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The emergence of blockchains has fueled the development of resilient systems that can deal with Byzantine failures due to crashes, bugs, or even malicious behavior. Recently, we have also seen the exploration of sharding in these resilient systems, this to provide the scalability required by very large data-based applications. Unfortunately, current sharded resilient systems all use system-specific specialized approaches toward sharding that do not provide the flexibility of traditional sharded data management systems. To improve on this situation, we fundamentally look at the design of sharded resilient systems. We do so by introducing BYSHARD, a unifying framework for the study of sharded resilient systems. Within this framework, we show how two-phase commit and two-phase locking ---two techniques central to providing atomicity and isolation in traditional sharded databases---can be implemented efficiently in a Byzantine environment, this with a minimal usage of costly Byzantine resilient primitives. Based on these techniques, we propose eighteen multi-shard transaction processing protocols. Finally, we practically evaluate these protocols and show that each protocol supports high transaction throughput and provides scalability while each striking its own trade-off between throughput, isolation level, latency , and abort rate. As such, our work provides a strong foundation for the development of ACID-compliant general-purpose and flexible sharded resilient data management systems.

The success of online creative communities depends on the will of participants to create and derive content in a collaborative environment. Despite their growing popularity, the factors that lead to remixing existing content in online creative communities are not entirely understood. In this article, we focus onoverdubbing, a dyadic collaboration in which one author mixes one new track with an audio recording previously uploaded by another. We study musicians who collaborate regularly, frequently overdubbing each other's songs. Building on frequent pattern–mining techniques, we develop an approach to seek instances of such recurring collaborations in the Songtree community. We identify 43 instances involving two or three members with a similar reputation in the community. Our findings highlight common and different remix factors in occasional and recurring collaborations. Specifically, fresh and less mature songs are generally overdubbed more. Exchanging messages and invitations to collaborate are significant factors only for songs generated through recurring collaborations, whereas author reputation (ranking) and applying metadata tags to songs have a positive effect only in occasional collaborations.

Internet of things (IoT) plays significant role in the fourth industrial revolution and attracts an increasing interest due to the rapid development of smart devices. IoT comprises factors of twofold. Firstly, a set of things (i.e., appliances, devices, vehicles, etc.) connected together via network. Secondly, human-device interaction to communicate with these things. Speech is the most natural methodology of interaction that can enrich user experience. In this paper, we propose a novel and effective approach for building customized voice interaction for controlling smart devices in IoT environments (i.e., Smart home). The proposed approach is based on extracting customized tiny decoding graph from a large graph constructed using weighted finite sates transducers. Experimental results showed that tiny decoding graphs are very efficient in terms of computational resources and recognition accuracy in clean and noisy conditions. To emphasize the effectiveness of the proposed approach, the standard Resources Management (RM1) dataset was employed and promising results were achieved when compared with four competitive approaches.

In order to reach a compromise between adhering to the traditional culture and embracing the modern lifestyle, more and more Asian moms are heading towards postpartum care centres for postpartum recovery. However, research regarding the quality of care of these postpartum care centres is nearly missing from the literature. This paper investigated the status quo of the postpartum care centres in Shanghai, China from mothers’ perspectives by means of analysing the 34280 pairs of ratings and reviews posted by postpartum care centre customers on the internet with machine learning and text mining. Results show that the mothers are generally satisfied with the studied care centres. Meanwhile, the 13 major topics in the customer online reviews were identified, which provide an overview of the interaction between a mother and a care centre. In addition, weight of topic analysis suggests that the studied care centres can further improve in the areas of support team, environment, and facility.

Among the tools used in teaching and learning, computers are very im-portant. Determining what kind of computer-related studies are carried out in the learning and teaching processes for university students will contribute to future research. In this context, the aim of the study is to examine university students’ documents related to computers in teaching and learning, scanned in the Web of Science database, thematically and methodologically. The study was designed by adopting the case study method from the qualitative research models. A sample was not determined for the articles to be included in the study but it was aimed to reach the whole population. In this context, all documents in the Web of Science database have been accessed through the library system of the university. In the document search, the keywords ‘learning and teaching’ and ‘computer’ were searched in all areas, and the studies were found by searching the keyword ‘university student’ within the title. All the studies were analysed by year, document type, Web of Science category, country and language of publication. The findings obtained from the research are explained in detail by content analysis method. A total of 305 documents were reached in the research findings. The findings obtained as a result of the research carried out are given in detail in the findings and results section.

