Emotions are a central aspect of communication. Consequently, emotion analysis (EA) is a rapidly growing field in natural language processing (NLP). However, there is no consensus on scope, direction, or methods. In this paper, we conduct a thorough review of 154 relevant NLP publications from the last decade. Based on this review, we address four different questions: (1) How are EA tasks defined in NLP? (2) What are the most prominent emotion frameworks and which emotions are modeled? (3) Is the (...) subjectivity of emotions considered in terms of demographics and cultural factors? and (4) What are the primary NLP applications for EA? We take stock of trends in EA and tasks, emotion frameworks used, existing datasets, methods, and applications. We then discuss four lacunae: (1) the absence of demographic and cultural aspects does not account for the variation in how emotions are perceived, but instead assumes they are universally experienced in the same manner; (2) the poor fit of emotion categories from the two main emotion theories to the task; (3) the lack of standardized EA terminology hinders gap identification, comparison, and future goals; and (4) the absence of interdisciplinary research isolates EA from insights in other fields. Our work will enable more focused research into EA and a more holistic approach to modeling emotions in NLP. (shrink)

The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and classify cyberbullying (...) behavior in real-time, analyzing textual data from social media posts to detect harmful content. The model is trained on a large dataset of labeled instances of bullying and non-bullying content, using supervised ML algorithms such as Support Vector Machines (SVM), Decision Trees, and Random Forest. NLP techniques, including sentiment analysis, keyword extraction, and text vectorization, are employed to preprocess and transform the data into a format suitable for machine learning. T. (shrink)

Artificial Intelligence (AI) technologies are now widely used in a variety of fields to aid with knowledge acquisition and decision-making. Health information systems, in particular, can gain the most from AI advantages. Recently, symptoms-based illness prediction research and manufacturing have grown in popularity in the healthcare business. Several scholars and organisations have expressed an interest in applying contemporary computational tools to analyse and create novel approaches for rapidly and accurately predicting illnesses. In this study, we present a paradigm for assessing (...) the efficacy of combining Machine Learning (ML) and Natural Language Processing (NLP) technologies in a disease prediction system. We scraped a disease-symptom dataset with NLP characteristics from one of the UK's most trusted National Health Service (NHS) websites as an example. In addition, we will thoroughly examine our data using symptom frequency, similarity, and clustering analysis. As a consequence, we can observe that the forecast has a high efficiency rate, but there are still some challenges to work out. (shrink)

The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and classify cyberbullying (...) behavior in real-time, analyzing textual data from social media posts to detect harmful content. The model is trained on a large dataset of labeled instances of bullying and non-bullying content, using supervised ML algorithms such as Support Vector Machines (SVM), Decision Trees, and Random Forest. NLP techniques, including sentiment analysis, keyword extraction, and text vectorization, are employed to preprocess and transform the data into a format suitable for machine learning. To optimize the performance of the detection model, techniques such as Grid Search, Genetic Algorithms, and Particle Swarm Optimization are used to fine-tune hyperparameters, resulting in improved accuracy and reduced false positives. The system's effectiveness is validated through experiments conducted on various social media platforms, demonstrating its potential to detect cyberbullying with high precision. Future work will focus on enhancing the model's adaptability to emerging slang and evolving language patterns in social media. Key words: Cyberbullying Detection, Social Media, Supervised Machine Learning, Natural Language Processing (NLP), Optimization Techniques. (shrink)

The main objective of our project is to construct a fully functional voice-based home automation system that uses the Internet of Things and Natural Language Processing. The home automation system is user-friendly to smartphones and laptops. A set of relays is used to connect the Node MCU to homes under controlled appliances. The user sends a command through the speech to the mobile devices, which interprets the message and sends the appropriate command to the specific appliance. The voice command given (...) by the user is interpreted by the mobile device using Natural Language Processing. The system utilizes a node microcontroller unit (Node MCU) as a Wi-Fi-based gateway to connect to the Google Assistant. It determines operation must be completed by which the appliance to fulfill the user’s request. The user increased mobile capabilities we will be using a smart phone in this project. A smartphone application as nearly all of the data transferred can be processed by cloud. The user increased mobile capabilities we will be using a smart phone in this project. The IoT -based system for home automation can easily and efficiently control appliances. The Node MCU is interfaced with the appliances and programmed in a manner that they respond to mobile inputs. (shrink)

The intersection of intelligent computing and linguistics has emerged as a vibrant field of study, offering innovative solutions and applications that transform how we understand and interact with language. This paper explores the diverse applications of intelligent computing in linguistics, encompassing natural language processing (NLP), computational linguistics, language modeling, speech recognition, and more. It delves into the underlying technologies, methodologies, and the impact of these advancements on various linguistic subfields. Through an extensive review of recent literature, case studies, and practical (...) examples, this paper aims to provide a comprehensive overview of the current state and future prospects of intelligent computing in linguistics. (shrink)

