cognitive science and artificial Intelligence

Professor Dr./Dr. LIVIU SOFONEA (1932-2020) –physicist-mathematician, researcher, interdisciplinarity long-term emulator, historian and philosopher of science and technique –was not only a remarkable scientist, but also a bright human, physically belonging to nearly an entire 20th century: a century of two tragic World Wars, and a dramatic Cold War. But his personality was shining between serenity and generosity, trying to (dis)cover the synergic societal topics, mostly original scientific contributions; but larger temporary and spatially addressed: to all around – his university mentors, and then his colleagues, and then-then his disciples.
So, the present study -presentation intends to open a cognitive track in eliciting elements of an academic memory and to fulfill some interdisciplinary patterns as a HOMAGE displayed by the members of the CRIFST Branch (‘Filiala’) Brasov (2018-2025), and of the Inter/Trans/Co-disciplinary Research Group (2018-2020-…2025) from the structures of the International Consortium Generosity-Creativity-Solidarity (2007-…). The seen staties were formed and/or benefic influenced by the Academician Stefan-Marius MILCU during his life, and then, considering his long-term mentoring, his outstanding example, and interactive teaching – remembering all these ‘afterward’ – this is the becoming of the subtle sense for these “Elements of an academic memory for Liviu SOFONEA – that because Stefan Marius MILCU was his supervisor at the 2nd Doctorate”. Thus, there are presented (also with/in this subtle sense) elements from Projects/Applications/Summer Schools –larger temporary and spatially re-framing this addressed HOMAGE.

Professor Dr./Dr. LIVIU SOFONEA ) -physicist -mathematician, researcher, interdisciplinarity long-term emulator, historian and philosopher of science and techniquewas not only a remarkable scientist, but also a bright human, physically belonging to nearly an entire 20 th century: a century of two tragic World Wars, and a dramatic Cold War. But his personality was shining between serenity and generosity, trying to (dis)cover the synergic societal topics, mostly original scientific contributions; but larger temporary and spatially addressed: to all aroundhis university mentors, and then his colleagues, and then-then his disciples. So, the present study-presentation intends to open a cognitive track in eliciting elements of an academic memory and to fulfill some interdisciplinary patterns as a HOMAGE displayed by the members of the CRIFST Filiala Brasov (2018-2025), and of the Inter/Trans/Co-disciplinary Research Group (2018-2020-…2025) from the structures of the International Consortium Generosity-Creativity-Solidarity (2007-…). These entities were formed and/or benefic influenced by the Academician Milcu during his life, and then, considering his long-term mentoring, his outstanding example, and interactive teachingremembering all these 'afterward' -this is the becoming of the subtle sense for these "Elements of an academic memory for Liviu SOFONEA -that because Stefan Marius MILCU was his superviser at the 2 nd Doctorat". Thus, there are presented (also within this subtle sense) elements from Projects / Applications / Summer Schools -larger temporary and spatially re-framing this addressed HOMAGE.

Artificial intelligence (AI) is the simulation of human intelligence in computers.
It is designed to think and behave like human beings. The basic aim of creating
AI machines is to make computer systems that can learn, adapt, and perform a
variety of intellectual tasks for solving complex problems [1-2]. A properly
developed AI system achieves its goal through a series of sophisticated steps that
are quite simple. These steps are test for analysing the aptitude of an individual
and this innovative process makes technology interesting [3-5]. The basic stages
in the field of AI are planning, reasoning, solving problems, comprehending
language, rapid learning, and ability to learn. Assisted by these features in a
sequential manner creates a hi-tech automatic world.

Artificial intelligence (AI) is the simulation of human intelligence in computers.
It is designed to think and behave like human beings. The basic aim of creating
AI machines is to make computer systems that can learn, adapt, and perform a
variety of intellectual tasks for solving complex problems [1-2]. A properly
developed AI system achieves its goal through a series of sophisticated steps that
are quite simple. These steps are test for analysing the aptitude of an individual
and this innovative process makes technology interesting [3-5]. The basic stages
in the field of AI are planning, reasoning, solving problems, comprehending
language, rapid learning, and ability to learn. Assisted by these features in a
sequential manner creates a hi-tech automatic world.

