User Attributes Research Articles

New Media Advertising Information Search Method Based on XML Technology

The sheer volume and complexity of new media advertising information pose significant challenges for users in searching and filtering advertisements. To enhance the match between search content and keywords, this paper proposes a multifunctional search method for new media advertising information based on eXtensible Markup Language (XML) technology. The collected user attribute data are organized into a set, and a membership function for user attributes is established. An information certainty index and a query certainty index are constructed to obtain the integrated attributes of new media advertisements and classify their attributes. The average value of the constant threshold element is calculated, and an attraction model of new media advertisements for random Internet users is constructed. The similarity between text information and query methods is determined, along with the calculation of browsing duration and frequency in any given area. According to the search model for new media advertising information, the sorted new media advertising information documents are merged to obtain search results. Experimental results demonstrate that the introduction of interactive elements enhances attraction, and dynamic new media advertisements exhibit stronger attraction compared to static advertisements. The proposed method shows significant advantages in search efficiency, achieving over 90% search efficiency, with the match between search content and keywords consistently remaining above 98%, the user satisfaction is higher, the system response time is shorter, the results can be obtained faster, and the search efficiency is improved.

The influence maximization algorithm for integrating attribute graph clustering and heterogeneous graph transformer

In social networks, maximizing influence is an important research direction. However, traditional influence maximization algorithms often overlook the attribute information of nodes and the heterogeneity of networks, leading to inefficiency and inaccuracy in the propagation process. To address this issue, this study first constructs a social network influence maximization propagation model, and then combines auto-encoder and graph convolutional autoencoder to extract social network user attributes. Finally, Transformer is used to learn the feature representation of social network nodes. Moreover, a heterogeneous graph neural network is introduced to combine with Transformer to further learn the feature representation of nodes, so as to design an influence maximization algorithm combining attributed graph clustering and heterogeneous graph Transformer. The results showed that the loss values of the fusion algorithm on different datasets were 0.619 and 0.17, respectively, proving its good fitting performance. The recall rate and F1 of this fusion algorithm were 92.5 % and 0.90, respectively, proving its high clustering accuracy. The influence maximization model based on fusion algorithm achieved active node coverage of 67 % and 48 % on two datasets, respectively, proving its good influence propagation effect. The above results demonstrate that the designed model can effectively spread influence. This helps to better understand and utilize the influence dissemination mechanisms in social networks, thereby promoting the development of social network research.

PrivateGaze: Preserving User Privacy in Black-box Mobile Gaze Tracking Services

Eye gaze contains rich information about human attention and cognitive processes. This capability makes the underlying technology, known as gaze tracking, a critical enabler for many ubiquitous applications and has triggered the development of easy-to-use gaze estimation services. Indeed, by utilizing the ubiquitous cameras on tablets and smartphones, users can readily access many gaze estimation services. In using these services, users must provide their full-face images to the gaze estimator, which is often a black box. This poses significant privacy threats to the users, especially when a malicious service provider gathers a large collection of face images to classify sensitive user attributes. In this work, we present PrivateGaze, the first approach that can effectively preserve users' privacy in black-box gaze tracking services without compromising gaze estimation performance. Specifically, we proposed a novel framework to train a privacy preserver that converts full-face images into obfuscated counterparts, which are effective for gaze estimation while containing no privacy information. Evaluation on four datasets shows that the obfuscated image can protect users' private information, such as identity and gender, against unauthorized attribute classification. Meanwhile, when used directly by the black-box gaze estimator as inputs, the obfuscated images lead to comparable tracking performance to the conventional, unprotected full-face images.