The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and classify cyberbullying (...) behavior in real-time, analyzing textual data from social media posts to detect harmful content. The model is trained on a large dataset of labeled instances of bullying and non-bullying content, using supervised ML algorithms such as Support Vector Machines (SVM), Decision Trees, and Random Forest. NLP techniques, including sentiment analysis, keyword extraction, and text vectorization, are employed to preprocess and transform the data into a format suitable for machine learning. To optimize the performance of the detection model, techniques such as Grid Search, Genetic Algorithms, and Particle Swarm Optimization are used to fine-tune hyperparameters, resulting in improved accuracy and reduced false positives. The system's effectiveness is validated through experiments conducted on various social media platforms, demonstrating its potential to detect cyberbullying with high precision. Future work will focus on enhancing the model's adaptability to emerging slang and evolving language patterns in social media. (shrink)

Machine-based automation has long been a key factor in the modern era. However, lately, many people have been shocked by artificial intelligence (AI) applications, such as ChatGPT (OpenAI), that can perform tasks previously thought to be human-exclusive. With recent advances in natural language processing (NLP) technologies, AI can generate written content that is similar to human-made products, and this ability has a variety of applications. As the technology of large language models continues to progress by making use of colossal reservoirs (...) of digital information, AI is becoming more capable of recognizing patterns and associations within given contexts, making it especially helpful in assisting with various professional writing tasks [1]. In this paper, we will discuss several key points regarding the current state and future of AI in academia. Furthermore, since in certain aspects, acts can speak louder than words, we will also present some of our hands-on experiences of working with ChatGPT. (shrink)

Despite its centrality in the philosophy of cognitive science, there has been little prior philosophical work engaging with the notion of representation in contemporary NLP practice. This paper attempts to fill that lacuna: drawing on ideas from cognitive science, I introduce a framework for evaluating the representational claims made about components of neural NLP models, proposing three criteria with which to evaluate whether a component of a model represents a property and operationalising these criteria using probing classifiers, a popular analysis (...) technique in NLP (and deep learning more broadly). The project of operationalising a philosophically-informed notion of representation should be of interest to both philosophers of science and NLP practitioners. It affords philosophers a novel testing-ground for claims about the nature of representation, and helps NLPers organise the large literature on probing experiments, suggesting novel avenues for empirical research. (shrink)

As if 2020 were not a peculiar enough year, its fifth month has seen the relatively quiet publication of a preprint describing the most powerful Natural Language Processing (NLP) system to date, GPT-3 (Generative Pre-trained Transformer-3), by Silicon Valley research firm OpenAI. Though the software implementation of GPT-3 is still in its initial Beta release phase, and its full capabilities are still unknown as of the time of this writing, it has been shown that this Artificial Intelligence can comprehend prompts (...) in natural language, on virtually any topic, and generate relevant, original text content that is indistinguishable from human writing. Moreover, access to these capabilities, in a limited yet worrisome enough extent, is available to the general public as of the time of this writing. This paper presents select examples of original content generated by the author using GPT-3. These examples illustrate some of the capabilities of GPT-3 in comprehending prompts in natural language and generating convincing content in response. We use these examples to raise specific, fundamental questions pertaining to the intellectual property of this content and the potential use of GPT-3 to facilitate plagiarism. Our goal is to instigate not just a sense of urgency, but of a present tardiness on the part of the academic community in addressing these questions. (shrink)

Gatekeeping, or determining membership of your group, is crucial to science: the moniker ‘scientific’ is a stamp of epistemic quality or even authority. But gatekeeping in science is fraught with dangers. Gatekeepers must exclude bad science, science fraud and pseudoscience, while including the disagreeing viewpoints on which science thrives. This is a difficult tightrope, not least because gatekeeping is a human matter and can be influenced by biases such as groupthink. After spelling out these general tensions around gatekeeping in science, (...) we shed light on them with a case study from psychology. This concerns whether academic psychologists rightly or wrongly classify the applied-psychology framework of NLP (‘neuro-linguistic programming’) as unscientific and even pseudoscientific. This example of gatekeeping is particularly instructive because both the NLP community and the psychology community, we argue, make legitimate but also illegitimate moves. This case gives rise to several general insights about gatekeeping in science more generally. (shrink)

Large language models (LLMs) reflect societal norms and biases, especially about gender. While societal biases and stereotypes have been extensively researched in various NLP applications, there is a surprising gap for emotion analysis. However, emotion and gender are closely linked in societal discourse. E.g., women are often thought of as more empathetic, while men's anger is more socially accepted. To fill this gap, we present the first comprehensive study of gendered emotion attribution in five state-of-the-art LLMs (open- and closed-source). We (...) investigate whether emotions are gendered, and whether these variations are based on societal stereotypes. We prompt the models to adopt a gendered persona and attribute emotions to an event like 'When I had a serious argument with a dear person'. We then analyze the emotions generated by the models in relation to the gender-event pairs. We find that all models consistently exhibit gendered emotions, influenced by gender stereotypes. These findings are in line with established research in psychology and gender studies. Our study sheds light on the complex societal interplay between language, gender, and emotion. The reproduction of emotion stereotypes in LLMs allows us to use those models to study the topic in detail, but raises questions about the predictive use of those same LLMs for emotion applications. (shrink)

At the heart of natural language processing is the understanding of context dependent meanings. This paper presents a preliminary model of formal contexts based on situation theory. It also gives a worked-out example to show the use of contexts in lifting, i.e., how propositions holding in a particular context transform when they are moved to another context. This is useful in NLP applications where preserving meaning is a desideratum.

While representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching. Unlike past work that has focused on feature engineering, we present a novel representation learning approach that is tailored to the ontology matching task. Our approach is based on embedding ontological terms in a high-dimensional Euclidean space. This embedding is derived on the basis of a novel phrase retrofitting strategy through which semantic (...) similarity information becomes inscribed onto fields of pre-trained word vectors. The resulting framework also incorporates a novel outlier detection mechanism based on a denoising autoencoder that is shown to improve performance. An ontology matching system derived using the proposed framework achieved an F-score of 94% on an alignment scenario involving the Adult Mouse Anatomical Dictionary and the Foundational Model of Anatomy ontology (FMA) as targets. This compares favorably with the best performing systems on the Ontology Alignment Evaluation Initiative anatomy challenge. We performed additional experiments on aligning FMA to NCI Thesaurus and to SNOMED CT based on a reference alignment extracted from the UMLS Metathesaurus. Our system obtained overall F-scores of 93.2% and 89.2% for these experiments, thus achieving state-of-the-art results. (shrink)

The current COVID-19 pandemic and the previous SARS/MERS outbreaks of 2003 and 2012 have resulted in a series of major global public health crises. We argue that in the interest of developing effective and safe vaccines and drugs and to better understand coronaviruses and associated disease mechenisms it is necessary to integrate the large and exponentially growing body of heterogeneous coronavirus data. Ontologies play an important role in standard-based knowledge and data representation, integration, sharing, and analysis. Accordingly, we initiated the (...) development of the community-based Coronavirus Infectious Disease Ontology in early 2020. -/- As an Open Biomedical Ontology (OBO) library ontology, CIDO is open source and interoperable with other existing OBO ontologies. CIDO is aligned with the Basic Formal Ontology and Viral Infectious Disease Ontology. CIDO has imported terms from over 30 OBO ontologies. For example, CIDO imports all SARS-CoV-2 protein terms from the Protein Ontology, COVID-19-related phenotype terms from the Human Phenotype Ontology, and over 100 COVID-19 terms for vaccines (both authorized and in clinical trial) from the Vaccine Ontology. CIDO systematically represents variants of SARS-CoV-2 viruses and over 300 amino acid substitutions therein, along with over 300 diagnostic kits and methods. CIDO also describes hundreds of host-coronavirus protein-protein interactions (PPIs) and the drugs that target proteins in these PPIs. CIDO has been used to model COVID-19 related phenomena in areas such as epidemiology. The scope of CIDO was evaluated by visual analysis supported by a summarization network method. CIDO has been used in various applications such as term standardization, inference, natural language processing (NLP) and clinical data integration. We have applied the amino acid variant knowledge present in CIDO to analyze differences between SARS-CoV-2 Delta and Omicron variants. CIDO's integrative host-coronavirus PPIs and drug-target knowledge has also been used to support drug repurposing for COVID-19 treatment. -/- CIDO represents entities and relations in the domain of coronavirus diseases with a special focus on COVID-19. It supports shared knowledge representation, data and metadata standardization and integration, and has been used in a range of applications. (shrink)

As the demand for explainable deep learning grows in the evaluation of language technologies, the value of a principled grounding for those explanations grows as well. Here we study the state-of-the-art in explanation for neural models for natural-language processing (NLP) tasks from the viewpoint of philosophy of science. We focus on recent evaluation work that finds brittleness in explanations obtained through attention mechanisms.We harness philosophical accounts of explanation to suggest broader conclusions from these studies. From this analysis, we assert the (...) impossibility of causal explanations from attention layers over text data. We then introduce NLP researchers to contemporary philosophy of science theories that allow robust yet non-causal reasoning in explanation, giving computer scientists a vocabulary for future research. (shrink)

The present comment concerns a recent AI & Ethics article which purports to report evidence of speciesist bias in various popular computer vision (CV) and natural language processing (NLP) machine learning models described in the literature. I examine the authors' analysis and show it, ironically, to be prejudicial, often being founded on poorly conceived assumptions and suffering from fallacious and insufficiently rigorous reasoning, its superficial appeal in large part relying on the sequacity of the article's target readership.

