Content Filtering Research Articles

Reducing Student Exposure to Digital Food and Beverage Marketing: Policy and Practice Recommendations.

Digital marketing of unhealthy foods and beverages to children and adolescents is pervasive, highly effective, undermines healthy eating, and contributes to health inequities. Expanded use of electronic devices and remote learning during the COVID-19 pandemic has increased the urgency for policy interventions to limit digital food marketing in schools and on school-issued devices. The US Department of Agriculture provides little guidance to schools for how to address digital food marketing. Federal and state privacy protections for children are inadequate. Considering these policy gaps, state and local education authorities can incorporate strategies to reduce digital food marketing into school policies for: content filtering on school networks and on school-issued devices; digital instructional materials; student-owned device use during lunch; and school use of social media to communicate with parents and students. Model policy language is provided. These policy approaches can leverage existing policy mechanisms to address digital food marketing from a variety of sources.

A doctor recommender system based on collaborative and content filtering

<span lang="EN-US">The volume of healthcare information available on the internet has exploded in recent years. Nowadays, many online healthcare platforms provide patients with detailed information about doctors. However, one of the most important challenges of such platforms is the lack of personalized services for supporting patients in selecting the best-suited doctors. In particular, it becomes extremely time-consuming and difficult for patients to search through all the available doctors. Recommender systems provide a solution to this problem by helping patients gain access to accommodating personalized services, specifically, finding doctors who match their preferences and needs. This paper proposes a hybrid content-based multi-criteria collaborative filtering approach for helping patients find the best-suited doctors who meet their preferences accurately. The proposed approach exploits multi-criteria decision making, doctor reputation score, and content information of doctors in order to increase the quality of recommendations and reduce the influence of data sparsity. The experimental results based on a real-world healthcare multi-criteria (MC) rating dataset show that the proposed approach works effectively with regard to predictive accuracy and coverage under extreme levels of sparsity.</span>

Making Bioinformatics Training Events and Material More Discoverable Using TeSS, the ELIXIR Training Portal

Many trainers and organizations are passionate about sharing their training material. Sharing training material has several benefits, such as providing a record of recognition as an author, offering inspiration to other trainers, enabling researchers to discover training resources for their personal learning path, and improving the training resource landscape using data-driven gap analysis from the bioinformatics community. In this article, we present a series of protocols for using the ELIXIR online training registry Training eSupport System (TeSS). TeSS provides a one-stop shop for trainers and trainees to discover online information and content, including training materials, events, and interactive tutorials. For trainees, we provide protocols for registering and logging in and for searching and filtering content. For trainers and organizations, we also show how to manually or automatically register training events and materials. Following these protocols will contribute to promoting training events and add to a growing catalog of materials. This will concomitantly increase the FAIRness of training materials and events. Training registries like TeSS use a scraping mechanism to aggregate training resources from many providers when they have been annotated using Bioschemas specifications. Finally, we describe how to enrich training resources to allow for more efficient sharing of the structured metadata, such as prerequisites, target audience, and learning outcomes using Bioschemas specification. As increasing training events and material are aggregated in TeSS, searching the registry for specific events and materials becomes crucial. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Searching for training events and materials in TeSS Support Protocol: Integrating TeSS widgets on your website Basic Protocol 2: Logging in to TeSS using an institutional account Alternate Protocol: Creating and logging in to a TeSS account Basic Protocol 3: Manual registration of training events in TeSS Basic Protocol 4: Manual registration of training materials in TeSS Basic Protocol 5: Registration of a content provider in TeSS Basic Protocol 6: Automated harvesting of training events and materials in TeSS.

Impact of Internet Culture on Indonesian Values and Moral Decision Making

As internet usage has grown, so has the influence of internet culture on Indonesian values and moral decision making. The internet provides a platform for the exchange of ideas and perspectives, and it has the potential to shape and transform social and cultural norms and values. For example, internet culture may promote the spread of liberal and progressive ideas, and it may provide a voice to marginalized and underrepresented groups. Exposure to internet culture can influence Indonesian values and moral decision making in a number of ways. One of the key ways is through the exposure to a wider range of ideas, perspectives, and behaviors, which can challenge and broaden existing cultural norms and values. For example, internet culture may expose individuals to more liberal and progressive ideas, promoting greater tolerance and inclusiveness. This study aims to find out how the impact of the internet for Indonesia, especially values and moral decision making. Based on the literature study conducted in this study, it is known that the internet has both positive and negative impacts on Indonesian values and moral decision-making. To minimize negative impacts, strategies such as content filtering, media and digital literacy programs, promoting responsible online behavior, and enforcing online regulations could be implemented. A proactive and multi-faceted approach that considers the specific context and needs of Indonesian society could help to maximize the internet's positive benefits while minimizing its negative impacts.

