Reviewerly/exHarmony huggingface dataset:
Binary Symbiotic Organism Search Algorithm for Feature Selection and Analysis

Feature selection is a challenging step in the field of data mining, because there are many local optimal solutions in a feature space.Feature selection can be considered an optimization problem, which requires as few feature combinations as possible and high accuracy.The binary symbiotic organism search (BSOS) algorithm is proposed in this paper.It maps the symbiotic organism search algorithm from a continuous space to a discrete space using an adaptive S-shaped transfer function and can be used to search for the optimal feature subset in a feature selection space.The proposed BSOS algorithm is evaluated using 19 datasets from the UCI repository.First, the results of four basic S-shaped transfer functions are compared with those of the adaptive S-shaped transfer function.Additionally, the experimental results are compared with the results obtained by the popular binary grasshopper optimization, binary gray wolf optimization, traditional binary particle swarm optimization, and binary differential evolution algorithms, which are also employed for feature selection in the existing literature.The experimental results show that the BSOS algorithm can find the fewest number of features in most datasets and achieve a high classification accuracy.Moreover, the experiments also show that the BSOS algorithm is still at a disadvantage in handling low-dimensional datasets and attains low sensitivity in hyperdimensional datasets.
Identification of internal fault against external abnormalities in power transformer using hierarchical ensemble extreme learning machine technique

Various unwanted phenomena that are taken place in the transformer may occasionally mal-operate selected fault classification based protective schemes. Hence, it is necessary to discriminate internal fault from external abnormal conditions for unit protection of power transformer. This study presents a new hierarchical ensemble extreme learning machine (HE-ELM) based classifier technique to identify faults in & out of transformer. The component extreme learning machine (ELM) is structured hierarchically to improve its fault data classification accuracy. The developed algorithm is evaluated by simulating multiple disorders on 100 MVA, 132/220 kV transformer with the help of PSCAD software. DWT is used to extract features from acquired current signals from transformer. The feature vector formed after extraction process is fed to the HE-ELM algorithm for data classification. The fault discrimination accuracy of HE-ELM technique is 99.91%. This shows its effectiveness with respect to other classifier techniques. Moreover, the developed algorithm is successfully tested on hardware prototype in laboratory environment under various inrush and fault conditions using Cortex M4 microcontroller (STM32F407) with maximum identification time of 27 ms. The proposed HE-ELM technique is compared with existing support vector machine, probabilistic neural network and ELM techniques for identical fault data. Results demonstrate that HE-ELM outperforms than existing schemes in cross-domain recognition task.
Gated Graph Convolutional Recurrent Neural Networks

Graph processes model a number of important problems such as identifying the epicenter of an earthquake or predicting weather. In this paper, we propose a Graph Convolutional Recurrent Neural Network (GCRNN) architecture specifically tailored to deal with these problems. GCRNNs use convolutional filter banks to keep the number of trainable parameters independent of the size of the graph and of the time sequences considered. We also put forward Gated GCRNNs, a time-gated variation of GCRNNs akin to LSTMs. When compared with GNNs and another graph recurrent architecture in experiments using both synthetic and real-word data, GCRNNs significantly improve performance while using considerably less parameters.
Multi-Level Sentiment Analysis of PolEmo 2.0: Extended Corpus of Multi-Domain Consumer Reviews

In this article we present an extended version of PolEmo -a corpus of consumer reviews from 4 domains: medicine, hotels, products and school.Current version (PolEmo 2.0) contains 8,216 reviews having 57,466 sentences.Each text and sentence was manually annotated with sentiment in 2+1 scheme, which gives a total of 197,046 annotations.We obtained a high value of Positive Specific Agreement, which is 0.91 for texts and 0.88 for sentences.PolEmo 2.0 is publicly available under a Creative
A robust statistics approach for plane detection in unorganized point clouds

Plane detection is a key component for many applications, such as industrial reverse engineering and self-driving cars. However, existing plane-detection techniques are sensitive to noise and to user-defined parameters. We introduce a fast deterministic technique for plane detection in unorganized point clouds that is robust to noise and virtually independent of parameter tuning. It is based on a novel planarity test drawn from robust statistics and on a split and merge strategy. Its parameter values are automatically adjusted to fit the local distribution of samples in the input dataset, thus leading to good reconstruction of even small planar regions. We demonstrate the effectiveness of our solution on several real datasets, comparing its performance to state-of-art plane detection techniques, and showing that it achieves better accuracy, while still being one of the fastest.
The path to the Hotel of Things: Internet of Things and Big Data converging in hospitality

