Reviewerly/exHarmony huggingface dataset:
Relative earthquake location procedure for clustered seismicity with a single station

SUMMARY Earthquake location is one of the oldest problems in seismology, yet remains an active research topic. With dense seismic monitoring networks, it is possible to obtain reliable locations for microearthquakes; however, in many cases dense networks are lacking, limiting the location accuracy, or preventing location when there are too few observations. For small events in all settings, recording may be sparse and location may be difficult due to low signal-to-noise ratio. In this work, we introduce a new, distance-geometry-based method to locate seismicity clusters using only one or two seismic stations. A distance geometry problem consists in determining the location of sets of points based only on the distances between member pairs. Applied to seismology, our approach allows earthquake location using the interevent distance between earthquake pairs, which can be estimated using only one or two seismic stations. We first validate the method with synthetic data that resemble common cluster shapes, and then test the method with two seismic sequences in California: the August 2014 Mw 6.0 Napa earthquake and the July 2019 Mw 6.4 Ridgecrest earthquake sequence. We demonstrate that our approach provides robust and reliable results even for a single station. When using two seismic stations, the results capture the same structures recovered with high-resolution double-difference locations based on multiple stations. The proposed method is particularly useful for poorly monitored areas, where only a limited number of stations are available.
Angular Deep Supervised Vector Quantization for Image Retrieval

Most of the deep quantization methods adopt unsupervised approaches, and the quantization process usually occurs in the Euclidean space on top of the deep feature and its approximate value. When this approach is applied to the retrieval tasks, since the internal product space of the retrieval process is different from the Euclidean space of quantization, minimizing the quantization error (QE) does not necessarily lead to a good performance on the maximum inner product search (MIPS). To solve these problems, we treat Softmax classification as vector quantization (VQ) with angular decision boundaries and propose angular deep supervised VQ (ADSVQ) for image retrieval. Our approach can simultaneously learn the discriminative feature representation and the updatable codebook, both lying on a hypersphere. To reduce the QE between centroids and deep features, two regularization terms are proposed as supervision signals to encourage the intra-class compactness and inter-class balance, respectively. ADSVQ explicitly reformulates the asymmetric distance computation in MIPS to transform the image retrieval process into a two-stage classification process. Moreover, we discuss the extension of multiple-label cases from the perspective of quantization with binary classification. Extensive experiments demonstrate that the proposed ADSVQ has excellent performance on four well-known image data sets when compared with the state-of-the-art hashing methods.
T2-UXT

The concept of transparency allows stakeholders to learn and understand the elements and processes in the context of web portals for collaborative development, being a fundamental requirement for a software ecosystem (SECO) platform. Its absence brings problems regarding the communication between developers as well as the understanding of the provided information, being harmful to SECO health. In this context, this paper presents a tool to allow the evaluation of characteristics that contribute to a SECO portal transparency, called T2-UXT. To do so, this tool produces visual artifacts and indicators from the user's experience. An initial evaluation with SECO developers was performed to demonstrate the use of T2-UXT. Android and iOS SECO portals were selected and developers' interaction in the execution of five tasks on both pages were monitored. The results show that T2-UXT generates indicators that assist in the transparency evaluation process for SECO portals.
Face Morphing Detection with Convolutional Neural Network based on Multi-Features

Face morphing attack has become an increasingly serious problem since the widely applying of face recognition techniques. This type of attack becomes a serious security issue to face recognition systems, especially in widely deployed border control applications. In this paper, a face morphing attack detection method is proposed based on a multi-features Convolutional Neural Network (CNN) with micro-texture and color features. In this method, HSV color space transformation, Local Binary Patterns (LBP) is applied to preprocess images as well as and Histogram of Oriented Gradients (HOG) to enhance the traces of morphing attacks in aspect of micro-texture and color. Then, above mentioned features and the original image are fed into a well-designed multi-features convolutional neural network and obtain the final detection results. Experimental results illustrate that micro-texture and color features are efficient for face morphing detection. The proposed scheme achieves better detection accuracy compared with previous proposed method. The influence of different network structures and hyper parameters are investigated.
Ce qui est écrit et ce qui est parlé. CRMtex for modelling textual entities on the Semantic Web