LEVERAGING WEB SCRAPING TO GATHER TOURISM INFORMATION DATA

The influence of Information and Communication Technologies (ICT) on both individuals' daily lives and the economy is of significant importance. In this context, the tourism industry plays a crucial role, and it is essential to recognise the contributions of tourists in terms of sharing their experiences through tourism websites. Analysing this data is key to improving future tourists' experiences. Therefore, the objective of this study is to employ web scraping to gather data on places of interest (POI) and user attributes, specifically in the state of Melaka via the TripAdvisor website. Melaka is chosen as it is one of the places recognised by the United Nations, Educational, Scientific and Cultural Organization (UNESCO). The study focuses on the 200 POI locations (UNESCO) Map, encompassing both Melaka's core and buffer zones. These POIs are categorised into four heritage types: built heritage, natural heritage, personal heritage, and living heritage, with some belonging to more than one category. For the data collection process, this study utilised the TripAdvisor website and extracted a total of 14 attributes. Specifically, 27282 user data entries were collected from 163 POIs in the core zone area, and 8305 data entries from 37 POIs in the buffer zone area. The data is managed and stored in various formats, including CSV, JSON, and Excel files in the repository. The data helps in the development of a tourism application. Furthermore, the tourism industry can benefit from this study by enhancing their services and conserving the cultural heritage.

A knowledge graph algorithm enabled deep recommendation system.

Personalized learning resource recommendations may help resolve the difficulties of online education that include learning mazes and information overload. However, existing personalized learning resource recommendation algorithms have shortcomings such as low accuracy and low efficiency. This study proposes a deep recommendation system algorithm based on a knowledge graph (D-KGR) that includes four data processing units. These units are the recommendation unit (RS unit), the knowledge graph feature representation unit (KGE unit), the cross compression unit (CC unit), and the feature extraction unit (FE unit). This model integrates technologies including the knowledge graph, deep learning, neural network, and data mining. It introduces cross compression in the feature learning process of the knowledge graph and predicts user attributes. Multimodal technology is used to optimize the process of project attribute processing; text type attributes, multivalued type attributes, and other type attributes are processed separately to reconstruct the knowledge graph. A convolutional neural network algorithm is introduced in the reconstruction process to optimize the data feature qualities. Experimental analysis was conducted from two aspects of algorithm efficiency and accuracy, and the particle swarm optimization, neural network, and knowledge graph algorithms were compared. Several tests showed that the deep recommendation system algorithm had obvious advantages when the number of learning resources and users exceeded 1,000. It has the ability to integrate systems such as the particle swarm optimization iterative classification, neural network intelligent simulation, and low resource consumption. It can quickly process massive amounts of information data, reduce algorithm complexity and requires less time and had lower costs. Our algorithm also has better efficiency and accuracy.

Meta-learning on dynamic node clustering knowledge graph for cold-start recommendation

Meta-learning has been introduced in the recommendation domain, and a possible direction to extend graph-based meta-learning is how to exploit higher-order information between nodes. The current studies primarily rely on pre-embedding methods to utilize user attributes, inadvertently overlooking the crucial co-occurrence patterns among users’ features. This oversight hinders the discovery of implicit connections between users. Furthermore, in scenarios of infrequent interactions, the reliance on initial edge information constrains connections between nodes in the same category. This constraint compromises the update capability of node embeddings, leading to reduced recommendation accuracy. In this paper, a meta-learning recommendation method with a dynamic node clustering knowledge graph(DCKG) is proposed. By introducing user attribute nodes and a triple-gated attention networks, this approach facilitates connections between users based on attribute relationships and enables the balancing of information across various pathways. Additionally, the integration of a dynamic aggregation module frees nodes of the same category from initial connection constraints, fostering more direct connections and further augmenting the propagation capability of higher-order information. The experimental results show that DCKG achieves the excellent results in the cold-start recommendation. DCKG can extend the use of higher-order features in the graph by direct connection.

