Different Classes Of Users Research Articles

Analyzing user activity on Twitter during long-lasting crisis events: a case study of the Covid-19 crisis in Spain

A pandemic crisis like the Covid-19 outbreak is a complex event, involving numerous aspects of the social life on multiple temporal scales. Focusing on the Spanish Twittersphere, we characterized users' activity behavior across the different phases of the Covid-19 first wave. Firstly, we analyzed a sample of timelines of different classes of users from the Spanish Twittersphere in terms of their propensity to produce new information or to amplify information produced by others. Secondly, by performing stepwise segmented regression analysis and Bayesian switchpoint analysis, we looked for a possible behavioral footprint of the crisis in the statistics of users’ activity. We observed that generic Spanish Twitter users and journalists experienced an abrupt increment of their tweeting activity between March 9 and 14, in coincidence with control measures being announced by regional and state-level authorities. However, they displayed a stable proportion of retweets before and after the switching point. On the contrary, politicians represented an exception, being the only class of users not experimenting this abrupt change and following a completely endogenous dynamics determined by institutional agenda. On the one hand, they did not increment their overall activity, displaying instead a slight decrease. On the other hand, in times of crisis, politicians tended to strengthen their propensity to amplify information rather than produce it.

Dynamic Pricing with Volume Discounts in Online Settings

According to the main international reports, more pervasive industrial and business-process automation, thanks to machine learning and advanced analytic tools, will unlock more than 14 trillion USD worldwide annually by 2030. In the specific case of pricing problems, which constitute the class of problems we investigate in this paper, the estimated unlocked value will be about 0.5 trillion USD per year. In particular, this paper focuses on pricing in e-commerce when the objective function is profit maximization and only transaction data are available. This setting is one of the most common in real-world applications. Our work aims to find a pricing strategy that allows defining optimal prices at different volume thresholds to serve different classes of users. Furthermore, we face the major challenge, common in real-world settings, of dealing with limited data available. We design a two-phase online learning algorithm, namely PVD-B, capable of exploiting the data incrementally in an online fashion. The algorithm first estimates the demand curve and retrieves the optimal average price, and subsequently it offers discounts to differentiate the prices for each volume threshold. We ran a real-world 4-month-long A/B testing experiment in collaboration with an Italian e-commerce company, in which our algorithm PVD-B - corresponding to A configuration - has been compared with human pricing specialists - corresponding to B configuration. At the end of the experiment, our algorithm produced a total turnover of about 300 KEuros, outperforming the B configuration performance by about 55%. The Italian company we collaborated with decided to adopt our algorithm for more than 1,200 products since January 2022.

Mining Mobile Network Fraudsters with Augmented Graph Neural Networks.

With the rapid evolution of mobile communication networks, the number of subscribers and their communication practices is increasing dramatically worldwide. However, fraudsters are also sniffing out the benefits. Detecting fraudsters from the massive volume of call detail records (CDR) in mobile communication networks has become an important yet challenging topic. Fortunately, Graph neural network (GNN) brings new possibilities for telecom fraud detection. However, the presence of the graph imbalance and GNN oversmoothing problems makes fraudster detection unsatisfactory. To address these problems, we propose a new fraud detector. First, we transform the user features with the help of a multilayer perceptron. Then, a reinforcement learning-based neighbor sampling strategy is designed to balance the number of neighbors of different classes of users. Next, we perform user feature aggregation using GNN. Finally, we innovatively treat the above augmented GNN as weak classifier and integrate multiple weak classifiers using the AdaBoost algorithm. A balanced focal loss function is also used to monitor the model training error. Extensive experiments are conducted on two open real-world telecom fraud datasets, and the results show that the proposed method is significantly effective for the graph imbalance problem and the oversmoothing problem in telecom fraud detection.