The Internet of Things (IoT) is an emerging paradigm that embodies the vision of merging smart objects while utilizing the internet as the backbone of the communication system to establish a smart interaction among physical entities in pervasive environments. In IoT, data is generated in realtime and stored in permanent repositories. Additional data in the form of meta-data that describes things adds to the data volume and to manage this data, architecture is required. IoT finds its applicability in a plethora of applications such as transportation, smart city, smart health, smart environment, home entertainment, sports, etc. but there is no universal architecture for all IoT implementations. We have layered architectures and domain-specific architectures for different IoT applications. Besides a large number of architectures for IoT, it faces several potential challenges such as scalability, reliability, heterogeneity, etc. This paper presents an understanding of the Internet of Things in terms of its functionality, layered and domain-specific architectures, and its potential challenges.

Manuscripts are usually seen as collections of material artefacts that are the by-products of authoring. Manuscripts are central in studies on authors and are used to disambiguate and reconstruct significant literary works. Digital scholarly editions are, for instance, hypertext systems that enable the collaborative, distributed study of digitised material manuscripts. However, digital and web authoring challenge the classical notion of manuscript as they generate traces that are different in form and nature (e.g. logs) while enabling a variety of collaborative practices. We address the epistemic differences between material and digital artefacts, highlighting what aspects of authoring they reflect and providing a digital-aware reframing of the manuscript.

Rapid growth of educational technology services today means that there are more applications in the market. Users may find it hard to choose the most suitable application, so they look for references. Experience shared in the form of text reviews and numerical rating can provide references. Text re-views are particularly specific and so they can provide insights to user satis-faction. In this study, we use text mining and multicriteria decision-making approach to measure the user satisfaction. The data is crawled and collected from seven educational applications: Coursera, edX, Khan Academy, LinkedIn Learning, Quipper, Socratic and Udemy. Nine attributes are used to measure the user reviews according to quality model of e-learning sys-tems. The result is in favor of Khan Academy, while Quipper is ranked the lowest. The v-values used range between 0 and1 and what is unique is that the rank of Khan Academy and Quipper are not affected by v-value while the ranks of the other applications are. It indicates that Khan Academy has high user satisfaction in terms of utility and low complaint from individuals. Quipper shows the opposite.

This paper discusses the tool for the main text and image extraction (extracting and parsing the important data) from a web document. This paper describes our proposed algorithm based on the Document Object Model (DOM) and natural language processing (NLP) techniques and other approaches for extracting information from web pages using various classification techniques such as support vector machine, decision tree techniques, naive Bayes, and K-nearest neighbor. The main aim of the developed algorithm was to identify and extract the main block of a web document that contains the text of the article and the relevant images. The algorithm on a sample of 45 web documents of different types was applied. In addition, the issue of web pages, from the structure of the document to the use of the Document Object Model (DOM) for their processing, was analyzed. The Document Object Model was used to load and navigation of the document. It also plays an important role in the correct identification of the main block of web documents. The paper also discusses the levels of natural language. These methods of automatic natural language processing help to identify the main block of the web document. In this way, the all-textual parts and images from the main content of the web document were extracted. The experimental results show that our method achieved a final classification accuracy of 88.18%.

Data consists of: README, qualitative data files in nvpx and qdpx formats, codebook and zipped file of codes.

In this paper, we present our effort on the development of a Maithili Named Entity Recognition (NER) system. Maithili is one of the official languages of India, with around 50 million native speakers. Although various NER systems have been developed in several Indian languages, we did not find any openly available NER resource or system in Maithili. For the development, we manually annotated a Maithili NER corpus containing around 200K words. We prepared a baseline classifier using Conditional Random Fields (CRF). Then we ran many experiments using various recurrent neural networks (RNN). We collected larger raw corpus to obtain better word embedding and character embedding. In our experiments, we found, neural models are better than CRF; a CRF layer is effective for the prediction of the final output in the RNN models; character embedding is effective in Maithili language. We also investigated the effectiveness of gazetteer lists in neural models. We prepared a few gazetteer lists from various web resources and used those in the neural models. The incorporation of the gazetteer layer caused performance improvement. The final system achieved an f-measure of 91.6% with 94.9% precision and 88.53% recall.

Nowadays, many platforms provide open educational resources to learners. So, they must browse and explore several suggested contents to better assimilate their courses. To facilitate the selecting task of these resources, the present paper proposes an intelligent tutoring system that can access teaching contents available on the web automatically and offers them to learners as additional information sources. In doing so, the authors highlight the description logic approach and its knowledge representation strength that underwrites the modulization, inference, and querying about a web ontology language, and enhanced traditional tutoring systems architecture using ontologies and description logic to enable them to access various data sources on the web. Finally, this article concludes that the combination of machine learning with the semantic web has provided a supportive study environment and enhanced the schooling conditions within open and distance learning.

