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Cyber Security - Location-Based Services.

 




Location-Based Services (LBSs) are systems that deliver information based on the location of people or devices [54]. 

Some LBSs go a step further in delivering valuable services by using the users' location to infer additional information about the area. 

To get there, existing IMSs track users' whereabouts so that they may be taken into account during management operations. 

An LBS may be either person-oriented or device-oriented, depending on the emphasis of services. 

The emphasis on applications in the person-oriented approach exploits a person's position to improve service. 

A friend-finder application is an example of an LBS that fits this concept. 

Device-oriented LBSs, on the other hand, may concentrate on a person's location but do not need it. 

Objects such as vehicles in navigation systems might also be found using this method. 

There are two types of application designs: push and pull services, in addition to this categorization. 

Users obtain information through push services without having to explicitly request it. 

Pull services, on the other hand, imply that users actively seek for information. 

The majority of early location services were pull services, however push services have gained prominence in particular areas in recent years. 

The ubiquitous computer paradigm evolved in the early 1990s, aided by the location information supplied by the mobile paradigm. 

Weiser [55] used the term "pervasive" to describe the seamless integration of electronics into consumers' daily lives. 

The invisibility or full disappearance of computer technology from the user's viewpoint, the number of users and computing resources that form the environment and the system's scalability, and context-awareness were the key aims of ubiquitous computing. 

People were prioritized above computer devices and technological difficulties in the ubiquitous paradigm. 

LBSs offered the users' movement records, which were critical pieces of information for this paradigm. 

IMSs took into account not just the users' location, but also information about their lives. 

Because ubiquitous computing is still a highly active and dynamic topic, the pace of penetration into daily life varies greatly depending on technological and nontechnical aspects including infrastructure, compute resources, security, and economics. 

The ubiquitous computing paradigm [55] is built on the context-awareness idea [56]. 

Context-awareness is a mobile paradigm in which services learn about the context or environment in which users are placed and adjust their behavior based on that knowledge without requiring users to engage. 

It's vital to note that consumers don't engage with context-aware systems, but they do agree to their data being collected and managed. 

Context-aware solutions rely on location-based systems to determine the users' position and acquire data about the people, objects, and factors that make up the context or environment in which they are situated. 

There are various definitions of context in the literature. 

Schilit et al. [57] were the first to publish one in 1994. They defined context as location, adjacent people's identities, things, and changes in those items. 

Following that, other writers added additional elements such as identity and time (Ryan et al. [58]), emotional states and focus of attention (Dey [59]), context dimensions (Prekop and Burnett [60]), and any environmental data (Gustavsen [61]). 

(Tajd and Ngantchaha [62]). Previous solutions' advancements have added to the complexity of the information management process. 

This fact suggests an increase in the amount of data evaluated during management processes, and hence an increase in the complexity of such procedures. 

Furthermore, the security of location information is a vital factor to consider. 

In another scenario, users' whereabouts may be recorded without their knowledge or agreement, and this information could be used maliciously. 

One example of this circumstance may be the ability to determine a user's present position in order to determine whether or not he or she is at home. 

It is now necessary to regulate not just the privacy of location information, as in location-based services, but also other sensitive pieces of information about the users' lives. 

In the other situation, and continuing with the previous example, it would be feasible to determine not only if a certain user is at home, but also his or her identity, whether he or she is married, his or her emails, and other details about his or her personal life. 

The quantity of heterogeneous bits of information maintained by the context-aware paradigm is quite large, as mentioned by the preceding definitions of context. 

This fact has been affected by the transition from established paradigms to new ones. 

For starters, the mobile paradigm included user and element location into IMS management procedures. 

Pervasive systems later integrated additional key aspects of the users' lives, such as their identities, emails, alerts, and information about smart places. 

Finally, the combination of information regarding the context in which users are positioned with information considered by earlier paradigms has enhanced the complexity of management operations. 



Figure depicts the development of IMSs through time, taking into account the effect of new emerging paradigms and the most representative bits of data that they typically handle. 





Due to the fact that a significant portion of this data is sensitive or private, contemporary IMSs must take into account the privacy of this data. 

In this regard, further research on automated techniques for safeguarding the privacy of sensitive data handled during IMS management activities is required. 

Otherwise, malevolent people might have access to additional information about a certain user's life. 

Context-awareness is a paradigm that may be useful in a variety of situations, including network administration. 

Contextual information may be used by network managers to manage network resources dynamically by adjusting their behavior to the network state. 