This paper presents the new developments of CRMtex, an ontological model based on CIDOC CRM, created to describe ancient texts and other semiotic features appearing on inscriptions, papyri, manuscripts and other similar supports. The model is also designed to describe in a formal way the phenomena related to the production, use, conservation, study and interpretation of textual entities. CRMtex was originally intended to detect the close relationship linking ancient texts with the physical objects on which they are supported, the tools and writing systems used for their production, and the various scientific investigations and readings carried out on the text by modern scholars. It eventually evolved to provide researchers with the fundamental concepts for the correct and complete rendering of textual objects, the events representing their history and the cultural and social environments in and for which they were created. The full compatibility of CRMtex with the CIDOC CRM ontology and its extensions ensures persistent interoperability of data encoded by means of its entities with other semantic information produced in cultural heritage and digital humanities. The new entities presented in this paper deal more closely with textual and intertextual structures and try to deepen the close relationships existing between fragments of text or sequences of signs and the underlying meaning they were originally intended to convey.
Blackbox Trojanising of Deep Learning Models: Using Non-Intrusive Network Structure and Binary Alterations

Recent advancements in Artificial Intelligence namely in Deep Learning has heightened its adoption in many applications. Some are playing important roles to the extent that we are heavily dependent on them for our livelihood. However, as with all technologies, there are vulnerabilities that malicious actors could exploit. A form of exploitation is to turn these technologies, intended for good, to become dual-purposed instruments to support deviant acts like malicious software trojans. As part of proactive defense, researchers are proactively identifying such vulnerabilities so that protective measures could be developed subsequently. This research explores a novel blackbox trojanising approach using a simple network structure modification to any deep learning image classification model that would transform a benign model into a deviant one with a simple manipulation of the weights to induce specific types of errors. Propositions to protect the occurrence of such simple exploits are discussed in this research. This research highlights the importance of providing sufficient safeguards to these models so that the intended good of AI innovation and adoption may be protected.
Recognition of Sign Language Using Leap Motion Controller Data

Sign language is a main communication tool for hearing impaired people. The sign language recognition systems are designed to convert it into text or speech, breaking the interaction barrier for deaf person. The paper reviews the state of the art in sign language recognition. The study focuses on data acquisition methods, because they put the major restrictions on a further recognition process. We perform the sign language recognition using data collected by the Leap Motion Controller. The device detects user's hands and transmits it into a three-dimensional model, providing hands and fingers location and posture. This paper presents a system that is capable of recognition and classification of the sign language using deep-convolutional neural network. We highlight the main features of the Leap Motion Controller that improves and simplifies feature extraction. Also, the paper discusses the problems we faced during the sign language recognition.
Expressivity of Some Versions of APAL

Arbitrary public announcement logic (APAL) is a logic of change of knowledge with modalities representing quantification over announcements. We present two rather different versions of APAL wherein this quantification is restricted to formulas only containing a subset of all propositional variables: FSAPAL and SCAPAL; and another version quantifying over all announcements implied by or implying a given formula: IPAL. We then determine the relative expressivity of these logics and APAL. The IPAL quantifier promises to provide a novel perspective on substructural implication as dynamic consequence.
Detecting Non-literal Translations by Fine-tuning Cross-lingual Pre-trained Language Models

Human-generated non-literal translations reflect the richness of human languages and are sometimes indispensable to ensure adequacy and fluency.Non-literal translations are difficult to produce even for human translators, especially for foreign language learners, and machine translations are still on the way to simulate human ones on this aspect.In order to foster the study on appropriate and creative non-literal translations, automatically detecting them in parallel corpora is an important step, which can benefit downstream NLP tasks or help to construct materials to teach translation.This article demonstrates that generic sentence representations produced by a pre-trained cross-lingual language model could be fine-tuned to solve this task.We show that there exists a moderate positive correlation between the prediction probability of being human translation and the non-literal translations' proportion in a sentence.The fine-tuning experiments show an accuracy of 80.16% when predicting the presence of non-literal translations in a sentence and an accuracy of 85.20% when distinguishing literal and non-literal translations at phrase level.We further conduct a linguistic error analysis and propose directions for future work.
A night-time outdoor data set for low-light enhancement

Low light Enhancement has been a hot topic in recent years, and many deep neural network (DNN)-based methods have achieved remarkable performance. However, the rapid development of DNNs also raises the urgent requirement of high-quality training sets, especially supervised night-time data sets. In this paper, we establish a night-time outdoor data set (NOD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> ) that contains 1214 groups of images. We also generate appropriate and high-quality reference images for each group based on multi-exposure fusion strategy, which not only focuses on dark areas but also provides details for over-exposed areas in low light images. Furthermore, a simple but efficient network is presented as the baseline of NOD. Experimental results on NOD and other data sets show the generalizability and effectiveness of the proposed data set and baseline model.
Cloud-assisted Augmented Reality Streaming Service System: Architecture Design and Implementation

