Reviewerly/exHarmony huggingface dataset:
Upper computer software design for fretting signal acquisition system based on Qt

Micro motion detection is an essential research direction in urban underground space and energy prospection. Aiming at the shortcomings of the traditional system for seismic exploration, the upper computer software of the micro motion signal acquisition system based on TCP/IP protocol is designed, which can be distributed offline acquisition and control. By formulating the communication protocol of the application layer and adding the heartbeat packet mechanism, the stable connection with the collector, data transmission and status monitoring is realized. Meanwhile the received data are stored and visualized, and the project management and task configuration are implemented by using the file reading and writing function of QT to achieve the effect of offline control. Through the test, the results show that each function can get the rapid and correct response of the lower computer, and the upper computer software of the system meets the design requirements. It has the advantages of high acquisition efficiency, reliability and convenience which has strong practicability.
Notebooks for Data Analysis and Visualization: Moving Beyond the Data

Notebooks are a relatively new way of analyzing data and creating visualizations. They differ from the common graphical user interfaces used for visualization tools in many ways, and have their own strengths and weaknesses. In particular, they allow easy sharing, experimentation, and collaboration, and provide context about the data for different kinds of users. They also integrate modeling, forecasting, and complex analyses directly with the visualization. We believe that notebooks provide a unique and fundamentally new way of working with and understanding data. By laying out their unique properties, we hope to inspire both researchers and practitioners to investigate their many uses, explore their pros and cons, and share their findings.
ВИХОВАННЯ В ЕПОХУ ЦИФРОВІЗАЦІЇ

The article discusses the main problems of education in the conditions of digitalization. The digital generation is characterized. The directions of the education process conditions of digitalization have been determined: the selection of pedagogical personnel, the preparation of methodological support, and the selection of new forms, methods, and means of education. The concerns of digitization of education and training have been identified
Object detection of VisDrone by stronger feature extraction FasterRCNN

Object detection and analysis in remote sensing images is a critical research subject for many businesses and agencies. At present, object detection based on convolutional neural network (CNN) in natural scenes has good performance. Due to the large number of small objects and similar characteristics between the objects in the VisDrone dataset, the current model cannot extract more small-scale features. Therefore, this paper proposes a stronger feature extraction FasterRCNN (SFE-FasterRCNN) that advances a feature extraction strengthening network to enhance the feature learning ability for different objects. Specifically, the pixel proposal network (PPN) is proposed by combining the low-resolution and strong semantic features with high-resolution and weak semantic features through a top-down approach and reusing these fusion blocks vertically to construct a comprehensive semantic feature map. Then hyperbolic pooling is proposed to minimize the loss of feature information during the activation mapping process. Finally, data clustering is used to adaptively generate better object proposals according to the characteristics of the dataset. Experimental results on the VisDrone dataset show that our method has excellent detection results.
Assuring the Evolvability of Legacy Systems in Devops Transformation/Adoption: Insights of an Experience Report

DevOps has changed the software industry to enable continuous delivery. While many studies have investigated on how to introduce DevOps into a software product from the organizational perspective, less is known about the technical challenges developers and practitioners face when transforming legacy codes into DevOps, despite the undisputed importance of this topic. In this paper, throughout the context of web applications, we report the results of a study case with the adoption of four legacy open-source projects into DevOps to understand which refactoring techniques and strategies influence developers’ decisions. We analyze two dependent variables: the technique used and how they are applied to the project. After every implementation, there was an overview of the process that just occurred and later a written report on how the strategies have been applied, their respective order, which strategy has been more fruitful, and such. Those reports have been the foundation of this study. The main findings of such study are that some strategies are more efficient when viewed from the evolution aspect and the sequence these techniques are employed matter.
Blockchain Technologies in the Design and Operation of Cyber-Physical Systems

A blockchain is an open, distributed ledger that can record transactions between two parties in an efficient, verifiable, and permanent way. Once recorded in a block, the transaction data cannot be altered retroactively. Moreover, smart contracts can be put in place to ensure that any new data added to the blockchain respects the terms of an agreement between the involved parties. As such, the blockchain becomes the single source of truth for all stakeholders in the system. These characteristics make blockchain technology especially useful in the context of Industry 4.0, distributed in nature, but with important requirements of trust and accountability among the large number of devices involved in the collaboration. In this chapter, we will see concrete scenarios where cyber-physical systems (CPSs) can benefit from blockchain technology, especially focusing on how blockchain works in practice, and which are the design and architectural trade-offs we should keep in mind when adopting this technology both for the design and operation of CPSs.
App Modernization Using GCP - Save Filter Data on an E-Commerce Platform

