Reviewerly/exHarmony huggingface dataset:
Using Multivariate Time Series Data via Long-Short Term Memory Network for Temperature Forecasting

This paper presents a Long-Short Term Memory, as a special kind of Recurrent Neural Network, capable of learning long-term dependencies to estimate the temperature. In order to improve the performance of the proposed model, a multivariate time series is considered. A Multivariate time series has more than one time-dependent variable. Each variable depends not only on its past values but also has some dependency on other variables (i.e., temperature, humidity, atmosphere pressure, wind speed, cloud cover and among others). A pre-processing is applied to dataset provided by the National Institute of Meteorology and Hydrology to obtain daily weather variables. The proposed model is compared with a previous approach using standard metrics, obtaining better results on the average temperature in Guayaquil city.
Multi-sensor fusion approach based on nonlinear H∞ filter with interval type 2 fuzzy adaptive parameters tuning for unmanned vehicle localization

In most applications of autonomous navigation, the state of a system must be estimated from noisy sensors. Accurate estimation of the true system state can be achieved using data fusion algorithms. Furthermore, the fusion scheme can be affected by many factors such as modeling errors and parameters uncertainties. The gaps and inconsistencies due to the sensors noise and modeling errors can be reached with robust nonlinear filtering. In this article, a new framework has been developed for data fusion algorithms based on nonlinear NH∞ filter with fuzzy adaptive bound and adaptive disturbances attenuations. Type-1 Fuzzy Adaptive NH ∞ algorithm has been proposed and compared with the Interval Type 2 Fuzzy Adaptive NH ∞ , for unmanned vehicle localization. The proposed algorithms fuse data from low-cost sensors using inertial navigation system, Global Positioning System and monocular vision. Type-1 Fuzzy Adaptive NH ∞ and Interval Type 2 Fuzzy Adaptive NH ∞ algorithms, adaptively, handle the effects of noisy sensors, parameters uncertainties and modeling errors. Both algorithms use adaptive bounds [Formula: see text] and adaptive disturbance attenuation [Formula: see text] for higher-order terms of the Taylor development. The adaptive bounds consider issues associated to Gyro drift, image distortion and Global Positioning System dilution of precision which make the proposed algorithms more accurate than the classical NH∞. The validating experiment and the efficiency of Type-1 Fuzzy Adaptive NH ∞ and Interval Type 2 Fuzzy Adaptive NH ∞ have been conducted in real scenario and in unstructured outdoor environment without any a priori knowledge of the evolution of the vehicle. The experimental results have demonstrated the robustness and efficiency of these two filtering strategies against uncertainties and their online sensor switching capability. The Interval Type 2 Fuzzy Adaptive NH ∞ algorithm provides more accuracy and robustness compared to the Type-1 Fuzzy Adaptive NH ∞ .
Combining Machine Learning and Formal Techniques for Small Data Applications - A Framework to Explore New Structural Materials

The massive increase in computation power leads to a renaissance of supervised learning techniques, which were published decades ago but have so far been confined to theory. These techniques form the increasingly important field of Machine Learning (ML), which contributes to a large variety of research concerning industrial, automotive but also consumer applications strongly influencing our daily life. Commonly, the learning techniques require a set of labeled data, which involves a resource-intensive generation, to conduct the training. Depending on the dimensionality of the data and the required precision as needed by the application, the amount of training data varies. In case of insufficient training data, the prediction is of low-quality or not even possible at all, restricting the applicability of ML. This work proposes a combination of formal techniques and ML to implement a framework that allows coping with high-dimensional, training data while retaining a high prediction quality. The efficacy of this method is exemplarily demonstrated on the basis of an interdisciplinary material science research problem concerning the development of new structural materials, though it can be adapted to further applications.
2D and 3D A* Algorithm Comparison for UAS Traffic Management Systems

In this work, we propose to implement a path planning algorithm for simulating an UAS Traffic Management system using ROS and Gazebo environment. From the 3D model generated for the simulation, both two-dimensional and threedimensional node grids were extracted in order to allow the application of the search algorithm in both scenarios. After selecting the A* algorithm for this application, a preliminary efficiency analysis was carried out, considering the distance to be covered by the drone and the execution time of the route planning method, aiming to support the choice of the algorithm based on the environment in which it will be applied. As a result of the tradeoff analysis between distance and execution time for small maps (200m x 200m x 120m), the routes generated by the 2D algorithm are about 6% longer compared to the 3D approach, but with the execution time nine times lower, on average.
Sequential changepoint detection in neural networks with checkpoints

