Popularity Profile Research Articles

Instagram as a Communication Space for Painters

Instagram is a social network platform intended for communication between the creator of information and its recipients, as well as between the recipients themselves. The driving force of Instagram has been recognised by many artists that decided to promote their creative work on this platform. This social network is also used to fulfil advertising contracts that the artists have concluded with companies. The question therefore is whether the number of followers is mainly affected by the artistic achievements of their authors or by out-of-art marketing activities? Instagram contains many profiles of people involved in other branches of art, including painting. It seems that due to the field of art they deal with, the private life of an artist painting pictures is not as interesting as the life of popular actors or singers. As a result, their profiles are mainly concentrated on presenting artistic achievements using a platform that helps them reach a broad spectrum of viewers. This article provides a selective review of popular profiles of artists, taking into consideration the way in which they present their art on the Internet and how the artist communicates with users, as well as how the communication between users themselves looks.

Learning-based cooperative content caching and sharing for multi-layer vehicular networks

Caching and sharing the content files is critical and fundamental for various future vehicular applications. However, how to satisfy the content demands in a timely manner with limited storage is an open issue owing to the high mobility of vehicles and the unpredictable distribution of dynamic requests. To better serve the requests from the vehicles, a cache-enabled multi-layer architecture, consisting of a Micro Base Station (MBS) and several Small Base Stations (SBSs), is proposed in this paper. Considering that vehicles usually travel through the coverage of multiple SBSs in a short time period, the cooperative caching and sharing strategy is introduced, which can provide comprehensive and stable cache services to vehicles. In addition, since the content popularity profile is unknown, we model the content caching problems in a Multi-Armed Bandit (MAB) perspective to minimize the total delay while gradually estimating the popularity of content files. The reinforcement learning-based algorithms with a novel Q-value updating module are employed to update the caching files in different timescales for MBS and SBSs, respectively. Simulation results show the proposed algorithm outperforms benchmark algorithms with static or varying content popularity. In the high-speed environment, the cooperation between SBSs effectively improves the cache hit rate and further improves service performance.

Quality of Information About Cosmetic Products in Social Media: One-Year Analysis of Portuguese Influential Instagram Profiles

This work characterizes scientific and health information from posts addressing cosmetic products on Instagram for one-year. Portuguese profiles that cover cosmetic products were selected based on popularity and expertise. Posts concerning cosmetic products were recorded, and those containing scientific and health information were selected. A new method was used, which allows rating each post as “correct,” “confuse,” or “incorrect”. Ten popular profiles and three expert profiles were selected. Forty-five posts containing scientific and health information about cosmetic products were analyzed. Popular profiles were approximately ten times more likely to convey confusing information about cosmetic products than expert profiles.

Cache content placement in the presence of fictitious requests in mmWave 5G IAB networks

Caching popular content at the edge of wireless networks leads to backhaul congestion mitigation. To come up with an effective caching policy, content popularity distribution should be taken into account, which is not accurately known in most practical scenarios. Moreover, the mobile users’ (MU) request pattern may not always follow a well-defined distribution since some malicious MUs may deliberately issue their requests incompatible with the content popularity statistics. In this paper, we consider the problem of cache content placement in a 5G mmWave small cell network that relies on integrated access and backhaul (IAB) technology for pushing contents to MUs. We assume that the IAB node is equipped with a cache and has no prior knowledge about the content popularity profiles; instead, it only relies on the observation of the instantaneous demands to shape its caching policy. Also, malicious MUs may exist whose goals are to increase cache miss by issuing fictitious requests. The IAB node decides on which contents to cache and for how long, given that frequently replacing contents incurs administrative costs. We model the content placement problem as an ”adversarial combinatorial multi-armed bandit process with switching costs (ACMAB-SC)” and present an online learning algorithm for shaping the caching policy. We conduct extensive simulation experiments to evaluate the convergence property and assess the performance of our algorithm in terms of backhaul congestion, delay, and cache hit ratio. We also compare against two baseline online learning schemes, including a CMAB-based approach and a generic caching policy based on the ”Follow the Perturbed Leader (FPL)” algorithm.