This work presents a cloud-assisted augmented reality (AR) streaming service system over wireless network. The goal of the proposed system is to provide interactive AR streaming services of high quality with low latency through a time-varying wireless network. In the proposed system, we employ progressive mesh technique to enhance the scalability of AR streaming services, and adopt meta-file structure to include various attributes of progressive meshes of AR contents necessary for rendering and displaying according to the wireless network conditions. Based on this meta-file provided by a cloud AR server, the proposed system requests the next chunk among AR contents to improve the human visual perceptual quality of all selected AR contents while considering the marker sizes detected by the camera of the mobile AR device. The proposed system is fully implemented by using well-known open sources (e.g., Android, ARCore, and Nexus) and C/C++. Finally, the system is examined in real wireless network environments.
Optimizing Virtual Machine Migration in Multi-Clouds

Cloud computing is susceptible to failures. Allocating Virtual machine (VM) in a reliable fashion is considered as one of the crucial problems in Cloud computing environment. Most researchers choose the optimal VM allocation based on resource utilization and cost minimization. However, to protect the reputation of cloud providers, service reliability should be addressed appropriately. In this paper, we propose a Markov-based failure prediction model to anticipate the failure of Cloud servers. Our model anticipates a deteriorating server state based on historical data. Server reliability prediction is then integrated into a VM re-allocation approach in a Multi-Cloud setting to optimize fault tolerance by maximizing Cloud reliability while reducing communication delay. The optimization problem is solved optimally and heuristically using the Artificial Bee Colony (ABC) algorithm. The results show that our model enhances reliability and minimizes communication delay between the VMs following service migration.
Methods of cryptographically steady elliptic curves generation

Abstract This article is devoted to a complex study of elliptic curves and description and characteristics of it are presented. Moreover, algorithms for generating cryptographically stable elliptic properties that ensure the greatest curves are revealed. The detailed description is given, and the resistance of the elliptic curves to the different types of cryptographic attacks are determined, grounding on the comparative analysis of information security system.
Multiclass spectral feature scaling method for dimensionality reduction

Irregular features disrupt the desired classification. In this paper, we consider aggressively modifying scales of features in the original space according to the label information to form well-separated clusters in low-dimensional space. The proposed method exploits spectral clustering to derive scaling factors that are used to modify the features. Specifically, we reformulate the Laplacian eigenproblem of the spectral clustering as an eigenproblem of a linear matrix pencil whose eigenvector has the scaling factors. Numerical experiments show that the proposed method outperforms well-established supervised dimensionality reduction methods for toy problems with more samples than features and real-world problems with more features than samples.
Sentiment analysis via dually-born-again network and sample selection

Text sentiment analysis is an important natural language processing (NLP) task and has received considerable attention in recent years. Numerous deep-learning based methods have been proposed in previous literature in terms of new deep neural networks (DNN) including new embedding strategies, new attention mechanisms, and new encoding layers. In this study, an alternative technical path is investigated to further improve the state-of-the-art performance of text sentiment analysis. An new effective learning framework is proposed that combines knowledge distillation and sample selection. A dually-born-again network (DBAN) is presented in which the teacher network and the student network are simultaneously trained through an iterative approach. A selection gate is defined to deal with training samples which are useless or even harmful for model training. Moreover, both the DBAN and sample selection are further improved by ensemble. The proposed framework can improve the existing state-of-the-art DNN models in sentiment analysis. Experimental results indicate that the proposed framework enhances the performances of existing networks. In addition, DBAN outperforms existing born-again network.
Online collaboration-based visual tracking for unmanned aerial vehicle with spatial-to-semantic information and multi-recommender voting.

