Reviewerly/exHarmony huggingface dataset:
An Effective Integration of Domain Knowledge in-to Deep Neural Networks

Machine learning in recent years has become an integral part of our day to day life and the ease of use has im-proved a lot in the past decade.There are various ways to make the model to work in smaller devices.A modest method to advance any machine learning algorithm to work in smaller devices is to provide the output of large complex models as input to smaller models which can be easily deployed into mobile phones .We provided a framework where the large models can even learn the domain knowledge which is integrated as first-order logic rules and explicitly includes that knowledge into the smaller model by simultaneously training of both the models.This can be achieved by transfer learning where the knowledge learned by one model can be used to teach the other model.Domain knowledge integra-tion is the most critical part here and it can be done by using some of the constraint principles where the scope of the data is reduced based upon the constraints mentioned. One of the best representation of domain knowledge is logic rules where the knowledge is encoded as predicates.This framework provides a way to integrate human knowledge into deep neural networks that can be easily deployed into any devices.
THE ROLE OF THE HUMANITIES IN THE FORMATION OF ADVANCED EDUCATION

The article substantiates the role of the humanities in the formation of a model for advanced education in the interests of sustainable development within the framework of global transformations, taking into account the influence of modern threats and challenges (technogenic, biogenic, sociocultural, etc.). In the context of the modern educational policy of Russia, the authors present the key elements in the humanitarian component of the model under consideration (professional-competence, value-worldview, spiritual-patriotic and humanitarian-ecological components, digital competencies and soft skills).
Cross-modal Data Linkage for Common Entity Identification

Multi-modal data is becoming pervasive in the digital era, providing compelling scenarios that require cross-modal linkage such as linking image data with databases. We outline a critical matching/linking task within that space, which we call cross-modal common entity identification. This involves linking images with structured databases with the aid of available unstructured information. We propose a framework and method, ICE, which embodies a structured approach for the same involving information extraction from images and person matching followed by identifying a common entity that unites people represented in the image. We curate data sources from the entertainment domain, upon which we illustrate the effectiveness of our method. We hope ICE will generate interest in other tasks within the realm of multi-modal data processing in the intersection of image processing, NLP and databases.
Predicting Medical Procedures from Diagnostic Sequences Using Neural Machine Translation

Automating diagnosis coding is a predictive healthcare application that involves building predictive models for large-scale patient data. This chapter proposes an approach for automatically predicting suitable diagnostic procedures given patients' medical records. The problem essentially is viewed as one of neural machine translation, where the sequence is translated from the diagnosis space to the procedure space. The results are presented in the form of Bilingual Evaluation Understudy Score (BLEU) scores, because the performance of the proposed approach as a machine translation problem is to be measured. The method used in this chapter is a novel idea proposed in the field of predictive health care that provides an intuitive insight into the relation between the diagnoses and their corresponding procedures.
Customizable service level agreement (SLA) generator platform using FCAPS management to enhance Quality of Experience (QoE) on Internet of Things (IoT)

Defining an ontology to support all possible scenarios in the Internet of Things (IoT) is challenging, given the range of IoT applications, the context in which they are used, and their continued evolution. Furthermore, a desire for flexibility and ability to accommodate all scenarios using a standardized approach renders this a complex operational and management environment. An approach taken by some researchers in defining ontologies is to integrate components from different ontological schemes into a single schema. This approach is rationalized through the subsequent ability to support interoperability across IoT deployments. It also allows niche areas in individual ontologies to be merged to produce a more encompassing approach. However, this continues to be a pieced-together strategy and depends on service provider intelligence to ensure all applications are fulfilled: service level agreements (SLAs) provisioned on this basis therefore continue to require manual intervention. In response to this gap, we have proposed an ontology for the IoT. The ontology is unique in that it incorporates details associated with customers and their preferences, such as their ability to tolerate the observed dataset becoming unavailable or the data collection frequency of a dataset changing. Furthermore, it supports ability to accommodate domain-specific angles when working on a cross-domain approach; we believe that this is key in overcoming the limitations in other IoT ontologies and works toward a single standardized ontology. Through accommodating domain-specific elements, it is our objective that Quality of Service (QoS) needs of the applications are fulfilled without a customer needing significant technical knowledge. Taking these unique aspects into account, we believe the ontology will facilitate automatic SLA provision, service setup, and service management throughout the SLA lifetime. This will help to support the business objectives of the Internet service provider. The ontology has been defined in our previous work. In this document, the use of these ontology terms to generate personalized SLAs is presented.
Sensor Management for Multitarget tracking with Less Computational Effort using the Probability Hypothesis Density Filter