Text classification is one of the most important task in natural language processing, In this research, we carried out several experimental research on three (3) of the most popular Text classification NLP classifier in Convolutional Neural Network (CNN), Multinomial Naive Bayes (MNB), and Support Vector Machine (SVN). In the presence of enough training data, Deep Learning CNN work best in all parameters for evaluation with 77% accuracy, followed by SVM with accuracy of 76%, and multinomial Bayes with least performance of (...) 69% accuracy. CNN has the best performance in the presence of large enough training dataset because of the presence of filter/ kernels which help to indentify patterns in text data regardless of their position in the sentence. We repeated the training again with just one-third of our data, at this point SVM comes with the best performance, the performance of CNN noticeably drops but still better than Multinomial Naive Bayes, the reason why SVM works best when we reduce the training data was because of its ability to look for a hyper-plane that creates a boundary between different classes of data so as to properly classify them, so we believed that getting the hyper-plane was more efficient when we reduce the dataset, hence reason for the good performance. Multinomial Naive Bayes have the least performance which we attributed to its assumption of independency between the features which sometimes does not hold true. We concluded that availability of data should be an important factor when choosing classifier for Natural Language Processing Text Classification task. CNN should be use in the presence of enough dataset, and SVM should be use when data is not enough. Multinomial Naive Bayes must not be trusted with state of the art NLP task due to its assumption of independency between the features . (shrink)

There is a common misconception across the lntelligence Community (IC) to the effect that information trapped within multiple heterogeneous data silos can be semantically integrated by the sorts of meaning-blind statistical methods employed in much of artificial intelligence (Al) and natural language processlng (NLP). This leads to the misconception that incoming data can be analysed coherently by relying exclusively on the use of statistical algorithms and thus without any shared framework for classifying what the data are about. Unfortunately, such approaches (...) do not yield sustainable results where we are dealing with widely distributed, highly heterogeneous and often changing bodies of data. We argue here that the needed integration requires the use of what we call an lntegrating Semantic Framework (ISF), which provldes a consistent set of categories and relationships that can be reused over and over again to tag successive bodies of data in ways which foster more coherent representation and reasoning. (shrink)

Text classification is one of the most important task in natural language processing, In this research, we carried out several experimental research on three (3) of the most popular Text classification NLP classifier in Convolutional Neural Network (CNN), Multinomial Naive Bayes (MNB), and Support Vector Machine (SVN). In the presence of enough training data, Deep Learning CNN work best in all parameters for evaluation with 77% accuracy, followed by SVM with accuracy of 76%, and multinomial Bayes with least performance of (...) 69% accuracy. CNN has the best performance in the presence of large enough training dataset because of the presence of filter/ kernels which help to indentify patterns in text data regardless of their position in the sentence. -/- We repeated the training again with just one-third of our data, at this point SVM comes with the best performance, the performance of CNN noticeably drops but still better than Multinomial Naive Bayes, the reason why SVM works best when we reduce the training data was because of its ability to look for a hyper-plane that creates a boundary between different classes of data so as to properly classify them, so we believed that getting the hyper-plane was more efficient when we reduce the dataset, hence reason for the good performance. Multinomial Naive Bayes have the least performance which we attributed to its assumption of independency between the features which sometimes does not hold true. We concluded that availability of data should be an important factor when choosing classifier for Natural Language Processing Text Classification task. CNN should be use in the presence of enough dataset, and SVM should be use when data is not enough. Multinomial Naive Bayes must not be trusted with state of the art NLP task due to its assumption of independency between the features. (shrink)

Knowledge is power. Knowledge about human psychology is increasingly being produced using natural language processing (NLP) and related techniques. The power that accompanies and harnesses this knowledge should be subject to ethical controls and oversight. In this chapter, we address the ethical pitfalls that are likely to be encountered in the context of such research. These pitfalls occur at various stages of the NLP pipeline, including data acquisition, enrichment, analysis, storage, and sharing. We also address secondary uses of the results (...) and tools developed through psychometric NLP, such as profit-driven targeted advertising, political campaigns, and domestic and international psyops. Along the way, we reflect on potential ethical guidelines and considerations that may help researchers navigate these pitfalls. (shrink)

