Content Filtering Research Articles

Internet Use by Children Aged six to nine: Parents’ Beliefs and Knowledge about Risk Prevention

The majority of studies on children’s Internet use have focused on children aged 9 years or older. However, children start using the Internet at increasingly younger ages, making research on these children necessary. Available studies warn that parents should be aware of the importance of protecting minors when using the Internet. This study focuses on the evaluation of parents’ beliefs and knowledge about their 6 to 9-year-old children’s Internet use. The sample is composed of 1827 parents who received a specially designed questionnaire. Results of the descriptive analysis show that 78% of children of these ages use the Internet, mainly for homework and games. Most of the parents are aware of the benefits and risks of the Internet, but roughly half of them state that they do not know how to set up content filters or parental controls, which is rather relevant, taking into account that 40% of these young children are sometimes left alone when online. At this age, some children have already had a bad experience when using the Internet. This study highlights the importance of working with parents and their children from very young ages in order to prevent victimization.

Ontological Model for Managing Context-aware Assembly Instructions

High model variance and complexity in mixed model assembly systems causes a vast amount of information. Current information systems neglect the user’s situation. Consequently, the user has to process loads of information that may be unnecessary. Information filtering and personalization are for this reason indispensable. By marking assembly instructions with context information, content filtering can be applied. Based on current research in several manual assembly environments in Flanders, a generic ontological model for managing all assembly related context is designed. The model consists of eight categories that define the four main domain context categories (people, places, things and work tasks) and their interrelationships (experience, condition, use and requirement). In order to validate the context model, an experimental approach on a lab set-up is proposed.

Media Content Access: Image-based Filtering

As the content on the internet contains sensitive adult material, filtering and blocking this content is essential for the social and ethical values of the many societies and organizations. In this paper, the content filtering is explored from still images perspectives. Thus, this article investigates and analyses the content based filtering which can help in the flagging of the images as adult nature or safe images. As the proposed approach is based on the Chroma (colour) based skin segmentation and detection for detecting the objectionable content in images, therefore, the approach proceeds in the direction of the classical Machine Learning approaches and uses the two well-known classifiers: The Random Forest and the Neural Network. Their fusion is also investigated. Skin colour is analyzed in the YCbCr colour space and in the form of blob analysis. With the “Adult vs Safe” classification, an Accuracy of 0.88 and the low RMSE of 0.313 is achieved, indicating the usefulness of the detection model.

Deep Learning Features Fusion with Classical Image Features for Image Access

Depending on the society, the access to the adult content can create social problems. This paper thus proposes a fusion approach for image based adult content filtering. The proposed approach merges the Deep Learning (DL) architecture and classical hand crafted feature extraction approaches. From the DL, we fuse the rich feature extraction capabilities of the Convolutional Neural Networks (CNNs) with the Correlograms features. We optimize the classification by integrating and modifying the Correlograms into skin Correlograms. The results show an increased performance by combining the DL learnt features with the classical hand crafted features. From an evaluation, the proposed approach achieves an Accuracy of 0.93. This work thus motivates the usage of classical hand crafted features to be exploited in the DL architectures for segmentation and detection scenarios.

Cyberwellness Learning Resources (CLR) Access Content Filter As Dangerous and Learning Network On the Internet

Educating the use of information and communication technology (ICT) to improve the teaching has become a commitment and an important goal in all courses at the State University of Malang. The era of innovative and ICT-based learning has changed the way teachers and students interact, and now teaches the method in one direction, with non-Internet learning resources cannot provide maximum learning outcomes for student learning. Application cyberwellness learning resources have the ability to learn resource management filter harmful content on student access and replace them with learning resources specific to students in the Educational Technology State University of Malang. Learning model using management information system with the concept of continuous, convergence, and concentric. Method development model CLR development using models Davidson-Shivers and Rasmussen (2007) and selected based on suitability to the needs of development characteristics.

