Reviewerly/exHarmony huggingface dataset:
Rapid Public Information and Situational Awareness After the November 26, 2019, Albania Earthquake: Lessons Learned From the LastQuake System

The use of LastQuake information system, its app, the associated Twitter account and to a lesser extent the EMSC’s websites have been analysed for the 7-days following the Nov. 26, 2019 M6.4 Albania destructive earthquake to evaluate what can be improved and how crowdsourcing of information and monitoring of both use and absence of use of the app can contribute to rapid situational awareness. The mainshock and its numerous felt aftershocks triggered a strong public desire for information which in turn led to rapid and massive adoption of the LastQuake app by up to 5% of the country’s population. The constant flow of new app users created a stress test of the app’s crowdsourcing features and led to errors in the association of felt reports with their appropriate earthquake. However these errors had no identifiable impact supporting the conclusion that the curation mechanisms currently in place are efficient. The rapid succession of felt aftershocks contributed to these errors by making information related to the mainshock difficult to access within hours of its occurrence, especially for new users who were not attuned to the app. This revealed that prioritization of information within the app layout is lacking and is an important requirement during these kinds of events. LastQuake has been shown to be a powerful tool for rapid situational awareness. The possibility of damage was detected within 8 min of the mainshock earthquake by a lack of LastQuake app activity close to the epicenter. This possibility was then gradually strengthened as new data became available and was finally confirmed by the reception of the first geo-located pictures of structural damage and building collapse within 60-70 min. Direct exchanges on Twitter were appreciated by eyewitnesses and were reported to reduce their anxiety. Questions mainly focused on the possible evolution of the seismicity. Attempts to debunk prediction claims were difficult and we report on how this could be eased and possibly made more efficient by the use of a clear concise, pre-prepared statement in the local language explaining the state of scientific knowledge and the difference between prediction, early warning or forecasts.
TransRes: A Deep Transfer Learning Approach to Migratable Image Super-Resolution in Remote Urban Sensing

Recent advances in remote sensing provide a powerful and scalable sensing paradigm to capture abundant visual information about the urban environments. We refer to such a sensing paradigm as remote urban sensing. In this paper, we focus on a migratable satellite image super-resolution problem in remote urban sensing applications. Our goal is to reconstruct satellite images of a high resolution in a target area where the high-resolution training data is not available by transferring a super-resolution model learned in a source area where such data is available. This problem is motivated by the limitation of current solutions that primarily rely on a rich set of high-resolution satellite images in the studied area that are not always available. Two important challenges exist in solving our problem: i) the target and source areas often have very different urban characteristics that prevent the direct application of a super-resolution model learned from the source area to the target area; ii) it is not a trivial task to ensure effective model migration with desirable quality without sufficient high quality training data. To address the above challenges, we develop TransRes, a deep adversarial transfer learning framework, to effectively reconstruct high-resolution satellite images without requiring any ground-truth training data from the studied area. We evaluate the TransRes framework using the real-world satellite imagery data collected from three different cities in Europe. The results show that TransRes consistently outperforms the state-of-the-art baselines by achieving the lowest perception errors under various application scenarios.
Improving Image Description with Auxiliary Modality for Visual Localization in Challenging Conditions

Image indexing for lifelong localization is a key component for a large panel of applications, including robot navigation, autonomous driving or cultural heritage valorization. The principal difficulty in long-term localization arises from the dynamic changes that affect outdoor environments. In this work, we propose a new approach for outdoor large scale image-based localization that can deal with challenging scenarios like cross-season, cross-weather and day/night localization. The key component of our method is a new learned global image descriptor, that can effectively benefit from scene geometry information during training. At test time, our system is capable of inferring the depth map related to the query image and use it to increase localization accuracy. We show through extensive evaluation that our method can improve localization performances, especially in challenging scenarios when the visual appearance of the scene has changed. Our method is able to leverage both visual and geometric clues from monocular images to create discriminative descriptors for cross-season localization and effective matching of images acquired at different time periods. Our method can also use weakly annotated data to localize night images across a reference dataset of daytime images. Finally we extended our method to reflectance modality and we compare multi-modal descriptors respectively based on geometry, material reflectance and a combination of both.
Argumentation as a Framework for Interactive Explanations for Recommendations

