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Gotowa bibliografia na temat „Sentient Machine”

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Data publikacji: 25 lipca 2024

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Artykuły w czasopismach na temat "Sentient Machine"

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Bronfman, Zohar, Simona Ginsburg i Eva Jablonka. "When Will Robots Be Sentient?" Journal of Artificial Intelligence and Consciousness 08, nr 02 (6.08.2021): 183–203. http://dx.doi.org/10.1142/s2705078521500168.

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The current failure to construct an artificial intelligence (AI) agent with the capacity for domain-general learning is a major stumbling block in the attempt to build conscious robots. Taking an evolutionary approach, we previously suggested that the emergence of consciousness was entailed by the evolution of an open-ended domain-general form of learning, which we call unlimited associative learning (UAL). Here, we outline the UAL theory and discuss the constraints and affordances that seem necessary for constructing an AI machine exhibiting UAL. We argue that a machine that is capable of domain-general learning requires the dynamics of a UAL architecture and that a UAL architecture requires, in turn, that the machine is highly sensitive to the environment and has an ultimate value (like self-persistence) that provides shared context to all its behaviors and learning outputs. The implementation of UAL in a machine may require that it is made of “soft” materials, which are sensitive to a large range of environmental conditions, and that it undergoes sequential morphological and behavioral co-development. We suggest that the implementation of these requirements in a human-made robot will lead to its ability to perform domain-general learning and will bring us closer to the construction of a sentient machine.
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Castro, Paulo. "Lying, computers and self-awareness". Kairos. Journal of Philosophy & Science 24, nr 1 (1.12.2020): 10–34. http://dx.doi.org/10.2478/kjps-2020-0009.

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Abstract From the initial analysis of John Morris in 1976 about if computers can lie, I have presented my own treatment of the problem using what can be called a computational lying procedure. One that uses two Turing Machines. From there, I have argued that such a procedure cannot be implemented in a Turing Machine alone. A fundamental difficulty arises, concerning the computational representation of the self-knowledge a machine should have about the fact that it is lying. Contrary to Morris’ claim, I have thus suggested that computers – as far as they are Turing Machines – cannot lie. Consequently, I have claimed that moral agency attribution to a robot or any other automated AI system, cannot be made, strictly grounded on imitating behaviors. Self-awareness as an ontological grounding for moral attribution must be evoked. This can pose a recognition problem from our part, should the sentient system be the only agent capable of acknowledging its own sentience.
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Tellols, Dolça, Maite Lopez-Sanchez, Inmaculada Rodríguez, Pablo Almajano i Anna Puig. "Enhancing sentient embodied conversational agents with machine learning". Pattern Recognition Letters 129 (styczeń 2020): 317–23. http://dx.doi.org/10.1016/j.patrec.2019.11.035.

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Biever, Celeste. "9 Will we ever build a sentient machine?" New Scientist 206, nr 2754 (marzec 2010): 32–33. http://dx.doi.org/10.1016/s0262-4079(10)60801-9.

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Torrens, Paul M. "Smart and Sentient Retail High Streets". Smart Cities 5, nr 4 (29.11.2022): 1670–720. http://dx.doi.org/10.3390/smartcities5040085.

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Here, we examine the extension of smart retailing from the indoor confines of stores, outward to high streets. We explore how several technologies at the union of retail intelligence and smart city monitoring could coalesce into retail high streets that are both smart and sentient. We examine the new vantages that smart and sentient retail high streets provide on the customer journey, and how they could transform retailers’ sway over customer experience with new reach to the public spaces around shops. In doing so, we pursue a three-way consideration of these issues, examining the technology that underpins smart retailing, new advances in artificial intelligence and machine learning that beget a level of street-side sentience, and opportunities for retailers to map the knowledge that those technologies provide to individual customer journeys in outdoor settings. Our exploration of these issues takes form as a review of the literature and the introduction of our own research to prototype smart and sentient retail systems for high streets. The topic of enhancing retailers’ acuity on high streets has significant currency, as many high street stores have recently been struggling to sustain custom. However, the production and application of smart and sentient technologies at hyper-local resolution of the streetscape conjures some sobering considerations about shoppers’ and pedestrians’ rights to privacy in public.
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Smith, G. W. "Art, Aliens and the Machine". Leonardo 51, nr 5 (październik 2018): 551–52. http://dx.doi.org/10.1162/leon_a_01222.

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With the assumption that the extraordinary discoveries being made in planetary science will soon trigger a conviction of the likelihood of contact with sentient extraterrestrial beings—and with the further realization that such beings may well depend upon our visual arts as their primary point of reference in respect to our own species—this short paper uses an imaginative approach to develop some corollary ideas, and, in addition, to throw a spotlight on pioneering “systems art” theorist and visionary cosmic citizen Jack Burnham.
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Smith, Dale. "The Trouble with Sentient Beings, and: The Time Machine Paradox". Colorado Review 37, nr 3 (2010): 144–46. http://dx.doi.org/10.1353/col.2010.0037.

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Jocz, Artur. "If Machines Want to Dream... Adam Wiśniewski-Snerg on Ethical Consequences of There Being No Substantial Distinction between Humans and Robots". ETHICS IN PROGRESS 10, nr 2 (10.10.2019): 45–51. http://dx.doi.org/10.14746/eip.2019.2.5.

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Adam Wiśniewski-Snerg (1937-1995) was a Polish science fiction writer. In his novel Robot (1973), he made an attempt at a literary visualization of a machine acquiring human identity. In this article I would like to follow the ethical consequences of such situations in created literary worlds. It is worth remembering, however, that these artistic worlds often serve to test non-literary reality. In his novel, Wiśniewski-Snerg also dealt with the problem of human feelings (e.g. moral dilemmas) in a thinking machine, which is formed in the image and likeness of a human being. Such literary reflection is valuable, partly because it enters into an interesting dialogue with the work of Bruno Schulz (1892- 1942), one of the most important Polish writers of the 20th century. It is also one of the first attempts in Polish literature to address the issue of sentient machines, and is a kind of preview of contemporary dilemmas connected with the work on the creation of artificial intelligence. An example of such a dilemma is the issue of the sentient machine’s perception of the tasks imposed on it by the human-constructor. Perhaps it will start to experience them as a kind of unethical oppression. In Wiśniewski-Snerg’s writing this problem of is, of course, expressed in a metaphorical way.
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PATRA, INDRAJIT. "The Battle Within and the Battle Without: The Posthuman Worldview of Ken MacLeod’s The Corporation Wars Trilogy". American, British and Canadian Studies 37, nr 1 (1.12.2021): 85–104. http://dx.doi.org/10.2478/abcsj-2021-0019.