Distributed Service Function Chaining

Middleboxes have become a vital part of modern networks by providing service functions such as content filtering, load balancing and optimization of network traffic. An ordered sequence of middleboxes composing a logical service is called service chain. Service Function Chaining (SFC) enables us to define these service chains. Recent optimization models of SFCs assume that the functionality of a middlebox is provided by a single software appliance, commonly known as Virtual Network Function (VNF). This assumption limits SFCs to the throughput of an individual VNF and resources of a physical machine hosting the VNF instance. Moreover, typical service providers offer VNFs with heterogeneous throughput and resource configurations. Thus, deploying a service chain with custom throughput can become a tedious process of stitching heterogeneous VNF instances. In this paper, we describe how we can overcome these limitations without worrying about underlying VNF configurations and resource constraints. This prospect is achieved by distributed deploying multiple VNF instances providing the functionality of a middlebox and modeling the optimal deployment of a service chain as a mixed integer programming problem. The proposed model optimizes host and bandwidth resources allocation, and determines the optimal placement of VNF instances, while balancing workload and routing traffic among these VNF instances. We show that this problem is NP-Hard and propose a heuristic solution called Kariz. Kariz utilizes a tuning parameter to control the trade-off between speed and accuracy of the solution. Finally, our solution is evaluated using simulations in data-center networks.

Educação em tempos de bolhas online: uma abordagem peirceana

O desenvolvimento das redes sociais digitais e dos sistemas de busca na internet tm nos levado a repensar os modos como interagimos e adquirimos informao na internet. Apesar de potencialmente a internet, como um repositrio gigantesco de dados, aumentar as possibilidades de acesso aos mais diversos dados, assim como o aumento da quantidade de informao, nossos modos de interao em rede hoje tm mostrado algumas restries nesse universo de possibilidades. Neste artigo, analisaremos o surgimento das bolhas online como um fenmeno que mostra como certos modos de interao na internet podem nos levar a ficar fechados em universos muito restritos e familiares ao invs de nos proporcionar a experincia com o desconhecido a fim de descobr-lo. Sabendo que as redes sociais digitais viraram uma das mais importantes fontes de notcia para as geraes jovens e que grande parte da informao adquirida por esses jovens vm da internet, precisamos refletir sobre os modos como acessamos esse enorme banco de dados.

Implementation of Management and Network Security Using Endian UTM Firewall

Internet is a source of information which is widely used today. However, the mode of internet abused becomes more various and unavoidable. The internet abused can be done from external or internal networks. Unified Threat Management (UTM) is one of a good solution to secure the networks, because it has several security features such as firewall, proxy, Intrusion Prevention System (IPS) and several other security features in one package. Endian is an UTM distro which is an open source in large community. Besides having some security features, Endian also has some network management features such as DHCP, routing, and VPN. This research put Endian as the center of a network topology that connected to the internal network/LAN, DMZ Server, and Internet Network/WAN. The tests are conducted in the form of implementation of DHCP feature, content filtering, port restrictions on interzone, and the response of the IPS features that exist on the Endian while receiving the attack. The results showed that Endian UTM is quite well in maintaining the security of the networks.

A personalized recommendation system with combinational algorithm for online learning

With the fast development of online and mobile technologies, individualized or personalized learning is becoming increasingly important. Online courses especially Massive Open Online Courses (MOOCs) often have students from many countries, with different prior knowledge, expectations, and skills. They in particular could benefit from learning materials or learning systems that are customized to meet their needs. On this note, this paper suggests a personalized recommendation system for learners in online courses. The system recommends learning resources such as relevant courses to learners enrolled in formal online courses, by using a combination of association rules, content filtering, and collaborative filtering. Pilot testing of this system in the Shanghai Lifelong Learning Network, a platform for free and open education, indicates that this recommendation system can improve the utilization rate of educational resources and also promote the learning autonomy and efficiency of students.

