User-related Information Research Articles

RGMeta: Enhancing Cold-Start Recommendations with a Residual Graph Meta-Embedding Model

Traditional recommendation models grapple with challenges such as the scarcity of similar user or item references and data sparsity, rendering the cold-start problem particularly formidable. Meta-learning has emerged as a promising avenue to address these issues, particularly in solving the item cold-start problem by generating meta-embeddings for new items as their initial ID embeddings. This approach has shown notable success in enhancing the accuracy of click-through rate predictions. However, prevalent meta-embedding models often focus solely on the attribute features of the item, neglecting crucial user information associated with it during the generation of initial ID embeddings for new items. This oversight hinders the exploitation of valuable user-related information to enhance the quality and accuracy of the initial ID embedding. To tackle this limitation, we introduce the residual graph meta-embedding model (RGMeta). RGMeta adopts a comprehensive approach by considering both the attribute features and target users of both old and new items. Through the integration of residual connections, the model effectively combines the representation information of the old neighbor items with the intrinsic features of the new item, resulting in an improved initial ID embedding generation. Experimental results demonstrate that RGMeta significantly enhances the performance of the cold-start phase, showcasing its effectiveness in overcoming challenges associated with sparse data and limited reference points. Our proposed model presents a promising step forward in leveraging both item attributes and user-related information to address cold-start problems in recommendation systems.

Advance Payment System using Wireless RFID Technology

As the world is moving toward a digital era, cashless payments are quite in demand. Smart cards that need to be swiped are vulnerable to skimming attacks and data theft. Thus, there is a need to have a cashless transaction system that is fast as well as secure and allows hassle-free transactions. This Work propose a novel approach to develop an advanced cashless payment system based on RFID that can be deployed anywhere to enable faster smoother, and secure transactions. The users of the system will have to be pre- registered and all the user-related information will be saved in a MySQL database the system will be programmed using HTML, CSS, and PHP or any supporting back-end programming language. The RFID reader will interact with a microcontroller ESP32 WROOM and the Microcontroller will be connected with an application using a WIFI module built into the micro-controller. Key Words: RFID Technology, Wireless Payment Systems, Secure Transactions.

Leveraging Multiyear, Geospatial Social Media Data for Health Policy Evaluations: Lessons From the Philadelphia Beverage Tax.

Public reactions to health policies are vital to understand policy sustainability and impact but have been elusively difficult to dynamically measure. The 2021 launch of the Twitter Academic Application Programming Interface (API), allowing for historical tweet analyses, represents a potentially powerful tool for complex, comprehensive policy analyses. Using the Philadelphia Beverage Tax (implemented January 2017) as a case study, this research extracted longitudinal and geographic changes in sentiments, and key influencers in policy-related conversations. The Twitter API was used to retrieve all publicly available tweets related to the Tax between 2016 and 2019. Twitter. Users who posted publicly available tweets related to the Philadelphia Beverage Tax (PBT). Tweet content, frequency, sentiment, and user-related information. Tweet content, authors, engagement, and location were analyzed in parallel to key PBT events. Published emotional lexicons were used for sentiment analyses. A total of 45 891 tweets were retrieved (1311 with geolocation data). Changes in the tweet volume and sentiment were strongly driven by Tax-related litigation. While anger and fear increased in the months prior to the policy's implementation, they progressively decreased after its implementation; trust displayed an inverse trend. The 50 tweeters with the highest positive engagement included media outlets (n = 24), displaying particularly high tweet volume/engagement, and public personalities (n = 10), displaying the greatest polarization in tweet sentiment. Most geo-located tweets, reflecting 321 unique locations, were from the Philadelphia region (55.2%). Sentiment and positive engagement varied, although concentrations of negative sentiments were observed in some Philadelphia suburbs. Findings highlighted how longitudinal Twitter data can be leveraged to deconstruct specific, dynamic insights on public policy reactions and information dissemination to inform better policy implementation and evaluation (eg, anticipating catalysts for both heightened public interest and geographic, sentiment changes in policy conversations). This study provides policymakers a blueprint to conduct similar cost and time efficient yet dynamic and multifaceted health policy evaluations.

