Intelligent technologies: basics, review, research and real-life application examples

Assoc. Prof. Dr. Eugenijus KurilovasVilnius Gediminas Technical University, Lithuania

EPL202-EPL463, UCY, Erasmus+, 17 Sept. 2015

The lecture aims at presenting basics, review, and real-life application examples of intelligent (smart) technologies.

What are intelligent (smart) technologies?

What are intelligent technologies for?

Literature review on Web 3.0, ontologies, artificial intelligence, intelligent agents, recommender systems and decision support systems.

Some author’s research results and real-life application examples of Web 3.0, semantic search, ontologies, recommender systems and decision support systems in education.

Abstract

1. Basics and applications

Intelligent technologies explore the fundamental roles and practical impacts of artificial intelligence and knowledge management in various paradigms.

A main factor of intelligence is the ability to create new ideas and correlate them with existing knowledge. Technology is good at accomplishing one of these tasks, but the "creative thinking" aspect is more than just coming up with something new or uncommon / random.

It means that one draws upon past experiences, feelings, questions, and answers, and contemplates unique ways of interchanging them. The difference is that the resulting ideas are understood based on more than the parts that lead to their inception.

Intelligent technology should, therefore, lead to a method of self-actualization where the technology would be able to improve itself through "creative thought".

What are intelligent technologies?

“Smart” is electronic device or system that can be connected to the Internet, used interactively, as is for some extent intelligent.

Examples:

– Smart TV– Smart home devices (e.g. smart washing machine, fridge)– Smart phone– Smart watch– Smart glasses– Smart table– Smart board– Smart buildings– Smart traffic light– Smart farming– Smart connected vehicles (automatically calls the emergency services in the event of an accident)

– Smart surveillance (a security systems based on face recognition)– Smart education– Tablet devices

What are intelligent technologies for? Applications

2. Literature review

Semantic Web (or Web 3.0) is an extension of the Web through standards by the World Wide Web Consortium (W3C). The standards promote common data formats and exchange protocols on the Web, most fundamentally the Resource Description Framework (RDF).

In addition to the classic “Web of documents” W3C is helping to build a technology stack to support a “Web of data,” the sort of data you find in databases. The ultimate goal of the Web of data is to enable computers to do more useful work and to develop systems that can support trusted interactions over the network. The term “Semantic Web” refers to W3C’s vision of the Web of linked data. Semantic Web technologies enable people to create data stores on the Web, build vocabularies, and write rules for handling data.

According to the W3C, "The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries". The term was coined by Tim Berners-Lee in 2001 for a web of data that can be processed by machines.

Semantic Web

In computer science and information science, an ontology is a formal naming and definition of the types, properties, and interrelationships of the entities that really or fundamentally exist for a particular domain of discourse. It is thus a practical application of philosophical ontology, with a taxonomy. An ontology compartmentalizes the variables needed for some set of computations and establishes the relationships between them.

What many ontologies have in common in both computer science and in philosophy is the representation of entities, ideas, and events, along with their properties and relations, according to a system of categories. In both fields, there is considerable work on problems of ontological relativity. Computer scientists are more concerned with establishing fixed, controlled vocabularies, while philosophers are more concerned with first principles, such as whether there are such things as fixed essences or whether entities must be ontologically more primary than processes.

The fields of artificial intelligence, the Semantic Web, systems engineering, software engineering, biomedical informatics, library science, enterprise bookmarking, and information architecture all create ontologies to limit complexity and to organize information. The ontology can then be applied to problem solving.

Ontologies

Artificial intelligence (AI) is the intelligence exhibited by machines or software. It is also the name of the academic field of study which studies how to create computers and computer software that are capable of intelligent behavior. Researchers and textbooks define this field as "the study and design of intelligent agents", in which an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success. AI is also "the science and engineering of making intelligent machines".

The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects. General intelligence is still among the field's long-term goals. Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. There are a large number of tools used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics, and many others.

The AI field is interdisciplinary, in which a number of sciences and professions converge, including computer science, mathematics, psychology, linguistics, philosophy and neuroscience, as well as other specialized fields such as artificial psychology.