Întrebarea de bază este, ce este o pseudoştiinţă? Una din cele mai disputate delimitări ale ştiinţei. Mulţi savanţi de renume mondial, unanim recunoscuţi (ca de ex. Charles Darwin) au cochetat de-a lungul timpului cu diverse aspecte ale pseudoştiinţei considerându-le, cu bună credinţă, drept ştiinţă. Şi multe domenii ale pseudoştiinţei actuale au fost, la vremea lor, considerate drept domenii onorabile ale ştiinţei. Chiar şi în prezent, practicanţii pseudoştiinţelor nu recunosc valabilitatea etichetei puse domeniului lor de activitate. Oamenii de ştiinţă au tendinţa (...) să claseze pe loc un domeniu de activitate, în funcţie de cunoştinţele şi cultura lor, drept ştiinţă sau pseudoştiinţă. Dar graniţa dintre ştiinţă şi pseudoştiinţă este în multe cazuri foarte vagă. Într-un fel, această problemă seamănă cu problema filozofică a stabilirii a ce e bine şi ce e rău. Filosofii ştiinţei, cum ar fi Paul Feyerabend, au susţinut că nu este nici posibil, nici de dorit, o distincţie între ştiinţă şi non-ştiinţă. Printre problemele care pot face distincţia dificilă este viteza variabilă de evoluţie între teoriile şi metodologiile ştiinţei ca răspuns la noile date. În plus, standardele specifice aplicabile unui domeniu al ştiinţei ar putea să nu fie aplicabile în alte domenii. După cum se spune în diferite cursuri despre introducerea în ştiinţe, pseudoştiinţa este oricare subiect care pare a fi ştiinţific la prima vedere, sau ai cărui susţinători susţin că este ştiinţific, dar care contravine condiţiilor de testare sau deviază mult de la alte aspecte fundamentale ale metodelor ştiinţifice. Pseudoştiinţa este caracterizată de folosirea unor afirmaţii vagi, exagerate sau care nu se pot testa, supralicitarea confirmării în detrimentul argumentării, lipsa transparenţei pentru teste realizate de alţi experţi, şi o stagnare în dezvoltarea teoriei. CUPRINS: Pseudoştiinţa - Ştiinţa şi pseudoştiinţa - - Metodologie ştiinţifică - - Falsificabilitatea - - Normele Merton - - Refuzul de a recunoaşte problemele - - Critica termenului - Identificarea pseudoştiinţei - Ştiinţa patologică - - Exemplele lui Langmuir - - - Raze N - - - Exemple ulterioare - - Exemple mai noi - - - Poliapa - - - Fuziunea la rece - - - Memoria apei - Antropomorfism - - În preistorie - - În religie şi mitologie - - - Critici - - În ştiinţă - - - Antonim - Criptozoologia - - Referinţe - - Critici - Pseudomatematica - - Unele taxonomii ale pseudomatematicii - - - Încercările de rezolvare a problemelor clasice imposibil de rezolvat - - Practicanţii - - Un exemplu ilustrativ - Radiestezia - - Originea radiesteziei - - Dispozitivele de detecţie - - - Anse - - Evaluarea ştiinţifică a radiesteziei - - - Studiul Kassel - - - Studiu Betz - - - Explicaţii sugerate - Scientologia - - Credinţe - - - Puntea Libertăţii Totale - - - Morala şi etica - - - Triunghiurile ARC şi KRC - - - Viaţa de apoi - - - Dumnezeu - - - Ştiinţa - - Învăţături şi practici - - - Terminologie - - - Interpretare şi context - - - Învăţarea treptată - - - Audit - - - Organismul - - - Naşterea silenţioasă - - - Sărbători - Sincronicitatea - - Principiul - - Exemplu - - Relaţia cu cauzalitatea - - Critici - Programarea neuro-lingvistică - - Tehnici sau set de practici - - Aplicaţii - - - Psihoterapie - - - Alte utilizări - - NLP ca o cvasi-religie - Manipularea şi automanipularea prin cuvinte - - Aflaţi când şi unde se folosesc cuvintele - - Testaţi pentru a vedea cum reacţionaţi la cuvinte - - Controlul sau manipularea prin cuvinte - - Uitaţi-vă la nivelul cuvintelor - - Vedeţi cum vă pot ajuta cuvintele, făcându-se nu doar utile, ci şi frumoase - - Exploraţi lumea mentală a cuvintelor - - - Încercaţi să exploraţi lumea fără cuvinte - - Priviţi din nou totul, pentru prima dată fără cuvinte Pseudofizica - Anti-gravitaţia - - Efecte convenţionale care imită efectele anti-gravitaţiei - - Soluţii ipotetice - - - Scuturi gravitaţionale - - - Cercetări în relativitatea generală în anii 1950 - - - A cincea forţă - - - "Unităţi distorsionate" în relativitatea generală - - - Breakthrough Propulsion Physics Program - - Încercări experimentale şi comerciale - - - Dispozitive giroscopice - - - Gravitatorul lui Thomas Townsend Brown - - - Cuplarea gravitoelectrică - - - Premiul Göde - Energia orgonică - - Dezvoltarea de către Reich a teoriilor sale orgonice - - Cărţile lui Reich - - Evaluarea teoriei - Eter luminifer - - Dezavantaje şi critici - Fuziunea la rece - - Fuziunea cu bule - - Rezultate - - - Producţia de energie şi căldura în exces - - - Heliu, elemente grele, şi neutroni - - Mecanisme propuse - Planete ipotetice: X / Nibiru - - Teorii ale conspiraţiei - - Reacţii publice - - Influenţa culturală - Poliapa (Apa polimerizată) - Maşini alimentate cu apă - - Ce nu sunt maşini alimentate cu apă - - Extragerea energiei din apă - - Hidrogenul ca supliment - - Pilula de benzină şi aditivi Pseudoarheologia - Caracteristici - - Lipsa metodei ştiinţifice - - Opoziţia la regulile arheologice - - Motivaţii naţionaliste - - Motivaţii religioase - Rasele de bază - - Rase de bază, epoci şi subrase - - - Prima rasă de bază (eterică) - - - A doua rasă de bază (hiperboreană) - - - A treia rasă de bază (lemuriană) - - - A patra rasă de bază (atlantidă) - - - A cincea rasă de bază (ariană) - - - A şasea rasă de bază - - - A şaptea rasă de bază - - Migrarea spre Mercur - - Credinţa teosofică în fraternitatea umanităţii - Protocronism - - Istoria protocronismului - - Scrierea dacică - Pământul gol (Intratereştrii) - - Ipoteza Pământului gol concav - Fenomenul 2012 - - Tortuguero - - Chilam Balam - Creaţia inteligentă - - Concepte - - - Complexitatea ireductibilă - - - Complexitatea specifică - - - Universul bine pus la punct - - - Creatorul inteligent - - Mişcarea creaţia inteligentă - - - Religia şi susţinătorii importanţi - Creaţionismul ştiinţific - - Abordări ale ştiinţei creaţiei - - - Religia - - - Afilieri religioase moderne - - - Abordări ştiinţifice - - Critici - - - Ipoteze metafizice - - - Critici religioase - - - Critici ştiinţifice - - - Critici istorice, filozofice, şi sociologice - - Domenii de studiu - - - Biologia creaţionistă - - - Ştiinţele Pământului şi Geofizica - - - Astronomia şi cosmologia - Ufologia - - Statutul ca disciplină ştiinţifică - - - Pseudoştiinţă - - - Aspecte metodologice - Teoria astronautului extraterestru - - Argumente şi susţinători - - Erich von Däniken - - Ipoteza creaţiei - - - Argumentul genetic - - - Cercetarea genetică şi evoluţia creierului Medicina alternativă - Tipuri de medicină alternativă - - Sisteme bazate pe convingeri neştiinţifice sau practici tradiţionale - - Energii supranaturale şi interpretări eronate ale energiilor fizice - Ayurveda - Homeopatia - - Remedii şi tratamente - - - Pregătirea - - - Diluţii - - - Patogeneza - - Reglementări şi prevalenţe - - Opoziţia publică - Medicina energetică - - Clasificarea metodelor - - Convingeri - - Cercetări