Network Users Research Articles

Circumventing Stragglers and Staleness in Distributed CNN using LSTM

INTRODUCTION: Using neural networks for these inherently distributed applications is challenging and time-consuming. There is a crucial need for a framework that supports a distributed deep neural network to yield accurate results at an accelerated time.
 METHODS: In the proposed framework, any experienced novice user can utilize and execute the neural network models in a distributed manner with the automated hyperparameter tuning feature. In addition, the proposed framework is provided in AWS Sage maker for scaling the distribution and achieving exascale FLOPS. We benchmarked the framework performance by applying it to a medical dataset. 
 RESULTS: The maximum performance is achieved with a speedup of 6.59 in 5 nodes. The model encourages expert/ novice neural network users to apply neural network models in the distributed platform and get enhanced results with accelerated training time. There has been a lot of research on how to improve the training time of Convolutional Neural Networks (CNNs) using distributed models, with a particular emphasis on automating the hyperparameter tweaking process. The study shows that training times may be decreased across the board by not just manually tweaking hyperparameters, but also by using L2 regularization, a dropout layer, and ConvLSTM for automatic hyperparameter modification.
 CONCLUSION: The proposed method improved the training speed for model-parallel setups by 1.4% and increased the speed for parallel data by 2.206%. Data-parallel execution achieved a high accuracy of 93.3825%, whereas model-parallel execution achieved a top accuracy of 89.59%.

Social networks in the context of postmodern theories � A case of Kazakhstan

Relevance. Social networks are developed and spread to an increasing number of people every day. This is due to the dynamic process of digitalization, which provokes changes in all vectors and categories of society.Purpose. The purpose of the work was to determine the role of the blogger and social networks in the process of shaping and demonstrating the lifestyle that a wide range of people aspire to.Methodology. The following methods of scientific research were used in the article, namely analysis, synthesis, comparison, content analysis and abstraction.Results. As a result, it was possible to establish that the dissemination of information in society, as well as the development of mechanisms for monitoring the consumer needs of society, were characteristic of the postmodern period. They have undergone significant changes as a result of reforming the conditions for the development and activities of the state. However, it has been established that their role is one of the determining ones in the development of the institution of blogging as a driving force in the field of marketing and consumption of things and services. The study managed to prove that bloggers, based on the demonstration of various objects, events directly related to their lives, provoke the formation of a certain associative series. The latter concerns the description of the features of a rich life, its dissemination to the masses, for the acquisition of high consumer value among individual products and services.Conclusions. The article found that consumer behaviour in modern Kazakh society is formed under the influence of social networks. The results of scientific research can be used in the formation of advertising integration plans or the description of marketing ways to form consumer behaviour among social network users.

Comparative Study of Radio Resource Distribution Algorithms

The equitable distribution of radio resources among different users in wireless networks is a difficult problem and has attracted the interest of many studies. This study presents the Proportional Fair Q-Learning Algorithm (PFLA) to enable the equitable distribution of radio resources among diverse users through the integration of Q-learning and proportional fairness principles. The PFLA, Round Robin (RR), and Max Throughput (MaxTP) algorithms were compared to evaluate their effectiveness in resource allocation. Performance was measured in terms of sum-rate throughputs and fairness index. The comparison results showed an improvement in the fairness index metrics for PFLA compared to the other algorithms. PFLA showed gains of 11.62 and 43% in the fairness index compared to RR and MaxTP, respectively. These results show that PFLA is more efficient in utilizing available resources, leading to higher overall system throughput and demonstrating its ability to balance performance metrics between users, especially when the number of users increases.