ИЗУЧЕНИЕ НАПРАВЛЕНИЙ ПРОФЕССИОНАЛЬНОГО ОБУЧЕНИЯ В КОЛЛЕДЖАХ И ОБЩЕОБРАЗОВАТЕЛЬНЫХ ШКОЛАХ, РЕАЛИЗУЮЩИХ АДАПТИРОВАННЫЕ ПРОГРАММЫ ДЛЯ ОБУЧАЮЩИХСЯ С НАРУШЕНИЕМ ИНТЕЛЛЕКТА: СРАВНИТЕЛЬНЫЙ АНАЛИЗ

Introduction. For successful socialization of adolescents with intellectual disabilities, it is extremely important to take into account their professional preferences. Teachers work on each student’s development and acquisition of skills necessary for the chosen profession. At the same time, it is crucial to take into account the capabilities and specifics of professional preferences of each student with intellectual disabilities when choosing a profession. Vocational training begins in labor lessons at school and requires a long propaedeutic period. This study aims to examine high school and college job profiles to identify the most in-demand training areas for persons with intellectual disabilities. Materials and methods. 210 graduates of grades 9 and 11 with intellectual disabilities took part in the experiment in the period from 2019 to 2023. We used such methods as analysis of scientific literature, descriptive method, qualitative and quantitative data analysis, comparative method. Results. As a result of the data analysis, the most popular areas of specialized work at the stage of school education have been identified including sewing, cardboard bookbinding, carpentry, and floriculture (gardening). At the college preparation stage, the most popular profiles are “cook”, “green farm worker”, and “computer operator”. Conclusion. Some job training profiles that are popular at the school stage are not so in demand in college such as “seamstress “, “carpenter” and “bookbinder”. The lack of training and continuity between school and college in matters of professional self-determination and vocational training of persons with intellectual disabilities has been revealed. Therefore, it is important to develop organizational and methodological foundations for the professional self-determination of students, taking into account their individual characteristics, it is necessary to ensure compliance between preferences and the real needs of the labor market.

Using TikTok as a platform for science communication

The pandemic has changed the way we interact. The popularity of the TikTok platform, which allows creating short video content, was on the rise even before 2020 and skyrocketed during quarantine. Scientists have recognized its potential as a new means to communicate and share experience.This poster will take a look at the content posted by the most popular science communicators and at the reasons for creating content of this type.As the hashtags on the platform can be used to categorize content for algorithms and AI to understand it, we will explore science-related hashtags using information available at https://tiktokhashtags.com/ and select the most popular profiles (personal TikTok profiles of science communicators) for analysis in terms of science content, differences between video creators and presenters, and their background. The background will be analyzed from content posted on the platform (if they are openly talking about it on the platform) and comparison with content posted on other platforms. We will also look for reasons for their switching to TikTok. Our hypothesis is that shifts in social dynamics in the wake of coronavirus pandemic favoured a switch from big productions to user-friendly platforms like TikTok, and that its video-making options encourage those less experienced in video-making to create and share (science-related) content.As TikTok is an open platform with egalitarian public engagement model, it can be used to build trust in science by, say, showing the process “behind the scenes”.

Joint Transmission Scheme and Coded Content Placement in Cluster-Centric UAV-Aided Cellular Networks

