Typology emerges from simplicity in representations and learning

Inferring descriptive generalisations of formal languages

We characterize the classes of languages described by P automata, i.e., accepting P systems with communication rules only. Motivated by properties of natural computing systems, we study computational complexity classes with a certain restriction on the use of the available workspace in the course of computations and relate these to the language classes described by P automata. We prove that if the rules of the P system are applied sequentially, then the accepted language class is strictly included in the class of languages accepted by one-way Turing machines with a logarithmically bounded workspace, and if the rules are applied in the maximal parallel manner, then the class of context-sensitive languages is obtained.KeywordsTuring MachineMathematical LinguisticsRule ApplicationInput SymbolInput TapeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

We present the first polynomial time algorithm to learn nontrivial classes of languages of infinite trees. Specifically, our algorithm uses membership and equivalence queries to learn classes of omega-tree languages derived from weak regular omega-word languages in polynomial time. The method is a general polynomial time reduction of learning a class of derived omega-tree languages to learning the underlying class of omega-word languages, for any class of omega-word languages recognized by a deterministic Büchi acceptor. Our reduction, combined with the polynomial time learning algorithm of Maler and Pnueli [Maler and Pneuli, Inform. Comput., 1995] for the class of weak regular omega-word languages yields the main result. We also show that subset queries that return counterexamples can be implemented in polynomial time using subset queries that return no counterexamples for deterministic or non-deterministic finite word acceptors, and deterministic or non-deterministic Büchi omega-word acceptors. A previous claim of an algorithm to learn regular omega-trees due to Jayasrirani, Begam and Thomas [Jayasrirani et al., ICGI, 2008] is unfortunately incorrect, as shown in [Angluin, YALEU/DCS/TR-1528, 2016].

In this paper, we define three classes of languages of bi-infinite words, namely local bi-ω languages, recognizable bi-ω languages and Buchi local bi-ω languages as subclasses of the class of regular bi-ω languages and prove some basic results. We observe that the class of recognizable bi-ω languages coincides with the well-known class of rational bi-adherence languages and show that the class of regular bi-ω languages is the class of morphic images of Buchi local bi-ω languages. We provide learning algorithms for Buchi local bi-ω languages and regular bi-ω languages.

Closure properties of linear context-free tree languages with an application to optimality theory

Learning approximately regular languages with reversible languages

In the last few decades, research on how to best teach and assess heritage language (HL) learners has grown as enrollment of these students in language classes has increased. Recent work on the assessment of HL learners has been conducted in the context of higher education in the USA and has focused almost exclusively on placement testing. This chapter first reviews the various characterizations and characteristics of the heritage language population. The next section discusses approaches to placement testing of HL learners and reviews specific studies that have been conducted related to these different approaches. The approaches include the use of self‐placements and demographic questionnaires, standardized foreign language exams, and locally developed placement tests. Finally, the chapter concludes by discussing challenges in the placement testing of HL learners and future directions for research.

Machine learning algorithms provide the ability to quickly adapt and find patterns in large diverse data sources and therefore are a potential asset to application developers in enterprise systems, networks, and security domains. They make analyzing the security implications of these tools a critical task for machine learning researchers and practitioners alike, spawning a new subfield of research into adversarial learning for security-sensitive domains. The work presented in this book advanced the state of the art in this field of study with five primary contributions: a taxonomy for qualifying the security vulnerabilities of a learner, two novel practical attack/defense scenarios for learning in real-world settings, learning algorithms with theoretical guarantees on training-data privacy preservation, and a generalization of a theoretical paradigm for evading detection of a classifier. However, research in adversarial machine learning has only begun to address the field's complex obstacles—many challenges remain. These challenges suggest several new directions for research within both fields of machine learning and computer security. In this chapter we review our contributions and list a number of open problems in the area. Above all, we investigated both the practical and theoretical aspects of applying machine learning in security domains. To understand potential threats, we analyzed the vulnerability of learning systems to adversarial malfeasance. We studied both attacks designed to optimally affect the learning system and attacks constrained by real-world limitations on the adversary's capabilities and information.We further designed defense strategies, which we showed significantly diminish the effect of these attacks. Our research focused on learning tasks in virus, spam, and network anomaly detection, but also is broadly applicable across many systems and security domains and has farreaching implications to any system that incorporates learning. Here is a summary of the contributions of each component of this book followed by a discussion of open problems and future directions for research. Framework for Secure Learning The first contribution discussed in this book was a framework for assessing risks to a learner within a particular security context (see Table 3.1). The basis for this work is a taxonomy of the characteristics of potential attacks. From this taxonomy (summarized in Table 9.1), we developed security games between an attacker and defender tailored to the particular type of threat posed by the attacker.

Powerful methods and algorithms are known to learn regular languages. Aiming at extending them to more complex grammars, we choose to change the way we represent these languages. Among the formalisms that allow to define classes of languages, the one of string-rewriting systems (SRS) has outstanding properties. Indeed, SRS are expressive enough to define, in a uniform way, a noteworthy and non trivial class of languages that contains all the regular languages, { a n b n : n ≥ 0 }, {w∈{a,b}*:|w| a =|w| b }, the parenthesis languages of Dyck, the language of Lukasewitz, and many others. Moreover, SRS constitute an efficient (often linear) parsing device for strings, and are thus promising and challenging candidates in forthcoming applications of Grammatical Inference. In this paper, we pioneer the problem of their learnability. We propose a novel and sound algorithm which allows to identify them in polynomial time. We illustrate the execution of our algorithm throughout a large amount of examples and finally raise some open questions and research directions.