ştiinţifice - - - Vindecarea la distanţă - - - Vindecarea prin contact - - - Baza de date - - Explicaţii alternative pentru rapoarte pozitive - Reiki - - Practica - - - Vindecarea - - - Tratamentul întregului organism - - - Tratament localizat - - - Respiraţia - - - Trei piloni de bază - Terapia de conversie - Metoda Bates pentru îmbunătăţirea vederii - - Concepte - - - Acomodarea - - - Cauze ale problemelor de vedere - Detoxifierea - - Detoxifierea în medicina alternativă - - - Istoric - - - Tampoane de curăţare şi detoxifiere - - Tipuri de detoxifiere - - - Dezintoxicarea de alcool - - - Dezintoxicarea de droguri - - - Detoxifiere metabolică - - - Medicina alternativă - - Modalităţi de detoxifiere - - - Diete de detoxifiere - - - Curăţirea colonului - - - Metale grele - - - Dispozitive de detoxifiere - - Terapia de chelare - - - Detoxifierea proteică - - Detoxifierea anusului şi intestinelor cu cafea - - - Efecte şi riscuri - - Plasturi (tampoane) de detoxifiere aplicate pe picioare - - - Ce sunt plasturii de detoxifiere (tampoanele de detoxifiere) pentru picior? - - - De ce se aplică plasturii pe picioare? - Fizionomia - - Origini moderne - - Perioada de popularitate - - Ştiinţa modernă - - Utilizarea modernă - Frenologia Paranormal - Activităţi paranormale - - Fantome şi alte entităţi spirituale - - Viaţa extraterestră şi OZN-urile - - Criptide - Cercetări în paranormal - - Abordări anecdotice - - Abordarea observaţiilor participanţilor - - Anomalistica - - Neuroştiinţa - - Critici - Vitalism - - Filozofia - - Ştiinţa - - - Relaţia cu emergentismul - - Mesmerism - - Medicina complementară şi alternativă - Aura - - Tradiţii spirituale - - Teste - - Explicaţii - Profeţii - - Definiţii - - Civilizaţii antice - - Credinţa Bahá'í - - Budism - - China - Religiile abrahamice - - Iudaism - - - She'ol - Experienţe aproape de moarte - - Caracteristici - Viaţa de Apoi (Viaţa de după moarte) - - Filozofia vieţii de Apoi - - - Filozofia modernă - - - Filozofia procesului - - Ştiinţa despre viaţa de Apoi - Modele metafizice ale Vieţii de Apoi - - Reîncarnarea - - Raiul şi iadul - - Limbo - - Purgatoriul - Nemurirea - - Abordări ştiinţifice - - Abordări religioase - - Nemurirea biologică - - - Specii biologice nemuritoare - - - Evoluţia îmbătrânirii - - Speranţe privind nemurirea biologică umană - - - Substanţe care prelungesc durata de viaţă - - - Nemurirea tehnologică - - - Crionica - - - Încărcarea minţii la calculator - - - Cibernetica - - - Nemurirea evoluţionară - Strigoi - Fantome - - Tipologia - - - Context antropologic - - Fantomele şi viaţa de apoi - - Teama de fantome - - Atribute comune - - Locaţii - Locaţii paranormale - România - - Judeţul Bacău - - - Conacul Zarifopol, în Filipeşti - - Bucureşti - - - Mănăstirea Chiajna - - - Hotel Cişmigiu - - - "Casa Diavolului", pe strada General Praporgescu - - - Orfelinatul pe strada Franceză - - - Casa Vernescu pe Calea Victoriei - - - Iazul Vrăjitoarelor - - Judeţul Călăraşi - - - Pădurea Călugăreasa - - Judeţul Cluj - - - Castelul Bánffy - - - Pădurea Hoia - - Judeţul Dâmboviţa - - - Trinitatea Miresei pe DN7 - - Judeţul Dolj - - - Pădurea Radovan - - Judeţul Iaşi - - - Casa Gavril Buzatu pe Manta Roşie - - - Dealul Lunganilor - - Judeţul Maramureş - - - Drumul European E58 lângă Cicărlău Parapsihologia - Terminologie - Cercetări - - Domenii - - Metodologie - - Cercetări experimentale - Percepţiile extrasenzoriale - - Investigarea parapsihologică - - Percepţiile extrasenzoriale şi hipnoza - Psihochinezia (telechinezia) - - Credinţa - - - Subdomenii ale telechineziei - - - Aptitudini notabile ale capabilităţilor psihocinetice Ezoterism - Etimologie - Cosmologia ezoterică - - Gnosticism - - Cabala - - Neoplatonism - - Teosofia şi antroposofia - - Max Theon şi "filosofia cosmică" - Ocultism - - Ştiinţă şi ocultism - - - Calităţi oculte - Magia - - Caracteristici comune în practica magiei - - - Ritualuri - - - Simboluri magice - - - Limbajul magiei - - - Magicieni - - - Vrăjitoria - - Teorii - - - Origini antropologice şi psihologice - Clarviziunea - - Aspecte ale clarviziunii - - Vizualizarea de la distanţă - Geomanţia - Tarot - - Arcana mare şi arcana mică - - Utilizare - - - Cărţi inversate - - - Desfăşurări - Masoneria - - Istoria masoneriei - - Loja masonică - - Structura masoneriilor - - - Recunoaşterea masonică reciprocă - Taoism - - Categorisire - - Ceaiul şi taoismul - - Credinţe taoiste - - Principii - - - Tao - - - De (Te) - - - Wu wei - - - Pu - - Spiritualitatea - - Escatologia Astrologia - Principii şi practici - - Astrologia occidentală - - Astrologia hindusă - - Astrologia chineză şi din Asia de Est - Astrologia şi ştiinţa - - Delimitarea - Astrohărţi - Principiile de bază ale astrologiei - - Modele astrologice - - Astrologia predictivă - Tradiţii în astrologie - - Tradiţii istorice - - Tradiţii ezoterice - Horoscop - - Etimologie - - Concepte în astrologia occidentală - - - Unghiuri - - - Zodiacul - - - Case - - Construcţia unui horoscop în astrologia occidentală - - - Case - - - Plasamente ale planetelor - - - Aspecte - - - Ascendentul - - Horoscopul chinez - Bioritm - - Teoria - - Calculul - - Plauzibilitate Vrăjitoria - Definiţii - Practici invocate de vrăjitoare - - Descântece - - Invocarea morţii - Vrăjitoarele bune - Demonologia Referinţe Despre autor - Nicolae Sfetcu - - De același autor - - Contact Editura - MultiMedia Publishing . 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Sự tiến bộ nhanh chóng của các nền tảng Công nghệ Thông tin (CNTT) và ngôn ngữ lập trình đã làm thay đổi hình thái vận động và phát triển của xã hội loài người. Không gian mạng và các tiện ích đi kèm ngày càng được mở rộng, dẫn đến sự chuyển dịch dần từ đời sống trong thế giới thực sang đời sống trong thế giới ảo (còn gọi là không gian mạng hay không gian số). Sự mở (...) rộng và phát triển của các Mô hình Xử lý Ngôn ngữ Tự nhiên (Natural Language Processing – NLP) và Mô hình Ngôn ngữ Lớn (Large Language Model – LLM) thể hiện những đặc điểm giống con người về lập luận, nhận thức, sự chú ý và sáng tạo đã giúp con người vượt qua rào cản về khả năng vận hành. Đi kèm với các tiềm năng to lớn của AI là những lỗ hổng bảo mật mới và các rủi ro về an ninh thông tin phức tạp hơn. Khi xã hội vẫn đang cố gắng chuyển mình sang giai đoạn mới để có thể thích ứng với sự biến đổi công nghệ, cuộc cách mạng AI lại tiếp tục diễn ra đòi hỏi chúng ta phải suy nghĩ lại quỹ đạo của quá trình chuyển đổi xã hội vì nó có thể làm trầm trọng hơn các rủi ro an ninh thông tin được trình bày ở trên. Cụ thể, xã hội cần tiến hóa như thế nào để có thể bắt kịp với sự biến đổi mang tính đột phá tạo ra bởi làn sóng công nghệ AI hiện nay? Làm sao để quản lý và tận dụng sức mạnh của chúng trong khi vẫn đảm bảo được an ninh thông tin khi không gian và thời gian sống trong thế giới ảo của chúng ta ngày càng tăng? Bài viết này sẽ giúp làm sáng tỏ và trả lời các câu hỏi này. (shrink)