Recently, as a consequence of the COVID-19 pandemic, dependence on telecommunication for remote learning/working and telemedicine has significantly increased. In this context, preserving high Quality of Service (QoS) and maintaining low-latency communication are of paramount importance. In cellular networks, the incorporation of unmanned aerial vehicles (UAVs) can result in enhanced connectivity for outdoor users due to the high probability of establishing Line of Sight (LoS) links. The UAV’s limited battery life and its signal attenuation in indoor areas, however, make it inefficient to manage users’ requests in indoor environments. Referred to as the cluster-centric and coded UAV-aided femtocaching (CCUF) framework, the network’s coverage in both indoor and outdoor environments increases by considering a two-phase clustering framework for Femto access points (FAPs)’ formation and UAVs’ deployment. Our first objective is to increase the content diversity. In this context, we propose a coded content placement in a cluster-centric cellular network, which is integrated with the coordinated multipoint (CoMP) approach to mitigate the intercell interference in edge areas. Then, we compute, experimentally, the number of coded contents to be stored in each caching node to increase the cache-hit-ratio, signal-to-interference-plus-noise ratio (SINR), and cache diversity and decrease the users’ access delay and cache redundancy for different content popularity profiles. Capitalizing on clustering, our second objective is to assign the best caching node to indoor/outdoor users for managing their requests. In this regard, we define the movement speed of ground users as the decision metric of the transmission scheme for serving outdoor users’ requests to avoid frequent handovers between FAPs and increase the battery life of UAVs. Simulation results illustrate that the proposed CCUF implementation increases the cache-hit-ratio, SINR, and cache diversity and decrease the users’ access delay, cache redundancy, and UAVs’ energy consumption.

An Investigation of Wood Baseball Bat Durability as a Function of Bat Profile and Slope of Grain Using Finite Element Modeling and Statistical Analysis

To counter a perceived increase in multi-piece fracturing of wood baseball bats, Major League Baseball implemented standards to regulate the quality of wood used in the making of professional-grade baseball bats. These specifications included a minimum density as a function of wood species and a standard related to slope of grain (SoG). Following the implementation of these specifications in 2008, there was a 65% reduction in the multi-piece failure rate. It is hypothesized that a further reduction in the breakage rate can be realized through the implementation of regulations on allowable bat profiles. In the current work, a parametric study was conducted to develop a quantitative understanding of the relationship between bat durability (i.e., resistance to breaking), SoG, and bat profile, thereby obtaining data to support or refute the hypothesis. Finite element models of the bat/ball impact of four different popular bat profiles were created using LS-DYNA software. Similarities and differences between bat profiles impacted at two relatively vulnerable axial locations are presented and discussed. Lastly, the respective bat durabilities for all of the profiles were compared using a probability analysis that considers the SoG, impact location, impact velocity, and it predicts an in-service bat durability.

An international cross-sectional investigation on social media, fitspiration content exposure, and related risks during the COVID-19 self-isolation period

With the global COVID-19 pandemic, governments from many countries in the world implemented various restrictions to prevent the SARS-Cov-2 virus's spread, including social distancing measures, quarantine, in-home lockdown, and the closure of services and public spaces. This led to an in-creased use of social media platforms to make people feel more connected, but also to maintain physical activity while self-isolating. Concerns about physical appearance and the desire to keep or reach a muscular and toned ideal body, might have further reinforced the engagement in fitness-related social media activities, like sharing progresses in training achievements or following more fitness contents on popular profiles. To better understand the underlying relation among these factors, the present study investigates 729 responses to the Exercise Addiction Inventory (EAI), the Appearance Anxiety Inventory (AAI), the Self-Compassion Scale (SCS) and their association to social media usage and compares the results cross-culturally in five countries (Spain, Lithuania, United Kingdom, Japan, and Hungary). Findings highlight significant differences between males and females, espe-cially in regard to the time spent online (U = 477.5, p = 0.036). Greater levels of appearance anxiety were associated with the exposure to fitness-related contents on social media. These results strongly confirm the previously highlighted association between fitspiration media and body image anxiety predominantly in females. Clinical implications and future considerations in terms of prevention and treatment in a situation of global emergency are also discussed.

Adolescent Popularity: Distinct Profiles and Associations with Excessive Internet Usage and Interpersonal Sensitivity.