Consumers often rate online product reviews in terms of helpfulness. To explore the linguistic features that may contribute to helpfulness ratings, a linguistic inquiry and word count analysis compared 377 helpful and unhelpful Amazon.com product reviews, showing that helpful and unhelpful reviews differed across 23 of 67 linguistic categories. Results also suggested a consumer preference for messages that contain objective characteristics and high-quality arguments. A follow-up study tested hypothesized relationships among helpfulness ratings and review relevance, descriptiveness, and evaluation discrepancy. The data showed that descriptive reviews were rated as helpful. Implications, limitations, and directions for future research are discussed.

Speech perception consists of a set of computations that take continuously varying acoustic waveforms as input and generate discrete representations that make contact with the lexical representations stored in long-term memory as output. Because the perceptual objects that are recognized by the speech perception enter into subsequent linguistic computation, the format that is used for lexical representation and processing fundamentally constrains the speech perceptual processes. Consequently, theories of speech perception must, at some level, be tightly linked to theories of lexical representation. Minimally, speech perception must yield representations that smoothly and rapidly interface with stored lexical items. Adopting the perspective of Marr, we argue and provide neurobiological and psychophysical evidence for the following research programme. First, at the implementational level, speech perception is a multi-time resolution process, with perceptual analyses occurring concurrently on at least two time scales (approx. 20-80 ms, approx. 150-300 ms), commensurate with (sub)segmental and syllabic analyses, respectively. Second, at the algorithmic level, we suggest that perception proceeds on the basis of internal forward models, or uses an 'analysis-by-synthesis' approach. Third, at the computational level (in the sense of Marr), the theory of lexical representation that we adopt is principally informed by phonological research and assumes that words are represented in the mental lexicon in terms of sequences of discrete segments composed of distinctive features. One important goal of the research programme is to develop linking hypotheses between putative neurobiological primitives (e.g. temporal primitives) and those primitives derived from linguistic inquiry, to arrive ultimately at a biologically sensible and theoretically satisfying model of representation and computation in speech.

This paper provides a critical overview of the theoretical, analytical, and practical questions most prevalent in the study of the structural and the sociolinguistic dimensions of code-switching (CS). In doing so, it reviews a range of empirical studies from around the world. The paper first looks at the linguistic research on the structural features of CS focusing in particular on the code-switching versus borrowing distinction, and the syntactic constraints governing its operation. It then critically reviews sociological, anthropological, and linguistic perspectives dominating the sociolinguistic research on CS over the past three decades. Major empirical studies on the discourse functions of CS are discussed, noting the similarities and differences between socially motivated CS and style-shifting. Finally, directions for future research on CS are discussed, giving particular emphasis to the methodological issue of its applicability to the analysis of bilingual classroom interaction.

This article discusses the concept of homework in cognitive-behavioral therapy (CBT), and reviews the articles in this special issue of Cognitive Therapy and Research. The article underscores the pivotal role of between- session activities and demonstrates that this role has been recognized for many years. This article reviews the articles from this special issue and uses them as a springboard to review other research and then discuss potential directions for future theory and research. The initial research in this area focused on documenting the magnitude of the relationship between homework adherence and outcome in CBT. Research has now advanced to incorporate studies that examine issues such as homework competence, the complicated relationship between homework and other aspects of CBT such as the therapeutic relationship, and the use of technology to try to enhance homework completion and clinical outcomes. The use of homework assignments in CBT remains a critical issue for development. Limitations of the literature are addressed, and directions for future research in the field are provided.

This article analyses the field of research on information and communication technology (ICT) in education. It reviews the results of ICT researches carried out in the world. First, the article presents the retrospective of researches on ICT implementation into education. Summarizing them, it reviews main research findings in five areas of ICT implementation: 1) the impact of ICT use on students achievements and attitudes; 2) the impact of ICT use on teachers; 3) the effect of teachers' factors and instructional methods on ICT efficiency; 4) the influence of infrastructure and organizational factors on ICT efficiency; 5) the effect of specific software design features. Then, the paper reviews the main recent and future directions of researches on ICT in education, in the world. Finally, it shortly reviews the most important future directions of ICT research and development in Lithuania. The article concludes that in spite of numerous ICT studies worldwide the national research and experiments have to be carried out in order to implement ICT in an appropriate for Lithuania and efficient way.

Literature reviews about pedagogical innovations are of importance to determine further interventions for learning refinement and future research directions. The present study describes existing pedagogical innovations, identifies learning problems underlie the innovations, and outlines development models used for developing elementary mathematics educational innovations in Indonesia. A systematic literature review was administered to review scholarly articles published between 2014 and 2019. This time frame was drawn in relation to a new curriculum called Kurikulum 2013 introduced in early 2014. The results of this study reveal that the majority of pedagogical innovations focus on developing learning materials and techniques while fewer innovations develop learning environments. Problems underlie existing innovations mainly due to the elementary students who reluctant to learn mathematics, the less competent teachers, and the old-fashioned learning resources. Research and development in elementary mathematics were commonly conducted using Borg & Gall, ADDIE, and 4D models. The evidence from this study suggests that forthcoming mathematics teaching and learning process should be conducted in more enjoyable manners such as practicing microgame-based learning in either physical or digital learning spaces. One of the most sensible research agendas is to develop joyful learning environments to address the interrelated complex problems among students, teachers, and learning resources in elementary mathematics learning in Indonesia.