(2021). Online collaboration-based visual tracking for unmanned aerial vehicle with spatial-to-semantic information and multi-recommender voting. International Journal of Remote Sensing: Vol. 42, No. 5, pp. 1664-1687.
Conceptual Framework of Cloud Computing Implementation On Start-Up Companies With Approach

The use of information technology in business today is inevitable The use of information technology is used by all business scales, including micro, small and medium enterprises (MSME) where start-up businesses are included in MSME Many MSME businesses and start-ups are closed in the Covid-19 pandemic for a variety of reasons, and one of them is not that they do not utilize information technology in their operations One of the essential information technologies used in the start-up business is cloud computing This research provides a way to evaluate the use of cloud computing using the Technological, Organizational, and Environmental (TOE) Framework approach Thus, this conceptual evaluation is expected that start-up companies can appropriately assess and use cloud computing services so that start-up businesses become going-concern companies introduction © 2020 IOP Conference Series: Materials Science and Engineering
Combining Deep and Manifold Learning For Nonlinear Feature Extraction in Texture Images

This paper applies a two-step approach for texture classification by combining Manifold learning with Deep CNN feature extractors. The first step is to use CNN architecture to compute the feature vector of a given image. The second step is to apply Manifold Learning algorithms on the features computed in the first step to making a refined feature vector. Eventually, this final representation is used to train SVM classifier. In the first step, we adopted VGG-19 network trained from scratch in order to extract texture features. In the next step, we used the DIMAL (Deep Isometric Manifold Learning Using Sparse Geodesic Sampling) configuration to train a neural network to reduce the dimensionality of the feature space in a nonlinear manner for generating the refined feature vector of the input image. Our concept is that the combination of a deep-learning framework with manifold learning techniques has the potential to select discriminative texture features from a high dimensional space. Based on this idea, we adopted this combination to perform nonlinear feature extraction in texture images. The resulting learned features were then used to train SVM classifier. The experiments demonstrated that our approach achieved better accuracy in texture classification than existing models if trained from scratch.
Power Transmission Line Foreign Object Detection based on Improved YOLOv3 and Deployed to the Chip

The application of object detection is becoming more and more widely in various fields, including the power industry, of course. And YOLOv3 is one of the most popular algorithms in the field of object detection owing to its high performance and efficiency. However, the conventional YOLOv3 algorithm is still too heavy to deploy on mobile or embedded platforms. Consequently, this paper proposes a method to improve the YOLOv3 thus it can be easily deployed to embedded platforms without losing performance. First, substitutes the backbone of YOLOv3, i.e. Darknet53 for MobileNet, which has been proven to be a very efficiency framework for lightweight network. Second, there are numerous redundancies in the detection heads of YOLOv3 and will take a lot of time in the inference process, so we prune the detection heads to a dead-simple structure. Various experiments on our own Power Transmission Line datasets verify our method has state-of-the-art performance while can meet the requirements for deployment to the mobile platforms.
Resolving Dilemmas Arising during Design and Implementation of Digital Repository of Heterogenic Scientific Resources

The creation of digital repositories for archiving and disseminating scientific resources faces many challenges. These challenges relate not only to the modelling of the processes of preparing, depositing, sharing, maintaining, and curating resources. They also face the feasibility of the adopted assumptions and final implementation. Such kind of issues become particularly important in the case of processing of resources containing multimedia. The critical factor then becomes a properly designed architecture that supports efficient data processing and universal data presentation. This article aims to answer questions that may arise when approaching various designing and implementation dilemmas, such as how to handle processes in a digital repository, how to use cloud solutions in its construction, how to work with user interfaces, and how to process collected multimedia. The presented study explores their practical context based on the experiences gained during the AZON platform’s implementation. This platform stores tens of thousands of scientific resources: books, articles, magazines, teaching materials, presentations, photos, 3D scans, audio and video files, databases, and many more. It serves as a running example for all presented proposals.
Random Forest Based Multi-View Fighting Detection with Direction Consistency Feature Extraction

Nowadays, with the increasing number of surveillance cameras, the demand for intelligent video surveillance systems is continuously growing. Detection of fight behaviors is an important and challenging research field of intelligent video surveillance systems. In real life, the camera shooting views are usually different in complex scenarios, so a multi-view approach which performs well in videos with different shooting views is critical. In order to improve the performance of existing methods in videos with different shooting views and solve the misjudgment on non-fight, such as running, talking, etc., we analyze the motion characteristics of fight behaviors and propose two features named Direction Consistency feature and Weighted Direction Consistency feature to distinguish fight and non-fight behaviors. Based on the statistics of features, we define the final feature which is fed into the Random Forest classifier. Moreover, the proposed method is evaluated with the CASIA dataset, and the results indicate that the proposed approach can improve the accuracy, missing alarm and false alarm for the detection of fight behaviors, and it is very robust against videos with different shooting views.