Reviewerly/exHarmony huggingface dataset:
Human Position Estimation Based on Filtered Sonar Scan Matching: A Novel Localization Approach Using DENCLUE

In this paper, a novel human localization approach called Human Position Estimation based on Filtered Sonar Scan Matching (HPE-FSSM) is proposed for the purposes of estimating the trajectory of a walking person in human-robot coexisting environments, by matching consecutive pairs of sonar scans obtained with a mobile robot. The work proposes a novel concept of removing large amounts of noise and spurious readings present in two consecutive raw scans by using a spatial clustering method called DENCLUE (DENsity-based CLUstEring). Moreover, an Edge Feature Based Leg Recognition (EFBLR) algorithm is developed, which, in both scans, extracts the data points characterizing the human legs. Finally, a Likelihood field (LF) based scan matching technique is implemented to estimate the roto-translation of leg pair between the refined scans. This procedure is repeatedly implemented to estimate human position, required for following. Extensive experiments carried out in different environments establish the supremacy of the proposed algorithm, compared to other state-of-the-art algorithms.
Combination of SIFT and Canny Edge Detection for Registration Between SAR and Optical Images

Scale-invariant feature transform (SIFT) has been successfully used for optical image registration, but it cannot produce satisfying results when directly applied to synthetic aperture radar (SAR) images. In the present study, a novel method is proposed for registration between SAR and optical images. First, candidate keypoints are detected using SIFT algorithm. Then, Canny edge detection algorithm is adopted to remove the edge points which are wrong candidate points. Next, SIFT descriptors are generated from these correct keypoints. Last, the fast library for approximate nearest neighbors (FLANNs) algorithm is applied to search matching points in high-dimensional space. Experimental results show that the proposed approach is significantly more accurate and much faster than the original SIFT algorithm.
Generating Tips from Product Reviews

Product reviews play a key role in e-commerce platforms. Studies show that many users read product reviews before purchase and trust them as much as personal recommendations. However, in many cases, the number of reviews per product is large and finding useful information becomes a challenging task. A few websites have recently added an option to post tips - short, concise, practical, and self-contained pieces of advice about products. These tips are complementary to the reviews and usually add a new non-trivial insight about the product, beyond its title, attributes, and description. Yet, most if not all major e-commerce platforms lack the notion of a tip as a first class citizen and customers typically express their advice through other means, such as reviews. In this work, we propose an extractive method for tip generation from product reviews. We focus on five popular e-commerce domains whose reviews tend to contain useful non-trivial tips that are beneficial for potential customers. We formally define the task of tip extraction in e-commerce by providing the list of tip types, tip timing (before and/or after the purchase), and connection to the surrounding context sentences. To extract the tips, we propose a supervised approach and provide a labeled dataset, annotated by human editors, over 14,000 product reviews using a dedicated tool. To demonstrate the potential of our approach, we compare different tip generation methods and evaluate them both manually and over the labeled set. Our approach demonstrates especially high performance for popular products in the Baby, Home Improvement and Sports & Outdoors domains, with precision of over 95% for the top 3 tips per product.
OPLA-Tool v2.0

The Multi-objective Optimization Approach for Product Line Architecture Design (MOA4PLA) is the seminal approach that successfully optimizes Product Line Architecture (PLA) design using search algorithms. The tool named OPLA-Tool was developed in order to automate the use of MOA4PLA. Over time, the customization of the tool to suit the needs of new research and application scenarios led to several problems. The main problems identified in the original version of OPLA-Tool are environment configuration, maintainability and usability problems, and PLA design modeling and visualization. Such problems motivated the development of a new version of this tool: OPLA-Tool v2.0, presented in this work. In this version, those problems were solved by the source code refactoring, migration to a web-based graphical user interface (GUI) and inclusion of a new support tool for PLA modeling and visualization. Furthermore, OPLA-Tool v2.0 has new functionalities, such as new objective functions, new search operators, intelligent interaction with users during the optimization process, multi-user authentication and simultaneous execution of several experiments to PLA optimization. Such a new version of OPLA-Tool is an important achievement to PLA design optimization as it provides an easier and more complete way to automate this task.
Geochemical anomalies from a survey of stream sediments in the Maquelab area (Oecusse, Timor-Leste) and their bearing on the identification of mafic-ultramafic chromite rich complex