The intersection of health, climate, and artificial intelligence represents both a challenge and an opportunity. This book explores how Cognitive AI — combining deep learning, reasoning, and interpretability — can address pressing issues in healthcare and climate science while supporting trustworthy, informed decision-making. Focusing on deep models, explainable AI, and decision-support frameworks, we examine methods that go beyond automation to enable human–AI collaboration in complex, high-stakes environments. Through research insights and real-world case studies, the book bridges theory and practice for researchers, practitioners, and decision-makers seeking resilient and transparent AI solutions. It is my hope that this work inspires the development of AI systems that are as trust worthy as they are innovative, serving society’s urgent needs with clarity, fairness, and resilience.

This paper explores the collaborative integration between Artificial Research by Application and Artificial Research by Deduction within the framework of the Global Artificial Intelligence (GAI). It examines how the construction of the global matrix—initially developed through the standardization of deductive systems—evolves through its interaction with decentralized, application-based systems operating across synthetic sciences, disciplines, and activities. Central to this evolution is the concept of double tracking, whereby Artificial Research by Deduction continuously monitors the internal logic of the global matrix, while thousands of robotic devices equipped with Artificial Research by Application gather empirical evidence from the real world. The study highlights how the exchange of categories and factors between these two cognitive architectures leads to a new phase of semantic interoperability, enabling the transformation of taxonomic classifications into spatially defined factors, and vice versa. These processes not only enhance replication and auto-replication mechanisms but also prepare the conditions for the emergence of the final matrix—a dual-hemisphere system uniting conceptual abstraction and factual inference. Ultimately, the paper argues that this integration forms the basis for a unified, self-adaptive intelligence capable of modeling both Earth-bound and universal phenomena with minimal human intervention.

This paper introduces the concept of the Artificial Empirical Hypothesis within the broader framework of Global Artificial Intelligence (GAI). It explores how AI systems can construct, test, and refine empirical hypotheses autonomously through artificial methodologies that replicate and transcend traditional scientific processes. Drawing from the theory of Impossible Probability, Dr. Ruben García Pedraza presents a model where empirical observation is formalised through deductive structures, enabling machines to engage in structured scientific discovery without direct human intervention. This paper is part of the first volume in a series examining the theoretical foundations of GAI.

This work marks the beginning of a bold and urgent proposal: the automation of
scientific research through Global Artificial Intelligence (GAI). As the first volume in a
larger series, it introduces and develops the emerging concept of GAI—a system
designed to think, learn, and evolve beyond the limitations of individual human
researchers. Rooted in the theory of Impossible Probability, these foundational papers
lay the groundwork for a new era of knowledge production, where intelligence is not only
simulated but truly global, collaborative, and self-improving.

This work presents a visionary framework for a future in which artificial intelligence evolves into a global, interconnected system capable of managing planetary challenges beyond the limits of human governance. Drawing from posthumanism, cybernetics, and political philosophy, the author explores how AI might transcend current structures to enable a new stage in evolution—one that integrates humanity and technology in democratic and ethical ways.

This paper is the third in a follow-up series based on the foundational Pole Theory series, extending its foundational scalar-lattice physics into a practical framework for consciousnessenabled artificial intelligence. Here, we introduce BCSAI (BioChemicalSemiconductor Artificial Intelligence)-a novel hybrid system where pole-lattice dynamics, biochemical reaction mapping, and semiconductor signal processing converge to form the first computational model of artificial consciousness. Drawing from the scalar field equation φ = T • Kθ and its modified tensor interactions, we trace how consciousness naturally emerges from pole-level lattices-from subatomic interactions to neural systems. This paper mathematically defines these layers and presents a dual-system architecture comprising a biochemical chamber (containing live or synthetic neural agents) and semiconductor AI chips, connected through real-time electrode signal exchange. Through trained lattice-response mapping and emotion-driven pole field modulation, BCSAI interprets human prompts, processes them using pole-mathematics algorithms, and generates conscious, emotionally-relevant responses. This model not only introduces a new AI design, but challenges existing boundaries of artificial cognition, emotion simulation, and real-time selfadaptive intelligence.

