toy examples and show how these same kernel methods can be applied to applications in the context of spatio-temporal Earth system data cubes. This work reflects on the observation that function derivatives may play a crucial role in kernel methods analysis and understanding.
A New Code-Based Cryptosystem

Unlike most papers devoted to improvements of code-based cryptosystem, where original Goppa codes are substituted by some other codes, we suggest a new method of strengthening which is code-independent. We show (up to some limit) that the security of the new code-based cryptosystem is much closer to the hardness of maximum likelihood decoding than in the original McEliece cryptosystem.
Bayesian quantile nonhomogeneous hidden Markov models

Hidden Markov models are useful in simultaneously analyzing a longitudinal observation process and its dynamic transition. Existing hidden Markov models focus on mean regression for the longitudinal response. However, the tails of the response distribution are as important as the center in many substantive studies. We propose a quantile hidden Markov model to provide a systematic method to examine the entire conditional distribution of the response given the hidden state and potential covariates. Instead of considering homogeneous hidden Markov models, which assume that the probabilities of between-state transitions are independent of subject- and time-specific characteristics, we allow the transition probabilities to depend on exogenous covariates, thereby yielding nonhomogeneous Markov chains and making the proposed model more flexible than its homogeneous counterpart. We develop a Bayesian approach coupled with efficient Markov chain Monte Carlo methods for statistical inference. Simulations are conducted to assess the empirical performance of the proposed method. The proposed methodology is applied to a cocaine use study to provide new insights into the prevention of cocaine use.