Reviewerly/exHarmony huggingface dataset:
Ecological Technosphere as the Basic Context of the Formation of the Sociotechnical Landscape

Relevance. The rapid development of the technosphere has played a huge role in the qualitative change of numerous processes in all spheres of human social life. The evolution of the modern anthropo-techno sphere, including the formation of the latest technologies, has led to the emergence of the sociotechnical landscape (STL). STL, being a system of representation of various phenomena of the ecological technosphere, at the same time causes various problems and provides significant opportunities for humanity. All this makes it necessary to determine the prospects for the greening of the technosphere, as well as the main trends in the influence of STL models on modern society, man and the environment. The purpose is conducting a philosophical analysis of the ecological technosphere as the basic context of the formation of the sociotechnical landscape (STL). Objectives: to define the essence of the concept of "ecological technosphere" in its relationship with the new socio-cultural model of the lifestyle of human civilization; consider the TABLE construct as an explication of the ecological technosphere and the anthropoecological system as a whole; to trace the formation of STL through the prism of industrial revolutions; describe Industry 4.0 as a result of the integration and combination of the effects of multiple technologies; to identify the main prospects for further interaction between the technosphere and the ecosphere. Methodology. To substantiate the concept of anthropological complementarity, assuming the fact that human activity includes simultaneous subordination to nature and control over it, an analysis of the processes and mechanisms that underlie the formation of STL as an explication of the ecological technosphere was carried out. Results. The presented manuscript presents the results of the philosophical analysis of the STEEL construct as an explication of the ecological technosphere and the anthropoecological system as a whole. In the course of this study, the formation of STL was considered through the prism of industrial revolutions, and the main prospects for further interaction between the technosphere and the ecosphere were identified. Conclusions. Conducting an interdisciplinary analysis of the role of the technosphere as a determining factor in the development and formation of all other technical systems and products is an urgent and significant problem. A comprehensive study of the ecological technosphere as the basic context of the formation of STL is necessary, due to the fact that it created the prerequisites for the emergence of artificial intelligence and convergent technologies, which currently determine global economic, cultural, social and industrial trends.
Deductive Knowledge

As humans, we can deduce more from the data graph of Figure 2.1 than what the edges explicitly indicate. We may deduce, for example, that the $${\rm{\tilde N}}$$ am festival ((eidis)) will be located in Santiago, even though the graph does not contain an edge (eidis)— location →(santiago). We may further deduce that the cities connected by flights must have some airport nearby, even though the graph does not contain nodes referring to these airports. In these cases, given the data as premises, and some general rules about the world that we may know a priori, we can use a deductive process to derive new data, allowing us to know more than what is explicitly given by the data. These types of general premises and rules, when shared by many people, form part of “commonsense knowledge” [McCarthy, 1990]; conversely, when rather shared by a few experts in an area, they form part of “domain knowledge,” where, for example, an expert in biology may know that hemocyanin is a protein containing copper that carries oxygen in the blood of some species of Mollusca and Arthropoda.
Hyperspectral denoising based on the principal component low-rank tensor decomposition

Abstract Due to the characteristics of hyperspectral images (HSIs), such as their high spectral resolution and multiple continuous narrow bands, HSI technology has become widely used in fields such as target recognition, environmental detection, and agroforestry detection. HSIs are subject, for various reasons, to noise in the processes of data acquisition and transmission. Therefore, the denoising of HSIs is very necessary and important. In this article, according to the characteristics of HSIs, an HSI denoising model combining principal component analysis (PCA) and CANDECOMP/PARAFAC decomposition (CP decomposition) is proposed, which is called PCA-TensorDecomp. First, we use PCA to reduce the dimension of HSI signals by obtaining the first K principal components and get the principal composite components. The low-rank part corresponding to the first K principal components is considered the characteristic signal. Then, low-rank CP decomposition is carried out, to denoise the first principal components and the remaining minor components, the secondary composite components, which contain a large amount of noise. Finally, the inverse PCA is then used to restore the HSIs denoised, such that the effect of comprehensive denoising is achieved. To test the effectiveness of the improved algorithm introduced in this article, we compare it with several methods on simulated and real hyperspectral data. The results of the analysis herein indicate that the proposed algorithm possesses a good denoising effect.
Storage Enhancement in the Cloud Using Machine Learning Technique and Novel Hash Algorithm for Cloud Data Security

