User Attributes Research Articles

Application of space syntax in neighbourhood park research: an investigation of multiple socio-spatial attributes of park use

ABSTRACT This case study investigates the actual park use as determined by the socio-spatial attributes of neighbourhoods and parks. As a contribution to the research about park accessibility, it integrates the space syntax analysis with the observation-based fieldwork data about the attributes of neighbourhoods, parks, and park users in 42 parks of 2 adjacent neighbourhoods in Izmir City (Turkey). With its syntactic measures (connectivity, integration, and choice), the study analysis describes the street configuration around these neighbourhood parks. Also, 3 multiple regression analyses are deployed to examine how the syntactic data along with the other neighbourhood and park attributes affect the number of users observed in 42 parks. The study contributes to the research about space syntax tools for analysing the organisational logic of parks in the neighbourhoods while also integrating other socio-spatial attributes of parks.

Robustness of Multi-Project Knowledge Collaboration Network in Open Source Community

Multi-project parallelism is an important feature of open source communities (OSCs), and multi-project collaboration among users is a favorable condition for an OSC's development. This paper studies the robustness of this type of community. Based on the characteristics of knowledge collaboration behavior and the large amount of semantic content generated from user collaboration in open source projects, we construct a directed, weighted, semantic-based multi-project knowledge collaboration network. Using analysis of the KCN's structure and user attributes, nodes are divided into knowledge collaboration nodes and knowledge dissemination nodes that participate in either multi- or single-project collaboration. From the perspectives of user churn and behavior degradation, two types of failure modes are constructed: node failure and edge failure. Based on empirical data from the Local Motors open source vehicle design community, we then carry out a dynamic robustness analysis experiment. Our results show that the robustness of our constructed network varies for different failure modes and different node types: the network has (1) a high robustness to random failure and a low robustness to deliberate failure, (2) a high robustness to edge failure and a low robustness to node failure, and (3) a high robustness to the failure of single-project nodes (or their edges) and a low robustness to the failure of multi-project nodes (or their edges). These findings can be used to provide a more comprehensive and targeted management reference, promoting the efficient development of OSCs.

Enhanced Clustering Based OSN Privacy Preservation to Ensure k-Anonymity, t-Closeness, l-Diversity, and Balanced Privacy Utility

Online Social Networks (OSN) sites allow end-users to share a great deal of information, which may also contain sensitive information, that may be subject to commercial or non-commercial privacy attacks. As a result, guaranteeing various levels of privacy is critical while publishing data by OSNs. The clustering-based solutions proved an effective mechanism to achieve the privacy notions in OSNs. But fixed clustering limits the performance and scalability. Data utility degrades with increased privacy, so balancing the privacy utility trade-off is an open research issue. The research has proposed a novel privacy preservation model using the enhanced clustering mechanism to overcome this issue. The proposed model includes phases like pre-processing, enhanced clustering, and ensuring privacy preservation. The enhanced clustering algorithm is the second phase where authors modified the existing fixed k-means clustering using the threshold approach. The threshold value is determined based on the supplied OSN data of edges, nodes, and user attributes. Clusters are k-anonymized with multiple graph properties by a novel one-pass algorithm. After achieving the k-anonymity of clusters, optimization was performed to achieve all privacy models, such as k-anonymity, t-closeness, and l-diversity. The proposed privacy framework achieves privacy of all three network components, i.e., link, node, and user attributes, with improved utility. The authors compare the proposed technique to underlying methods using OSN Yelp and Facebook datasets. The proposed approach outperformed the underlying state of art methods for Degree of Anonymization, computational efficiency, and information loss.

Reachability Analysis for Attributes in ABAC With Group Hierarchy

Attribute-based access control (ABAC) models are widely used to provide fine-grained and adaptable authorization based on the attributes of users, resources, and other relevant entities. Hierarchical group and attribute based access control (HGABAC) model was recently proposed which introduces the novel notion of attribute inheritance through group membership. GURA <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</sub> was subsequently proposed to provide an administrative model for user attributes in HGABAC, building upon the ARBAC97 and GURA administrative models. The GURA model uses administrative roles to manage user attributes. The reachability problem for the GURA model is to determine what attributes a particular user can acquire, given a predefined set of administrative rules. This problem has been previously analyzed in the literature. In this article, we study the user attribute reachability problem based on directly assigned attributes of the user and attributes inherited via group memberships. We first define a restricted form of GURA <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</sub> , called rGURA <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</sub> scheme, as a state transition system with multiple instances having different preconditions and provide reachability analysis for each of these schemes. In general, we show PSPACE-complete complexity for all rGURA <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</sub> schemes. We further present polynomial time algorithms with empirical experimental evaluation to solve special instances of rGURA <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</sub> schemes under restricted conditions.

