Reviewerly/exHarmony huggingface dataset:
Fully convolutional network for P-wave arrival detection and time picking using strong-motion data in China

PreviousNext No AccessSEG 2021 Workshop: 4th International Workshop on Mathematical Geophysics: Traditional & Learning, Virtual, 17–19 December 2021Fully convolutional network for P-wave arrival detection and time picking using strong-motion data in ChinaAuthors: Bao-rui HouJin-dong SongYong-xiang WeiShan-you LiBao-rui HouChina Earthquake AdministrationSearch for more papers by this author, Jin-dong SongChina Earthquake AdministrationSearch for more papers by this author, Yong-xiang WeiEarthquake Administration of Fujian ProvinceSearch for more papers by this author, and Shan-you LiChina Earthquake AdministrationSearch for more papers by this authorhttps://doi.org/10.1190/iwmg2021-12.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract Over the past decade, the amount of available strong-motion records in China mainland has increased significantly, prompting the need for automatic processing to extract the vast amount of information from such data sets. Meanwhile, the need for seismic analysis and earthquake disaster estimation during quaking demands even more reliable P-wave detection and accurate P-wave arrival time picking. Here, we present a fully convolutional network (FCN) for on-set P-wave detection and time picking for China strong-motion Network. The FCN uses three-component seismic acceleration waveforms as input and generates probability distributions of P-wave arrival time as output. The FCN is trained and tested on the datasets (1708 events) selected from China Earthquake Network Center Catalog (CENC, 2017). The trained network is tested against the short-term average/long-term average united Akaike Information Criterion (STA/LTA-AIC) method, the FCN has an advantage over the STA/LTA method, achieves 99.3% probability in P-wave arrival detection and 98.3% probability in P-wave arrival time picking within the residual error of 0.1s. Keywords: P-wave, neural networks, algorithm, earthquake, divergencePermalink: https://doi.org/10.1190/iwmg2021-12.1FiguresReferencesRelatedDetails SEG 2021 Workshop: 4th International Workshop on Mathematical Geophysics: Traditional & Learning, Virtual, 17–19 December 2021ISSN (online):2159-6832Copyright: 2022 Pages: 193 publication data© 2022 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 24 Feb 2022 CITATION INFORMATION Bao-rui Hou, Jin-dong Song, Yong-xiang Wei, and Shan-you Li, (2022), "Fully convolutional network for P-wave arrival detection and time picking using strong-motion data in China," SEG Global Meeting Abstracts : 45-48. https://doi.org/10.1190/iwmg2021-12.1 Plain-Language Summary KeywordsP-waveneural networksalgorithmearthquakedivergencePDF DownloadLoading ...
Improved Side-Channel Resistance by Dynamic Fault-Injection Countermeasures.

Side-channel analysis and fault-injection attacks are known as serious threats to cryptographic hardware implementations and the combined protection against both is currently an open line of research. A promising countermeasure with considerable implementation overhead appears to be a mix of first-order secure Threshold Implementations and linear Error-Correcting Codes.In this paper we employ for the first time the inherent structure of non-systematic codes as fault countermeasure which dynamically mutates the applied generator matrices to achieve a higher-order side-channel and fault-protected design. As a case study, we apply our scheme to the PRESENT block cipher that do not show any higher-order side-channel leakage after measuring 150 million power traces.
“EVERYONE THROUGHOUT HIS LIFE INEVITABLY AND REPEATEDLY FACES A NEED TO USE VARIOUS DOCUMENTS…”. TOWARDS THE PUBLICATION OF THE BOOK “HISTORY OF DOCUMENTS IN RUSSIA IN PERSONALITIES AND DESTINIES” (HISTORICAL, LEGAL AND RECORDS MANAGEMENT ASPECTS) / ED. BY V.N. YEGOROV. ULl’YANOVSK: CORPORATION OF PROMOTION TECHNOLOGIES. 2020. 376 P.