We introduce a framework for online changepoint detection and simultaneous model learning which is applicable to highly parametrized models, such as deep neural networks. It is based on detecting changepoints across time by sequentially performing generalized likelihood ratio tests that require only evaluations of simple prediction score functions. This procedure makes use of checkpoints, consisting of early versions of the actual model parameters, that allow to detect distributional changes by performing predictions on future data. We define an algorithm that bounds the Type I error in the sequential testing procedure. We demonstrate the efficiency of our method in challenging continual learning applications with unknown task changepoints, and show improved performance compared to online Bayesian changepoint detection.
Corpus Development for Indonesian Product Named Entity Recognition Using Semi-supervised Approach

PRONER is a NER system that is aimed to recognize product entities in a text. To develop a PRONER system using supervised approach, we need labeled dataset. However, building a labeled dataset needs a lot of effort and expensive. In this paper, we propose an approach to build PRONER dataset on beauty product domain using semi-supervised method. The dataset was built based on posts on Indonesian beauty online forum. We implemented a semi-supervised learning approach using Conditional Random Field (CRF) classifier to expand small labeled dataset. We used list lookup and handcrafted features for our CRF classifier. The overall process consists of two steps: initial manual labeling and automatic labeling. We started with 2,2759 manually labeled sentences and expanded it to 9,165 sentences using automatic labeling process. The experimental results show that the automatically labeled dataset has 73.01% accuracy. Based on our analysis of miss-labeled tokens, other features from the text should be explored to improve the accuracy.
An Attention Enhanced Graph Convolutional Network for Semantic Segmentation

Global modeling and integrating over feature relations are beneficial for semantic segmentation. Previous researches excel at modeling features through convolution operations but are inefficient at associating features between distant regions. Consequently, key information within one semantic category can be neglected if the features between distant regions are not bridged. To solve this issue, we propose a novel Attention Enhanced Graph Convolutional Network to explore relational information and preserve details in semantic segmentation tasks. We first build a graph over feature maps, where each node is represented by features in spatial and channel dimensions. Then the interdependent semantic information is obtained through applying the GCN to globally bridge co-occurrence features regardless of their distance. Finally, we introduce the self-attention mechanism in spatial and channel dimensions respectively to project the semantic-aware information into feature maps so as to capture the discriminative information. We examine the merit of our model on three challenging scene segmentation datasets: Cityscapes, PASCAL VOC2012 and COCO Stuff. Experimental results show that our model boosts the performance over other state-of-the-arts. Code will be available.
Particle Gibbs sampling for Bayesian phylogenetic inference

Abstract Motivation The combinatorial sequential Monte Carlo (CSMC) has been demonstrated to be an efficient complementary method to the standard Markov chain Monte Carlo (MCMC) for Bayesian phylogenetic tree inference using biological sequences. It is appealing to combine the CSMC and MCMC in the framework of the particle Gibbs (PG) sampler to jointly estimate the phylogenetic trees and evolutionary parameters. However, the Markov chain of the PG may mix poorly for high dimensional problems (e.g. phylogenetic trees). Some remedies, including the PG with ancestor sampling and the interacting particle MCMC, have been proposed to improve the PG. But they either cannot be applied to or remain inefficient for the combinatorial tree space. Results We introduce a novel CSMC method by proposing a more efficient proposal distribution. It also can be combined into the PG sampler framework to infer parameters in the evolutionary model. The new algorithm can be easily parallelized by allocating samples over different computing cores. We validate that the developed CSMC can sample trees more efficiently in various PG samplers via numerical experiments. Availability and implementation The implementation of our method and the data underlying this article are available at https://github.com/liangliangwangsfu/phyloPMCMC. Supplementary information Supplementary data are available at Bioinformatics online.
On Deep Learning Solutions for Joint Transmitter and Noncoherent Receiver Design in MU-MIMO Systems

This paper aims to handle the joint transmitter and noncoherent receiver design for multiuser multiple-input multiple-output (MU-MIMO) systems through deep learning. Given the deep neural network (DNN) based noncoherent receiver, the novelty of this work mainly lies in the multiuser waveform design at the transmitter side. According to the signal format, the proposed deep learning solutions can be divided into two groups. One group is called pilot-aided waveform, where the information-bearing symbols are time-multiplexed with the pilot symbols. The other is called learning-based waveform, where the multiuser waveform is partially or even completely designed by deep learning algorithms. Specifically, if the information-bearing symbols are directly embedded in the waveform, it is called systematic waveform. Otherwise, it is called non-systematic waveform, where no artificial design is involved. Simulation results show that the pilot-aided waveform design outperforms the conventional zero forcing receiver with least squares (LS) channel estimation on small-size MU-MIMO systems. By exploiting the time-domain degrees of freedom (DoF), the learning-based waveform design further improves the detection performance by at least 5 dB at high signal-to-noise ratio (SNR) range. Moreover, it is found that the traditional weight initialization method might cause a training imbalance among different users in the learning-based waveform design. To tackle this issue, a novel weight initialization method is proposed which provides a balanced convergence performance with no complexity penalty.
Reflectance spectroscopy-guided broadband spectral derivative approach to detect glauconite-rich zones in fossiliferous limestone, Kachchh region, Gujarat, India

