Reviewerly/exHarmony huggingface dataset:
An Improved Minimax-Q Algorithm Based on Generalized Policy Iteration to Solve a Chaser-Invader Game

In this paper, we use reinforcement learning and zero-sum games to solve a Chaser-Invader game, which is actually a Markov game (MG). Different from the single agent Markov Decision Process (MDP), MG can realize the interaction of multiple agents, which is an extension of game theory to a MDP environment. This paper proposes an improved algorithm based on the classical Minimax-Q algorithm. First, in order to solve the problem where Minimax-Q algorithm can only be applied for discrete and simple environment, we use Deep Q-network instead of traditional Q-learning. Second, we propose a generalized policy iteration to solve the zero-sum game. This method makes the agent use linear programming method to solve the Nash equilibrium action at each moment. Finally, through comparative experiments, we prove that the improved algorithm can perform as well as Monte Carlo Tree Search in simple environments and better than Monte Carlo Tree Search in complex environments.
Online Learning for Beta-Liouville Hidden Markov Models: Incremental Variational Learning for Video Surveillance and Action Recognition

Challenges in realtime installation of surveillance systems is an active area of research, especially with the use of adaptable machine learning techniques. In this paper, we propose the use of variational learning of Beta-Liouville (BL) hidden Markov models (HMM) for AR in an online setup. This proposed incremental framework enables continuous adjustment of the system for better modelling. We evaluate the proposed model on the visible IOSB dataset to validate the framework.
Predicting a Cold from Speech Using Fisher Vectors; SVM and XGBoost as Classifiers

Screening a cold may be beneficial in the sense of avoiding the propagation of it. In this study, we present a technique for classifying subjects having a cold by using their speech. In order to achieve this goal, we make use of frame-level representations of the recordings of the subjects. Such representations are exploited by a generative Gaussian Mixture Model (GMM) which consequently produces a fixed-length encoding, i.e. Fisher vectors, based on the Fisher Vector (FV) approach. Afterward, we compare the classification performance of the two algorithms: a linear kernel SVM and a XGBoost Classifier. Due to the data sets having a high class imbalance, we undersample the majority class. Applying Power Normalization (PN) and Principal Component Analysis (PCA) on the FV features proved effective at improving the classification score: SVM achieved a final score of 67.81% of Unweighted Average Recall (UAR) on the test set. However, XGBoost gave better results on the test set by just using raw Fisher vectors; and with this combination we achieved a UAR score of 70.43%. The latter classification approach outperformed the original (non-fused) baseline score given in 'The INTERSPEECH 2017 Computational Paralinguistics Challenge'.
Computer Vision Techniques for Improving Structured Light Vision Systems

In this paper, we propose computer vision techniques for 3D reconstruction and object height measurement using a single camera and multi-laser emitters which have an intersection on the projected image plane. Time-division and color division methods are first investigated. Although the color division method offers better accuracy for height measurement, it requires the laser emitters equipped with different color light, and the color division method is also sensitive to light exposure in the measurement environment. Next, a new multi-level random sample consensus (MLRANSAC) algorithm has been developed. The proposed MLRANSAC method not only offers high accuracy for height measurement but also eliminates the requirement for the laser emitters with different colors. Our experiment results have validated the effectiveness of the MLRANSAC algorithm.
Generalized Local Aggregation for Large Scale Gaussian Process Regression

Despite being one of the most popular nonparametric approaches, Gaussian process regression (GPR) suffers from O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) computational burden and the computation is infeasible for large-scale scenarios. To reduce the computational complexity, many Shannon-mutual-information-based aggregation methods were proposed, whereas these methods can not effectively identify the importance of experts in some cases. To address this problem, we generalize the traditional mutual information-based methods (GPoE, RBCM, GRBCM) based on Tsallis mutual information. Accordingly, the generated weight distribution is more sparse tending to focus on those experts with good performance. To obtain adaptive and data-dependent entropic-index in Tsallis entropy, we propose three heuristic algorithms to solve our model. Extensive experiments show that, the proposed method can improve the prediction of both the mean and variance, and the improvement of variance prediction is significant in many cases.
Revisiting Object-Rule Fusion in Query Languages

Object-oriented (O2) and declarative rule-based (RB) paradigms are known and intensively used for decades. There have been many approaches to combine O2 and RB concepts in a uniform framework. However, they either focus on embedding rules into O2 languages, or provide a declarative semantics and proof theory for the O2 machinery. In the current paper we focus on understanding objects as basic building blocks for deductive queries. To achieve the goal of object-rule fusion we use carefully chosen elementary constructs, encompassed in a simple and uniform framework easily extendable and adaptable to many existing RB languages.
Contact-based Bounding Volume Hierarchy for Assembly Tasks

