Different Kinds Of Users Research Articles

Blockchain privacy: Fundamental aspects and challenges for the future Internet data sharing

AbstractPrivacy concerns the majority of individuals, governments, and organizations that share data over dissimilar networks of nodes. Every kind of participant requires awareness of the data journey that unfolds inside a blockchain network with its own trustworthy rules of data management and accessibility. This paper provides a methodological approach on privacy for data sharing within blockchain environments. Specific technological aspects of blockchains that relate to on‐chain privacy such as network nature, party join, smart contracts, blockchain states, transactions, and ledger flows, are analysed with respect to the involvement and impact of privacy in distributed ledgers. A high‐level architectural approach is suggested that is intended to address significant privacy concerns on data sharing among different kinds of users in the context of blockchain networks deployment and the broader Web 3.0 ecosystem building. Simultaneously, many pertinent challenges are discussed regarding network nature, configurable privacy, ownership, confidentiality, secured computation, and data monetization that appear in the field and ought to be carefully tackled for the future mass adoption of privacy‐matured distributed ledgers that interconnect delivering the future Internet.

Efficient Fingerprinting Attack on Web Applications: An Adaptive Symbolization Approach

Website fingerprinting is valuable for many security solutions as it provides insights into applications that are active on the network. Unfortunately, the existing techniques primarily focus on fingerprinting individual webpages instead of webpage transitions. However, it is a common scenario for users to follow hyperlinks to carry out their actions. In this paper, an adaptive symbolization method based on packet distribution information is proposed to represent network traffic. The Profile Hidden Markov Model (PHMM exploits positional information contained in network traffic sequences and is sensitive to webpage transitional information) is used to construct users’ action patterns. We also construct user role models to represent different kinds of users and apply them to our web application identification framework to uncover more information. The experimental results demonstrate that compared to the equal interval and K-means symbolization algorithms, the adaptive symbolization method retains the maximum amount of information and is less time-consuming. The PHMM-based user action identification method has higher accuracy than the existing traditional classifiers do.

Methodologic Recommendations to Implement Pavement Management Systems and Eco-Sustainable Solutions for Local Road Administrations

Local Road Administrations (LRA) manage wide and fragmented road networks with constrained financial and human resources. Though LRA manage the most road networks and the relative development and maintenance have a huge impact on environment and society, frequently, LRA cannot implement new technologies and methodological advancements because innovations are generally fitted to different kinds of users. For these reasons, the adoption of a customised pavement management system (PMS) for LRA is strongly recommended to define workflows, make investments, and find long-lasting and cost-effective solutions. Considering the goal of a sustainable development, new complex aspects must be also considered in the PMS matching policies, investment strategies and engineered solutions. Eco-sustainable techniques for the maintenance of road pavements and utility trenches must be gradually introduced in PMS involving stakeholders to preserve natural resources, to reduce atmospheric pollutions and to stimulate the local economic growth. The definition of a PMS guideline for LRA considering new concepts of a circular economy is a contemporary and open challenge. This paper shows a new PMS approach for LRA, including the strategy and requirements for environmentally friendly road materials that can be progressively adopted by each LRA to lessen the negative effects of maintenance activities on the environment.

Design and performance of a Martian autonomous navigation system based on a smallsat constellation

Deciphering the genesis and evolution of the Martian polar caps can provide crucial understanding of Mars' climate system. The growing scientific interest for the exploration of Mars at high latitudes, and the need of minimizing the resources onboard landers and rovers, motivates the need for adequate navigation support from orbit. We propose a novel concept based on a constellation that can support autonomous navigation of different kind of users devoted to scientific investigations of those regions. We study two constellations, that differ mainly for the semi-major axis, composed of 5 small satellites (based on the SmallSats design being developed in Argotec), offering dedicated coverage of the Mars polar regions. We focus on the architecture of the inter-satellite links (ISL), the key elements providing both ephemerides and time synchronization for the broadcasting of the navigation message. Our concept is based on suitably configured coherent links, able to suppress the adverse effects of on-board clock instabilities and to provide excellent range-rate accuracies between the constellation's nodes. The data quality allows attaining good positioning performance for both constellations with a largely autonomous system. Indeed, we show that ground support can be heavily reduced by employing an ISL communication architecture.