Cloud computing refers to the supply of a variety of services through the Internet, including data storage, servers, databases, networking, and software. Cloud-based storage allows files to be saved to a remote database instead of being stored on a proprietary hard drive or local storage device. In cloud-fog storage integrated situations, data redundancy is a major issue that wastes a lot of data storage. By removing redundant data from cloud storage systems, data redundancy issues can be efficiently decreased and handled. In a cloudcomputing environment, implementing redundant data over encrypted data is always a significant issue. This study builds secure non-redundant systems using the SHA-512 cryptographic hash algorithm in a cloud environment. The proposed Bayesian model concentrates on the two most important objectives of the system, first is the data redundancy must be reduced to a bare minimum; and the second is a strong encryption strategy must be created to assure data security. The experimental results of the Bayesian technique enable the cloud users to effectively fulfil their cloud data storage space by avoiding the storage of duplicated data and saving bandwidth. The proposed method surpasses existing methods in terms of computational complexity and security levels, based on the results of the testing.
Chapter 4 Smittestopp analytics: Analysis of position data

Abstract Contact tracing applications generally rely on Bluetooth data. This type of data works well to determine whether a contact occurred (smartphones were close to each other) but cannot offer the contextual information GPS data can offer. Did the contact happen on a bus? In a building? And of which type? Are some places recurrent contact locations? By answering such questions, GPS data can help develop more accurate and better-informed contact tracing applications. This chapter describes the ideas and approaches implemented for GPS data within the Smittestopp contact tracing application.We will present the pipeline used and the contribution of GPS data for contextual information, using inferred transport modes and surrounding POIs, showcasing the opportunities in the use of GPS information. Finally,we discuss ethical and privacy considerations, as well as some lessons learned.
Cyber-Physical Behaviour Detection and Understanding using Artificial Intelligence

The advancement of cyber-physical behaviour detection and understanding in context of urban environment safety and security has been developed in the S4AllCities project (S4AllCities, 2020). Specifically, various concepts of fundamental artificial intelligence and reasoning have been successfully developed and will subsequently be tested in situ in S4AllCities pilot sites during the coming year 2022 (Sabeur et al, 2021). The detection of anomalies in TCP and UDP communication-based protocols taking place in context of urban spaces have been investigated. These were also complemented with the detection of unusualness in crowd physical behaviour in the same urban spaces. The aim is to combine both modes (cyber and physical) of detection and behaviour understanding, in order to advance our situation awareness in context of native knowledge and reasoning for efficiently maintaining safety and security across the urban space. Native knowledge concerns the evaluated risks and mitigation measures for response to potential cyber-physical attacks on the urban space. In this study, the deployed machine learning techniques achieved good performances for classifying cyber and physical behaviour under various scenarios of potential attacks. Our future work is to exercise the performance, evaluation and validation of our intelligent algorithms using in situ cyber and physical observation scenarios of the urban spaces of the three S4AllCities pilot sites in Europe.References:S4AllCities (2020). Safe and Secure Smart Spaces for all Cities H2020 project ID number 883522. https://www.s4allcities.eu/project. Sabeur Z., Angelopoulos C.M., Collick L., Chechina N., Cetinkaya D., Bruno A. (2021) Advanced Cyber and Physical Situation Awareness in Urban Smart Spaces. In: Ayaz H., Asgher U., Paletta L. (eds) Advances in Neuroergonomics and Cognitive Engineering. AHFE 2021. Lecture Notes in Networks and Systems, vol 259. pp. 428-441. Springer, Cham. https://doi.org/10.1007/978-3-030-80285-1_50
SAP S/4 on Microsoft Azure – Deployment

This chapter will deepen the theoretical foundations from the previous chapter in a practice-oriented manner by deploying an S/4HANA system based on Azure VM step by step. Firstly, we will present the example architecture demonstrating how the system resources will be implemented to receive the end result. After that, we will deploy the basic network architecture, the HANA HA cluster, and the S/4HANA HA cluster in order. We will show how to provision resources manually as well as how to create and use custom ARM templates. Finally, we will shortly look at alternative deployment automations based on SAP CAL, as well as Ansible and Terraform.
The Importance of Validation Metrics in Chaotic Image Encryption

Nowadays, the technological growth is remarkable in various fields and domains. Besides, the computing environments will be completely in distributed mode in near future. That is, even the development, training and testing of computing models or algorithms and other artificial intelligence algorithms will be running in several locations for many hours. Hence, the data supplied to this type of environment must be secure. The distributed environment also should be completely secured to avoid adversarial attacks. This perhaps leads to deal with different types of data in which the images are the important one. Particularly, the medical images have vital part in medical field for diagnosing and treatment of diseases. In order to provide confidentiality for medical images, several encryption algorithms are proposed. Among them chaotic encryption plays an important role. This study deals and elaborates the importance of validation metrics used for chaotic cryptosystem. Ultimately, few suggestions are also proposed for validating chaotic cryptosystem.
Community for data integration 2020 annual report