The location of connected devices and network resources, the types of devices connected to the network infrastructure, and the network's status and statistics have all been identified as valuable pieces of context information that network administrators should consider before making management decisions. 

These options might range from reconfiguring a network component that isn't being utilized in an energy-efficient manner to deploying additional resources in a specific location to assure the QoS of certain users. 




~ Jai Krishna Ponnappan

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References & Further Reading:



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Artificial Intelligence - Intelligent Tutoring Systems.

  



Intelligent tutoring systems are artificial intelligence-based instructional systems that adapt instruction based on a variety of learner variables, such as dynamic measures of students' ongoing knowledge growth, personal interest, motivation to learn, affective states, and aspects of how they self-regulate their learning.

For a variety of problem areas, such as STEM, computer programming, language, and culture, intelligent tutoring systems have been created.

Complex problem-solving activities, collaborative learning activities, inquiry learning or other open-ended learning activities, learning through conversations, game-based learning, and working with simulations or virtual reality environments are among the many types of instructional activities they support.

Intelligent tutoring systems arose from a field of study known as AI in Education (AIED).

MATHia® (previously Cognitive Tutor), SQL-Tutor, ALEKS, and Rea soning Mind's Genie system are among the commercially successful and widely used intelligent tutoring systems.

Intelligent tutoring systems are frequently more successful than conventional kinds of training, according to six comprehensive meta-analyses.

This efficiency might be due to a number of things.

First, intelligent tutoring systems give adaptive help inside issues, allowing classroom instructors to scale one-on-one tutoring beyond what they could do without it.

Second, they allow adaptive problem selection based on the understanding of particular pupils.

Third, cognitive task analysis, cognitive theory, and learning sciences ideas are often used in intelligent tutoring systems.

Fourth, the employment of intelligent tutoring tools in so-called blended classrooms may result in favorable cultural adjustments by allowing teachers to spend more time working one-on-one with pupils.

Fifth, more sophisticated tutoring systems are repeatedly developed using new approaches from the area of educational data mining, based on data.

Finally, Open Learner Models (OLMs), which are visual representations of the system's internal student model, are often used in intelligent tutoring systems.

OLMs have the potential to assist learners in productively reflecting on their current level of learning.

Model-tracing tutors, constraint-based tutors, example-tracing tutors, and ASSISTments are some of the most common intelligent tutoring system paradigms.

These paradigms vary in how they are created, as well as in tutoring behaviors and underlying representations of domain knowledge, student knowledge, and pedagogical knowledge.

For domain reasoning (e.g., producing future steps in a problem given a student's partial answer), assessing student solutions and partial solutions, and student modeling, intelligent tutoring systems use a number of AI approaches (i.e., dynamically estimating and maintaining a range of learner vari ables).

To increase systems' student modeling skills, a range of data mining approaches (including Bayesian models, hidden Markov models, and logistic regression models) are increasingly being applied.

Machine learning approaches, such as reinforcement learning, are utilized to build instructional policies to a lesser extent.

Researchers are looking at concepts for the smart classroom of the future that go beyond the capabilities of present intelligent tutoring technologies.

AI systems, in their visions, typically collaborate with instructors and students to provide excellent learning experiences for all pupils.

Recent research suggests that rather than designing intelligent tutoring systems to handle all aspects of adaptation, such as providing teachers with real-time analytics from an intelligent tutoring system to draw their attention to learners who may need additional support, promising approaches that adaptively share regulation of learning processes across students, teachers, and AI systems—rather than designing intelligent tutoring systems to handle all aspects of adaptation, for example—by providing teachers with real-time analytics from an intelligent tutoring system to draw their attention to learners who may need additional support.



Jai Krishna Ponnappan


You may also want to read more about Artificial Intelligence here.



See also: 


Natural Language Processing and Speech Understanding; Workplace Automation.



Further Reading:




Aleven, Vincent, Bruce M. McLaren, Jonathan Sewall, Martin van Velsen, Octav Popescu, Sandra Demi, Michael Ringenberg, and Kenneth R. Koedinger. 2016. “Example-Tracing Tutors: Intelligent Tutor Development for Non-Programmers.” International Journal of Artificial Intelligence in Education 26, no. 1 (March): 224–69.