A Flexible Web Traffic Generator for the dimensioning of a 5G backhaul in NPN

Web-based services have nowadays aggregated the majority of applications, ranging from multimedia delivery to instant messaging, embracing requirements from different classes of users (i.e., gamers, streamers, professionals, etc.). The common factor is the need for connectivity everywhere and every time at high performance and quality, while the services traffic profiles and the users’ behaviour can be drastically different. This need is fully satisfied by the recent 5G mobile networks, where the coexistence of heterogeneous applications with different Quality of Service (QoS) levels is possible, defining new vertical markets and cutting-edge applications. In this context, we propose a Flexible Web Traffic Generator (FWTG) tool to model the dynamics of recent applications and user interactions in a realistic way, allowing the generation of real-time HTTP traffic and the possibility to inject it into real networks. FTWG represents a valid in-laboratory solution for the rapid configuration and applications performance evaluation of network slices in the context of 5G Non-Public Networks (NPN), pursuing an effective emulation approach. The advantage of this approach is that it allows the evaluation of real traffic response to different network configurations (and vice-versa), using components, equipment and network functions of the target network under test. After the description of the rationale and the methodology for defining FWTG models and their implementation, we defined a set of Key Performance Indicators (KPI) generated by the tool (e.g., number of requested objects and size, request rate, throughput, etc.). Then, we present initial validation tests on a Linux-based testbed representing the 5G-based scenarios of interest, emulating also a satellite backhaul as representative of a use-case requiring cost-effective dimensioning. Numerical outcomes of running FWTG in different configurations are then provided, considering a variation of the number of users sharing the same resources, their composition and traffic type, showing with concrete examples the usefulness and flexibility of FWTG in supporting 5G network configuration optimizations. Definitively, the tool is released as Open Source software on Gitlab.

A business class for autonomous mobility-on-demand: Modeling service quality contracts in dynamic ridesharing systems

With the popularization of transportation network companies (TNCs) (e.g., Uber, Lyft) and the rise of autonomous vehicles (AVs), even major car manufacturers are increasingly considering themselves as autonomous mobility-on-demand (AMoD) providers rather than individual vehicle sellers. However, matching the convenience of owning a vehicle requires providing consistent service quality, taking into account individual expectations. Typically, different classes of users have different service quality (SQ) expectations in terms of responsiveness, reliability, and privacy. Nonetheless, AMoD systems presented in the literature do not enable active control of service quality in the short term, especially in light of unusual demand patterns, sometimes allowing extensive delays and user rejections. This study proposes a method to control the daily operations of an AMoD system that uses the SQ expectations of heterogeneous user classes to dynamically distribute service quality among riders. Additionally, we consider an elastic vehicle supply, that is, privately-owned freelance AVs (FAVs) can be hired on short notice to help providers meeting user service-level expectations. We formalize the problem as the dial-a-ride problem with service quality contracts (DARP-SQC) and propose a multi-objective matheuristic to address real-world requests from Manhattan, New York City. Applying the proposed service-level constraints, we improve user satisfaction (in terms of reached service-level expectations) by 53% on average compared to conventional ridesharing systems, even without hiring additional vehicles. By deploying service-quality-oriented on-demand hiring, our hierarchical optimization approach allows providers to adequately cater to each segment of the customer base without necessarily owning large fleets.

Active mobility perception from an intersectional perspective: insights from two European cities

The investigation of active mobility perception for different classes of users is crucial to include all mobility needs and increase the accessibility of these modes. Elements like the perception of safety, security and the shape of the urban environment (e.g., provision of cycle paths) are to be considered while analyzing the different mobility choices between men and women. The current study focuses on the answers of a survey among representative samples of the population of two European cities, namely Valencia and Turin. Data collected in these similarly structured ‘bike friendly’ sites are compared following an intersectional approach based on gender and age. The results showed, for example, that, in general, cyclists are somewhat affected by safety issues, especially in terms of sharing space with motor vehicles. Or that women are less satisfied with the level of safety during walking. When considering bike-sharing, women seem more responsive to adding a dedicated space for children to start using this mode. The analysis of the results helps in providing interesting indications and suggestions for transport planning.

User Specific Efficiency Parameters for Brain Computer Interface (BCI) based Upper Limb Stroke Rehabilitation System

Rehabilitation after stroke through conventional manner is not quite successful due to a number of patient related issues including lack of interest in lengthy exercises, cost of therapy and dependency on healthcare professionals. In addition, around 50% of stroke survivors worldwide belong to the low and middle income countries that are unable to afford expensive rehabilitation systems. Advancements in Brain Computer Interface (BCI) technology enabling the researchers to design and develop BCI based strokerehabilitation systems by exploiting neural plasticity. This is achieved via Electroencephalogram (EEG) based computer gaming rehabilitation exercises through Motor Imagery (MI) to achieve successful neural plasticity. However, current research is largelybased on expensive bio-signal amplifiers and processing hardware that are beyond the affordability of a large population of stroke patients living in low and middle-income countries. Moreover, the efficiency of BCI based stroke rehabilitation systems thatare generally considered as the accuracy of EEG signal classifications is not the only parameter to rate the efficiency.Since the requirements of BCI based rehabilitation therapy are highly subject specific, efficiency of such systems also depends on many user specific features related to cost and performance.This paper describes a research that proposes a number of parameters for cost and efficiency along with their weightage set by the domestic users to determine the overall efficiency of the system.Inputs from different groups of users were obtained that are classified as deserving class, middle class and rich class. Results indicated that the users of different groups are giving different weights to different performance and cost parameters. The overall efficiency requirements are therefore having different meanings for different classes of users