Peer popularity constitutes a pivotal developmental task to adolescents' current and future adaptation. This study identified distinct adolescent popularity profiles and explored their links with excessive Internet usage and interpersonal sensitivity. The sample included 2090 students attending Greek high schools (Mage = 16.16, SD = 0.91). Their popularity was measured via self-report and peer sociometric means. They also responded to the Internet Addiction Test (IAT) and the Interpersonal Sensitivity subscale of the Symptom Checklist-90-Revised (SCL-90-R). A sequence of latent profile analysis, ANOVAs and linear regression models were performed. Three distinct popularity profiles were revealed: the "Average Confident" (68.4%), the "Socially Vulnerable" (26.8%), and the "Insecure Bi-Strategic" (4.8%). These profiles did not significantly vary regarding their Internet usage and interpersonal sensitivity behaviours. Interestingly, lower self-perceived popularity predicted higher interpersonal sensitivity, whereas higher actual popularity predicted excessive Internet use. Findings have important implications for student-tailored mental health prevention and intervention practices.

On the Role of Micro-categories to Characterize Event Popularity in Meetup

Event-based social networking platforms such as Meetup have recently witnessed a huge growth. However, with the rise in the volume of groups and events, making individual events attractive has become increasingly challenging for its organizers. As a result, we find that events hosted by groups of same category at similar venues and similar times, also widely differ in their popularity. Data study reveals that the topics specified in textual descriptions of events may be key to their popularity. In this paper, we introduce a novel concept of topical micro-categories in the context of EBSNs for accurately characterizing events, such that events belonging to the same micro-category exhibit similar popularity profile. We develop a principled method to detect such micro-categories from the textual descriptions of individual events. Our experiments reveal the significance of the detected micro-categories in determining the popularity of associated Meetup events and groups. We also investigate the effectiveness of the micro-categories in a real-world application scenario by developing a recommendation model; this model recommends relevant micro-categories to a group for hosting its future events with enhanced popularity. Notably, our model achieves an average NDCG score of around 0.75 showing a straight 5% improvement over the best performing competing method.

Contextual Learning for Content Caching With Unknown Time-Varying Popularity Profiles via Incremental Clustering

With the rapid development of social networks and high-quality video sharing services, the demand for delivering large quantity and high quality contents under stringent end-to-end delay requirement is increasing. To meet this demand, we study the content caching problem modelled as a Markov decision process in the network edge server when the popularity profiles are unknown and time-varying. In order to adapt to the changing trends of content popularity, a context-aware popularity learning algorithm is proposed. We prove that the learning error of this scheme is sublinear in the number of requests. In light of the learned popularities, a reinforcement learning-based caching scheme is designed on top of the state-action-reward-state-action algorithm with a function approximation. A reactive caching algorithm is also proposed to reduce the complexity. The time complexities of both the caching schemes are studied to demonstrate their feasibility in real time systems and a theoretical analysis is performed to prove that the cache hit rate of the reactive caching algorithm asymptotically converges to the optimal cache hit rate. Finally the simulations are presented to demonstrate the superiority of the proposed algorithms.

Function Approximation Based Reinforcement Learning for Edge Caching in Massive MIMO Networks

Caching popular contents in advance is an important technique to achieve low latency and reduced backhaul congestion in future wireless communication systems. In this article, a multi-cell massive multi-input-multi-output system is considered, where locations of base stations are distributed as a Poisson point process. Assuming probabilistic caching, average success probability (ASP) of the system is derived for a known content popularity (CP) profile, which in practice is time-varying and unknown in advance. Further, modeling CP variations across time as a Markov process, reinforcement Q-learning is employed to learn the optimal content placement strategy to optimize the long-term-discounted ASP and average cache refresh rate. In the Q-learning, the number of Q-updates are large and proportional to the number of states and actions. To reduce the space complexity and update requirements towards scalable Q-learning, two novel (linear and non-linear) function approximations-based Q-learning approaches are proposed, where only a constant (4 and 3 respectively) number of variables need updation, irrespective of the number of states and actions. Convergence of these approximation-based approaches are analyzed. Simulations verify that these approaches converge and successfully learn the similar best content placement, which shows the successful applicability and scalability of the proposed approximated Q-learning schemes.

