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As a two-dimensional material, graphene has attracted increasing attention as heat dissipation material owing to its excellent thermal transport property. In this work, we fabricated sisal nanocrystalline cellulose/functionalized graphene papers (NPGs) with high thermal conductivity by vacuum-assisted self-assembly method. The papers exhibit in-plane thermal conductivity as high as 21.05 W m−1 K−1 with a thermal conductivity enhancement of 403% from the pure cellulose paper. The good thermal transport properties of NPGs are attributed to the strong hydrogen-bonding interaction between nanocrystalline cellulose and functionalized graphene and the well alignment structure of NPGs.

The confluence of significant computational power and inexpensive sensors provides the opportunity to reliably collect large volumes of information from the world and extract humanly useful information resources. This paper reviews a coherent body of work over the last 20+ years focused on development of advanced bio-inspired computing techniques, and their applications primarily for human related data in behaviour and human centered computing. We close with a synthesis proposing an experiment analysis methodology combining these tools.

In this project our goal is to build a tweet sentimental analysis in which user can search and analyse thousands of tweets in one go. For backend we will use python and Django web framework and for front end we are going to use HTML, Css and bootstrap. It will help to analyse any hashtag and companies about their product feedback reviews. This project contains only the one user side. It is like a tweets search analyser. The design of this project is pretty simple so that the user won’t find any difficulties while working on it.

Background Geotagging is the process of attaching geospatial tags to various media data types. In health care, the goal of geotagging is to gain a better understanding of health-related questions applied to populations. Although there has been a prevalence of geographic information in public health, in order to effectively use and expand geotagging across health care there is a requirement to understand other factors such as the disposition, standardization, data sources, technologies, and limitations. Objective The objective of this document is to serve as a resource for new researchers in the field. This report aims to be comprehensive but easy for beginners to understand and adopt in practice. The optimal geocodes, their sources, and a rationale for use are suggested. Geotagging’s issues and limitations are also discussed. Methods A comprehensive review of technical instructions and articles was conducted to evaluate guidelines for geotagging, and online resources were curated to support the implementation of geotagging practices. Summary tables were developed to describe the available geotagging resources (free and for fee) that can be leveraged by researchers and quality improvement personnel to effectively perform geospatial analyses primarily targeting US health care. Results This paper demonstrated steps to develop an initial geotagging and geovisualization project with clear structure and instructions. The geotagging resources were summarized. These resources are essential for geotagging health care projects. The discussion section provides better understanding of geotagging’s limitations and suggests suitable way to approach it. Conclusions We explain how geotagging can be leveraged in health care and offer the necessary initial resources to obtain geocodes, adjustment data, and health-related measures. The resources outlined in this paper can support an individual and/or organization in initiating a geotagging health care project.

Purpose This paper aims to provide a snapshot of key learnings about paid online peer-to-peer accommodation trading, as it relates to tourism and hospitality, and to identify future research questions. Design/methodology/approach The paper paints a picture and discusses research conducted in the past, which relates to paid online peer-to-peer accommodation, in brief. It also lists a number of specific research questions which should be investigated in future. Findings Some of the key topics, such as the business model of facilitators of peer-to-peer trading and the necessary regularly responses, have been extensively studied. The focus should now turn on how peer-to-peer trading of travel-related services can best be leveraged to the benefit of economies, communities and people. Originality/value The main value of this perspective paper lies in offering a succinct overview of research into paid online peer-to-peer accommodation and pointing to key questions for future research.

Authentication devices transform cheap paper into legitimate documents. They are the sensory, informational, and computational features that make up valuable papers like banknotes and passports, and they provide the confidence required in moments of exchange and passage. These devices – which include techniques like watermarks and specialized threads, proprietary substrates and inks, or RFID chips – are the product of security printing, an industry that continuously reinvents the possibilities of paper. Importantly, these components protect paper things from counterfeiting, allowing it to function as an original and authentic copy and to do the logistical work of connecting quotidian materials to global networks. The value of valuable papers is therefore not purely extrinsic, socially or discursively established, but is also performed through its intrinsic material qualities. These are the authentication devices that are read, assessed, and trusted as paper things are circulated, and they are what securely connects paper to infrastructures of mobility.