Artificial intelligence

In artificial intelligence, an intelligent agent (IA) is an autonomous entity which observes through sensors and acts upon an environment using actuators and directs its activity towards achieving goals (i.e. it is "rational"). IAs may also learn or use knowledge to achieve their goals.

IAs are often described schematically as an abstract functional system similar to a computer program. For this reason, IAs are sometimes called abstract intelligent agents to distinguish them from their real world implementations as computer systems, biological systems, or organizations. Some definitions of intelligent agents emphasize their autonomy, and so prefer the term autonomous IAs. Still others considered goal-directed behavior as the essence of intelligence and so prefer a term borrowed from economics, "rational agent".

IAs are also closely related to software agents (an autonomous computer program that carries out tasks on behalf of users). In computer science, the term IA may be used to refer to a software agent that has some intelligence, regardless if it is not a rational agent. For example, autonomous programs used for operator assistance or data mining are also called "intelligent agents".

Intelligent agents

Recommender systems (or recommendation systems) are a subclass of information filtering system that seek to predict the 'rating' or 'preference' that a user would give to an item.

Recommender systems have become extremely common in recent years, and are applied in a variety of applications. The most popular ones are movies, music, news, books, research articles, search queries, social tags, and products in general.

Recommender systems typically produce a list of recommendations in one of two ways - through collaborative or content-based filtering. Collaborative filtering approaches building a model from a user's past behavior (items previously purchased or selected and/or numerical ratings given to those items) as well as similar decisions made by other users. This model is then used to predict items (or ratings for items) that the user may have an interest in. Content-based filtering approaches utilize a series of discrete characteristics of an item in order to recommend additional items with similar properties. These approaches are often combined (Hybrid Recommender Systems).

Recommender systems

A Decision Support System (DSS) is a computer-based information system that supports business or organizational decision-making activities. DSSs serve the management, operations, and planning levels of an organization (usually mid and higher management) and help people make decisions about problems that may be rapidly changing and not easily specified in advance (i.e. Unstructured and Semi-Structured decision problems).

Decision support systems can be either fully computerized, human-powered or a combination of both.

While academics have perceived DSS as a tool to support decision making process, DSS users see DSS as a tool to facilitate organizational processes. Some authors have extended the definition of DSS to include any system that might support decision making.

DSSs include knowledge-based systems. A properly designed DSS is an interactive software-based system intended to help decision makers compile useful information from a combination of raw data, documents, and personal knowledge, or business models to identify and solve problems and make decisions.

Decision support systems

3. Research and real-life application

examples

What learning content, methods and technologies are the most suitable to achieve better learning quality and efficiency? In Lithuania, we believe that there is no correct answer to this question if we don’t apply personalised learning approach. We strongly believe that “one size fits all” approach doesn’t longer work in education.

It means that, first of all, before starting any learning activities, we should identify students’ personal needs: their preferred learning styles, knowledge, interests, goals etc.

After that, teachers should help students to find their suitable (optimal) learning paths: learning methods, activities, content, tools, mobile applications etc. according to their needs.

But, in real schools practice, we can’t assign personal teacher for each student. This should be done by intelligent technologies. Therefore, we believe that future school means personalisation plus intelligence.

In this presentation, Lithuanian Intelligent Future School (IFS) project is presented aimed at implementing both learning personalisation and educational intelligence.

Introduction

IFS concept

5: Empower Redefinition & innovative use

1. Technology supports new learning services that go beyond institutional boundaries.

2. Mobile and locative ICT support ‘agile’ teaching and learning.

3. The learner as a ‘co-designer’ of the learning journey, supported by intelligent content and analytics.

4: Extend Network redesign & embedding

1. Ubiquitous, integrated, seamlessly connected ICT support learner choice and personalisation beyond the classroom.

2. Teaching and learning are distributed, connected and organised around the learner.

3. Learners take control of learning using ICT to manage their own learning

3: EnhanceProcess redesign

1. Teaching and learning redesigned to incorporate ICT, building on research in learning and cognition.

2. Institutionally embedded ICT supports the flow of content and data, providing an integrated approach to teaching, learning and assessment.