Systematic Literature Review on Statistics and Machine Learning Predictive Models for Rice Phenotypes

Predicting the best-quality of rice phenotypes is the priority among agricultural researchers to fulfill worldwide food security. Trend development of predictive models from statistics to machine learning is the subject of this review. Gathered from the Google Scholar database, 14 appropriate papers (2016-2020) related to the rice phenotypes prediction were selected through title and abstract content filtering. The outputs show that Support Vector Machine, Multi-layer Perceptron, and regression are the most used models, while yield is the priority prediction point besides tiller, panicle, and 1000-grain weight of rice. However, finding the accurate predictor is invariably challenging due to distinct rice varieties in the world and high confounding factors. Thus, developing an advanced deep learning model that accommodates these needs is worth considering further.

ALGORITHM OF DATA MINING AND PROCESSING OF RELATED DATA IN SOCIAL NETWORKS

We live in a time of rapid growth of information technology, which is firmly entrenched in our daily lives. It is simply impossible to imagine a modern person without social networks, because they perform a communicative and informational function, namely: communication, information retrieval, news exchange, etc. Five hundred million tweets are posted daily, making Twitter a major social media platform from which topical information on events can be extracted. So, there is a lot of information available to the user, which is difficult to identify something specific and necessary in the usual way viewing. Accordingly, there is a need for technologies that can quickly process large amounts of data and highlight only the information that is useful to a particular user. This technology called recommender systems. It automatically suggest items to users that might be interesting for them. Due to the desire to unite people with common interests, it is relevant to develop a recommendation system based on social networks that help in personification of the user and compilation of his psychotype using his profile. The paper has description and results of the creation of recommendation system. The basis of this work is one of the algorithms used in recommendation systems – the recommendation system is based on content filtering. It analyzes users’ Twitter posts and calculates their interests. If we consider all the words, our model will not have good results and do not pay attention to what is important to use. Therefore, the most important step is always filtering data, so the number one task is to speed up the time of filtering text and retrieving data from the social network for further processing. The feature of this system is that this algorithm uses parallel calculations and frequency analysis of the text.

Careful consumption and aspirational ethics in the media and cultural industries: Cancelling, quitting, screening, optimising

We are witnessing an era of increased intensity of consumer activism (and its discontents) within the arts, cultural and media industries. Ethical, radical, activist and even ‘woke’ consumer interests are now actively catered to across almost all goods and services, from food, fashion and fast-moving consumer goods to tourism, transport and finance. The aim of this paper is to analyse another field where these practices have recently focussed – the media and cultural industries. Drawing on interviews with 20 self-identified feminist and ethical consumers, this article examines how hyperconscious ethical consumption of cultural and media content is lived out and experienced as careful consumption. How are these careful audience activities described, rationalised and understood by the interview participants? What deliberative processes do they undertake and how does that guide them to certain conclusions about what media, art and culture they are willing to watch or not, where they draw the line, and why? This article shows how perceptions of consumer choice, responsibility and culpability are being channelled into an aspirational ethics, involving forms of self-improvement, self-care and self-control such as screening and filtering content, ‘cancelling’ and boycotting media, and attempts to correct, optimise and diversify our tastes and interests.

