Reviewerly/exHarmony huggingface dataset:
Automatic microseismic signals classification with Deep Learning using multi-input Convolutional Neural Networks

Summary Microseismic monitoring is a key tool for many industry activities, where human-induced seismicity must be monitored for risk mitigation, production enhancement or operations continuity. Recorded signals are complex and composed of different classes: actual microearthquakes, anthropogenic noise, electromagnetic interferences, natural earthquakes, quarry blasts, etc. requiring time-consuming manual expert review. While manual processing can feel like a quality assurance, it is impacted by individual’s interpretation, fatigue and availability. Furthermore, profitability is reduced due to the blend of pertinent and non-essential data. In this paper, we use Deep Learning with multi-input Convolutional Neural Networks (CNNs) to automate microseismic monitoring signals classification. Goals are threefold: improved quality thanks to consistent machine decisions, enhanced productivity by removing the need for manual sorting of false positives, and new technologies applications such as early warning of risks through 24/7 automatic processing. For that purpose, we train multi-input CNNs to identify images of our labelled data transformed into a time-frequency representation known as the scalogram. We demonstrate the efficiency of the method, capable of successfully reproducing expert classifications in real time, and providing a tool that reduces workload by 90 up to 100%.
Transformational Leadership, Organizational Innovation and Performance: The Role of CSR from Pakistani Perspective

The study focuses and examines the relationship between transformational leadership and organizational innovation and organizational performance with the mediation analysis of CSR. We modified Khan et. al. (2017) model, by including different dimensions of CRS namely customers, employees, shareholders, and society. This study presents CSR and organizational innovation as potential mediators of affiliations between the key constructs of transformational leadership and organizational performance. Data was collected by means of a close-ended questionnaire, from 201 respondents from more than 50 different organizations comprising of top and middle management level positions, working in the banking sector, FMCGs, Consulting firms, Services Industry, operating in Karachi. The direct and mediation effects of predictors were tested using partial least squares structural equation modeling (PLS-SEM). The results illustrate the direct effect of transformational leadership is significant and positive for all elements of CRS. The mediation analysis showed that customers, shareholders, and society dimensions significantly mediate the Transformational Leadership (TL) and Organizational Innovation (OI) relationship. Moreover, shareholders' dimensions also mediate TL and performance. Lastly, (OI) effectively mediate society and performance nexus. This study discusses the findings and provides theoretical, managerial, and research implications.
A Moderat ed Mediat ion model of t he Relat ionship between Part icipat ory Decision-making and Team Performance in Hotel Industry

Purpose: This study aimed to verify the impact of participatory decision-making (PDM) on team performance (TPF) in the hotel s department/team. In particular, it investigated the role of a mediator for creative self-efficacy (CSE) in the relationship between PDM and TPF. This study determined the optimal decision-making process in a service organization by verifying the controlled mediating effect of collective efficacy (CEF) in the relationship among PDM, CSE, and TPF. Design/methodology/approach: In this study, the 242 samples comprised luxury hotel workers. The analysis verified the hypothesis set by means of causal correlations among four variables using regression analysis and the process macro developed by Hayes (2013). Findings: The study revealed significant effects through the medium of CSE, although PDM did not have a direct impact on TPF. The moderating mediating effect of CEF on the mediating effect of CSE in the relationship between PDM and TPF was statistically significant. Research limitations/implications: This study focused on luxury hotels. The same hierarchy and team culture were assumed without considering the specific characteristics of each hotel team or department. Originality/value: This study proposed a suitable decision-making process model for efficient operation and employee motivation in hotel companies. The findings will contribute to a horizontal and open organizational culture, as well as the spread of PDM.
Cluster Analysis for Mixed Methods Research

Cluster analysis is not a statistical method per se but a vast collection of algorithms for grouping objects based on their similarity. In this chapter, the authors focus on the most commonly used clustering techniques, explain how they work, and discuss their limitations and various issues related to their use. Quantitative researchers using cluster analysis will often refer to attributes as &quot;variables&quot; and objects as &quot;cases&quot;, but clustering can be used to partition either cases or variables. Although a description of the various indices proposed in the literature would be lengthy and unnecessarily tedious, the silhouette method proposed by Rousseeuw possesses specific characteristics that make it suitable for an introduction to cluster analysis. The chapter reviews some of those applications but will also stress how clustering techniques could be used differently to support additional research activities.
Fingerprinting Blank Paper and Printed Material by Smartphones