In this study, we processed visible near-infrared (VNIR) and shortwave infrared (SWIR) bands of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data to delineate glauconite-bearing zones in fossiliferous limestones, Quaternarysediments and Paleogene shales in the Lakhpat. Region of the Kachchh Province in India. Glauconitic limestone has subtle absorption features at 2.3 µm when compared to the absorption features of pure limestones that are dominated by the strong absorption feature of calcite at 2.33 µm. Additionally, the spectrum of glauconite-rich limestone also has an absorption feature at about 0.9 µm due to the crystal field effect of Fe2+ present in the glauconite structure and a feeble spectral feature due to the presence of Al-OH bonds at about 2.2 µm. ASTER resampled reflectance spectra of these rocks have been used as the reference to construct different image-enhanced products. We derived index images and Eigen matrix analysis-based principal components (PCs), using ASTER bands, to delineate different rocks in the study area. These index images are derived to enhance broadband absorption features observed in shales, glauconite-bearing and pure limestones, respectively. Principal component images are prepared using the spectral bands defining the broadband absorption features of these rocks. We found that the PC image composite, derived using PC 8, PC 2 and PC 7, is suitable to discriminate basalts, limestones and shales from each other, while an index image colour composite, prepared using three independent index images, is suitable for the identification of lithological contacts between Quaternary sediments, shales and fossiliferous limestones, respectively. In addition, the ASTER resampled rock spectrum of glauconite has a relatively lower spectral gradient in comparison to the gradient observed in the spectrum of fossiliferous limestone within the spectral range of bands 6 and 8. The spectral derivative images are derived for discriminating glauconite-rich zones within the limestone using the contrast in the spectral gradient of limestone and glauconite-rich limestone. The image composite of three spectral derivatives was compiled from the derivative image of bands 4 to 3, bands 5 to 7, and bands 6 to 8 to discriminate glauconite-rich zones from pure fossiliferous limestone. Our study proposes the potential use of broadband reflectance spectroscopy in the mineral mapping of sedimentary provinces.
Combining Hand Detection and Gesture Recognition Algorithms for Minimizing Computational Cost

Hand gesture recognition is very important in human-computer interactions (HCI). The most common way to build a recognition system is to use a pre-trained convolution neural network. Relatively new architectures called convolution pose machine can represent a skeleton model of a hand or body from an image with sufficiently high accuracy. However, systems based on these architectures require valuable computational resources which might be inaccessible in practice. Convolutional layers of neural networks take a significant part of computer resources even if the target object (hand) is absent in the frame. This paper proposes a possible solution to this problem. It presents a combined hand gesture recognition system that uses a hand detector to detect hand in the frame and then switches to gesture classifier if a hand was detected. The paper illustrates the proposed combined algorithm. Descriptions of used hand detector and gesture recognition algorithms also are given. Equations for the evaluation of potential performance increase and experimental results are presented. The proposed system is tested on publicly accessible gesture bases and on video sequences prepared by the authors. The experimental results are consistent with theoretical estimates and demonstrate the benefits of the proposed gesture recognition system design.
Enabling Image Recognition on Constrained Devices Using Neural Network Pruning and a CycleGAN

Smart cameras are increasingly used in surveillance solutions in public spaces. Contemporary computer vision applications can be used to recognize events that require intervention by emergency services. Smart cameras can be mounted in locations where citizens feel particularly unsafe, e.g., pathways and underpasses with a history of incidents. One promising approach for smart cameras is edge AI, i.e., deploying AI technology on IoT devices. However, implementing resource-demanding technology such as image recognition using deep neural networks (DNN) on constrained devices is a substantial challenge. In this paper, we explore two approaches to reduce the need for compute in contemporary image recognition in an underpass. First, we showcase successful neural network pruning, i.e., we retain comparable classification accuracy with only 1.1% of the neurons remaining from the state-of-the-art DNN architecture. Second, we demonstrate how a CycleGAN can be used to transform out-of-distribution images to the operational design domain. We posit that both pruning and CycleGANs are promising enablers for efficient edge AI in smart cameras.
Approach of a Word2Vec Based Tourist Spot Collection Method Considering COVID-19