Paper recommendation is a research topic to provide users with personalized papers of interest. However, most existing approaches equally treat title and abstract as the input to learn the representation of a paper, ignoring their semantic relationship. In this paper, we regard the abstract as a sequence of sentences, and propose a two-level attentive neural network to capture: (1) the ability of each word within a sentence to reflect if it is semantically close to the words within the title. (2) the extent of each sentence in the abstract relative to the title, which is often a good summarization of the abstract document. Specifically, we propose a Long-Short Term Memory (LSTM) network with attention to learn the representation of sentences, and integrate a Gated Recurrent Unit (GRU) network with a memory network to learn the long-term sequential sentence patterns of interacted papers for both user and item (paper) modeling. We conduct extensive experiments on two real datasets, and show that our approach outperforms other state-of-the-art approaches in terms of accuracy.

Web of Science and Scopus are two world-leading and competing citation databases. By using the Science Citation Index Expanded and Social Sciences Citation Index, this paper conducts a comparative, dynamic, and empirical study focusing on the use of Web of Science (WoS) and Scopus in academic papers published during 2004 and 2018. This brief communication reveals that although both Web of Science and Scopus are increasingly used in academic papers, Scopus as a new-comer is really challenging the dominating role of WoS. Researchers from more and more countries/regions and knowledge domains are involved in the use of these two databases. Even though the main producers of related papers are developed economies, some developing economies such as China, Brazil and Iran also act important roles but with different patterns in the use of these two databases. Both two databases are widely used in meta-analysis related studies especially for researchers in China. Health/medical science related domains and the traditional Information Science & Library Science field stand out in the use of citation databases., Forthcoming in Scientometrics

This paper empirically indicates the production process and operating system of South India Paper Mill, Nanjangud (SIPM). The study indicates that the organization must be aware of some dependent and independent variables which influence the organization’s production process and operating system, which might affect the efficiency of the company. The study was conducted on the quantitative research methodology by collecting the secondary data from the organization. Descriptive statistics and correlation has been used for selecting statistical techniques. The study includes various literature reviews to decide the right component to improve the production and operation of the company. The study has come up with a result showing that the dependent and independent variables influence the production process and might also affect the operating system of the company. This paper mainly considers the four factors for analysand the company’s production process and its operating system, the factors are productivity, labour efficiency, employees age and employees efficiency. These four factors are further divided into independent variable (labour efficiency and employees age) and dependent variable (productivity and employee’s efficiency). Z-Test and Karl Pearson’s Correlation analysis is done to find out the relation between the variables and to draw conclusions. The study includes various literature reviews to decide the relation between the components and to understand the process of production.

A method for image retrieval based on supervised local regression and global alignment (sLRGA) with relevance feedback for insect identification is presented in this paper. Based on the novel sLRGA, which is an extended version of LRGA, the proposed method estimates ranking scores for image retrieval in such a way that the neighborhood structure of a feature space of the database can be optimally preserved with consideration of class information. This is the main contribution of this paper. By measuring the relevance between all of the images and the query image in the database, sLRGA realizes accurate image retrieval. Furthermore, when positive/negative labels to retrieved images are given by users, the proposed method can improve image retrieval performance considering the query relevance information via use of both relevance feedback and sLRGA. This is the second contribution of this paper. Experimental results show the effectiveness of the proposed method.

The aim of this contribution is to show the detection of seminal papers using data mining techniques. To achieve the objective of this research, Rapidminer Studio software and its data mining tools are used, based on data created with information extracted from Google Scholar and Scopus, in three different areas of knowledge. In this process, other softwares such as Microsoft Excel and Publish or Perish are used. Comparing the results obtained for the searches in Knowledge Management, Entrepreneurship and Marketing, it was obtained that there is no marked similarity between the sets of articles that were obtained in Google Scholar and Scopus. The values for the Similarity Index remained below 0.52%, similar between Knowledge Management and Entrepreneurship but decreasing for Marketing. The detection of outliers using Data Mining techniques and in particular using Rapidminer, allowed to determine the seminals papers for the three search terms analyzed and allowed to characterize these in the space, in Google Scholar and Scopus. It was shown that the seminal articles can be different if Google Scholar or Scopus is used. The results suggest determining for other search terms whether the trend found is maintained or not.