SOCIAL PREVENTION OF CYBERBULING AMONG STUDENTS

The problem of virtual space violence is very relevant in Ukraine. The state's unstable situation and lack of proper punishment for cyberbullying and its prevention cause an increase in cases of this phenomenon. Cyberbullying is a problem that is rapidly spreading and causing irreparable damage to young people. The purpose of the article is to highlight the problem of cyberbullying and determine the main directions of the social worker’s work on the social prevention of cyberbullying among students. To achieve the goal of the study, a set of methods was used: analysis of social-pedagogical, psychological and pedagogical literature, comparison of scientific sources, which became the basis for determining the degree of scientific development of the problem, systematization in order to determine and clarify the basic concepts of research and existing scientific approaches to solving the specified problem, generalization - to formulate final provisions and conclusions. Theoretical study and empirical research allow us to draw the following conclusions regarding the concept of "cyberbullying" and examine its occurrence, causes, consequences and main directions of social prevention of cyberbullying. Therefore, cyberbullying is a type of bullying, but the specifics are the use of electronic communications. Cyberbullying can occur in various forms. Therefore, it can be classified into eight types, namely, disputes, or fleming, assaults, and constant debilitating attacks, slander and restatement, impostor (impersonation of oneself into another person), erase confidential information and spread it, alienate, cyber-harassment, and hepisleping. Preventive work is carried out in two directions. The first area is associated with the development of technical means that restrict unwanted content (filters, censorship), which provides social networks and websites "complain" buttons, as well as setting up the privacy of personal accounts. This area includes the creation of a system of rapid response of content providers and services, law enforcement agencies, communication operators for illegal activities and the Internet. The second area of cyberbullying prevention involves teaching Internet users the basic rules of security and correct behavior towards other community users. On the basis of theoretical study and empirical research, the main directions of social prevention of cyberbullying were revealed and the project "Social prevention of cyberbullying among young people in the Internet space" was developed

Institute Recommendation System Based on Online Review Comments and Machine Learning

Abstract: Recommendation system has become a necessity in real world. In recommendation system, we are searching any product based on the reviews, if any product shows the best ratings, then we can select that product. Recommendation system has been used in wide range of application like, amazon, Netflix, Flipkart, Facebook etc. Now institution also become a part of recommendation system. In institution recommendation system, we select those colleges which is having a best reviews, which meets our eligibility criteria and our branch also. Recommendation system helps the user to discover the information and settle on a right choices where they do not have the required learning to judge a specific item. Only Naves Bayes algorithm is best algorithm in this system that we can easily check, and recommends them. This system has been used in education. Keywords: Artificial Intelligence, Machine Learning, Artificial Immune System, Naives Bayes, Decision Tree, Collaborative Filtering, Content Filtering, Hybrid Filtering

Zoom gesture analysis for age-inappropriate internet content filtering

The Internet contains large amounts of adult content. With only a few taps, or mis-taps, an under-aged user can be exposed to age-inappropriate content. Currently, this can be avoided by creating age-restricted profiles or restricting users to child-friendly applications (apps). However, these existing measures are time-consuming, laborious, and require a higher level of technical literacy than many parents can afford. We believe a better solution is to use a browser or an app that automatically detects the user’s age then applies any appropriate content filters. For such a browser/app to be developed, we must learn that age estimation can indeed be performed with an acceptable rate of error. To that end, we created an Android app that collects biometric touchscreen data from elementary school, middle school, high school, and university students. Touch samples were collected from participants aged 5 to 61 on both smartphones and tablets. We focused exclusively on zoom-in and zoom-out touchscreen data samples. We made this decision because we found the zoom gesture to be rich with data and highly used among the most popular applications. Furthermore, we identify a niche within the current research landscape: no other machine learning experiments have leveraged the benefits of the zoom gesture for age estimation. We collected a total of 41,911 zoom data samples. From each zoom sample, 90 features were extracted. Those features were then used to train and test on six regressors and six classifiers to build a method that can accurately estimate the user’s age from their touchscreen behavior. The regressors performed with the best mean absolute errors (MAEs) of 2.27 and 2.54 years for smartphones and tablets, respectively. The classifiers performed with the best accuracies of 90% and 91% for smartphones and tablets, respectively. Given these results, it is our belief that not only is touch-based age estimation viable, but developing a child-safe browser or a parental control app with this underlying technology is a worthwhile endeavor.

RECOMMENDATION SYSTEM FOR BSNL NETWORK IN SSA SRINAGAR

Recommender is a Web service that recommends users using their browsing patterns for the type of material they prefer. It tries to forecast a user's preferences or ratings for an item. It leverages the web mining idea, which includes a collection of data mining techniques employed to gather usable knowledge and implicit information from web data. My research is built on a system of collaborative advice. It delivers better result than content filter algorithm. I use a collaborative filtering algorithm. I propose a BSNL telecom network recommendation system based on a variety of collaborative algorithms applied for BSNL networking sites' complicated data set. Experiments are carried out to examine the varying effects of various algorithms on a dataset supplied from BSNL at SSA Srinagar BTS locations on recommended system performance and accuracy. I have identified the high traffic locations and the channel traffic rate. I evaluate SDCCH block calls sites, cell use, SDCCH dropping percentage, total site calls, TCH drop, total cell derivatives. During the analysis of this data set of BSNL telecommunications websites, I found the losing sites, the biggest income sites and those typically downloaded sites (non-working sites).