Colouring of COVID-19 Affected Region Based on Fuzzy Directed Graphs

Graph colouring is the system of assigning a colour to each vertex of a graph. It is done in such a way that adjacent vertices do not have equal colour. It is fundamental in graph theory. It is often used to solve real-world problems like traffic light signalling, map colouring, scheduling, etc. Nowadays, social networks are prevalent systems in our life. Here, the users are considered as vertices, and their connections/interactions are taken as edges. Some users follow other popular users’ profiles in these networks, and some don’t, but those non-followers are connected directly to the popular profiles. That means, along with traditional relationship (information flowing), there is another relation among them. It depends on the domination of the relationship between the nodes. This type of situation can be modelled as a directed fuzzy graph. In the colouring of fuzzy graph theory, edge membership plays a vital role. Edge membership is a representation of flowing information between end nodes of the edge. Apart from the communication relationship, there may be some other factors like domination in relation. This influence of power is captured here. In this article, the colouring of directed fuzzy graphs is defined based on the influence of relationship. Along with this, the chromatic number and strong chromatic number are provided, and related properties are investigated. An application regarding COVID-19 infection is presented using the colouring of directed fuzzy graphs.

Facial Plastic Surgery on Instagram: What Is Trending? What Is Working?

Instagram has become a prominent way for facial plastic surgeons to advertise their work and communicate with patients. Some providers develop globally popular profiles, becoming "influencers" in their field. We sought to characterize posts found on top profiles by type and determine the degree of patient interaction within each type. A social media tracking tool (Awario, Belarus) compiled a list of the top 33 facial plastic surgery Instagram profiles based on number of followers and "reach," obtained on September 22, 2019. Profiles not belonging to American Board of Facial Plastic and Reconstructive Surgery (ABFPRS)-certified surgeons were excluded, leading to 20 profiles. The most recent 18 posts at the time of data extraction were categorized into: (1) clinical or professional, (2) lifestyle, and (3) patients. The average number of "likes," as a percentage of total followers, was calculated for each category for each profile. Data were gathered from September to November 2019. Most top profiles belong to ABFPRS-certified surgeons and were not verified. Clinical pictures and patient posts were 42.78% and 41.94% of those sampled, respectively. Lifestyle content was 19.05%. Followers interacted with clinical posts the most, having an average of 6.79% of follower interaction. Patient and lifestyle posts had 2.88% and 3.81%, respectively. Instagram is an important communication tool for facial plastic surgeons to engage in promotion and education. It is important to understand what trends tend to attract followers and engagement. Our study showed that more clinical content, separate from patient pictures, tended to be popular with both providers and followers.

Diversity-improved caching of popular transient contents in Vehicular Named Data Networking

In-network caching, natively enabled by Named Data Networking (NDN), is effective to speed up content retrieval in vehicular environments, where a large variety of contents are typically transient, i.e., they expire after a certain amount of time, and may exhibit different popularity profiles. Lifetime and popularity play a crucial role in the content caching decision: intuitively, caching a popular content with long lifetime can be more useful than caching an unpopular one that is ready to expire and then to be dropped from the content store. At the same time, making nearby nodes caching different contents and, therefore, improving the caching diversity, can be crucial to get better delivery performance over the broadcast wireless medium. In this paper, we devise a novel distributed caching strategy where vehicles autonomously decide which content is to be locally cached according to the content residual lifetime, its popularity and the perceived availability of the same content in the neighborhood. The target is to cache with higher probability more popular contents with a longer lifetime, which are not already cached by a nearby node, thus improving the caching diversity in the neighborhood. As a result, vehicles can find the majority of distinct fresh and popular contents nearby, without flooding the network with content requests that have to reach the original source. Performance evaluation shows that the conceived solution outperforms representative benchmark schemes, by guaranteeing, among others, the shortest content retrieval time and the lowest network traffic load.