This work shows a fingerprint method for the unique identification of blank and printed paper by a smartphone. This allows a secure authentication by authorities or end users of products or documents. The digital file includes no hidden data. The fingerprint method uses uncontrollable printing variabilities and paper structure as features. The uncontrollable variabilities are mapped into a binary sequnce, which is used as representation of the features and acts as our fingerprint. The variabilities can be extracted from low and high quality paper as well as from printed material created with low-cost office printers and high-end offset printing machines. Based on this fingerprint, various applications can be realized where the distinction between original and copy or forgery is essential, such as piracy of packaging, tickets, coupons or official documents. From the results of the evaluation it can be concluded that the proposed method is independent of the smartphones used, the paper, the printing technology and the color temperature of the ambient light. Furthermore, the test results show that the proposed method works robustly at different distances, from the smartphone camera to the paper.
Earthquake Forecast and Niels Bohr Postulates

People live, love, create families, build flowering cities, but earthquakes come and cities with all the inhabitants turn to dust. People are doing everything possible to protect themselves from underground catastrophes, but time is running out, and no progress has been made in the forecast of underground cataclysms, which could be gave people hope for a bright future. Scientists do not give up [1-5], and every year they develop more and more new methods of earthquake prediction, but all in vain. The problem, which seemed simple and understandable within the theory of the occurrence of earthquakes in the interpretation of Mr. Reid in which two tectonic plates (blocks) rub against each other and ”carves sparks” in harsh reality turned into an unsolvable mystery of nature. No matter how many seismologists struggle to solve it, no matter how much money the governments of many countries donate, geophysicists haven’t invented anything that deserves serious attention, and every next destructive earthquake turns out to be discouraged by researchers, like snow on the streets of New York in July. People tired of the promises of scientists, and they have suspicions crept: - Is there a solution to this puzzle in principle? In search of the answer to these questions, we came to the conclusion that the accurate forecast of earthquakes, unfortunately, contradicts the fundamental laws of physics, but thanks to the postulates of Niels Bohr, humanity has a great chance not to predict, but to prevent (!!!) seismic phenomena in densely populated areas or around technically important facilities.
Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning

Secure aggregation is a critical component in federated learning (FL), which enables the server to learn the aggregate model of the users without observing their local models. Conventionally, secure aggregation algorithms focus only on ensuring the privacy of individual users in a single training round. We contend that such designs can lead to significant privacy leakages over multiple training rounds, due to partial user selection/participation at each round of FL. In fact, we show that the conventional random user selection strategies in FL lead to leaking users' individual models within number of rounds that is linear in the number of users. To address this challenge, we introduce a secure aggregation framework, Multi-RoundSecAgg, with multi-round privacy guarantees. In particular, we introduce a new metric to quantify the privacy guarantees of FL over multiple training rounds, and develop a structured user selection strategy that guarantees the long-term privacy of each user (over any number of training rounds). Our framework also carefully accounts for the fairness and the average number of participating users at each round. Our experiments on MNIST, CIFAR-10 and CIFAR-100 datasets in the IID and the non-IID settings demonstrate the performance improvement over the baselines, both in terms of privacy protection and test accuracy.
Steganalysis of Adaptive Image Steganography using Convolution Neural Network and Blocks Selection

The method of detecting secret information in images in order to discriminate between a suspect and carrier images are known as image steganalysis. Since content-adapted steganographic methods adaptively integrate knowledge into regions with distortion-based rich textures, state-of-the-art steganalysis approaches with the development of steganography technology cannot achieve the required detection performance. This makes it difficult to extract effective steganalysis features. Due to the impressive performance of Convolution Neural Network (CNN) in the field of image processing, a growing number of research papers are focusing on developing steganalysis methods based on the CNN. Moreover, most research works rely on the entire image to extract the steganalysis features. In this paper, we introduce a new image steganalysis method by designing an entropy-based regional selecting method, and a new CNN framework to discriminate between a stego and cover image. We design a new approach for image block selection that finds the blocks with the highest entropy, allowing the CNN to concentrate on complex textures in image regions while reducing computational complexity. To allow feature diversity, we built a new CNN framework composed of two subnets of different kernel sizes, which we then repeatedly combined and separated. Those two aspects are consequently improved the performance with a reasonable number of epochs for training. When the results of the experiments are compared, our proposed method can lead to improving the detection accuracy of content-adaptive steganographic methods.
All-In-One: Artificial Association Neural Networks