The Community for Data Integration is a community of practice whose purpose is to advance the data integration capabilities of the U.S. Geological Survey.In fiscal year 2020, the Community for Data Integration held 11 monthly forums, facilitated 13 collaboration areas, and supported 13 projects.The activities supported the broad U.S. Geological Survey priority of producing building blocks for doing integrated predictive science.Specifically, the activities supported tools and methods for findable, accessible, interoperable, and reusable (FAIR) data and wildland fire and water prediction.Through these efforts, community members were informed of new and emerging technologies and data topics that helped them accomplish their professional responsibilities.
Does nationality affect nurses’ information security participation? A comparative study in Iran and Poland

Background Confidential personal data breaches are a serious problem for medical service providers. This may result in high financial costs for medical care facilities and unnecessary stress for patients. Research on information security (IS) in healthcare has been insufficient thus far. International studies may add a new perspective on this issue. Participants and procedure A quantitative approach was assumed to analyse relationships between IS climate and IS indicators in countries with different cultures (Iran and Poland). The Information Security Climate Index developed by Kessler et al. were used in the study. The PLS3 and SPSS 22 software was used for data analysis. Results Results showed that the predicted factors of nurses’ IS participation were different in Iran and Poland. In both countries, nurses' perception of the importance of IS had an effect on IS participation, but in Poland, IS compliance had an additional positive effect. In addition, IS compliance moderated the relationship between IS motivation and IS participation. Conclusions Components of IS climate have a significant effect on nurses’ IS participation in both Iran and Poland, though nationality can modify this relation. However, in both countries, by linking IS policies with human resource management activities such as performance appraisal and training, nurses’ perception of IS importance can be increased and IS participation can be facilitated.
Reasons for the Emergence of Body-Focus Repetitive Behaviors in Students and the Impact of Body-Focus Repetitive Behaviors on Students

Researchers have long explored the relationship between Body-Focus Repetitive Behaviors (BFRBs) and students, including school-age children and youth, and the effects of BFRBs on students.The paper summarizes previous research on BFRBs and related aspects and concludes that the presence of BFRBs is positively associated with students' stress and negative emotions and that BFRBs have a negative impact on student's physical and mental health.It is hoped that this paper will raise the awareness of parents and teachers about BFRBs.
Scoping natural language processing in Indonesian and Malay for education applications

Indonesian and Malay are underrepresented in the development of natural language processing (NLP) technologies and available resources are difficult to find.A clear picture of existing work can invigorate and inform how researchers conceptualise worthwhile projects.Using an education sector project to motivate the study, we conducted a wide-ranging overview of Indonesian and Malay human language technologies and corpus work.We charted 657 included studies according to Hirschberg and Manning's 2015 description of NLP, concluding that the field was dominated by exploratory corpus work, machine reading of text gathered from the Internet, and sentiment analysis.In this paper, we identify most published authors and research hubs, and make a number of recommendations to encourage future collaboration and efficiency within NLP in Indonesian and Malay.
Natural Language Processing and Big Data: A Strapping Combination

Business cannot just boot up with Microsoft Excel, make a graph and call it good. Traditional spreadsheets and analytical software are not capable of managing and processing Big data. Lots of data is generated in past few decades. In order to manage exploding amount of data and offer real time analytical power lot of programs and new tools have been introduced. Natural Language processing is becoming essential to many new business functions such as chatbots, digital assistance like Alexa, Siri, google home, health care industry, compliance monitoring business intelligence (BI), analytics and many more. There is lot of unstructured and semi- structured content available like queries, email communications, social media, videos, customer reviews, support requests and so on, that can bring significant insights. NLP tools and techniques help business process in analyzing and understanding all of such data in order to operate effectively and proactively. NLP can also analyze the pattern emerging in text/audio entries in big data by scrutinizing the linguistic and semantic statistics to extract the significant entities and relationship in the context of what the customers are trying to say in their feedbacks using automatic summarization, speaker diarization and entity extraction. NLP has ability to understand and derive meaning from both written and verbal communication. In this chapter we will discuss about the significant benefits of merging NLP with Big Data to accurately analyze the increasing amount of unstructured data, like email, text messages, voice calls to take a strong insight into human behavior, specially when combined with other structured data.
PTLchain: Privacy and Traceability Enhanced Scheme for Logistics by using Consortium Blockchain