Cloud computing has become an essential component of any IT environment, where companies want their data to be stored, processed and analyzed upon. Companies can also host their application on cloud platforms for users to have a seamless experience. This paper proposes an idea to migrate an application from Amazon Web Services (AWS) to Google Cloud Platform (GCP) and then modernize the application in a new environment. The main purpose of this paper is to bring light to an application modernization solution which is a pressing issue for all the users across the world, but has not been implemented as of now. Once the migration is in process, an Application Programming Interface (API) can be implemented, to save the user generated filters on the e-commerce application. The API has been created using python scripts using an Indian e-commerce platform as a reference. This process should help a user save time and create a better user experience, while providing the platform with valuable insights regarding user behavior.
Text Categorization Using Convolutional and Bidirectional Fast Gated Recurrent Unit

A novel supervised model is presented in this paper with One-Dimensional Convolutional Neural Networks (CNNs) followed by double Bidirectional Gated Recurrent Units (2-BiGRUs) for text categorization. The overall model contains six components, two of which are required. Starting with the input layer which converts the text into an embedding matrix and then passed to the embedding layer via GloVe and Word2Vec embeddings. The convolution block then extracts the bidirectional temporal characteristics from text data and flatting them by the Flatten layer, using Softmax activation and dropouts; the results are then normalized using the Batch Normalization layer and sent to the Dense or output Layer which generates the categorization results by using the Softmax activation. Multiple experiments with loss and precision scores are the primary criterion for evaluating the proposed model on WebKb, Reuters R8 and R52, AG-News (AG), and 20 NewsGroups (20NG) datasets. Our model achieves better precision results on the WebKB, AG, and 20 NG data sets than the best results of other approaches. The proposed model demonstrates that performance scores are superior.
A Secure Framework Design for Digital Sensitive Content Certification

This paper presents a digital system for issuing digital certificates for sensitive Internet contents through modern digital authentication techniques. Taking the Quran content as a case study and relying on blockchain technology for authentication and certification, we suggest designing a system that aims to reduce attempts to manipulate the Quran by appointing parties responsible for examining the product and others accountable for issuing certificates for the Quran product in a decentralized environment that allows the end-user to quickly verify the authenticity of the digital certificate issued for the content of the Quranic product over the internet.
Supplemental Material: Gold-mineralized diorite beneath the Linglong ore field, North China craton: New insights into the origin of decratonization-related gold deposits

&lt;p&gt;One text file (analytical methods), three figures, and twelve Excel files.&lt;/p&gt;
Immuno-Inspired Augmentation of Siamese Neural Network for Multi-class Classification

Siamese Neural Networks (SNN) provide a robust mechanism to learn similarities/dissimilarities between objects of different classes. The distinguishing features learnt by SNNs make them a good candidate for multi-class classification as well. However, the potential of an SNN to create a classification space that has, both higher accuracy and lower inference time, needs to be exploited further. In this paper, we present a novel multi-class classification approach using SNNs by drawing concepts from the Immune Network theory. This bio-inspired strategy aids in injecting class specific characteristics into the SNN architecture, thereby enhancing the classification process. Experimental results conducted on three benchmark datasets indicate that the approach consistently provides higher accuracies and lesser inference times as compared to recent SNN based multi-class classification approaches, indicating its efficacy.
Extending Temporal Data Augmentation for Video Action Recognition

Pixel space augmentation has grown in popularity in many Deep Learning areas, due to its effectiveness, simplicity, and low computational cost. Data augmentation for videos, however, still remains an under-explored research topic, as most works have been treating inputs as stacks of static images rather than temporally linked series of data. Recently, it has been shown that involving the time dimension when designing augmentations can be superior to its spatial-only variants for video action recognition [34]. In this paper, we propose several novel enhancements to these techniques to strengthen the relationship between the spatial and temporal domains and achieve a deeper level of perturbations. The video action recognition results of our techniques outperform their respective variants in Top-1 and Top-5 settings on the UCF-101 [55] and the HMDB-51 [38] datasets.
Velocity-Free Localization of Trapped People

It is vital for rescuers to accurately locate the trapped people and carry out rescue work in time. The blasting and drilling tests are conducted to simulate the distress signals sent by the trapped people knocking with tools. The velocity-free localization method and traditional method are used to determine the source positions. The results show that it is feasible to apply the AE location method to determine the location of trapped people. The velocity-free method performed better than the traditional method in such a situation.
Participatory Modelling of Information Technology Equipment Vulnerability Using Causal Loop Analysis