Notebooks for Data Analysis and Visualization: Moving Beyond the Data.

Notebooks are a relatively new way of analyzing data and creating visualizations. They differ from the common graphical user interfaces used for visualization tools in many ways, and have their own strengths and weaknesses. In particular, they allow easy sharing, experimentation, and collaboration, and provide context about the data for different kinds of users. They also integrate modeling, forecasting, and complex analyses directly with the visualization. We believe that notebooks provide a unique and fundamentally new way of working with and understanding data. By laying out their unique properties, we hope to inspire both researchers and practitioners to investigate their many uses, explore their pros and cons, and share their findings.

A Literature Review of Big Data-Based Urban Park Research in Visitor Dimension

Urban parks provide multiple benefits to human well-being and human health. Big data provide new and powerful ways to study visitors’ feelings, activities in urban parks, and the effect they themselves have on urban parks. However, the term “big data” has been defined variably, and its applications on urban parks have so far been sporadic in research. Therefore, a comprehensive review of big data-based urban park research is much needed. The review aimed to summarize the big data-based urban park research in visitor dimension by a systematic review approach in combination with bibliometric and thematic analyses. The results showed that the number of publications of related articles has been increasing exponentially in recent years. Users’ days data is used most frequently in the big data-based urban park research, and the major analytical methods are of four types: sentiment analysis, statistical analysis, and spatial analysis. The major research topics of big data-based urban park research in visitor dimension include visitors’ behavior, visitors’ perception and visitors’ effect. Big data benefits urban park research by providing low-cost, timely information, a people-oriented perspective, and fine-grained site information. However, its accuracy is insufficient because of coordinate, keyword classification and different kinds of users. To move forward, future research should integrate multiple big data sources, expand the application, such as public health and human–nature interactions, and pay more attention to the big data use for overcoming pandemic. This review can help to understand the current situation of big data-based urban park research, and provide a reference for the studies of this topic in the future.

A Flexible Top-Down Data-Driven Stochastic Model for Synthetic Load Profiles Generation

The study of the behavior of smart distribution systems under increasingly dynamic operating conditions requires realistic and time-varying load profiles to run comprehensive and accurate simulations of power flow analysis, system state estimation and optimal control strategies. However, due to the limited availability of experimental data, synthetic load profiles with flexible duration and time resolution are often needed to this purpose. In this paper, a top-down stochastic model is proposed to generate an arbitrary amount of synthetic load profiles associated with different kinds of users exhibiting a common average daily pattern. The groups of users are identified through a preliminary Ward’s hierarchical clustering. For each cluster and each season of the year, a time-inhomogeneous Markov chain is built, and its parameters are estimated by using the available data. The states of the chain correspond to equiprobable intervals, which are then mapped to a time-varying power consumption range, depending on the statistical distribution of the load profiles at different times of the day. Such distributions are regarded as Gaussian Mixture Models (GMM). Compared with other top-down approaches reported in the scientific literature, the joint use of GMM models and time-inhomogeneous Markov chains is rather novel. Furthermore, it is flexible enough to be used in different contexts and with different temporal resolution, while keeping the number of states and the computational burden reasonable. The good agreement between synthetic and original load profiles in terms of both time series similarity and consistency of the respective probability density functions was validated by using three different data sets with different characteristics. In most cases, the median values of synthetic profiles’ mean and standard deviation differ from those of the original reference distributions by no more than ±10% both within a typical day of each season and within the population of a given cluster, although with some significant outliers.

Exploiting Performance of Ambient Backscatter Systems in Presence of Hardware Impairment

In the context of ambient Backscatter systems, Backscatter devices (tags and readers) transmit data by employing existing Radio Frequency (RF) signals. Most prior works consider perfect hardware impairment and apply the Orthogonal Multiple Access (OMA) technique, but this paper investigates the case of Outage Probability (OP) reduction situation when the hardware is imperfect, especially when the Non-Orthogonal Multiple Access (NOMA) technology is applied. Consequently, we design a downlink of transmission from base station to destination to highlight different performances among users. Furthermore, to indicate the impact of levels of hardware impairment, we develop the closed-form expressions of OP for different kinds of users. Finally, extensive simulation results validate the analysis and illustrate the effectiveness of the proposed system.