Path planning of an object which is allowed to be in contact with other objects during assembly process is a significant challenge due to the variety of permitted or forbidden collisions between the distinct parts of the objects to be assembled. In order to put objects together in real-life scenarios, parts of assembled objects may be required to flex, whereas other parts may have to fit exactly. Consequently, existing collision checking and distance computation algorithms have to be modified to enable path planning of objects that can be in contact during the assembly process. In this paper, we analyze an improved broad phase proximity query algorithm to enable such contact-based assembly tasks we call CHAT (Contact-based Hierarchy for Assembly Tasks). We demonstrate that, compared to existing approaches, our proposed method is more than an order of magnitude faster for collision queries and up to three times faster for distance queries when the two objects contain a large number of parts (with some parts containing thousands or tens of thousands of triangles). Due to the nature of the algorithm, we expect the performance improvements to increase as the number of parts in an object becomes larger.
Quantum gates on asynchronous atomic excitations

Abstract A method for realising a universal system of quantum gates based on asynchronous excitations of two-level atoms in optical cavities is proposed. The entangling operator of the CSign type is implemented without beam splitters, approximately, using the incommensurability of the Rabi oscillation periods in a cavity with single and double excitations.
Preserving Contextual Awareness during Selection of Moving Targets in Animated Stream Visualizations

In many types of dynamic interactive visualizations, it is often desired to interact with moving objects. Stopping moving objects can make selection easier, but pausing animated content can disrupt perception and understanding of the visualization. To address such problems, we explore selection techniques that only pause a subset of all moving targets in the visualization. We present various designs for controlling pause regions based on cursor trajectory or cursor position. We then report a dual-task experiment that evaluates how different techniques affect both target selection performance and contextual awareness of the visualization. Our findings indicate that all pause techniques significantly improved selection performance as compared to the baseline method without pause, but the results also show that pausing the entire visualization can interfere with contextual awareness. However, the problem with reduced contextual awareness was not observed with our new techniques that only pause a limited region of the visualization. Thus, our research provides evidence that region-limited pause techniques can retain the advantages of selection in dynamic visualizations without imposing a negative effect on contextual awareness.
Improved Hierarchical Clustering with Non-locally Enhanced Features for Unsupervised Person Re-identification

Due to the high cost of data annotation in supervised person re-identification (re-ID) methods, unsupervised person re-ID methods have attracted more and more attention. The unsupervised person re-ID methods based on deep clustering have achieved good performance. However, the distance metrics used in existing unsupervised clustering methods ignore intra-cluster distance and are likely to cause some wrong merging situations and uneven distribution within clusters. Besides, these models based on deep clustering usually ignore the importance of global features for person re-ID. In this paper, we address the above problems by proposing an improved hierarchical clustering approach with non-locally enhanced features. To improve the clustering performance, we design a new metric which consists of intermediate distance as inter-cluster distance and compactness degree as intra-cluster distance. The former one can prevent some wrong merging situations and the latter one can promote the uniform distribution within clusters. In addition, we develop a non-locally enhanced feature network to take advantage of global features of images. Extensive experiments on Market-1501, DukeMTMC-reID, MARS and DukeMTMC-VideoReID demonstrate that our method obtains significant improvement over the state-of-the-art unsupervised methods.
Echo State Network Performance in Electrical and Industrial Applications

Echo State Network (ESN) attracted significant interest in the research activities in last years. In this paper some application to industrial cases are presented, considering in particular the energy and manufacturing sectors. In particular, load forecasting is crucial for penetrations of renewable energy sources and extension of programs in the paradigm of smart grids. Feed-Forward Neural Network (FFNN) based techniques have been widely used in recent years and applied to predict the electric load with high accuracy. This research work is focused on the use and comparison of neural network approaches, i.e. FFNN and ESN, on a dataset related to industrial application. The results of both models are compared based on their accuracy through experimental measurements and suitably defined metrics.
Trajectory Planning of UAV Group: Pythagorean Hodograph and Bernstein-Bezier Composite Curves in the Plane