Webpages have become increasingly complex in recent years, with longer loading times to match. This paper uses tailored edge computing to address this issue. As is customary, A grip server interacts with cloud web servers in edge computing. In A footing server, on the other hand, is a personalised edge computing system. referred to as a foothold The Server in the Middle (ESM) collaborates with other servers. users' cell phones This research focuses on two strategies based on personalised edge computing: edge aided caching and edge aided reprioritizing. Edge-assisted caching decreases the time it takes for a page to load. Because an ESM saves the cached data on mobile devices, So far, we've got components. Edge helps in the reprioritization of forces on the internet. browser to show visual components earlier and lowers the amount of white space Time spent in front of a screen.In addition, the ESM uses HTTP/2 rather than HTTP/1.1. This decreases the number of interactions between a mobile device and, as a result, the ESM, allowing advanced functionalities to be used. such as priority and server push Edge-assisted caching has been implemented. built in a high-end PC for Google's web browser Chrome for Android is a mobile web browser. Edge aided in an experiment, according to the results. The time it took for a popular website to load was cut in half because to caching. 59 percent in a network that is extremely congested. Another experiment found that edge-assisted reprioritization cut the white screen time of a webpage with a lot of photo photos by 21%. edge computing, reprioritization, mobile device, index terms browsing the web, caching

In this demo, we present RealGraph Web , a web-based platform that provides various kinds of graph analysis services. RealGraph Web is based on RealGraph, a graph engine that addresses the problem of performance degradation in processing real-world big graphs, achieving great performance improvement up to 44 times over existing state-of-the-art graph engines. RealGraph Web runs on a single machine with a web-based interface, thereby allowing users to easily and conveniently enjoy graph analysis services and perform various graph algorithms anywhere on the web . In this demo, we present how a user can analyze a graph on RealGraph Web in three steps and get the analysis result quickly via a graphical user interface.

Tanium Reveal is a federated search engine deployed on large-scale enterprise networks that is capable of executing data queries across billions of private data files within 60 seconds. Data resides at the edge of networks, potentially distributed on hundreds of thousands of endpoints. The anatomy of the search engine consists of local inverse indexes on each endpoint and a global communication platform called Tanium for issuing search queries to all endpoints. Reveal enables asynchronous parsing and indexing on endpoints without noticeable impact to the endpoints' primary functionality. The engine harnesses the Tanium platform, which is based on a self-organizing, fault-tolerant, scalable, linear chain communication scheme. We demonstrate a multi-tier workflow for executing search queries across a network and for viewing matching snippets of text on any endpoint. We analyze metrics for federated indexing and searching in multiple environments including a production network with 1.05 billion searchable files distributed across 4236 endpoints. While primarily focusing on Boolean, phrase, and similarity query types, Reveal is compatible with further automation (e.g., semantic classification based on machine learning). Lastly, we discuss safeguards for sensitive information within Reveal including cryptographic hashing of private text and role-based access control (RBAC).

Scientific software underpins a large and growing number of scientific discoveries and engineering advances. At the same time, it is a small portion of the total software produced, has little literature, or focus on improvement and community building relative to other, larger software domains. The Collegeville Workshop Series on Scientific Software provides a platform for scientific software teams and stakeholders to exchange information on the nature, challenges, and technical and cultural approaches to improve key elements of the scientific software enterprise. By soliciting white papers, posters, and tea-time themes, these workshops gather content to provide a three-day live event, plus recorded presentations and interviews that enable community members to learn from each other. The outcomes from the first two workshops, in 2019 and 2020, have provided a body of knowledge and experience that expands the size and recognition of software as an important part of the scientific enterprise. The 2021 workshop, focused on software teams, intends to further expand the conversation, and continue to build community around scientific software to improve its impact on science and engineering.

Data preparation is still a major bottleneck for many data science projects. Even though many sophisticated algorithms and tools have been proposed in the research literature, it is difficult for practitioners to integrate them into their data wrangling efforts. We present openclean, a open-source Python library for data cleaning and profiling, openclean integrates data profiling and cleaning tools in a single environment that is easy and intuitive to use. We designed openclean to be extensible and make it easy to add new functionality. By doing so, it will not only become easier for users to access state-of-the-art algorithms for their data wrangling efforts, but also allow researchers to integrate their work and evaluate its effectiveness in practice. We envision openclean as a first step to build a community of practitioners and researchers in the field. In our demo, we outline the main components and design decisions in the development of openclean and demonstrate the current functionality of the library on real-world use cases.