The relationship between user types and gamification designs

Gamification has been discussed as a standout approach to improve user experience, with different studies showing that users can have different preferences over game elements according to their user types. However, relatively less is known how different kinds of users may react to different types of gamification. Therefore, in this study (N=331) we investigate how user orientation (Achiever, Disruptor, Free Spirit, Philanthropist, Player, and Socializer) is associated with the preference for and perceived sense of accomplishment from different gamification designs. Beyond singular associations between the user orientation and the gamification designs, the findings indicate no comprehensive and consistent patterns of associations. From the six user orientations, five presented significant associations: Socializer orientation was positively associated with Social, Fictional, and Personal designs, while negatively associated with Performance design; Player orientation was positively associated with Social (Accomplishment), Personal, and Ecological designs, while negatively associated with the Social design (Preference); Disruptor orientation was positively associated with Social design; Achiever orientation was positively associated with Performance and Social designs; and Free Spirit orientation was negatively associated with Social design. Based on the results, we provide recommendations on how to personalize gamified systems and set further research trajectories on personalized gamification.

Optimal D-STATCOM Placement Tool for Low Voltage Grids

In low-voltage grids with a wide spread of domestic and/or small commercial consumers, mostly single-phase, problems can appear due to unbalanced power consumption between the different phases. These problems are mainly caused due to voltage unbalances between phases and the increase in distribution losses. This phenomenon occurs more frequently at the end of highly radial grids and can be stressed by the installation of renewable generators next to the consumers. Amongst the various techniques that have been proposed to solve this problem, this article explores the use of a D-STATCOM, presenting and testing a new method for the optimal location of this type of D-FACT. The developed method starts from a detailed analysis of the existing voltage unbalances in a distribution network and identifies the optimal location of the D-STATCOM (i.e., the one that reduces these unbalances while reducing energy losses). The developed method has been successfully tested for one year at four real European locations with different characteristics and different kinds of users.

Detecting malicious behavior in social platforms via hybrid knowledge- and data-driven systems

Among the wide variety of malicious behavior commonly observed in modern social platforms, one of the most notorious is the diffusion of fake news, given its potential to influence the opinions of millions of people who can be voters, consumers, or simply citizens going about their daily lives. In this paper, we implement and carry out an empirical evaluation of a version of the recently-proposed NetDER architecture for hybrid AI decision-support systems with the capability of leveraging the availability of machine learning modules, logical reasoning about unknown objects, and forecasts based on diffusion processes. NetDER is a general architecture for reasoning about different kinds of malicious behavior such as dissemination of fake news, hate speech, and malware, detection of botnet operations, prevention of cyber attacks including those targeting software products or blockchain transactions, among others. Here, we focus on the case of fake news dissemination on social platforms by three different kinds of users: non-malicious, malicious, and botnet members. In particular, we focus on three tasks: (i) determining who is responsible for posting a fake news article, (ii) detecting malicious users, and (iii) detecting which users belong to a botnet designed to disseminate fake news. Given the difficulty of obtaining adequate data with ground truth, we also develop a testbed that combines real-world fake news datasets with synthetically generated networks of users and fully-detailed traces of their behavior throughout a series of time points. We designed our testbed to be customizable for different problem sizes and settings, and make its code publicly available to be used in similar evaluation efforts. Finally, we report on the results of a thorough experimental evaluation of three variants of our model and six environmental settings over the three tasks. Our results clearly show the effects that the quality of knowledge engineering tasks, the quality of the underlying machine learning classifier used to detect fake news, and the specific environmental conditions have on smart policing efforts in social platforms.

O relacjach i doświadczeniach użytkowników Instagramu

Instagram — one of the biggest social media platforms of our times, brings together many different kinds of users. Content creators and companies try to engage their followers by building relationships with them. Both groups are connected by their willingness to have a positive experience with the plat-form. It uses many functions which greatly facilitate using it. However, why do we even need Instagram in our everyday life?

