Typology emerges from simplicity in representations and learning

We derive well-understood and well-studied subregular classes of formal languages purely from the computational perspective of algorithmic learning problems. We parameterise the learning problem along dimensions of representation and inference strategy. Of special interest are those classes of languages whose learning algorithms are necessarily not prohibitively expensive in space and time, since learners are often exposed to adverse conditions and sparse data. Learned natural language patterns are expected to be most like the patterns in these classes, an expectation supported by previous typological and linguistic research in phonology. A second result is that the learning algorithms presented here are completely agnostic to choice of linguistic representation. In the case of the subregular classes, the results fall out from traditional model-theoretic treatments of words and strings. The same learning algorithms, however, can be applied to model-theoretic treatments of other linguistic representations such as syntactic trees or autosegmental graphs, which opens a useful direction for future research.

HEURISTIC ORGANIZATION OF STUDENTS' LEARNING SOCIAL EXPERIENCE

В статье рассматривается эвристическая организация творческого усвоения обучающимися социального опыта, которая позволяет в системе непрерывного образования усваивать культурное наследие общества и готовить себя к его развитию в творческой профессиональной деятельности. Раскрывается значение эвристической организации учебного процесса в вузе для овладения обучающимися социокультурным опытом, исследуется характер взаимосвязи культуры и социального опыта, в том числе значение характера организации учебной деятельности как инструмента передачи социокультурного опыта последующим поколениям, обеспечивающего развитие психических функций человека и включение его в систему общественных отношений. Рассматривается понятие и иерархическая структура социального опыта, описывается взаимовлияние его элементов (подготавливающего и обеспечивающего, репродуктивного творческого и эмоционального). Характеризуется роль эмоциональной составляющей социального опыта в регулировании учебно-познавательной деятельности. Определяются основные задачи преподавателей высшей школы при эвристической организации усвоения социального опыта обучающимися, характеризуется сочетание эвристически организованного творческого и репродуктивно-алгоритмического обучения , описывается процесс преобразования фактов, изложенных преподавателем, в знания обучающихся при эвристической организации учебного процесса. Рассматривается эволюция учебно-практической деятельности субъекта от подражания до осознаваемого поиска. Раскрывается механизм эвристического усвоения знаний при участии эмоционально-личностного отношения субъекта к происходящему. The article examines the heuristic organization of students' creative assimilation of social experience, which allows in the system of lifelong education to assimilate the cultural heritage of society and prepare oneself for its development in creative professional activity. The significance of the heuristic organization of the educational process at the university for mastering the sociocultural experience by students is revealed, the nature of the relationship between culture and social experience is investigated, including the importance of the nature of the organization of educational activity as a tool for transferring sociocultural experience to subsequent generations, ensuring the development of human mental functions and its inclusion in the system of social relationships. The concept and the hierarchical structure of social experience are considered, the mutual influence of its elements (preparatory and providing, reproductive, creative and emotional) is described. The role of the emotional component of social experience in the regulation of educational and cognitive activity is characterized. The main tasks of higher school teachers in the heuristic organization of the assimilation of social experience by students are determined, the combination of heuristically organized creative and reproductive-algorithmic learning is characterized, the process of transforming the facts stated by the teacher into the knowledge of students in the heuristic organization of the educational process is described. The evolution of educational and practical activities of the subject from imitation to conscious search is considered. The mechanism of heuristic assimilation of knowledge with the participation of the subject's emotional-personal attitude to what is happening is revealed.

Forecasting Bitcoin Trends Using Algorithmic Learning Systems

This research has examined the ability of two forecasting methods to forecast Bitcoin’s price trends. The research is based on Bitcoin—USA dollar prices from the beginning of 2012 until the end of March 2020. Such a long period of time that includes volatile periods with strong up and downtrends introduces challenges to any forecasting system. We use particle swarm optimization to find the best forecasting combinations of setups. Results show that Bitcoin’s price changes do not follow the “Random Walk” efficient market hypothesis and that both Darvas Box and Linear Regression techniques can help traders to predict the bitcoin’s price trends. We also find that both methodologies work better predicting an uptrend than a downtrend. The best setup for the Darvas Box strategy is six days of formation. A Darvas box uptrend signal was found efficient predicting four sequential daily returns while a downtrend signal faded after two days on average. The best setup for the Linear Regression model is 42 days with 1 standard deviation.

Algorithmic Learning for Auto-deconvolution of GC-MS Data to Enable Molecular Networking within GNPS

Gas chromatography-mass spectrometry (GC-MS) represents an analytical technique with significant practical societal impact. Spectral deconvolution is an essential step for interpreting GC-MS data. No public GC-MS repositories that also enable repository-scale analysis exist, in part because deconvolution requires significant user input. We therefore engineered a scalable machine learning workflow for the Global Natural Product Social Molecular Networking (GNPS) analysis platform to enable the mass spectrometry community to store, process, share, annotate, compare, and perform molecular networking of GC-MS data. The workflow performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization, using a Fast Fourier Transform-based strategy to overcome scalability limitations. We introduce a “balance score” that quantifies the reproducibility of fragmentation patterns across all samples. We demonstrate the utility of the platform with breathomics analysis applied to the early detection of oesophago-gastric cancer, and by creating the first molecular spatial map of the human volatilome.

Attractors of variation in Hungarian inflectional morphology

We use algorithmic learning and statistical methods over a form frequency list (compiled from the Hungarian web corpus) to investigate variation in Hungarian verbal inflection. Our aims are twofold: (i) to give an adequate description of this variation, which has not been described in detail in the literature and (ii) to explore the range and depth of lexical attractors that potentially shape this variation. These attractors range from closely related ones, such as the shape of the word form or the behaviour of the verb’s paradigm, to broad ones, such as the behaviour of similar verbs or the phonotactics of related verb forms. We find that verbal variation is predominantly determined by similarity to related verb forms rather than by word shape or by word frequency. What is more, the effect of similarity is better approximated using inflected forms as opposed to base forms as points of comparison. This, in turn, supports a rich memory model of morphology and the mental lexicon.

STRENGTHENING STRATEGIC APPLICATIONS OF PROBLEM-SOLVING SKILLS FOR TAIWAN STUDENTS’ CHEMISTRY UNDERSTANDING

This study better research aimed at strategic applications for exploring students’ learning performances with conceptual understanding and algorithmic proficiency by problem-solving maps and six major learning activities. A quasi-experimental method was employed to detect the outcomes of students’ compared intervention, together with two learning groups, the experimental group and control group. All results demonstrated that the experimental group students who used the strategic applications showed better learning performances than those of the control group students. The experimental group students with more cognitive competency presented significant achievements and larger effect sizes after their two module executions of gas chemistry program. Moreover, these demonstrations were predominated with students’ conceptual and algorithmic learning developments in chemistry. The experimental group students witnessed a new advancement of self-performed modules to promote their feedback and intelligent analyses. Key words: algorithmic proficiency, conceptual understanding, gas chemistry, problem-solving.