Practical Privacy-Preserving Federated Learning in Vehicular Fog Computing

Benefitting from the outstanding capabilities of intelligent controlling and prediction, federated learning (FL) has been widely applied in Internet of Vehicle (IoV). However, applying FL into fog-computing-based IoV still suffers from two crucial problems: (i) how to achieve the privacy-preserving FL under the flexible architecture of fog computing with no assistance of cloud server, and (ii) how to guarantee the privacy-preserving FL to perform with high efficiency and low overhead in fog-computing settings. For addressing the above issues, we propose a practical framework, named <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Galaxy</small> , the first of its kind in the regime of privacy-preserving FL under the setting of non-cloud-assisted fog computing. Based on the secure multi-party computation (MPC) technology, our framework satisfies the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{(T,N)}$</tex-math></inline-formula> -threshold property, permitting <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{N}$</tex-math></inline-formula> (a scalable number) fog nodes to cooperate with multiple users for implementing privacy-preserving FL, while resisting the collusion up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{T}-\boldsymbol{1}$</tex-math></inline-formula> fog nodes, and being robust to at most <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{N}-\boldsymbol{T}$</tex-math></inline-formula> fog nodes simultaneously dropping out. Besides, considering the practical scenario that low-quality data may negatively impair the FL model convergence, our scheme can handle users’ low-quality data while protecting all user-related information under our secure framework. Based on the above superior properties, our scheme can perform with high scalability, high processing efficiency, and low resource overhead, being practical for fog-computing-based IoV. Extensive experiment results demonstrate our scheme with high-level performance.

Ontological user profile for E-orientation platforms

In recent years, E-orientation systems have played an increasingly significant role in the proposal of an academic and professional orientation to students. Research efforts have grown to provide more useful and effective E-orientation systems for research or other purposes. The implementation of E-orientation systems resulting from these efforts utilizes several techniques including Artificial Intelligence (AI) methodologies. This study proposes a personalised approach to support an E-orientation system that is tailored to the student’s characteristics. A key component of this system comprises an ontological model of the user profile. The objective of this research was to propose an ontology that is able to collect and analyze the user related information as well as customize the profiles with the most appropriate recommendation or orientation. The ontology employed in this study was developed using the OWL (Ontology Web Language), a knowledge representation language for authoring ontologies. In this paper we will present a definition for the user profile, and then we present our methodology of ontological modeling of the user profile, and finally the conceptual model of the user model for e-orientation systems.

Design and Development of Smartphone Application Control

Smartphone Application Control is a project designed to block other phone applications that have excessive use by users. Excessive phone use is often experienced by children aged 7-18 years old. At the same time, many parents nowadays do not have time all day long to monitor and limit the use of their children’s phone due to work. The efforts of the mass media to advise on the use of phone do not give much positive impact on these children. Therefore, a robust alternative such as Smartphone Application Control is essential to curb these issues. To develop this application, a methodological approach has been used namely the Evolutionary Prototyping model. Android Studio version 3.6.2 is used to develop this application while SQLite Database acts as a database for storing all application usage statistics and Google-Cloud Firebase is used to store user-related information for user authentication purposes. To create an attractive user interface, Adobe Photoshop CS6 is used to design background and interface buttons. This app is able to track usage hours of other apps and block them if they exceed their daily usage limits. The time limit for such use can only be set by parents. Additionally, notification of excess usage will be sent to parents via email as a notification. A pop-up alert notification will be issued when user has reached his or her application limit. Smartphone Application Control is very important in helping user to control their phone usage behaviour.

The role of legitimacy and reputation judgments in users’ selection of service providers on sharing economy platforms

Information systems literature suggests that reputation is the main judgment form that captures user-related information online. Drawing on social judgment literature, we contend that a user's online characteristics and past actions are not all reputation-based, but also legitimacy-based, and that transaction stake determines whether users will use reputation or legitimacy judgment to evaluate other users. Using Airbnb, we show that level of stake in a transaction determines the judgment form (reputation or legitimacy) that guests resort to when evaluating a host. Moreover, we find that providing extensive information on a host to potential guests in low-stake transactions is counterproductive.