Case Study of Cloud Computing Security and Emerging Security Research Challenges

In this technological era, cloud computing is bombarded with immense benefits that includes availability, flexibility, ubiquitary access, and cost effectiveness. Cloud Computing offers its services to different kinds of users with the help of the World Wide Web on the virtual platform regardless of devices. Hence, all the resources kept on the same-shared storage device, which will lead to a considerable rise in various cloud security concerns for both; user and their private information. Data privacy can be compromised with a broad usage of cloud as smaller companies to bigger ones are adapting this versatile system. This paper examines several recent security attacks and proposed solutions of secure cloud computing from the perspective of organizations. Threat action varieties, other attacks with date, information exposed and number of record breach is presented with IT attack at risk. Finally, we have presented research challenges that can be worth noticing.

Complex PM2.5 Pollution and Hospital Admission for Respiratory Diseases over Big Data in Cloud Environment

With the establishment of China’s national air quality monitoring network, large amounts of monitoring data are available for different kinds of users. How to process and use this big data is a tough problem for users: most users have limited computing power, and new data are collected at every moment. Cloud computing may be an efficient and low-cost way to solve this problem. This paper investigates a problem of a complex system: the impact of PM2.5 on hospitalization for respiratory diseases. A change-point detection method based on grey relation analysis was used to solve this problem. Daily air pollution monitoring data and patient data were used in this study. Our results showed that (1) PM2.5 pollution showed a positive correlation on hospital admission for respiratory disease; (2) most patients went to hospital 2 days after PM2.5 pollution events; and (3) male, children, and old people were significantly affected by PM2.5 pollution. Our study is of great significance to help the government formulate suitable policies to reduce the damage caused by PM2.5 pollution and help hospitals allocate medical resources efficiently.

Online users' attitudes toward fake news: Implications for brand management

AbstractThis study examines brands' vulnerability to fake news. The rapid spread of online misinformation poses challenges to brand managers, whose brands are cocreated online, sometimes to the detriment of the brand. There is a need to identify the information sources that are likely to be trustworthy and to promote positive consumer attitudes toward brands. The data for this study were taken from a Flash Eurobarometer of 26,576 respondents across 28 European countries. Cluster analysis and partial least squares structural equation modeling were used to analyze the data and unveil users' attitudes toward fake news. The findings show that users' attitudes toward fake news differ among European countries. Younger and tech‐savvy users are more likely to recognize fake news and are consequently able to evaluate digital information sources without relying on policy interventions to limit the impact of fake news. Brand managers can use the findings of this study to better understand different kinds of users' susceptibility to fake news and reshape their social media branding strategies accordingly. It is hoped that this paper will encourage further research on brand management in relation to fake news and promote the widespread adoption of best practices in social media communication.

Evaluation of Industry 4.0 Data formats for Digital Twin of Optical Components

A wide range of software and hardware components are present in today’s production systems and plants using a variety of interfaces and data formats for information exchange on different levels of the system. To increase the traceability, the lifecycle management and providing a single point of source of component-specific data, the Digital Twin technology is proposed, linking different data sets tailored to the requirements of different kind of users (e.g., machines, technicians, logistics, manufacturing execution systems). The data exchange between entities in the manufacturing network relies on machine-readable, flexible and self-describing data formats. When implementing or integrating different components into complex systems, the interoperability challenge is a major concern to address by the system designers and becomes a central task for the creation and integration of Digital Twin technology. In this paper, we evaluate different formats that are used in real environments and create a requirements framework for an ideal format for exchanging flexible and self-describing data in context of optical components manufacturing process and their special requirements.

Spectrum Knowledge and Real-Time Observing Enabled Smart Spectrum Management

Spectrum is the indispensable resource for 5G wireless systems and beyond. The dynamic spectrum management (DSM) framework with spectrum sharing among different kinds of users is essential to satisfy the sustainable rapid increase of the bandwidth requirement. Based on the analysis of the DSM as well as the latent applications of the machine learning and artificial intelligence (AI) in the spectrum access, this paper will address the problem of incorporating intelligence into the spectrum management. Firstly, the spectrum opportunity (SOP) related information is layered into the spectrum data, the spectrum information and the spectrum knowledge based on the processing and abstractive level. The spectrum knowledge is formally defined as the extendible and scalable information to reason and predict the SOP usability as well as the outcome of the SOP occupancy. Then the smart spectrum model (SSM) is proposed with the spectrum knowledge as the key enabler and the coupled SOP exploration and exploitation as the core feature. Under the SSM framework, the spectrum knowledge and real-time observing (SKRO) enabled SOP exploration scheme is designed, which can make use of both the historical information and the real-time sensing information for smart SOP exploration. Extensive simulations are provided which demonstrate that the SKRO enabled SSM can achieve much better SOP utilization while satisfying the required legacy assurance. Specifically, in the low signal to noise environment, at least 16.55% gains on the SOP utilization ratio can be obtained compared with the DSM.