An Efficient Decentralized Identity Management System Based on Range Proof for Social Networks

Online social networks (OSNs) are becoming more and more popular in people's lives as the demand for online interaction continues to grow. Current OSNs are using centralized identity management system (IDM), which has some problems of single point of failure and privacy data leakage. The emergence of decentralized identity (DID) can solve these problems. However, most existing DID systems have some privacy issues that a user's attributes value are disclosed while accessing service. In this paper, we design a DID protocol to solve these challenges. The proposed protocol includes a range proof protocol to provide attribute privacy. The range proof protocol works with anonymous credentials and does not need a trusted setup. Moreover, the identity model behind the DID protocol is extended from an existing model, which achieves identity revocation. Finally, we implement a system prototype on the blockchain for evaluation. The security analysis shows that our protocol provides stronger privacy protection. The performance evaluation indicates that the computation cost and blockchain overheads of our protocol are acceptable in OSNs.

Marketing Strategy and Customer Engagement Analysis of Catering Brands – From Short Video Perspective

In recent years, with the popularity of short video platforms, more and more peo-ple tend to use short videos to share their lives. In the catering industry, many phenomenal pop videos created by many catering brands are continuously shared on the short video platform, which has brought about a doubling of brand reve-nue. In order to specifically explore the relationship between short videos and ca-tering marketing, in this paper, we first design a questionnaire and collected hun-dreds of samples. Then, based on the samples, we analyze the users’ preferences for catering brands and related short video attributes. We also analyze the correla-tion between user attributes, sharing behavior, and customer engagement. Finally, we establish a discriminant model, a machine learning method, to predict users’ sharing and visiting behaviors according to their attributes. The experimental re-sults show the effectiveness of our method.

Quantifying the Vulnerability of Attributes for Effective Privacy Preservation Using Machine Learning

Personal data have been increasingly used in data-driven applications to improve quality of life. However, privacy preservation of personal data while sharing it with analysts/ researchers has become an essential requirement to be met by data owners (hospitals, banks, insurance companies, etc.). The existing literature on privacy preservation does not precisely quantify the vulnerability of each item among user attributes, thereby leading to explicit privacy disclosures and poor data utility during published data analytics. In this work, we propose and implement an automated way of quantifying the vulnerability of each item among the attributes by using a machine learning (ML) technique to significantly preserve the privacy of users without degrading data utility. Our work can solve four technical problems in the privacy preservation field: optimization of the privacy-utility trade-off, privacy guarantees (i.e., safeguard against identity and sensitive information disclosures) in imbalanced data (or clusters), over-anonymization issues, and rectifying or enabling the applicability of prior privacy models when data have skewed distributions. The experiments were performed on two real-world benchmark datasets to prove the feasibility of the concept in practical scenarios. Compared with state-of-the-art (SOTA) methods, the proposed method effectively preserves the equilibrium between utility and privacy in the anonymized data. Furthermore, our method can significantly contribute towards responsible data science (extracting enclosed knowledge from data without violating subjects’ privacy) by controlling higher changes in data during its anonymization.

A Movie Recommender System Based on User Profile and Artificial Bee Colony Optimization.

In this study, a new algorithm for recommending movies to viewers has been proposed. To do this, the suggested method employs data mining techniques. The proposed method includes three steps for generating recommendations: "preprocessing of user profile information," "feature extraction," and "recommendation." In the first step of proposed method, the user information will be examined and transformed into a form that can be handled in the next phases. In the second step of the proposed method, user attributes are then extracted as a collection of their individual qualities, as well as the average rating of each user for various genres. The bee colony optimization algorithm is then used to select the optimal features. Finally, in the third step of the proposed method, the ratings of similar users are utilized to offer movies to the target user, and the similarities between various users are determined using the characteristics calculated for them, as well as the Euclidean distance criteria. The proposed method was evaluated using the MovieLens database, and its output was assessed in terms of precision and recall criteria; these results show that the proposed method will increase the precision by an average of 1.39% and the recall by 0.8% compared to the compared algorithms.