A Fuzzy Recommender System for Public Library Catalogs

Recommendation engines are one of the “discovery” products built into integrated library systems. These are a subclass of enterprise systems designed specifically for public and research libraries that incorporate an electronic card catalogue, circulation and inventory management, personnel and payroll systems, etc. The system vendors offer customizations for different contexts of specific library systems, but cannot create a bespoke solution for every customer. Our partner, an Edmonton-area company, is filling this gap for a consortium of rural libraries in Alberta by creating a mobile app that interfaces with their electronic card catalog. Rural libraries are generally smaller than major urban public libraries, meaning that their holdings are limited overall, and within any given genre. This poses a severe problem for traditional collaborative-filtering recommender algorithms, as the item sets for recommendations are limited by supply rather than by readers’ interests. The library's relatively small clientele also limits the item sets available for comparison. To deal with this ongoing “cold-start” problem, we propose a hybridization of collaborative filtering with a content filter using a fuzzy taste vector. Experiments on two benchmark recommender data sets show that this approach is at least as accurate as existing fuzzy recommenders and is particularly effective on sparse data sets.

Censorship: Filtering Content on the Web

Censorship: Filtering Content on the Web

Distributed Agents for Web Content Filtering

This paper describe Web Content Filtering that aimed to block out offensive material by using DistributedAgents. The proposed system using FCM algorithm and other page's features (Title, Metadata , Warning Message) to classifythe websites (using as candidate) into two types:- white that considered acceptable, and black that contain harmful materialtaking the English Pornographic websites as a case study.

Secured Client-Side Content Filtering Using Machine Learning Algorithms

Objectives: The sheer amount of content available in cloud service providers (News, Social media, etc.) poses a fundamental problem for users: The excess amount of diverse information available for public access without regard to age, sex or other classifiers. Methods/Statistical analysis: Ability to filter and control this content while maintaining anonymity is a challenge which isn’t addressed properly, yet. Findings: Previous Solutions proposed to tackle this need integration on either server-side or client-side. However, these solutions seem to have two major drawbacks the ability to maintain client anonymity from the content provider and the ability to integrate server-side filtering with client side filtering. Application/Improvements: In this paper a system is proposed that integrates secured content classification and content filtering based on trained data. Security is achieved by using the identity of the user generated from the initial contact point.

Online Product Recommendation using Relationships and Demographic Data on Social Networks

Objectives: There have been many traditional product recommender systems in the past, but they were mostly based on collaborative or content filtering, historical transaction records or website browsing history of the users. This approach leads to sparsity and cold-start issues. Methods/Statistical Analysis: The proposed system is a hybrid one and can improve the suitability and the accuracy of recommender systems with the help of users’ and products’ demographic info and the ratings of a brand name, constantly updated in the e-commerce and social media websites. Findings: Experiments on the actual dataset reveal that friends with similar tendencies select the similar items and this approach can solve the relevant data sparsity and cold start problems. Initially, the system loads the data about the friends who have given product ratings and selects a target cold start user. Then it predicts his/her tech level and cost level using BMART algorithm. With the help of these, it finds the user-friends and user-items similarity using the cosine and Euclidean cluster similarity methods respectively. It then feeds these into the proposed ranking algorithm to find the relevance scores of the products to the target user and based on this the recommended product links are displayed. Application/Improvement: The outputs reveal that the proposed system gives very accurate and personalized product recommendations to the user. Keywords: Hybrid Ranking, Recommender System, Regression Ranking, Relevance Score, Social Media

Exploratory querying of SPARQL endpoints in space and time

The linked data Web provides a simple and flexible way of accessing information resources in a self-descriptive format. This offers a realistic chance of perforating existing data silos. However, in order to do so, space, time and other semantic concepts need to function as dimensions for effectively exploring, querying and filtering contents. While triple stores, SPARQL endpoints, and RDF were designed for machine access, large burdens are still placed on a user to simultaneously explore and query the contents of a given endpoint according to these dimensions. First, one has to know the semantic concepts and the type of knowledge contained in an endpoint a-priori in order to query content effectively. Second, one has to be able to write and understand SPARQL and RDF. And third, one has to understand complex data type literals for space and time. In this article, we propose a way to deal with these challenges by interactive visual query construction, i.e., by letting query results feedback into both (space-time) exploration and filtering, and thus enabling exploratory querying. We propose design principles for SPEX (Spatio-temporal content explorer), a tool which helps people unfamiliar with the content of SPARQL endpoints or their syntax to explore the latter in space and time. In a preliminary user study on a repository of historical maps, we found that our feedback principles were effective, however, that successful question answering still requires improvements regarding space-time filtering, vocabulary explanation and the linking of space-time windows with other displays.