Timor-Leste and Oecusse in particular have very little geological data coverage due to the many conflicts generated mainly by Portuguese colonization and Indonesian occupation. The main work that has been done was aimed at oil prospecting on the south coast. A regional stream sediment campaign was started from the Maquelab area in the Oecusse enclave, west of Timor-Leste, where no mineralization had been reported before. The geochemical characteristics of stream sediments in Maquelab area drainage basins, located in NW of Oecusse enclave, Timor-Leste, were investigated in order to establish the geochemical baseline conditions prior to any type of new mineral occurrences. Stream sediments demonstrated a different chemical composition with elevated values mainly for Cr (49–12,520 mg kg−1), Ni (11.6–863 mg kg−1) and Co (19.8–77.1 mg kg−1) which was not expected going by the present geological cartography of the area where the existence of ultramafic rocks is not supported. Principal component analysis (PCA) was used after centered log-ratio (CLR) transformation of data that allowed for the observance of different compositional groupings and the identification of the drainage basins they represented. Aided by the stream sediments geochemical results, a geological recognition of the area was then made, which allowed for the discovery of a new ultramafic complex, consisting of dunites, peridotites and serpentinites, containing metamorphic rocks, predominantly calc-silicate, basalts and gabbros. This complex is named the Maquelab Complex due to its proximity to this village. Inside the peridotitic rocks, small boulders of massive chromite with about 56% Cr2O3 were also found.
A Demonstration of Symbiotic Academic-Social Enterprise in Subsistence Marketplaces: Researching and Designing Customized Sustainability Literacy Education in Tanzania

This article demonstrates symbiotic academic-social enterprise (SASE), a bottom-up approach intertwined with the subsistence marketplace research stream. The SASE approach is unique in coevolving academic and social initiatives in parallel for the express purpose of achieving dual objectives: societally relevant research and social impact over an extended period. Distinct from typical action research approaches, the directionality between research and practice in this approach is circular or mutual rather than linear, the time frame continuous rather than discrete, and the unit of analysis the entire enterprise rather than a single project. Thus, SASE is fundamentally a bottom-up, learning-by-doing approach that developed in contexts characterized by a confluence of uncertainties for communities and a confluence of unfamiliarities for researchers and practitioners. The authors demonstrate this approach in the context of creating sustainability literacy education in Tanzania based on unique climate change impacts in the region. The academic research enterprise provides bottom-up insights about climate change and potential approaches to sustainability literacy education. A sustainability literacy education pilot project demonstrates an initiative in the social enterprise aspect of the approach. Finally, the authors derive public policy and marketing implications of SASE.
TiledSoilingNet: Tile-level Soiling Detection on Automotive Surround-view Cameras Using Coverage Metric

Automotive cameras, particularly surround-view cameras, tend to get soiled by mud, water, snow, etc. For higher levels of autonomous driving, it is necessary to have a soiling detection algorithm which will trigger an automatic cleaning system. Localized detection of soiling in an image is necessary to control the cleaning system. It is also necessary to enable partial functionality in unsoiled areas while reducing confidence in soiled areas. Although this can be solved using a semantic segmentation task, we explore a more efficient solution targeting deployment in low power embedded system. We propose a novel method to regress the area of each soiling type within a tile directly. We refer to this as coverage. The proposed approach is better than learning the dominant class in a tile as multiple soiling types occur within a tile commonly. It also has the advantage of dealing with coarse polygon annotation, which will cause the segmentation task. The proposed soiling coverage decoder is an order of magnitude faster than an equivalent segmentation decoder. We also integrated it into an object detection and semantic segmentation multi-task model using an asynchronous back-propagation algorithm. A portion of the dataset used will be released publicly as part of our WoodScape dataset [1] to encourage further research.
AI Playground: Unreal Engine-Based Data Ablation Tool for Deep Learning

Machine learning requires data, but acquiring and labeling real-world data is challenging, expensive, and time-consuming. More importantly, it is nearly impossible to alter real data post-acquisition (e.g., change the illumination of a room), making it very difficult to measure how specific properties of the data affect performance. In this paper, we present AI Playground (AIP), an open-source, Unreal Engine-based tool for generating and labeling virtual image data. With AIP, it is trivial to capture the same image under different conditions (e.g., fidelity, lighting, etc.) and with different ground truths (e.g., depth or surface normal values). AIP is easily extendable and can be used with or without code. To validate our proposed tool, we generated eight datasets of otherwise identical but varying lighting and fidelity conditions. We then trained deep neural networks to predict (1) depth values, (2) surface normals, or (3) object labels and assessed each network's intra- and cross-dataset performance. Among other insights, we verified that sensitivity to different settings is problem-dependent. We confirmed the findings of other studies that segmentation models are very sensitive to fidelity, but we also found that they are just as sensitive to lighting. In contrast, depth and normal estimation models seem to be less sensitive to fidelity or lighting, and more sensitive to the structure of the image. Finally, we tested our trained depth-estimation networks on two real-world datasets and obtained results comparable to training on real data alone, confirming that our virtual environments are realistic enough for real-world tasks.
More relaxed stability analysis and positivity analysis for positive polynomial fuzzy systems via membership functions dependent method