As AI systems become ever more intertwined in our personal lives, the way in which they explain themselves to and interact with humans is an increasingly critical research area. The explanation of recommendations is thus a pivotal functionality in a user’s experience of a recommender system (RS), providing the possibility of enhancing many of its desirable features in addition to its effectiveness (accuracy wrt users’ preferences). For an RS that we prove empirically is effective, we show how argumentative abstractions underpinning recommendations can provide the structural scaffolding for (different types of) interactive explanations (IEs), i.e. explanations supporting interactions with users. We prove formally that these IEs empower feedback mechanisms that guarantee that recommendations will improve with time, hence rendering the RS scrutable. Finally, we prove experimentally that the various forms of IE (tabular, textual and conversational) induce trust in the recommendations and provide a high degree of transparency in the RS’s functionality.
Stack-VS: Stacked Visual-Semantic Attention for Image Caption Generation

Recently, automatic image caption generation has been an important focus of the work on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multimodal</i> translation task. Existing approaches can be roughly categorized into two classes, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">top-down</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bottom-up</i> , the former transfers the image information (called as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">visual-level feature</i> ) directly into a caption, and the later uses the extracted words (called as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">semantic-level attribute</i> ) to generate a description. However, previous methods either are typically based one-stage decoder or partially utilize part of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">visual</i> -level or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">semantic</i> -level information for image caption generation. In this paper, we address the problem and propose an innovative multi-stage architecture (called as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Stack-VS</i> ) for rich fine-grained image caption generation, via combining <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bottom-up</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">top-down</i> attention models to effectively handle both <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">visual</i> -level and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">semantic</i> -level information of an input image. Specifically, we also propose a novel well-designed stack decoder model, which is constituted by a sequence of decoder cells, each of which contains two LSTM-layers work interactively to re-optimize attention weights on both visual-level feature vectors and semantic-level attribute embeddings for generating a fine-grained image caption. Extensive experiments on the popular benchmark dataset <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MSCOCO</i> show the significant improvements on different evaluation metrics, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> the improvements on BLEU-4 / CIDEr / SPICE scores are 0.372, 1.226 and 0.216, respectively, as compared to the state-of-the-art.
Modeling and optimization of photovoltaic cells with GA algorithm

In this research, the modeling and analysis of the solar tracking system were carried out to obtain the optimal angle in photovoltaic systems for generating maximum power using genetic algorithm (GA). In this paper, the control system is proposed by the GA genetic algorithm that optimizes the output energy of the PV system by adjusting the spatial angles of the solar panel in both vertical and horizontal axes. In this method, without the need for additional hardware, the optimal panel position angles are calculated by using the Matlab software to capture the most sun and maximize output energy. The main advantage is that the system operates discretely during operation and losses are reduced, as well as in the clouds, solar radiation is received and the output energy rises. The important results of this study can be the system is optimized, the output power of the photovoltaic system in a fixed array mode increases by 15.85%.
Ontology Fixing by Using Software Engineering Technology

This paper presents OntologyFixer, a web-based tool that supports a methodology to build, assess, and improve the quality of ontology web language (OWL) ontologies. Using our software, knowledge engineers are able to fix low-quality OWL ontologies (such as those created from natural language documents using ontology learning processes). The fixing process is guided by a set of metrics and fixing mechanisms provided by the tool, and executed primarily through automated changes (inspired by quick fix actions used in the software engineering domain). To evaluate the quality, the tool supports numerical and graphical quality assessments, focusing on ontology content and structure attributes. This tool follows principles, and provides features, typical of scientific software, including user parameter requests, logging, multithreading execution, and experiment repeatability, among others. OntologyFixer architecture takes advantage of model view controller (MVC), strategy, template, and factory design patterns; and decouples graphical user interfaces (GUI) from ontology quality metrics, ontology fixing, and REST (REpresentational State Transfer) API (Application Programming Interface) components (used for pitfall identification, and ontology evaluation). We also separate part of the OntologyFixer functionality into a new package called OntoMetrics, which focuses on the identification of symptoms and the evaluation of the quality of ontologies. Finally, OntologyFixer provides mechanisms to easily develop and integrate new quick fix methods.
Sensor Location Analysis and Minimal Deployment for Fall Detection System