Near-reversible efficient image resizing for devices supporting different spatial resolutions

Many devices in cloud environments support different spatial resolutions, necessitating image resizing of the original image contents. The goal of this paper is to combine multiple operators for image resizing in a stochastic optimization framework, seeking an optimal balance among essential resizing operators such as cropping and linear scaling. Specifically, we formulate image resizing as a MAP (maximum a posteriori) optimization problem with a Gibbs energy function. To reduce computational complexity we seek a sub-optimal solution of the MAP criterion with a deterministic implementation of the Metropolis algorithm. Since the optimization is carried out on the basis of a straight horizontal or vertical line in an image instead of the curved seam pixels, the optimization should converge quickly to have a fast image resizing. In addition, our image resizing can be associated with various user-defined content filtering such as a color masking. Finally, our resizing method is reversible, meaning that the image with the original size can be reconstructed from the retargeted image. This allows us to apply the proposed image resizing method to a prioritized image transmission with a scalable and progressive structure.

A Systematic Analysis of Errors in Target Localization and Treatment Delivery for Stereotactic Radiosurgery Using 2D/3D Image Registration.

To determine the localization uncertainties associated with 2-dimensional/3-dimensional image registration in comparison to 3-dimensional/3-dimensional image registration in 6 dimensions on a Varian Edge Linac under various imaging conditions. The systematic errors in 6 dimensions were assessed by comparing automatic 2-dimensional/3-dimensional (kV/MV vs computed tomography) with 3-dimensional/3-dimensional (cone beam computed tomography vs computed tomography) image registrations under various conditions encountered in clinical applications. The 2-dimensional/3-dimensional image registration uncertainties for 88 patients with different treatment sites including intracranial and extracranial were evaluated by statistically analyzing 2-dimensional/3-dimensional pretreatment verification shifts of 192 fractions in stereotactic radiosurgery and stereotactic body radiotherapy. The systematic errors of 2-dimensional/3-dimensional image registration using kV-kV, MV-kV, and MV-MV image pairs were within 0.3 mm and 0.3° for the translational and rotational directions within a 95% confidence interval. No significant difference ( P > .05) in target localization was observed with various computed tomography slice thicknesses (0.8, 1, 2, and 3 mm). Two-dimensional/3-dimensional registration had the best accuracy when pattern intensity and content filter were used. For intracranial sites, means ± standard deviations of translational errors were -0.20 ± 0.70 mm, 0.04 ± 0.50 mm, and 0.10 ± 0.40 mm for the longitudinal, lateral, and vertical directions, respectively. For extracranial sites, means ± standard deviations of translational errors were -0.04 ± 1.00 mm, 0.2 ± 1.0 mm, and 0.1 ± 1.0 mm for the longitudinal, lateral, and vertical directions, respectively. Two-dimensional/3-dimensional image registration for intracranial and extracranial sites had comparable systematic errors that were approximately 0.2 mm in the translational direction and 0.08° in the rotational direction. The standard 2-dimensional/3-dimensional image registration tool available on the Varian Edge radiosurgery device, a state-of-the-art system, is helpful for robust and accurate target positioning for image-guided stereotactic radiosurgery.

Aggregating social media data with temporal and environmental context for recommendation in a mobile tour guide system