Most deep learning models are limited to specific datasets or tasks because of network structures using fixed layers. In this paper, we discuss the differences between existing neural networks and real human neurons, propose association networks to connect existing models, and describe multiple types of deep learning exercises performed using a single structure. Further, we propose a new neural data structure that can express all basic models of existing neural networks in a tree structure. We also propose an approach in which information propagates from leaf to a root node using the proposed recursive convolution approach (i.e., depth-first convolution) and feed-forward propagation is performed. Thus, we design a ``data-based,'' as opposed to a ``model-based,'' neural network. In experiments conducted, we compared the learning performances of the models specializing in specific domains with those of models simultaneously learning various domains using an association network. The model learned well without significant performance degradation compared to that for models performing individual learning. In addition, the performance results were similar to those of the special case models; the output of the tree contained all information from the tree. Finally, we developed a theory for using arbitrary input data and learning all data simultaneously.
Human learning follows the dynamics of gradient descent

Artificial neural networks (ANNs) have achieved near human-level performance on many tasks and can account for human behavioural and brain measures in a number of domains. Although a principal strength of ANNs is learning representations from experience, only a handful of contributions have evaluated this process to ask whether ANN learning dynamics provide a good model of human learning. We investigated whether humans learn similarly to an ANN, which adjusts its representations through gradient descent. Gradient descent learning is steep at first and initially ignores covariance between features. ANNs can theoretically display a non-monotonic behaviour in which early in learning, multiple weak predictors determine the ANN’s decision whereas late in learning a single strong predictor can dominate. This initial behaviour was confirmed in a simple ANN and in half of human participants performing a comparable task. Later in gradient descent learning, the ANN changed to placing a greater weight on the stronger predictor, and humans also shifted their preferences in the same way. Hidden Markov modelling of the behaviour of ANNs and humans predicted similar transitions from weak-feature to strong-feature states. Our results suggest a significant proportion of people learn about categories in a manner analogous to ANNs, possibly by updating their mental representations by a process akin to gradient descent. Our findings demonstrate how ANNs can be used to not only explain the products of human learning but also the process.
Automation of the development of ontologies of scientific subject domains based on ontology design patterns

At present, ontologies are recognized as the most effective means of formalizing and systematizing knowledge and data in scientific subject domains (SSDs). However, the development of an ontology is a rather complicated and timeconsuming process. All indications are that when developing SSDs ontologies, it is especially effective to use ontology design patterns (ODPs). This is due to the fact that the SSD ontology, as a rule, contains a large number of typical fragments, which are well described by the ODPs. In addition, due to the fact that the use of ODPs greatly facilitates the development of an SSD ontology, it is possible to involve experts in a modeled SSD not possessing the skills of ontological modeling. To obtain an ontology that adequately describes the SSD, it is necessary to process a huge number of publications relevant to the modeled SSD. It is possible to facilitate and accelerate the process of populating the ontology with information from such sources by using the lexical and syntactic patterns of ontological design. The paper presents an approach to the automated development of SSDs ontologies based on a system of heterogeneous ODPs. This system includes both ODPs intended for ontology developers and lexical and syntactic patterns built on the basis of the above-mentioned types of the ODPs and the current version of the SSD ontology.
<i>Call for Special Issue Papers:</i> Virtual Emotions: Understanding Affective Experiences in the Metaverse

Cyberpsychology, Behavior, and Social NetworkingVol. 25, No. 2 Calls For PapersCall for Special Issue Papers: Virtual Emotions: Understanding Affective Experiences in the MetaverseDeadline for Manuscript Submission: July 31, 2022Guest Editors: Andrea Gaggioli and Alice ChiricoGuest Editors: Andrea GaggioliAndrea Gaggioli, MSc(Psych), PhD; ExperienceLab, Università Cattolica del Sacro Cuore, Milan, Italy.Search for more papers by this author and Alice ChiricoAlice Chirico, MSc (Psych), PhD; ExperienceLab, Università Cattolica del Sacro Cuore, Milan, Italy.Search for more papers by this authorPublished Online:10 Feb 2022https://doi.org/10.1089/cyber.2022.29239.cfpAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View article"Call for Special Issue Papers: Virtual Emotions: Understanding Affective Experiences in the Metaverse." Cyberpsychology, Behavior, and Social Networking, 25(2), pp. 85–86FiguresReferencesRelatedDetailsCited byAdvances in Metaverse Investigation: Streams of Research and Future Agenda29 October 2022 | Virtual Worlds, Vol. 1, No. 2 Volume 25Issue 2Feb 2022 InformationCopyright 2022, Mary Ann Liebert, Inc., publishersTo cite this article:Guest Editors: Andrea Gaggioli and Alice Chirico.Call for Special Issue Papers: Virtual Emotions: Understanding Affective Experiences in the Metaverse.Cyberpsychology, Behavior, and Social Networking.Feb 2022.85-86.http://doi.org/10.1089/cyber.2022.29239.cfpPublished in Volume: 25 Issue 2: February 10, 2022PDF download
A Deep SAS ATR explainability framework assessment