Lattice-based multi-authority ciphertext-policy attribute-based searchable encryption with attribute revocation for cloud storage

Multi-authority attribute-based searchable encryption (MABSE) is an flexible and efficient way to securely share and search encrypted data. Compared with single-authority systems, MABSE has more complex access control policies and key management mechanism. However, most existing MABSE schemes rely on traditional number-theoretic assumptions, which maybe vulnerable to attack in the era of quantum-computers. Besides, the effective revocation of user attributes is also crucial in searchable encryption. To overcome these challenges, this paper proposes a new multi-authority ciphertext-policy attribute-based searchable encryption scheme for securely sharing encrypted data in the cloud. By calling Shamir’s threshold secret-sharing technology twice, we achieve co-management of the master key by attribute authorities and interactive generation of user private keys. Furthermore, the KUNodes algorithm is employed for attribute revocation, offering a mechanism to update private keys for non-revoked users. Compared to other schemes, MCP-ABSE-AR introduces multiple attribute authorities responsible for managing user attributes collectively. Additionally, it provides functionalities for keyword searching and attribute revocation. Finally, the proposed scheme is proved to be semantically secure under the decision learning with errors problem in the standard model.

A method to assist designers in optimizing the exterior styling of vehicles based on key features

Aiming to assist the design and management personnel of automotive products to make correct judgments and give optimization solutions for the design of exterior styling, and to solve the defects of the traditional method which relies too much on experience and personal ability, a neural network-based key feature analysis and optimization method of styling is proposed. Firstly, the user attributes are analyzed and quantified, and the weighted optimization of user evaluation data is completed based on the multidimensional Gaussian distribution function to construct a more objective car styling evaluation dataset. Secondly, a particle swarm algorithm is proposed to optimize the prediction model of radial basis function neural network (PSO-RBFNN) which deeply analyzes the mapping relationship between different car perspectives and users’ perceptual preferences. The identification of styling components is completed with the annotation of feature points, and a parametric expression method of styling feature data with multi-viewpoint weight optimization is proposed. Third, the model interpretation is based on the embedded feature selection method to discover the key feature points affecting user perception, and the key common features of styling are obtained through the coupling relationship analysis of feature points. Finally, the optimal distribution range of key features is given based on statistical analysis, and the application process is shown by an example of optimization to verify the feasibility of the theory. Compared with the traditional methods, PSO-RBFNN has better prediction accuracy. Meanwhile, the modeling optimization method based on key features has the great advantages of objective accuracy and high efficiency compared with the traditional theory.

A cross-chain access control mechanism based on blockchain and the threshold Paillier cryptosystem

With the continuous maturation of blockchain technology and the increasing demands for various industry applications, data sharing and interoperability among different blockchain networks face significant challenges. Research on cross-chain interoperability mechanisms has facilitated data collaboration across organizations and industries, enhancing the value and utility of data. When engaging in cross-chain data interactions, access control ensures the security and privacy of data while promoting collaboration and information exchange among multiple chains. Attribute-based access control can provide fine-grained authorization support, matching complex business scenarios. However, publicly disclosed policies and attributes in a transparent blockchain network may pose privacy and security issues. To address these issues, this paper proposes a cross-heterogeneous multichain data access control scheme based on attributes and threshold homomorphic encryption, achieving fine-grained and secure cross-domain access control in cross-chain networks. This scheme uses the threshold Paillier cryptosystem to encrypt and conceal user attributes and access policies. Through smart contracts, homomorphic differential computation is performed on the ciphertext of policies and attributes, to protect data privacy. This solution leverages private key decryption shares from multiple relay nodes in the cross-chain network to jointly decrypt the computation results, providing secure access control in complex cross-chain scenarios. Security analysis and experimental results demonstrate that the proposed scheme ensures security with reasonable computational overhead, to meet the access control requirements in cross-chain networks.

