I outline and defend a theory of mental representation based on three ideas that I extract from the work of the mid-twentieth century philosopher, psychologist, and cybernetician Kenneth Craik: first, an account of mental representation in terms of idealised models that capitalize on structural similarity to their targets; second, an appreciation of prediction as the core function of such models; and third, a regulatory understanding of brain function. I clarify and elaborate on each of these ideas, relate them to contemporary advances in neuroscience and machine learning, and favourably contrast a predictive model-based theory of mental representation with other prominent accounts of the nature, importance, and functions of mental representations in cognitive science and philosophy.

<p>The subject of this dissertation encompasses a comprehensive overview<br />and analysis of the impact of Sakoe-Chiba global constraint on the most<br />commonly used elastic similarity measures in the field of time-series data<br />mining with a focus on classification accuracy. The choice of similarity<br />measure is one of the most significant aspects of time-series analysis&nbsp; -&nbsp; it<br />should correctly reflect the resemblance between the data presented in<br />the form of time series. Similarity measures represent a critical<br />component of many tasks of mining time series, including: classification,<br />clustering, prediction, anomaly detection, and others.</p><p>The research covered by this dissertation is oriented on several issues:</p><p>1.&nbsp; review of the effects of&nbsp; global constraints on the<br />performance of computing similarity measures,</p><p>2.&nbsp; a detailed analysis of the influence of constraining the elastic<br />similarity measures on the accuracy of classical classification<br />techniques,</p><p>3.&nbsp; an extensive study of the impact of different weighting<br />schemes on the classification of time series,</p><p>4.&nbsp; development of an open source library that integrates the<br />main techniques and methods required for analysis and<br />mining time series, and which is used for the realization of<br />these experiments</p> / <p>Predmet istraživanja ove disertacije obuhvata detaljan pregled i analizu uticaja Sakoe-Chiba globalnog ograničenja na najče&scaron;će kori&scaron;ćene elastične mere sličnosti u oblasti data mining-a vremenskih serija sa naglaskom na tačnost klasifikacije. Izbor mere sličnosti jedan je od najvažnijih aspekata analize vremenskih serija&nbsp; -&nbsp; ona treba&nbsp; verno reflektovati sličnost između podataka prikazanih u obliku vremenskih serija.&nbsp; Mera sličnosti predstavlјa kritičnu komponentu mnogih zadataka&nbsp; mining-a vremenskih serija, uklјučujući klasifikaciju, grupisanje (eng.&nbsp; clustering), predviđanje,&nbsp;otkrivanje anomalija i drugih.</p><p>Istraživanje obuhvaćeno ovom disertacijom usmereno je na nekoliko pravaca:</p><p>1.&nbsp; pregled efekata globalnih ograničenja na performanse računanja mera sličnosti,</p><p>2.&nbsp; detalјna analiza posledice ograničenja elastičnih mera sličnosti na tačnost klasifikacije klasičnih tehnika klasifikacije,</p><p>3.&nbsp; opsežna studija uticaj različitih načina računanja težina (eng. weighting scheme) na klasifikaciju vremenskih serija,</p><p>4.&nbsp; razvoj biblioteke otvorenog koda (Framework for Analysis and Prediction&nbsp; -&nbsp; FAP) koja će integrisati glavne tehnike i metode potrebne za analizu i mining&nbsp; vremenskih serija i koja je kori&scaron;ćena za realizaciju ovih eksperimenata.</p> / <p>Predmet istraživanja ove disertacije obuhvata detaljan pregled i analizu uticaja Sakoe-Chiba globalnog ograničenja na najče&scaron;će kori&scaron;ćene elastične mere sličnosti u oblasti data mining-a vremenskih serija sa naglaskom na tačnost klasifikacije. Izbor mere sličnosti jedan je od najvažnijih aspekata analize vremenskih serija&nbsp; -&nbsp; ona treba&nbsp; verno reflektovati sličnost između podataka prikazanih u obliku vremenskih serija.&nbsp; Mera sličnosti predstavlja kritičnu komponentu mnogih zadataka&nbsp; mining-a vremenskih serija, uključujući klasifikaciju, grupisanje (eng.&nbsp; clustering), predviđanje,&nbsp;otkrivanje anomalija i drugih.</p><p>Istraživanje obuhvaćeno ovom disertacijom usmereno je na nekoliko pravaca:</p><p>1.&nbsp; pregled efekata globalnih ograničenja na performanse računanja mera sličnosti,</p><p>2.&nbsp; detaljna analiza posledice ograničenja elastičnih mera sličnosti na tačnost klasifikacije klasičnih tehnika klasifikacije,</p><p>3.&nbsp; opsežna studija uticaj različitih načina računanja težina (eng. weighting scheme) na klasifikaciju vremenskih serija,</p><p>4.&nbsp; razvoj biblioteke otvorenog koda (Framework for Analysis and Prediction&nbsp; -&nbsp; FAP) koja će integrisati glavne tehnike i metode potrebne za analizu i mining&nbsp; vremenskih serija i koja je kori&scaron;ćena za realizaciju ovih eksperimenata.</p>