Network Function Virtualization and Service Function Chaining Frameworks: A Comprehensive Review of Requirements, Objectives, Implementations, and Open Research Challenges

Network slicing has become a fundamental property for next-generation networks, especially because an inherent part of 5G standardisation is the ability for service providers to migrate some or all of their network services to a virtual network infrastructure, thereby reducing both capital and operational costs. With network function virtualisation (NFV), network functions (NFs) such as firewalls, traffic load balancers, content filters, and intrusion detection systems (IDS) are either instantiated on virtual machines (VMs) or lightweight containers, often chained together to create a service function chain (SFC). In this work, we review the state-of-the-art NFV and SFC implementation frameworks and present a taxonomy of the current proposals. Our taxonomy comprises three major categories based on the primary objectives of each of the surveyed frameworks: (1) resource allocation and service orchestration, (2) performance tuning, and (3) resilience and fault recovery. We also identify some key open research challenges that require further exploration by the research community to achieve scalable, resilient, and high-performance NFV/SFC deployments in next-generation networks.

DAMPAK MEDIA SOSIAL TERHADAP KONFLIK DI MASYARAKAT

Along with the development of technology, social media has become one of the products of the 4.0 revolution period, which has progressed rapidly. Through social media, everything can be reached in a short distance and time. However, there is an impact from this progress, one of which is the development of social conflict in society. The purpose of this research is to provide an analysis and study of the impact of social media on society. This research method is in the form of library research taken from texts that discuss social media. The results of the study show that social media can trigger conflicts in the community if users are not wise in filtering content on social media. The existence of social media also has a positive impact on society as an example of how information is received by the public more quickly in educational programs. The ease of life is also felt by the community with the existence of social media. So social media users need to be educated on the wise use of social media and take benefits in people's lives to overcome conflicts that exist in society.

Natural Language Processing and its applications in e-business

The advent of big data and the ability to extract insights from unstructured data has opened new avenues for companies. In this scientific article we have gathered different examples of application of Natural Language Processing (NLP) specifically in the electronic business environment. The main objective was to investigate what kind of analyzes and techniques are being used to extract knowledge from Big data, more concretly unstructured data, and within this type, textual data and what concrete applications derive from NLP in the context of e-business. Being NLP a type of Artificial Intelligence (AI) that uses Machine Learning (ML) and Deep Learning (DL) with the objective of developing a technology that learns, and takes decisions based on what it learned, it was important to understand what a ML workflow is like and therefore, the literatuere revision is very focused on this point. It was concluded that NLP is a complex process whose applications are diverse and relevant in the context of Electronic Business. Today, NLP is used to complete tasks such as text classification, content filtering, sentiment analysis, language modeling, translation, and summarization and applications such as chatbots, voice assistants and recommendation systems. In the future, it is expected that the NLP will have greater influence in different business areas such as the management of new products and recommendation systems; segmentation, segmentation and analysis of customers and users; brand positioning, communication and marketing; competitor analysis; and risk management, sustainability and social responsibility. Specialists in the area also believe that there will possibly be a paradigm shift in AI that will use Reinforcement Learning techniques, which will allow the development of more advanced, adaptive and multipurpose AI agents. It is also to be expected that humans and machines cohabit in a more collaborative way.

DETERMINE RECOMMENDATION SYSTEMS TO SEARCH FOR BOOKS BY PREFERENCES OF WEB USERS

Currently, the question of state, formation and development of the information source interaction system, the scientific interaction and users' requestsin certain fields of activity remains relevant under the conditions of the development of the use of Internet services. Recommendation systems are oneof the types of artificial intelligence technologies for predicting parameters and capabilities.Due to the rapid increase in data on the Internet, it is becoming more difficult to find something really useful. And the recommendations offered by theservice itself may not always correspond to the user's preferences. The relevance of the topic is to develop a personal recommendation system forsearching books, which will not only reduce time and amount of unnecessary information, but also meet the user's preferences based on the analysis oftheir assessments and be able to provide the necessary information at the right time. All this makes resources based on referral mechanisms attractiveto the user. Such a system of recommendations will be of interest to producers and sellers of books, because it is an opportunity to provide personalrecommendations to customers according to their preferences.The paper considers algorithms for providing recommender systems (collaborative and content filtering systems) and their disadvantages.Combinations of these algorithms using a hybrid algorithm are also described. It is proposed to use a method that combines several hybrids in onesystem and consists of two elements: switching and feature strengthening. This made it possible to avoid problems arising from the use of each of thealgorithms separately.A literature web application was developed using Python using the Django and Bootstrap frameworks, as well as SQLite databases, and a system ofrecommendations was implemented to provide the most accurate suggestion. During the testing of the developed software, the work of the literatureservice was checked, which calculates personal recommendations for users using the method of hybrid filtering. The recommendation system wastested successfully and showed high efficiency.