Purpose The purpose of this study is to explore the use of Internet of Things (IoT) in hospitality and examine its relationship with Big Data. Drawing upon theoretical and practical considerations, it lays a foundation for its adoption in practice and future research. Design/methodology/approach This paper uses a conceptual approach. It demonstrates the use of IoT and its impact on Big Data in hospitality through exemplars. The paper further explores the convergence of IoT, Big Data and hospitality in the context of the literature, value attributes and vendor offerings. Theoretical models from information systems and business are used to support the concepts proposed. Findings The study compiles and contextualizes the applications of IoT in hospitality by applying an input–process–output model, demonstrating the link to Big Data. The resulting value dimensions are represented by the IoT–Big Data triple impact intensity model. Research limitations/implications An outlook toward the future trajectory of IoT adoption is provided by proposing to extend the prevalent social, mobile, analytics and cloud framework with an IoT component. Practical implications Practical implications of the use of IoT and Big Data in hospitality on information technology infrastructure, business models, security and standardization highlight the scope for further empirical research. Originality/value By synthesizing IoT applications in hospitality and by bringing to light their relationship with Big Data, the study demonstrates how IoT, Big Data and hospitality converge – a synthesis that has thus far been largely unexplored. This study lays the groundwork for increased deployment of IoT and Big Data in hospitality and future academic research in this area.
Rapid collaborative knowledge building via Twitter after significant geohazard events

Abstract. Twitter is an established social media platform valued by scholars as an open way to disseminate scientific information and to publicly discuss research results. Scientific discussions on Twitter are viewed by the media, who can then pass on information to the wider public. Social media is used widely by geoscientists, but there is little documentation currently available regarding the benefits or limitations of this for the scientist or the public. Here, we use the example of two 2018 earthquake-related events that were widely commented on by geoscientists on Twitter: the Palu Mw 7.5 earthquake and related tsunami in Indonesia and the long-duration Mayotte island seismovolcanic crisis in the Indian Ocean. We built our study on a content and contextual analysis of selected Twitter threads about the geophysical characteristics of these events. From the analysis of these two examples, we show that Twitter promotes a very rapid building of knowledge in the minutes to hours and days following an event via an efficient exchange of information and active discussion between the scientists themselves and the public. We discuss the advantages and potential pitfalls of this relatively novel way of making scientific information accessible to scholarly peers and lay people. We argue that scientific discussion on Twitter breaks down the traditional “ivory tower” of academia, contributes to the growing trend towards open science, and may help people to understand how science is developed and, in turn, to better understand the risks related to natural/environmental hazards.
Systematic review of 3D facial expression recognition methods

The three-dimensional representation of the human face has emerged as a viable and effective way to characterize the facial surface for expression classification purposes. The rapid progress in the area continually demands its up-to-date characterization to guide and support research decisions, specially for newcomer researchers. This systematic literature review focus on investigating three major aspects of 3D facial expression recognition methods: face representation, preprocessing and classification experiments. The investigation of 49 specialized studies revealed the preferential types of data and regions of interest for face representation in recent years, as well as a trend towards keypoint-independent methods. In addition, it brings to light current weaknesses regarding the report of preprocessing techniques and identifies challenges concerning the current possibility of fair comparison among multiple methods. The presented findings outline essential research decisions whose the regardful report is of great value to this research community.
Joint Demosaicking and Denoising by Fine-Tuning of Bursts of Raw Images

Demosaicking and denoising are the first steps of any camera image processing pipeline and are key for obtaining high quality RGB images. A promising current research trend aims at solving these two problems jointly using convolutional neural networks. Due to the unavailability of ground truth data these networks cannot be currently trained using real RAW images. Instead, they resort to simulated data. In this paper we present a method to learn demosaicking directly from mosaicked images, without requiring ground truth RGB data. We apply this to learn joint demosaicking and denoising only from RAW images, thus enabling the use of real data. In addition we show that for this application fine-tuning a network to a specific burst improves the quality of restoration for both demosaicking and denoising.
Single-Network Whole-Body Pose Estimation

We present the first single-network approach for 2D~whole-body pose estimation, which entails simultaneous localization of body, face, hands, and feet keypoints. Due to the bottom-up formulation, our method maintains constant real-time performance regardless of the number of people in the image. The network is trained in a single stage using multi-task learning, through an improved architecture which can handle scale differences between body/foot and face/hand keypoints. Our approach considerably improves upon OpenPose~\cite{cao2018openpose}, the only work so far capable of whole-body pose estimation, both in terms of speed and global accuracy. Unlike OpenPose, our method does not need to run an additional network for each hand and face candidate, making it substantially faster for multi-person scenarios. This work directly results in a reduction of computational complexity for applications that require 2D whole-body information (e.g., VR/AR, re-targeting). In addition, it yields higher accuracy, especially for occluded, blurry, and low resolution faces and hands. For code, trained models, and validation benchmarks, visit our project page: https://github.com/CMU-Perceptual-Computing-Lab/openpose_train.
Learning Local Descriptors With a CDF-Based Dynamic Soft Margin