Aleven, Vincent, Elizabeth A. McLaughlin, R. Amos Glenn, and Kenneth R. Koedinger. 2017. “Instruction Based on Adaptive Learning Technologies.” In Handbook of Research on Learning and Instruction, Second edition, edited by Richard E. Mayer and Patricia Alexander, 522–60. New York: Routledge.

du Boulay, Benedict. 2016. “Recent Meta-Reviews and Meta-Analyses of AIED Systems.” International Journal of Artificial Intelligence in Education 26, no. 1: 536–37.

du Boulay, Benedict. 2019. “Escape from the Skinner Box: The Case for Contemporary Intelligent Learning Environments.” British Journal of Educational Technology, 50, no. 6: 2902–19.

Heffernan, Neil T., and Cristina Lindquist Heffernan. 2014. “The ASSISTments Ecosystem: Building a Platform that Brings Scientists and Teachers Together for Minimally Invasive Research on Human Learning and Teaching.” International Journal of Artificial Intelligence in Education 24, no. 4: 470–97.

Koedinger, Kenneth R., and Albert T. Corbett. 2006. “Cognitive Tutors: Technology Bringing Learning Sciences to the Classroom.” In The Cambridge Handbook of the Learning Sciences, edited by Robert K. Sawyer, 61–78. New York: Cambridge University Press.

Mitrovic, Antonija. 2012. “Fifteen Years of Constraint-Based Tutors: What We Have Achieved and Where We Are Going.” User Modeling and User-Adapted Interaction 22, no. 1–2: 39–72.

Nye, Benjamin D., Arthur C. Graesser, and Xiangen Hu. 2014. “AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring.” International Journal of Artificial Intelligence in Education 24, no. 4: 427–69.

Pane, John F., Beth Ann Griffin, Daniel F. McCaffrey, and Rita Karam. 2014. “Effectiveness of Cognitive Tutor Algebra I at Scale.” Educational Evaluation and Policy Analysis 36, no. 2: 127–44.

Schofield, Janet W., Rebecca Eurich-Fulcer, and Chen L. Britt. 1994. “Teachers, Computer Tutors, and Teaching: The Artificially Intelligent Tutor as an Agent for Classroom Change.” American Educational Research Journal 31, no. 3: 579–607.

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Artificial Intelligence - Algorithmic Composition And Generative Music.

 


A composer's approach for producing new musical material by following a preset limited set of rules or procedures is known as algorithmic composition.

In place of normal musical notation, the algorithm might instead be a set of instructions defined by the composer for the performer to follow throughout a performance. 

According to one school of thinking, algorithmic composition should include as little human intervention as possible.

In music, AI systems based on generative grammar, knowledge-based systems, genetic algorithms, and, more recently, deep learning-trained artificial neural networks have all been used.

The employment of algorithms to assist in the development of music is far from novel.

Several thousand-year-old music theory treatises provide early examples.

These treatises compiled lists of common-practice rules and conventions that composers followed in order to write music correctly.

Johann Joseph Fux's Gradus ad Parnassum (1725), which describes the precise rules defining species counter point, is an early example of algorithmic composition.

Species counterpoint presented five techniques of composing complimentary musical harmony lines against the primary or fixed melody, which was meant as an instructional tool.

Fux's technique gives limited flexibility from the specified rules if followed to the letter.

Chance was often used in early instances of algorithmic composition with little human intervention.

Chance music, often known as aleatoric music, dates back to the Renaissance.

Mozart is credited with the most renowned early example of the technique.

The usage of "Musikalisches Würfelspiel" (musical dice game) is included in a published manuscript claimed to Mozart dated 1787.

In order to put together a 16-bar waltz, the performer must roll the dice to choose one-bar parts of precomposed music (out of a possible 176) at random.



John Cage, an American composer, took these early aleatoric approaches to a new level by composing a work in which the bulk of the composition was determined by chance.

In the musical dice game, chance is only allowed to affect the sequence of brief pre-composed musical snippets, but in his 1951 work Music of Changes, chance is allowed to govern almost all choices.

To decide all musical judgments, Cage consulted the ancient Chinese divi nation scripture I Ching (The Book of Changes).

For playability considerations, his friend David Tudor, the work's performer, had to convert his highly explicit and intricate score into something closer to conventional notation.

This demo shows two types of aleatoric music: one in which the composer uses random processes to generate a set score, and the other in which the sequence of the musical pieces is left to the performer or chance.

Arnold Schoenberg created a twelve-tone algorithmic composition process that is closely related to fields of mathematics like combinatorics and group theory.

Twelve-tone composition is an early form of serialism in which each of the twelve tones of traditional western music is given equal weight.

After placing each tone in a chosen row with no repeated pitches, the row is rotated by one at a time until a 12 12 matrix is formed.

The matrix contains all variants of the original tone row that the composer may use for pitch material.