A Study of Costs and Benefits of Content Sharing in Personal Cloud Storage

Personal Cloud Storage (PCS) is a very popular Internet service. In addition to backup, PCS allows content sharing among multiple devices, which is a valuable functionality for many users. Yet maintaining a PCS service incurs both storage and bandwidth costs to service providers, as the update and successive downloads of shared files may generate extra traffic on the PCS cloud servers. This becomes particularly worrisome as a large fraction of users (e.g., over 90%) does not pay for the service, joining a free version with limited storage capacity but typically without content sharing restrictions. Thus, a natural concern arises on the costs and benefits of such service, for both provider and users. In this paper, we propose a model to analyze the cost-benefit tradeoffs of content sharing in PCS services for both parties. Our model uses a macroeconomic concept, notably user surplus, to capture the satisfaction of different classes of users, as well as a cost saving function to capture the interest of the provider in reducing bandwidth costs. We use our model to evaluate two alternative policies to the content sharing architecture in use in existing PCS services, searching for scenarios in which both parties have benefits. The proposed policies rely on incentives given to users in exchange of their participation to offload shared content from cloud servers. Our investigation, based on analytical modeling and data-driven experiments, shows that the incentive leads to greater satisfaction to both parties, and that the alternative policies can reach scenarios which benefit both provider and users, with reductions in provider’s bandwidth costs by 20% and increases in user satisfaction from 51 to 82% under reasonable model assumptions.

Multi-Level Reversible Data Anonymization via Compressive Sensing and Data Hiding

Recent advances in intelligent surveillance systems have enabled a new era of smart monitoring in a wide range of applications from health monitoring to homeland security. However, this boom in data gathering, analyzing and sharing brings in also significant privacy concerns. We propose a Compressive Sensing (CS) based data encryption that is capable of both obfuscating selected sensitive parts of documents and compressively sampling, hence encrypting both sensitive and non-sensitive parts of the document. The scheme uses a data hiding technique on CS-encrypted signal to preserve the one-time use obfuscation matrix. The proposed privacy-preserving approach offers a low-cost multi-tier encryption system that provides different levels of reconstruction quality for different classes of users, e.g., semi-authorized, full-authorized. As a case study, we develop a secure video surveillance system and analyze its performance.

THE STRUCTURAL ROLE OF USER CLASS IN CHAT INTERACTIONS ON TWITCH

Twitch.tv has become an important platform for video streaming, especially of games, with more than 100 million monthly users. The structure of content on the platform, which merges live video feeds with chat rooms for user feedback, problematizes existing theoretical frameworks for understanding the roles and hierarchies of different types of users. Combined with efforts to monetize user engagement for the benefit of both platform owners and user-generators of content, there is a need for greater understanding of the new interaction paradigm introduced by the platform. The present study introduces a framework for systematic, quantitative analysis of user interactions in the chat rooms associated with Twitch channels, as well as a preliminary data set. Social network analysis techniques are used to analyse the centrality and homophily of different classes of users, and the theoretical significance of these observations is briefly discussed.

Analysis of irregular repetition spatially-coupled slotted ALOHA

Contention-based access is a promising technology for massive and sporadic transmissions. In this paper, we propose a novel contention-based multiple access scheme, named irregular repetition spatially-coupled slotted ALOHA (IRSC-SA), motivated by the spatial coupling and irregular repetition techniques. There are different classes of users and slots in IRSC-SA, which result in unequal protection for different users. Considering that, we derive a novel density evolution (DE) method, which deals with unequal packet protection and introduces Bayesian reasoning to analyze the throughput threshold of the proposed IRSC-SA.Theoretical analysis and simulation results show that the proposed scheme achieves better asymptotic threshold and system packet throughput performance than the conventional spatially-coupled slotted ALOHA.