Usually, scientific research begins with the collection of data in which online social media tools can be some of the most rewarding and informative resources. The extensive measure of accessible information pulls in users from undergraduate students to postdoc. The search for scientific themes has popularized due to the availability of abundant publications that resides in scientific social networks such as Mendeley, ResearchGate etc. Articles are published on these media inform of text for knowledge dissemination, scientific support, research, updates etc, and are frequently uploaded after its publication in a proceedings or journal. In this sense, data collected from database often contains high noise and its analysis can be treated as a characterization undertaking as it groups the introduction of a content into either good or bad. In this text, we present quantitative and qualitative analysis of papers popularity in Mendeley repository by using naive Bayes Classifier.

Virtual Reality (VR) and 360 film have caught the attention of audiences and content creators and emerged as a new media, however, the market penetration of VR and head mounted hardware has remained slow despite the availability of more affordable mobile options. This has resulted in some audiences turning to VR cinemas, festivals and out-of-home exhibitions. Creating affordable, scalable VR cinemas presents a number of challenges and many of the decisions taken in both developing and facilitating these curated exhibitions directly impact audience's reception of spatial audio soundtracks. This workshop position paper looks to discuss the potential issues and future solutions in the use of current synchronous exhibition applications, the competing formats, standardised Head Related Transfer Functions, headphone build/colourisation, and the on-boarding process.

DDoS attack detection using entropy-based features in network traffic has become a popular approach among researchers in the last five years. The use of traffic distribution features constructed using entropy measures has been proposed as a better approach to detect Distributed Denial of Service (DDoS) attacks compared to conventional volumetric methods, but it still lacks in the generality of detecting various intensity DDoS attacks accurately. In this paper, we focus on identifying effective entropy-based features to detect both high- and low-intensity DDoS attacks by exploring the effectiveness of entropy-based features in distinguishing the attack from normal traffic patterns. We hypothesise that using different entropy measures, window sizes, and entropy-based features may affect the accuracy of detecting DDoS attacks. This means that certain entropy measures, window sizes, and entropy-based features may reveal attack traffic amongst normal traffic better than the others. Our experimental results show that using Shannon, Tsallis and Zhou entropy measures can achieve a clearer distinction between DDoS attack traffic and normal traffic than Renyi entropy. In addition, the window size setting used in entropy construction has minimal influence in differentiating between DDoS attack traffic and normal traffic. The result of the effectiveness ranking shows that the commonly used features are less effective than other features extracted from traffic headers.

This paper focuses on referential coherence, which is seen as a crucial attribute of effective academic writing. Findings are reported from a corpus study of Russian students’ research proposals. The learners’ use of anaphoric expressions is compared with a reference corpus, which comprises research articles published in peer-reviewed journals. It was hypothesised that learners use anaphora less frequently than professional writers and face some difficulties when using anaphoric expressions. The results of the analysis partly confirmed the hypothesis and allowed the identification of particular problems connected with the students’ use of anaphoric expressions, which were then classified into several groups. Examples of exercises aimed at dealing with the identified problems are also provided. It is hoped that the reported findings, as well as the author’s suggested reasons for the problems and possible ways of dealing with them, will be useful for EAP practitioners, researchers, and students writing their research papers in English.

A paper’s novelty enhances its impact and citation. In this paper, we examine two dimensions of a paper’s novelty: new combinations and new components. We define new combinations as new pairs of knowledge elements in a related research area, and new components as new knowledge elements that have never appeared in a related research area previously. The importance of both dimensions is stressed, and we analyze the mechanisms that affect the frequency of a paper’s citation; we believe that a paper’s new combinations and new components both have an inverted U-shaped effect on its citation count. Utilizing a text-mining approach, we develop a novel method for constructing new combinations and new components using a paper’s keywords. Using keywords from papers published in the wind energy field between 2002 and 2015 as our sample, we conduct an empirical analysis on the above-mentioned relationships. To do so, we use the negative binomial regression method and several robustness tests. The results provide support for our hypotheses that propose a paper’s new combinations and new components significantly affect its impact. Specifically, new combinations and new components lead to more citation counts up to a specific threshold. When the number of new combinations and new components exceed the threshold, the paper is likely to be cited less frequently. Finally, we discuss the theoretical contributions, methodological contributions, and practical implications of these findings.