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Abstract The present essay seeks to analyze Scottish science fiction writer Ken MacLeod’s The Corporation Wars trilogy (2016-2017) as an amalgam of politico-philosophical ideas set against the background of posthumanism. MacLeod’s far-future posthuman world-building relies on the conventional tropes of science fiction (man-machine hybrids, brain uploading, digital resurrection, and the agency of sentient machines) to engage with pressing ideologies (the master-slave dialectics, the historical perpetuation of age-old conflict between progressive and reactionary forces, the ethics of machinic consciousness). MacLeod’s novels project a postbinarist worldview where outmoded binary oppositions between life and death, the real and the virtual, the human and the machinic are constantly abolished, but which still preserves persistent ideological divisions.
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Wilson, Daniel C. S., Mariona Coll Ardanuy, Kaspar Beelen, Barbara McGillivray i Ruth Ahnert. "The Living Machine: A Computational Approach to the Nineteenth-Century Language of Technology". Technology and Culture 64, nr 3 (lipiec 2023): 875–902. http://dx.doi.org/10.1353/tech.2023.a903976.

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abstract: This article examines a long-standing question in the history of technology concerning the trope of the living machine. The authors do this by using a cutting-edge computational method, which they apply to large collections of digitized texts. In particular, they demonstrate the affordances of a neural language model for historical research. In a deliberate maneuver, the authors use a type of model, often portrayed as sentient today, to detect figures of speech in nineteenth-century texts that portrayed machines as self-acting, automatic, or alive. Their masked language model detects unusual or surprising turns of phrase, which could not be discovered using simple keyword search. The authors collect and close read such sentences to explore how figurative language produced a context that conceived humans and machines as interchangeable in complicated ways. They conclude that, used judiciously, language models have the potential to open up new avenues of historical research.
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Rozprawy doktorskie na temat "Sentient Machine"

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OGURI, PEDRO. "MACHINE LEARNING FOR SENTIMENT CLASSIFICATION". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2006. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=9947@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
Sentiment Analysis é um problema de categorização de texto no qual deseja-se identificar opiniões favoráveis e desfavoráveis com relação a um tópico. Um exemplo destes tópicos de interesse são organizações e seus produtos. Neste problema, documentos são classificados pelo sentimento, conotação, atitudes e opiniões ao invés de se restringir aos fatos descritos neste. O principal desafio em Sentiment Classification é identificar como sentimentos são expressados em textos e se tais sentimentos indicam uma opinião positiva (favorável) ou negativa (desfavorável) com relação a um tópico. Devido ao crescente volume de dados disponível na Web, onde todos tendem a ser geradores de conteúdo e expressarem opiniões sobre os mais variados assuntos, técnicas de Aprendizado de Máquina vem se tornando cada vez mais atraentes. Nesta dissertação investigamos métodos de Aprendizado de Máquina para Sentiment Analysis. Apresentamos alguns modelos de representação de documentos como saco de palavras e N-grama. Testamos os classificadores SVM (Máquina de Vetores Suporte) e Naive Bayes com diferentes modelos de representação textual e comparamos seus desempenhos.
Sentiment Analysis is a text categorization problem in which we want to identify favorable and unfavorable opinions towards a given topic. Examples of such topics are organizations and its products. In this problem, docu- ments are classifed according to their sentiment, connotation, attitudes and opinions instead of being limited to the facts described in it. The main challenge in Sentiment Classification is identifying how sentiments are expressed in texts and whether they indicate a positive (favorable) or negative (unfavorable) opinion towards a topic. Due to the growing volume of information available online in an environment where we all tend to be content generators and express opinions on a variety of subjects, Machine Learning techniques have become more and more attractive. In this dissertation, we investigate Machine Learning methods applied to Sentiment Analysis. We present document representation models such as bag-of-words and N-grams.We compare the performance of the Naive Bayes and the Support Vector Machine classifiers for each proposed model
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Alotaibi, Saud Saleh. "Sentiment analysis in the Arabic language using machine learning". Thesis, Colorado State University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3720340.

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Sentiment analysis has recently become one of the growing areas of research related to natural language processing and machine learning. Much opinion and sentiment about specific topics are available online, which allows several parties such as customers, companies and even governments, to explore these opinions. The first task is to classify the text in terms of whether or not it expresses opinion or factual information. Polarity classification is the second task, which distinguishes between polarities (positive, negative or neutral) that sentences may carry. The analysis of natural language text for the identification of subjectivity and sentiment has been well studied in terms of the English language. Conversely, the work that has been carried out in terms of Arabic remains in its infancy; thus, more cooperation is required between research communities in order for them to offer a mature sentiment analysis system for Arabic. There are recognized challenges in this field; some of which are inherited from the nature of the Arabic language itself, while others are derived from the scarcity of tools and sources.

This dissertation provides the rationale behind the current work and proposed methods to enhance the performance of sentiment analysis in the Arabic language. The first step is to increase the resources that help in the analysis process; the most important part of this task is to have annotated sentiment corpora. Several free corpora are available for the English language, but these resources are still limited in other languages, such as Arabic. This dissertation describes the work undertaken by the author to enrich sentiment analysis in Arabic by building a new Arabic Sentiment Corpus. The data is labeled not only with two polarities (positive and negative), but the neutral sentiment is also used during the annotation process.

The second step includes the proposal of features that may capture sentiment orientation in the Arabic language, as well as using different machine learning classifiers that may be able to work better and capture the non-linearity with a richly morphological and highly inflectional language, such as Arabic. Different types of features are proposed. These proposed features try to capture different aspects and characteristics of Arabic. Morphological, Semantic, Stylistic features are proposed and investigated. In regard with the classifier, the performance of using linear and nonlinear machine learning approaches was compared. The results are promising for the continued use of nonlinear ML classifiers for this task. Learning knowledge from a particular dataset domain and applying it to a different domain is one useful method in the case of limited resources, such as with the Arabic language. This dissertation shows and discussed the possibility of applying cross-domain in the field of Arabic sentiment analysis. It also indicates the feasibility of using different mechanisms of the cross-domain method.

Other work in this dissertation includes the exploration of the effect of negation in Arabic subjectivity and polarity classification. The negation word lists were devised to help in this and other natural language processing tasks. These words include both types of Arabic, Modern Standard and some of Dialects. Two methods of dealing with the negation in sentiment analysis in Arabic were proposed. The first method is based on a static approach that assumes that each sentence containing negation words is considered a negated sentence. When determining the effect of negation, different techniques were proposed, using different word window sizes, or using base phrase chunk. The second approach depends on a dynamic method that needs an annotated negation dataset in order to build a model that can determine whether or not the sentence is negated by the negation words and to establish the effect of the negation on the sentence. The results achieved by adding negation to Arabic sentiment analysis were promising and indicate that the negation has an effect on this task. Finally, the experiments and evaluations that were conducted in this dissertation encourage the researchers to continue in this direction of research.

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Paknejad, Sepideh. "Sentiment classification on Amazon reviews using machine learning approaches". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233551.