Optimal and Fast Real-Time Resource Slicing With Deep Dueling Neural Networks

Effective network slicing requires an infrastructure/network provider to deal with the uncertain demands and real-time dynamics of the network resource requests. Another challenge is the combinatorial optimization of numerous resources, e.g., radio, computing, and storage. This paper develops an optimal and fast real-time resource slicing framework that maximizes the long-term return of the network provider while taking into account the uncertainty of resource demands from tenants. Specifically, we first propose a novel system model that enables the network provider to effectively slice various types of resources to different classes of users under separate virtual slices. We then capture the real-time arrival of slice requests by a semi-Markov decision process. To obtain the optimal resource allocation policy under the dynamics of slicing requests, e.g., uncertain service time and resource demands, a Q-learning algorithm is often adopted in the literature. However, such an algorithm is notorious for its slow convergence, especially for problems with large state/action spaces. This makes Q-learning practically inapplicable to our case, in which multiple resources are simultaneously optimized. To tackle it, we propose a novel network slicing approach with an advanced deep learning architecture, called deep dueling, that attains the optimal average reward much faster than the conventional Q-learning algorithm. This property is especially desirable to cope with the real-time resource requests and the dynamic demands of the users. Extensive simulations show that the proposed framework yields up to 40% higher long-term average return while being few thousand times faster, compared with the state-of-the-art network slicing approaches.

A QoS Aware RA Algorithm for OFDM Network

Orthogonal Frequency Division Multiplexing (OFDM) is modulation technique used for new generation wireless system. The Resource allocation (RA) in this scheme is done by allotting multiple subcarriers, power to multiple users which ensures impeccable performance in spectral efficiency and throughput of the network. Quality of Service (QoS), and resource utilization are interrelated for providing users a level of satisfaction in wireless communication services. In this research paper, a novice resource allocation algorithm based on integrated scheduling and queuing with RA to enhance QoS is being introduced. The given RA supports QoS to enhance the performance of the network. Simulation and performance evaluation exhibits the efficiency and usefulness of given algorithm which achieves high throughput while considering QoS constraints and maintains fairness among different classes of users.

Performance Analysis and Optimization of a $N$ -Class Bipolar Network

A wireless network with unsaturated traffic and N classes of users sharing a channel under random access is analyzed here. Necessary and sufficient conditions for the network stability are derived, along with simple closed formulas for the stationary packet transmission success probabilities and mean packet delays for all classes under stability conditions. We also show, through simple and elegant expressions, that the channel sharing mechanism in the investigated scenario can be seen as a process of partitioning a well-defined quantity into portions, each portion assigned to each user class, the size of which determined by system parameters and performance metrics of that user class. Using the derived expressions, optimization problems are then formulated and solved to minimize the mean packet delay and to maximize the channel throughput per unit of area. These results indicate that the proposed analysis is capable of assessing the trade-off involved in radio-resource management when different classes of users are considered.

Adaptive Remote Banking System

The purpose of the article is to study the processes of on-line servicing of various bank clients, review and the author's system description of remote banking services.Research methods are methods of developing web applications, methods of semantic analysis of the main concepts of the subject domain (banking services of banking products). The article deals with the main functions of banking systems for servicing different classes of users (individuals, corporate legal entities, etc.). The article proposes and describes the function of the author's adaptive remote banking system, which allows the use of the wide possibilities of modern mobile applications and the Internet. The described functional involves the processing of accounts, corporate cards, banking products, and the like. All functionalities have been tested in the developed author adaptive system of remote banking services for clients of Bank PUMB.The novelty of the research. The authors have proposed approaches to the flexibility of embedding the developed software product in the banking systems of other banks, minimizing the computational complexity of the algorithm of banking services for various banking products, and so on. These approaches are implemented in the author's adaptive system of remote banking services and provide a new level of efficiency of on-line servicing of bank clients. The developed interface of the system meets all the requirements for the banking systems interfaces.The conclusion of the research consists in the proposed approach to banking customer service with the help of the author's adaptive remote banking system helps reduce the time for banking operations and minimize the information and technological resources necessary for the functioning of the system. Therefore, the proposed approach (in the form of an author's adaptive remote banking system) is an important factor in ensuring efficient banking services for banking products.