The rapid emergence of cloud data warehouses like Google BigQuery has redefined the landscape of data analytics. With the growth of data volumes, such systems need to scale to hundreds of EiB of data in the near future. This growth is accompanied by an increase in the number of objects stored and the amount of metadata such systems must manage. Traditionally, Big Data systems have tried to reduce the amount of metadata in order to scale the system, often compromising query performance. In Google BigQuery, we built a metadata management system that demonstrates that massive scale can be achieved without such tradeoffs. We recognized the benefits that fine grained metadata provides for query processing and we built a metadata system to manage it effectively. We use the same distributed query processing and data management techniques that we use for managing data to handle Big metadata. Today, BigQuery uses these techniques to support queries over billions of objects and their metadata.

In such an unfortunate situation of a medical emergency, a lot of people tend to lose their lives which can be a result of misplaced/delayed paperwork. Thus it is essential to have the patient’s medical record history before going into major surgery and it is often unavailable at the required time due to the chain of communication between different hospitals. As a possible solution to this problem we propose a central chain of all medical records of a patients in an electronic format. The electronic health records (EHRs) are patient-centered, real-time records that make information available to authorized users and help doctors diagnose cases more quickly, reduce medical errors, and deliver safer care. EHRs improve the communication gap and make the process of getting medical attention quicker. Our methodology includes an easy to use 2 module approach (doctor portal and patient portal) with multiple sub modules all linked to a common database. We create a secure and centralized database for EHRs with an easy approach and manageable methodology. Security is important in our use case and we thus include- Secure authentication (and verification of doctor’s ID while creating a new account) , two factor authentication and OTP verification while accessing patient reports. We are using block chain for safely storing multiple patient records for maintaining a safe storage of multiple records. The doctors will be able to access the patients with few clicks and verification and understand the patient background. This will majorly help in maintaining records, accessing prescription and getting an overview of the patient's medical history while saving a lot of manual paper work.

An improved Web community discovery algorithm is proposed in this paper based on the attraction between Web pages to effectively reduce the complexity of Web community discovery. The proposed algorithm treats each Web page in the Web pages collection as an individual with attraction based on the theory of universal gravitation, elaborates the discovery and evolution process of Web community from a Web page in the Web pages collection, defines the priority rules of Web community size and Web page similarity, and gives the calculation formula of the change in Web page similarity. Finally, an experimental platform is built to analyze the specific discovery process of the Web community in detail, and the changes in cumulative distribution of Web page similarity are discussed. The results show that the change in the similarity of a new page satisfies the power-law distribution, and the similarity of a new page is proportional to the size of Web community that the new page chooses to join.

With the immense use of android operating systems in today’s technology, Android apps became part of our daily lives and we use different apps for different purposes. The motivation behind every location-based application is to give the user a live experience with real data in real-time. To ease the searching of nearby places for the user, this android application provides the details of nearby locations like restaurants, hospitals, emergency services, travel services and also COVID vaccination centres like an assistant that answers the user’s query based on different queries. The apps that are available already use location and show the google maps inside the application which makes the application heavy and the cost of API request is high, but in this application, the user interface will be simple and it will fetch only the required data from different API Service providers and the data adapters for the application change based on different API service providers.

CAP messages are the standard ways for the government to broadcast emergency messages, which are represented in the XML format to meet the open data initiatives. However, such formats are hard for ...

— Currently, higher education must open their mindset to change the learning process. This process cannot still stay on a conventional process that only involved students and lecturers as learning participants because to capture what the current industry needs and the trend of knowledge, higher education institutions must collaborate with external parties as learning partners to give a global perspective about the industrial needs and trends. The process to identify a learning partner to contribute to the learning process is not easy way. The higher education institution must select which partner has appropriate skills and competency suitable for the subject’s course. Many parameters will be involved in the selection process to identify the right partner. In this proposed system, external parties in this context consist of professionals or educators from the external institution easily can be defined to be selected as learning partners based on their competency and experience, that listed on social media LinkedIn as a professional platform. The method to build this interactive recommender system in this study is based on design science research that identifies step by step design stage to propose the best way solution for the recommender system. The result of this study is an interactive recommender system that can help higher education to find out the best candidate for their learning partner so the collaboration of learning can be implemented effectively

In order to improve the translation effect of political text metaphors, based on Web data mining technology, this paper constructs a political text metaphor translation system based on Web data mining technology. Aiming at the two shortcomings of the selection of the initial center point of the K-Means algorithm and the isolated points, this paper gives a solution to the ICKM algorithm that combines the density parameter and the coordinate rotation algorithm. The algorithm uses the object with the largest density parameter as the first center point, and uses the KCR algorithm to find the next center point, which avoids the influence of isolated points on the data sample to a certain extent. The constructed political text metaphor translation system based on Web data mining technology needs to accurately translate political texts and also needs to meet the requirements of metaphor translation. Finally, this paper designs experiments to verify the system performance. The research results show that the system constructed in this paper can meet the needs of political text metaphor translation.