HA-Med: A Blockchain-Based Solution for Sharing Medical Data with Hidden Policies and Attributes

Existing healthcare data-sharing solutions often combine attribute-based encryption techniques with blockchain technology to achieve fine-grained access control. However, the transparency of blockchain technology may introduce potential risks of exposing access structures and user attributes. To address these concerns, this paper proposes a novel healthcare data-sharing scheme called HA-Med. By leveraging blockchain technology, HA-Med ensures the concealment of access policies and attributes, providing a secure solution for fine-grained access control of medical data. Furthermore, the scheme supports attribute revocation and forward secrecy to enhance user privacy. The security of HA-Med is rigorously verified through theoretical analysis, and its feasibility is demonstrated through experiments conducted using the Java-based JPBC library.

Immersive technologies in the formation of musical audio space

Immersive audio technology is increasingly used in music, with devices like cell phones and televisions replicating formats. The increasing adoption of immersive audio technologies by both consumers and professionals in the music industry has made this topic more relevant. The purpose of the research is to analyse immersive technologies that contribute to the creation of the musical audio environment. The research employed methods: literature and historical review, technical analysis, practical cases, statistical method, and the author’s personal empirical experience. The article examines the function of audio space in relation to the perception of musical art and the characteristics of musical audio space. The proposed classification categories musical audio spaces into two distinct types: natural and artificial, as well as offline and online. The article’s statistical data illustrates the changing patterns of growth in online audio consumers and the rivalry between radio stations and streaming platforms to enhance their impact on shaping listeners’ audio spaces. The research demonstrates that streaming platforms actively encourage the incorporation of musical content in immersive formats as a means to expand their market share and evoke heightened emotional and perceptual responses in customers. The article outlines techniques used in immersive music spaces to provide a distinct aural experience for the listener. The article’s practical analysis of immersive audio technologies encompasses the examination of requisite microphones and digital audio workstations that facilitate the transition from two-dimensional audio space formation to threedimensional. In contemporary circumstances, the distribution of musical items is progressively employing threedimensional immersive technologies. The article enumerates audio technologies that guarantee the creation of an immersive auditory environment for both listeners and musicians: Ambisonics, KLANG: technologies, and DPA 5100. The practical significance of this work rests in examining the perceptual attributes of users of immersive technologies and exploring the potential for developing immersive technologies, such as immersive musical instruments or immersive radio stations

Task allocation algorithm for distributed large data stream group computing in the era of digital intelligence

In the era of digital intelligence, this paper studies the task allocation algorithm of distributed large data stream group computing, and reasonably allocates the task of group computing to meet the needs of massive computing and analysis of distributed large data stream. According to the idea of swarm intelligence perception and crowdsourcing platform, the task allocation model of distributed large data stream group computing is constructed to realize the task allocation of group computing. A distributed large data stream group computing task model and a user model are constructed, user attributes are initialized by using the accuracy of the answers submitted by users, the possibility that users can participate in the group computing task is predicted by a logistic regression algorithm, so that user candidate sequences participating in the computing task can be obtained, and the accuracy of the user’s real topics and corresponding topics can be grasped by capturing the candidate users’ real topics and evaluating the accuracy algorithm. Select the users who meet the subject area, update the candidate user sequence, and filter the users again on the basis of fully considering the factors such as information gain, user integrity and cost, so as to get the final user sequence and complete the task allocation of group computing. Experiments show that this method can solve the problem of distributed large data flow group computing task allocation, achieve high accuracy, reduce the cost, and effectively improve the information gain.

User readiness as a determinant for use of big data analytics: A case of state corporations in Kenya

AbstractState corporations in Kenya are installing systems to provide real‐time data to enhance decision making, customer base growth, and revenue generation. However, these organizations must still determine their readiness for big data analytics. User readiness as a determinant for use of big data analytics in developing countries like Kenya has received limited coverage in literature. Yet, successful implementation necessitates users' preparedness and willingness to use the technology. Grounded theory was used in this study, which involved eight state corporations in Kenya. Four categories emerged from the data: user attributes, change management, ICT competence, and decision‐making culture. The proposed interventions to improve user readiness are top leadership support, user engagement, change management strategies, capacity‐building programs, and data governance. The article contributes to literature by exploring the gap between user readiness and use of big data analytics in state corporations in Kenya. It additionally offers practitioners, decision‐makers, and government officials in developing countries, factors that can increase use of big data analytics.