Sự tiến bộ nhanh chóng của các nền tảng Công nghệ Thông tin (CNTT) và ngôn ngữ lập trình đã làm thay đổi hình thái vận động và phát triển của xã hội loài người. Không gian mạng và các tiện ích đi kèm ngày càng được mở rộng, dẫn đến sự chuyển dịch dần từ đời sống trong thế giới thực sang đời sống trong thế giới ảo (còn gọi là không gian mạng hay không gian số). Sự mở (...) rộng và phát triển của các Mô hình Xử lý Ngôn ngữ Tự nhiên (Natural Language Processing – NLP) và Mô hình Ngôn ngữ Lớn (Large Language Model – LLM) thể hiện những đặc điểm giống con người về lập luận, nhận thức, sự chú ý và sáng tạo đã giúp con người vượt qua rào cản về khả năng vận hành. Đi kèm với các tiềm năng to lớn của AI là những lỗ hổng bảo mật mới và các rủi ro về an ninh thông tin phức tạp hơn. Khi xã hội vẫn đang cố gắng chuyển mình sang giai đoạn mới để có thể thích ứng với sự biến đổi công nghệ, cuộc cách mạng AI lại tiếp tục diễn ra đòi hỏi chúng ta phải suy nghĩ lại quỹ đạo của quá trình chuyển đổi xã hội vì nó có thể làm trầm trọng hơn các rủi ro an ninh thông tin được trình bày ở trên. Cụ thể, xã hội cần tiến hóa như thế nào để có thể bắt kịp với sự biến đổi mang tính đột phá tạo ra bởi làn sóng công nghệ AI hiện nay? Làm sao để quản lý và tận dụng sức mạnh của chúng trong khi vẫn đảm bảo được an ninh thông tin khi không gian và thời gian sống trong thế giới ảo của chúng ta ngày càng tăng? Phần một của bài viết sẽ bàn về các vấn đề và rủi ro ảnh hưởng đến an ninh thông tin trong kỷ nguyên AI, cũng như là vai trò, lợi thế, và cơ hội của việc vận dụng AI cho mục đích đảm bảo an ninh thông tin. (shrink)