A new imputation-based incomplete data-driven fuzzy modeling for accuracy improvement in ubiquitous computing applications

Purpose In real-world decision-making, high accuracy data analysis is essential in a ubiquitous environment. However, we encounter missing data while collecting user-related data information because of various privacy concerns on account of a user. This paper aims to deal with incomplete data for fuzzy model identification, a new method of parameter estimation of a Takagi–Sugeno model in the presence of missing features. Design/methodology/approach In this work, authors proposed a three-fold approach for fuzzy model identification in which imputation-based linear interpolation technique is used to estimate missing features of the data, and then fuzzy c-means clustering is used for determining optimal number of rules and for the determination of parameters of membership functions of the fuzzy model. Finally, the optimization of the all antecedent and consequent parameters along with the width of the antecedent (Gaussian) membership function is done by gradient descent algorithm based on the minimization of root mean square error. Findings The proposed method is tested on two well-known simulation examples as well as on a real data set, and the performance is compared with some traditional methods. The result analysis and statistical analysis show that the proposed model has achieved a considerable improvement in accuracy in the presence of varying degree of data incompleteness. Originality/value The proposed method works well for fuzzy model identification method, a new method of parameter estimation of a Takagi–Sugeno model in the presence of missing features with varying degree of missing data as compared to some well-known methods.

CoLEAP: Cooperative Learning-Based Edge Scheme With Caching and Prefetching for DASH Video Delivery

The outstanding increase in video traffic, puts increasing pressure on network transmission. Since the Dynamic Adaptive Streaming over HTTP (DASH) adjusts the delivery to the dynamic network conditions, it has emerged as a popular approach for video transmissions. However, bitrate switching and video rebuffering may still occur and influence negatively quality of experience (QoE). Additionally the popular videos are transmitted multiple times, which leads to high bandwidth consumption, despite large transmission redundancy. In this context, we propose a Cooperative Learning-based scheme for the smart Edge servers with cAching and Prefetching (CoLEAP) to improve the QoE of adaptive video streaming. CoLEAP employs edge servers which cache the most beneficial contents to reduce redundant video transmissions and prefetches content to decrease network transmission delay. Considering user-related information and the state of network, CoLEAP intelligently makes the most advantageous decisions of caching and prefetching by employing a novel QoE-oriented deep neural network model. To demonstrate the performance of our scheme, we test the proposed solution in comprehensive simulated scenarios and against four alternative solutions. When compared with the existing schemes, CoLEAP increases average bitrate by up to 181.8%, reduces video rebuffering by up to 70.8% as well as decreases response time by up to 28.0%. These values result in minimum improvements of 57.4% and 29.0%, respectively in terms of cache hit rate and QoE.

Stance Analysis of Twitter Users: The Case of the Vaccination Topic in Italy

People’s opinion around social and political issues is currently witnessed by messages ordinarily posted on social media. With reference to a specific case study, namely the vaccination topic in Italy, this article discusses a crucial aspect in structuring the data processing pipeline in intelligent systems aimed at monitoring the public opinion through Twitter messages: A plain analysis of tweet contents is not sufficient to grasp the diversity of behavior across users. To get a sharper picture of the public opinion as expressed on social media, user-related information must be incorporated in the analysis. Relying on a dataset of tweets about vaccination and on an established text classification system, we present the results of a stance monitoring campaign with advanced analysis on temporal and spatial scales. The overall methodological workflow provides a sound solution for public opinion assessment from Twitter data.

홈 IoT 개인정보활용 및 보호에 대한 선호도 연구

홈 IoT 개인정보활용 및 보호에 대한 선호도 연구

Chebyshev Polynomial-Based Authentication Scheme in Multiserver Environment

Nowadays, communication technologies are more and more strongly advanced, such as 4G or 5G. There are many useful online applications, and one of them is the telecare medical information system (TMIS). If the TMIS is widely deployed, patients and doctors will have more frequently connection. Clearly, this enhances our quality of life. One of the most important modules securely constructing this convenient TMIS is the user-authentication scheme. We should prevent user identity and related information from the adversary’s eavesdropping. Therefore, the authentication scheme could provide user anonymity and concern some kinds of attacks, such as impersonation or password-guessing attacks. Common solutions are a combination of hash function and public-key cryptosystem (RSA or elliptic curve cryptosystem, ECC), but current schemes do not consider identity protection as one main task necessary for medical information environment. In this paper, our results consist of some important analyses of previous works and a multiserver user-authentication scheme suitable for TMIS using Chebyshev polynomial with two models: random oracle and BAN-logic.

MHealth for schizophrenia spectrum disorders management: A systematic review.

