Reviewerly/exHarmony huggingface dataset:
Beyond sensory conflict: The role of beliefs and perception in motion sickness

Illusory self-motion often provokes motion sickness, which is commonly explained in terms of an inter-sensory conflict that is not in accordance with previous experience. Here we address the influence of cognition in motion sickness and show that such a conflict is not provocative when the observer believes that the motion illusion is indeed actually occurring. Illusory self-motion and motion sickness were elicited in healthy human participants who were seated on a stationary rotary chair inside a rotating optokinetic drum. Participants knew that both chair and drum could rotate but were unaware of the actual motion stimulus. Results showed that motion sickness was correlated with the discrepancy between participants’ perceived self-motion and participants’ beliefs about the actual motion. Together with the general motion sickness susceptibility, this discrepancy accounted for 51% of the variance in motion sickness intensity. This finding sheds a new light on the causes of visually induced motion sickness and suggests that it is not governed by an inter-sensory conflict per se, but by beliefs concerning the actual self-motion. This cognitive influence provides a promising tool for the development of new countermeasures.
Technology, Education, Life and Non-life Insurance in Africa

This article examines the relevance of information and communication technology (ICT) in modulating the effect of education on life insurance and non-life insurance consumption in 48 African countries for the period 2004–2014. The empirical evidence is based on generalized method of moments. The following main findings are established. First, from the nexuses between education, ICT and life insurance, there are positive conditional effects from the interaction between: (i) broadband subscriptions and primary school enrollment; (ii) broadband subscriptions and secondary school enrollment and (iii) internet penetration and tertiary school enrollment. Second, from the nexuses between education, ICT and non-life insurance: (i) there is a negative net effect from the interactions between mobile phone penetration and primary education while positive net effects are apparent from the interactions between: mobile phone penetration and secondary school enrollment; secondary school enrollment and broadband subscriptions and; tertiary school enrollment and broadband subscriptions.
A new sun-tracking approach for energy saving

The aim of this paper is to introduce a novel solar tracking alternative whose principle is based on continuous tests comparing the produced thermal energy corresponding to the new apparent sun position with the electrical energy consumption required to drive the solar collector to the new position. Accordingly, the sun-tracking occurs whenever the net energy gain, the gap between thermal production and electrical consumption, is higher than an optimal threshold which reflects the required limits from which the sun-tracking process should begin. The proposed approach was investigated through modeling and optimization. Furthermore, to check the accuracy of the theoretical model, a series of comparisons were also performed, which have shown a reasonable agreement between the simulation results and the available data in the literature. The findings have revealed that there is an optimal threshold to start the sun-tracking process depending on the sun-tracking mode, sky clearness and season. In terms of comparison with conventional sun-tracking systems, it has been proven that, even though the suggested sun-tracking approach enhances slightly the net energy during typical clear sunny days, the improvement of the net energy gain becomes more significant under partly cloudy conditions.
Hierarchical self-adaptation network for multimodal named entity recognition in social media

Multimodal Named Entity Recognition task aims to identify named entities in user-generated posts containing both images and texts. Previous multimodal named entity recognition methods greatly benefit from visual features when the text and the image are well aligned, but this is not always the case in social media. On condition that the image is missing or mismatched with the text, these models usually fail to provide excellent performance. Besides, previous models use only single attention to capture the semantic interaction between different modalities, which largely ignore the existence of multiple entity objects in images and texts of the posts. To alleviate these issues, we present a novel model named Hierarchical Self-adaptation Network (HSN) to address these issues. The HSN contains 1) a Cross-modal Interaction Module to promote semantic interaction for the multiple entity objects in different modalities, which is proved to suppress wrong or incomplete attention in multimodal interactivity; 2) a Self-adaptive Multimodal Integration module to handle the problems that the images are missing or mismatched with the texts. Additionally, to evaluate the adaptability of HSN in real-life social media, we construct a Real-world NER dataset consisting of plain text posts and multimodal posts from Twitter. Extensive experiments demonstrate that our model achieves state-of-the-art results on the Real-world multimodal NER dataset and the Twitter multimodal NER dataset.
How can I help you? An Intelligent Virtual Assistant for Industrial Robots