Artificial Intelligence for Wireless Caching: Schemes, Performance, and Challenges

Wireless data traffic is growing unprecedentedly and it may impede network performance by consuming an ever-greater amount of bandwidth. With the advancement in technology there exist profound techniques having potentials to improve performance of wireless networks. Artificial Intelligence (AI) is one such evolving technology that enables systems to take intelligent decisions. AI can be incorporated in wireless networks for performing an optimal data caching based on accurate predictions of users' data requests and data popularity profile. AI-based data caching is a promising candidate to effectively harness the issues of rising backhaul data traffic of future wireless networks such as duplicate data transmission and data access delay. In this paper, we provide a systematic survey of state-of-the-art intelligent data caching approaches based on learning mechanism to optimize data caching. First we give an overview of traditional caching approaches and their limitations. Then, after rendering brief introduction of several AI techniques, we introduce state-of-the-art learning approaches in cache-enabled wireless networks. We unfold significant research efforts utilizing AI for efficient data placement for optimizing network performance in terms of cache hit rate, throughput, and offloading etc. Finally, we highlight existing challenges and research directions of AI-based data caching.

Reinforcement Learning Based Cooperative Coded Caching Under Dynamic Popularities in Ultra-Dense Networks

For ultra-dense networks with wireless backhaul, caching strategy at small base stations (SBSs), usually with limited storage, is critical to meet massive high data rate requests. Since the content popularity profile varies with time in an unknown way, we exploit reinforcement learning (RL) to design a cooperative caching strategy with maximum-distance separable (MDS) coding. We model the MDS coding based cooperative caching as a Markov decision process to capture the popularity dynamics and maximize the long-term expected cumulative traffic load served directly by the SBSs without accessing the macro base station. For the formulated problem, we first find the optimal solution for a small-scale system by embedding the cooperative MDS coding into Q-learning. To cope with the large-scale case, we approximate the state-action value function heuristically. The approximated function includes only a small number of learnable parameters and enables us to propose a fast and efficient action-selection approach, which dramatically reduces the complexity. Numerical results verify the optimality/near-optimality of the proposed RL based algorithms and show the superiority compared with the baseline schemes. They also exhibit good robustness to different environments.

Machine-Learning Approach for User Association and Content Placement in Fog Radio Access Networks

The joint user association and cache placement problem is challenging in fog radio access networks (F-RANs) due to its difficulty to present the optimal solution with low complexity. Motivated by the recent development of artificial intelligence, we divide the original optimization problem into two subproblems. In particular, the user association problem is solved by a reinforcement-learning-based algorithm in which the enhanced fog access point content placement profiles and the fronthaul constraint are considered. On the other hand, since the popularity profile of the contents is hard to acquire in practice, a stacked autoencoder-based scheme is presented to predict the content popularity, which considers both the local and global user request status within a specified time interval. Based on the popularity prediction, the edge content placement problem is solved by a deep-reinforcement-learning-based algorithm, aiming at maximizing the F-RAN network payoff. Moreover, the complicated interactions and the cyclic dependency among the short time-scale user association and the long time-scale content popularity prediction and placement problems are studied by applying the Stackelberg game theory. The simulation validates the accuracy of the analytical results and proves that the proposal can further improve the performance of F-RANs.

Edge Caching in Multi-UAV-Enabled Radio Access Networks: 3D Modeling and Spectral Efficiency Optimization

Unmanned-aerial-vehicle (UAV) enabled radio access is an effective technology to improve the wireless coverage, in particular for remote and disaster-struck areas. It will become a key enabler in the forthcoming 5G heterogeneous cellular networks to provide improved and resilient coverage. In this article, we study edge caching for multiple UAV-enabled radio access networks (UAV-RANs) and investigate how the overall spectral efficiency (SE) can be improved by efficient edge caching. In this context, UAV base stations (UBSs) may not serve the immediate close-by users, but serve the users based on the requested contents, which brings the service contents closer to users. Based on analyses of the SE achieved by the content-centric UAV-RAN, a hybrid caching strategy is proposed to further improve the SE. In order to cache more files with limited cache resources while guaranteeing the quality-of-service, the contents are divided into two subsets, a popular set and a less popular set, based on their popularity profile that follows Zipf distribution. The popular files, according to the proposed hybrid caching strategy, are cached at all the UBSs, while each less popular file is cached at one UBS only. The overall SE is maximized by finding an optimal popularity threshold between the two subsets. By relaxing the formulated problem, analytical expression for the optimized threshold is derived. The effectiveness of the proposed method is verified by numerical examples where existing benchmark schemes are compared and outperformed.