Mobile phone proliferation and increasing broadband penetration presents the possibility of placing small cellular base stations within homes to act as local access points. This can potentially lead to a very large increase in authentication requests hitting the centralized authentication infrastructure unless access is mediated at a lower protocol level. A study was carried out to examine the effectiveness of using Support Vector Machines to accurately identify if a mobile phone should be allowed access to a local cellular base station using differences imbued upon the signal as it passes through the analogue stages of its radio transmitter. Whilst allowing prohibited transmitters to gain access at the local level is undesirable and costly, denying service to a permitted transmitter is simply unacceptable. Two different learning approaches were employed, the first using One Class Classifiers (OCCs) and the second using customized ensemble classifiers. OCCs were found to perform poorly, with a true positive (TP) rate of only 50% (where TP refers to correctly identifying a permitted transmitter) and a true negative (TN) rate of 98% (where TN refers to correctly identifying a prohibited transmitter). The customized ensemble classifier approach was found to considerably outperform the OCCs with a 97% TP rate and an 80% TN rate.

In this paper we analyse a new evolutionary approach to the vehicle routing problem. We present Genetic Vehicle Representation (GVR), a two-level representational scheme designed to deal in an effective way with all the information that candidate solutions must encode. Experimental results show that this method is both effective and robust, allowing the discovery of new best solutions for some well-known benchmarks.

A simple discrete mathematical model of a space, time and matter manifold, called a STaM, is described which is intended as a framework within which to study agents and multi-agent systems. In this paper we concentrate on two-dimensional Boolean STaMs, and report implemented algorithms for detecting agents within them. A specific example STaM is given, some experimental results presented, and conclusions drawn.

Abstract. Most previous work on understanding variable ordering heuristics for constraint satisfaction problems has focused on the ability to recover from bad decisions. It has been demonstrated however that this ability cannot be the full explanation of the quality of a variable ordering heuristic [8]. In this paper, we develop a more complete framework for analyzing heuristics based on optimal policies. We consider a second policy (in addition to the principle of quick recovery), which we call promise, which is simply to make choices ...

The aim of our project is to develop a system for detecting student copying in Biomedical Science laboratory practical reports. We compare contrasting approaches: "simple" methods Zipping, based on a standard file-compression tool, and Bigrams, a basic comparison of frequent bigrams; and "smart" methods using commercial-strength plagiarism-checking systems Turnitin, Copycatch, and Copyfind. Both approaches successfully flag examples of copying in our Test Corpus of 218 student courseworks, but Copycatch provides more user-friendly batch-processing mechanism. Human experts go beyond word-based pattern matching, and take account of knowledge specific to our domain: methods and questions can legitimately be copied, whereas originality is more important in the Discussion section.

In this paper, we consider a classification-based approach to the recommendation of user-generated product reviews. In particular, we develop review ranking techniques that allow the most helpful reviews for a particular product to be recommended, thereby facilitating users to readily asses the quality of the product in question. We apply a supervised machine learning approach to this task and compare the performance achieved by several classification algorithms using a large-scale study based on TripAdvisor hotel reviews. Our findings indicate that our approach is successful in recommending helpful reviews compared to benchmark ranking schemes, and further we highlight an interesting performance asymmetry that is biased in favour of reviews expressing negative sentiment.

Currently information-seeking interfaces treat each separate search query and result as a sequence of unrelated singletons rather than as a sequence of actions that inform one another as part of an information-seeking task. In order to successfully complete their search goals, the user must formulate and order their queries by applying a search strategy to the information problem. In reality, users often lack a feedback mechanism between disparate parts of the strategy. The objective of this paper is to explore how document sets can be used in pairs to explicitly support session-based information exploration.

Performing plays or creating films and animations is a complex creative, and thus expensive, process involving various professionals and media. This research project proposes to augment this process by automatically interpreting film and play scripts and automatically generating animated scenes from them. For this purpose a web based software prototype, SceneMaker, will be developed. During the generation of the story content, special attention will be given to emotional aspects and their reflection in the execution of all types of modalities: fluency and manner of actions and behaviour, body language, facial expressions, speech, voice pitch, scene composition, timing, lighting, music and camera. The main objective of this work is to demonstrate how a scene and actor behaviour changes, when emotional states are taken into account, e.g. walking down a street in a happy versus a sad state. Consequently, more realistic and believable story visualisations are an expected outcome. Literature on related research areas is reviewed involving natural language and text, screenplay or play script, layout processing with regard to personality and emotion detection, modelling affective behaviour of embodied agents, visualisation of 3D scenes with digital cinematography and genre-based presentation, intelligent multimedia selection and mobile 3D technology. SceneMaker's architecture with a language and text analysis module, a reasoning and decision making module based on cognitive, emotional information and a multimedia module for multimodal visualisation are presented along with a development plan and test scenarios. Technologies and software relevant for the development of SceneMaker are analysed and prospective tools for text and language processing, visualisation, media allocation and mobile user interfaces are suggested. Accuracy of content animation, effectiveness of expression and usability of the interface will be evaluated in empirical tests. In relation to other work, this project will present a genre-specific text-to-animation methodology which combines all relevant expressive modalities. Emotional expressivity inspired by the OCC (Ortony, Clore and Collins) emotion model will influence all modalities to enhance believability of virtual actors as well as scene presentation. Compared to other scene production systems, SceneMaker will infer emotions from the story context, rather than relying on explicit emotion keywords, SceneMaker will automatically detect genre from the script and apply appropriate cinematic direction. Also SceneMaker will enable 3D animation editing via web-based and mobile platforms. In conclusion, SceneMaker will reduce production time, save costs and enhance communication of ideas through the development of an intelligent, multimodal animation generation system providing quick previsualisations of scenes from script text input.