In this article the method of the trajectory building of the UAV group motion by approximation of Pythagorean Hodograph and Bernstein-Bezier polynomial has been presented. The group consisted of three UAVs moves in the safety circles along the trajectories, which provide a minimum of the energy functionality. The motion of three UAVs from the starting point to the goal has been presented. The flight was carried out under ideal condition without any obstacles on the way. Focusing on the points of the beginning of the target motion, every UAV built its trajectory on five supportive points of the leading UAV. The presented algorithm illustrates the satisfying level of performance. It deals successfully with the trajectory building of the UAV group motion.
A Lightweight Approach to the Multi-perspective Modeling of Processes and Objects

Process modeling has a broad range of applications, varying from business and system engineering, via artifact design, up to natural process modeling utilized in natural sciences. Over the last decades, various sophisticated languages and frameworks have been developed to support process modeling. The current paper discusses an approach to process modeling, which is, in contrast to many existing solutions, intended for the integrated process and object modeling. Furthermore, it is designed to be a lightweight approach with only a few constructs, which, however, permit the representation of processes from various perspectives. The developed solution provides an abstract language-independent model (ontology), partial formalization in first-order logic as well as a Web Ontology Language (OWL) implementation.
Research on network security protection technology of energy industry based on blockchain

The upsurge of comprehensive automation and digitization in the energy industry is accelerating, while making the energy business management more efficient, it also brings new hidden dangers to network security. The blockchain-based network security mechanism has the characteristics of decentralization, non-tampering, traceability, high credibility and multi-party consensus, and will become an important technical means to improve network security. Aiming at the main network security problems faced by the energy industry, this paper proposes a security protection solution based on blockchain technology from the aspects of identity authentication, data protection and security operation and maintenance, which provides a new technical idea for improving the network security protection ability of the energy industry.
Variable Neighbourhood Search for Bi-Objective Travelling Thief Problem

In this paper, Travelling Thief Problem (TTP) which is a hybridization of two well-known hard optimization problems-namely Travelling salesman problem and Knapsack Problem, is dealt using metaheuristic approach. We have designed and implemented Variable Neighbourhood Search, a widely used metaheuristic, to tackle TTP. The results indicate comparable performance with the available best-known work for TTP.
Experimental Evaluation of Test-Driven Development With Interns Working on a Real Industrial Project

Context: There is still little evidence on differences between Test-Driven Development and Test-Last Development, especially for real-world projects, so their impact on code/test quality is an ongoing research trend. An empirical comparison is presented, with 19 participants working on an industrial project developed for an energy market software company, implementing real-world requirements for one of the company's customers. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> Examine the impact of TDD and TLD on quality of the code and the tests. The aim is to evaluate if there is a significant difference in external code quality and test quality between these techniques. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Method:</i> The experiment is based on a randomized within-subjects block design, with participants working for three months on the same requirements using different techniques, changed from week to week, within three different competence blocks: Intermediate, Novice and Mixed. The resulting code was verified for process conformance. The participants developed only business logic and were separated from infrastructural concerns. A separate group of code repositories was used to work without unit tests, to verify that the requirements were not too easy for the participants. Also, it was analysed if there is any difference between the code created by shared efforts of developers with different competences and the code created by participants isolated in the competence blocks. The resulting implementations had LOC order of magnitude of 10k. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> Statistically significant advantage of TDD in terms of external code quality (1.8 fewer bugs) and test quality (5 percentage points higher) than TLD. Additionally, TDD narrows the gap in code coverage between developers from different competence blocks. At the same time, TDD proved to have a considerable entry barrier and was hard to follow strictly, especially by Novices. Still, no significant difference w.r.t. code coverage has been observed between the Intermediate and the Novice developers - as opposed to TLD, which was easier to follow. Lastly, isolating the Intermediate developers from the Novices had significant impact on the code quality. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</i> TDD is a recommended technique for software projects with a long horizon or when it is critical to minimize the number of bugs and achieve high code coverage.
Document classification using convolutional neural networks with small window sizes and latent semantic analysis

A parsimonious convolutional neural network (CNN) for text document classification that replicates the ease of use and high classification performance of linear methods is presented. This new CNN architecture can leverage locally trained latent semantic analysis (LSA) word vectors. The architecture is based on parallel 1D convolutional layers with small window sizes, ranging from 1 to 5 words. To test the efficacy of the new CNN architecture, three balanced text datasets that are known to perform exceedingly well with linear classifiers were evaluated. Also, three additional imbalanced datasets were evaluated to gauge the robustness of the LSA vectors and small window sizes. The new CNN architecture consisting of 1 to 4-grams, coupled with LSA word vectors, exceeded the accuracy of all linear classifiers on balanced datasets with an average improvement of 0.73%. In four out of the total six datasets, the LSA word vectors provided a maximum classification performance on par with or better than word2vec vectors in CNNs. Furthermore, in four out of the six datasets, the new CNN architecture provided the highest classification performance. Thus, the new CNN architecture and LSA word vectors could be used as a baseline method for text classification tasks.
Generating nonlinear codes for multi-bit symbol error correction using cellular automata