How players across gender and age experience Pok\xe9mon Go?

The purpose of this study is to provide insights into player experiences and motivations in Pokémon Go, a relatively new phenomenon of location-based augmented reality games. With the increasing usage and adoption of various forms of digital games worldwide, investigating the motivations for playing games has become crucial not only for researchers but for game developers, designers, and policy makers. Using an online survey (N = 1190), the study explores the motivational, usage, and privacy concerns variations among age and gender groups of Pokémon Go players. Most of the players, who are likely to be casual gamers, are persuaded toward the game due to nostalgic association and word of mouth. Females play Pokémon Go to fulfill physical exploration and enjoyment gratifications. On the other hand, males seek to accomplish social interactivity, achievement, coolness, and nostalgia gratifications. Compared to females, males are more concerned about the privacy aspects associated with the game. With regard to age, younger players display strong connotation with most of the studied gratifications and the intensity drops significantly with an increase in age. With the increasing use of online and mobile games worldwide among all cohorts of society, the study sets the way for a deeper analysis of motivation factors with respect to age and gender. Understanding motivations for play can provide researchers with the analytic tools to gain insight into the preferences for and effects of game play for different kinds of users.

A service recommendation algorithm with the transfer learning based matrix factorization to improve cloud security

Recommendation system (RS) is designed to provide personalized services based on the users’ historical data. It has been applied in various fields and is expected to recommend the suitable services for the different kinds of users. Considering the importance of individual privacy, current users gradually tend not to expose personal information. This means RS may face the highly sparse datasets in the fields of cloud security. In general, the accuracy of recommendation will be improved with the growth of individual data, but the cold start problem is exactly in this contradictory phenomenon: this question evolves to produce sufficiently accurate recommendation result under the data scarcity problem. RS has to recommend services for the rarely historical data users and the latent users might drain along with the production of counter effects. To alleviate data scarcity problem in cloud security environment, this work is to introduce similar domain knowledge based on the transfer learning. Besides, the content and location based methods have been proved that these ideas work under this situation. So, this work also employs latent dirichlet allocation (LDA) to analysis the service descriptions and explore the relationship between the content and location information. In this framework, the suitable combination of LDA and word2vec models will balance the accuracy and speed which benefit service recommendation particularly. The related experiments demonstrate the effectiveness on the real word dataset. It can be found that the transfer learning based word2vec model shows the potentiality to explore the relationship between topic words, and improve the LDA algorithm from the content relationship. This proves that in both cold start environment and warm start environment, the proposed algorithm is more robust than other model-based state-of-art methods.

WebGIS-based suitability evaluation system for Chinese table grape production

China is the largest worldwide producer of table grape, and table grape production has expanded throughout the country with technology advance. However, the vineyards in different regions show discrepant performance due to the multiple natural, locational, economic and technological conditions. This paper aims to develop a WebGIS-based suitability evaluation system for table grape production, and implement efficient and real-time assessment for grape production suitability. Firstly, different kinds of users’ requirements were analyzed, base on which the function modules and the system architecture were designed. Analytic Hierarchy Process (AHP) was applied to make the suitability evaluation for the system and the suitability indicators concerning economic benefits, climate, location factors and soil conditions were established. Finally, the system was implemented and then applied in suitability evaluation in sampled areas of China. The system application showed the main functions of the system have been realized and the system can satisfy users’ requirements. The evaluation results show that vineyards with high suitability of table grape production are distributed both in traditional grape planting areas and southern areas where used to be deemed unsuited for grape cultivation. The reasons may be that some areas in south China own superiority in economic conditions, location and the technology.