The Novel Coronavirus disease 2019 (COVID-19) is raging around the world and is seriously affecting daily lives and economic activities for people. For example, the work and class are now taking place online, and major lifestyle changes are also taking place. It is possible that COVID-19 will continue to change lifestyle habits in the future. To cope with this change, COVID-19 needs to be taken into account in daily life and in economic activities, and measures need to be taken based on a variety of information. A variety of approaches are currently being tried and tested around the world to assist in combating COVID-19. This paper focuses on measures taken in the tourism industry and aims to propose a tourist spot collection method that takes into account COVID-19.
An Interdisciplinary Approach for Teaching Artificial Intelligence to Computer Science Students

Artificial intelligence (AI) is a demanding and important course for computer science education in the universities and open online courses (MOOCs). It includes various introductory and specialized courses for artificial intelligence like knowledge representation, machine learning, reasoning under uncertainty, natural language processing, robotics, and perception of computer vision, etc. We observed that mostly AI courses focus on the Computer Science (CS)-centric approach and lacks core explanation from their roots including philosophy, neuroscience, psychology, cognitive science, linguistics, economics, social science, etc. In this paper, we propose to engage the interdisciplinary approach along with CS-centric approach for teaching AI that includes the disciplines that have been established to tackle the age-old problem of understanding the science of thinking.
Facial reshaping operator for controllable face beautification

Posting attractive facial photos is part of everyday life in the social media era. Motivated by the demand, we propose a lightweight method to automatically and efficiently beautify the shapes of both portrait and non-portrait faces in photos, while allowing users to customize the beautification of individual facial features. Previous methods focus on the beautification of mostly frontal and neutral faces, without incorporating user controllability in the beautification process. To address these restrictions, we propose the Facial Reshaping Operator representation, which is affine-invariant, captures the pairwise geometric configuration of facial landmarks, and allows for efficient face beautification with the user-specified weights of individual facial parts. We also propose an unsupervised beautification method in the operator space of faces, where an input face is iteratively pulled towards a local nearby density mode with improved attractiveness. Our method distinguishes itself from the commercial beautification tools in that it mildly enhances facial shapes without altering makeups or complexions, which complements these tools that lack fine-grained control on the attractiveness of facial shapes for users. The experimental results show that our method improves facial shape attractiveness for a large range of poses and expressions, demonstrating the potential of applicability to photos seen on the social media such as Facebook and Instagram everyday.
Integrated Perception and Tactical Behaviours in an Auto-Organizing Aerial Sensor Network

We conducted real proof-of-concept demonstrations of an auto-organizing sensor network composed of UAVs and ground cameras, for urban surveillance. We adopted a decentralised paradigm with tightly coupled perception and tactical behaviour algorithms. The network would reconfigure when cameras are added or removed so that high priority tasks are always served. Tracked targets could be handed over from one camera to another, to ensure continuous tracking.
Integrated Double Estimator Architecture for Reinforcement Learning

Estimation bias is an important index for evaluating the performance of reinforcement learning (RL) algorithms. The popular RL algorithms, such as Q -learning and deep Q -network (DQN), often suffer overestimation due to the maximum operation in estimating the maximum expected action values of the next states, while double Q -learning (DQ) and double DQN may fall into underestimation by using a double estimator (DE) to avoid overestimation. To keep the balance between overestimation and underestimation, we propose a novel integrated DE (IDE) architecture by combining the maximum operation and DE operation to estimate the maximum expected action value. Based on IDE, two RL algorithms: 1) integrated DQ (IDQ) and 2) its deep network version, that is, integrated double DQN (IDDQN), are proposed. The main idea of the proposed RL algorithms is that the maximum and DE operations are integrated to eliminate the estimation bias, where one estimator is stochastically used to perform action selection based on the maximum operation, and the convex combination of two estimators is used to carry out action evaluation. We theoretically analyze the reason of estimation bias caused by using nonmaximum operation to estimate the maximum expected value and investigate the possible reasons of underestimation existence in DQ. We also prove the unbiasedness of IDE and convergence of IDQ. Experiments on the grid world and Atari 2600 games indicate that IDQ and IDDQN can reduce or even eliminate estimation bias effectively, enable the learning to be more stable and balanced, and improve the performance effectively.