The intelligent task of semantically assigning a paper to a reviewer with respect to his knowledge domain remains a challenging task in academic conferences. From literature, a number of automated reviewer assignment systems have been presented which are based on distributional semantic models such as Term Frequency-Inverse Document Frequency (TF-IDF), Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA) have been used to capture semantics. Thus, this study presents the comparative study of the three models based on their derived suitability scores between a paper meant for review and a reviewer’s representation papers. From the experimental results obtained, it shows that TF-IDF outperformed the accuracy level of the other two models by a substantial margin.

The use of reliable, valid measures in implementation practice will remain limited without pragmatic measures. Previous research identified the need for pragmatic measures, though the characteristic identification used only expert opinion and literature review. Our team completed four studies to develop a stakeholder-driven pragmatic rating criteria for implementation measures. We published Studies 1 (identifying dimensions of the pragmatic construct) and 2 (clarifying the internal structure) that engaged stakeholders—participants in mental health provider and implementation settings—to identify 17 terms/phrases across four categories: Useful, Compatible, Acceptable, and Easy. This paper presents Studies 3 and 4: a Delphi to ascertain stakeholder-prioritized dimensions within a mental health context, and a pilot study applying the rating criteria. Stakeholders (N = 26) participated in a Delphi and rated the relevance of 17 terms/phrases to the pragmatic construct. The investigator team further defined and shortened the list, which were piloted with 60 implementation measures. The Delphi confirmed the importance of all pragmatic criteria, but provided little guidance on relative importance. The investigators removed or combined terms/phrases to obtain 11 criteria. The 6-point rating system assigned to each criterion demonstrated sufficient variability across items. The grey literature did not add critical information. This work produced the first stakeholder-driven rating criteria to assess whether measures are pragmatic. The Psychometric and Pragmatic Evidence Rating Scale (PAPERS) combines the pragmatic criteria with psychometric rating criteria, from previous work. Use of PAPERS can inform development of implementation measures and to assess the quality of existing measures.

Introduction: Assessment is a very important component of medical education and written examination is widely used tool of assessment. The instrument used in the written examination is the “Question Paper” (QP). The QP often lacks the validity, reliability, relevance and objectivity. 1,2 The present study is undertaken to assess the characteristics in construction of University question papers. Aim and Objectives: To analyze the theory question papers of Microbiology subject of Saurashtra University, with respect to; question form, learning objectives, relevance to core syllabus, relevance to teaching hours, and language and grammatical errors. Materials and Methods: Total 28 question papers of Saurashtra University of Microbiology subject (Mar-05 to Mar-15) were analyzed. The questions were analyzed with respect to types of question form and categorized in long answer type, short note type and short answer type. All questions were analyzed with respect to learning objectives and were classified in to three cognitive domains of learning knowledge, understanding and synthesis. All questions were analyzed to determine the focus of questions with respect to the syllabus and classified into must know, desirable to know and nice to know areas. Questions were analyzed to determine the weather questions cover the area of syllabus with regards to teaching hours dedicated. Questions were analyzed to identify the clarity and ambiguity in language, spelling and grammatical errors and scientific and taxonomic errors. Results: There were two paper styles noted. Question papers have long answer; short note and short answer questions. 95% questions were framed to test basic knowledge level of cognitive domain, and 5% question were framed to test the understanding level, while none of the questions were framed to test the synthesis level of cognitive domain. The majority of question were framed from must know area (97%) of syllabus, while less number of questions were from desirable to know (2%) and nice to know (1%) area. 30% of teaching hours were dedicated to bacteriology section and carried 22% weightage in examination. 6% of teaching hours were dedicated to mycology section which carried 11% weightage in examination. A balance was observed for General microbiology, Immunology and Virology sections with regards to teaching hours and weightage in examination. On an average of 11.1 errors per paper was noted. Spelling errors were common (8.8) followed by taxonomic errors (2.2) and grammatical errors (0.1). Conclusions: Major numbers of questions were subjective type than objective type. Majority of questions were framed to test basic knowledge level of cognitive domain. The majority of question were framed from must know area of syllabus which may have poor discriminatory ability between low and high achievers. A balance was observed for General microbiology, Immunology and Virology sections for dedicated teaching hours and its weightage in examination while Bacteriology has less and Parasitology and Mycology has more weightage in comparison to teaching hours. Many different types of errors were noted in question papers which include spelling errors, taxonomic errors and grammatical errors. A standard “Blue Print” is essential to bring the uniform standard in theory examination. Keywords: Question paper analysis, Blue-printing.