In order to construct an efficient translation system, this paper constructs a corpus translation system based on Web Services. Moreover, this paper builds a network term detection system based on machine learning algorithms, expands the corpus data with the support of the crawler system, and uses WEB retrieval translation technology. At the same time, in response to the problem of sentence length changes in English abstracts, this paper proposes a method to obtain standard sentence length changes based on edit distance and SVM sorting. Based on requirements, this paper designs the architecture and data integration process of the data integration system. In addition, this paper outlines the detailed design and implementation process of each module of the system, and proposes a system performance optimization plan, and combines translation requirements to construct a corpus translation system based on Web Services. Finally, this paper designs experiments to verify the performance of the model. The research results show that the system constructed in this paper has a good application effect.

In the Dark and Stormy Archives (DSA) project, we focus on storytelling techniques to summarize collections of archived web pages. Since collections can have hundreds or even thousands of seeds (initial URLs) and each seed can be recrawled many times, with each version separately maintained, techniques that include information about all members of the collection can be overwhelming. The premise of storytelling is to focus on sampling exemplar pages from the collection, and present them in a social media interface familiar to users. We present Hypercane , the tool in the DSA suite responsible for selecting exemplar pages. Hypercane offers eight action statements that can be combined in various ways to customize the sample that is produced. Because of its modular design, Hypercane can also be used to analyze large web archive collections outside of the DSA suite.

The sports industry is considered the industry with the most vitality, participation, and penetration in the 21st century. From the perspective of practice on a global scale, the demand for the sports market has gradually developed from low-level simplification to high-level multi-polarity, and sports consumption has also shifted from single sports goods to sports services and entertainment sports. The sport industry has become modern society containing emerging industries with large business opportunities. This article mainly uses literature, expert interviews, data collation, logical analysis, and other methods to conduct a comparatively systematic study on the development of the new form of the “Internet+” sports industry. In the study, by comparing the commodity types, organizational forms, and management of the traditional sports industry in terms of management and other aspects, the connotation of the new format of the “Internet+” sports industry is introduced in detail, which is divided into four industries: network broadcasting, smart software and hardware, vertical e-commerce, and e-sports. The research in this article shows that, after accounting, the total scale (total output) of the national sports industry in 2019 was 2,948.3 billion yuan, with a year-on-year added value of 1,124.8 billion yuan. In terms of growth, total output increased by 10.9% over 2018 and 11.6% year-on-year. Among them, sports activities integrated with the Internet have reached 58%. Finally, it will help the transformation and upgrading of Internet+sports and improve its own development level. Returning to the real economy and serving the real economy have become a consensus for the future development of Internet finance.

With the rapid development of computer communication technology, the level of archives information services in colleges and universities continues to improve. More and more universities have created archives management halls to promote digital archives services in depth. As an important part of information resources, the development and use of archive information resources have also attracted the attention of all walks of life. University archives are the pioneers in the development of archives in my country, and their computerization level will directly affect the development and utilization of information resources in my country’s archives. This article aims to analyze the management of online education archives in colleges and universities under the background of big data, analyze the management of online education archives in colleges and universities, and explore the management of archives under the background of online education in colleges and universities. Use the university network archives construction evaluation model calculation and investigation research method to study the current situation and mode of university network education archives management, and provide reference value for the rational connection of various tasks of university network education archives management under the background of big data. The experimental results of this article show that 55% of college students believe that the current college archives need to be combined with the requirements of the development of the times, and it is necessary to innovate the archive management methods of colleges and universities to improve the quality of archive management services in universities and ensure the real-time storage of network information and maximize the development of archive information the value of.

Aerobics was officially introduced into my country in the 1980 s. With the further development of sports, Chinese universities and primary and secondary schools gradually introduced aerobics and related courses into teaching, making aerobics develop rapidly. Due to objective reasons, the teaching of aerobics has always been at a low-efficiency teaching level. Therefore, it is urgent to establish a digital system of aerobics information. The aerobics knowledge database is for aerobics teachers to publish teaching knowledge, students learn aerobics knowledge and interact with teachers. Platform, the platform can use various technical means to increase the relevant knowledge content, enrich the means of interaction between the platform and students, the system architecture mainly solves the connection of various links in the system application, the deployment of subsystems, and the storage of information. On the basis of the platform, it provides a powerful technical support for the teaching and promotion of aerobics. The experimental results of this paper show that the system shows that the self-evaluation of aerobics of the experimental class in the male group is 0% in the 6th class, and 100% is satisfied or above. The self-evaluation of the aerobics of the experimental class of the female group is in the fourth. The dissatisfaction rate is 0% during the course, and 100% is satisfied or above. From this point of view, the system meets the requirements.