In digital intelligence era, the authenticity , privacy and traceability of data are key factors in building a good logistics information system. However, current logistics system does not pay attention to protecting user’s privacy information, once the user’s privacy is leaked, it will seriously damage the interests of the user. Besides, when there is a logistics dispute, the logistics system cannot ensure the credibility of the traceable process, which reduces the security and availability of the system. Compared with the traditional logistics system, the blockchain-based logistics scheme ensures the security of users’ personal privacy, the authenticity and traceability of logistics information to a certain extent. However, most of the existing blockchain-based privacy-preserving logistics schemes only propose corresponding system models, which cannot be directly applied. Therefore, to address the leaking problems of logistics information in today’s logistics system and inefficiency in tracing the logistics delivery process, we propose a privacy and traceability enhanced scheme for logistics based on consortium blockchain. The blockchain guarantees the authenticity of logistics data through tamper-resistance and traceability, it avoids logistics information and transportation process tampering and supports efficient logistics traceability. Furthermore, we utilize asymmetric encryption and attribute-based encryption to encrypt users’ privacy and logistics information, to protect the private information and realize fine-grained access control. Finally, we implement the prototype system by Hyperledger Fabric platform, and give the performance evaluation to verify the feasibility and practical significance of our scheme.
TCAS System Fault Research and Troubleshooting Process

The function of TCAS system is to identify other aircraft equipped with TCAS equipment in its surveillance airspace, determine whether the aircraft has potential threats by calculating the possible flight paths of other aircraft, and send visual and voice reminding information to the pilot to remind the crew to take necessary maneuver measures. All the above tasks are completed automatically without crew intervention. The consultation information sent by TCAS is divided into two levels: traffic consultation TA and decision consultation RA. Traffic advisory information is used to remind pilots to pay close attention to the existence, movement status, and trend of the intruding aircraft around the aircraft, while decision advisory information requires pilots to immediately take the recommended vertical avoidance measures to avoid imminent danger approaching and ensure the safety of the aircraft.
The AISP-SJTU Simultaneous Translation System for IWSLT 2022

This paper describes AISP-SJTU's submissions for the IWSLT 2022 Simultaneous Translation task.We participate in the text-totext and speech-to-text simultaneous translation from English to Mandarin Chinese.The training of the CAAT is improved by training across multiple values of right context window size, which achieves good online performance without setting a prior right context window size for training.For speech-to-text task, the best model we submitted achieves 25.87, 26.21, 26.45 BLEU in low, medium and high regimes on tst-COMMON, corresponding to 27.94, 28.31,
Language Models as Context-sensitive Word Search Engines

Context-sensitive word search engines are writing assistants that support word choice, phrasing, and idiomatic language use by indexing large-scale n-gram collections and implementing a wildcard search.However, search results become unreliable with increasing context size (e.g., n ≥ 5), when observations become sparse.This paper proposes two strategies for word search with larger n, based on masked and conditional language modeling.We build such search engines using BERT and BART and compare their capabilities in answering English context queries with those of the ngram-based word search engine Netspeak.Our proposed strategies score within 5 percentage points MRR of n-gram collections while answering up to 5 times as many queries. 1
Measure of translation transformations in rendering a science text

An adequate measure of translation transformations is a distinctive feature of a good translation. It allows to realise the two major requirements to the target text – equivalent regulatory effect of source and target texts and maximum possible semantic and structural similarity of source and target texts. A faithful translation should be made in keeping with the three principles determining the adequate measure of transformations in translation: the motivation principle, the minimalism principle and the boundedness principle. A particular research interest is taken in the degree of translation transformations in the science text in conformity with these principles. To produce a quality translation with (an) identical pragmatic effect, the translator should clearly define the measure of translation transformations – when certain translation modifications are necessary and admissible. It is important for translators to be knowledgeable about the functions of each determinant principle and avoid unmotivated transformations, reasonable but excessive modifications, and motivated transformations that go beyond the admissible translation limits. The translator should also avoid extremes – literal and free translations, which hamper the perception of the text. The adequate measure of translation transformations is in the middle between literal and loose translations. To take an appropriate translation solution, the translator of a science text should be trained in using translation techniques and bring the source text in conformity with the norms of the language of science. A specific admissible measure of transformations in translation is impossible to determine as it varies in different text genres and different contexts. The translator should be competent enough in the branch of knowledge the translation text belongs to. Our analysis of the Ukrainian translation of the history (historical) monograph “The Celts: A Chronological History” by Dáithí Ó hÓgáin, aims to establish the degree of semantic and structural divergences between the source and target texts and to evaluate the translation solutions that were used to achieve the pragmatically equivalent translation.