No presente estudo, são investigados os processos fonológicos e morfológicos na adapta ção/nativização de itens lexicais, incorporados no século XX, provenientes de outras línguas (sobretudo do espanhol, holandês, inglês e português) para o papiamentu moderno, língua falada nas ilhas de Aruba, Bonaire e Curaçao por mais de 200 mil pessoas. A partir do corpus formado pela seleção de aproximadamente mil palavras de nativização recente, este trabalho limitou-se aos domínios lexicais do Esporte, Política, Economia, Tecnologia e Desenvolvimento. A escolha teve por objetivo limitar os dados obtidos para apenas itens de empréstimos recentes, ou seja, constituir um corpus com palavras incorporadas a partir do século XX em diante. O material utilizado para pesquisa foi retirado de fontes como manuais e livros de esporte (DANIELS, 1986; PERSAUD, 1992; COSTER, 2010); guias e livros sobre política e economia (HOYER, 1944; SINDIKATONAN, 1985; HEILIGERSHALABI, 1988; KOMERSIO, 1996); jornal impresso Èxtra (2011), produzido e vendido em Curaçao; dicionário bilíngue papiamentu/inglês (RATZLAFF-HENRIQUEZ, 2008). A análise de itens lexicais de adaptação recente buscou avaliar se os empréstimos são nativizados segundo o padrão linguístico do papiamentu ou se há uma gramática especial para estas palavras (PARADIS & LABEL, 1994; PARADIS, 1996; KENSTOWICZ, 2001; KENSTOWICZ & SUCHATO, 2004) e também buscou identicar os padrões fonológicos e morfológicos existentes na adaptação. Para realizar tais propósitos, os itens lexicais coletados das fontes escritas foram gravados com pelo menos dois falantes nativos (para cada palavra). Posteriormente, deu-se início ao processo de análise de dados que foi baseado em padrões de correspondência de som entre palavras do papiamentu e palavras de empréstimo de outras línguas. Foram coletados 930 itens relativos ao empréstimo recente. Desse número total, pôde-se traçar, em 892 palavras, uma única língua fonte, enquanto que 38 itens ou são palavras resultantes do processo de hibridismo, em que o item lexical é formado por palavras fontes de duas ou mais línguas ou possuem mais de um étimo possível. Ao analisar os números obtidos pelo espanhol e português em todos os campos lexicais, constata-se que tais línguas são as que mais participam no processo de empréstimo de itens lexicais para o papiamentu. Esse predomínio sinaliza que o papiamentu moderno ainda é inuenciado pelas línguas que formaram o seu superstrato, embora não se possa ignorar a presença de um número signicativo de itens de étimo holandês e inglês na formação de seu vocabulário recente. Tendo como embasamento teórico a Teoria de Restrições e Estratégias de Reparo (TCRS), proposta por Paradis (1988) e a Teoria da Similaridade Perceptual (STERIADE, 2002; FLEISCHHACKER, 2001, 2002; WALKER 2003), o estudo conrmou que a informação segmental tende a ser maximamente preservada. O apagamento não foi o recurso mais usado, ocorrendo apenas quando era necessário evitar a violação de uma restrição na L1. Além disso, os falantes tendem a maximizar a similaridade perceptual entre a forma adaptada e o input estrangeiro nos processos de empréstimo. / This study investigates the phonological and morphological processes involved in the adaptation/ nativization of lexical items from other languages (especially Spanish, Dutch, English, and Portuguese) incorporated during the twentieth century into modern Papiamentu, a language spoken on the islands of Aruba, Bonaire and Curaçao by over 200 thousand people. Based on a corpus made up of a selection of approximately a thousand recently nativized words, the study limits itself to the lexical domains of Sport, Politics, Economics, Technology, and Development. The choice aimed to limit data obtained to only items of recent loans, that is, the aim was to constitute a corpus of words incorporated from the twentieth century onwards. The material used for the study was taken from sources such as sports manuals and books (DANIELS, 1986; PERSAUD, 1992; COSTER, 2010); guides and books on politics and economics (HOYER, 1944; SINDIKATONAN, 1985; HEILIGERSHALABI, 1988; KOMERSIO, 1996); the newspaper Èxtra (2011), produced and sold in Cura çao; and a Papiamentu/English bilingual dictionary (RATZLAFF-HENRIQUEZ, 2008). The analysis of recently adapted lexical items seeks to evaluate whether the loanwords studied were nativized according to the linguistic patterns of Papiamentu, or if there is a special grammar for these words. (PARADIS & LABEL, 1994; PARADIS, 1996; KENSTOWICZ, 2001; KENSTOWICZ & SUCHATO, 2004) and it also seeks to identify the existing phonological and morphological patterns in the adaptation. In order to achieve these aims, the lexical items collected from the written sources were recorded by at least two native speakers (for each word). Then, a process of data analysis was initiated, based on patterns of sound correspondence between Papiamentu words and loanwords from other languages. A total of 930 items were collected in relation to recent loans. Of these, a single language source can be traced in 892 words, while 38 items are either words formed by hybridism, in which the lexical item is formed by source words from two or more languages, or words with more than one possible etymon. In the analysis of the numbers obtained relating to Spanish and Portuguese in all the lexical elds, it can be seen that these two languages are the ones with the largest part in the process of loaning of lexical items to Papiamentu. This predominance indicates that modern Papiamentu continues to be inuenced by the languages that framed its superstratum, although the presence in recent Papiamentu vocabulary of a signicant number of items with Dutch and English etymons should also not be ignored. Taking as a theoretical framework the Theory of Constraints and Repair Strategies (TCRS), proposed by Paradis (1988) and the Theory of Perceptual Similarity (STERIADE, 2002; FLEISCHHACKER, 2001, 2002; WALKER 2003), this study attests that segmental information tends to be maximally preserved. Deletion was not the most commonly used resource, apparently occurring only when there was a need to avoid violation of a restriction in the L1. Additionally, speakers tend to maximize the perceptual similarity between the adapted from and the foreign input in the loan processes.