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As online marketplaces have been popular during the past decades, the online sellers and merchants ask their purchasers to share their opinions about the products they have bought. As a result, millions of reviews are being generated daily which makes it difficult for a potential consumer to make a good decision on whether to buy the product. Analyzing this enormous amount of opinions is also hard and time consuming for product manufacturers. This thesis considers the problem of classifying reviews by their overall semantic (positive or negative). To conduct the study two different supervised machine learning techniques, SVM and Naïve Bayes, has been attempted on beauty products from Amazon. Their accuracies have then been compared. The results showed that the SVM approach outperforms the Naïve Bayes approach when the data set is bigger. However, both algorithms reached promising accuracies of at least 80%.
Eftersom marknadsplatser online har varit populära under de senaste decennierna, så har online-säljare och inköpsmän ställt kunderna frågor om deras åsikter gällande varorna de har köpt. Som ett resultat genereras miljontals recensioner dagligen vilket gör det svårt för en potentiell konsument att fatta ett bra beslut om de ska köpa produkten eller inte. Att analysera den enorma mängden åsikter är också svårt och tidskrävande för produktproducenter. Denna avhandling tar upp problemet med att klassificera recensioner med deras övergripande semantiska (positiva eller negativa). För att genomföra studien har två olika övervakade maskininlärningstekniker, SVM och Naïve Bayes, testats på recensioner av skönhetsprodukter från Amazon. Deras noggrannhet har sedan jämförts. Resultaten visade att SVM-tillvägagångssättet överträffar Naïve Bayes-tillvägagångssättet när datasetet är större. Båda algoritmerna nådde emellertid lovande noggrannheter på minst 80%.
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WESTLING, ANDERS. "Sentiment Analysisof Microblog Posts from a Crisis Eventusing Machine Learning". Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-138428.

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With social media services becoming more and more popular, there now exists a constant stream of opinions publicly available on the Internet. These opinions can be analyzed to find the users’ sentiments towards things. One example of interest is to see how people are feeling during a crisis situation to get a better understanding about what kind of help that would be the most useful at the moment. The goal of this degree project has been to see if it is possible to create an automatic classifier, based on machine learning techniques, that can accurately determine whether a microblog post written during a political event in Russia is for, against, or neutral towards the group of people being at the center of the event. Because of the shortness of microblog texts and the informal language often used in them, the problem is expected to be more difficult compared to sentiment analysis of normal length texts. A number of different machine learning algorithms were studied along with different ways to convert the microblog texts into a representation that can be used by the classifier algorithms. The most promising of these algorithms and representations were implemented and tested to see if an accurate classifier could be obtained. The results show that the algorithms are not good enough to create a sufficiently accurate classifier with the training data used. One major factor is believed to be the small training data set used. A better classifier could potentially be achieved by training the classifier with more microblog posts. It is of interest to examine other sentiment classifications of microblog posts, since the one used in this project is believed to be especially difficult. This study and previous research on similar classifications suggest that this is a difficult problem that requires more work if an accurate classifier is to be obtained.
I och med att tjänster för sociala medier blir allt mer populära, existerar det nu en konstant ström av åsikter fritt tillgängliga på internet. Dessa åsikter kan analyseras för att finna användarnas känslor kring olika ämnen. Ett exempel av intresse är att se hur folk känner under en krissituation för att få en bättre uppfattning om vilken typ av hjälp som skulle vara till mest nytta för tillfället. Målet med detta examensarbete har varit att se om det är möjligt att skapa en automatisk klassificerare, baserad på maskininlärningsmetoder, som med precision kan avgöra huruvida ett mikroblogginlägg skrivet under en politisk händelse i Ryssland är för, emot, eller neutral till den grupp människor som händelsen kretsar kring. Problemet väntas vara svårare än sentimentanalys av normallånga texter, detta eftersom mikroblogginlägg är mycket kortare och ofta har ett informellt språk. Ett antal olika algoritmer för maskininlärning studerades tillsammans med olika metoder för att representera mikroblogginläggen på ett format som algoritmerna kan arbeta med. De mest lovande utav dessa algoritmer och representationer implementerades och testades för att se om en effektiv klassificerare kunde åstakommas. Resultaten visar att algoritmerna inte är tillräckligt bra för att skapa en tillräckligt precis klassificerare med den träningsdata som användes. En stor faktor tros vara den lilla mängden träningsdata som användes. En bättre klassificerare skulle potentiellt kunna uppnås om genom att använda fler mikrobloginlägg som träningsdata. Det vore även intressant att utforska andra sentimentklassificeringar utav mikroblogginlägg, då den som användes i det här arbetet tros vara särskilt svår. Den här studien och tidigare forskning på liknande klassificeringar talar för att detta är ett svårt problem som kräver mer arbete för att en precis klassificerare ska kunna erhållas.
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Erogul, Umut. "Sentiment Analysis In Turkish". Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610616/index.pdf.

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Sentiment analysis is the automatic classification of a text, trying to determine the attitude of the writer with respect to a specific topic. The attitude may be either their judgment or evaluation, their feelings or the intended emotional communication. The recent increase in the use of review sites and blogs, has made a great amount of subjective data available. Nowadays, it is nearly impossible to manually process all the relevant data available, and as a consequence, the importance given to the automatic classification of unformatted data, has increased. Up to date, all of the research carried on sentiment analysis was focused on English language. In this thesis, two Turkish datasets tagged with sentiment information is introduced and existing methods for English are applied on these datasets. This thesis also suggests new methods for Turkish sentiment analysis.
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Di, Gennaro Pierluigi. "Due approcci alla sentiment polarity classification di tweet per la lingua italiana". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/13270/.

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Questo lavoro di tesi si pone l'obiettivo di fornire un'ampia panoramica sull'attuale stato dell'arte della ricerca sulla sentiment analysis mostrando le metodologie, le tecniche e le applicazioni realizzate negli ultimi anni e di presentare le implementazioni concrete (ed i risultati ottenuti) di due diversi sistemi per la sentiment polarity classification di tweet per la lingua italiana. Il primo sistema (FICLIT+CS@Unibo System) utilizza un approccio basato sull'orientamento semantico tramite la realizzazione e l'utilizzo di un lessico annotato e la propagazione della polarità lungo alberi sintattici mentre il secondo utilizza algoritmi stocastico/statistici di machine learning per la creazione di un modello generalizzato per la classificazione del sentimento a partire da un training set annotato.
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Vaswani, Vishwas. "Predicting sentiment-mention associations in product reviews". Thesis, Kansas State University, 2012. http://hdl.handle.net/2097/13714.