The performance of tracking algorithms that use bearing measurements is dependent on the sensor-target geometry. As a result, a sensor can maneuver to maximize the accuracy of estimates. The Rényi divergence has been shown to be an effective metric for selecting guidance commands that maximize information gain, thereby increasing the accuracy of estimates. However, there is no closed form solution to the Rényi divergence in applications with nonlinear measurements. As a result, the Rényi divergence is computed for a set of discretized guidance commands. Computing the Rényi divergence for each admissible command is computationally expensive, and there is no clear guidance on how many or which discrete guidance commands should be evaluated. We address multitarget tracking scenarios where a mobile sensor estimates the trajectory of one and three targets using the probability hypothesis density (PHD) filter. We show that for a PHD filter implementation the Rényi divergence need be computed only for guidance commands (heading) in a specific closed subset. The results from our analysis greatly reduce computational time, as commands outside of the subset need not be examined.
Low Cost Robust Service Overloading Fusion Model for Cloud Environments

Principle security of the valuable data is the largest part and an essential issue on cloud environment as it is directly linked to the cloud end-user. Since the Internet derives all the cloud data services, data security (DS) is a biggest problem in the cloud. Security of data entails a variety of risks involved and the proposed SOFM model resolve addresses the security concerns of the secure data transaction on cloud environment. For safe transmission of data (both sending and receiving) over the cloud environment, a layer of security has been employed between the base overloaded cloud end and derived cloud server. A new scheme comprised of service exchange has been executed to give the intended security in the environment and lessen those security risks respective to the cloud consumers. Currently available techniques improved for the crafty of the proposed model. It builds the service overloaded cloud server (OCS) and provides a secure cloud environment. Offered SOFM scheme has provided preferable safety and enhanced the performance than any other existing major management techniques.
GEOLOGICAL, STRUCTURAL AND RADIOACTIVITY STUDIES OF WADI SUTRAHUMM GHEIG AREA, CENTRAL EASTERN DESERT, EGYPT

Wadi Sutrah-Umm Gheig area is located in the Central Eastern Desert, 20 km west of Marsa Alam international airport and 50 km south of the Quseir city.The area represents a part of the Arabian-Nubian Shield.It is also pertaining to the pan-African complex which consists of a folded belt of metasediments and metavolcanics subjected to greenschist-amphibolite facies metamorphism associated with partial melting of amphibolites and development of gneissic and migmatitic rocks.The rocks of the metasediments are tectonically enclosed sheets and lenses of serpentinites, forming an ophiolitic mélange.These rock units intruded by four granitoid groups related to different magmatic and tectonic events.The area was affected by three distinct deformational events (D 1 -D 3 ).D 1 interpreted to have resulted from the closure of inter-arc basins, ophiolite obduction, and collision of East and West Gondwana, representing the earliest phase of deformation in the area.In the area of Umm Ghamis, D 1 represented by a series of tight overturned folds which have a NW-SE strike with NE and SW dip directions.During D 2 transpression, which marked the terminal stages of the Pan-African Orogeny, along Wadi Sutrah a series of open folds with NW-SE trending axes that plunge mostly to the SE.D 3 deformation is thought to be belongs to the regional Najd system, represented by several prominent shear zones and strike-slip faults.The F 3 phase folds are trending nearly E-W discordant to the other older two phases F 1 and F 2 .
Mellon Fellowships for Digital Publication (NEH)

Federal Grants & ContractsVolume 45, Issue 4 p. 3-3 Grants Alerts Mellon Fellowships for Digital Publication (NEH) First published: 15 January 2021 https://doi.org/10.1002/fgc.31506Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume45, Issue421 January 2021Pages 3-3 RelatedInformation
Women and minorities in STEM fields program (USDA)

Federal Grants & ContractsVolume 46, Issue 5 p. 7-7 Grants alerts Women and minorities in STEM fields program (USDA) First published: 29 January 2022 https://doi.org/10.1002/fgc.32203Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume46, Issue53 February 2022Pages 7-7 RelatedInformation
Biometrics firms join global push for ‘standard’ Covid passports

A number of leading biometric tech vendors – including CLEAR, Daon, iProov and SITA – are backing a new global initiative that aims to create a unifying open standard for biometric-based ‘passports’ that prove people are free from Covid-19.
Design of an Academic CSIRT – A Proposal Based on Strategic Planning Principles