Model-Based Quantitative Evaluation of Repair Procedures in Gas Distribution Networks

We propose an approach for assessing the impact of multi-phased repair procedures on gas distribution networks, capturing load profiles that can depend on time for different classes of users, suspension of activities during non-working hours, and random execution times depending on topological, physical, and geographical characteristics of the network. The problem is characterized through a semi-formal specification based on artifacts of the Systems Modeling Language (SysML), which is then translated into a formal model based on stochastic time Petri nets. The solution method interleaves fluid-dynamic analysis of the gas behavior and stochastic analysis of the time spent in the repair process, decoupling complexities and making stochastic analysis almost insensitive to the network size and topology. Hence, our approach turns out to be applicable to real scale cases, notably computing the optimal time of day to start the repair procedure. Moreover, by encompassing general (non-Markovian) distributions, the approach enables effective fitting of durations.

Users' Distribution and Behavior in Academic Social Networking Sites

Academic social networking sites (SNSs) are growing rapidly. Worldwide, academicians use academic SNSs for many reasons regardless of their nation, gender, position, and discipline. In this paper, the authors extend their previous work in exploring the distribution and behavior of a particular academic SNS (academia.edu) on a large scale. The authors classify users into different groups based on their position, discipline, and continent. This study gives a better understanding of usage patterns in academic SNS, especially in the lack of large-scale studies about different classes of users on academic SNSs.

Optimal Representations for Adaptive Streaming in Interactive Multiview Video Systems

Interactive multiview video streaming (IMVS) services permit to remotely navigate within a 3D scene with an immersive experience. This is possible by transmitting a set of reference camera views (anchor views), which are used by the clients to freely navigate in the scene and possibly synthesize additional viewpoints of interest. From a networking perspective, the big challenge in IMVS systems is to deliver to each client the best set of anchor views that maximizes the navigation quality, minimizes the view-switching delay and yet satisfies the network constraints. Integrating adaptive streaming solutions in free-viewpoint systems offers a promising solution to deploy IMVS in large and heterogeneous scenarios, as long as the multiview video representations on the server are properly selected. Therefore, we propose to optimize the multiview data at the server by minimizing the overall resource requirements while offering a good navigation quality to the different users. We propose a representation set optimization problem for multiview adaptive streaming systems, and we show that it is NP-hard. Therefore, we introduce the concept of multiview navigation segment that permits to cast the video representation set selection as an integer linear programming problem with a bounded computational complexity. We then show that the proposed solution reduces the computational complexity, while preserving optimality in most of the 3D scenes. We finally provide simulation results for different classes of users and show the gain offered by an optimal multiview video representation selection compared to recommended representation sets (e.g., Netflix and Apple ones) or to a baseline representation selection algorithm, where the encoding parameters are decided a priori for all the camera views.

A Pareto-improving hybrid rationing and pricing policy with multiclass network equilibria

ABSTRACTThis paper extends the work on Pareto-improving hybrid rationing and pricing policy for general road networks by considering heterogeneous users with different values of time. Mathematical programming models are proposed to find a multiclass Pareto-improving pure road space rationing scheme (MPI-PR) and multiclass hybrid rationing and pricing schemes (MHPI and MHPI-S). A numerical example with a multimodal network is provided for comparing both the efficiency and equity of the three proposed policies. We discover that MHPI-S can achieve the largest reduction in total system delay, MHPI can induce the least spatial inequity and MHPI-S is a progressive policy which is appealing to policy makers. Furthermore, numerical results reveal that different classes of users react differently to the same hybrid policies and multiclass Pareto-improving hybrid schemes yield less delay reduction when compared to their single-class counterparts.

Decoupled signal detection for the uplink of massive MIMO in 5G heterogeneous networks

Massive multiple-input multiple-output (MIMO) systems are strong candidates for future fifth-generation (5G) heterogeneous cellular networks. For 5G, a network densification with a high number of different classes of users and data service requirements is expected. Such a large number of connected devices needs to be separated in order to allow the detection of the transmitted signals according to different data requirements. In this paper, a decoupled signal detection (DSD) technique which allows the separation of the uplink signals, for each user class, at the base station (BS) is proposed for massive MIMO systems. A mathematical signal model for massive MIMO systems with centralized and distributed antennas in heterogeneous networks is also developed. The performance of the proposed algorithm is evaluated and compared with existing detection schemes in a realistic scenario with distributed antennas. A sum-rate analysis and a computational cost study for DSD are also presented. Simulation results show an excellent performance of the proposed algorithm when combined with linear and successive interference cancellation detection techniques.