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Master of Science
Department of Computing and Information Sciences
Doina Caragea
With the rising trend in social networking, more people express their opinions on the web. As a consequence, there has been an increase in the number of blogs where people write reviews about the products they buy or services they experience. These reviews can be very helpful to other potential customers who want to know the pros and cons of a product, and also to manufacturers who want to get feedback from customers about their products. Sentiment analysis of online data (such as review blogs) is a rapidly growing field of research in Machine Learning, which can leverage online reviews and quickly extract the sentiment of a whole blog. The accuracy of a sentiment analyzer relies heavily on correctly identifying associations between a sentiment (opinion) word and the targeted mention (token or object) in blog sentences. In this work, we focus on the task of automatically identifying sentiment-mention associations, in other words, we identify the target mention that is associated with a sentiment word in a sentence. Support Vector Machines (SVM), a supervised machine learning algorithm, was used to learn classifiers for this task. Syntactic and semantic features extracted from sentences were used as input to the SVM algorithm. The dataset used in the work has reviews from car and camera domain. The work is divided into two phases. In the first phase, we learned domain specific classifiers for the car and camera domains, respectively. To further improve the predictions of the domain specific classifiers we investigated the use of transfer learning techniques in the second phase. More precisely, the goal was to use knowledge from a source domain to improve predictions for a target domain. We considered two transfer learning approaches: a feature level fusion approach and a classifier level fusion approach. Experimental results show that transfer learning can help to improve the predictions made using the domain specific classifier approach. While both the feature level and classifier level fusion approaches were shown to improve the prediction accuracy, the classifier level fusion approach gave better results.
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Svensson, Kristoffer. "Sentiment Analysis With Convolutional Neural Networks : Classifying sentiment in Swedish reviews". Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-64768.

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Today many companies exist and market their products and services on social medias, and therefore may receive reviews and thoughts from their end-users directly in these social medias. Reading every text by hand can be time-consuming, so by analysing the sentiment for all texts give the companies an overview how positive or negative the users are on a specific subject. Sentiment analysis is a feature that Beanloop AB is interested in implementing in their future projects and this thesis research problem was to investigate how deep learning could be used for this task. It was done by conducting an experiment with deep learning and neural networks. Several convolutional neural network models were implemented with different settings to find a combination of settings that gave the highest accuracy on the given test dataset. There were two different kind of models, one kind classifying positive and negative, and the second classified the previous two categories but also neutral. The training dataset and the test dataset contained data from two recommendation sites, www.reco.se and se.trustpilot.com. The final result shows that when classifying three categories (positive, negative and neutral) the models had problems to reach an accuracy at 85%, were only one model reached 80% accuracy as best on the test dataset. However, when only classifying two categories (positive and negative) the models showed very good results and reached almost 95% accuracy for every model.
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CAMBA, GIACOMO. "Machine Learning in Social Media Sentiment Classification and Trading Strategy Design". Doctoral thesis, Università degli Studi di Cagliari, 2022. http://hdl.handle.net/11584/333407.

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The goal of this thesis is to build a trading strategy that jointly uses quantitative and qualitative sentiment variables. In particular, we want to see if we can improve the equity line of a trading bot when trained in a trading environment in which we also insert sentiment variables and attention measures in addition to price and volume variables. Our target market is the US stock market and in particular the S&P 500. As a proxy for the equity investors' attention, we use the S&P 500 Google Search Volume Index downloaded from Google Trend, while the sentiment variable is built from textual data of the 4 main financial social media. The text corpus includes the tweets posted on StockTwits and Twitter and the comments published on the Yahoo Finance and Investing Message Board concerning the ticker of the American stock index and its Etf. The downloaded messages are over 5.7 million and cover a period of 15 years from 2006 to 2021. 32% of this data has been labeled by users as bullish or bearish, while the remainder is unlabeled. This meant for us to research the best sentiment classifier and use it to label messages that didn't have one, as we wanted our sentiment variable to include the full amount of data collected. To do this, we adopted the two main financial sentiment analysis approaches on the labeled data, namely the lexicon approach and the machine learning model approach. After testing the classification skills of 16 of the main financial and non-financial sentiment lexicons, and having verified their poor performance, we necessarily had to undertake the machine learning strategy. This meant, first of all establishing the best word embedding techniques distinct between frequentist and probabilistic methods, then comparing different unsupervised learning algorithms to understand if there could be some data dimensionality reduction techniques without losing the most precious information, and finally testing the classification capabilities of the most advanced machine learning models in textual data classification field. Supervised model training included exhaustive parametric research via 5-folds cross-validation for simpler models and random parametric research for more complex models. Ultimately, we find that the best sentiment classifier on our data is the LSTM model, with a test accuracy of 77%. After having employed it to label the unlabeled data, we were able to build a sentiment variable expressing investors' bullish and/or bearish moods. Subsequently, the sentiment and attention variables were aggregated to the price and volume data of the US stock market ETF to create a reinforcement learning environment in which to train our agent. By doing several tests, we discover that our agent achieves a significantly higher return when the sentiment and attention variables are also included in the RL environment.
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CAPUA, M. DI. "A DEEP LEARNING APPROACH FOR SENTIMENT ANALYSIS". Doctoral thesis, Università degli Studi di Milano, 2017. http://hdl.handle.net/2434/467844.