This work aims to design and implement an organizational model that supports the macro processes of an academic institution in response to computer incidents to raise the level of information security. This study intends to include the research, development, and innovation (R&D&I) in an academic CSIRT. To achieve this, we developed a systematic literature review using the guidance of Barbara Kitchenham through a broad search for information, which allows us to identify the characteristics of the different types of organizations, services, infrastructure, and procedures to design and implement Academic CSIRTs. We then apply the science of design with its specific guidelines for evaluation and iteration within research projects and carry out improvement and knowledge construction. Specifically, we employed the European Network and Information Security Agency (ENISA) guides and the Forum of Incident Response and Security Teams (FIRST) directions, as these organizations are dedicated to preventing and addressing network security and information security problems. We also applied the Strategic Planning Process principles to obtain the Organizational Model Proposal, the operation and services proposal of the Academic CSIRT, and the research areas.
Offene Quantensysteme

No AccessNumerische Physik mit Octave und MatlabJan 2022Offene QuantensystemeHans Jürgen KorschHans Jürgen KorschSearch for more papers by this authorhttps://doi.org/10.3139/9783446473164.010SectionsAboutPDF ToolsAdd to FavoritesDownload CitationTrack CitationsCopy LTI LinkPDF key 'share (en)' returned an object instead of string.FacebookTwitterEmailLinkedIn previous chapternext chapter FiguresReferencesRelatedDetails 2022Pages: 269-300Print ISBN: 978-3-446-47026-2eISBN: 978-3-446-47316-4 Copyright & Permissions© 2022 Carl Hanser Verlag GmbH & Co. KGPDF downloadLoading ...
Python Data Analytics of Influence on Temperature and Humidity of City from Mountains: Case Study of Chengdu Qingcheng Mountains

The purpose of data analysis is to transform original data into information, and then into knowledge. Here, the mountains where Qingcheng Mountain is located as the research case, and the Chengdu area as the research scope, using Python data analysis tools, following the steps of data acquisition, data preprocessing, data analysis and visualization, the influence of mountains on regional temperature and humidity is briefly discussed, demonstrating the general process and methods of Python data analysis.
Starting a compliance program

In Chapter 1 of the Field Guide, readers are introduced to key considerations and practical steps for designing and implementing a compliance program, including appointing a data privacy officer or other person in charge; working with internal stakeholders and outside advisors; determining which laws apply; setting priorities; preparing a task list and executing on implementation.
A Survey of Cloud Service Events and Their Connections

Any unexpected service interruption or failure may cause customer dissatisfaction or economic losses. To distinguish the rights and interests or security disputes between cloud service providers and customers, explore the essence and rules of cloud service events and their various connections, such as: Normal contact of service scheduling, normal contact of service dependence, abnormal contact of resource competition, abnormal contact of service delay, abnormal contact of service dependence, etc., as well as their rules in time, resources, scheduling and other aspects, and the form of the rules; The purpose is to provide the above abnormal connections, as well as the rule and presentation form in terms of time, resources and load, for the study of violation determination and failure tracing in the cloud service accountability mechanism.
Blockchain in healthcare: The current situation and challenges

The lack of traceability of transactions and the complicated network of middlemen in healthcare cause a slew of issues. To name a few: healthcare data is scattered across multiple silos, adversely impacting research and services; about half of clinical trials are never published; the cost of drug development continues to rise; and counterfeit and inferior medications remain a major issue. Because it offers trust without middlemen, has traceability as a default feature, and promises new business models by allowing innovative incentive structures, blockchain has the potential to address these issues. Blockchain has sparked a lot of interest in the healthcare sector because of its potential. We examine key application cases of blockchain in healthcare, including patient data management, pharmaceutical research, medical supply chain management, prescription administration, billing claims management, analytics, and telemedicine, as well as associated initiatives, in this paper. We discovered that the majority of blockchain initiatives are just white papers, proof of ideas, or products with a small user base. However, we saw an increase in the number, quality, and maturity of the initiatives. We also go through the technological, regulatory, and commercial barriers to blockchain implementation in the healthcare sector.
Designing rule-based conversational agents with behavioral programming: a study of human subjects