SROS2 with OIDC（OpenID Connect）—サービスロボットに対する命令の信頼性を担保する技術

More robots are involved with humans, such as cooperative robots. When providing services based on user attributes (name, authority, location information, etc.) or authentication (face, fingerprints, etc.), developers must be careful to protect personal information. Therefore, we developed and released a package called “sros2_oidc”. The app adopts the OIDC (OpenID Connect) as an authentication protocol. It is one of the standard authentication protocols for online services. And combined with SROS2, personal information is sent through a secure route to the end of the robots.

Intelligent Attributes of Voice Assistants and User’s Love for AI: A SEM-Based Study

Adoption of Voice Assistants (VA) has been a hot topic of research currently. However, the existing studies are based on traditional models like the Technology Acceptance Model that is less suitable for investigating adoption of AI-based technologies like VAs. This is because the conventional models do not consider the anthropomorphic nature of AI-based technologies that has the potential to form emotional bonds between man and machines. Therefore, in this work we propose artificial autonomy as an intelligent AI—based attribute founded on three task primitives of sensing, thinking, and action autonomy. Further, we use the concept of interpersonal love and apply it in the context of human-AI relationship based on Sternberg’s Triangular Theory of love. Data is collected from 607 participants across two Asian countries. Partial Least Squares (PLS) based Structural Equation Modelling (SEM) has been used for the purpose of model building and testing. The results highlight the importance of artificial autonomy on all the three aspects of love, indicating the presence of consummate love. “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Love for AI</i> ” is different from “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">brand love</i> ” since in the later case some of the love aspects of passion, intimacy, and commitment are missing. Theoretically, this study improves our understanding of artificial autonomy as an intelligent AI-attribute and the perceptions of love in Information Systems research. The findings can help technology companies develop novel strategies for improving the human-likeliness of these VAs.

Learning Personalized Image Aesthetics From Subjective and Objective Attributes

Due to the widespread popularity of social media, researchers have developed a strong interest in learning the personalized image aesthetics of online users. Personalized image aesthetics assessment (PIAA) aims to study the aesthetic preferences of individual users for images, which should be affected by the properties of both users and images. Existing PIAA approaches usually use the generic aesthetics learned from images as a prior model and adapt it to PIAA models through a small number of data annotated by individual users. However, the prior model merely learns the objective attributes of images, which is agnostic to the subjective attributes of users, complicating efficient learning of the personalized image aesthetics of individual users. Therefore, we propose a personalized image aesthetics assessment method that integrates the subjective attributes of users and objective attributes of images simultaneously. To characterize these two attributes jointly, an attribute extraction module is introduced to learn users’ personality traits and image aesthetic attributes. Then, an aesthetic prior model is built from numerous individual users’ annotated data, which leverages the personality traits of users and the aesthetic attributes of rated images as prior knowledge to model both the image aesthetic distribution and users’ residual scores relative to generic aesthetics simultaneously. Finally, a PIAA model is obtained by fine-tuning the aesthetic prior model with an individual user’s annotated data. Experiments demonstrate that the proposed method is superior to existing PIAA methods in learning individual users’ personalized image aesthetics.

Enabling Attribute-based Access Control in NoSQL Databases.

NoSQL databases are being increasingly used for efficient management of high volumes of unstructured data in applications like information retrieval, natural language processing, social computing, etc. However, unlike traditional databases, data protection measures such as access control for these databases are still in their infancy, which could lead to significant vulnerabilities and security/privacy issues as their adoption increases. Attribute-based Access Control (ABAC), which provides a flexible and dynamic solution to access control, can be effective for mediating accesses in typical usage scenarios for NoSQL databases. In this paper, we propose a novel methodology for enabling ABAC in NoSQL databases. Specifically we consider MongoDB, which is one of the most popular NoSQL databases in use today. We present an approach to both specify ABAC access control policies and to enforce them when an actual access request has been made. MongoDB Wire Protocol is used for extracting and processing appropriate information from the requests. We also present a method for supporting dynamic access decisions using environmental attributes and handling of ad-hoc access requests through digitally signed user attributes. Results from an extensive set of experiments on the Enron corpus as well as on synthetically generated data demonstrate the scalability of our approach. Finally, we provide details of our implementation on MongoDB and share a Github repository so that any organization can download and deploy the same for enabling ABAC in their own MongoDB installations.