Hypernet Model of Media Communication: new qualities of risks for children

The technologically determined hyperbolic growth of communicative connectivity and the imperfection of administrative mechanisms of control over this process lead to an emergence of new phenomena and new risks, including risks in relation to the most vulnerable social group – children. The article proposes a new approach to describing the nature of the risks of the media space in relation to the health and development of children. Minimization of these risks is defined by the author as the goal of ensuring the information security of children. New qualities of risks are described on the basis of a hypernet model of media communication and developed on the basis of the hypernetwork brain theory. The author specifies the properties of hypernet education and their riskiness, mediated by the network morphology of connections, which implies the impossibility or ineffectiveness of the application of customary management practices. The vulnerability of the child group to these risks stem from the psychological immaturity. The importance of the phenomenalization of “media” and “information” is noted for the purposes of information policy and security – media security. The article reveals the tendencies of the media hypernetwork that increase the risks, especially the growth in the speed and volume of information transfer provided by technologies, which excludes the action of the well-known mechanisms for regulating the circulation of information and filtering content.

Use of artificial intelligence technologies for building individual educational trajectories of students

Problem and goal. Developed and tested solutions for building individual educational trajectories of students, focused on improving the educational process by forming a personalized set of recommendations from the optional disciplines. Methodology. Data mining and machine learning methods were used to process both numeric and textual data. The approaches based on collaborative and content filtering to generate recommendations for students were also used. Results. Testing of the developed system was carried out in the context of several periods of elective courses selection, in which 4,769 first- and second-year students took part. A set of recommendations was automatically generated for each student, and then the quality of the recommendations was evaluated based on the percentage of students who used these recommendations. According to the results of testing, the recommendations were used by 1,976 students, which was 41.43% of the total number of participants. Conclusion. In the study, a recommendation system was developed that performs automatic ranking of subjects of choice and forms a personalized set of recommendations for each student based on their interests for building individual educational trajectories.

A new age for content filters

A new age for content filters

Multitask adversarial attack with dispersion amplification

Recently, adversarial attacks have drawn the community’s attention as an effective tool to degrade the accuracy of neural networks. However, their actual usage in the world is limited. The main reason is that real-world machine learning systems, such as content filters or face detectors, often consist of multiple neural networks, each performing an individual task. To attack such a system, adversarial example has to pass through many distinct networks at once, which is the major challenge addressed by this paper. In this paper, we investigate multitask adversarial attacks as a threat for real-world machine learning solutions. We provide a novel black-box adversarial attack, which significantly outperforms the current state-of-the-art methods, such as Fast Gradient Sign Attack (FGSM) and Basic Iterative Method (BIM, also known as Iterative-FGSM) in the multitask setting.

Joint Reliability-Aware and Cost Efficient Path Allocation and VNF Placement using Sharing Scheme

Network Function Virtualization (NFV) is a vital player of modern networks providing different types of services such as traffic optimization, content filtering, and load balancing. More precisely, NFV is a provisioning technology aims at reducing the large Capital Expenditure (CapEx) of network providers by moving services from dedicated hardware to commodity servers using Virtualized Network Functions (VNF). A sequence of VNFs/services following a logical goal is referred to as a Service Function Chain (SFC). The movement toward SFC introduces new challenges to those network services which require high reliability. To address this challenge, redundancy schemes are introduced. Existing redundancy schemes using dedicated protection enhance the reliability of services, however, they do not consider the cost of redundant VNFs. In this paper, we propose a novel reliability enhancement method using a shared protection scheme to reduce the cost of redundant VNFs. To this end, we mathematically formulate the problem as a Mixed Integer Linear Programming (MILP). The objective is to determine optimal reliability that could be achieved with minimum cost. Although the corresponding optimization problem can be solved using existing MILP solvers, the computational complexity is not rational for realistic scenarios. Thereafter, we propose a Reliability-aware and minimum-Cost based Genetic (RCG) algorithm to solve this problem with low computational complexity. In order to evaluate the proposed solution, we have compared it with four different solutions. Simulation results show that RCG achieves near-optimal performance at a much lower complexity compared with the optimal solution.