004 Datenverarbeitung; Informatik
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Website blocking in the European Union: Network interference from the perspective of Open Internet
(2024)
By establishing an infrastructure for monitoring and blocking networks in accordance with European Union (EU) law on preventive measures against the spread of information, EU member states have also made it easier to block websites and services and monitor information. While relevant studies have documented Internet censorship in non-European countries, as well as the use of such infrastructures for political reasons, this study examines network interference practices such as website blocking against the backdrop of an almost complete lack of EU-related research. Specifically, it performs and demonstrates an analysis for the total of 27 EU countries based on three different sources. They include first, tens of millions of historical network measurements collected in 2020 by Open Observatory of Network Interference volunteers from around the world; second, the publicly available blocking lists used by EU member states; and third, the reports issued by network regulators in each country from May 2020 to April 2021. Our results show that authorities issue multiple types of blocklists. Internet Service Providers limit access to different types and categories of websites and services. Such resources are sometimes blocked for unknown reasons and not included in any of the publicly available blocklists. The study concludes with the hurdles related to network measurements and the nontransparency from regulators regarding specifying website addresses in blocking activities.
The object of the study is the process of identifying the state of a computer network. The subject of the study are the methods of identifying the state of computer networks. The purpose of the paper is to improve the efficacy of intrusion detection in computer networks by developing a method based on transformer models. The results obtained. The work analyzes traditional machine learning algorithms, deep learning methods and considers the advantages of using transformer models. A method for detecting intrusions in computer networks is proposed. This method differs from known approaches by utilizing the Vision Transformer for Small-size Datasets (ViTSD) deep learning algorithm. The method incorporates procedures to reduce the correlation of input data and transform data into a specific format required for model operations. The developed methods are implemented using Python and the GOOGLE COLAB cloud service with Jupyter Notebook. Conclusions. Experiments confirmed the efficiency of the proposed method. The use of the developed method based on the ViTSD algorithm and the data preprocessing procedure increases the model's accuracy to 98.7%. This makes it possible to recommend it for practical use, in order to improve the accuracy of identifying the state of a computer system.
Purpose
This study investigates whether the artificial neural network approach, when used on a large organizational soft HR performance dataset, results in a better (R2/RMSE) model compared to the linear regression. With the use of predictive modelling, a more informed base for managerial decision making within soft HR performance management is offered.
Design/methodology/approach
The study builds on a dataset (n > 43 k) stemming from an annual employee MNC survey. It covers several soft HR performance drivers and outcomes (such as engagement, satisfaction and others) that either have evidence of a dual-role nature or non-linear relationships. This study applies the framework for artificial neural network analysis in organization research (Scarborough and Somers, 2006).
Findings
The analysis reveals a substantial artificial neural network model performance (R2 > 0.75) with an excellent fit statistic (nRMSE <0.10) and all drivers have the same relative importance (RMI [0.102; 0.125]). This predictive analysis revealed that the organization has to increase six of the drivers, keep two on the same level and decrease one.
Originality/value
Up to date, this study uses the largest dataset in soft HR performance management. Additionally, the predictive results reveal that specific target values lay below the current levels to achieve optimal performance.
Understanding Website Privacy Policies—A Longitudinal Analysis Using Natural Language Processing
(2023)
Privacy policies are the main method for informing Internet users of how their data are collected and shared. This study aims to analyze the deficiencies of privacy policies in terms of readability, vague statements, and the use of pacifying phrases concerning privacy. This represents the undertaking of a step forward in the literature on this topic through a comprehensive analysis encompassing both time and website coverage. It characterizes trends across website categories, top-level domains, and popularity ranks. Furthermore, studying the development in the context of the General Data Protection Regulation (GDPR) offers insights into the impact of regulations on policy comprehensibility. The findings reveal a concerning trend: privacy policies have grown longer and more ambiguous, making it challenging for users to comprehend them. Notably, there is an increased proportion of vague statements, while clear statements have seen a decrease. Despite this, the study highlights a steady rise in the inclusion of reassuring statements aimed at alleviating readers’ privacy concerns.
Research on the robustness of networks, and in particular the Internet, has gained critical importance in recent decades because more and more individuals, societies and firms rely on this global network infrastructure for communication, knowledge transfer, business processes and e-commerce. In particular, modeling the structure of the Internet has inspired several novel graph metrics for assessing important topological robustness features of large complex networks. This survey provides a comparative overview of these metrics, presents their strengths and limitations for analyzing the robustness of the Internet topology, and outlines a conceptual tool set in order to facilitate their future adoption by Internet research and practice but also other areas of network science.
Background: Searching a biological sequence database with a query sequence looking for homologues has become a routine operation in computational biology. In spite of the high degree of sophistication of currently available search routines it is still virtually impossible to identify quickly and clearly a group of sequences that a given query sequence belongs to. Results: We report on our developments in grouping all known protein sequences hierarchically into superfamily and family clusters. Our graph-based algorithms take into account the topology of the sequence space induced by the data itself to construct a biologically meaningful partitioning. We have applied our clustering procedures to a non-redundant set of about 1,000,000 sequences resulting in a hierarchical clustering which is being made available for querying and browsing at http://systers.molgen.mpg.de/. Conclusions: Comparisons with other widely used clustering methods on various data sets show the abilities and strengths of our clustering methods in producing a biologically meaningful grouping of protein sequences.