In the light of recent trends toward introducing Artificial Intelligence (AI) to enhance Human-Robot Interaction (HRI), intelligent virtual assistants (VA) driven by Natural Language Processing (NLP) receives ample attention in the manufacturing domain. However, most VAs either tightly bind with a specific robotic system or lack efficient human-robot communication. In this work, we implement a layer of interaction between the robotic system and the human operator. This interaction is achieved using a novel VA, called Max, as an intelligent and robust interface. We expand the research work in three directions. Firstly, we introduce a RESTful style Client-Server architecture for Max. Secondly, inspired by studies of human-human conversations, we embed conversation strategies into human-robot dialog policy generation to create a more natural and humanized conversation environment. Finally, we evaluate Max over multiple real-world scenarios from the exploration of an unknown environment to package delivery, with the means of an industrial robot.
Hardware-based solutions for trusted cloud computing

The increasing number of threats targeting cloud computing and the exploitation of specifically privileged software vulnerabilities have pushed the security managers of cloud service providers to deploy hardware-based solutions. These solutions can offer better hardware-assisted security features for a broad range of computing platforms including both CISC and RISC architecture families in datacenters. Their goal is to reduce the attack surface by rooting the trust into the hardware instead of some high-privileged pieces of system software such as the operating system or the hypervisor which have been demonstrated that they include severe security vulnerabilities, thus limiting the adoption of the cloud computing model for some security-skeptical users. In this paper, we give cloud users and customers, application developers and security managers a comprehensive overview of four major industrial-scale commercial hardware-based solutions brought by major vendors in the cloud market. We present, analyze and compare Intel TXT, ARM TrustZone, AMD SEV, and Intel SGX technologies with respect to more than twenty criteria fitting within three categories: security, functional and deployment. We discuss each of these technologies and show the cases where they particularly excel. Our comparison can help IT managers to take the right decision about which better industrial technology to adopt for their particular security requirements and future cloud migrations.
Guided Zoom: Zooming into Network Evidence to Refine Fine-Grained Model Decisions

In state-of-the-art deep single-label classification models, the top- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> <inline-formula><tex-math notation="LaTeX">$(k=2,3,4, \dots)$</tex-math></inline-formula> accuracy is usually significantly higher than the top-1 accuracy. This is more evident in fine-grained datasets, where differences between classes are quite subtle. Exploiting the information provided in the top <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> predicted classes boosts the final prediction of a model. We propose Guided Zoom, a novel way in which explainability could be used to improve model performance. We do so by making sure the model has “the right reasons” for a prediction. The reason/evidence upon which a deep neural network makes a prediction is defined to be the grounding, in the pixel space, for a specific class conditional probability in the model output. Guided Zoom examines how reasonable the evidence used to make each of the top- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> predictions is. Test time evidence is deemed reasonable if it is coherent with evidence used to make similar correct decisions at training time. This leads to better informed predictions. We explore a variety of grounding techniques and study their complementarity for computing evidence. We show that Guided Zoom results in an improvement of a model's classification accuracy and achieves state-of-the-art classification performance on four fine-grained classification datasets. Our code is available at <uri>https://github.com/andreazuna89/Guided-Zoom</uri> .
Generating Images From Spoken Descriptions

Text-based technologies, such as text translation from one language to another, and image captioning, are gaining popularity. However, approximately half of the world's languages are estimated to be lacking a commonly used written form. Consequently, these languages cannot benefit from text-based technologies. This paper presents 1) a new speech technology task, i.e., a speech-to-image generation (S2IG) framework which translates speech descriptions to photo-realistic images 2) without using any text information, thus allowing unwritten languages to potentially benefit from this technology. The proposed speech-to-image framework, referred to as S2IGAN, consists of a speech embedding network and a relation-supervised densely-stacked generative model. The speech embedding network learns speech embeddings with the supervision of corresponding visual information from images. The relation-supervised densely-stacked generative model synthesizes images, conditioned on the speech embeddings produced by the speech embedding network, that are semantically consistent with the corresponding spoken descriptions. Extensive experiments are conducted on four public benchmark databases: two databases that are commonly used in text-to-image generation tasks, i.e., CUB-200 and Oxford-102 for which we created synthesized speech descriptions, and two databases with natural speech descriptions which are often used in the field of cross-modal learning of speech and images, i.e., Flickr8k and Places. Results on these databases demonstrate the effectiveness of the proposed S2IGAN on synthesizing high-quality and semantically-consistent images from the speech signal, yielding a good performance and a solid baseline for the S2IG task.
Self-supervised graph convolutional clustering by preserving latent distribution