Error Correcting Codes can be used in cryptography since the security of cryptosystems depends heavily on their resilience against attacks such as fault attacks. In the last three decades, Cellular Automata (CA) have been explored as efficient crypto-primitives as well as to generate error correcting codes. In earlier works, CA are mainly used in bit error correction for Single Error Correcting (SEC) and Double Error Detecting (DED) codes. However, in the practical scenario, when fault is injected by a laser beam or using a clock glitch, multiple bits get infected. The situation thus demands detection/correction of multi-bit errors instead of SEC-DED. In literature, linear CA are used for bit as well as byte error correction. But the CA generating linear codes are of very little use in cryptography as they provide little or no security when used as a component in a cryptosystem. In this paper, linear cellular automata (LCA) with nonlinearity injection at some specific cell positions are used for generating error correcting codes (ECC) which can detect and correct errors in m-bit symbols (m = 8 for a byte). In this paper, we propose coding schemes for single bit as well as multi-bit error correcting codes. For multi-bit error correcting codes, two schemes are proposed. The schemes can easily be extended for generating ECC for different m-bit symbols by simply exploiting m-bit LCA with nonlinearity injection. Moreover, the simple and regular structure of the CA makes the ECC scheme very well suited for VLSI implementation.
Multivariate Predictive Clustering Trees for Classification

Decision trees are well established machine learning models that combined in ensembles produce state-of-the-art predictive performance. Predictive clustering trees are a generalization of standard classification and regression trees towards structured output prediction and semi-supervised learning. Most of the research attention is on univariate decision trees, whereas multivariate decision trees, in which multiple attributes can appear in a test, are less widely used. In this paper, we present a multivariate variant of predictive clustering trees, and experimentally evaluate it on 12 classification tasks. Our method shows good predictive performance and computational efficiency, and we illustrate its potential for performing feature ranking.
On the Performance Analysis for CSIDH-Based Cryptosystems

In this paper, we present the performance and security analysis for various commutative SIDH (CSIDH)-based algorithms. As CSIDH offers a smaller key size than SIDH and provides a relatively efficient signature scheme, numerous CSIDH-based key exchange algorithms have been proposed to optimize the CSIDH. In CSIDH, the private key is an ideal class in a class group, which can be represented by an integer vector. As the number of ideal classes represented by these vectors determines the security level of CSIDH, it is important to analyze whether the different vectors induce the same public key. In this regard, we generalize the existence of a collision for a base prime p≡7mod8. Based on our result, we present a new interval for the private key to have a similar security level for the various CSIDH-based algorithms for a fair comparison of the performance. Deduced from the implementation result, we conclude that for a prime p≡7mod8, CSIDH on the surface using the Montgomery curves is the most likely to be efficient. For a prime p≡3mod8, CSIDH on the floor using the hybrid method with Onuki’s collision-free method is the most likely to be efficient and secure.
Hybridization of Data and Model based Object Detection for Tracking in Flash Lidars

In recent times deep neural networks have become very successful in solving traditionally hard problems in Computer Vision such as Object Detection. This is due to their ability to find hidden patterns in high dimensional data such as images. But if there is a known structure within data that can be accurately represented by a pre-defined model, then by merging this model based algorithm and deep neural network, overall system accuracy can be increased. We apply this idea for solving the task of flash lidar object detection and tracking. Flash lidar is an emerging lidar sensing technology which is getting a lot of attention lately due to their lack of moving parts compared to prevalent scanning lidars. Samples from flash lidar suffer from both spatial and temporal noise which coupled with low angular resolution and FoV lead to low accuracy in object detection. In this paper we present a data driven deep learning based flash lidar object detector and tracker. To our knowledge, this is the first work to use deep learning for flash lidar object detection/tracking. Our tracker has two detectors- 1. supervised object detector and 2. unsupervised class agnostic foreground/moving object detector which are merged to achieve multi-object tracking accuracy of 47.9% on CAMEL dataset.