Pattern of Cybercrime Among Adolescents: An Exploratory Study

Background: Cybercrime is common phenomenon at present both developed and developing countries. Young generation, especially teenagers now engaged internet frequently and they commit cybercrime frequently in Bangladesh. Objective: To assess consequences of cybercrime among teenagers was the motto of the study. Methods and tools: This study was a cross-sectional study, descriptive in nature. Non-probability accidental sampling technique has been applied to select the sample because of the nonfinite population and the sample size was 167. A printed semi-structured questionnaire was used to collect data. Results: The study identified consequences of cybercrime on various aspects of individual and social life such as lack of trust and confidence hinders profitable transaction, economic loss, tarnishing the country reputation towards foreigners, denial of innocent Bangladeshi’s opportunity abroad, loss of life in various ways, inimical to the progress and development in the country, and favor of loss of employment. Respondents thought that social network users are the prime victims of cybercrime (92.8%). Moreover, financial institutions also another victim (82.6%) followed by mobile internet users (80.2%). About 80.2% said that criminals were always on the net, 82.0% told of spend money lavishly, 67.1% in favor of riding the best automobiles, 65.9% for date the most beautiful lady, 62.3% for mainly clubbers, 47.3% on indulged in religious activities and 45.5% in live like kings in the society. Conclusion: The study concluded that consequences of cybercrimes are alarming and increasing day by day. Finally, it is recommending that the private public partnership and execution of existing laws can be controlling this crime along with gray area of social media and net-work.

Joint Alignment Networks For Few-Shot Website Fingerprinting Attack

Abstract Website fingerprinting (WF) attacks based on deep neural networks pose a significant threat to the privacy of anonymous network users. However, training a deep WF model requires many labeled traces, which can be labor-intensive and time-consuming, and models trained on the originally collected traces cannot be directly used for the classification of newly collected traces due to the concept drift caused by the time gap in the data collection. Few-shot WF attacks are proposed for using the originally and few-shot newly collected labeled traces to facilitate anonymous trace classification. However, existing few-shot WF attacks ignore the fine-grained feature alignment to eliminate the concept drift in the model training, which fails to fully use the knowledge of labeled traces. We propose a novel few-shot WF attack called Joint Alignment Networks (JAN), which conducts fine-grained feature alignment at both semantic-level and feature-level. Specifically, JAN minimizes a distribution distance between originally and newly collected traces in the feature space for feature-level alignment, and utilizes two task-specific classifiers to detect unaligned traces and force these traces mapped within decision boundaries for semantic-level alignment. Extensive experiments on public datasets show that JAN outperforms the state-of-the-art few-shot WF methods, especially in the difficult 1-shot tasks.

Students' participation in social networks' conflicts (based on the materials of a sociological study of Barnaul universities)

In the modern world, there is a significant number of collisions in the Internet space, which occur not only in interpersonal communication online, but also on various social networks with a large number of participants. Almost any social network that supports the function of discussion, commenting on the information provided, allows you to observe a number of pre-conflict and conflict situations. Despite the fact that the space in which communication takes place is virtual, the consequences of conflicts in it can be more than real, both for an individual and for a number of participants. Conflict situations can have an impact on the daily life of opponents and collectively affect social processes in society. In his work, the author studies in depth the nature of conflicts in social networks. The study was implemented through a quantitative method of collecting information – a survey in the form of a questionnaire. The general totality of the study was made up of students from Barnaul who are users of social networks. The study was conducted using a selective method. The sample totals 367 respondents. The sample size was determined by the capabilities of the researcher. A single-stage sampling was used, the selection unit was specific students. According to the results of the study, the following conclusions were made. Firstly, the vast majority of students use social networks every day, but only half of them have participated or are involved in conflicts. Secondly, students most often conflict on social networks with strangers and in community comments. Thirdly, trolling is still one of the important problems of social networks. A significant part of the (interviewed) students became victims of trolls. Fourth, most of the conflicts in social networks in which students participate are short-lived and occur through verbal debates. Fifth, most students choose a more "peaceful" behavior strategy. Sixth, many students determine the beginning of a conflict in a social network by negative emotions from interaction and consider the presence of physical distance to be the main feature of the described conflicts.

Joint Optimization of Resource Allocation and Trajectory Control for Mobile Group Users in Fixed-Wing UAV-Enabled Wireless Network

Joint Optimization of Resource Allocation and Trajectory Control for Mobile Group Users in Fixed-Wing UAV-Enabled Wireless Network

Interference-Aware Resource Allocation Algorithm for D2D-Enabled Cellular Networks Using Matching Theory

To enhance spectral efficiency and network throughput in Device-to-Device (D2D)-enabled cellular networks, various resource sharing methods can be utilized. However, when users in such dense and complicated networks share the same resources, co-channel interference arises. Therefore, more efficient and comprehensive frameworks should be used to accurately model the network and then decrease co-channel interference and increase resource efficiency. This work proposes a novel and practical resource allocation framework that considers more realistic assumptions based on matching theory with externalities. The goal of the proposed framework is to maximize network throughput by minimizing the interference between co-channel cellular and D2D communications. Moreover, the effect of the interference between various types of communications on the network’s quality of service is studied. The spatial positions of the users’ equipments are modeled by using the Poisson Point Process. It is shown that the proposed method results in a stable resource allocation matching after a finite number of iterations. Based on the analytical and simulation results, the proposed algorithm achieves near-optimal network performance with a low computational cost.

