Reviewerly/exHarmony huggingface dataset:
Detection of Deep Low‐Frequency Tremors From Continuous Paper Records at a Station in Southwest Japan About 50 Years Ago Based on Convolutional Neural Network

Abstract Since deep low‐frequency tremors are considered to be associated with large earthquakes that occur adjacently on the same subducting plate interface, it is important to investigate tremors that occurred before the establishment of modern seismograph networks such as the High Sensitivity Seismograph Network (Hi‐net). We propose a deep‐learning solution to detect evidence of tremors in scanned images of paper seismogram records from over 50 years ago. In this study, we fine‐tuned a convolutional neural network (CNN) based on the Residual Network, which was pre‐trained using images of synthetic waveforms from our previous study, using a data set comprised of images generated from real seismic data recorded digitally by Hi‐net to facilitate a supervised analysis. The fine‐tuned CNN was able to predict the presence or absence of tremors in the Hi‐net images with an accuracy of 98.64%. Gradient‐weighted Class Activation Mapping heatmaps created to visualize model predictions indicated that the CNN's ability to detect tremors is not degraded by the presence of teleseisms. Once validated using the Hi‐net images, the CNN was applied to paper seismograms recorded from 1966 to 1977 at the Kumano observatory in southwest Japan, operated by Earthquake Research Institute, The University of Tokyo. The CNN showed potential for detecting tremors in scanned images of paper seismogram records from the past, facilitating downstream tasks such as the creation of new tremor catalogs. However, further training using an augmented data set to control for variables such as inconsistent plotting pen thickness is required to develop a universally applicable model.
Process Mining IPTV Customer Eye Gaze Movement Using Discrete-Time Markov Chains

Human-Computer Interaction (HCI) research has extensively employed eye-tracking technologies in a variety of fields. Meanwhile, the ongoing development of Internet Protocol TV (IPTV) has significantly enriched the TV customer experience, which is of great interest to researchers across academia and industry. A previous study was carried out at the BT Ireland Innovation Centre (BTIIC), where an eye tracker was employed to record user interactions with a Video-on-Demand (VoD) application, the BT Player. This paper is a complementary and subsequent study of the analysis of eye-tracking data in our previously published introductory paper. Here, we propose a method for integrating layout information from the BT Player with mining the process of customer eye movement on the screen, thereby generating HCI and Industry-relevant insights regarding user experience. We incorporate a popular Machine Learning model, a discrete-time Markov Chain (DTMC), into our methodology, as the eye tracker records each gaze movement at a particular frequency, which is a good example of discrete-time sequences. The Markov Model is found suitable for our study, and it helps to reveal characteristics of the gaze movement as well as the user interface (UI) design on the VoD application by interpreting transition matrices, first passage time, proposed ‘most likely trajectory’ and other Markov properties of the model. Additionally, the study has revealed numerous promising areas for future research. And the code involved in this study is open access on GitHub.
How blockchain and microservices are being used together

Microservices and blockchain are recent technologies that are attracting attention not only individually, but also when used together to achieve a certain goal. This study aims to map how both technologies have been integrated with applications, presenting the state of the art in the subject. To do that we conducted a systematic mapping study that searched five significant research bases and selected 40 papers to extract information. The results indicate that healthcare has been the most addressed domain using the combination of blockchain and microservices. On the other hand, the majority of studies have not been evaluated or presented only a proof of concept as an empirical validation, which shows that the proposals are not mature yet and there are still open research opportunities.
An exploratory study on solidity guards and ether exchange constructs

Ethereum is a blockchain platform that enables the use of smart contracts. Smart contracts will execute a set of instructions without an intermediary party when called upon. The possibility to make calls to another contract or exchange cryptocurrency allows for potential exploits to occur, most notable reentrancy. The Solidity language for coding smart contracts has syntactic constructs created to be safer alternatives, and guards to aid in securing code against exploits. In this paper, we collect a total of 26,799 verified Solidity smart contracts from Etherscan, to analyze the language constructs used in calling another contract or exchanging ether. We also analyze the usage of guards to make the code more secure. For instance, even though call is the unsafest function, it is still used by 50% of the contracts in our dataset. The safe method transfer is used by approximately one-third of contracts, and send is rarely used. We noticed that contracts using call have a higher average and median size in Lines of Code than normal. We also found an increased percentage of call contracts using more guards. Moreover, 97% of all contracts are using the require guard, with 23 uses of require on average per contract. This may be an indication that Solidity developers are using more guards to prevent exploits in their contracts
The Autobiography of a Language: Emanuel Carnevali's Italian/American Writing