The article presents the analysis of a new collective monograph “History of Documents in Russia in Personalities and Destinies”, which is concerned with the development history of national personal documents of official origin The publication was prepared by joint efforts of experts in records management, state and administrative law from both Ulyanovsk State University and the Institute for History and Archives of the Russian State University for the Humanities. The authors recreated the historical outline of the national documents development basing on the sources found in the capital and regional archives of the Ulyanovsk Region. The book features the analysis of the archival sources on the basis of which the lives of the people from various classes of the Russian Empire, citizens of the Soviet Union and the Russian Federation are revealed. The information presented in the monograph is hardly known and it contributes significantly to the research in the history of documents in Russia. The scientific publication outlined the prospects for future research in the field of records management. Published to mark the 100 th anniversary of the Archival Service of the Ulyanovsk Region, the monograph became an important contribution to studies of the history, records management and archival science, jurisprudence, genealogy, civil law and deserves attention of the general public. The article analyzes the contents of the monograph. The originality of the compositional structure chosen by the authors, as well as the high educational and scientific value of the book are emphasized.
GP-ALPS: Automatic Latent Process Selection for Multi-Output Gaussian Process Models

A simple and widely adopted approach to extend Gaussian processes (GPs) to multiple outputs is to model each output as a linear combination of a collection of shared, unobserved latent GPs. An issue with this approach is choosing the number of latent processes and their kernels. These choices are typically done manually, which can be time consuming and prone to human biases. We propose Gaussian Process Automatic Latent Process Selection (GP-ALPS), which automatically chooses the latent processes by turning off those that do not meaningfully contribute to explaining the data. We develop a variational inference scheme, assess the quality of the variational posterior by comparing it against the gold standard MCMC, and demonstrate the suitability of GP-ALPS in a set of preliminary experiments.
Practice-led and Interdisciplinary Research

This chapter discusses practice-led and interdisciplinary methods in sound design research. Sound design can be undertaken as a creative activity in combination with theoretical investigation, so that theory informs practice; while conversely, the generation of new practical approaches provides new theoretical insights. The authors discuss how this may provide a suitable methodology for research in sound design, where sound artifacts themselves may transmit and advance knowledge in the field, but may also be complemented and disseminated through means of papers and publications. Furthermore, the authors also argue that practice-led methods can be used in combination with other empirical approaches, which may help to reinforce the outcomes. For instance, it is possible to develop artifacts of sound design or prototype systems, which are informed by, or provide a basis for, quantitative, qualitative or mixed methods evaluations. How such practice-led and interdisciplinary studies may be formulated, is specifically explored in this chapter with reference to the authors’ previous research in affective sound design, which considers aspects of human emotion and perception as they relate to sound. Through the discussion of various projects, the chapter explores how practice-led and interdisciplinary approaches may lead to positive research outcomes, and may also have an enriching effect on the skillsets, knowledge and attributes of individual researchers and their teams.
Explainable robotic systems: Interpreting outcome-focused actions in a reinforcement learning scenario.

Robotic systems are more present in our society everyday. In human-robot environments, it is crucial that end-users may correctly understand their robotic team-partners, in order to collaboratively complete a task. To increase action understanding, users demand more explainability about the decisions by the robot in particular situations. Recently, explainable robotic systems have emerged as an alternative focused not only on completing a task satisfactorily, but also in justifying, in a human-like manner, the reasons that lead to making a decision. In reinforcement learning scenarios, a great effort has been focused on providing explanations using data-driven approaches, particularly from the visual input modality in deep learning-based systems. In this work, we focus on the decision-making process of a reinforcement learning agent performing a simple navigation task in a robotic scenario. As a way to explain the goal-driven robot's actions, we use the probability of success computed by three different proposed approaches: memory-based, learning-based, and introspection-based. The difference between these approaches is the amount of memory required to compute or estimate the probability of success as well as the kind of reinforcement learning representation where they could be used. In this regard, we use the memory-based approach as a baseline since it is obtained directly from the agent's observations. When comparing the learning-based and the introspection-based approaches to this baseline, both are found to be suitable alternatives to compute the probability of success, obtaining high levels of similarity when compared using both the Pearson's correlation and the mean squared error.
Issues On Innovative Approaches To The Formation Of Independent Work Of University Students

The article deals with the organization of students' independent work,   innovative forms and teaching methods that ensure the implementation of an individual personal and professional development process for future specialist.
Implementing a Multi-lingual Chatbot for Positive Reinforcement in Young Learners

This is a humanitarian work –a counter-terrorism effort. The presentation describes the experiences of developing a multi-lingua, interactive chatbot trained on the corpus of two Nigerian Languages (Hausa and Fulfude), with simultaneous translation to a third (Kanuri), to stimulate conversations, deliver tailored contents to the users thereby aiding in the detection of the probability and degree of radicalization in young learners through data analysis of the games moves and vocabularies. As chatbots have the ability to simulate a human conversation based on rhetorical behavior, the system is able to learn the need of individual user through constant interaction and deliver tailored contents that promote good behavior in Hausa, Fulfulde and Kanuri languages.
Control of Knowledge of Programming in The Digital Educational Resources on The Basis of Expert Systems Theory