DSVN: A Flexible and Secure Data-Sharing Model for VANET Based on Blockchain

Vehicular Ad Hoc Network (VANET) is an important part of the modern intelligent transportation system, which can provide vehicle communication at a certain distance. More importantly, VANET can provide route planning and autonomous driving for drivers by analyzing data. However, VANET’s data privacy and security are a huge challenge when serving drivers. In this paper, we propose a VANET data-sharing model (DSVN) that combines ciphertext-based attribute encryption (CP-ABE), blockchain, and InterPlanetary File System (IPFS). DSVN uses an outsourced and revocable ciphertext policy attribute-based encryption (ORCP-ABE) scheme, which is improved based on CP-ABE. ORCP-ABE uses key encryption key (KEK) trees to manage user attribute groups and revoke user-level attributes. It eliminates redundant attributes in the access policy by attribute-weighted access trees. Moreover, DSVN has no single point of failure. We demonstrate the indistinguishability under the chosen-plaintext attack (IND-CPA) security of DSVN by a game based on the computational Diffie–Hellman (CDH) assumption. Experimental results show that DSVN can store and share data with low overhead. Additionally, it can revoke attributes of users safely.

MLI: A Multi-level Inference Mechanism for User Attributes in Social Networks

In the social network, each user has attributes for self-description called user attributes, which are semantically hierarchical. Attribute inference has become an essential way for social platforms to realize user classifications and targeted recommendations. Most existing approaches mainly focus on the flat inference problem neglecting the semantic hierarchy of user attributes, which will cause serious inconsistency in multi-level tasks. In this article, we propose a multi-level model MLI, where information propagation part collects attribute information by mining the global graph structure, and the attribute correction part realizes the mutual correction between different levels of attributes. Further, we put forward the concept of generalized semantic tree, a way of representing the hierarchical structure of user attributes, whose nodes are allowed to have multiple parent nodes unlike the regular tree. Both regular and generalized semantic trees are commonly used in practice, and can be handled by our model. Besides, by making the inference start from sub-networks with sufficient attribute information, we design a “Ripple” algorithm to improve the efficiency and effectiveness of our model. For evaluation purposes, we conduct extensive verification experiments on DBLP datasets. The experimental results show the superior effect of MLI, compared with the state-of-the-art methods.

JARUA: Joint Embedding of Attributes and Relations for User Alignment across Social Networks

User alignment (UA), a central issue for social network analysis, aims to recognize the same natural persons across different social networks. Existing studies mainly focus on the positive effects of incorporating user attributes and network structure on UA. However, there have been few in-depth studies into the existing challenges for the joint integration of different types of text attributes, the imbalance between user attributes and network structure, and the utilization of massive unidentified users. To this end, this paper presents a high-accuracy embedding model named Joint embedding of Attributes and Relations for User Alignment (JARUA), to tackle the UA problem. First, a mechanism that can automatically identify the granularity of user attributes is introduced for handling multi-type user attributes. Second, a graph attention network is employed to extract the structural features and is integrated with user attributes features. Finally, an iterative training algorithm with quality filters is introduced to bootstrap the model performances. We evaluate JARUA on two real-world data sets. Experimental results demonstrate the superiority of the proposed method over several state-of-the-art approaches.

Users taking the blame? How service failure, recovery, and robot design affect user attributions and retention

Firms use robots to deliver an ever-expanding range of services. However, as service failures are common, service recovery actions are necessary to prevent user churn. This research further suggests that firms need to know how to design service robots that avoid alienating users in case of service failures. Robust evidence across two experiments demonstrates that users attribute successful service outcomes internally, while robot-induced service failures are blamed on the firm (and not the robot), confirming the well-known self-serving bias. While this external attributional shift occurs regardless of the robot design (i.e., it is the same for warm vs. competent robots), the findings imply that service recovery minimizes the undesirable external shift and that this effect is particularly pronounced for warm robots. For practitioners, this implies prioritizing service robots with a warm design for maximizing user retention for either type of service outcome (i.e., success, failure, and failure with recovery). For theory, this work demonstrates that attribution represents a meaningful mechanism to explain the proposed relationships.