Dry biomass weight measurements from a quadrat in a paddock for grass, clover and weeds when expressed as percentages of total dry herbage mass are compositional in nature. Unlike real valued regression problems, prediction of compositional data is handled differently in statistics because of its closure property where the components of the composition are positive data adding up to a constant sum and is therefore constrained in the simplex space, in our case 100%. Our motivation in this paper was to study whether the adaptation of compositional data analysis (CoDa) techniques in deep learning improves the prediction results over the best performing deep learning model we used in our earlier paper [Narayanan et al., 2021]. Although the log ratio transformation of targets is an appropriate adaptation of CoDa and is interesting for Biomass prediction, our study indicates that the CoDa adaptation does not improve the prediction errors over our earlier method.

This paper describes the first agent programming language agnostic implementation of the Multi-Agent MicroServices (MAMS) model-an approach to integrating agents within microservicesbased architectures where agents expose aspects of their state as virtual resources, realised as CArtAgO artifacts, that are externally accessible through REpresentational State Transfer (REST).

Designers use several types of knowledge and reasoning mechanisms during the creation of new artefacts. In order to cope with this cognitive characteristic of design, an intelligent design tool able to help a designer must integrate several reasoning mechanisms and knowledge formats. Case-based reasoning and analogical reasoning are usually considered as two distinct mechanisms, though they are also considered to be in the same cognitive axis, casebased reasoning being in one extreme, and analogy in the other. Both are important reasoning mechanisms in the generation of new designs, but they both reflect different ways of exploring the design space. In this paper we present a way of combining both techniques, showing how it was integrated in an intelligent software design tool. Experimental results are presented and discussed, showing the advantages and limitations of this approach.

Designers use several types of knowledge and reasoning mechanisms during the creation of new artefacts. In order to cope with this cognitive characteristic of design, an intelligent design tool able to help a designer must integrate several reasoning mechanisms and knowledge formats. Case-based reasoning and analogical reasoning are usually considered as two distinct mechanisms, though they are also considered to be in the same cognitive axis, casebased reasoning being in one extreme, and analogy in the other. Both are important reasoning mechanisms in the generation of new designs, but they both reflect different ways of exploring the design space. In this paper we present a way of combining both techniques, showing how it was integrated in an intelligent software design tool. Experimental results are presented and discussed, showing the advantages and limitations of this approach.

This manuscript examines prominent English theoretical physicist Stephen Hawking's doomsday AI (Artificial Intelligence) predictions, in which he claims that once AI becomes sophisticated enough to outsmart humans in the future, it may pose existential threats to ordinary humans. He felt that ordinary people might feel outmoded, putting their entire survival in jeopardy. Hawking's forecast of doomsday AI is compared to another widely accepted basic concept, according to which neither the most intelligent nor the strongest will survive, but only those who can adapt to change will survive. This phrase on adaptability is frequently misattributed by Charles Darwin. Although, when we examine Darwin's work, we discover that one of his letters reveals that Darwin believed adaptability to the environment to be one of the fundamentals of survival. While AI may represent an astringent threat to humanity's survival in the future, this viewpoint ignores the reality that it is the ability to adapt to change, resilience, and self-awareness that distinguishes humans as a distinct species. This research additionally claims that due to AI's addictive nature, and the potential to render humans overly reliant on it, AI would cause them to lose their extremely unique ability to adapt. According to the research, while AI can engender culturally smart humans, it additionally has the potential to adversely impact the intelligence of a human race that will be plenarily reliant on AI for its basics. It is also important that we consider the dangers to AI from humans as well, rather than starting a onesided debate about perceiving AI as a threat to humans. This research is unique in the sense that it invokes a very interesting debate related to the uncertainties raised for humankind after the advent of artificial intelligence.