Book Review| August 01 2020 The Autobiography of a Language: Emanuel Carnevali's Italian/American Writing The Autobiography of a Language: Emanuel Carnevali's Italian/American Writing by Andrea Ciribuco. Albany: SUNY University Press, 2019. 242 pp. Perri Giovannucci Perri Giovannucci American University in Dubai Search for other works by this author on: This Site Google Italian Americana (2020) XXXVIII (2): 200–203. https://doi.org/10.5406/2327753X.38.2.30 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn MailTo Permissions Search Site Citation Perri Giovannucci; The Autobiography of a Language: Emanuel Carnevali's Italian/American Writing. Italian Americana 1 August 2020; XXXVIII (2): 200–203. doi: https://doi.org/10.5406/2327753X.38.2.30 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All Scholarly Publishing CollectiveUniversity of Illinois PressItalian Americana Search Advanced Search The text of this article is only available as a PDF. Copyright 2016 Carla Anne Simonini & Carol Bonomo Albright2016 Article PDF first page preview Close Modal You do not currently have access to this content.
Challenges Performance Facing Global Virtual Teams In Global Software Development:Literature Review

Global technological progress has increased the demand for software applications to manage new technology and employ it in various fields. Moreover, it has contributed to the emergence of a new approach in software development, which is (software globalization), that is, global software development (GSD) by building global virtual teams (GVT) that are geographically spread and different Culturally, you can work from anywhere in the world without the need to be in one place or to see each other daily. Teams communicate with each other through information and communication technology (such as e-mail, telephone, and online video conferencing) and through which information and data are exchanged, which helped reduce travel costs between company branches and reduce the money, time, and effort required to develop software. (GVTs) spread to benefit from the diversity of global expertise and obtain them at low wages in addition to the possibility of working 24 hours and seven days a week, in addition to the proximity to the markets, which helped to understand the requirements of the market and customers and reduce the time for the product to reach the markets, which helped to develop programs Global high quality and affordable cost that can compete and meet the needs of the global software market. A set of challenges and factors have emerged that face (GVTs), represented by: (Leadership, Communication, Cultural differences, Language problems, Time-zone differences, Lack of trust, Group Cohesion, training, Coordination of Expertise, Mutual Support, Team size, ICT problems, ….)These factors have greatly affected the performance of (GVTs) in developing global software. This paper aims to conduct a literature review to determine the Challenges Performance Facing Global Virtual Teams In Global Software Development.
Sentiment Analysis on Food Review from Website Zomato Using K-Nearest Neighbor (KNN) Algorithm

Product reviews based on other people's opinions are very important to be a source of information to build the trust of potential buyers to judge the product. Even though there have been many reviews about restaurant products, there are still many reviews that are not convincing or biased, whether they are good or bad reviews. This problem is essential to be solved because biased judgments can affect the judgment of potential buyers and affect the business side of the restaurant. To solve this problem, a document-level sentiment analysis system was built with the input of a single review sentence and produces a positive or negative class output. The system was built using TF-IDF as a feature extraction method with the K-Nearest Neighbor (KNN) classification algorithm. The selection of the K-Nearest Neighbor (KNN) classification algorithm is because based on existing research, KNN results can be better than others because KNN performance is based on good features and the appropriate number of features. The final result of this study shows that the TF-IDF combined with the KNN classification algorithm with the Euclidean Distance calculation method without going through the Stopwords Removal preprocessing technique and with a ratio of 80:20 produces the best performance accuracy and f1-measure, namely 93.09% and 93.08% by using the value of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{k}=53$</tex> .
A Comparative Study Among Some Natural-Inspired Optimization Algorithms

Biologically inspired algorithms such as Ant colony optimization (ACO), Firefly algorithm (FA), Artificial Bee Colony (ABC), biogeography-based optimization algorithm (BBO) and teaching learning-based optimization (TLBO) are among the most effective algorithms used for optimization. These algorithms are able to solve real-world optimization problems and discover optimal or nearly optimal solutions in a realistic time. Actually, these algorithms have different performance quality in solving various problems based on convergence speed and finding the optimal solution. So, this paper intends to make a comparative study among ABC, FA and TLBO algorithms in terms of minimum, maximum, mean and standard deviation based on some benchmark functions which are Sphere, Rosenbrock, Ackley, Schwefel and Rastrigin functions. The experimental results show that the performance of the TLBO algorithm is superior to other optimization algorithms.
Providing Trust in a Dynamic Distributed Energy Production Scenario by means of a Blockchain