The triplet loss is adopted by a variety of learning tasks, such as local feature descriptor learning. However, its standard formulation with a hard margin only leverages part of the training data in each mini-batch. Moreover, the margin is often empirically chosen or determined through computationally expensive validation, and stays unchanged during the entire training session. In this work, we propose a simple yet effective method to overcome the above limitations. The core idea is to replace the hard margin with a non-parametric soft margin, which is dynamically updated. The major observation is that the difficulty of a triplet can be inferred from the cumulative distribution function of the triplets' signed distances to the decision boundary. We demonstrate through experiments on both real-valued and binary local feature descriptors that our method leads to state-of-the-art performance on popular benchmarks, while eliminating the need to determine the best margin.
Online Topology Learning by a Gaussian Membership-Based Self-Organizing Incremental Neural Network

In order to extract useful information from data streams, incremental learning has been introduced in more and more data mining algorithms. For instance, a self-organizing incremental neural network (SOINN) has been proposed to extract a topological structure that consists of one or more neural networks to closely reflect the data distribution of data streams. However, SOINN has the tradeoff between deleting previously learned nodes and inserting new nodes, i.e., the stability-plasticity dilemma. Therefore, it is not guaranteed that the topological structure obtained by the SOINN will closely represent data distribution. For solving the stability-plasticity dilemma, we propose a Gaussian membership-based SOINN (Gm-SOINN). Unlike other SOINN-based methods that allow only one node to be identified as a “winner” (the nearest node), the Gm-SOINN uses a Gaussian membership to indicate to which degree the node is a winner. Hence, the Gm-SOINN avoids the topological structure that cannot represent the data distribution because previously learned nodes overly deleted or noisy nodes inserted. In addition, an evolving Gaussian mixture model is integrated into the Gm-SOINN to estimate the density distribution of nodes, thereby avoiding the wrong connection between two nodes. Experiments involving both artificial and real-world data sets indicate that our proposed Gm-SOINN achieves better performance than other topology learning methods.
Conditional Coupled Generative Adversarial Networks for Zero-Shot Domain Adaptation

Machine learning models trained in one domain perform poorly in the other domains due to the existence of domain shift. Domain adaptation techniques solve this problem by training transferable models from the label-rich source domain to the label-scarce target domain. Unfortunately, a majority of the existing domain adaptation techniques rely on the availability of the target-domain data, and thus limit their applications to a small community across few computer vision problems. In this paper, we tackle the challenging zero-shot domain adaptation (ZSDA) problem, where the target-domain data is non-available in the training stage. For this purpose, we propose conditional coupled generative adversarial networks (CoCoGAN) by extending the coupled generative adversarial networks (CoGAN) into a conditioning model. Compared with the existing state of the arts, our proposed CoCoGAN is able to capture the joint distribution of dual-domain samples in two different tasks, i.e. the relevant task (RT) and an irrelevant task (IRT). We train the CoCoGAN with both source-domain samples in RT and the dual-domain samples in IRT to complete the domain adaptation. While the former provide the high-level concepts of the non-available target-domain data, the latter carry the sharing correlation between the two domains in RT and IRT. To train the CoCoGAN in the absence of the target-domain data for RT, we propose a new supervisory signal, i.e. the alignment between representations across tasks. Extensive experiments carried out demonstrate that our proposed CoCoGAN outperforms existing state of the arts in image classifications.
From Strings to Things: Knowledge-Enabled VQA Model That Can Read and Reason

Text present in images are not merely strings, they provide useful cues about the image. Despite their utility in better image understanding, scene texts are not used in traditional visual question answering (VQA) models. In this work, we present a VQA model which can read scene texts and perform reasoning on a knowledge graph to arrive at an accurate answer. Our proposed model has three mutually interacting modules: i. proposal module to get word and visual content proposals from the image, ii. fusion module to fuse these proposals, question and knowledge base to mine relevant facts, and represent these facts as multi-relational graph, iii. reasoning module to perform a novel gated graph neural network based reasoning on this graph. The performance of our knowledge-enabled VQA model is evaluated on our newly introduced dataset, viz. text-KVQA. To the best of our knowledge, this is the first dataset which identifies the need for bridging text recognition with knowledge graph based reasoning. Through extensive experiments, we show that our proposed method outperforms traditional VQA as well as question-answering over knowledge base-based methods on text-KVQA.
Dynamic-Net: Tuning the Objective Without Re-Training for Synthesis Tasks