Various types of computational technologies can be used to access, store and wirelessly share private and sensitive user-related information. The 'big data' provided by these technologies may enable researchers and clinicians to better identify behavioral patterns and to provide a more personalized approach to care. The information collected, however, can be misused or potentially abused, and therefore could be detrimental to the very people who provided their medical data with the hope of improving care. This article focuses on the use of emerging mobile technologies that allow the collection of data about patients experiencing schizophrenia spectrum and related disorders. Schizophrenia has been recognized by the Sustainable Development Goals of the United Nations for its burden on our health care system and society [1]. Our analysis provides an overview of the range of available mobile technologies for people with schizophrenia and related conditions along with the technology's reported capabilities and limitations, and efficacy of mHealth interventions based on the data from articles. Thus, the focus of this review is first and foremost to update the scope of existing technologies as previous systematic reviews such as the ones by Alvarez-Jimenez et al. and by Firth and Torous are outdated [2, 3]. Although we also examine the ethical issues arising from the use of these technologies, for an in-depth analysis of the ethical implications of mobile Health technologies (mHealth), we refer the readers to our follow-up article. In terms of the ubiquitous availability of technology on the internet, our article summarizes significant information for mental health specialists and apprises the reader about the existence of these technologies. In terms of the ubiquitous availability of technology on the internet, our article summarizes significant information for mental health specialists and apprises the reader about the existence of these technologies.

Privacy-Preserving Federated Deep Learning with Irregular Users

Federated deep learning has been widely used in various fields. To protect data privacy, many privacy-preserving approaches have also been designed and implemented in various scenarios. However, existing works rarely consider a fundamental issue that the data shared by certain users (called irregular users) may be of low quality. Obviously, in a federated training process, data shared by many irregular users may impair the training accuracy, or worse, lead to the uselessness of the final model. In this paper, we propose PPFDL, a Privacy-Preserving Federated Deep Learning framework with irregular users. In specific, we design a novel solution to reduce the negative impact of irregular users on the training accuracy, which guarantees that the training results are mainly calculated from the contribution of high-quality data. Meanwhile, we exploit Yaos garbled circuits and additively homomorphic cryptosystems to ensure the confidentiality of all user-related information. Moreover, PPFDL is also robustness to users dropping out during the whole implementation. This means that each user can be offline at any subprocess of training, as long as the remaining online users can still complete the training task. Extensive experiments demonstrate the superior performance of PPFDL in terms of training accuracy, computation, and communication overheads.

INNV-13. A NOVEL TELEGRAM CHAT APPLICATION BASED BOT (FLORENCEBOT) FOR BRAIN TUMOUR AFFECTED PATIENTS

Abstract The discoverability of reliable information online is difficult. As the network grows exponentially, unregulated content has the potential to cause harm. Telegram is a popular mainstream chat application. It allows automated software called bots that interact passively with an individual. Florence bot is unique in this space for the patient support; for those affected by brain tumours. It has been developed in PHP and hosted privately by the developer. Florence links to physician generated content (hosted externally on a blogging website) such as introduction, signs and symptoms of brain tumours and various treatment options along with their side effects. The content generates “web view” in Telegram itself, preserving written and multimedia formats. It ensures minimal overheads on the bot, making it responsive. It links to brain tumour channel (automated streamed information working as a public broadcast) and physician-administered support group. A physician designed bot is preferable to a third party mobile application to ensure the accurateness of information. No user-related information is available to bot developers as it does not have tracking and advertising elements; hence safeguards users privacy. It is more accessible and cheaper to deploy than a mobile application as it uses an instant messaging app as its container, which works across computing platforms. Florence bot is expected to bridge the gap for reliable information and real-time support independent of geographical limitations.

Multi-level encryption algorithm for user-related information across social networks

AbstractThe traditional RSA information encryption algorithm uses one-dimensional chaotic equations to generate pseudo-random sequences that meet the encryption requirements. This encryption method is too simple and the security performance is poor. A multi-level encryption algorithm for user-related information across social networks is proposed, and a user association model across social networks is constructed to obtain user-related information across social networks. This multi-level chaotic encryption algorithm based on neural network is used to select three different chaotic mapping models based on user-related information, and a multi-level chaotic encryption algorithm is designed. According to the characteristics of error sensitivity of chaotic system, the neural network is used to inversely propagate the error. A chaotic encryption algorithm that implements multi-level encryption of user-related information across social networks is optimized. The experimental results show that the average rate for which the proposed algorithm correctly identified the user-related information across social networks was 97.6%, the highest frequency of average character distribution probability in cipher text was 0.021, and the average time for encryption was 18.45 Mbps. The average time for decryption was 21.90Mbps.