Spatial visualization—the ability to mentally imagine and manipulate objects—has frequently been measured using the Paper Folding Test (PFT). In this task, participants view diagrams of a paper being folded and a hole being punched. They then identify the resulting punch pattern. Although task instructions promote mentally unfolding the paper, the extent to which people follow this spatial visualization strategy is unknown. The present work assesses hypothesized PFT solution strategies and their relation to problem attributes. Accuracy was impacted by the interaction between fold types, linear mixed models revealed greater use of simple heuristics compared to the suggested unfolding. Furthermore, most participants used a single strategy but simple heuristics were more often used than unfolding. Given this, we recommend redesigning the PFT to utilize the prevalence of strategy use to assess individual differences.

In this work, a kind of nanocomposite paper was obtained by evaporation-induced self-assembly of a mixture of sisal cellulose nanofibers (CNF) and polyethylene glycol (PEG) as the matrix and citric acid (CA) as a cross-linking agent. The CNF/PEG/CA paper exhibited good water swelling resistance which could be controlled by changing the concentration of CA. In addition, this nanocomposite paper exhibited good mechanical properties and water-induced shape memory performance. In particular, when the dosage of CA was 30 wt.%, the tensile strength and the tensile modulus of the CNF/PEG/CA paper after swelling were 25.2 MPa and 813.0 MPa, respectively. Further, this nanocomposite showed great potential for water-induced shape memory materials with fast response speed. The shape recovery rate (Rr) of the CNF/PEG/CA paper reached 90.2% with 30 wt.% CA after being immersed in water for 11 s. It is anticipated that our current work can be used to exploit more efficient methods to overcome the poor water swelling resistance of the cellulose-based shape memory materials.

Security inks based on photoluminescent materials are mostly investigated for security applications, such as information encryption and decryption, anti-counterfeiting, and data storage. Although they are invisible to the naked eye under ambient light, they can be detected under ultraviolet or near-infrared light. Herein, a new kind of secret paper made from network-structured ultralong hydroxyapatite nanowires and cellulose fibers has been developed. White vinegar, a common cooking ingredient, is used as an invisible security ink. Covert information on the secret paper written with white vinegar is totally invisible under natural light, but it can be decrypted and clearly read after exposure to fire; the response time to fire is short (

The uneven utilisation of modes of transport has a big impact on traffic in transport pathway infrastrutures. For motor vehicles for instance, this situation explains rapid road deterioration and the large amounts of money invested in maintenance and development due to overuse. There are many approaches to managing this problem; however, the impact of individual users in infrastructural maintenance is mostly ignored. In this position paper, we hypothesise that important changes torwards a more efficient use of the transport network start with its users and their behavioural changes. To this end, we introduce our vision on how to employ data driven, intelligent agent-based modelling, incorporating human factors aspects, as a toolset to understand travellers and to stimulate behavioural changes. The aim is to achieve better balanced and integrated mobility usage within the transport network. The idea is explored with a few guided questions, and a methodology is proposed. We employ 1) cognitive work analysis to investigate the reasons for travellers' mode choice; 2) computational intelligence to extract and represent knowledge from related datasets; 3) agent-based modelling to represent the real-world and to observe both individual and emergent behaviours. Future directions to adapt our methodology to alternative smart mobility projects are also discussed.

Even though AI/ML is of strategic importance for many companies, it is not easy to come up with an AI life cycle in a multi-cloud world that is being increasingly embraced. In this paper, we present our position on an AI strategy that is future proof and does not force vendor lock in.