The purpose of this study was to model the relationships between the vulnerability factors that relate to unintentional compromising electromagnetic emanations from Information Technology Equipment (ITE). ITE vulnerabilities of this type can occur in many contexts including office and manufacturing environments using for example thin client and laptop technologies. The study applies a Causal Loop Analysis to the vulnerability factors to show the inter-relationships between them. This enables a clearer understanding of their relative significance when assessing vulnerability likelihood. The resulting causal loop analysis will be used in future work to develop a decision support tool for cyber security practitioners, when assessing the likelihood of unintentional emanations from ITE leading to the compromise of sensitive information.
FAIR4PGHD: A framework for FAIR implementation over PGHD

Patient Generated Health Data (PGHD) are being considered for integration with health facilities, however little is known about how such data can be made machine-actionable in a way that meets FAIR guidelines. This article proposes a 5-stage framework that can be used to achieve this.
Support and defense strategy-assisted social network search algorithm and its application

A novel state-of-the-art optimization strategy, named support and defense strategy, is proposed to enhance the research ability for the social network search algorithm. In the improved social network search algorithm (ISNSA), the support and defense strategy (SDS) is used to improve the conversation and disputation efficiency. The SDS can improve the correlation between the supporters and opponents in the ISNSA. The research ability of the ISNSA is verified with three constrained engineering problems. The optimization results show that the ISNSA is a competitive algorithm, which can obtain the global optimal solution.
Strategy and tools for an inclusive, development-oriented online open educational resource in allergen science

Open scientific education resources for health care professionals, researchers, patients and public are encouraged. In line with the 2019 UNESCO Open Educational Resource (OER) guidelines, we conducted a gap-analysis of free online allergen OERs. The lack of a comprehensive OER prompted us to establish a free OER for allergen science, including tools for assessing the impact of OER language in native/official English-speaking (E) and non-English speaking countries (non-E).
Cloud resource management using adaptive firefly algorithm and artificial neural network

Cloud computing can be characterised in basic terms as one of the stages for giving conveyed figuring assets. Registering assets may incorporate capacity, data transfer capacity, memory space, handling components, etc. These assets are leased to the clients utilising the compensation per-utilised model. The interest for the assets may not be static, yet they can be mentioned on-request because of the developing web office. The clients of the cloud want to have a limited reaction time and cost yet then again, the cloud supplier centres on accomplishing proficient cloud assets distribution and limiting the support costs. Resource management is a practice of provisioning and managing the cloud resources efficiently. It also provides the techniques to provision the resources, schedule the jobs, and balance the loads. This proposal provides a resource management technique for efficient provisioning of resources and the scheduling of jobs on the static and dynamic cloudlet requests.
Towards a Software Development Process Based on Ontologies

The relevance and complexity of software are growing more and more.Hence, it is necessary to create and apply new approaches that contribute to deal with the complexity of software development process.Ensuring the consistency and traceability of the software artefacts are two of the challenges that software engineers face during the development of a complex software.On the other hand, the ontologies are one of the main technologies of the semantic web for representing knowledge.Adopting ontologies to support the modelling and validation in software development projects is a feasible alternative that has been widely discussed.Therefore, this paper aims to analyze some ontology-based approaches which address some of the gaps that can be faced during software development projects.For example, the lack of consistency and traceability of software artefacts.
Breast cancer detection in digital mammography using a novel hybrid approach of Salp Swarm and Cuckoo Search algorithm with deep belief network classifier

The Computer Aided Diagnosis (CAD) system has evolved as a useful tool for radiologists to classify breast cancer images into various categories, enabling early diagnosis and treatment. In CAD model construction, feature selection is essential for determining a subset of appropriate features to diagnose breast cancer. Salp Swarm Algorithm (SSA) is an evolutionary algorithm that simulates the swarming behaviour of salps. SSA has some advantages such as simplicity, speed in searching and ease of hybridization with other optimization algorithms. However, it suffers from being stuck in local optima and having slow convergence. To address these issues, this work proposes a novel hybridization algorithm called SSACS by combining the SSA with Cuckoo Search (CS) to improve convergence and exploitation capabilities. Further, the Deep Belief Network (DBN) classifier is applied to classify the mammogram images and improve the diagnosis rates. The proposed system’s efficacy is validated with the benchmark database of the Mammographic Image Analysis Society (mini-MIAS) dataset. The experimental findings indicate that the proposed SSACS with DBN classifier outperforms the state-of-the-art methods.
Markov chain latent space probabilistic for feature optimization along with Hausdorff distance similarity matching in content-based image retrieval