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La Sentiment Analysis si riferisce alla analisi qualitativa volta ad identificare e classificare opinioni contenute in frasi e testi, allo scopo di stabilire lo “stato d’animo” dell’autore rispetto ad un particolare argomento o prodotto, e di determinare se tale stato è di fatto positivo, negativo oppure neutrale. Le opinioni espresse in un testo, come ad esempio giudizi, sentimenti ed emozioni, sono di recente diventate oggetto di studio e di ricerca sia in ambito accademico che industriale. Sfortunatamente la comprensione del linguaggio, applicata a commenti di utenti, è un attività estremamente complessa per una macchina, specialmente se ci si riferisce ai contesti dei moderni social network. Le modalità in cui le persone si esprimono in linguaggio naturale, sono molteplici, e l’utilizzo “informale” della lingua adottato tipicamente nei social netowrks, genera frasi spesso dense di errori, modi di dire (slang), costrutti sintattici ”personalizzati”, o anche frasi arricchite da caratteri speciali (come l’hashtag in Twitter), il che complica notevolmente l’analisi. Recentemente, le tecniche di Deep Learning, stanno emergendo nel panorama del machine learning, come un modello computazionale che può essere adoperato con efficacia per scoprire relazioni semantiche complesse, all’interno di un testo, anche senza la necessità di dover individuare a priori caratteristiche (features) di tali relazioni. Questi approcci hanno migliorato l’attuale stato dell’arte in diversi settori della Sentiment Analysis, come ad esempio la classificazione di frasi o di documenti, l’apprendimento basato su lexicon, fino ad arrivare alla analisi di fenomeni complessi come il cyber bullismo. I contributi di questa tesi sono di due tipi. Il primo contributo fornito, relativo ad aspetti generali di Sentiment Analysis, riguarda la proposta di un modello di rete neurale semi supervisionata, basato sulle reti di tipo Deep Belief, in grado di affrontare l’incertezza dei dati insita nelle frasi testuali, con particolare riferimento alla lingua italiana. Il modello proposto è stato testato rispetto a diversi datasets presi dalla letteratura di riferimento, composti da testi relativi a critiche cinematografiche, adottando una rappresentazione dell’informazione basata su vettori (Word2Vec) ed introducendo anche metodi derivati dal campo del Natural Language Processing (NLP). Il secondo contributo fornito in questa tesi, partendo dall’assunto che il cyber bullismo può essere considerato come un caso particolare di Sentiment Analysis, propone un approccio non supervisionato alla rilevazione automatica di tracce di cyber bullismo all’interno di social networks, basato sia su di una rete neurale di tipo GHSOM (Growing Hierarchical Self Organizing Map), sia su di un modello di caratteristiche (features) predefinito. Il modello non supervisionato proposto dimostra di raggiungere comunque risultati interessanti rispetto ai tipici modelli supervisionati, applicati solitamente in questo ambito.
Sentiment Analysis refers to the process of computationally identifying and categorizing opinions expressed in a piece of text, in order to determine whether the writer’s attitude towards a particular topic or product is positive, negative, or even neutral. The views expressed and its related concepts, such as feelings, judgments, and emotions have become recently a subject of study and research in both academic and industrial areas. Unfortunately language comprehension of user comments, especially in social networks, is inherently complex to computers. The ways in which humans express themselves with natural language are nearly unlimited and informal texts is riddled with typos, misspellings, badly set up syntactic constructions and also specific symbols (e.g. hashtags in Twitter) which exponentially complicate this task. Recently, deep learning approaches are emerging as powerful computational models that discover intricate semantic representations of texts automatically from data without hand-made feature engineering. These approaches have improved the state-of-the-art in many Sentiment Analysis tasks including sentiment classification of sentences or documents, sentiment lexicon learning and also in more complex problems as cyber bullying detection. The contributions of this work are twofold. First, related to the general Sentiment Analysis problem, we propose a semi-supervised neural network model, based on Deep Belief Networks, able to deal with data uncertainty for text sentences in Italian language. We test this model against some datasets from literature related to movie reviews, adopting a vectorized representation of text (Word2Vec) and exploiting methods from Natural Language Processing (NLP) pre-processing. Second, assuming that the cyber bullying phenomenon can be treated as a particular Sentiment Analysis problem, we propose an unsupervised approach to automatic cyber bullying detection in social networks, based both on Growing Hierarchical Self Organizing Map (GHSOM) and on a new specific features model, showing that our solution can achieve interesting results, respect to classical supervised approaches.
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Książki na temat "Sentient Machine"

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Affective computing and sentiment analysis: Emotion, metaphor and terminology. Dordrecht: Springer, 2011.

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Husain, Amir. Sentient Machine: The Coming Age of Artificial Intelligence. Scribner, 2017.

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Amir, Husain. The sentient machine: The coming age of artificial intelligence. 2017.

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The Sentient Machine: The Coming Age of Artificial Intelligence. Scribner, 2018.

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GILCHRIST, Alasdair. Thinking Machines : Book I Robotics: From Mechanical to Sentient Machines. Independently Published, 2017.

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Grimm, Joshua. Ex Machina. Liverpool University Press, 2020. http://dx.doi.org/10.3828/liverpool/9781800348301.001.0001.

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Ex Machina (2014) impressed critics and audiences alike with its bold ideas and all-too-realistic depiction of the unexpected consequences of constructing a sentient being. In his feature directorial debut, Alex Garland uses efficient storytelling, a compelling narrative, and heady concepts to create a modern science fiction masterpiece that explores gender, scientific advancement, and the very concept of humanity, all in a compelling, suspenseful film. Artificial intelligence has long been a sci-fi staple, but here, Garland posits what would happen if, for once, humans, rather than AI, were the real villains. In exploring Ex Machina's ideas about consciousness, embodiment, and masculinity, all through the lens of a misogynist mad scientist, Joshua Grimm argues the result is a fascinating, truly unique film that immediately established Garland as a breakout voice in the landscape of science fiction film.
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Goodman, Adam. The Deportation Machine. Princeton University Press, 2020. http://dx.doi.org/10.23943/princeton/9780691182155.001.0001.

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Constant headlines about deportations, detention camps, and border walls drive urgent debates about immigration and what it means to be an American in the twenty-first century. This book traces the long and troubling history of the U.S. government's systematic efforts to terrorize and expel immigrants over the past 140 years. The book provides needed historical perspective on one of the most pressing social and political issues of our time. It examines how federal, state, and local officials have targeted various groups for expulsion, from Chinese and Europeans at the turn of the twentieth century to Central Americans and Muslims today. It reveals how authorities have singled out Mexicans, nine out of ten of all deportees, and removed most of them not by orders of immigration judges but through coercive administrative procedures and calculated fear campaigns. The book uncovers the machine's three primary mechanisms—formal deportations, “voluntary” departures, and self-deportations—and examines how public officials have used them to purge immigrants from the country and exert control over those who remain. Exposing the pervasive roots of anti-immigrant sentiment in the United States, the book introduces the politicians, bureaucrats, businesspeople, and ordinary citizens who have pushed for and profited from expulsion. It chronicles the devastating human costs of deportation and the innovative strategies people have adopted to fight against the machine and redefine belonging in ways that transcend citizenship.
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Papacharissi, Zizi. Networked Self and Human Augmentics, Artificial Intelligence, Sentience. Taylor & Francis Group, 2018.

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Papacharissi, Zizi. Networked Self and Human Augmentics, Artificial Intelligence, Sentience. Taylor & Francis Group, 2018.

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Papacharissi, Zizi. Networked Self and Human Augmentics, Artificial Intelligence, Sentience. Taylor & Francis Group, 2018.

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Części książek na temat "Sentient Machine"

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Moreno-Ortiz, Antonio. "Sentiment". W Making Sense of Large Social Media Corpora, 141–68. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-52719-7_6.

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AbstractSentiment analysis tools are very powerful when it comes to obtaining a description of the emotional aspect of the contents of a corpus. This chapter describes the methods and tools available, and illustrates what can be achieved with them. Both machine learning ad lexicon-based approaches are described and used, as they can provide different advantages. Whereas machine/deep learning approaches are the state of the art in sentiment classification tasks, lexicon-based tools can provide further insights, as they are able to retrieve the actual sentiment words and expressions used in the corpus. Finally, the role of emojis is discussed and illustrated with a frequency analysis of the most prominent emojis used in the CCTC.
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Cerulli, Giovanni. "Sentiment Analysis". W Fundamentals of Supervised Machine Learning, 365–84. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-41337-7_8.