Topology Identification of Active Low-Voltage Distribution Network Based on Regression Analysis and Knowledge Reasoning

Due to the access of distributed energy and a new flexible load, the electrical characteristics of a new distribution network are significantly different from those of a traditional distribution network, which poses a new challenge to the original topology identification methods. To address this challenge, a hierarchical topology identification method based on regression analysis and knowledge reasoning is proposed for an active low-voltage distribution network (ALVDN). Firstly, according to the new electrical characteristics of bidirectional power flow and voltage jump caused by the ALVDN, active power is selected as the electric volume for hierarchical topology identification. Secondly, considering the abnormal fluctuation of active power caused by bidirectional power flow characteristics of distributed energy users, a user attribution model based on the Elastic-Net regression algorithm is proposed. Subsequently, based on the user identification results, the logic knowledge reflecting the hierarchical topology of the ALVDN is extracted by the AMIE algorithm, and the “transformer-phase-line-user” hierarchical topology of the ALVDN is deduced by a knowledge reasoning model. Finally, the effectiveness of the proposed method is verified by an IEEE example.

Pricing Models for Crowdsourced Logistics Platforms: A Two-Sided Market Perspective

The application of the crowdsourcing model to instant delivery has achieved remarkable success. Various crowdsourced logistics platforms have successfully addressed the challenges of last-mile delivery in urban areas by eliciting the active involvement of the public. This study takes a dual-market perspective and, considering the high requirements of instant delivery for timeliness, introduces two crucial factors: platform subsidies and the degree of public involvement. We establish a pricing model based on the Hotelling model and conduct in-depth research on the platform’s maximum profit and equilibrium pricing under different user attribution conditions. This study reveals that when the dispatching party has a single attribution, the platform can increase profits by reducing the intensity of cross-network externalities or increasing user transfer costs. In cases where the dispatching party has partial multiple attributions and the receiving party has a single attribution, lowering network externalities, increasing platform subsidies, enhancing public involvement, improving platform technical matching rates, and increasing the expected order quantity of the dispatching party all effectively increase the platform’s maximum profit. When both sides of users have partial multiple attributions, increasing public involvement increases the platform’s maximum profit. This research provides new theoretical support for the pricing strategy of crowdsourced logistics platforms.

Gamified online course design: a scoping review of the research

The purpose of this scoping review is to synthesize knowledge of gamified online course design processes and practices extracted from the peer-reviewed research literature. The scoping review followed a systematic process involving five main stages: (a) identifying research questions, (b) identifying relevant studies, (c) study selection, (d) charting the data, and (e) collating, summarizing, and reporting the results. A total of 58 studies, published from 2013 through 2021 were included in this review. Five focus areas of gamified online course design were identified that include gamification elements, activities, tools, frameworks, and user attributes. The well-known ADDIE (Analyze, Design, Develop, Implement, Evaluate) framework was used as a conceptual lens through which gamified online course design processes were considered. This scoping review addresses a gap in the knowledge base by aggregating and interpreting current gamified online course design practices through the common language of a well-known instructional design framework.