Human falls are considered as an important health problem worldwide. Fall detection systems can alert when a fall occurs reducing the time in which a person obtains medical attention. In this regard, there are different approaches to design fall detection systems, such as wearable sensors, ambient sensors, vision devices, and more recently multimodal approaches. However, these systems depend on the types of devices selected for data acquisition, the location in which these devices are placed, and how fall detection is done. Previously, we have created a multimodal dataset namely UP-Fall Detection and we developed a fall detection system. But the latter cannot be applied on realistic conditions due to a lack of proper selection of minimal sensors. In this work, we propose a methodological analysis to determine the minimal number of sensors required for developing an accurate fall detection system, using the UP-Fall Detection dataset. Specifically, we analyze five wearable sensors and two camera viewpoints separately. After that, we combine them in a feature level to evaluate and select the most suitable single or combined sources of information. From this analysis we found that a wearable sensor at the waist and a lateral viewpoint from a camera exhibits 98.72% of accuracy (intra-subject). At the end, we present a case study on the usage of the analysis results to deploy a minimal-sensor based fall detection system which finally reports 87.56% of accuracy (inter-subject).
FFA-DMRI: A Network Based on Feature Fusion and Attention Mechanism for Brain MRI Denoising

Magnetic Resonance Imaging (MRI) is an indispensable tool in the diagnosis of brain diseases due to its painlessness and safety. Nevertheless, Rician noise is inevitably injected in the process of image acquisition, which results in poor observation and interferes with the treatment. Owing to the complexity of Rician noise, using the elimination method of Gaussian to remove noise does not perform well. Therefore, the feature fusion and attention network (FFA-DMRI) is proposed to separate noise from observed MRI. Inspired by the Attention-guided CNN network (ADNet) and Convolutional Block Attention Module (CBAM), a spatial attention mechanism is specially designed to obtain the area-of-interest in MRI. Additionally, the feature fusion block concatenates local with global information, which makes full use of the multilevel information and boosts the expressive ability of network. The comprehensive experiments on Alzheimer’s Disease Neuroimaging Initiative dataset (ADNI) have exhibited high effectiveness of FFA-DMRI. Meanwhile, it preserves the crucial brain details. Moreover, in terms of visual inspection, the denoising results are also consistent with human perception.
Geological and Mineralogical mapping in Moroccan central Jebilet using multispectral and hyperspectral satellite data and Machine Learning

The Central Jebilet Massif is one of the main Palaeozoic outcrops in Morocco.This massif is characterized by its arid climate, its significant mining potential and the absence of plant cover, which favors the use of spatial remote sensing for geological mapping and mineral prospecting in this site.The objective of this study is the comparison of hyperspectral data from the Hyperion sensor of the Earth Observing-1 (EO-1) satellite and multispectral data from the Operational Land Imager (OLI) sensor of Landsat 8 in the discrimination of geological units and detection of iron caps in the study area.The classification by the Support Vector Machine (SVM) method allowed for a good mapping of the lithological units in the study area.The accuracy of the SVM classification of hyperspectral data is higher than that of multispectral data, which was demonstrated by the confusion matrix, notably an overall accuracy of 93.05% and 89.24%, respectively, and a kappa coefficient of 91.25% and 84.36%, respectively.Concerning the iron detection, the band rationing using both sensors have demonstrated a performance of detecting areas that contain more iron ores, especially, the iron caps of Kettara mine, with a small advantage of hyperspectral data.In overall, our results highlight the efficiency of machine learning classifier and hyperspectral data for the detection of iron ores and the discrimination of lithological units in arid regions.The use of hyperspectral and multispectral images has been shown to be a good technique for the characterization of iron deposits and lithological units, which may help in in mineral exploration engineering with reduced fieldwork and geochemistry.
Development of Interactive Learning Media for Android-Based Correspondence Subjects Using Articulate Storyline 3

The pandemic period due to Covid-19 currently demands teachers to be able to use technology as a distance learning medium. But in reality there are still many teachers who have not been able to utilize technology as using learning media. Therefore, the purpose of this research to produce interactive learning media based on Android using articulate storyline 3 on correspondence subjects in SMK Muhammadiyah 5 Kepanjen. This type of research is Research &amp; Development. The data analysis techniques used are qualitative descriptive analytical techniques and quantitative descriptive analyses. The result of this research and development is an Android learning media application that can help facilitate the delivery of materials with additional competency features, quizzes, learning videos, and glossaries that can be accessed offlline as well as the ease of downloading on the web that has been provided equipped with the user manual. This Learning Media has been validated by material experts and media experts whose results are very worthy to use. So that the conclusion of interactive learning media based on Android using articulate storyline 3 on correspondence subjects can be used as learning resources and media support distance learning.
Steganography Techniques Using Convolutional Neural Networks