Mobile systems are becoming increasingly important, and new promising paradigms such as Mobile Cloud Computing. Mobile Cloud Computing is an application that allows data to be stored and processed outside of the mobile node. There is a lot of interest in using the resources that can be accessed by transparently using distributed resource pooling offered by nearby mobile nodes. This type of device is used in emergency, education, and tourism. Systems basically use dynamic network topologies in which network partitions and disconnection occurs frequently, so the availability of the services has been compromised. In this paper proposes the context aware architecture to provide availability of the services deployed in mobile and dynamic network environments which provides better response time, the services need not be migrated at real time, so the bandwidth and energy used has been more efficient.

The purpose of this paper is to study and analyse the various tools that are used in modern day web application systems, which include but are not limited to Flask, Django, PostgreSQL, MongoDB, Docker containers, virtual machines, and so on. The main aim is to allow users of these technologies to be able to choose the right technology based on their needs and the scale of their applications. This is done with the help of sysbench and Docker and Linux based containers, along with basic Flask and Django web applications. Flask could be preferred for simpler web applications over Django. Docker and LXD do perform similarly for the most part, but due to its low storage footprint (only essential libraries are installed in the container, not an entire OS), and its ease of configurability in almost all operating systems, Docker is generally preferred over the others. PostgreSQL seemed to perform 2 times better than MongoDB in terms of the number of queries it handled.

With the advancement of information construction, as an important part of smart city, the construction of smart society is invested. As a new mode of social management innovation under the new situation, the advantage of smart society is that it can complete the full integration of existing public resources and infrastructure, realize the knowledge mining of appearance information, so as to make social services and management more information-based and intelligent based on the common needs of smart social management, this paper analyzes the overall function of social management system in detail, puts forward the architecture and simulation of social management service system based on internet of things information model, and realizes the smart social management system, so as to make social service more high-quality and efficient, and social management more standardized and scientific. This paper describes how to promote social management services, and the specific needs of social information as the research basis of this paper, realize the application of social information integrated management service platform, and then give play to the advantages of information technology, realize the transformation of social management work mode, facilitate the social management work, improve the overall quality of life of the community, and reduce the cost of management.

This paper studies the implementation of distributed building smart construction technology. We analyze the role of building information models, Internet of Things, and pervasive computing in smart construction. Based analysis, we use integrated ideas and integrated frameworks for integration construction in the full life cycle of distributed buildings, and research from the perspective of full life cycle on distributed architectural intelligence construction management concepts. Equipment modeling and data storage management mainly include the establishment of equipment models and the storage and management of equipment real-time data. We design the database table structure by analyzing personnel, equipment, virtual space and the relationship between them, and establish the corresponding E-R model. We research and investigate the types and operating characteristics of equipment in buildings, classify IoT equipment, model several commonly used building networking equipment, and build equipment model libraries. We analyze the business process and data flow of building networked equipment management, and conduct research on the application of equipment virtualization management to realize the intelligent and personalized management of IoT equipment. Taking three smart buildings based on building information models as examples for empirical analysis, the evaluation grades of three projects are obtained by calculating the correlation degree, and then the projects are sorted according to the evaluation grades and the best projects are selected.

Focused crawlers, as fundamental components of vertical search engines, focus on crawling the web pages related to a specific topic. Existing focused crawlers commonly suffer from the problems of low efficiency of crawling pages and subject migration. In this paper, we propose a learning-based focused crawler using a URL knowledge base. To improve the accuracy of similarity, the similarity of the topic is measured with the parent page content, anchor information, and URL content. The URL content is also learned and updated iteratively and continuously. Within the crawler, we implement a crawling mechanism based on a combination of content analysis and simple link analysis crawler strategy, which decreases computational complexity and avoids the locality problem of crawling. Experimental results show that our proposed algorithm achieves a better precision than traditional methods including the shark-search and best-first search algorithms, and avoids the local optimum problem of crawling.

The continuous adoption of service-oriented architecture (SOA) and web services across vertical industries, geographies, and organization sizes generates new major security concerns both for individuals and organizations. Many of the features that make web services attractive, including greater accessibility of data, dynamic application-to-application connections, and relative autonomy are at odds with traditional security models and controls. Current web service composition technologies still present serious risks to individual and enterprise security and privacy. In the presence of multiple providers that coordinate to provide a composite web service, these issues are amplified. Web services security, privacy, and forensics investigations are therefore becoming a serious concern, especially with the tremendous increase in cybernetic crimes. The aim of this work is to draw a comprehensive overview about security aspects related to web services composition by considering different branches related to security, privacy, and forensics investigations. For each security aspect, the problem statement and related works are discussed. Then, a proposed approach to deal with the aspect under consideration is presented.