In the near future, a large number of actors will be involved in the production, exchange and consumption of green energy. Such actors are generally small and no central authority is put into place for operations such as e.g. the handling of monetary accounts, for letting consumers paying and producers being paid, etc. For embracing a large amount of actors in a system that does not rely on a central authority, some mechanisms of trust have to be properly embedded. On top of such mechanisms, then the balancing of monetary accounts can be achieved. We have developed a distributed solution catering for the recording of counterfeit-resistant data that characterise energy production by small producers, as well as energy consumption from others. For this, we have devised the need of aggregators that, while gathering data, compute a signature (or hash) for the data received. To make such a signature immutable, we resort to the blockchain technology. In this way, the authenticity of data stored off-chain can be checked by matching them with the immutable signature read from the blockchain storage.
Digital competencies: perceptions of primary school teachers pursuing master’s degrees from eight African countries

The United Nations Educational, Scientific, and Cultural Organization (UNESCO) highlights the relevance of using information and communications technology (ICT) in education for improving the quality of education. To achieve this goal, it is necessary to extend research on digital competences in education. To advance the development of digital competencies it is necessary to take account of how teachers perceive these. In addition, systematic reviews of the literature on ICT and education show an imbalance regarding the amount of research from Africa compared to other regions of the world. In this sense, the objective of this study carried out between March 2019 and April 2020 was to analyse the perceptions of primary school teachers from 8 African countries about their digital competences. The teachers were master’s students in teacher training on virtual platforms. A mixed methodological perspective (quantitative-qualitative) was adopted and a questionnaire with closed and open-ended questions was applied. The quantitative and qualitative analyses show that the teachers recognised their digital competence at all 3 levels. The needs highlighted by teachers were in developing their knowledge of how to create content with the support of technology. However, the available resources, which differed in the participants’ work contexts and did not enable the equal use of ICT in all African countries, was an important issue highlighted by the participants. It is recommended that teacher training in digital competence is prepared using instructional design that promotes innovation and contact with real teaching-learning situations.
Univariate Analysis of Variance as a Method of Solving Professional Pedagogical Tasks in Higher Education

Abstract. Introduction. The new challenges facing the world community in the context of post-industrial development bring to the fore the problems of human capital development. Thus, education plays a key role in ensuring the social and economic well-being of each individual, each country and the world community as a whole. In our opinion, the achievement of this goal will be possible in the case of skillful application of modern didactic methods, not simple exercises, but real pedagogical tasks � tasks of a high level of complexity by lecturers. Professional pedagogical activity is defined as a constant process of solving pedagogical problems, so lecturers should pay considerable attention to the technology of their solution. Purpose. The purpose of this publication is to reveal and clarify the meaning of the term �professional pedagogical task� in the context of the method of its solution in higher education establishment. Results. The article presents, using a practical example, the method of using one-factor variance analysis in solving professional problems of higher school teachers, in particular, identifying the main factors influencing the success of students in higher education institutions. The main purpose of variance analysis is to statistically reveal the influence of various factors on the variability of the characteristic being studied. Of particular interest is the use of the method in the analysis of economic processes and phenomena, when the variability of the resulting characteristic is caused by the simultaneous action of several factors with unequal influence. In particular, this is observed when analyzing the effective synthetic indicators of the economic efficiency of production. Conclusions. Our research material makes it possible to justify the choice of univariate analysis of variance for its application in solving professional pedagogical problems of teachers in higher educational institutions. The results of data processing using analysis of variance give a quantitative or qualitative assessment of research, which, in turn, increases the efficiency of the educational process, improving it.
SAR Target Recognition with Limited Training Samples in Open Set Conditions

It is difficult to collect training samples for all types of synthetic aperture radar (SAR) targets. A realistic problem comes when unseen categories exist that are not included in training and benchmark data at the time of recognition, which is defined as open set recognition (OSR). Without the aid of side-information, generalized OSR methods used on ordinary optical images are usually not suitable for SAR images. In addition, OSR methods that require a large number of samples to participate in training are also not suitable for SAR images with the realistic situation of collection difficulty. In this regard, a task-oriented OSR method for SAR is proposed by distribution construction and relation measures to recognize targets of seen and unseen categories with limited training samples, and without any other simulation information. The method can judge category similarity to explain the unseen category. Distribution construction is realized by the graph convolutional network. The experimental results on the MSTAR dataset show that this method has a good recognition effect for the targets of both seen and unseen categories and excellent interpretation ability for unseen targets. Specifically, while recognition accuracy for seen targets remains above 95%, the recognition accuracy for unseen targets reaches 67% for the three-type classification problem, and 53% for the five-type classification problem.
RCaNmodel: An R package for Chance and Necessity modelling