This chapter focuses on cost control by analyzing the intelligent control system of waste paper deinking production line, and finally designs a solution. In the aspect of control index, machine learning has designed a solution that pays less attention to the benefit of a single control loop and focuses on maximizing the overall benefit of the production line. In the aspect of control demand, according to the complexity of waste paper deinking production line, a distributed control solution is designed Finally, according to the dynamic fuzziness in machine learning system, the basic concept of dynamic fuzzy machine learning is constructed by using dynamic fuzzy set The dynamic fuzzy machine learning model is described and the related learning algorithm is given. Finally, a simulation example is given, and satisfactory results are obtained by comparing with the elastic BP algorithm RPROP of neural network.

This companion paper supports the replication of the fashion trend forecasting experiments with the KERN (Knowledge Enhanced Recurrent Network) method that we presented in the ICMR 2020. We provide an artifact that allows the replication of the experiments using a Python implementation. The artifact is easy to deploy with simple installation, training and evaluation. We reproduce the experiments conducted in the original paper and obtain similar performance as previously reported. The replication results of the experiments support the main claims in the original paper.

The entire scientific and academic community has been mobilized to gain a better understanding of the COVID-19 disease and its impact on humanity. Most research related to COVID-19 needs to analyze large amounts of data in very little time. This urgency has made Big Data Analysis, and related questions around the privacy and security of the data, an extremely important part of research in the COVID-19 era. The White House OSTP has, for example, released a large dataset of papers related to COVID research from which the research community can extract knowledge and information. We show an example system with a machine learning-based knowledge extractor which draws out key medical information from COVID-19 related academic research papers. We represent this knowledge in a Knowledge Graph that uses the Unified Medical Language System (UMLS). However, publicly available studies rely on dataset that might have sensitive data. Extracting information from academic papers can potentially leak sensitive data, and protecting the security and privacy of this data is equally important. In this paper, we address the key challenges around the privacy and security of such information extraction and analysis systems. Policy regulations like HIPAA have updated the guidelines to access data, specifically, data related to COVID-19, securely. In the US, healthcare providers must also comply with the Office of Civil Rights (OCR) rules to protect data integrity in matters like plasma donation, media access to health care data, telehealth communications, etc. Privacy policies are typically short and unstructured HTML or PDF documents. We have created a framework to extract relevant knowledge from the health centers’ policy documents and also represent these as a knowledge graph. Our framework helps to understand the extent to which individual provider policies comply with regulations and define access control policies that enforce the regulation rules on data in the knowledge graph extracted from COVID-related papers. Along with being compliant, privacy policies must also be transparent and easily understood by the clients. We analyze the relative readability of healthcare privacy policies and discuss the impact. In this paper, we develop a framework for access control decisions that uses policy compliance information to securely retrieve COVID data. We show how policy compliance information can be used to restrict access to COVID-19 data and information extracted from research papers.

ObjectiveTo identify and highlight research papers representing noteworthy developments in signals, sensors, and imaging informatics in 2020.MethodA broad literature search was conducted on PubMed and Scopus databases. We combined Medical Subject Heading (MeSH) terms and keywords to construct particular queries for sensors, signals, and image informatics. We only considered papers that have been published in journals providing at least three articles in the query response. Section editors then independently reviewed the titles and abstracts of preselected papers assessed on a three-point Likert scale. Papers were rated from 1 (do not include) to 3 (should be included) for each topical area (sensors, signals, and imaging informatics) and those with an average score of 2 or above were subsequently read and assessed again by two of the three co-editors. Finally, the top 14 papers with the highest combined scores were considered based on consensus.ResultsThe search for papers was executed in January 2021. After removing duplicates and conference proceedings, the query returned a set of 101, 193, and 529 papers for sensors, signals, and imaging informatics, respectively. We filtered out journals that had less than three papers in the query results, reducing the number of papers to 41, 117, and 333, respectively. From these, the co-editors identified 22 candidate papers with more than 2 Likert points on average, from which 14 candidate best papers were nominated after intensive discussion. At least five external reviewers then rated the remaining papers. The four finalist papers were found using the composite rating of all external reviewers. These best papers were approved by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board.ConclusionsSensors, signals, and imaging informatics is a dynamic field of intense research. The four best papers represent advanced approaches for combining, processing, modeling, and analyzing heterogeneous sensor and imaging data. The selected papers demonstrate the combination and fusion of multiple sensors and sensor networks using electrocardiogram (ECG), electroencephalogram (EEG), or photoplethysmogram (PPG) with advanced data processing, deep and machine learning techniques, and present image processing modalities beyond state-of-the-art that significantly support and further improve medical decision making.