Intelligent RAN Automation for 5G and Beyond

5G is becoming more agile and inevitably more complicated for supporting various deployment scenarios and diversified applications. Artificial Intelligence (AI) is becoming one of the most essential driving forces for 5G evolution to handle the complexity and further improve the network spectrum efficiency, energy efficiency and user experience. In this article, we share the latest standardization progress of the industry on AI in 5G. Evolving directions for the intelligent radio access networks (RAN) are analyzed and further enhancements on both AI for RAN and RAN for AI are proposed based on a hierarchical and distributed intelligent RAN architecture. Case studies are presented along with field trial results. An innovative AI-empowered RAN and service-aware cross layer optimization framework is illustrated for the quality of experience (QoE) optimization of cloud virtual reality (VR) with converged communication and computing services provided by RAN. The extension of the solution to support the AI/ML services is also discussed to shed some light on how to evolve network for AI towards the 6G vision.

Dynamic Traffic Grooming Based on Virtualization-Plane-Aided Optimization for Elastic Optical Satellite Networks

With the increase in global wireless traffic, the use of large-scale satellite networking to provide ubiquitous access is one of the essential trends of future 6G network development. Elastic optical satellite networks (EOSNs) are widely considered a flexible solution for future satellite communication. However, with the continuous proliferation of network devices and users, the growing disparity between user demands and the limited bandwidth and capacity of the network is becoming increasingly noticeable. This has led to issues such as constrained network resource utilization and resource fragmentation. Therefore, EOSNs must efficiently address the challenge of allocating scarce bandwidth resources. Effective traffic grooming methods will be applied to EOSNs to solve the problem of bandwidth shortage. This paper proposed a dynamic traffic grooming algorithm based on virtualization-plane-aided optimization (DTG-VPO) to facilitate the bandwidth allocation for EOSNs. Firstly, the nodes of the alternative paths were graded, and the weights of the subsequent hop links were modified. Then, the path was evaluated using link weights, alternative paths were selected in the virtual and physical topologies, respectively, and a path set was constructed. Finally, a resource block evaluation parameter was designed to quantify the quality of candidate resource blocks and rank them. A series of simulations have evaluated the traffic-blocking probability and wavelength utilization under different traffic loads. The link resource was more fully utilized compared with other traffic grooming algorithms. The blocking probability can be reduced by 75%, while wavelength utilization can be improved by 8.1%.

Underlay Loosely Coupled Model for Public Safety Networks Based on Device-to-Device Communication

In several emergency situations, during natural or human-caused disasters, frontline responders need to be able to communicate and collaborate to properly carry out relief missions. Some countries build their national Public Safety Mobile Broadband based on cellular LTE technology to provide fast, safe, and secure emergency services. However, in several emergency situations, cellular antennas can be overloaded or partially damaged in a manner that affects group communication services. In the last few years, direct device-to-device (D2D) communications have been proposed by the 3GPP as an underlay of long-term evolution (LTE) networks based on proximity, reuse, and hop gains. This paper focuses on a loosely coupled model based on direct D2D communication in a public safety context. Many scenarios related to user membership and network management are detailed. Both the “less cost” and “optimized tree” approaches are proposed and implemented, and their performance is evaluated in terms of the network update number and the resulting average Channel Quality Indicator (CQI). Other optimization approaches, with different CQI thresholds and optimization interval parameters, are simulated to compare their performance with the “optimized tree” approach. By conducting simulations that combine a CQI threshold = 1 and optimization interval = 2 s, it becomes possible to keep an average CQI level close to the “optimized tree” approach, while the costs related to network updates significantly decrease by almost 35%. Other simulations are also carried out to measure the bandwidth required by the control messages between the server and active users. It was found that both inbound and outbound traffic on the server side can be well supported with LTE and 5G networks.