The personality congruence of supervisors and subordinates and its influence on work outcomes is a relatively new topic in social and behavioral sciences. Most well-known personality theory is Big Five that includes openness, conscientiousness, neuroticism, extraversion and agreeableness traits. LMX theory focuses on the mutual relationship between a supervisor and a subordinate. There is a gap in the literature regarding the mediating role of LMX perceptions of subordinates on the relationship between personality congruence of supervisors and subordinates and affective commitment (AC). The purpose of this cross-sectional design was first to explore the direct relationship between supervisors and subordinates personality congruence and AC of subordinates. The second purpose of this study was to explore the role of LMX as a mediator between the personality congruence of supervisors and subordinates and AC of the subordinates. A cluster sampling method was used to gather 400 supervisor-subordinate dyads from 3 technopolises in Ankara, who completed self-reported questionnaires. A technopolis is a technology science park. Polynomial regression analysis was conducted to measure the congruence level of dyads' personality traits and structural equation modeling was used to analyze the mediating effect of LMX. Results revealed that, LMX has no mediating effect on personality congruence and AC. The results also revealed that there is a significant relation between the agreeableness congruence of supervisors and subordinates, and AC. This information can be used by organizations by pairing up agreeable dyad members to increase affective commitment. The findings of this study may create positive social change by promoting optimum functioning organizations that have committed employees which would affect the society and economy in a positive way.