Cloud computing is a new paradigm. In recent years, cloud computing has been a popular model for hosting and providing services, as it has gained great popularity in providing multiple services such as storage and cloud hosting. Moreover, the cloud provides a powerful computing platform that enables many individuals and organizations to perform many tasks such as: use of online storage space, approval on business applications, developing custom computer software, and creating a virtual network environment. In previous years, the number of people using cloud services increased dramatically and a lot of data was stored in cloud computing environments. Meanwhile, data breaches of cloud services are also increasing every year due to hackers who are always trying to exploit security holes in the cloud architecture, so the security problems in these systems are horrific because they store a lot of private and confidential data. In this paper, the authors review security and privacy issues, bottlenecks associated with cloud computing, and the specific security issues in cloud computing layers and vulnerabilities, and how to avoid and control them. This paper helps the reader understand the problems related to cloud computing.

Currently, readers often prefer to search for their interested papers based on a set of typed query keywords. As the keywords of a paper is often limited, paper recommender systems often need to recommend a set of papers which collectively satisfy the readers’ keyword query. However, the topics of recommended papers are probably not correlated with each other, which fail to meet the readers’ requirements on in-depth and continuous academic research. Furthermore, although existing paper citation graphs can model the papers’ correlations, they often face the data sparse problem which blocks accurate paper recommendations. To address these issues, we propose a keywords-driven and weight-aware paper recommendation approach, named LP-PRk+w (link prediction-paper recommendation), based on a weighted paper correlation graph. Concretely, we firstly optimize the existing paper citation graph modes by introducing a weighted similarity, after which we obtain a weighted paper correlation graph. Then we recommend a set of correlated papers based on the weighted paper correlation graph and the query keywords from readers. At last, we conduct large-scale experiments on a real-world Hep-Th dataset. Experimental results demonstrate that our proposal can improve the paper recommendation performances considerably, compared to other related solutions.

Video cameras are becoming ubiquitous in our daily lives. With the recent advancement of Artificial Intelligence (AI), live video analytics are enabling various useful services, including traffic monitoring and campus surveillance. However, current video analytics systems are highly limited in leveraging the enormous opportunities of the deployed cameras due to (i) centralized processing architecture (i.e., cameras are treated as dumb streaming-only sensors), (ii) hard-coded analytics capabilities from tightly coupled hardware and software, (iii) isolated and fragmented camera deployment from different service providers, and (iv) independent processing of camera streams without any collaboration. In this paper, we envision a full-fledged system for software-defined video analytics with cross-camera collaboration that overcomes the aforementioned limitations. We illustrate its detailed system architecture, carefully analyze the key system requirements with representative app scenarios, and derive potential research issues along with a summary of the status quo of existing works.

Storm and flood disasters such as typhoons and torrential rains are becoming more intense and frequent. The national government and municipalities must respond to such natural disasters as soon as possible. When the scale of damage is large; however, it takes much time to investigate the severity of damage, and the initial response can be delayed. If we could precisely and rapidly estimate the severity of damage for each city at an early stage, the national government would be able to better support the municipalities, and consequently respond quickly to help citizens. In this paper, we propose a novel approach to estimate the severity of disaster damage within a short time period after a disaster occurs by exploiting real-time population data generated from cellular networks. First, we investigate the relationship between real-time population data and the severity of damage. Then, we design a Graph Convolutional Networks for Disaster Damage Estimation, called D2E-GCN, which fully exploits the directed and weighted characteristics of human mobility graph. We conduct an offline evaluation on real-world datasets including two typhoons that hit Japan. The evaluation results show that the proposed method outperforms baseline methods which do not consider the graph structure of cities, and the proposed method can estimate the severity of damage approximately 48 hours after typhoons passed. Moreover, we find the experimental insight that the estimation performance can be significantly affected by the graph construction method for GCN models.