In recent years, Educational Recommender Systems (ERSs) have attracted great attention as a solution towards addressing the problem of information overload in e-learning environments and providing relevant recommendations to online learners. These systems play a key role in helping learners to find educational resources relevant and pertinent to their profiles and context. So, it is necessary to identify information that helps learner’s profile definition and in identifying requests and interests. In this context, we suggest to take advantage of the annotation activity used usually in the learning context for different purposes and which may reflect certain learner’s characteristics useful as input data for the recommendation process. Therefore, we propose an educational recommender system of web services based on learner’s annotative activity to assist him in his learning activity. This process of recommendation is founded on two preparatory phases: the phase of modelling learner’s personality profile through analysis of annotation digital traces in learning environment realized through a profile constructor module and the phase of discovery of web services which can meet the goals of annotations made by learner via the web service discovery module. The evaluation of the developed annotation based recommendation system through empirical studies realized on groups of learners based on the Student’s t-test showed significant results.

Information is the cornerstone of the development of a person, social group, and society as a whole. In modern conditions, information plays a key role in all spheres of the national economy, and its timely search and analysis is fundamentally important. The mass of information on the Internet includes data that meet various (sometimes paradoxical) user requests and, at the same time, when accessing virtual resources, the key problem is often the redundancy of links and the difficulty of finding information that is strictly relevant to the user’s request. The article provides an overview of approaches to organizing the search for necessary information in the continuum of the information space; it is emphasized that success in finding the necessary information is determined by two main factors (conditions): the correct, clear formulation of the search query and knowledge of search engines and search tools.

Frente ao desafio de explorar novos meios de uma mitigação eficiente e acessível para inativação de patógenos e limitar sua transmissão em ambientes ocupados por pessoas, surgiu a iniciativa desta investigação que teve como objetivo primário realizar uma revisão sistemática, buscando na literatura oestado da arte em relação à utilização de radiação UVC e Ozônio como agentes germicidas no combate de vírus e bactérias.Foram pesquisados nos bancos de dados trabalhos que continham as strings “UVC" AND "wave" AND "ultraviolet" AND "radiation" AND "light" AND" intensity" e também “ozone" AND "air"AND "disinfection" AND "virus" AND "bacteria" AND "environment" AND "decontamination" AND "air decontamination". A busca foi feita nas bases de dados ScienceDirect, Web of Science, pubMED, SciELO, Google Scholar e World Wide Science. Foram encontrados 189 artigos relacionados à utilização de UVC comoagente germicida e 16 artigos acerca da utilização do ozônio, totalizando assim 205 artigos. Destes somente 44 foram selecionados para a revisão, os outros trabalhos não foram selecionados, pois não se enquadraram no critério de seleção, seja por possuir uma aplicação diferente da proposta pelo grupo, exemplo artigos que relacionavam a utilização da radiação UVC no tratamento de águas residuais ou por a data de publicação não se enquadrar no período de 2016 a 2020, nenhum artigo utilizando ambos os agentes germicidas ozônio e UVC foi encontrado. Entretanto, entres os artigos selecionados, foram relatadas informações importantes e promissoras para dar firmamento e veracidade às premissas adotadas pelos pesquisadores.

The objective of this project was the development of an information management system for healthcare purpose using cloud computing and IoT technology. The system was designed to receive data coming from medical instruments, as well as documents issued by healthcare professionals and doctor responsible for the care of a patient. For medical consultation, patients must use an identification card that allows access to the corresponding medical history stored in the cloud. This access can be done through an RFID reader, making it possible to consult patient information on a human-machine interface, where new information can also be registered. For the development of the interface, an application for the Android operating system was created, where information can be accessed, and documents can be stored. Storage was implemented on Google's Firebase® platform. Akyiama's RFID reader integrated in a Raspberry Pi was used to access the information. Exemplifying the connection with biomedical instruments, a sen-11574 heart beats sensor was connected to a Raspberry Pi, whose measurements were converted to the I2C format with an ADS-1115 module. Results obtained are very promising to be applied in the automation of hospitals, first aid and private assistance in medical offices. the main benefit for patients is the fact that they can keep their medical history in the cloud, which can prevent loss of patient information in usual cases such as changing doctors, offices, or hospitals, which is commonly the case. Therefore, the system allows the preservation of the patient's historical record. The medical care system would make it more effective as it facilitates the work of medical professionals and assists in the patient's day-to-day life with updated health information