Dynamics of changes in the psychosocial characteristics of online media consumers under the influence of official information and rumors about COVID-19

The purpose of this study is to study changes in the psychosocial characteristics of network users who spread online rumors on during the pandemic COVID–19. Special attention is paid to the psychological reaction of consumers to reports of pandemic in the context of infodemiс. The subject is the peculiarities of the dissemination and perception of official information and rumors about the pandemic in network media. The object is information about the pandemic on two Chinese network platforms that posts with label "rumors". The research methods were Internet monitoring and statistical analysis. The entire early stage of pandemic is included in the research period (from January to March 2020). The author's special contribution is the analysis of the impact of social networks on the psychological state of consumers and the formation of public consciousness in the context of an infodemiс generated by mass and contradictory information COVID-19. The main conclusions are the need to take into account changes in the mental states of the audience and communicative behavior in information activities, depending on the stage of attitude to the event, pragmatic motives and social interaction. Different requirements should be imposed on the information disseminated at different stages, taking into account the psychological state of the audience, also the existence of various languages of consciousness used by an individual in cognitive and emotional manifestations. Government departments can appropriately publish, clarify and disclose reliable and useful information in a timely manner to prevent the spread of rumors. The scientific novelty consists in analyzing the cognitive and emotional reactions of the mass audience to the flow of information about COVID-10 in all periods, deepening media experts' understanding of the relationship between the behavior of users and the spread of disinformation, determining approaches to developing strategies to combat rumors on the Internet.

The Speech Genre of Commentary as a Way to Realize the Social Needs of Internet Communication Participants (Based the Survey Results).

The Speech Genre of Commentary as a Way to Realize the Social Needs of Internet Communication Participants (Based the Survey Results). The article studies commenting as a speech activity for creating and reading comments and an effective way to realize the social needs of representatives of a digital society. It reveals the features of comments as a primary network genre existing in the context of network discourse. During the research, a survey of Russian-speaking Internet users was conducted. The following conclusions are made: network users create a digital discourse designed to meet their needs for social interaction on a new emotional and cognitive level, as well as the need for self-expression in the outer world. The specific speech norms of commenting are: sincerity, focus on personal experience; constructiveness; positivity; benevolence; usefulness, relevance; "the right of the speaker". The results of the work can be used as a basis for further research of network discourse, communication on the Internet, in the process of codification of communicative and ethical norms of network communication, as well as in sociological researches of digital society.

A Graph Sampling-Based Model for Influence Maximization in Large-Scale Social Networks

Social networks have attracted a great deal of attention and have, in fact, changed the way we produce, consume, and diffuse information. This change gave rise to the notion of social influence and today we talk about influential nodes. The process of detecting influential nodes in social networks aims to find entities that propagate information to a large portion of the network users. This process is often known as the influence maximization (IM) problem. Due to the explosive growth of social networks’ data, their structure is more complex and we talk about “big graph data.” Moreover, modern networks are dynamic and their topology or/and information is likely to change over time. Detecting influential nodes in such networks is a challenging task. Several methods have been developed in this context. However, they concentrate on static networks and there is little work on large-scale social networks. We propose in this article a new model for IM called MapReduce-based dynamic selection of influential nodes (MR-DSINs) that has the ability to cope with the huge size of real social networks. In fact, our approach is based on a graph sampling step in order to reduce the network’s size. Given that reduced version, MR-DSIN is able to select dynamically influential nodes. Our proposal has the advantage of considering the dynamics of information that can be modeled by users’ social actions (e.g.“, share”, “comment”, “retweet”). Experimental results on real-world social networks and computer-generated artificial graphs demonstrate that MR-DSIN is efficient for identifying influential nodes, compared with three known proposals. We prove that our model is able to detect in the reduced graph an influence as important as in the original one.