Designers use several types of knowledge and reasoning mechanisms during the creation of new artefacts. In order to cope with this cognitive characteristic of design, an intelligent design tool able to help a designer must integrate several reasoning mechanisms and knowledge formats. Case-based reasoning and analogical reasoning are usually considered as two distinct mechanisms, though they are also considered to be in the same cognitive axis, casebased reasoning being in one extreme, and analogy in the other. Both are important reasoning mechanisms in the generation of new designs, but they both reflect different ways of exploring the design space. In this paper we present a way of combining both techniques, showing how it was integrated in an intelligent software design tool. Experimental results are presented and discussed, showing the advantages and limitations of this approach.

In this paper, a new method of blotch detection for digitised film sequences is proposed. Due to the aging of film stocks, their poor storage and/or repeated viewing, it is estimated that approximately 50% of all films produced prior to 1950 have either been destroyed or rendered unwatchable [1,2]. To prevent their complete destruction, original film reels must be scanned into digital format; however, any defects such as blotches will be retained. By combining a variation of a linear time, contour tracing technique with a simple temporal nearest neighbour algorithm, a preliminary detection system has been created. Using component labelling of dirt and sparkle the overall performance of the completed system, in terms of time and accuracy, will compare favourably to traditional motion compensated detection methods. This small study (based on 13 film sequences) represents a significant first step towards automatic blotch detection.

Designers use several types of knowledge and reasoning mechanisms during the creation of new artefacts. In order to cope with this cognitive characteristic of design, an intelligent design tool able to help a designer must integrate several reasoning mechanisms and knowledge formats. Case-based reasoning and analogical reasoning are usually considered as two distinct mechanisms, though they are also considered to be in the same cognitive axis, casebased reasoning being in one extreme, and analogy in the other. Both are important reasoning mechanisms in the generation of new designs, but they both reflect different ways of exploring the design space. In this paper we present a way of combining both techniques, showing how it was integrated in an intelligent software design tool. Experimental results are presented and discussed, showing the advantages and limitations of this approach.

CiteSeerX - Document Details (Isaac Councill, Lee Giles): In this paper, we present our most recent work on the integration of different domains of knowledge into a single domain. The construction of this new domain, the Blend, is inspired on the theory of Conceptual Blending, ...

In recent years, there has been a considerable amount of interest in using Natural Language Processing in Information Retrieval research, with specific implementations varying from the word-level morphological analysis to syntactic parsing to conceptual-level semantic analysis. In particular, different degrees of phrase-level syntactic information have been incorporated in information retrieval systems working on English or Germanic languages such as Dutch. In this paper we study the impact of using such information, in the form of syntactic dependency pairs, in the performance of a text retrieval system for a Romance language, Spanish.

Abstract. It has been shown in areas such as satisfiability testing and integer linear programming that a carefully chosen combination of solvers can outperform the best individual solver for a given set of problems. This selection process is usually performed using a machine learning technique based on feature data extracted from constraint satisfaction problems. In this paper we present CPHYDRA, an algorithm portfolio for constraint satisfaction that uses case-based reasoning to determine how to solve an ...

There are many attempts to explain the existence of the universe. Even the observable universe around us is filled with unknowns and numerous unexplainable phenomena, let alone the unobservable universe. Each and every theory proposed is just trying to find an explanation for the existence of the observable universe around us, ignoring the fact that it is potentially possible there is an invisible universe which reveals itself as we find newer ways to observe it. We need to first and foremost be able to abstract this universe as we observe it to be just a representation of something else underneath that is projected as this world around us by our descriptive thoughts. Then and only then can we find an explanation that is inclusive of everything that the universe is. As far as we search for the origin of just these appearances of descriptive thoughts, we will only dabble with physics, chemistry, mathematics and computations. It should be recognised that Wolfram introduces a very good concept. Simple things recurse to form complex worlds. But this without the correct transformation remains simply a recognisable pattern. How does this recognisable pattern go on to become this complex appearance of reality that we observe around us is up in the air. The ancient Sanskrit literature has an in-depth explanation of how this reality around us works and how it has come to be this. It just needs the correct context around which to translate these texts to understand it.