In this paper, the conservatism source of the positivity and stability analysis results of positive polynomial fuzzy-model-based (PPFMB) control system are studied. Also, in order to improve the flexibility of controller design, a fuzzy controller that does not depend on the membership functions of the fuzzy model is designed. In the existing literatures, it is proved that the LCLF can reduce the conservatism of stability results. However, the LCLF generally results in non-convex conditions which is still a conservatism source. To handle the non-convex conditions, the sector nonlinear concept is applied to handle non-convex terms in stability conditions, and the obstacles caused by mismatched membership functions can be eliminated by PLMF dependent method. In addition, to relax the conservatism caused by the lack of membership functions information, the PLMF dependent positivity analysis are performed for the first time. Meanwhile, PLMF dependent method is extended to stability conditions to obtained more relaxed conditions. Finally, a simulation example is presented to verify the feasibility of this method.
Anomaly Detection for Non-Stationary and Non-Periodic Univariate Time Series

This study proposes an anomaly detection method called wavelet autoencoder anomaly detection (WAAD) for non-stationary and non-periodic univariate time series. The method first applies discrete wavelet transform to time series of a sliding time window to obtain wavelet transform coefficients. It then uses an autoencoder to encode and decode (reconstruct) these coefficients. WAAD calculates the reconstruction error for every time window. An anomaly is assumed to occur for specific conditions of the errors. By five NAB datasets, the performance of WAAD is evaluated and compared with other methods to show its superiority.
Next generation antivirus endowed with bitwise morphological extreme learning machines

Every second, on average, 8 (eight) new malware are created. So, our goal is to propose an antivirus, endowed with artificial intelligence, able of identifying malwares through models based on fast training and high-performance neural networks. Our NGAV (Next Generation Antivirus) is equipped with an authorial ELM (Extreme Learning Morphological) machine. Our bmELMs (Bitwise-Morphological ELMs) are inspired by the image processing theory of Mathematical Morphology. We claim that bmELMs are able to adapt in any machine learning dataset. Inspired by Mathematical Morphology, our bmELMs are capable of modeling any form present at the decisions boundaries of neural networks. Our bmELMs results are compared with classical ELMs and evaluated through widely used classification metrics. Our antivirus, provided with Bitwise-Morphology, achieves an average accuracy of 97.88%, 93.07%, 93.07% and 91.74% in malware detection of PE (Portable Executable), Java, JavaScript and PHP, respectively. Our NGAV enables high performance, large capacity of parallelism, and simple, low-power architecture with low power consumption. We concluded that our Bitwise-Morphology assists to the main requirements for the proper operation and confection of antivirus in hardware.
Rethinking Design of Digital Platforms for Emergent Users: Findings from a Study with Rural Indian Farmers

With increasing penetration of low cost smartphones and affordable data plans in developing countries like India, there is a definite potential that the benefits of access to information can reach a wider audience. However, researchers have also been cognizant that many from this segment are first time users of digital technology and are limited by low text-literacy and low capability to handle and use digital platforms. The established norms of application building for a more tech-savvy and text-literate audience might need to be revisited. We conduct detailed interviews in an ecologically valid setting with 20 farmers from rural Maharashtra and Chhattisgarh to understand the information seeking behaviour and culture of digital platform usage among these populations. Through these interviews we are able to distill some very salient characteristics of the new Internet users and draw a broad set of guidelines for designing information systems for these users. We find the constructs of trust, external invisibility, peers and family play a very important role in the uptake of digital platforms in addition to the accessibility features of text-free design.
Learning Photometric Stereo via Manifold-based Mapping

Three-dimensional reconstruction technologies are fundamental problems in computer vision. Photometric stereo recovers the surface normals of a 3D object from varying shading cues, prevailing in its capability for generating fine surface normal. In recent years, deep learning-based photometric stereo methods are capable of improving the surface-normal estimation under general non-Lambertian surfaces, due to its powerful fitting ability on the non-Lambertian surface. These state-of-the-art methods however usually regress the surface normal directly from the high-dimensional features, without exploring the embedded structural information. This results in the underutilization of the information available in the features. Therefore, in this paper, we propose an efficient manifold-based framework for learning-based photometric stereo, which can better map combined high-dimensional feature spaces to low-dimensional manifolds. Extensive experiments show that our method, learning with the low-dimensional manifolds, achieves more accurate surface-normal estimation, outperforming other state-of-the-art methods on the challenging DiLiGenT benchmark dataset.
Structure-Feature Fusion Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition

Human skeleton contains intuitive information of actions and has high robustness in dynamic environment. Therefore, it has been widely studied in action recognition tasks. Most existing methods of skeleton recognition are based on graph convolutional networks (GCNs), which extract the topological structure of graphs to describe the dependencies between joints. However, the GCNs pay excessive attention to skeleton structure and neglect the feature information of skeleton joints. Accordingly, how to fuse the feature of both skeleton structure and joints is a problem to be solved. In addition, non-linear temporal convolutional network (TCN), which has more robustness and learning capability, is rarely investigated in existing methods. With the comprehensive consideration of the dependence between structure and feature on graphs, we propose a novel structure-feature fusion adaptive GCN (SFAGCN) for skeleton based action recognition. The topological structure of the skeleton graph and the feature of the joints can be fused by the decoupled spatiotemporal correlation in our model effectively. The relevance of spatiotemporal data can be preserved well by the fusion strategy, with data integrity ensured. Moreover, Gated TCN is used to extract temporal feature, improving the network performance further. We choose two-stream adaptive GCNs and shift-GCN as the baseline. To demonstrate the effectiveness of our methods, extensive experiments are implemented on the three large-scale datasets, namely, NTU-RGBD 60, NTU-RGBD 120 and Kinetics-Skeleton 400. The accuracy of top-1 on above datasets are improved by more than 0.6% on average, where the performance of SFAGCN exceeds the state-of-the-art methods.
A Path Planning Algorithm for a Dynamic Environment Based on Proper Generalized Decomposition

A necessity in the design of a path planning algorithm is to account for the environment. If the movement of the mobile robot is through a dynamic environment, the algorithm needs to include the main constraint: real-time collision avoidance. This kind of problem has been studied by different researchers suggesting different techniques to solve the problem of how to design a trajectory of a mobile robot avoiding collisions with dynamic obstacles. One of these algorithms is the artificial potential field (APF), proposed by O. Khatib in 1986, where a set of an artificial potential field is generated to attract the mobile robot to the goal and to repel the obstacles. This is one of the best options to obtain the trajectory of a mobile robot in real-time (RT). However, the main disadvantage is the presence of deadlocks. The mobile robot can be trapped in one of the local minima. In 1988, J.F. Canny suggested an alternative solution using harmonic functions satisfying the Laplace partial differential equation. When this article appeared, it was nearly impossible to apply this algorithm to RT applications. Years later a novel technique called proper generalized decomposition (PGD) appeared to solve partial differential equations, including parameters, the main appeal being that the solution is obtained once in life, including all the possible parameters. Our previous work, published in 2018, was the first approach to study the possibility of applying the PGD to designing a path planning alternative to the algorithms that nowadays exist. The target of this work is to improve our first approach while including dynamic obstacles as extra parameters.
Filter Selection for Speaker Diarization Using Homomorphism

In the current scenario, audio classification and recognition of a particular source of voice is a major challenge when several speakers are speaking at a time. The process of identifying each speaker in an audio segment is called speaker diarization. The major steps involved in speaker diarization are speech detection, speaker change, and speaker merges. Finding and suggesting the best filter is one of the most important task involved in every step of this process. No current researches at present have yet focused on the impact of filter with optimized approaches. In this chapter, a simple yet effective method using homorphism has been implemented to recommend the best filter for any audio classification task for this purpose. After having performed the classification task for a number of commonly used filters, their accuracies have been compared at every step of speaker diarization. In this work, a module using Bayesian information criterion, convolutional neural networks is implemented, and diarization algorithm that performs the task of speaker diarization is proposed.
BiTe-GCN: A New GCN Architecture via Bidirectional Convolution of Topology and Features on Text-Rich Networks

Graph convolutional networks (GCNs), aiming to obtain node embeddings by integrating high-order neighborhood information through stacked graph convolution layers, have demonstrated great power in many network analysis tasks such as node classification and link prediction. However, a fundamental weakness of GCNs, that is, topological limitations, including over-smoothing and local homophily of topology, limits their ability to represent networks. Existing studies for solving these topological limitations typically focus only on the convolution of features on network topology, which inevitably relies heavily on network structure. Moreover, most networks are text-rich, so it is important to integrate not only document-level information, but also the local text information which is particularly significant while often ignored by the existing methods. To solve these limitations, we propose BiTe-GCN, a novel GCN architecture modeling via bidirectional convolution of topology and features on text-rich networks. Specifically, we first transform the original text-rich network into an augmented bi-typed heterogeneous network, capturing both the global document-level information and the local text-sequence information from texts. We then introduce discriminative convolution mechanisms, which performs convolution on this augmented bi-typed network, realizing the convolutions of topology and features altogether in the same system, and learning different contributions of these two parts (i.e., network part and text part), automatically for the given learning objectives. Extensive experiments on text-rich networks demonstrate that our new architecture outperforms the state-of-the-arts by a breakout improvement. Moreover, this architecture can also be applied to several e-commerce search scenes such as JD searching, and experiments on JD dataset show the superiority of the proposed architecture over the related methods.