Purpose Manufacturers of smartphone devices are increasingly utilising a diverse range of sensors. This innovation has enabled developers to accurately determine a user’s current context. One area that has been significantly enhanced by the increased use of context in mobile applications is tourism. Traditionally, tour guide applications rely heavily on location and essentially ignore other types of context. This has led to problems of inappropriate suggestions and tourists experiencing information overload. These problems can be mitigated if appropriate personalisation and content filtering is performed. This research proposes an intelligent context-aware recommender system that aims to minimise the highlighted problems. Design/methodology/approach Intelligent reasoning was performed to determine the weight or importance of different types of environmental and temporal context. Environmental context such as the weather outside can have an impact on the suitability of tourist attractions. Temporal context can be the time of day or season; this is particularly important in tourism as it is largely a seasonal activity. Social context such as social media can potentially provide an indication of the “mood” of an attraction. These types of contexts are combined with location data and the context of the user to provide a more effective recommendation to tourists. The evaluation of the system is a user study that utilised both qualitative and quantitative methods, involving 40 participants of differing gender, age group, number of children and marital status. Findings This study revealed that the participants selected the context-based recommendation at a significantly higher level than either location-based recommendation or random recommendation. It was clear from analysing the questionnaire results that location is not the only influencing factor when deciding on a tourist attraction to visit. Research limitations/implications To effectively determine the success of the recommender system, various combinations of contextual conditions were simulated. Simulating contexts provided the ability to randomly assign different contextual conditions to ensure an effective recommendation under all circumstances. This is not a reflection of the “real world”, because in a “real world” field study the majority of the contextual conditions will be similar. For example, if a tourist visited numerous attractions in one day, then it is likely that the weather conditions would be the same for the majority of the day, especially in the summer season. Practical implications Utilising this type of recommender system would allow the tourists to “go their own way” rather than following a prescribed route. By using this system, tourists can co-create their own experience using both social media and mobile technology. This increases the need to retain user preferences and have it available for multiple destinations. The application will be able to learn further through multiple trips, and as a result, the personalisation aspect will be incrementally refined over time. This extensible aspect is increasingly important as personalisation is gradually more effective as more data is collated. Originality/value This paper contributes to the body of knowledge that currently exists regarding the study of utilising contextual conditions in mobile recommender systems. The novelty of the system proposed by this research is the combination of various types of temporal, environmental and personal context data to inform a recommendation in an extensible tourism application. Also, performing sentiment analysis on social media data has not previously been integrated into a tourist recommender system. The evaluation concludes that this research provides clear evidence for the benefits of combining social media data with environmental and temporal context to provide an effective recommendation.

Use of Linked Data principles for semantic management of scanned documents

The study addresses the use of the Semantic Web and Linked Data principles proposed by the World Wide Web Consortium for the development of Web application for semantic management of scanned documents. The main goal is to record scanned documents describing them in a way the machine is able to understand and process them, filtering content and assisting us in searching for such documents when a decision-making process is in course. To this end, machine-understandable metadata, created through the use of reference Linked Data ontologies, are associated to documents, creating a knowledge base. To further enrich the process, (semi)automatic mashup of these metadata with data from the new Web of Linked Data is carried out, considerably increasing the scope of the knowledge base and enabling to extract new data related to the content of stored documents from the Web and combine them, without the user making any effort or perceiving the complexity of the whole process.

Accountability 2.0: Towards a Special Responsibility for Internet Intermediaries

On May 16th, 2011, the UN Special Rapporteur for the Promotion and Protection of the Right to Freedom of Opinion and Expression issued its Annual Report to the Human Rights Council, devoted to the issue of freedom of expression on the Internet. The Report highlighted inter alia a number of challenges to the enjoyment of such right in the cyberspace, including arbitrary blocking and filtering of content, disconnection of users from the Internet and inadequate protection of the right to privacy. While the need to tackle these problems has been recognized and discussed in multiple fora over the last decade, both the strategic use of Internet and communication services by the Egyptian and Syrian regimes and the recent discovery of the existence of extensive surveillance conducted by the United States’ National Security Agency brought to the fore the instrumental importance of overcoming some of these challenges for the promotion of democracy. In fact, in all three examples mentioned above governments faced the technical obstacle of having to request the intermediaries of information on the Internet (namely, internet providers, social networks and search engines) to shut down specific user accounts, provide user data, or limit the scope of their services altogether. In light of the rights and principles highlighted by the Special Rapporteur, this paper poses the questions of what international law has to say regarding these practices, and importantly, how compliance with international law can be secured (or at least, induced) in this domain. For this purpose, the paper focuses on the role of Internet intermediaries in enabling or preventing the violation of human rights, and more particularly, freedom of expression and privacy. After defining the category of intermediaries (section 1) and the international rules and standards applicable to this type of interaction (section 2), a third section of the paper proposes interpretative criteria that would enable Internet intermediaries not only to function as effective “choke points” for illegal content, but also to stand up against flagrant violations of fundamental rights.