RCaNmodel is an R package dedicated to the development and exploration of time-dynamic food-web models.The associated user-friendly JavaFX graphical user interface (GUI) RCaNconstructor is specifically developed to facilitate the co-construction of such models with non-modelers.Together, RCaNmodel and RCaNconstructor ensure the traceability and replicability of food-web model developments, and provide a fast and efficient solution to handle the complex mathematical steps that arise from dynamic linear inverse modelling and polytope sampling in high dimensions.The software includes standard graphical outputs for the exploration of food-webs dynamics and the detection of trophic controls, which are key questions in trophic ecology.
A Joint Framework to Privacy-Preserving Edge Intelligence in Vehicular Networks

The number of internet-connected devices has been exponentially growing with the massive volume of heterogeneous data generated from various devices, resulting in a highly intertwined cyber-physical system. Currently, the Edge Intelligence System (EIS) concept that leverages the merits of edge computing and Artificial Intelligence (AI) is utilized to provide smart cloud services with powerful computational processing and reduce decision-making delays. Thus, EIS offers a possible solution to realizing future Intelligent Transportation Systems (ITS), especially in a vehicular network framework. However, since the central aggregator server is responsible for supervising the entire system orchestration, the existing EIS framework faces several challenges and is still potentially susceptible to numerous malicious attacks. Hence, to solve the issues mentioned earlier, this paper presents the notion of secure edge intelligence, merging the benefits of Federated Learning (FL), blockchain, and Local Differential Privacy (LDP). The blockchain-assisted FL approach is used to efficiently improve traffic prediction accuracy and enhance user privacy and security by recording transactions in immutable distributed ledger networks as well as providing a decentralized reward mechanism system. Furthermore, LDP is empowered to strengthen the confidentiality of data sharing transactions, especially in protecting the user's private data from various attacks. The proposed framework has been implemented in two scenarios, i.e., blockchain-based FL to efficiently develop the decentralized traffic management for vehicular networks and LDP-based FL to produce the randomized privacy protection using the IBM Library for differential privacy.
ARMing-Sword: Scabbard on ARM

Scabbard, one of the Post-quantum Key Encapsulation Mechanisms (KEM), is a improved version of Saber that Lattice-based Key Encapsulation Mechanism. Scabbard has three schemes, called Florete, Espada, and Sable. Florete is a Ring-LWR-based KEM that effectively reuses the hardware architecture module used in Saber. Espada is a Module-LWR-based KEM that can be parallelized, requires very little memory, and is advantageous for operating in a resource-constrained environment. Finally, Sable adjusted the parameters to reduce the standard deviation of errors occurring in the Saber. In this paper, we propose ARMing-sword that optimized implementation of Scabbard on ARM processor. For the efficient implementation, a parallel operation technique using vector registers and vector instructions of the ARM processor is used. We focused on optimizing the multiplier, which takes majority execution time for Scabbard computation, and propose a Direct Mapping and Sliding Window methods for accumulating computation results. ARMing-sword has a performance difference of up-to 6.34 $$\times $$ in the multiplier and a performance difference of up-to 2.17 $$\times $$ in the encryption algorithm to which the optimization technique is applied.
Maximization of lung segmentation of generative adversarial network for using taguchi approach

Conditional generative adversarial network (c-GAN) is one of the best-performing convolutional neural networks (CNN) for the segmentation of lung computed tomography (CT). However, lung segmentation from CT images becomes complicated in the presence of various dense abnormalities. The performance in the presence of dense abnormalities can be improved by tuning the c-GAN architecture and parameters of the network. This study focuses on maximizing lung segmentation performance of a c-GAN segmentation algorithm by configuring and tuning the network using the Taguchi optimization method. We have considered the benchmark interstitial lung disease (ILD) dataset for evaluating the performance of the proposed approach. The comparative performance analysis of the proposed algorithm shows that the proposed algorithms outperform the existing state-of-the-art methods, even in the presence of dense abnormalities in lung CT scans. Furthermore, the proposed approach has been demonstrated for lung segmentation in the presence of large nodules.