A Blockchain Technology Introduction Strategy for Asymmetric Sharing Platforms under Different Homing Behaviors of Both Sides

To address user privacy concerns and improve user trust levels, sharing platforms are commencing to focus on investing in blockchain technology. This study focuses on blockchain technology investment and pricing strategies for two asymmetric sharing platforms. By constructing a Hotelling model, we investigate the investment strategies of the two asymmetric platforms regarding blockchain technology under different user attribution behaviours, i.e., single-homing or multi-homing, and the optimal pricing under different investment decisions. Afterwards, we compare and analyse the investment strategies under different conditions, obtain the influence of relevant market factors on the pricing strategies of the platforms, and finally determine the optimal timing of blockchain technology investment for asymmetric sharing platforms. The results indicate that when users' perception of blockchain value is high, both platforms are motivated to introduce blockchain technology, and, conversely, a stronger platform exits the blockchain market. In multi-homing markets, platforms are more likely to implement blockchain strategies and the cost of technology investment is significantly higher than in a single-homing market. In addition, we also find that the degree of differentiation has a significant impact on the blockchain strategies of weaker platform under multi-homing market.

A User Portrait of Express Software Based on Full Life Cycle Data

With the rapid development of traditional express industry, it is of great significance to study the portrait of express users. The express industry can realize the industrial transformation and upgrading by means of the express customer portrait. However, the research on the user portrait of express delivery is still scarce, at present. In this paper, a user portrait model based on life cycle data is proposed, which comprehensively describes the behavior preferences of users through multi-dimensional feature vectors. The model takes into account the basic attributes, preference attributes and feedback attributes of users, and uses the idea of classification before clustering to realize the segmentation of express users. In the classification stage, this paper fuses the K-means and the Support vector machine algorithm, adds the preference coefficient, and designs a new objective function to complete the user classification. In the process of clustering, we follow the idea of density-based peak clustering, combine the characteristic attributes of the multidimensional data of express users, add the characteristic coefficient, modify the index calculation formula in the model, and complete the clustering of express users. The simulation data show that the user portrait model proposed in this paper can achieve better segmentation of courier users, with better accuracy and timeliness.

Personalized Image Aesthetics Assessment via Multi-Attribute Interactive Reasoning

Due to the subjective nature of people’s aesthetic experiences with respect to images, personalized image aesthetics assessment (PIAA), which can simulate the aesthetic experiences of individual users to estimate images, has received extensive attention from researchers in the computational intelligence and computer vision communities. Existing PIAA models are usually built on prior knowledge that directly learns the generic aesthetic results of images from most people or the personalized aesthetic results of images from a large number of individuals. However, the learned prior knowledge ignores the mutual influence of the multiple attributes of images and users in their personalized aesthetic experiences. To this end, this paper proposes a personalized image aesthetics assessment method via multi-attribute interactive reasoning. Different from existing PIAA models, the multi-attribute interaction constructed from both images and users is used as more effective prior knowledge. First, we designed a generic aesthetics extraction module from the perspective of images to obtain the aesthetic score distribution and multiple objective attributes of images rated by most users. Then, we propose a multi-attribute interactive reasoning network from the perspective of users. By interacting multiple subjective attributes of users with multiple objective attributes of images, we fused the obtained multi-attribute interactive features and aesthetic score distribution to predict personalized aesthetic scores. Experimental results on multiple PIAA datasets demonstrated our method outperformed state-of-the-art PIAA methods.

IMGC-GNN: A multi-granularity coupled graph neural network recommendation method based on implicit relationships.

In the application recommendation field, collaborative filtering (CF) method is often considered to be one of the most effective methods. As the basis of CF-based recommendation methods, representation learning needs to learn two types of factors: attribute factors revealed by independent individuals (e.g., user attributes, application types) and interaction factors contained in collaborative signals (e.g., interactions influenced by others). However, existing CF-based methods fail to learn these two factors separately; therefore, it is difficult to understand the deeper motivation behind user behaviors, resulting in suboptimal performance. From this point of view, we propose a multi-granularity coupled graph neural network recommendation method based on implicit relationships (IMGC-GNN). Specifically, we introduce contextual information (time and space) into user-application interactions and construct a three-layer coupled graph. Then, the graph neural network approach is used to learn the attribute and interaction factors separately. For attribute representation learning, we decompose the coupled graph into three homogeneous graphs with users, applications, and contexts as nodes. Next, we use multilayer aggregation operations to learn features between users, between contexts, and between applications. For interaction representation learning, we construct a homogeneous graph with user-context-application interactions as nodes. Next, we use node similarity and structural similarity to learn the deep interaction features. Finally, according to the learned representations, IMGC-GNN makes accurate application recommendations to users in different contexts. To verify the validity of the proposed method, we conduct experiments on real-world interaction data from three cities and compare our model with seven baseline methods. The experimental results show that our method has the best performance in the top-k recommendation.