Social capital development on interest-based networks: examining its antecedents, process, and consequences

Social capital development has been extensively studied on relationship-based networks (i.e., Facebook, WeChat), whereas scant attention has been devoted to another critical category: interest-based networks. People join interest-based networks primarily to exchange information on shared topics and interests, as opposed to relationship development or maintenance. Guided by social capital theory, the current research takes an initial step by looking into whether and how bridging and bonding social capital accrues on interest-based networks. A cross-sectional survey was conducted among users of a well-known Chinese interest-based network, Douban.com (N = 624). The participants reported their bridging and bonding social capital, frequency of online interactions with content (i.e., posting, favoriting) and with humans (i.e., chatting, discussing), and sense of belonging. The mediation analysis results informed us of a theoretical model of social capital development on interest-based networks with both online interactions with content and with humans as the antecedents, and a sense of belonging as the consequence. The findings also revealed the steps of bridging and bonding social capital development on interest-based networks. This study advanced our understanding of social capital theory by examining its applicability in an interest-based SNS, identifying OIs with humans and content as potential antecedents, and elucidating the association between two types of social capital. The findings hold practical significance for designers of interest-based networks.

Optimizing throughput value through re-examining of 4G LTE infrastructure link budgets: A Case Study in Lubuk Kilangan

Broadband wireless access technology has taken the lead in communication technology development, enabling users to communicate with fast data speeds and extensive coverage.. Problems that frequently arise in mobile communication systems are the wide range of services and the level of network service quality. Thus, optimization is carried out to improve network quality in the Lubuk Kilangan area by using the drive test method to determine the quality of the network and bad spot areas and analyzed using terms discovery software. The reason behind the findings of this study is that the optimal link budget value for 4G LTE infrastructure in Lubuk Kilangan is higher than the current industry standard due to the unique characteristics of the area. According to the analysis and calculation of the link budget, power additions, and antenna tilting alterations were carried out for optimization. The power quality of the RSRP parameter increased from the fair (orange) area of 2.58 to 0.29. For the SINR parameter, For the SINR parameter, there was also an increase in quality at poor category locations in the KPI parameters (Range RSRP) locations from 0.08% to 0.05%, resulting in SINR bad spots decreasing. The proportion for Throughput after optimization remains the same, at 100%. Indicating that there had been a good increase in the coverage area, making the coverage in the Lubuk Kilangan area good. It is necessary to conduct a deeper study by comparing several types of antenna orientation, including tilt, depending on various factors such as the deployment scenario, coverage requirements, interference considerations, and the specific characteristics of picocells, femtocells, and repeaters. Future research could explore the impact of other factors, such as network topology and user behavior, on link budget optimization of 4G LTE infrastructure in different contexts

Community relations discovery methods for users in Fancircle based on sentiment analysis in China

PurposeThe identification of network user relationship in Fancircle contributes to quantifying the violence index of user text, mining the internal correlation of network behaviors among users, which provides necessary data support for the construction of knowledge graph.Design/methodology/approachA correlation identification method based on sentiment analysis (CRDM-SA) is put forward by extracting user semantic information, as well as introducing violent sentiment membership. To be specific, the topic of the implementation of topology mapping in the community can be obtained based on self-built field of violent sentiment dictionary (VSD) by extracting user text information. Afterward, the violence index of the user text is calculated to quantify the fuzzy sentiment representation between the user and the topic. Finally, the multi-granularity violence association rules mining of user text is realized by constructing violence fuzzy concept lattice.FindingsIt is helpful to reveal the internal relationship of online violence under complex network environment. In that case, the sentiment dependence of users can be characterized from a granular perspective.Originality/valueThe membership degree of violent sentiment into user relationship recognition in Fancircle community is introduced, and a text sentiment association recognition method based on VSD is proposed. By calculating the value of violent sentiment in the user text, the annotation of violent sentiment in the topic dimension of the text is achieved, and the partial order relation between fuzzy concepts of violence under the effective confidence threshold is utilized to obtain the association relation.

Interference Mitigation and Power Consumption Reduction for Cell Edge users in Future Generation Networks