Natural Language Processing (NLP) is the field of study that focuses on the interactions between human language and computers. By “natural language” we mean a language that is used for everyday communication by humans. Different from programming languages, natural languages are hard to be defined with accurate rules. NLP is developing rapidly and it has been widely used in different industries. Technologies based on NLP are becoming increasingly widespread, for example, Siri or Alexa are intelligent personal assistants using NLP build in an algorithm to communicate with people. “Natural Language Processing Based Generator of Testing Instruments” is a stand-alone program that generates “plausible” multiple-choice selections by analyzing word sense disambiguation and calculating semantic similarity between two natural language entities. The core is Word Sense Disambiguation (WSD), WSD is identifying which sense of a word is used in a sentence when the word has multiple meanings. WSD is considered as an AI-hard problem. The project presents several algorithms to resolve WSD problem and compute semantic similarity, along with experimental results demonstrating their effectiveness.

Swede midge, Contarinia nasturtii Kieffer (Diptera: Cecidomyiidae), is an invasive pest causing marketable losses on Brassica crops in the Northeastern United States and throughout southern Canada. Heading brassicas, like cauliflower and broccoli, are particularly susceptible because larvae feed concealed inside meristematic tissues of the plant, where head formation occurs. Our work details the development of a sustainable, affordable pest management tactic for swede midge – plant derived repellents. First, it was necessary to establish both a damage and marketability threshold for swede midge, so we developed a technique to manipulate larval density of swede midge on cauliflower, We asked: (1) What is the swede midge damage threshold? (2) How many swede midge larvae can render cauliflower crowns unmarketable? and (3) Does the age of cauliflower at infestation influence the severity of damage? We found that even a single larva causes mild twisting and scarring rendering cauliflower unmarketable 52% of the time, with more larvae causing more severe damage and additional losses, regardless of cauliflower age at infestation. Repellency is an important management approach to consider for swede midge. Since the host range of specialist insects appears constrained by plant phylogeny, we hypothesized that odors from less phylogenetically related plants would be more repellent to swede midge. To test our hypothesis, we performed no-choice and choice biological assays, asking: (1) How do essential oils from different plant species influence midge densities on broccoli? (2) What is the relationship between phylogenetic distance of non-host odors and larval densities on broccoli? Biological assays identified multiple essential oils that reduced larval densities, and phylogenetic analyses showed that less related plants were more effective. In addition to the biological assays, we tested 15 essential oils for their ability to repel gravid females from broccoli tissue in y-tube olfactometer assays. While most of the essential oils reduced the frequency at which females chose host plant meristems, wintergreen, thyme, lemongrass, eucalyptus lemon, garlic, cinnamon, and star anise were most effective. Additionally, we used chemical fingerprints (physical/chemical properties) from PubChem to compare the essential oil volatile compounds and develop an index for their similarity. We found that physicochemical similarity was predictive of repellency. Finally, for repellency to be an effective, long-term strategy, it was important to consider how and whether the repellent response of midges changes over time or previous experience. In our final chapter, we performed electroantennography trials testing how previous experience with garlic or eucalyptus lemon odor for one or 10 s influences the neurophysiological response of swede midge to host (broccoli) or non-host (garlic or eucalyptus lemon) odors. We asked: (1) Does previous experience with garlic or eucalyptus lemon influence the physiological response of swede midge to host or non-host odors? (2) Does the time of previous exposure to non-host odors influence their physiological response to host or non-host odors? Our findings show that swede midge, after 10 s of exposure to either repellent, was more responsive to repellents than host compounds, suggesting that the effectiveness of repellents will not diminish over time.

No description available.