Autoencoder-enabled eye-tracking data analytics for objective assessment of user preference in humanoid robot appearance design

Humanoid robots have been extensively utilized in service industries to provide information and product delivery through direct interactions with users. As the design of humanoid robot appearance significantly impacts human-robot interactions, it is crucial to assess user preference towards it. Traditional evaluation tools, such as surveys, field observations, and interviews, are often time-consuming and subjective. Therefore, this study aims to develop a novel eye-tracking-based assessment tool to investigate user preference towards humanoid robot appearance design. We analyze the critical factors influencing user preference from two perspectives: the attributes of robot appearance and users' selective attention distribution. Accordingly, we propose an integrated machine learning method, combining an autoencoder neural network with a support vector machine to handle the collected visual data. This method, named ASVM, extracts several novel indicators from the eye-tracking data via an unsupervised autoencoder neural network and manual entropy analysis. The proposed ASVM achieves an accuracy of 91%, outperforming other classical machine learning methods, including decision tree, naive Bayes, and support vector machine. ASVM can objectively assess user preference towards humanoid robot appearance design with high time resolution. Furthermore, it can enhance humanoid robot design by revealing the visual attention distribution in assessing robot appearance.

A federated authentication schema among multiple identity providers

Single Sign-On (SSO) methods are the primary solution to authenticate users across multiple web systems. These mechanisms streamline the authentication procedure by avoiding duplicate developments of authentication modules for each application. Besides, these mechanisms also provide convenience to the end-user by keeping the user authenticated when switching between different contexts. To ensure this cross-application authentication, SSO relies on an Identity Provider (IdP), which is commonly set up and managed by each institution that needs to enforce SSO internally. However, the solution is not so straightforward when several institutions need to cooperate in a unique ecosystem. This could be tackled by centralizing the authentication mechanisms in one of the involved entities, a solution raising responsibilities that may be difficult for peers to accept. Moreover, this solution is not appropriate for dynamic groups, where peers may join or leave frequently.In this paper, we propose an architecture that uses a trusted third-party service to authenticate multiple entities, ensuring the isolation of the user's attributes between this service and the institutional SSO systems. This architecture was validated in the EHDEN Portal, which includes web tools and services of this European health project, to establish a Federated Authentication schema.

Intelligent Security Detection and Defense in Operating Systems Based on Deep Learning

With the development of network technology, APT(advanced persistentthreat) attacks are increasing, and research on the security of enterprise assets requires effective detection of digital assets in the network space and effective management of assets through screening and combing, which is the key to real-time monitoring of the safe operation of the system. However, the accompanying malware also poses a threat to the user's property and privacy, so an effective method of detecting Android malware is necessary. In this research direction, although the feature processing capability of traditional machine learning has been improved, there are problems that feature extraction relies on expert experience and the accuracy is low. Therefore, this paper combines deep learning reinforcement technology with the operating system to detect and defend the system in advance, so as to achieve network security.

Backdoor Adjustment via Group Adaptation for Debiased Coupon Recommendations

Accurate prediction of coupon usage is crucial for promoting user consumption through targeted coupon recommendations. However, in real-world coupon recommendations, the coupon allocation process is not solely determined by the model trained with the history interaction data but is also interfered with by marketing tactics desired to fulfill specific commercial goals.This interference creates an imbalance in the interactions, which causes the data to deviate from the user's natural preferences. We refer to this deviation as the matching bias. Such biased interaction data affects the efficacy of the model, and thus it is necessary to employ debiasing techniques to prevent any negative impact. We investigate the mitigation of matching bias in coupon recommendations from a causal-effect perspective. By treating the attributes of users and coupons associated with marketing tactics as confounders, we find the confounders open the backdoor path between user-coupon matching and the conversion, which introduces spurious correlation. To remove the bad effect, we propose a novel training paradigm named Backdoor Adjustment via Group Adaptation (BAGA) for debiased coupon recommendations, which performs intervened training and inference, i.e., separately modeling each user-coupon group pair. However, modeling all possible group pairs greatly increases the computational complexity and cost. To address the efficiency challenge, we further present a simple but effective dual-tower multi-task framework and leverage the Customized Gate Control (CGC) model architecture, which separately models each user and coupon group with a separate expert module. We instantiate BAGA on five representative models: FM, DNN, NCF, MASKNET, and DEEPFM, and conduct comprehensive offline and online experiments to demonstrate the efficacy of our proposed paradigm.