Estimating word complexity is a well-established task in computer-assisted language learning. So far, however, complexity estimation has been largely limited to comprehension. This neglects words that are easy to comprehend, but hard to produce. We introduce semantic error prediction (SEP) as a novel task that assesses the production complexity of content words. Given the corrected version of a learner-produced text, a system has to predict which content words replace tokens from the original text. We present and analyse one example of (...) such a semantic error prediction dataset, which we generate from an error correction dataset. As neural baselines, we use BERT, RoBERTa, and LLAMA2 embeddings for SEP. We show that our models can already improve downstream applications, such as predicting essay vocabulary scores. (shrink)

Unless and until our society recognizes cyber bullying for what it is, the suffering of thousands of silent victims will continue.” ~ Anna Maria Chavez. There had been series of research on cyber bullying which are unable to provide reliable solution to cyber bullying. In this research work, we were able to provide a permanent solution to this by developing a model capable of detecting and intercepting bullying incoming and outgoing messages with 92% accuracy. We also developed a chatbot automation (...) messaging system to test our model leading to the development of Artificial Intelligence powered anti-cyber bullying system using machine learning algorithm of Multinomial Naïve Bayes (MNB) and optimized linear Support Vector Machine(SVM). Our model is able to detect and intercept bullying outgoing and incoming bullying messages and take immediate action. (shrink)