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Röchert, Daniel, German Neubaum i Stefan Stieglitz. "Identifying Political Sentiments on YouTube: A Systematic Comparison Regarding the Accuracy of Recurrent Neural Network and Machine Learning Models". W Disinformation in Open Online Media, 107–21. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61841-4_8.

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Abstract Since social media have increasingly become forums to exchange personal opinions, more and more approaches have been suggested to analyze those sentiments automatically. Neural networks and traditional machine learning methods allow individual adaption by training the data, tailoring the algorithm to the particular topic that is discussed. Still, a great number of methodological combinations involving algorithms (e.g., recurrent neural networks (RNN)), techniques (e.g., word2vec), and methods (e.g., Skip-Gram) are possible. This work offers a systematic comparison of sentiment analytical approaches using different word embeddings with RNN architectures and traditional machine learning techniques. Using German comments of controversial political discussions on YouTube, this study uses metrics such as F1-score, precision and recall to compare the quality of performance of different approaches. First results show that deep neural networks outperform multiclass prediction with small datasets in contrast to traditional machine learning models with word embeddings.
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Sarkar, Dipanjan, Raghav Bali i Tushar Sharma. "Analyzing Movie Reviews Sentiment". W Practical Machine Learning with Python, 331–72. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-3207-1_7.

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Aggarwal, Charu C. "Opinion Mining and Sentiment Analysis". W Machine Learning for Text, 413–34. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73531-3_13.

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Aggarwal, Charu C. "Opinion Mining and Sentiment Analysis". W Machine Learning for Text, 491–514. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96623-2_15.

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Mahata, Sainik Kumar, Anupam Mondal, Monalisa Dey i Darothi Sarkar. "Sentiment Analysis using Machine Translation". W Applications of Machine Intelligence in Engineering, 371–77. New York: CRC Press, 2022. http://dx.doi.org/10.1201/9781003269793-40.

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Denecke, Kerstin. "Machine Learning-Based Sentiment Analysis Approaches". W Sentiment Analysis in the Medical Domain, 71–78. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-30187-2_11.

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Gerard, Charlie. "Text classification and sentiment analysis". W Practical Machine Learning in JavaScript, 67–134. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6418-8_4.

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Li, Qingyuan, Kai Zhang, Lin Sun i Ruichen Xia. "Detecting Negative Sentiment on Sarcastic Tweets for Sentiment Analysis". W Artificial Neural Networks and Machine Learning – ICANN 2023, 479–91. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44204-9_40.

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Streszczenia konferencji na temat "Sentient Machine"

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Beesley, Philip, Asya Zeliha Ilgun, Giselle Bouron, David Kadish, Jordan Prosser, Rob Gorbet, Dana Kulic, Paul Nicholas i Mateusz Zwierzycki. "Hybrid Sentient Canopy: An implementation and visualization of proprioreceptive curiosity-based machine learning". W ACADIA 2016: Post-Human Frontiers. ACADIA, 2016. http://dx.doi.org/10.52842/conf.acadia.2016.362.

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R. Hodeghatta, Umesh, i Sanath V. Haritsa. "Covid-19 Twitter Sentiments Across the United States in August 2020". W International Conference on AI, Machine Learning and Applications (AIMLA 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.111305.

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COVID-19 has drastically affected the entire nation. This study involved collecting tweets and analyzing the COVID tweets for August 2020. The aim was to understand whether people have expressed sentiments related to COVID-19 across all the states of the United States and find any correlation between the sentiment tweets and the number of actual cases reported. Around 400000 COVID-19 Twitter data was collected for August 2020 from the primary Twitter database. A simple NLP-based unigram sentiment analyser, a novel approach different from the traditional machine learning approach, was adopted to identify twitter sentiments. The results indicate that tweets related to COVID demonstrate the two types of sentiments, one related to the deaths and the other about the COVID symptoms. Furthermore, the results show that the sentiments for each category vary from State to State. For example, states of New York, California, Texas are higher tweets sentiments regarding expressing death sentiment, and states of New York, California, Nevada, are higher regarding sentiments of expressing COVID-19 symptoms with an accuracy of 83%. As a part of the research, a new sentiment scorecard was created to provide a sentiment score based on the sentiments of the tweets expressed to the actual reported death cases. The sentiment scores for the ‘symptoms’ class are higher for Maryland, New Jersey, and Oregon, whereas sentiment scores for the 'death' class are higher for Virginia, Delaware, and Hawaii. These sentiment scores indicate that the Twitter users of these states are actively tweeting about symptoms and deaths even though the actual reported cases are less in these states. The analysis results also found no or little correlation between the COVID Tweets and the number of COVID death cases reported across all the states.
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La Russa, Federico Mario, i Cettina Santagati. "From the Cognitive to the Sentient Building - Machine Learning for the preservation of museum collections in historical architecture". W eCAADe 2020: Anthropologic : Architecture and Fabrication in the cognitive age. eCAADe, 2020. http://dx.doi.org/10.52842/conf.ecaade.2020.2.507.

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Bhatt, Saachin, Mustansar Ghazanfar i Mohammad Hossein Amirhosseini. "Machine Learning based Cryptocurrency Price Prediction using Historical Data and Social Media Sentiment". W 5th International Conference on Machine Learning & Applications. Academy & Industry Research Collaboration, 2023. http://dx.doi.org/10.5121/csit.2023.131001.

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The purpose of this research is to investigate the impact of social media sentiments on predicting the Bitcoin price using machine learning models, with a focus on integrating onchain data and employing a Multi Modal Fusion Model. For conducting the experiments, the crypto market data, on-chain data, and corresponding social media data (Twitter) has been collected from 2014 to 2022 containing over 2000 samples. We trained various models over historical data including K-Nearest Neighbors, Logistic Regression, Gaussian Naive Bayes, Support Vector Machine, Extreme Gradient Boosting and a Multi Modal Fusion. Next, we added Twitter sentiment data to the models, using the Twitter-roBERTa and VADAR models to analyse the sentiments expressed in social media about Bitcoin. We then compared the performance of these models with and without the Twitter sentiment data and found that the inclusion of sentiment feature resulted in consistently better performance, with TwitterRoBERTa-based sentiment giving an average F1 scores of 0.79. The best performing model was an optimised Multi Modal Fusion classifier using Twitter-RoBERTa based sentiment, producing an F1 score of 0.85. This study represents a significant contribution to the field of financial forecasting by demonstrating the potential of social media sentiment analysis, onchain data integration, and the application of a Multi Modal Fusion model to improve the accuracy and robustness of machine learning models for predicting market trends, providing a valuable tool for investors, brokers, and traders seeking to make informed decisions
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Thapa, Bipun. "Sentiment Analysis of Cyber Security Content on Twitter and Reddit". W 3rd International Conference on Data Mining and Machine Learning (DMML 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120708.