Node clustering has attracted widespread concern due to the fact that graph structure helps to seek the geometric structure of data. Existing graph convolutional networks (GCNs) based node clustering methods usually contain two steps. First, learning latent nodes representations. Second, inferring the unknown labels. Despite the promising preliminary results, it cannot take full advantage of the information embedded in current pseudo clustering labels, resulting in suboptimal performance. In fact, it is the truth we do not know the true label of the node in clustering, but the pseudo clustering label of the node generated by each iteration is known. Under this condition, although the pseudo labels of some nodes are incorrect, the partial correct label information is useful and significant. Moreover, GCNs based node clustering aims to learn satisfactory low dimensional node representations which are usually used for clustering. However, existing GCNs based clustering methods fail to take into account distribution consistency between the raw data space and the latent space of nodes representations, resulting in insufficient representations. To address the above problems, we propose a novel GCN-based node clustering method. By introducing a self-supervision module, it can employ the good property of pseudo clustering labels to self-supervise the learning of node representations. Moreover, a latent distribution preserving term, which is measured by KL divergence, is employed to help the latent representations of the same sample to further have a consistent distribution in dimension space as well as in the original dimension space. We formulate the above two concerns into a unified optimization framework. Experimental results on several public datasets indicate our method is effective compared with the state-of-the-art node clustering methods.
Automatic Insulator Detection for Power Line Using Aerial Images Powered by Convolutional Neural Networks

Abstract The inspection and fault diagnosis of power transmission line via aerial images is becoming widely used in smart grids and electrical power systems. However, the accuracy of detecting objects with complicated backgrounds still remains an important challenge in the defect detection system. In this article, a deep-learning based method is presented to realize the automation of insulator fault detection. In the proposed approach, the insulator localization network based on Single Shot Multibox Detector (SSD), a one-stage detecting backbone, is utilized to extract multi-level features and make predictions in order to determine the insulator situation in the aerial images. Additionally, Densely Connected Convolutional Networks (DenseNet) is newly introduced to strengthen the insulator detecting system’s classification ability. To address the data scarcity problem, a two-stage data augmenting strategy is also applied. The first stage is mainly based on the image combination, while the second stage includes random affine transformation, Gaussian blur, brightness and contrast transformation, and salt-and-pepper noise. The insulator detection network and the classification network precisions of the proposed method have reached 0.95 and 0.98 using the text dataset, respectively. The results have shown that the proposed method can ensure robustness and accuracy during insulator detection.
Fast and Resilient Manipulation Planning for Object Retrieval in Cluttered and Confined Environments

In this article, we present a task and motion planning method for retrieving a target object from clutter using a robotic manipulator. We consider dense and cluttered environments where some objects must be removed in order to retrieve the target without collisions. To ensure a successful execution, the interplay between task planning ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">what to remove in what order</i> ) and motion planning ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">how to remove</i> ) is crucial. Thus, the task and motion planning approach combining a symbolic task planner and a geometric motion planner becomes one of the major paradigms in manipulation planning. However, motion planning in dense clutter often leads to frequent failures, so repetitive task replanning is inevitable. Although symbolic task planners are general and domain-independent, they do not scale; so we need an efficient task planner specialized for dense clutter for fast completion of tasks. We propose a polynomial-time task planner for object manipulation in clutter that can be combined with any motion planner. We aim to optimize the number of pick-and-place actions which often determines the efficiency of object manipulation tasks. We consider common situations that could occur in clutter: 1) all object locations are known, 2) some hidden objects are revealed while relocating some front objects, and 3) the target is hidden until some objects are removed. Our method is shown to reduce the number of pick-and-place actions compared to baseline methods (e.g., at least 28.0% of reduction in a known static environment with 20 objects). We also deploy the proposed method to two physical robots with vision systems to show that our method can solve real-world problems.
GPU-based aim point optimization for solar tower power plants

In solar tower power plants, aim point optimization is suitable to find aim point distributions resulting in intercept powers close to the theoretical maximum. However, the application in real time operation often faces the problem of long optimization duration. To counteract this issue, the convergence of an existing strategy, the ant colony optimization meta-heuristic, is enhanced. The raytracing is already replaced by pre-calculated flux maps of the individual heliostats in previous works to increase the optimization speed. In this work, the optimization is merged with a grouping strategy and implemented on a GPU to achieve further time reductions. Here, a k-means clustering algorithm performs the heliostats grouping. The use of groups reduces the solution space for the optimizer and additionally the amount of pre-calculated flux maps, so that the data fits in the global memory of the GPU. Over 100 billion flux values can be evaluated per second using this adapted approach. In this way, the algorithm finds suitable aim point distributions within a few seconds up to a minute. The achieved intercepts are 1–4% higher then those found by a single factor aiming strategy for the evaluated central receiver reference power plant. Moreover, the approach has proved its applicability in clouded environments that lead to spatially fluctuating solar radiation. There, a spillage reduction compared to the single factor aiming of 35% is reached.
Memetic differential evolution methods for clustering problems

