Virtual user geography of library websites

The author generalizes the results of library user survey. The survey was held within the framework of the study “Webometric monitoring of libraries”. The study goal was to assess the virtual geography of library website users and to specify target audience. The user audience of 11 library websites was assessed by following criteria: geographical data (user location, probable user residence area); user demographic data (sex, age); loyalty indicators (bounce rate, visit depth, visit duration); website user desired actions (estimation of desired action conversion). Based on the findings, the author concludes that: library offline services impact website user geography; there is a close interdependence between library location and website traffic; users resident in the library region make the most loyal website users; women prevail in the library user audience. The target website user audience is identified; recommendations are offered for the further strategic development in the web-space. The study conclusions and recommendations would facilitate generated demanded content and efficient promotion of library information resources and services in the web-space.

BFR-SE: A Blockchain-Based Fair and Reliable Searchable Encryption Scheme for IoT with Fine-Grained Access Control in Cloud Environment

Due to capacity limitations, large amounts of data generated by IoT devices are often stored on cloud servers. These data are usually encrypted to prevent the disclosure, which significantly affects the availability of this data. Searchable encryption (SE) allows a party to store his data created by his IoT devices or mobile in encryption on the cloud server to protect his privacy while retaining his ability to search for data. However, the general SE techniques are all pay-then-use. The searchable encryption service providers (SESP) are considered curious but honest, making it unfair and unreliable. To address these problems, we combined ciphertext-policy attribute-based encryption, Bloom filter, and blockchain to propose a blockchain-based fair and reliable searchable encryption scheme (BFR-SE) in this paper. In BFR-SE, we constructed an attribute-based searchable encryption model that can provide fine-grained access control. The data owner stores the indices on SESP and stores some additional auxiliary information on the blockchain. After a data user initiates a request, SESP must return the correct and integral search results before the deadline. Otherwise, the data user can send an arbitration request, and the blockchain will make a ruling. The blockchain will only perform arbitrations based on auxiliary information when disputes arise, saving the computing resources on-chain. We analyzed the security and privacy of BFR-SE and simulated our scheme on the EOS blockchain, which proves that BFR-SE is feasible. Meanwhile, we provided a thorough analysis of storage and computing overhead, proving that BFR-SE is practical and has good performance.

Data Standards for the Phenology of Plant Specimens

Phenological data (i.e., data on growth and reproductive events of organisms) are increasingly being used to study the effects of climate change, and biodiversity specimens have arisen as important sources of phenological data. However, phenological data are not expressly treated by the Darwin Core standard (Wieczorek et al. 2012), and specimen-based phenological data have been codified and stored in various Darwin Core fields using different vocabularies, making phenological data difficult to access, aggregate, and therefore analyze at scale across data sources. The California Phenology Network, an herbarium digitization collaboration launched in 2018, has harvested phenological data from over 1.4 million angiosperm specimens from California herbaria (Yost et al. 2020). We developed interim standards by which to score and store these data, but further development is needed for adoption of ideal phenological data standards into the Darwin Core. To this end, we are forming a Plant Specimen Phenology Task Group to develop a phenology extension for the Darwin Core standard. We will create fields into which phenological data can be entered and recommend a standardized vocabulary for use in these fields using the Plant Phenology Ontology (Stucky et al. 2018, Brenskelle et al. 2019). We invite all interested parties to become part of this Task Group and thereby contribute to the accesibility and use of these valuable data. In this talk, we will describe the need for plant phenological data standards, current challenges to developing such standards, and outline the next steps of the Task Group toward providing this valuable resource to the data user community.

Analysis and simulation of plant disease progress curves in R: introducing the epifitter package

The analysis of the disease progress curves (DPCs) is central to understanding plant disease epidemiology. The shape of DPCs can vary significantly and epidemics can be better understood and compared with an appropriate depiction and analysis. This paper introduces epifitter, an open-source tool developed in R for aiding in the simulation and analysis of DPC data. User-level functions were developed and their use is demonstrated to the reader using actual disease progress curve data for facilitating the conduction of several tasks, including (a) simulation of synthetic DPCs using four population dynamics models (exponential, monomolecular, logistic, and Gompertz); (b) calculation of the areas under disease progress curve and stairs; (c) fitting and ranking the four above-mentioned models to single or multiple DPCs; and (d) generation and customization of graphs. The package requires the installation of R in any desktop computer and the scripted analysis can be fully documented, reproduced, and shared. The epifitter R package provides a flexible suite for temporal analysis of epidemics that is useful for both research and teaching purposes.

ANALYSIS OF USER EXPERIENCE RESOURCE PLANNING WITH USER EXPERIENCE QUESTIONNAIRE FRAMEWORK (CASE STUDY: UNIVERSITAS MULTIMEDIA NUSANTARA)

ERP or Enterprise Resource Planning is a design that has been applied in an educational institution or company that can be used to organize, coordinate and manage the system resources in it to be more integrated. The refusal of system users to use the ERP system (new system) is one of the failure factors for ERP system implementation that should be considered by higher education companies. User's lack of understanding by users can cause users to simply be forced to use without being adapted to a qualified use of the ERP system. In addition to the importance of the ERP implementation process in a company or university, there is also an influence on the system's user experience as the key to the success of ERP implementation to minimize failures in the ERP system process. This study uses the User Experience Questionnaire Framework to analyze, measure and find out significantly how much influence the user experience level has on the PeopleSoft Oracle Campus Solution ERP system using the six aspects contained in the framework UEQ on the modules Student Record, Academic Advisement, and Student Financial in the respective departments at UMN. The research was conducted quantitatively, namely the collection and collection of data to be carried out through the distribution of questionnaires online and carried out using SPSS software. The results of the measurement level obtained based on the mean and benchmark data user experience from the six UEQ scales from the highest to the lowest value level are Dependability is in the category (Excellent), Stimulation is in the category (Excellent), Novelty is in the category (Excellent), Perspicuity category is in the category (Good), Attractiveness is in the category (Good), and Efficiency is in the category (Good) and partially or completely can have a significant 61% influence on the use of modules in the ERP system at UMN. Keywords: ERP (Enterprise Resource Planning), UEQ (User Experience Questionnaire), Quantitative, SPSS.