3. The learner as a ‘producer’ using networked ICT to model and make.

2: Enrich InternalCoordination

1. ICT used interactively to make differentiated provision within the classroom.

2. ICT supports a variety of routes to learning. 3. The learner as a ‘user’ of ICT tools and resources

1: ExchangeLocalised use

1. ICT is used within current teaching approaches. 2. Learning is teacher-directed and classroom-

located. 3. The learner as a ‘consumer’ of learning content

and resources

___________________________________________

Future school means personalisation plus intelligence

IFS implementation stages (based on iTEC schools innovation maturity model):

(1) Creating learners’ models (profiles) based on their learning styles and other particular needs

(2) Interconnecting learners’ models with relevant learning components (learning content, methods, activities, tools, apps etc.) and creating corresponding ontologies

(4) Creating intelligent agents and recommender systems

(5) Creating and implementing personalised learning scenarios (e.g. in STEM – Science, Technology, Engineering and Mathematics – subjects)

(6) Creating educational multiple criteria decision making models and methods

Personalisation

(1) Selecting good taxonomies (models) of learning styles, e.g., (Felder & Silverman, 1988), (Honey & Mumford, 2000), the VARK style (Fleming, 1995)

(2) Creating integrated learning style model which integrates characteristics from several models. Dedicated psychological questionnaire(s)

(3) Creating open learning style model

(4) Using implicit (dynamic) learning style modelling method

(5) Integrating the rest features in the student profile (knowledge, interests, goals)

Personalisation: creating students’ profiles

Personalisation: identifying learning styles

VARK inventory was designed by Fleming in 1987 and is an acronym made from Visual, Aural, Read/write and Kinaesthetic. These modalities are used for preferable ways of learning (taking and giving out) information:

Visual learners prefer to receive information from depictions in figures: in charts, graphs, maps, diagrams, flow charts, circles, hierarchies, and others. It does not include pictures, movies and animated websites that belong to Kinaesthetic.

The aural perceptual mode describes a preference for spoken or heart information. Aural learners learn best by discussing, oral feedback, email, chat, discussion boards, and oral presentations.

Read/write learners prefer information displayed as words: quotes, lists, texts, books, and manuals.

The kinaesthetic perceptual mode describes a preference for reality and concrete situations. They prefer videos, teaching others, pictures of real things, examples of principles, practical sessions, and others.

Multimodals are those learners who have preferences in more than one mode.

Personalisation: identifying learning styles

Creating recommender system

Learning styles

(Honey and Mumford, 1992)

Preferred learning activities

Suitable teaching / learning methods

(iCOPER D3.1, 2009))

Suitable LO types

(LRE AP v4.7, 2011)

Activists are those people who learn by doing. Have an open-minded approach to learning, involving themselves fully and without bias in new experiences

Brainstorming, problem solving, group discussion, puzzles, competitions, and role-play

Active Learning, Blogging, Brainstorming and Reflection, Competitive Simulation, E-Portfolio, Creation of Personalised Learning Environments, Creative Workshops, Exercise Unit, Games Genre, Presenting Homework, Image Sharing, In-class Online Discussion, Mini Conference, Modelling, Online Reaction Sheets, Online Training, Peer Assessment, Process-based Assessment, Process Documentation, Project-based Learning, Resource-based Analysis, Role Play, Student Wiki Collaboration, World Café, Web Quest

Application, Assessment, Broadcast, Case study, Drill and practice, Educational game, Enquiry-oriented activity, Experiment, Exploration, Glossary, Open activity, Presentation, Project, Reference, Role play, Simulation, Tool, Website

Creating recommender system

Creating recommender system

Creating recommender system

iOS (Apple iPad) Android (Samsung)

iOS / Android Suitable LO types

Idea Sketch – lets you easily draw a diagram – mind map, concept map, or flow chart - and convert it to a text outline, and vice versa. You can use Idea Sketch for anything, such as brainstorming new ideas, illustrating concepts, making lists and outlines, planning presentations, creating organizational charts, and more

Mindjet for Android – rated as one of the best mind mapping apps for Android. Create nodes and notes, add images of your own or icons provided, and add attachments and hyperlinks. Sync to your Dropbox

Mind Mapping – lets you create, view and edit mind maps online or offline and lets the app synch with your online account whenever connected. You can share mind maps directly from the device, inviting users via email. You can add icons, colours and styles, view notes, links and tasks and apply map themes, drag and drop and zoom

Application, Broadcast, Enquiry-oriented activity, Glossary, Open activity, Presentation, Reference, Role play, Simulation, Tool, Website

Interconnection of Activists Brainstorming learning activity with suitable apps and LOs types

Creating recommender system

Example: Integrating Web 2.0 tools into learning activities

Recommender systems (as a kind of services in the e-learning environment) can provide personalised learning recommendations to learners.