In federated learning (FL), model updates from mutually distrusting parties are aggregated in a centralized fusion server. The concentration of model updates simplifies FL's model building process, but might lead to unforeseeable information leakage. This problem has become acute due to recent FL attacks that can reconstruct large fractions of training data from ostensibly "sanitized" model updates. In this paper, we re-examine the current design of FL systems under the new security model of reconstruction attacks. To break down information concentration, we build TRUDA, a new cross-silo FL system, employing a trustworthy and decentralized aggregation architecture. Based on the unique computational properties of model-fusion algorithms, we disassemble all exchanged model updates at the parameter-granularity and re-stitch them to form random partitions designated for multiple hardware-protected aggregators. Thus, each aggregator only has a fragmentary and shuffled view of model updates and is oblivious to the model architecture. The deployed security mechanisms in TRUDA can effectively mitigate training data reconstruction attacks, while still preserving the accuracy of trained models and keeping performance overheads low.

Blind natural video quality assessment (BVQA), also known as no-reference video quality assessment, is a highly active research topic. In our recent contribution titled "Blind Natural Video Quality Prediction via Statistical Temporal Features and Deep Spatial Features" published in ACM Multimedia 2020, we proposed a two-level video quality model employing statistical temporal features and spatial features extracted by a deep convolutional neural network (CNN) for this purpose. At the time of publishing, the proposed model (CNN-TLVQM) achieved state-of-the-art results in BVQA. In this paper, we describe the process of reproducing the published results by using CNN-TLVQM on two publicly available natural video quality datasets.

We introduce a comprehensive benchmark for local features and robust estimation algorithms, focusing on the downstream task -- the accuracy of the reconstructed camera pose -- as our primary metric. Our pipeline's modular structure allows easy integration, configuration, and combination of different methods and heuristics. This is demonstrated by embedding dozens of popular algorithms and evaluating them, from seminal works to the cutting edge of machine learning research. We show that with proper settings, classical solutions may still outperform the perceived state of the art. Besides establishing the actual state of the art, the conducted experiments reveal unexpected properties of Structure from Motion (SfM) pipelines that can help improve their performance, for both algorithmic and learned methods. Data and code are online https://github.com/vcg-uvic/image-matching-benchmark, providing an easy-to-use and flexible framework for the benchmarking of local features and robust estimation methods, both alongside and against top-performing methods. This work provides a basis for the Image Matching Challenge https://vision.uvic.ca/image-matching-challenge., Comment: Added: KeyNet-SOSNet, AffNet-HardNet, TFeat, MKD from kornia

Background: In recognition of the ongoing discussion on euthanasia and physician-assisted suicide, the Board of Directors of the European Association for Palliative Care commissioned this white paper from the palliative care perspective. Aim: This white paper aims to provide an ethical framework for palliative care professionals on euthanasia and physician-assisted suicide. It also aims to provide an overview on the available evidence as well as a discourse of ethical principles related to these issues. Design: Starting from a 2003 European Association for Palliative Care position paper, 21 statements were drafted and submitted to a five-round Delphi process Participants: A panel with 17 experts commented on the paper in round 1. Board members of national palliative care or hospice associations that are collective members of European Association for Palliative Care were invited to an online survey in rounds 2 and 3. The expert panel and the European Association for Palliative Care board members participated in rounds 4 and 5. This final version was adopted as an official position paper of the European Association for Palliative Care in April 2015. Results: Main topics of the white paper are concepts and definitions of palliative care, its values and philosophy, euthanasia and physician-assisted suicide, key issues on the patient and the organizational level. The consensus process confirmed the 2003 European Association for Palliative Care white paper and its position on the relationship between palliative care and euthanasia and physician-assisted suicide. Conclusion: The European Association for Palliative Care feels that it is important to contribute to informed public debates on these issues. Complete consensus seems to be unachievable due to incompatible normative frameworks that clash.