The Euclidean Minimum Sum-of-Squares Clustering(MSSC) is one of the most important models for the clustering problem. Due to its NP-hardness, the problem continues to receive much attention in the scientific literature and several heuristic procedures have been proposed. Recent research has been devoted to the improvement of the classical K-MEANS algorithm, either by suitably selecting its starting configuration or by using it as a local search method within a global optimization algorithm. This paper follows this last approach by proposing a new implementation of a Memetic Differential Evolution (MDE) algorithm specifically designed for the MSSC problem and based on the repeated execution of K-MEANS from selected configurations. In this paper we describe how to adapt MDE to the clustering problem and we show, through a vast set of numerical experiments, that the proposed method has very good quality, measured in terms of the minimization of the objective function, as well as a very good efficiency, measured in the number of calls to the local optimization routine, with respect to state of the art methods.
Graph partitioning and graph neural network based hierarchical graph matching for graph similarity computation

Graph similarity computation aims to predict a similarity score between one pair of graphs to facilitate downstream applications, such as finding the most similar chemical compounds similar to a query compound or Fewshot 3D Action Recognition. Recently, some graph similarity computation models based on neural networks have been proposed, which are either based on graph-level interaction or node-level comparison. However, when the number of nodes in the graph increases, it will inevitably bring about reduced representation ability or high computation cost. Motivated by this observation, we propose a graph partitioning and graph neural network-based model, called PSimGNN, to effectively resolve this issue. Specifically, each of the input graphs is partitioned into a set of subgraphs to extract the local structural features directly. Next, a novel graph neural network with an attention mechanism is designed to map each subgraph into an embedding vector. Some of these subgraph pairs are automatically selected for node-level comparison to supplement the subgraph-level embedding with fine-grained information. Finally, coarse-grained interaction information among subgraphs and fine-grained comparison information among nodes in different subgraphs are integrated to predict the final similarity score. Experimental results on graph datasets with different graph sizes demonstrate that PSimGNN outperforms state-of-the-art methods in graph similarity computation tasks using approximate Graph Edit Distance (GED) as the graph similarity metric.
Systemizing the Challenges of Auditing Blockchain-Based Assets

ABSTRACT Presently, auditing firms are hesitant to accept mandates from companies that hold a significant amount of cryptoassets, primarily because the blockchain sector introduces novel, technically sophisticated, and risky propositions that auditors are unequipped to handle. Abrupt recusals by auditors operating in this sector have led to several enterprises being placed on cease trade by securities regulators for failure to produce audited financial statements on time, thus impeding these companies from raising capital and bringing new investments to fund innovation in this space. Through an iterative process of interviews with senior accounting professionals, structured brainstorming among a multidisciplinary team of accountants and blockchain experts, and a focus group with experienced auditors, we critically analyze the purported roadblocks to auditing blockchain firms and map them to traditional auditing practices. We urge auditors to reconsider their resistance to the blockchain sector by demonstrating that providing an audit opinion is challenging but not insurmountable.
Improving word embedding quality with innovative automated approaches to hyperparameters

Abstract Deep learning practices have a great impact in many areas. Big data and significant hardware developments are the main reasons behind deep learning success. Recent advances in deep learning have led to significant improvements in text analysis and classification. Progress in the quality of word representation is an important factor among these improvements. In this study, we aimed to develop word2vec word representation, also called embedding, by automatically optimizing hyperparameters. Minimum word count, vector size, window size, negative sample, and iteration number were used to improve word embedding. We introduce two approaches for setting hyperparameters that are faster than grid search and random search. Word embeddings were created using documents of approximately 300 million words. We measured the quality of word embedding using a deep learning classification model on documents of 10 different classes. It was observed that the optimization of the values of hyperparameters alone increased classification success by 9%. In addition, we demonstrate the benefits of our approaches by comparing the semantic and syntactic relations between word embedding using default and optimized hyperparameters.