Monitoring and evaluation of students' knowledge is one of the main issues of organization of the educational process. Since the creation of expert systems associated with the identification of declarative knowledge (facts and ideas), structural knowledge (knowledge about the mutual relations between the concepts and memory) and procedural knowledge (how to apply the previous), the expert system can also work as a learning tools. Digital educational resources have been designed to meet the educational requirements for the drawing up of control - measuring materials. This paper discusses the issues of organization of the control of knowledge on programming in the digital educational resources that have been implemented on the basis of expert systems theory. This article also explains that the holding of the control of knowledge on programming using digital educational resources as teaching tools helps to logical and critical thinking, imagination and self-training. Highlights. developed digital educational resources for programming on the basis of expert systems; based thesaurus approach, semantic network, the concept of construction of the graph model designed substantive content of digital educational resources; programming; experimentally proved the efficiency of the developed digital educational resources.
Controlled Privacy-Aware (CPA) Protocol for Machine-to-Machine Communication in Internet of Things

Internet of things (IoT) is buzzword nowadays, and rapid growth of IoT in the market makes the impact in private and public domains. It is a giant network of devices, people, and services. The devices and things are integrated with a variety of sensors generate heterogeneous data, and these devices are connected to the IoT platform. This data is a very important asset of many organizations. There are a variety of applications that carry sensitive data. The security of such applications becomes a challenging responsibility for security researchers due to the resource constraint environment of IoT. There are various aspects of security, but confidentiality of data is one the major aspect that needs to be addressed. In IoT communication, the data traveling from one to another node, there are chances of a replay attack, Man-in-Middle attack, eavesdropping, etc. To overcome these attacks and challenges, there is a need for efficient lightweight algorithms that maintain confidentiality and privacy of data. At the same time, algorithms must efficiently work in a resource constraint environment. This paper presents Controlled Privacy-Aware (CPA) Protocol using elliptic curve cryptography Diffie–Hellman algorithm to exchange key, as well as for encryption and decryption of data to maintain the privacy. This paper presents formal security analysis against the above-mentioned attacks and gives a comparison of performance in terms of computational time with state of art.
Rotating Black Hole Universe and Anti-gravity

On the basis of a black-hole model of the static Universe, a black-hole model of the spinning Universe was proposed. At the same mass, the spinning Universe has (relative to static) a larger radius, lower density, energy density and thickness of the anti-gravity halo.
A Survey on Face Recognition System by using a Principle Component Analysis and Fully Convolutional Network

Face recognition has gained a significant position among most commonly used applications of image processing. With the rapid growth in multimedia contents, among such content face recognition has got much attention especially in past few years. Face as an object consists of distinct features for detection; therefore, it remains most challenging research area for scholars in the field of computer vision and image processing. Partial face images are produced in an unconstrained environment. A face may be occluded by sunglasses, a hat and a scarf, captured in various poses, positioned partially out of cameras ﬁeld of view. Human face plays an important role in our social interaction, conveying people’s identity but it is a dynamic object and has a high degree of variability in its appearances. The problem of recognizing an arbitrary patch of a face image remains largely unsolved. This study proposes a new partial face recognition approach, called Dynamic Feature Matching, which combines Fully Convolutional Networks, Principle Component Analysis and Sparse Representation Classiﬁcation to address partial face recognition problem regardless of various face sizes. DFM does not require prior position information of partial faces against a holistic face.
Empirical Analysis of Overfitting and Mode Drop in GAN Training

We examine two key questions in GAN training, namely overfitting and mode drop, from an empirical perspective. We show that when stochasticity is removed from the training procedure, GANs can overfit and exhibit almost no mode drop. Our results shed light on important characteristics of the GAN training procedure. They also provide evidence against prevailing intuitions that GANs do not memorize the training set, and that mode dropping is mainly due to properties of the GAN objective rather than how it is optimized during training.
Capstone Project: REST API for Polling App

We have, by far, made everything we need for a data-driven REST API. Even if we don’t learn further, we will be able to make a rich REST API application, considering what we know about hapi and a little bit more research about Sequelize and other plugins we’ve used. The little loose ends, those things we call bugs, are included in the Appendix. So what’s left now? Well, the book wasn’t just about Hapi, it was more about preparing you for the industry where Hapi is catching up quickly. Preparing for the industry, if you’re not already in it, includes a ground-up approach to understanding a use case. Let’s get right into the show.
Designing computer-assisted problem-based learning (CAPBL) environment for performance analysis of isolation forest algorithm