Recommender systems are information processing systems that gather various kinds of data in order to create their recommendations.

The data are primarily about the items (objects that are recommended) to be suggested and the users who will receive these recommendations.

The data can be formalised in domain ontology, thus the knowledge about a user and items becomes reusable for people and software agents. Also, the ontology could contain a useful knowledge that can be used to infer more interests than can be seen by just an observation.

The aim of TEL is to improve learning. It is therefore an application domain that generally covers technologies that support all forms of learning activities. An important activity in TEL is search-ability relevant learning resources and services as well as their better finding. Recommender systems support such an information retrieval.

There are different types of recommender systems based on the recommendation approaches: content-based, collaborative filtering, demographic, knowledge-based, community-based, utility-based, hybrid, and semantic.

In this research, knowledge-based recommender system using rules-based reasoning is used. Knowledge-based systems recommend items based on the specific domain knowledge about how certain item features satisfy users’ needs and preferences as well as how the item is useful for the user.

Knowledge-based recommender systems can be rule-based or case-based. The form of data collected by the knowledge-based system about user’s preferences can be statements, rules, or ontologies.

The knowledge base of the rule-based system comprises the knowledge that is specific to the domain of the application.

The rule-based reasoning system represents knowledge of the system in terms of a bunch of rules (facts). These rules are in the form of IF THEN rules such as “IF some condition THEN some action”. If the ‘condition’ is satisfied, the rule will take the ‘action’.

The proposed method for Web 2.0 tools integration into learning activities is based on the ontology developed.

With the view to find a particular Web 2.0 tool suitable for the accomplishment of the learning activity, a link between the tool and the learning activity must be identified. This relationship can be established by interconnections between the defined tool and activity elements.

The learning activity is defined as consisting of the following elements: (1) Learning Activity (what action a learner performs); (2) Content (which object a learner manages); (3) Interaction (with whom a learner interacts); and (4) Synchronicity (at what time a learner performs the intended action).

Web 2.0 tool is defined as set of universal functions. This universal function is defined as consisting of the following elements:

(1) Function (what action can be performed by using a tool); (2) Artefact (which object can be managed by using a tool); (3) Interaction (what kind of interaction the tool enables); and (4) Synchronicity (at what time the intended action is enabled by a tool to take place).

The Learning activities and Functions of tools are classified mostly based on the [Conole, 05] media taxonomy. These types and particular elements are presented in Table 2:

Type Learning activities

Subtype (1-8)

Web 2.0 tool function

Narrative Revise 1: View Explore ( Read, view, listen)

Information management

Find 2: Search Search

Collect 3: Host Host (Store), Syndicate

Productive Prepare 4: Create Create (draw, write, record, edit)

Communicative

Present 5: Share Share, publicise

Dispute 6: Discuss Communicate

Imitative Role play 7: Imitate

Simulate (Game simulation)

Observation 8: Model Model (Phenomenon modelling)

Table 2: Learning activities and Web 2.0 tools functions types

Thus, Web 2.0 tools could be divided based on their usage possibilities, managed objects, communication form, and sort of imitation process into three groups as follows: (1) Artefacts management, (2) Communication, and (3) Imitation tools.

We have defined the following components in the domain ontology visualised with Protégé 4.3 ontology editor:

Concepts (Main Classes) (Figure 1), and

Relationships between Concepts (Properties) (Figure 2):

The stages of the method of integrating Web 2.0 tools into learning activities are as follows:

1. Identification of learner’s learning style (i.e. preferences of the learning content and communication modes)

2. Selection of the learning objective and the learning method

3. Determination of the elements of chosen learning method activities

4. Determination of universal function elements of each Web 2.0 tool

5. Finding of the link between tool and learning activity elements

6. Selection of a suitable tool based on specified elements: Action, Interaction, Synchronicity. Artefact is determined based on individual learning style.

Description of each stage and the detailed presentation of the method are provided in [Juskeviciene, Kurilovas, 14].