Purpose In this work, the authors propose to harness the advantages of behavioral programming as a new technique for designing rule-based conversational agents. Design/methodology/approach To examine the study’s hypotheses, the authors perform a first-of-its-kind user study through which the authors examine how potential designers, both expert designers, computationally-oriented designers, and otherwise, leverage behavioral programming (BP) and dialog graphs for designing conversational agents (CAs). The authors also use two standard CA settings common in the literature: designing a CA representative for a user in an online dating service and a non-character player in a role-playing game (RPG). Findings The study’s results indicate that BP can be successfully utilized by computationally-oriented designers, with or without prior knowledge in CA design, and can facilitate the design of better CAs (i.e. more accurate and more robust). However, to capitalize on these potential advantages, designers may be required to devote more time to the design process and are likely to encounter higher temporal demand levels. These results suggest that BP, which was initially proposed and evaluated in the general context of software design, can constitute a valuable alternative to the classic rule-based CA design technique commonly practiced today. Research limitations/implications An important limitation of this study is the relatively small participant pool. While the authors do plan to extend this study in the future, the current coronavirus disease 2019 (COVID-19) situation makes it ever more complex to conduct formal user studies of this kind. It is, however, important to note that despite the low number of participants, many of the results are found to be statistically significant. Practical implications The authors plan to continue this line of work and conduct human studies for additional design techniques in other popular agent-based settings. Specifically, the authors seek to explore how people of different backgrounds should design agents for various tasks such as automated negotiation (e.g. how should a person design a representative agent to negotiate on her behalf?) and social choice (e.g. how should a person design a voting bot to represent her in online voting systems?). Originality/value People are increasingly interacting with conversational agents in various settings and for a variety of reasons, as the market size of those agents keeps on growing every year. Through a first-of-its-kind human study ( N = 41), consisting of both expert designers, computationally-oriented designers, and otherwise, the authors demonstrate a few key advantages and limitations of BP in the realm of conversational agents and propose its consideration as an alternative to the classic dialog graph technique.
Light-weight Enhanced Semantics-Guided Neural Networks for Skeleton-Based Human Action Recognition

In the skeleton-based human action recognition domain, the methods based on graph convolutional networks have had great success recently. However, most graph neural networks rely on large parameters, which is not easy to train and take up a large computational cost. In the above, a simple yet effective semantics-guided neural network (SGN) obtains with a few parameters and has achieved good results. However, the simple use of semantics is limited to the improvement of recognition rate. Moreover, using only one fixed temporal convolution kernel, which is not enough to extract the temporal details comprehensively. To this end, we propose an enhanced semantics-guided neural network (ESGN) in this paper. Some simple but effective strategies are applied to ESGN, such as semantic expansion, graph pooling methods, and regularization loss function, which do not significantly increase the parameter size but improve the accuracy on two large datasets than SGN. The proposed method with an order of magnitude smaller size than most previous papers is evaluated on the NTU60 and NTU120, the experimental results show that our method achieves the state-of-the-art performance.
The relationship between affordable housing and prevalence of COVID-19-like illness

Graph-based methods have shown their potentialities for saliency detection. In this paper, a graph-based framework is proposed for saliency detection, which incorporates perceptual cues into the framework and uses the background-excluded seeds to propagate saliency. Firstly, a graph is constructed by two perceptual cues, including proximity and similarity. Secondly, probable background nodes are generated by a novel background probability measure and used to pick out reliable seeds. Then a label propagation model is developed to diffuse saliency based on these reliable seeds. Lastly, another perceptual cue called rareness is integrated into a cost function to optimize the propagation result. Results on four datasets demonstrate that the proposed method achieves superior performance against fifteen state-of-the-art methods in terms of different evaluation metrics.
Prospects Of Using Best European Practices For Modernizing Doctoral Education In Russia

Throughout its history, doctoral education has always experienced significant changes due to arising challenges and shifts in higher education. Due to the changes in academic and non-academic career opportunities for doctoral school graduates, there is a growing need for the changes in the pathways towards a doctoral degree. Doctoral education in Russia is currently going through the reformation process with changes in the state educational standards and cancellation of state accreditation procedures. Recent European experience of doctoral education reforms can help Russian universities in shaping the new infrastructure and contents of PhD studies. The paper aims at studying the best European practices of modernizing doctoral education and developing possible solutions for the Russian universities in reformation of their doctoral schools. The authors analyze the current state and trends in EU doctoral education based on recent research publications, reports of European Universities Association and involved observation in the framework of Erasmus+ joint CBHE MODEST project. The results show that the best European practices include Doctoral Training Centers as a new model of doctoral education management. Creating such centers at Russian universities could improve international networking cooperation, quality assurance, collaboration with non-academic sector, and development of doctoral students’ soft skills, namely, leadership, teamwork, academic writing, etc. Further research could support the set-up of regulatory documents and development of teaching and learning materials for such centers.