A fresh row may be employed once the aggregate—that is, all of the pitches from one row—has been included into the score.

Instead of writing melodic lines, the rows may be further separated into subsets to provide harmonic content (a vertical collection of sounds) (horizontal setting).

Later composers like Pierre Boulez and Karlheinz Stockhausen experimented with serializing additional musical aspects by building matrices that included dynamics and timbre.

Some algorithmic composing approaches were created in response to serialist composers' rejection or modification of previous techniques.

Serialist composers, according to Iannis Xena kis, were excessively concentrated on harmony as a succession of interconnecting linear objects (the establishment of linear tone-rows), and the procedures grew too difficult for the listener to understand.

He presented new ways to adapt nonmusical algorithms for music creation that might work with dense sound masses.

The strategy, according to Xenakis, liberated music from its linear concerns.

He was motivated by scientific studies of natural and social events such as moving particles in a cloud or thousands of people assembled at a political rally, and he focused his compositions on the application of probability theory and stochastic processes.

Xenakis, for example, used Markov chains to manipulate musical elements like pitch, timbre, and dynamics to gradually build thick-textured sound masses over time.

The likelihood of the next happening event is largely influenced by previous occurrences in a Markov chain; hence, his use of algorithms mixed indeterminate aspects like those in Cage's chance music with deterministic elements like serialism.

This song was dubbed stochastic music by him.

It prompted a new generation of composers to incorporate more complicated algorithms into their work.

Calculations for these composers ultimately necessitated the use of computers.

Xenakis was a forerunner in the use of computers in music, using them to assist in the calculation of the outcomes of his stochastic and probabilistic procedures.

With his album Ambient 1: Music for Airports, Brian Eno popularized ambient music by building on composer Erik Satie's notion of background music involving live performers (known as furniture music) (1978).

The lengths of seven tape recorders, each of which held a distinct pitch, were all different.

With each loop, the pitches were in a new sequence, creating a melody that was always shifting.

The composition always develops in the same manner each time it is performed since the inputs are the same.




Eno invented the phrase "generative music" in 1995 to describe systems that generate constantly changing music by adjusting parameters over time.

Ambient and generative music are both forerunners of autonomous computer-based algorithmic creation, most of which now uses artificial intelligence techniques.

Noam Chomsky and his collaborators invented generative grammar, which is a set of principles for describing natural languages.

The rules define a range of potential serial orderings of items by rewriting hierarchically structured elements.

Generative grammars, which have been adapted for algorithmic composition, may be used to generate musical sections.

Experiments in Musical Intelligence (1996) by David Cope is possibly the best-known use of generative grammar.

Cope taught his program to produce music in the styles of a variety of composers, including Bach, Mozart, and Chopin.

Information about the genre of music the composer desires to replicate is encoded as a database of facts that may be used to develop an artificial expert to aid the composer in knowledge-based systems.

Genetic algorithms are a kind of composition that mimics the process of biological evolution.

The similarity of a population of randomly made compositions to the intended musical output is examined.

Then, based on natural causes, artificial methods are applied to improve the likelihood of musically attractive qualities increasing in following generations.

The composer interacts with the system, stimulating new ideas in both the computer and the spectator.

Deep learning systems like generative adversarial networks, or GANs, are used in more contemporary AI-generated composition methodologies.

In music, generative adversarial networks pit a generator—which makes new music based on compositional style knowledge—against a discriminator, which tries to tell the difference between the generator's output and that of a human composer.

When the generator fails, the discriminator gets more information until it can no longer distinguish between genuine and created musical content.

Music is rapidly being driven in new and fascinating ways by the repurposing of non-musical algorithms for musical purposes.


Jai Krishna Ponnappan


You may also want to read more about Artificial Intelligence here.



See also: 


Computational Creativity.


Further Reading:

 

Cope, David. 1996. Experiments in Musical Intelligence. Madison, WI: A-R Editions.

Eigenfeldt, Arne. 2011. “Towards a Generative Electronica: A Progress Report.” eContact! 14, no. 4: n.p. https://econtact.ca/14_4/index.html.

Eno, Brian. 1996. “Evolving Metaphors, in My Opinion, Is What Artists Do.” In Motion Magazine, June 8, 1996. https://inmotionmagazine.com/eno1.html.

Nierhaus, Gerhard. 2009. Algorithmic Composition: Paradigms of Automated Music Generation. New York: Springer.

Parviainen, Tero. “How Generative Music Works: A Perspective.” http://teropa.info/loop/#/title.








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