Steganography has been used since centuries for concealment of messages in a cover media where messages were physically hidden.The goal in our paper is to hide digital messages using modern steganography techniques.An N * N RGB pixel secret message (either text or image) is to be transmitted in another N * N RGB container image with minimum changes in its contents.The cover image also called the carrier can be publicly visible.In this paper, along with LSB encoding, deep learning modules using the Adam algorithm are used to train the model that comprises a hiding network and a reveal network.The encoder neural network determines where and how to place the message, dispersing it throughout the bits of cover image.The decoder network on the receiving side, which is simultaneously trained with the encoder, reveals the secret image.The main aspect of this work is it produces minimal distortion to the secret message.Thus, preserving its integrity.Also, other steganography softwares cannot be used to reveal the message since the model is trained using a deep learning algorithm which complicates its steganalysis.The network is only trained once, irrespective of the different container images and secret messages given as inputs.Thus, this work has wide and secure applications in many fields.
Deep Hashing Based on Class-Discriminated Neighborhood Embedding

Deep-hashing methods have drawn significant attention during the past years in the field of remote sensing (RS) owing to their prominent capabilities for capturing the semantics from complex RS scenes and generating the associated hash codes in an end-to-end manner. Most existing deep-hashing methods exploit pairwise and triplet losses to learn the hash codes with the preservation of semantic-similarities which require the construction of image pairs and triplets based on supervised information (e.g., class labels). However, the learned Hamming spaces based on these losses may not be optimal due to an insufficient sampling of image pairs and triplets for scalable RS archives. To solve this limitation, we propose a new deep-hashing technique based on the class-discriminated neighborhood embedding, which can properly capture the locality structures among the RS scenes and distinguish images class-wisely in the Hamming space. An extensive experimentation has been conducted in order to validate the effectiveness of the proposed method by comparing it with several state-of-the-art conventional and deep-hashing methods. The related codes of this article will be made publicly available for reproducible research by the community.
SHIELDeNN: Online Accelerated Framework for Fault-Tolerant Deep Neural Network Architectures

We propose SHIELDeNN, an end-to-end inference accelerator frame-work that synergizes the mitigation approach and computational resources to realize a low-overhead error-resilient Neural Network (NN) overlay. We develop a rigorous fault assessment paradigm to delineate a ground-truth fault-skeleton map for revealing the most vulnerable parameters in NN. The error-susceptible parameters and resource constraints are given to a function to find superior design. The error-resiliency magnitude offered by SHIELDeNN can be adjusted based on the given boundaries. SHIELDeNN methodology improves the error-resiliency magnitude of cnvW1A1 by 17.19% and 96.15% for 100 MBUs that target weight and activation layers, respectively.
Semantic Segmentation of Remote-Sensing Imagery Using Heterogeneous Big Data: International Society for Photogrammetry and Remote Sensing Potsdam and Cityscape Datasets

Although semantic segmentation of remote-sensing (RS) images using deep-learning networks has demonstrated its effectiveness recently, compared with natural-image datasets, obtaining RS images under the same conditions to construct data labels is difficult. Indeed, small datasets limit the effective learning of deep-learning networks. To address this problem, we propose a combined U-net model that is trained using a combined weighted loss function and can handle heterogeneous datasets. The network consists of encoder and decoder blocks. The convolutional layers that form the encoder blocks are shared with the heterogeneous datasets, and the decoder blocks are assigned separate training weights. Herein, the International Society for Photogrammetry and Remote Sensing (ISPRS) Potsdam and Cityscape datasets are used as the RS and natural-image datasets, respectively. When the layers are shared, only visible bands of the ISPRS Potsdam data are used. Experimental results show that when same-sized heterogeneous datasets are used, the semantic segmentation accuracy of the Potsdam data obtained using our proposed method is lower than that obtained using only the Potsdam data (four bands) with other methods, such as SegNet, DeepLab-V3+, and the simplified version of U-net. However, the segmentation accuracy of the Potsdam images is improved when the larger Cityscape dataset is used. The combined U-net model can effectively train heterogeneous datasets and overcome the insufficient training data problem in the context of RS-image datasets. Furthermore, it is expected that the proposed method can not only be applied to segmentation tasks of aerial images but also to tasks with various purposes of using big heterogeneous datasets.