Malware analysis and detection continues to be one of the central battlefields for cybersecurity industry. For the desktop malware domain, we observed multiple significant ransomware attacks in the past several years, e.g., it was estimated that in 2017 the WannaCry ransomware attack affected more than 200,000 computers across 150 countries with hundreds of millions damages. Similarly, we witnessed the increased impacts of Android malware on global individuals due to the popular smartphone and IoT devices worldwide. In this dissertation, we describe similarity comparison based novel techniques that can be applied to achieve large scale desktop and Android malware analysis, and the practical implications of machine learning based approaches for malware detection. First, we propose a generic and effective solution for accurate and efficient binary similarity analysis of desktop malware. Binary similarity analysis is an essential technique for a variety of security analysis tasks, including malware detection and malware clustering. Even though various solutions have been developed, existing binary similarity analysis methods still suffer from limited efficiency, accuracy, and usability. In this work, we propose a novel graphical fuzzy hashing scheme for accurate and efficient binary similarity analysis. We first abstract control flow graphs (CFGs) of binary codes to extract blended n-gram graphical features of the CFGs, and then encode the graphical features into numeric vectors (called graph signatures) to measure similarity by comparing the graph signatures. We further leverage a fuzzy hashing technique to convert the numeric graph signatures into smaller fixed size fuzzy hash outputs for efficient comparisons. Our comprehensive evaluation demonstrates that our blended n-gram graphical feature based CFG comparison is more effective and efficient compared to existing CFG comparison techniques. Based on our CFG comparison method, we develop BingSim, a binary similarity analysis tool, and show that BingSim outperforms existing binary similarity analysis tools while conducting similarity analysis based malware detection and malware clustering. Second, we identify the challenges faced by overall similarity based Android malware clustering and design a specialized system for solving the problems. Clustering has been well studied for desktop malware analysis as an effective triage method. Conventional similarity-based clustering techniques, however, cannot be immediately applied to Android malware analysis due to the excessive use of third-party libraries in Android application development and the widespread use of repackaging in malware development. We design and implement an Android malware clustering system through iterative mining of malicious payloads and checking whether malware samples share the same version of malicious payloads. Our system utilizes a hierarchical clustering technique and an efficient bit-vector format to represent Android apps. Experimental results demonstrate that our clustering approach achieves precision of 0.90 and recall of 0.75 for the Android Genome mal- ware dataset, and average precision of 0.98 and recall of 0.96 with respect to manually verified ground-truth. Third, we study the fundamental issues faced by traditional machine learning (ML) based Android malware detection systems, and examine the role of ML for Android malware detection in practice, which leads to a revised evaluation strategy that evaluates an ML based malware detection system by checking their zero-day detection capabilities. Existing machine learning based Android malware research obtains the ground truth by consulting AV products, and uses the same label set for training and testing. However, there is a mismatch between how the ML system has been evaluated, and the true purpose of using ML system in practice. The goal of applying ML is not to reproduce or verify the same potentially imperfect knowledge, but rather to produce something that is better — closer to the ultimate ground truth about the apps’ maliciousness. Therefore, it will be more meaningful to check their zero-day detection capabilities than detection accuracy for known malware. This evaluation strategy is aligned with how an ML algorithm can potentially benefit malware detection in practice, by acknowledging that any ML classifier has to be trained on imperfect knowledge, and such knowledge evolves over time. Besides the traditional malware prediction approaches, we also examine the mislabel identification approaches. Through extensive experiments, we demonstrate that: (a) it is feasible to evaluate ML based Android malware detection systems with regard to their zero-day malware detection capabilities; (b) both malware prediction and mislabel identification approaches can be used to achieve verifiable zero-day malware detection, even when trained with an old and noisy ground truth dataset.

Orientador: Rawlilson de Oliveira Araújo / Resumo: O presente trabalho trata das semelhanças de segmentos e figuras geométricas, em especial o triângulo. Iremos demonstrar alguns teoremas "famosos" utilizados na resolução de problemas que envolvem semelhanças, e outros que são desconhecidos para os jovens do ensino básico tais como Teorema de Ceva, Triângulo medial e a linha de Euler e Triângulos Pedal mas que têm a sua importância na matemática. / Abstract: The present work deals with the similarities of segments and geometric figures, especially the triangle. We will demonstrate some "famous" theorems used to solve problems involving similarities, and others that are unknown to the youngsters of elementary education such as Ceva Theorem, Medial Triangle and Euler aligns and Pedal Triangles but which have their importance in mathematics. / Mestre

A key factor of software architecting is the decision-making process. All phases of software development contain some kind of decision-making activities. However, the software architecture decision process is the most challenging part. To support the decision-making process, a research project named ORION provided a knowledge repository that contains a collection of decision cases. To utilize the collected data in an efficient way, eliciting correlations between decision cases needs to be automated.  The objective of this thesis is to select appropriate method(s) for automatically detecting correlations between decision cases. To do this, an experiment was conducted using a dataset of collected decision cases that are based on a taxonomy called GRADE. The dataset is stored in the Neo4j graph database. The Neo4j platform provides a library of graph algorithms which allow to analyse a number of relationships between connected data. In this experiment, five Similarity algorithms are used to find correlated decisions, then the algorithms are analysed to determine whether the they would help improve decision-making.  From the results, it was concluded that three of the algorithms can be used as a source of support for decision-making processes, while the other two need further analyses to determine if they provide any support.