CIDF: A Clustering-Based Interaction-Driven Friending Algorithm for the Next-Generation Social Networks

Online social networks, such as Facebook, have been massively growing over the past decade. Recommender algorithms are a key factor that contributes to the success of social networks. These algorithms, such as friendship recommendation algorithms, are used to suggest connections within social networks. Current friending algorithms are built to generate new friendship recommendations that are most likely to be accepted. Yet, most of them are weak connections as they do not lead to any interactions. Facebook is well known for its Friends-of-Friends approach which recommends familiar people. This approach has a higher acceptance rate but the strength of the connections, measured by interactions, is reportedly low. The accuracy of friending recommendations is, most of the time, measured by the acceptance rate. This metric, however, does not necessarily correlate with the level of interaction, i.e., how much friends do actually interact with each other. As a consequence, new metrics and friending algorithms are needed to grow the next generation of social networks in a meaningful way, i.e., in a way that actually leads to higher levels of social interactions instead of merely growing the number of edges. In this paper, we develop a novel approach to build friendship recommender algorithms for the next-generation social networks. We first investigate existing recommender systems and their limitations. We also highlight the side effects of generating easily accepted but weak connections between people. To overcome the limitations of current friending algorithms, we develop a clustering-based interaction-driven friendship recommender algorithm and show through extensive experiments that it does generate friendship recommendations that have a higher probability of leading to interactions between users than existing friending algorithms

A single-source shortest path algorithm for dynamic graphs

Graphs are mathematical structures used in many applications. In recent years, many applications emerged that require the processing of large dynamic graphs where the graph’s structure and properties change constantly over time. Examples include social networks, communication networks, transportation networks, etc. One of the most challenging problems in large scale dynamic graphs is the single-source shortest path (SSSP) problem. Traditional solutions (based on Dijkstra’s algorithms) to the SSSP problem do not scale to large dynamic graphs with a high change frequency. In this paper, we propose an efficient SSSP algorithm for large dynamic graphs. We first present our algorithm and give a formal proof of its correctness. Then, we give an analytical evaluation of the proposed solution

Precision Clinical Medicine Through Machine Learning: Using High and Low Quantile Ranges of Vital Signs for Risk Stratification of ICU Patients

Remote monitoring of patients in the intensive care unit (ICU) is a crucial observation and assessment task that is necessary for precision medicine. We have recently built a cloud-based intelligent remote patient monitoring (IRPM) framework in which we follow the state-of-the-art in risk stratification through machine learning-based prediction, but with minimal features that rely on vital signs, the most commonly used physiological variables obtained inside and outside hospitals. In this work, we significantly improve the functionality of the initial IRPM framework by building three machine learning models for readmission, abnormality, and next-day vital sign measurements. We provide a formal representation of a feature engineering algorithm and report the development and validation of three reproducible machine learning prediction models: ICU patient readmission, abnormality, and next-day vital sign measurements. For the readmission model, we proposed two solutions for data with imbalanced classes and applied five binary classification algorithms to each solution. For the abnormality model, we applied the same five algorithms to predict whether a patient will show abnormal health conditions. Our findings indicate that we can still achieve a reasonable performance using these machine learning models by focusing on low and high quantile ranges of vital signs. The best accuracy achieved in the readmission model was around 67.53%, with an area under the receiver operating characteristic (AUROC) of 0.7376. The highest accuracy achieved in the abnormality model was around 67.40%, with an AUROC of 0.7379. For the next-day vital sign measurements model, we provide three approaches for selecting model predictors and apply the eXtreme Gradient Boosting (XGB) and Random Forest Regression (RFR) algorithms to each solution. We found that, in general, the use of the most recent vital sign measurements achieves the least prediction error. Considering the large investment from the medical industry in patient monitoring devices, the developed models will be incorporated into an Intelligent ICU Patient Monitoring (IICUPM) module that can potentially facilitate the delivery of high quality care by implementing cost-efficient policies for handling the patients who utilize ICU resources the most

A Brief Review on Mathematical Tools Applicable to Quantum Computing for Modelling and Optimization Problems in Engineering

Since its emergence, quantum computing has enabled a wide spectrum of new possibilities and advantages, including its efficiency in accelerating computational processes exponentially. This has directed much research towards completely novel ways of solving a wide variety of engineering problems, especially through describing quantum versions of many mathematical tools such as Fourier and Laplace transforms, differential equations, systems of linear equations, and optimization techniques, among others. Exploration and development in this direction will revolutionize the world of engineering. In this manuscript, we review the state of the art of these emerging techniques from the perspective of quantum computer development and performance optimization, with a focus on the most common mathematical tools that support engineering applications. This review focuses on the application of these mathematical tools to quantum computer development and performance improvement/optimization. It also identifies the challenges and limitations related to the exploitation of quantum computing and outlines the main opportunities for future contributions. This review aims at offering a valuable reference for researchers in fields of engineering that are likely to turn to quantum computing for solutions. Doi: 10.28991/ESJ-2023-07-01-020 Full Text: PD

Automated conflict resolution in collaborative data sharing systems using community feedbacks

a b s t r a c t In collaborative data sharing systems, groups of users usually work on disparate schemas and database instances, and agree to share the related data among them (periodically). Each group can extend, curate, and revise its own database instance in a disconnected mode. At some later point, the group can publish its updates to other groups and get updates of other ones (if any). The reconciliation operation in the CDSS engine is responsible for propagating updates and handling any data disagreements between the different groups. If a conflict is found, any involved updates are rejected temporally and marked as deferred. Deferred updates are not accepted by the reconciliation operation until a user resolves the conflict manually. In this paper, we propose an automated conflict resolution approach that depends on community feedbacks, to handle the conflicts that may arise in collaborative data sharing communities, with potentially disparate schemas and data instances. The experiment results show that extending the CDSS by our proposed approach can resolve such conflicts in an accurate and efficient manner

Preserving Privacy in Web Services

Web services arein creasin gly bein g adopted as a viable mean s to access Web-based application . This has been en - abled by the tremen3 us stan3 rdization e#ort to describe, advertise, discover, an d in voke Web services. Digital government (DG) is a ma or application domain for Web services. It aims at improvin g govern men t-citizen in teraction s usin g in formation an commun cation techn logies. Govern5 n t agen cies collect, store, process,an d sharein formation about million s of citizen s who have di#eren t preferen ces regardin g their privacy. Thisn aturally raises an umber of legalan d techn ical issues that must be addressed to preserve citizen s' privacy through the con trol of the in formation flow amon gst di#eren ten tities (users, Web services, DBMSs). Solution s addressin g this issue are stillin their in fan cy. They con sist, essen tially, of en forcin g privacy by law or by self-regulation . In this paper, we propose a n w techn cal approach for preservin privacyin governPE t Web services. Our design is based d mobile privacy preserving agents. This work aims at establishin the feasibility an d provable reliability of techn ology-based privacy preservin solution for Web service in rastructures

Dynamic Privacy Policy Management in Services-Based Interactions

Technology advancements have enabled the distribution and sharing of patient personal health data over several data sources. Each data source is potentially managed by a different organization, which expose its data as aWeb service. Using suchWeb services, dynamic composition of atomic data type properties coupled with the context in which the data is accessed may breach sensitive data that may not comply with the users preference at the time of data collection. Thus, providing uniform access policies to such data can lead to privacy problems. Some fairly recent research has focused on providing solutions for dynamic privacy policy management. This paper advances these techniques, and fills some gaps in the existing works. In particular, dynamically incorporating user access context into the privacy policy decision, and its enforcement. We provide a formal model definition of the proposed approach and a preliminary evaluation of the model