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Sentiment Analysis provides an opportunity to understand the subject(s), especially in the digital age, due to an abundance of public data and effective algorithms. Cybersecurity is a subject where opinions are plentiful and differing in the public domain. This descriptive research analyzed cybersecurity content on Twitter and Reddit to measure its sentiment, positive or negative, or neutral. The data from Twitter and Reddit was amassed via technology-specific APIs during a selected timeframe to create datasets, which were then analyzed individually for their sentiment by VADER, an NLP (Natural Language Processing) algorithm. A random sample of cybersecurity content (ten tweets and posts) was also classified for sentiments by twenty human annotators to evaluate the performance of VADER. Cybersecurity content on Twitter was at least 48% positive, and Reddit was at least 26.5% positive. The positive or neutral content far outweighed negative sentiments across both platforms. When compared to human classification, which was considered the standard or source of truth, VADER produced 60% accuracy for Twitter and 70% for Reddit in assessing the sentiment; in other words, some agreement between algorithm and human classifiers. Overall, the goal was to explore an uninhibited research topic about cybersecurity sentiment.
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Jalalahmadi, Behrooz, John Slotwinski, Jingfu Liu, Jason Rios, Christopher Peitsch, Arnold Goldberg i Timothy Montalbano. "In-process Defect Monitoring and Correction in Additive Manufacturing of Aluminum Alloys". W Vertical Flight Society 75th Annual Forum & Technology Display. The Vertical Flight Society, 2019. http://dx.doi.org/10.4050/f-0075-2019-14623.

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Metal additive manufacturing (AM) has become increasingly popular to fabricate complex, light-weight, and highefficiency components for use in the aerospace industry; however, there are inherent limitations in existing AM processes that have delayed widespread implementation for aviation applications. Porosity is just one example of the key characteristics that can impact the mechanical strength of an AM part. This research focuses on a real-time feedback system to detect and correct defects during the powder bed fusion process of aluminum alloys. In this study, AlSi10Mg coupons were built using various AM parameters. The build process was continuously monitored via a high-frequency in-situ infrared camera which had been integrated into a commercial metal powder bed fusion machine. Porosity information (pore location and size) of the as-built AM coupons were characterized using x-ray computed tomography. The monitoring results were post processed and correlated with porosity location, indicating a strong relationship between abnormal sensing signal and pore formation. This demonstrates that the real-time abnormal sensing signal can be a good indicator for identifying pore formation during the AM process. Additionally, Sentient Science Corporation (Sentient) used its advanced modeling technique to simulate the AM build process regarding the melt pool geometry, porosity, and microstructure. Prediction of porosity level at different AM parameters aligned well with the experimental results. Advanced modeling results showed that careful selection of AM settings is required to correct in-process defects. Repair parameters must be tailored to achieve satisfactory correction of individual defects. Combining the in-situ defect monitoring and advanced simulation capabilities enables the creation of a closed-loop feedback control system that provides automatic defect detection and correction action in powder bed additive manufacturing process.
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Wright, Rewa, i Simon Howden. "Nga manawataki o te koiora: biological rhythms, posthuman design and decolonial thought". W 28th International Symposium on Electronic Art. Paris: Ecole des arts decoratifs - PSL, 2024. http://dx.doi.org/10.69564/isea2023-35-short-wright-et-al-nga-manawataki-o-te-koiora.

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SHORT PAPER. Western science, in fields such as computational ecology, has grown to accept the truths that Indigenous culture have long known: that computational ecology accepts that ecological models are too complex to be summarised in computational form. Since this complexity evades the codification of mere indexing, how then, should we work with computational companions (code, algorithms, programs, platforms). What new ways of intra-acting can we develop alongside computational frameworks, which bring us one more step closer to sentient machines? Most importantly, how can ethical ways of thinking and doing motivate transformations in the computational space, in areas such as machine learning where extreme problems of bias are now embedded? This research does not answer these complex questions, for they are genuinely ‘wicked problems’ that reach toward wider issues of equity, sustainability, and economy. Our aim is to use creative practice to generate gestures and markings that tentatively trace a way forward. This research contributes to new modalities of human computer interaction that attempt to restore the dynamic pathways developed by Indigenous thinking, challenging artificial boundaries such as nature/culture, instead giving respect to concepts of interconnection. Examining some of the differences between Western epistemology and Indigenous thinking opens a pathway toward Indigenous Futures that are crafted in support of a decolonial ecology.
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S. Chu, Jason, i Sindhu Ghanta. "Integrative Sentiment Analysis: Leveraging Audio, Visual, and Textual Data". W 4th International Conference on AI, Machine Learning and Applications. Academy & Industry Research Collaboration Center, 2024. http://dx.doi.org/10.5121/csit.2024.140211.

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Exploring the area of multimodal sentiment analysis, this paper addresses the growing significance of this field, driven by the exponential rise in multimodal data across platforms like YouTube. Traditional sentiment analysis, primarily focused on textual data, often overlooks the complexities and nuances of human emotions conveyed through audio and visual cues. Addressing this gap, our study explores a comprehensive approach that integrates data from text, audio, and images, applying state-of-the-art machine learning and deep learning techniques tailored to each modality. Our methodology is tested on the CMU-MOSEI dataset, a multimodal collection from YouTube, offering a diverse range of human sentiments. Our research highlights the limitations of conventional text-based sentiment analysis, especially in the context of the intricate expressions of sentiment that multimodal data encapsulates. By fusing audio and visual information with textual analysis, we aim to capture a more complete spectrum of human emotions. Our experimental results demonstrate notable improvements in precision, recall and accuracy for emotion prediction, validating the efficacy of our multimodal approach over single-modality methods. This study not only contributes to the ongoing advancements in sentiment analysis but also underscores the potential of multimodal approaches in providing more accurate and nuanced interpretations of human emotions.
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Chen, Zhijun. "High-Frequency Cryptocurrency Trading Strategy using Tweet Sentiment Analysis". W 2nd International Conference on Machine Learning Techniques and NLP (MLNLP 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.111410.

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Sentiments are extracted from tweets with the hashtag of cryptocurrencies to predict the price and sentiment prediction model generates the parameters for optimization procedure to make decision and re-allocate the portfolio in the further step. Moreover, after the process of prediction, the evaluation, which is conducted with RMSE, MAE and R2, select the KNN and CART model for the prediction of Bitcoin and Ethereum respectively. During the process of portfolio optimization, this project is trying to use predictive prescription to robust the uncertainty and meanwhile take full advantages of auxiliary data such as sentiments. For the outcome of optimization, the portfolio allocation and returns fluctuate acutely as the illustration of figure.
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Arora, Adwita, Krish Chopra, Divya Chaudhary, Ian Gorton i Bijendra Kumar. "Sentiment Analysis of Social Media Data on COVID-19". W 4th International Conference on Natural Language Processing and Machine Learning. Academy and Industry Research Collaboration Center (AIRCC), 2023. http://dx.doi.org/10.5121/csit.2023.130802.