Blockchain-Based Access Control and Data Sharing Mechanism in Cloud Decentralized Storage System

Access control is a major factor in enhancing data security in the cloud storage system. However, the existing data sharing and the access control method have privacy data leakage and key abuse, which is a major challenge in the research community. Therefore, an effective method named Blockchain-based access control and data sharing approach is developed in the cloud storage system to increase data security. The proposed Blockchain-based access control and data sharing approach effectively solve single-point failure in the cloud system. It provides more benefits by increasing the throughput and reducing the cost. The Data user (DU) makes the registration request using the ID and password and forwards it to the Data Owner (DO), which processes the request and authenticates the Data user. The information of the data owner is embedded in the transactional blockchain using the encrypted master key. The Data owner achieves the data encryption process, and encrypted files are uploaded to the Interplanetary File System (IPFS). Based on the encrypted file location and encrypted key, the Data owner generates the ciphertext metadata and is embedded in the transactional blockchain. The proposed Blockchain-based access control and data sharing approach achieved better performance using the metrics, like a better genuine user detection rate of 95% and lower responsiveness of 25sec with the blockchain of 100 sizes.

Periods as powerful data: User understandings of menstrual app data and information

Digital apps for tracking menstruation are widely used. Taking a critical menstruation and critical digital health approach, this research asks how menstrual app users perceive their data. Interviews with 25 menstrual app users across Aotearoa New Zealand, were analysed using reflexive thematic analysis. Some participants only track their period, while others track additional symptoms. They appreciated having a choice of data to track, entering extensive amounts of data. Most participants had not given much thought to their data, viewing it as uninteresting and unproblematic. A small group were concerned by the data risks and managed this in several ways. Participants across both groups supported using data for menstrual health research. This research demonstrates a need for digital literacy and for limits on the use of menstruation information, where menstruators themselves are controlling and benefitting from their data.

Data validation and data quality assessment

Once data have been recorded, data validation procedures have to be conducted to assess the quality of the data, i.e. give a confidence grade. Furthermore, gaps may occur in time series and, depending on the purposes, these can be given values by application of e.g. interpolation. Since both aspects are strongly correlated, this chapter gives an overview on the main data validation and data curation/imputation methods. Instead of offering exhaustive details on existing methods, this chapter aims at providing concepts for most popular techniques, a discussion of their advantages and disadvantages in the light of different cases of application, and some thoughts on potential impacts of the choices that must be made. Despite involving mathematical methods, data validation remains a largely subjective process: every data user must be aware of those subjectivities.

A Non-Interactive Attribute-Based Access Control Scheme by Blockchain for IoT

As an important method of protecting data confidentiality in the Internet of Things (IoT), access control has been widely concerned. Because attribute-based access control mechanisms are dynamic, it is not only suitable to solve the dynamic access problem in IoT, but also to deal with the dynamic caused by node movement and access data change. The traditional centralized attribute-based access control mechanism has some problems: due to the large number of devices in IoT, the central trusted entity may become the bottleneck of the whole system. Moreover, when a central trusted entity is under distributed denial-of-service (DDoS) attack, the entire system may crash. Blockchain is a good way to solve the above problems. Therefore, we developed a non-interactive, attribute-based access control scheme that applies blockchain technology in IoT scenarios by using PSI technology. In addition, the attributes of data user and data holder are hidden, which protects the privacy of both parties’ attributes and access policy. Furthermore, the experimental results indicate that our scheme has high efficiency.

A sampling approach to Debiasing the offline evaluation of recommender systems

Offline evaluation of recommender systems (RSs) mostly relies on historical data, which is often biased. The bias is a result of many confounders that affect the data collection process. In such biased data, user-item interactions are Missing Not At Random (MNAR). Measures of recommender system performance on MNAR test data are unlikely to be reliable indicators of real-world performance unless something is done to mitigate the bias. One widespread way that researchers try to obtain less biased offline evaluation is by designing new, supposedly unbiased performance metrics for use on MNAR test data. We investigate an alternative solution, a sampling approach. The general idea is to use a sampling strategy on MNAR data to generate an intervened test set with less bias — one in which interactions are Missing At Random (MAR) or, at least, one that is more MAR-like. An existing example of this approach is SKEW, a sampling strategy that aims to adjust for the confounding effect that an item’s popularity has on its likelihood of being observed. In this paper, after extensively surveying the literature on the bias problem in the offline evaluation of RSs, we propose and formulate a novel sampling approach, which we call WTD; we also propose a more practical variant, which we call WTD_H. We compare our methods to SKEW and to two baselines which perform a random intervention on MNAR data. We empirically validate for the first time the effectiveness of SKEW and we show our approach to be a better estimator of the performance that one would obtain on (unbiased) MAR test data. Our strategy benefits from high generality (e.g. it can also be employed for training a recommender) and low overheads (e.g. it does not require any learning).