One of the key ingredients for successful optimization of modern CNNs is identifying a suitable objective. To date, the objective is fixed a-priori at training time, and any variation to it requires re-training a new network. In this paper we present a first attempt at alleviating the need for re-training. Rather than fixing the network at training time, we train a ``Dynamic-Net'' that can be modified at inference time. Our approach considers an ``objective-space'' as the space of all linear combinations of two objectives, and the Dynamic-Net is emulating the traversing of this objective-space at test-time, without any further training. We show that this upgrades pre-trained networks by providing an out-of-learning extension, while maintaining the performance quality. The solution we propose is fast and allows a user to interactively modify the network, in real-time, in order to obtain the result he/she desires. We show the benefits of such an approach via several different applications.
A Deep-Learning-Based Vehicle Detection Approach for Insufficient and Nighttime Illumination Conditions

Most object detection models cannot achieve satisfactory performance under nighttime and other insufficient illumination conditions, which may be due to the collection of data sets and typical labeling conventions. Public data sets collected for object detection are usually photographed with sufficient ambient lighting. However, their labeling conventions typically focus on clear objects and ignore blurry and occluded objects. Consequently, the detection performance levels of traditional vehicle detection techniques are limited in nighttime environments without sufficient illumination. When objects occupy a small number of pixels and the existence of crucial features is infrequent, traditional convolutional neural networks (CNNs) may suffer from serious information loss due to the fixed number of convolutional operations. This study presents solutions for data collection and the labeling convention of nighttime data to handle various types of situations, including in-vehicle detection. Moreover, the study proposes a specifically optimized system based on the Faster region-based CNN model. The system has a processing speed of 16 frames per second for 500 × 375-pixel images, and it achieved a mean average precision (mAP) of 0.8497 in our validation segment involving urban nighttime and extremely inadequate lighting conditions. The experimental results demonstrated that our proposed methods can achieve high detection performance in various nighttime environments, such as urban nighttime conditions with insufficient illumination, and extremely dark conditions with nearly no lighting. The proposed system outperforms original methods that have an mAP value of approximately 0.2.
SoilingNet: Soiling Detection on Automotive Surround-View Cameras

Cameras are an essential part of sensor suite in autonomous driving. Surround-view cameras are directly exposed to external environment and are vulnerable to get soiled. Cameras have a much higher degradation in performance due to soiling compared to other sensors. Thus it is critical to accurately detect soiling on the cameras, particularly for higher levels of autonomous driving. We created a new dataset having multiple types of soiling namely opaque and transparent. It will be released publicly as part of our WoodScape dataset [15] to encourage further research. We demonstrate high accuracy using a Convolutional Neural Network (CNN) based architecture. We also show that it can be combined with the existing object detection task in a multi-task learning framework. Finally, we make use of Generative Adversarial Networks (GANs) to generate more images for data augmentation and show that it works successfully similar to the style transfer.
One Trace Is All It Takes: Machine Learning-Based Side-Channel Attack on EdDSA

Profiling attacks, especially those based on machine learning proved as very successful techniques in recent years when considering side-channel analysis of block ciphers implementations. At the same time, the results for implementations of public-key cryptosystems are very sparse. In this paper, we consider several machine learning techniques in order to mount a power analysis attack on EdDSA using the curve Curve25519 as implemented in WolfSSL. The results show all considered techniques to be viable and powerful options. Especially convolutional neural networks (CNNs) are effective as we can break the implementation with only a single measurement in the attack phase while requiring less than 500 measurements in the training phase. Interestingly, that same convolutional neural network was recently shown to perform extremely well for attacking the implementation of the AES cipher. Our results show that some common grounds can be established when using deep learning for profiling attacks on distinct cryptographic algorithms and their corresponding implementations.
An LSTM-Based Neural Network Architecture for Model Transformations

Model transformations are a key element in any model-driven engineering approach. But writing them is a time-consuming and error-prone activity that requires specific knowledge of the transformation language semantics. We propose to take advantage of the advances in Artificial Intelligence and, in particular Long Short-Term Memory Neural Networks (LSTM), to automatically infer model transformations from sets of input-output model pairs. Once the transformation mappings have been learned, the LSTM system is able to autonomously transform new input models into their corresponding output models without the need of writing any transformation-specific code. We evaluate the correctness and performance of our approach and discuss its advantages and limitations.