Effective hate-speech detection in Twitter data using recurrent neural networks

This paper addresses the important problem of discerning hateful content in social media. We propose a detection scheme that is an ensemble of Recurrent Neural Network (RNN) classifiers, and it incorporates various features associated with user-related information, such as the users' tendency towards racism or sexism. These data are fed as input to the above classifiers along with the word frequency vectors derived from the textual content. Our approach has been evaluated on a publicly available corpus of 16k tweets, and the results demonstrate its effectiveness in comparison to existing state of the art solutions. More specifically, our scheme can successfully distinguish racism and sexism messages from normal text, and achieve higher classification quality than current state-of-the-art algorithms.

Community-Structured Evolutionary Game for Privacy Protection in Social Networks

Social networks have attracted billions of users and supported a wide range of interests and practices. Users of social networks can be connected with each other by different communities according to professions, living locations, and personal interests. With the development of diverse social network applications, academic researchers, and practicing engineers pay increasing attention to the related technology. As each user on the social network platforms typically stores and shares a large amount of personal data, the privacy of such user-related information raises serious concerns. Most research on privacy protection relies on specific information security techniques such as anonymization or access control. However, the protection of privacy depends heavily on the incentive mechanisms of social networks, like users’ psychological decisions on security execution and socio-economic considerations. For example, the desire to influence the behaviors of other people may change a user’s choice of security setting. In this paper, a game theoretic framework is established to model users’ interactions that influence users’ decisions as to whether to undertake privacy protection or not. To model the relationship of user communities, community-structured evolutionary dynamics are introduced, in which interactions of users can only happen among those users who have at least one community in common. Then the dynamics of the users’ strategies to take a specific privacy protection or not is analyzed based on the proposed community structured evolutionary game theoretic framework. Experiments show that the proposed framework is effective in modeling the users’ relationships and privacy protection behaviors. Moreover, results can also help social network managers to design appropriate security service and payment mechanisms to encourage their users to take the privacy protection, which can promote the spreading of privacy behavior throughout the network.

Implicit relevance feedback from electroencephalography and eye tracking in image search

Objective. Methods from brain–computer interfacing (BCI) open a direct access to the mental processes of computer users, which offers particular benefits in comparison to standard methods for inferring user-related information. The signals can be recorded unobtrusively in the background, which circumvents the time-consuming and distracting need for the users to give explicit feedback to questions concerning the individual interest. The obtained implicit information makes it possible to create dynamic user interest profiles in real-time, that can be taken into account by novel types of adaptive, personalised software. In the present study, the potential of implicit relevance feedback from electroencephalography (EEG) and eye tracking was explored with a demonstrator application that simulated an image search engine. Approach. The participants of the study queried for ambiguous search terms, having in mind one of the two possible interpretations of the respective term. Subsequently, they viewed different images arranged in a grid that were related to the query. The ambiguity of the underspecified search term was resolved with implicit information present in the recorded signals. For this purpose, feature vectors were extracted from the signals and used by multivariate classifiers that estimated the intended interpretation of the ambiguous query. Main result. The intended interpretation was inferred correctly from a combination of EEG and eye tracking signals in 86% of the cases on average. Information provided by the two measurement modalities turned out to be complementary. Significance. It was demonstrated that BCI methods can extract implicit user-related information in a setting of human-computer interaction. Novelties of the study are the implicit online feedback from EEG and eye tracking, the approximation to a realistic use case in a simulation, and the presentation of a large set of photographies that had to be interpreted with respect to the content.

Feature weighted clustering for user profiling

Personalization is the adaptation of the services to fit the user’s interests, characteristics and needs. The key to effective personalization is user profiling. Apart from traditional collaborative and content-based approaches, a number of classification and clustering algorithms have been used to classify user related information to create user profiles. However, they are not able to achieve accurate user profiles. In this paper, we present a new clustering algorithm, namely Multi-Dimensional Clustering (MDC), to determine user profiling. The MDC is a version of the Instance-Based Learner (IBL) algorithm that assigns weights to feature values and considers these weights for the clustering. Three feature weight methods are proposed for the MDC and, all three, have been tested and evaluated. Simulations were conducted with using two sets of user profile datasets, which are the training (includes 10,000 instances) and test (includes 1000 instances) datasets. These datasets reflect each user’s personal information, preferences and interests. Additional simulations and comparisons with existing weighted and non-weighted instance-based algorithms were carried out in order to demonstrate the performance of proposed algorithm. Experimental results using the user profile datasets demonstrate that the proposed algorithm has better clustering accuracy performance compared to other algorithms. This work is based on the doctoral thesis of the corresponding author.