Abstract Anomaly detection is important applied in various fields of application to determine errors in the system. By detecting it can minimize losses on the system. Anomaly detection can be done using several algorithms. One such algorithm is the Isolation Forest algorithm. Isolation Forest Algorithm is an efficient and effective algorithm in detecting anomalies. Isolation Forest has better performance than other algorithms in terms of execution time, especially in large datasets. Although the Isolation Forest algorithm has many advantages, there are still very few tools that provide this algorithm. One tool that provides the Isolation Forest algorithm is Scikit Learn. However, using special tools such as Scikit Learning requires sufficient time and experience to be able to manage the features of the tool. Thus, in this study the authors developed a web-based application that is used to assist users in assisting and improving the performance of Isolation Forests by using sample data sets, setting the parameters of Isolation Forests, visualization, and evaluation. This application was developed using the waterfall process model and CAPBL concept. In determining the features of the application, an analysis of features is based on the CAPBL concept with the business process of CRISP-DM. The software testing result shows that this application is fit to be used as a new solution to facilitate users who want to learn and analyze the performance of Isolation Forest.
Exponential quantum communication reductions from generalizations of the Boolean Hidden Matching problem

In this work we revisit the Boolean Hidden Matching communication problem, which was the first communication problem in the one-way model to demonstrate an exponential classical-quantum communication separation. In this problem, Alice's bits are matched into pairs according to a partition that Bob holds. These pairs are compressed using a Parity function and it is promised that the final bit-string is equal either to another bit-string Bob holds, or its complement. The problem is to decide which case is the correct one. Here we generalize the Boolean Hidden Matching problem by replacing the parity function with an arbitrary function $f$. Efficient communication protocols are presented depending on the sign-degree of $f$. If its sign-degree is less than or equal to 1, we show an efficient classical protocol. If its sign-degree is less than or equal to $2$, we show an efficient quantum protocol. We then completely characterize the classical hardness of all symmetric functions $f$ of sign-degree greater than or equal to $2$, except for one family of specific cases. We also prove, via Fourier analysis, a classical lower bound for any function $f$ whose pure high degree is greater than or equal to $2$. Similarly, we prove, also via Fourier analysis, a quantum lower bound for any function $f$ whose pure high degree is greater than or equal to $3$. These results give a large family of new exponential classical-quantum communication separations.
Exploring Design Opportunities for Visually Congruent Proxemics in Information Visualization: A Design Space

This project was funded in part by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 753816
Secure Data Sharing in Cloud

Cloud storage is widely used for sharing data due to low cost maintenance. But, it is also necessary to secure data on cloud. Secure Data Sharing in Cloud focuses mainly on: a) privacy and confidentiality, b) key management and encryption, c) secure data sharing without re-encryption and d) forward and backward access control. When user wants to share data, it sends request to trusted party that generates a symmetric key which is used to encrypt the data for sharing. This key is used to compute two key shares for trusted party and user. The key is deleted using secure overwriting. Its working was formally verified using High Level Petri Nets, SMT Library, and Z3 solver. It was implemented in Visual Studio and its performance was evaluated based on time consumption for various operations which revealed that it has the potential to be effectively used for secure data sharing in cloud.
A New Generation of Readers in the Digital age: Attentive or Distracted?

Within a progressively high knowledge-based society, globally-oriented world, and diverse society, making sure that our students are well equipped with the necessary tools and competences to live in this digital age is, in fact, one of the prevalent challenges facing today’s education. Indeed, the novel nature of reading and readers has enormously changed as digital texts and technologies become more prevalent. Thus, teachers are under the pressure to shape their teaching visions to more mobile-based reading ways that will cope with learners’ new requirements to exist, struggle, and cooperate in a newly global scenario. Thus, the present paper attempts at reflecting upon the challenging task of teachers, from developing world, to incorporate new educational technologies into their typical classrooms in general and reading practice in particular for a better teaching/learning experience. Nonetheless, one should be cognizant of the fact that despite the growing importance of ICTs in education, there is no “magic bullet” that will answer all existing challenges (Schramm, 1977), still there is a lack of a structured approach based on collaboration, innovation, development and implementation of educational technologies. Hence, incorporating technology within the reading process may create a challenging problem at this level; do our learners, in such a technological scenario, read as attentively and thoroughly as required? How do their brains respond to onscreen text than to words on paper? Should teachers be worried about dividing learners’ attention between pixels and ink? This paper will answer these questions and many other concerns.