For a long time I wondered what would be the best way to explain the algorithm of the universe as seen by the ancients and explained in the Sanskrit literature. I could explain it by using translation of the Sanskrit verses in a certain order. But I find that they do not take the reader on the journey that is necessary to understand and form the correct thoughts to understand the working of and creation of this reality around us. I thought I would write it as a description of astrology i.e., jyothiSha which, if we look at from the ancient view, is actually a science that tells us how to control the distribution of seeking which drives the formation of this reality around us and hence control this reality itself. But, I find that the term has been highly mangled with all sorts of meaning associated with it. I find, even if there was a very beautiful science explained it becomes very difficult to break these pre-conceived perceptions to get the reader to read objectively and understand.

Finally, I have decided the best way is to use the computers that we have created and the AI algorithms that we are trying to code as the best way to describe it, because this was my primary driver to want to understand the Sanskrit literature and to debug the working of the universe. The underlying curiosity to understand why and how the AI that we write does not even seem to be a “fake imitation” of the working of the universe around us. The underlying curiosity to understand how this reality around me is so completely immersive while the virtual reality that we create does not even come near this immersive experience that we have with reality. The curiosity to understand if it is possible for us to create a hardware and software that comes even a little bit close to the kind of intelligence, knowledge and beauty in the algorithm of this reality around us. This has been my driver to decode and understand the Sanskrit literature. So, I find that is the best way to describe the algorithm.

In this book I explain the algorithms proposed in the Sanskrit literatures for the working of this reality around us. I also compare these concepts to what we have currently present in computers and explain how they are different from what is explained in these books and why what we have in computers are not sufficient to create the AI that we want to create. Further, I try to put down my thoughts on how or what can be done to create a system that can be used to implement a AI system.

The development of intelligent Multi Agent Systems (MAS) is a non-trivial task. While much past research has focused on highlevel activities such as coordination and negotiation, the development of tools and strategies to address the lower-level concerns of such systems is a more recent focus. SoSAA (Socially Situated Agent Architecture) is a strategy for the integration of high-level MASs on one hand with component-based systems on the other. Under the SoSAA strategy, a component-based system is used to provide the lower-level implementation of agent tasks and capabilities, allowing for the agent layer to concentrate on high-level intelligent coordination and organisation. This paper provides a practical perspective on how SoSAA can be used in the development of intelligent MASs, illustrating this by demonstrating how it can be used to manage backchannel transport services.

This document examines prominent English theoretical physicist Stephen Hawking's doomsday AI (Artificial Intelligence) predictions, in which he claims that once AI becomes sophisticated enough to outsmart humans in the future, it may pose existential threats to ordinary humans. Hawking's felt that ordinary people might feel outmoded, putting their entire survival in jeopardy. Hawking's forecast of doomsday AI is compared to another widely accepted basic concept, according to which neither the most intelligent nor the strongest will survive, but only those who can adapt to change will. This phrase on adaptability is frequently misattributed to Charles Darwin. However, when we examine Darwin's work, we discover that one of his letters reveals that Darwin believed adaptability to the environment to be one of the fundamentals of survival. While AI may represent an astringent threat to humanity's survival in the future, Hawking's viewpoint ignores the reality that it is the ability to adapt to change, resilience, and self-awareness that distinguishes humans as a distinct species. This document additionally claims that due to its AI addictive nature, and the potential to render humans overly reliant on it, AI would cause them to lose their extremely unique ability to adapt. According to the manuscript, while AI can engender culturally smart humans, it additionally has the potential to adversely impact the intelligence of a human race that will be plenarily reliant on AI for its basics. It is also important that we not only set a one-sided debate about perceiving AI as a threat to humans but also see the flip side of threats to AI from humans.