Content-Based Image Retrieval involves searching a database of photographs for the pictures that look like the query picture. With the help of Feature Extraction Technique, number of required images can be extracted from the database based on the query image. Fuzzy Cmean, Kmean, Gray-Level Co-Occurrence Matrix, Local Binary Pattern, Principal Component Analysis and Scale Invariant Feature transform are used to extract features. The main motive of this work is to improve the efficiency of Content-Based Image Retrieval system by eliminating characteristics from query and database images. The Markov Chain Rule Feature Optimization approach is used to choose the informative features from the features. For similarity search, Hausdarff Distance along with Gradient Boosting machine learning techniques improves matching accuracy and retrieval speed. To compare to the state of the art, we have got 97.71% accuracy and they got 93.89% and 94.79%.
Recognition Model for Activity Classification in Everyday Movements in the Context of Dementia Diagnostics – Cooking

By monitoring movements and activities, the progression of neurological diseases can be detected. The documentation required for this is associated with a high level of effort, which is hardly possible in view of the increasing shortage of nursing staff. In order to gradually relieve the nursing staff, we are developing an approach to automate documentation in cooperation with two dementia residential communities. The aim of this work is to facilitate everyday life of caregivers. Previous research results from this working group show that everyday activities of dementia patients can be recognized well by combining smartwatch sensor technology and machine learning. However, the state of research has gaps when it comes to recognize activities consisting of a variety of movement patterns. In this paper, we present an approach to classify the activity of cooking. We divide this activity into several sub-activities each consisting of a distinct motion pattern that a recurrent network recognizes. This is followed by a model for calculating the probability that cooking actually occurred based on the different sub-activities recognized. We show the advantages of different smartwatch sensor combinations and compare the different approaches of our model with the prediction accuracy of the classification. This model can later be integrated into the care documentation of the residential communities in addition to the activities that are easier to recognize.
Protection and Transfer of Financial Digital Data Through Open Source Software

Modern society is increasingly using ICT technologies to transfer information quickly. In addition to the purely technological advantages of IT tools, they enable the recipients of information data to be ultimately facilitated in their operational activities. On the other hand, data transfer, when it comes to transferring files with specific information, is a serious issue that concerns both available technological resources and security. This report aims to explore the current technological capabilities for the transfer of digital information containing banking, financial and other data, to be maximum protected from unauthorized or malicious users. Also into question are conventional methods of transmitting digital data via email or cloud. Open source software provides the possibility of reliable encryption and protection: Money Manager Ex, Tor Browser and OnionShare. In the present study, specific advantages of realizing the transfer of digital data from the point of transmission to the final recipient are presented.
Experimenting on the Enactment of Predictive AI: The Quest for a Future Proactive Healthcare Sector

Currently, a large number of AI projects are experimenting with the use of AI and big data for various purposes, especially in the public sector. In this article, we explore one such AI project. Specifically, we study a group of developers in Scandinavia and their efforts to enact predictive AI through the development of a clinical decision support system (CDSS) in pursuit of a future proactive healthcare sector. This yet-to-be system was envisioned to prevent unplanned hospitalizations by ‘turning’ what we term ‘potential patients’, i.e. the effective management of patient trajectories, in pursuit of a proactive healthcare sector. In the article, we investigate this particular project as an ‘experiment’ and conceptualize the developing CDSS as a ‘partially existing object’ with an uncertain ontological status. By studying the gradual enactment and emergence of the CDSS, we illuminate how this fuzzy data-driven object is performed and gradually attributed with solid reality: during its creation process, it advances from being a proactive device imagined to be used in primary healthcare to becoming a triage tool embedded in the prehospital emergency department. Along the way, the project developers are also transformed, learning what ‘moves’ and ‘actions’ to make, and, thereby, becoming skillful CDSS-operators. By using ‘experiment’ as our analytical lens, the article renders visible how persons, locations, and procedures have to be changed, revoked, and suspended in order for the AI project to succeed. Thus, the article contributes to showing how ‘social mangling’ is an essential precondition for predictive AI to succeed as a prolific solution to specific healthcare challenges, along with developers’ learning and transformation.
(Retracted) Mimicking human vision systems: deep-learning-based feature fusion for semantic image retrieval

The Editor-in-Chief and the publisher have retracted this article, which was submitted as part of a guest-edited special section. An investigation uncovered evidence of systematic manipulation of the publication process, including compromised peer review. The Editor and publisher no longer have confidence in the results and conclusions of the article.ZC and LZ agreed with retraction.