College English cross-cultural teaching has changed from offline to online teaching. Under the impetus of MOOC teaching mode, college English cross-cultural teaching online teaching has exposed problems such as insufficient intelligence and poor online teaching effects. In order to improve the efficiency of college English cross-cultural teaching, based on cloud computing technology and artificial intelligence technology, this article improves and analyzes traditional MOOC, improves traditional algorithms according to the actual needs of MOOC teaching, and proposes a new improved model. Moreover, this article sets up functional modules through requirements analysis. In addition, according to actual teaching, this paper designs control experiments to verify and analyze the model performance from experimental teaching and the effect of attracting students’ online learning. The research results show that the model proposed in this paper has good performance and can effectively improve the efficiency of English cross-cultural teaching.

As the most notable implementation of service-oriented architectures, web services has brought some challenging research issues, one of which is the composition. Correct interaction among web services is the prerequisite of their successful composition. In this paper, we propose a novel approach to model dynamic interactive behavior of web services, aiming to effectively take advantage of the flexibility of service composition technology and guarantee the behavior compatibility of web services and further to facilitate the application of web service composition in healthcare field. An interaction model of web services based on Kahn process network (KPN) is presented in this paper, which is a data-flow and channel-based model supporting parallel and cooperative computation. Formal behavioral semantics of this model is defined by Pi-calculus process algebra and formal properties of the proposed model are discussed. An application case is demonstrated to illustrate how the proposed model can be applied to describe services behavior and their interaction. The reachability analysis of the application case is performed based on modal -calculus. How the supporting tool, i.e. CWB-NC, can be utilized to verify the properties we anticipate is presented.

Background: With the advent of IoT, the deployment of batteries with a limited lifetime in remote areas is a major concern. In certain conditions, the network lifetime gets restricted due to limited battery constraints. Subsequently, the collaborative approaches for key facilities to reduce the constraint demands of the current security protocols. Objective: This work covers and combines a wide range of concepts linked by IoT based on security and energy efficiency. Specifically, this study examines the WSN energy efficiency problem among IoT devices and security for the management of threats in IoT through collaborative approaches and finally outlines the future. The concept of energy-efficient key protocols which clearly cover heterogeneous IoT communications among peers with different resources has been developed. Because of the low capacity of sensor nodes, the energy efficiency in WSNs has been an important concern. Methods: In this paper, we present an algorithm for Artificial Bee Colony (ABC) which reviews security and energy consumption to discuss their constraints in the IoT scenarios. Results: The results of a detailed experimental assessment are analyzed in terms of communication cost, energy consumption and security, which prove the relevance of a proposed ABC approach and a key establishment. Conclusion: The validation of DTLS-ABC consists of designing an inter-node cooperation trust model for the creation of a trusted community of elements that are mutually supportive. Initial attempts to design the key methods for management are appropriate individual IoT devices. This gives the system designers, an option that considers the question of scalability.

User satisfaction is the principle component in the prosperity of a recommender system to provide an exact recommendation within a rational amount of time. The recommendation system is an intelligent system that analyzes the large quantity of online data to predict the patterns. In this paper, various recommendation techniques are described as a literature survey and their classifications are explained based upon the attributes and characteristics required for the recommendation process. The categorization of the recommendation system hinge on the analysis of the research papers and identifies the areas of the future for the development of an intelligent system.

Rich user interfaces like Jupyter have the potential to make interacting with a supercomputer easier and more productive, consequently attracting new kinds of users and helping to expand the application of supercomputing to new science domains. For the scientist user, the ideal rich user interface delivers a familiar, responsive, introspective, modular, and customizable platform upon which to build, run, capture, document, re-run, and share analysis workflows. From the provider or system administrator perspective, such a platform would also be easy to configure, deploy securely, update, customize, and support. Jupyter checks most if not all of these boxes. But from the perspective of leadership computing organizations that provide supercomputing power to users, such a platform should also make the unique features of a supercomputer center more accessible to users and more composable with high-performance computing (HPC) workflows. Project Jupyter’s ( https://jupyter.org/about ) core design philosophy of extensibility, abstraction, and agnostic deployment has allowed HPC centers like National Energy Research Scientific Computing Center to bring in advanced supercomputing capabilities that can extend the interactive notebook environment. This has enabled a rich scientific discovery platform, particularly for experimental facility data analysis and machine learning problems.

The 29th ACM International Conference on Information and Knowledge Management (CIKM) was held online from the 19 th to the 23 rd of October 2020. CIKM is an annual computer science conference, focused on research at the intersection of information retrieval, machine learning, databases as well as semantic and knowledge-based technologies. Since it was first held in the United States in 1992, 28 conferences have been hosted in 9 countries around the world.