In order to ascertain the suitability of this approach, the recommender system prototype was developed. This prototype was developed following the working principles of the knowledge-based recommender system. The domain knowledge was conceptualised in the ontology.

The prototype of the knowledge-based recommender system implements this method completely:

Scheme of the recommender system

Recommender system prototype operation

Example: educational multiple criteria decision making

Multiple Criteria Decision Making

Scalarisation method:the experts’ additive utility function

The major is the meaning of the utility function the better LOs meet the quality requirements in comparison with the ideal (100%) quality According to scalarisation method, we need LOs evaluation criteria ratings (values) and weights

Literature review has shown that fuzzy numbers and scalarisation methods are applicable for e-textbooks and other LOs quality and reusability evaluation in terms of its simplicity and effectiveness. Scalarisation method is referred here as the experts’ additive utility function represented by the formula (1). According to this method, a possible decision here could be to transform a multi-criteria task into one-criterion task obtained by adding all the criteria ratings (values) together with their weights (Kurilovas & Serikoviene, 2012):

m

iii XfaXf

1

)()( , 1

1

m

iia , 0ia . (1)

Here fi(X) is the rating (i.e. non-fuzzy value) of the criterion i for the each of the examined e-textbooks and other LOs alternatives Xj, and ai are the weights of the quality criteria

Linguistic variables conversion into triangle non-fuzzy values and weights:

Linguistic variables Non-fuzzy values

Excellent / Extremely valuable0.850Good / Very valuable 0.675Fair / Valuable 0.500Poor / Marginally valuable 0.325Bad / Not valuable 0.150

In identifying quality criteria for the decision making, the following considerations are relevant to all multiple criteria decision making approaches:

•Value relevance•Understandability•Measurability•Non-redundancy•Judgmental independence•Balancing completeness and conciseness•Operationality•Simplicity versus complexity

E-textbooks and other learning objects quality model

Criteria group Nr. Quality criteria

Technological criteria

‘Internal’ quality 1 Interoperability 2 Architecture 3 Interactivity

Quality ‘in use’ 4

Design and usability: aesthetics, navigation, user-friendly interface and information structure, personalisation

Pedagogical criteria

E-textbook and other LO relevance to educate basic subject competences criteria: 5 E-textbook and other LO textual and visual material are

suitable to acquire knowledge, and to educate understanding, skills and values defined in the curriculum

6 Assignments provided in e-textbook and other LO are suitable to acquire knowledge, and to educate understanding, skills and values defined in the curriculum

7 E-textbook and other LO methodological structure is suitable to acquire knowledge, and to educate understanding, skills and values defined in the curriculum

Criterion of E-textbook and other LO material suitability to educate general competences defined in the curriculum: 8 E-textbook and other LO textual and visual material,

assignments and methodological structure suitability to educate general competences

IPR criterion 9 Clear license: e-textbook and other LO is open, free to

use, and cost-effective

IFS concept implementation vision

1. Collaboration agreements between Vilnius University and (20 pilot) schools on IFS implementation

2. Joint expert group on creating interconnections and intelligent agents

3. R&D, creation of technologies and scenarios, and validation at schools

4. Feedback, questionnaires, interviews, data mining

5. Return to (3) based on (4)

Kurilovas, E.; Juskeviciene, A.; Bireniene, V. (2015). Research on Mobile Learning Activities Using Tablets. In: Proceedings of the 11th International Conference on Mobile Learning (ML 2015). Madeira, Portugal, March 14–16, 2015, pp. 94–98.

Kurilovas, E.; Zilinskiene, I.; Dagiene, V. (2015). Recommending Suitable Learning Paths According to Learners’ Preferences: Experimental Research Results. Computers in Human Behavior, Vol. 51, 2015, pp. 945–951.

Kurilovas, E.; Juskeviciene, A. (2015). Creation of Web 2.0 Tools Ontology to Improve Learning. Computers in Human Behavior, Vol. 51, 2015, pp. 1380–1386.