In 5G heterogeneous networks (HetNets), a unique and promising option to address the growing demand for higher data rates is network densification of small cells (SCs) and macro cells (MCs). Unfortunately, the 5G HetNets are suffering severe issues due to the interference caused by these densely populated SCs and their high-power consumption. To lessen interference and boost network throughput, a New Soft Frequency Reuse (NSFR) technique is put forth in this work. The proposed scheme uses the Soft Frequency Reuse (SFR) for on/off switching of the SCs according to their Interference Contribution Rate (ICR) values. By splitting the cell region into edge and center zones, it resolves the interference issue caused by the densely packed SCs. Moreover, SC on/off switching addresses the issue of excessive power consumption and improves the 5G network's power efficiency. Furthermore, this work tackles the irregular shape nature problem of 5G HetNets and compares two different proposed shapes for the centre zone of the SC, existing irregular and proposed circular shapes. Additionally, the optimum radius of the centre zone, which maximizes the total system data rate, is obtained. A comparative analysis of power consumption, data rate and power efficiency was performed between the NSFR model, the SFR model and the proposed model. The results show that for 1000 number of equipment, the proposed model has a low power consumption of 1.72KW compared to 3.51KW for SFR and 3.73KW for NSFR. Data rate of 12.19kbps compared to 11.42kbps for SFR and 11.09kbps for NSFR. Also, power efficiency of 610kbps/W compared to 572kbps/W for SFR and 560kbps/W for NSFR. These results imply that the interference mitigation handled by the proposed scheme improves by approximately 22%.

Improving GNSS PPP-RTK through global forecast system zenith wet delay augmentation

The precise point positioning real-time kinematic (PPP-RTK) is a high-precision global navigation satellite system (GNSS) positioning technique that combines the advantages of wide-area coverage in precise point positioning (PPP) and of rapid convergence in real-time kinematic (RTK). However, the PPP-RTK convergence is still limited by the precision of slant ionospheric delays and tropospheric zenith wet delay (ZWD), which affects the PPP-RTK network parameters estimation and user positioning performance. The present study aims to construct a PPP-RTK model augmented with a priori ZWD values derived from the global forecast system (GFS) product (a global numerical weather prediction (NWP) model) to improve the PPP-RTK performance. This study gives a priori ZWD values and conversion based on the GFS products, and the full-rank GFS-augmented undifferenced and uncombined (UDUC) PPP-RTK network model is derived. To verify the performance of GFS-augmented UDUC PPP-RTK, a comprehensive evaluation using 10-day GNSS observation data from three different GNSS station networks in the United States (US), Australia, and Europe is conducted. The results show that with the GFS ZWD a priori information, PPP-RTK performance significantly improves at the initial filtering stage, but this advantage gradually decays over time. Based on 10-day positioning results for all user stations, the GFS ZWD-augmented PPP-RTK approach reduces the average convergence time by 46% from 10.0 to 5.4 min, the three-dimensional root-mean-square (3D-RMS) error by 5.7% from 3.5 to 3.3 cm, and the time to first fix (TTFF) value by 35.8% from 6.7 to 4.3 min, all when compared to the traditional PPP-RTK without GFS ZWD constraints.

ROLE OF MEDIA AND SOCIAL NETWORKS IN PROMOTING THE POSITION OF AFGHAN WOMEN’

This research endeavours to explore and identify the impact of media and social networks on advancing the position of Afghan women during the final year of Ashraf Ghani's presidency, prior to the Taliban's resurgence. Employing a qualitative approach, this study utilizes semi-structured interviews for data collection, and theme analysis is applied for data interpretation. The research focuses on educated women, social activists, and users of Afghan social networks as the target population. Sampling is conducted through targeted and snowball methods, resulting in 15 interviews until theoretical saturation is achieved. The findings of the study reveal divergent perspectives, categorized into two main patterns of thought: agreement and disagreement, regarding the role of media in promoting the status of Afghan women. Supporters of media's positive role highlight themes such as "leaving the traditional space," "reducing social isolation," and "modelling and identification." They advocate for the positive influence of media in enhancing the status of women. Conversely, opponent’s present themes such as the "Pathology of Media Activities," "Intra-Media Challenges," and "Causes of Media Failure," expressing scepticism about the effectiveness of media in promoting women's social eminence. The research also uncovers a noteworthy conflict between traditional religious media and secular media in Afghanistan concerning women's issues. This conflict is indicative of a virtual battle between traditional and modern media outlets in the representation of women's positions. The findings illuminate the complex landscape in which Afghanistan's media landscape operates, revealing the struggles and challenges faced in shaping public perception and discourse on the status of women. In conclusion, this research provides valuable insights into the nuanced interplay between media, social networks, and the position of Afghan women. The contrasting viewpoints and conflicts within the media landscape underscore the complexity of the societal discourse surrounding women's roles and the challenges faced in leveraging media for positive societal change.