In critical operational situations such as Mine Warfare, Automatic Target Recognition (ATR) algorithms are still hardly accepted. Despite their performances close to human experts, their decision-making complexity impedes prediction understanding. Explainability Artificial Intelligence (XAI) is a field of research that attempts to provide explanations for the decision-making of complex networks to promote their acceptability. In the context of image classifying networks such as ATR, the explanations often result in heat maps. These maps highlight pixels according to their importance in decision making. In this paper, we evaluate XAI benefits in the form of a heat map for collaboration with operator during Synthetic Aperture Sonar (SAS) image classification. We carry out various operator tests with several levels of explanation in order to compare their classification performances. We study the probability of classification, the probability of false alarm and the time taken by the operators. These characteristics are essential in an operational context and must be optimized. We also study the operators opinions and preferences on the presence of explanations to take into account the human aspect. This is essential for collaboration. The results obtained show that heat maps as an explanation have a disputed utility according to the operators. Their presence does not increase the quality of the classifications and on the contrary, it even increases the response time. However in terms of opinions, half of operators see a certain usefulness in heat maps.
Performance evaluation of dual axis solar tracker in North-East India

The efficiency of the solar panels is very less as it mainly depends on the irradiation received by Sun. The volume of the irradiation varies during the daytime as the Sun moves. The maximum power point is also affected with value of the voltage and current obtained at a certain instant, and the current generated is also depends on the Sun radiation. So to track the position of the Sun where the maximum radiation can be received by the panel is done in solar tracker system. In this paper dual axis solar tracker is proposed with two different methods, namely live tracking of the Sun with light-dependent resistor and Time based dual axis movement and finally, the output current is compared with and without tracker system. It has been found that there is an increase in the current received with dual axis tracker panel and hence improves efficiency. The whole setup is installed in the North East Renewable Energy Research Lab (NERERL), Jorhat, Assam.
Toward Understanding the Use of Centralized Exchanges for Decentralized Cryptocurrency

Cryptocurrency has been extensively studied as a decentralized financial technology built on blockchain. However, there is a lack of understanding of user experience with cryptocurrency exchanges, the main means for novice users to interact with cryptocurrency. We conduct a qualitative study to provide a panoramic view of user experience and security perception of exchanges. All 15 Chinese participants mainly use centralized exchanges (CEX) instead of decentralized exchanges (DEX) to trade decentralized cryptocurrency, which is paradoxical. A closer examination reveals that CEXes provide better usability and charge lower transaction fee than DEXes. Country-specific security perceptions are observed. Though DEXes provide better anonymity and privacy protection, and are free of governmental regulation, these are not necessary features for many participants. Based on the findings, we propose design implications to make cryptocurrency trading more decentralized.
Optical Network Sensing: Opportunities and Challenges

The scientific community is exploring complementary applications for optical fiber networks. We review recent developments in seismic sensing and discuss the challenge of telemetry pipelines to enable novel fundamental science and wider societal benefits.
A Model for the Build-Versus-Buy Decision in Developing Embedded Systems

We propose a model for recommending build-versus-buy decisions when developing embedded systems. It compares designing a custom unit with integrating a commercial unit into the final product. It accounts for expertise of the development team, tool resources available to the team, partitioning of the tasks, quality of commercial units, vendor support, premiums, and product lifecycles.
Improvement of Technique of Designing and Teaching Learning Process in the course “Methods of Teaching Mathematics”

This article is written about improving the design methods of training based on the proposed pedagogical technologies, the content of the discipline "Methods of teaching mathematics" (the sequence of actions of a teacher in the development of modern educational technologies) and the development of a modern concept for the design of teaching in the discipline of mathematics, the use of elements of practice, integrity, independence, action, drawing up the "large", "medium" and "small" stages of the module of design proposals concerning the content of the sequence of the trajectory of the functional operations. In particular, the practical results of the study are in the process of designing the teaching process for the discipline “Methods of Teaching Mathematics” in higher educational institutions, the methodology for introducing theoretical and practical lessons into practice has activated the students' knowledge, and has made it possible to develop a design model that helps to improve the quality of discipline learning. During the study for students studying in the field of education "Methods of teaching mathematics" developed the same manual.