Kurilovas, E.; Vinogradova, I.; Kubilinskiene, S. (2015). New MCEQLS Fuzzy AHP Methodology for Evaluating Learning Repositories: A Tool for Technological Development of Economy. Technological and Economic Development of Economy – in press, DOI: 10.3846/20294913.2015.1074950

Kurilovas, E. (2015). Future School: Personalisation plus Intelligence. Chapter in: “Handbook of Research on Information Technology Integration for Socio-Economic Development”. IGI Global – in print

Papers 2015

Kurilovas, E.; Juskeviciene, A.; Kubilinskiene, S.; Serikoviene, S. (2014). Several Semantic Web Approaches to Improving the Adaptation Quality of Virtual Learning Environments. Journal of Universal Computer Science, Vol. 20 (10), 2014, pp. 1418–1432.

Kurilovas, E.; Kubilinskiene, S.; Dagiene, V. (2014). Web 3.0 – Based Personalisation of Learning Objects in Virtual Learning Environments. Computers in Human Behavior, Vol. 30, 2014, pp. 654–662. [Q1]

Kurilovas, E.; Zilinskiene, I.; Dagiene, V. (2014). Recommending Suitable Learning Scenarios According to Learners’ Preferences: An Improved Swarm Based Approach. Computers in Human Behavior, Vol. 30, 2014, pp. 550–557. [Q1]

Kurilovas, E.; Serikoviene, S.; Vuorikari, R. (2014). Expert Centred vs Learner Centred Approach for Evaluating Quality and Reusability of Learning Objects. Computers in Human Behavior, Vol. 30, 2014, pp. 526–534. [Q1]

Juskeviciene, A.; Kurilovas, E. (2014). On Recommending Web 2.0 Tools to Personalise Learning. Informatics in Education, Vol. 13 (1), 2014, pp. 17–30.

Kurilovas, E. (2014). Research on Tablets Applications for Mobile Learning Activities. Journal of Mobile Multimedia, Vol. 10 (3&4), 2014, pp. 182–193.

Papers 2014

Kurilovas, E.; Serikoviene, S. (2013). New MCEQLS TFN Method for Evaluating Quality and Reusability of Learning Objects. Technological and Economic Development of Economy, Vol. 19 (4), 2013, pp. 706–723. [Q1]

Kurilovas, E.; Zilinskiene, I. (2013). New MCEQLS AHP Method for Evaluating Quality of Learning Scenarios. Technological and Economic Development of Economy, Vol. 19 (1), 2013, pp. 78–92. [Q1]

Kurilovas, E. (2013). MCEQLS Approach in Multi-Criteria Evaluation of Quality of Learning Repositories. Chapter 6 in the book: José Carlos Ramalho, Alberto Simões, and Ricardo Queirós (Ed.) “Innovations in XML Applications and Metadata Management: Advancing Technologies”. IGI Publishing, USA, 2013, pp. 96–117.

Kurilovas, E.; Serikoviene, S. (2013). On E-Textbooks Quality Model and Evaluation Methodology. International Journal of Knowledge Society Research, Vol. 4 (3), 2013, pp. 66–78.

Papers 2013

Kurilovas, E.; Zilinskiene, I. (2012). Evaluation of Quality of Personalised Learning Scenarios: An Improved MCEQLS AHP Method. International Journal of Engineering Education, Vol. 28 (6), 2012, pp. 1309–1315.

Kurilovas, E.; Serikoviene, S. (2012). New TFN Based Method for Evaluating Quality and Reusability of Learning Objects. International Journal of Engineering Education, Vol. 28 (6), 2012, pp. 1288–1293.

Zilinskiene, I.; Dagiene, V.; Kurilovas, E. (2012). A Swarm-based Approach to Adaptive Learning: Selection of a Dynamic Learning Scenario. In: Proceedings of the 11th European Conference on e-Learning (ECEL 2012). Groningen, the Netherlands, October 26–27, 2012, pp. 583–593.

Papers 2012

Conclusion

Future school means personalisation + intelligence

Learning personalisation means creating and implementing personalised learning paths based on recommender systems and personal intelligent agents suitable for particular learners according to their personal needs

Educational intelligence means application of intelligent technologies and methods enabling personalised learning to improve learning quality and efficiency

Lithuanian IFS project is aimed at implementing both learning personalisation and educational intelligence

Thank you for your attention.

Questions?

Dr. Eugenijus Kurilovas http://eugenijuskurilovas.wix.com/my_site