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The COVID-19 pandemic has forced people to resort to social media to express their thoughts and opinions, which could be analysed further. In this paper, we aim to analyse the impact of the COVID-19 pandemic on social media users by Sentiment analysis of data collected from popular social media platforms, Twitter and Reddit. The textual data is preprocessed and is made fit for proper sentiment analysis using two unsupervised methods, VADER and TextBlob. Special care is taken to translate tweets or comments not in the English language to ensure their proper classification. We perform a comprehensive analysis of the emotions of the users specific to the COVID pandemic along with a time-based analysis of the trends, and a comparison of the performance of both the tools used. Geographical distribution of the sentiments is also done to see how they vary across regional boundaries.
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Raporty organizacyjne na temat "Sentient Machine"

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Hua, Tianyu. Machine learning for sentiment analysis: Opportunities and challenges. Ames (Iowa): Iowa State University, maj 2022. http://dx.doi.org/10.31274/cc-20240624-974.

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Lasko, Kristofer, i Elena Sava. Semi-automated land cover mapping using an ensemble of support vector machines with moderate resolution imagery integrated into a custom decision support tool. Engineer Research and Development Center (U.S.), listopad 2021. http://dx.doi.org/10.21079/11681/42402.

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Land cover type is a fundamental remote sensing-derived variable for terrain analysis and environmental mapping applications. The currently available products are produced only for a single season or a specific year. Some of these products have a coarse resolution and quickly become outdated, as land cover type can undergo significant change over a short time period. In order to enable on-demand generation of timely and accurate land cover type products, we developed a sensor-agnostic framework leveraging pre-trained machine learning models. We also generated land cover models for Sentinel-2 (20m) and Landsat 8 imagery (30m) using either a single date of imagery or two dates of imagery for mapping land cover type. The two-date model includes 11 land cover type classes, whereas the single-date model contains 6 classes. The models’ overall accuracies were 84% (Sentinel-2 single date), 82% (Sentinel-2 two date), and 86% (Landsat 8 two date) across the continental United States. The three different models were built into an ArcGIS Pro Python toolbox to enable a semi-automated workflow for end users to generate their own land cover type maps on demand. The toolboxes were built using parallel processing and image-splitting techniques to enable faster computation and for use on less-powerful machines.
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Muñoz-Martínez, Jonathan Alexander, David Orozco i Mario A. Ramos-Veloza. Tweeting Inflation: Real-Time measures of Inflation Perception in Colombia. Banco de la República, listopad 2023. http://dx.doi.org/10.32468/be.1256.

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This study follows a novel approach proposed by Angelico et al. (2022) of using Twitter to measure inflation perception in in real-time in Colombia. By applying machine learning techniques, we implement two real-time indicators of inflation perception and show that both exhibit a similar dynamic pattern to that of inflation and inflation expectations for the sample period January 2015 to March 2023. Our interpretation of these results is that they suggest that our indicators are closely linked to the underlying factors driving inflation perception. Overall, this approach provides a valuable instrument to gauge public sentiment towards inflation and complements the traditional inflation expectation measures used in the inflation–targeting framework.
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Becker, Sarah, Megan Maloney i Andrew Griffin. A multi-biome study of tree cover detection using the Forest Cover Index. Engineer Research and Development Center (U.S.), wrzesień 2021. http://dx.doi.org/10.21079/11681/42003.

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Tree cover maps derived from satellite and aerial imagery directly support civil and military operations. However, distinguishing tree cover from other vegetative land covers is an analytical challenge. While the commonly used Normalized Difference Vegetation Index (NDVI) can identify vegetative cover, it does not consistently distinguish between tree and low-stature vegetation. The Forest Cover Index (FCI) algorithm was developed to take the multiplicative product of the red and near infrared bands and apply a threshold to separate tree cover from non-tree cover in multispectral imagery (MSI). Previous testing focused on one study site using 2-m resolution commercial MSI from WorldView-2 and 30-m resolution imagery from Landsat-7. New testing in this work used 3-m imagery from PlanetScope and 10-m imagery from Sentinel-2 in imagery in sites across 12 biomes in South and Central America and North Korea. Overall accuracy ranged between 23% and 97% for Sentinel-2 imagery and between 51% and 98% for PlanetScope imagery. Future research will focus on automating the identification of the threshold that separates tree from other land covers, exploring use of the output for machine learning applications, and incorporating ancillary data such as digital surface models and existing tree cover maps.
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Adegoke, Damilola, Natasha Chilambo, Adeoti Dipeolu, Ibrahim Machina, Ade Obafemi-Olopade i Dolapo Yusuf. Public discourses and Engagement on Governance of Covid-19 in Ekiti State, Nigeria. African Leadership Center, King's College London, grudzień 2021. http://dx.doi.org/10.47697/lab.202101.

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Numerous studies have emerged so far on Covid-19 (SARS-CoV-2) across different disciplines. There is virtually no facet of human experience and relationships that have not been studied. In Nigeria, these studies include knowledge and attitude, risk perception, public perception of Covid-19 management, e-learning, palliatives, precautionary behaviours etc.,, Studies have also been carried out on public framing of Covid-19 discourses in Nigeria; these have explored both offline and online messaging and issues from the perspectives of citizens towards government’s policy responses such as palliative distributions, social distancing and lockdown. The investigators of these thematic concerns deployed different methodological tools in their studies. These tools include policy evaluations, content analysis, sentiment analysis, discourse analysis, survey questionnaires, focus group discussions, in depth-interviews as well as machine learning., These studies nearly always focus on the national government policy response, with little or no focus on the constituent states. In many of the studies, the researchers work with newspaper articles for analysis of public opinions while others use social media generated contents such as tweets) as sources for analysis of sentiments and opinions. Although there are others who rely on the use of survey questionnaires and other tools outlined above; the limitations of these approaches necessitated the research plan adopted by this study. Most of the social media users in Nigeria are domiciled in cities and their demography comprises the middle class (socio-economic) who are more likely to be literate with access to internet technologies. Hence, the opinions of a majority of the population who are most likely rural dwellers with limited access to internet technologies are very often excluded. This is not in any way to disparage social media content analysis findings; because the opinions expressed by opinion leaders usually represent the larger subset of opinions prevalent in the society. Analysing public perception using questionnaires is also fraught with its challenges, as well as reliance on newspaper articles. A lot of the newspapers and news media organisations in Nigeria are politically hinged; some of them have active politicians and their associates as their proprietors. Getting unbiased opinions from these sources might be difficult. The news articles are also most likely to reflect and amplify official positions through press releases and interviews which usually privilege elite actors. These gaps motivated this collaboration between Ekiti State Government and the African Leadership Centre at King’s College London to embark on research that will primarily assess public perceptions of government leadership response to Covid-19 in Ekiti State. The timeframe of the study covers the first phase of the pandemic in Ekiti State (March/April to August 2020).
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