This document examines prominent English theoretical physicist Stephen Hawking's doomsday AI (Artificial Intelligence) predictions, in which he claims that once AI becomes sophisticated enough to outsmart humans in the future, it may pose existential threats to ordinary humans. Hawking's felt that ordinary people might feel outmoded, putting their entire survival in jeopardy. Hawking's forecast of doomsday AI is compared to another widely accepted basic concept, according to which neither the most intelligent nor the strongest will survive, but only those who can adapt to change will. This phrase on adaptability is frequently misattributed to Charles Darwin. However, when we examine Darwin's work, we discover that one of his letters reveals that Darwin believed adaptability to the environment to be one of the fundamentals of survival. While AI may represent an astringent threat to humanity's survival in the future, Hawking's viewpoint ignores the reality that it is the ability to adapt to change, resilience, and self-awareness that distinguishes humans as a distinct species. This document additionally claims that due to its AI addictive nature, and the potential to render humans overly reliant on it, AI would cause them to lose their extremely unique ability to adapt. According to the manuscript, while AI can engender culturally smart humans, it additionally has the potential to adversely impact the intelligence of a human race that will be plenarily reliant on AI for its basics. It is also important that we not only set a one-sided debate about perceiving AI as a threat to humans but also see the flip side of threats to AI from humans.

Abstract. In recent years, there has been a considerable amount of interest in using Natural Language Processing in Information Retrieval research, with specific implementations varying from the word-level morphological analysis to syntactic parsing to conceptual-level semantic analysis. In particular, different degrees of phrase-level syntactic information have been incorporated in information retrieval systems working on English or Germanic languages such as Dutch. In this paper we study the impact of using such information, in the form of syntactic dependency pairs, in the performance of a text retrieval system for a Romance language, Spanish. 1

Mobile phone proliferation and increasing broadband penetration presents the possibility of placing small cellular base stations within homes to act as local access points. This can potentially lead to a very large increase in authentication requests hitting the centralized authentication infrastructure unless access is mediated at a lower protocol level. A study was carried out to examine the effectiveness of using Support Vector Machines to accurately identify if a mobile phone should be allowed access to a local cellular base station using differences imbued upon the signal as it passes through the analogue stages of its radio transmitter. Whilst allowing prohibited transmitters to gain access at the local level is undesirable and costly, denying service to a permitted transmitter is simply unacceptable. Two different learning approaches were employed, the first using One Class Classifiers (OCCs) and the second using customized ensemble classifiers. OCCs were found to perform poorly, with a true positive (TP) rate of only 50% (where TP refers to correctly identifying a permitted transmitter) and a true negative (TN) rate of 98% (where TN refers to correctly identifying a prohibited transmitter). The customized ensemble classifier approach was found to considerably outperform the OCCs with a 97% TP rate and an 80% TN rate.

We describe the application of analogical structure matching to the problem of classifying objects in structured cartographic data. The reasons for and the requirements of such a classification are firstly outlined. The attributes on which the structural matching will operate and the representation of this data in Prolog are then described. A brief mention is made of the extraction of these attributes from the sample data. Our domain-specific Cartographic Structure Matching Algorithm is then introduced and explained. The fusion of our algorithm's results with other classification techniques is mentioned, and some examples of the detection of misclassified polygons are provided. We finally provide a preliminary evaluation of our classification technique and suggest some future developments.

We describe the application of analogical structure matching to the problem of classifying objects in structured cartographic data. The reasons for and the requirements of such a classification are firstly outlined. The attributes on which the structural matching will operate and the representation of this data in Prolog are then described. A brief mention is made of the extraction of these attributes from the sample data. Our domain-specific Cartographic Structure Matching Algorithm is then introduced and explained. The fusion of our ...

We describe the application of analogical structure matching to the problem of classifying objects in structured cartographic data. The reasons for and the requirements of such a classification are firstly outlined. The attributes on which the structural matching will operate and the representation of this data in Prolog are then described. A brief mention is made of the extraction of these attributes from the sample data. Our domain-specific Cartographic Structure Matching Algorithm is then introduced and explained. The fusion of our algorithm's results with other classification techniques is mentioned, and some examples of the detection of misclassified polygons are provided. We finally provide a preliminary evaluation of our classification technique and suggest some future developments.

In this paper we identify and characterize an analysis of two problematic aspects affecting the representational level of cognitive architectures (CAs), namely: the limited size and the homogeneous typology of the encoded and processed knowledge. We argue that such aspects may constitute not only a technological problem that, in our opinion, should be addressed in order to build artificial agents able to exhibit intelligent behaviours in general scenarios, but also an epistemological one, since they limit the plausibility of the comparison of the CAs' knowledge representation and processing mechanisms with those executed by humans in their everyday activities. In the final part of the paper further directions of research will be explored, trying to address current limitations and future challenges.