Miracle-dz/UniFAR-FTUs huggingface dataset:
Prenatally Exposed to Cocaine

The purpose of this study was to determine whether or not the label prenatally exposed to cocaine has an effect on how early intervention personnel rate the behavior of toddlers. One hundred seventy-nine subjects were shown videotape segments of two 24-month-old male children. One had been exposed prenatally to cocaine, the other had not. The subjects were randomly divided among three conditions. In one condition subjects were told that both children were cocaine-exposed; in a second condition they were told that neither was cocaine-exposed, and in a third condition they were told that one child was exposed and that the other child was not After viewing each segment subjects were asked to rate each child, using a rating scale developed for this study. It was hypothesized that children would be rated more favorably when subjects were told that they were not cocaine-exposed and less favorably when they were told that they were exposed. The hypothesis was upheld. Results are discussed within the context of other literature pointing to the need for more data concerning the long-term effects of prenatal cocaine exposure. Caution is suggested in the use of the label cocaine-exposed, especially in light of the results of this study.
Graph Clustering: Algorithms, Analysis and Query Design

A wide range of applications in engineering as well as the natural and social sciences have datasets that are unlabeled. Clustering plays a major role in exploring structure in such unlabeled datasets. Owing to the heterogeneity in the applications and the types of datasets available, there are plenty of clustering objectives and algorithms. In this thesis we focus on two such clustering problems: Graph Clustering and Crowdsourced Clustering. In the first part, we consider the problem of graph clustering and study convex-optimization-based clustering algorithms. Datasets are often messy -- ridden with noise, outliers (items that do not belong to any clusters), and missing data. Therefore, we are interested in algorithms that are robust to such discrepancies. We present and analyze convex-optimization-based clustering algorithms which aim to recover the low-rank matrix that encodes the underlying cluster structure for two clustering objectives: clustering partially observed graphs and clustering similarity matrices with outliers. Using block models as generative models, we characterize the performance of these convex clustering algorithms. In particular, we provide explicit bounds, without any large unknown constants, on the problem parameters that determine the success and failure of these convex approaches. In the second part, we consider the problem of crowdsourced clustering -- the task of clustering items using answers from non-expert crowd workers who can answer similarity comparison queries. Since the workers are not experts, they provide noisy answers. Further, due to budget constraints, we cannot make all possible comparisons between items in the dataset. Thus, it is important to design queries that can reduce the noise in the responses and design algorithms that can work with noisy and partial data. We demonstrate that random triangle queries (where three items are compared per query) provide less noisy data as well as greater quantity of data, for a fixed query budget, as compared to random edge queries (where two items are compared per query). We extend the analysis of convex clustering algorithms to show that the exact recovery guarantees hold for triangle queries despite involving dependent edges. In addition to random querying strategies, we also present a novel active querying algorithm that is guaranteed to find all the clusters regardless of their sizes and without the knowledge of any parameters as long as the workers are better than random guessers. We also provide a tight upper bound on the number of queries made by the proposed active querying algorithm. Apart from providing theoretical guarantees for the clustering algorithms we also apply our algorithms to real datasets.
Decision-making in fuzzy environment

Decision-making is a logical human judgment process for identifying and choosing alternatives based on the values and preferences of the decision maker that mostly applied in the managerial level of the concerned department of the organization/ supply chain. Recently, decision-making has gained immense popularity in industries because of their global competitiveness and to survive successfully in respective marketplace.Therefore, decision-making plays a vital role especially in purchase department for reducing material costs, minimizing production time as well as improving the quality of product or service. But, in today’s real life problems, decision-makers generally face lot of confusions, ambiguity due to the involvement of uncertainty and subjectivity in complex evaluating criterions of alternatives. To deal such kind of vagueness in human thought the title ‘Decision-Making in Fuzzy Environment’ has focused into the emerging area of research associated with decision sciences. Multiple and conflicting objectives such as ‘minimize cost’ and ‘maximize quality of service’ are the real stuff of the decision-makers’ daily concerns. Keeping this in mind, this thesis introduces innovative decision aid methodologies for an evaluation cum selection policy analysis, based on theory of multi criteria decision-making tools and fuzzy set theory. In the supplier selection policy, emphasis is placed on compromise solution towards the selection of best supplier among a set of alternative candidate suppliers. The nature of supplier selection process is a complex multi-attribute group decision making (MAGDM) problem which deals with both quantitative and qualitative factors may be conflicting in nature as well as contain incomplete and uncertain information. Therefore, an application of VIKOR method combined with fuzzy logic has been reported as an efficient approach to support decision-making in supplier selection problems. This dissertation also proposes an integrated model for industrial robot selection considering both objective and subjective criteria’s. The concept of Interval-Valued Fuzzy Numbers (IVFNs) combined with VIKOR method has been adapted in this analysis.
Evolutionary Model Discovery of Factors for Farm Selection by the Artificial Anasazi

Agent-based modeling has been criticized for its apparent lack of establishing causality of social phenomena. However, we demonstrate that when coupled with evolutionary computation techniques, agent-based models can be used to evolve plausible agent behaviors that are able to recreate patterns observed in real-world data, from which valuable insights into candidate explanations of the macro-phenomenon can be drawn. Existing methodologies have suggested the manual assembly and comparison or automated selection of pre-built models on their ability to fit patterns in data. We discuss the cons of existing manual approaches and how evolutionary model discovery, an evolutionary approach to explore the space of agent behaviors for plausible rule-sets, can overcome these issues. We couple evolutionary model discovery with concepts from the Agent_Zero framework, ensuring social connectivity, emotional theory components and rational mechanisms. In this study, we revisit the farm-seeking strategy of the Artificial Anasazi model, originally designed to simply select the closest potential farm plot as their next farming location. We use evolutionary model discovery to explore plausible farm seeking strategies, extending our previous study by testing four social connectivity strategies, four emotional theory components and five rational mechanisms for a more complex human-like approach towards farm plot selection. Our results confirm that, plot quality, dryness and community presence were more important in the farm selection process of the Anasazi than distance, and discover farm selection strategies that generate simulations that produce a closer fit to the archaeological data.
Using Bayesian statistical modelling as a bridge between quantitative and qualitative analyses: illustrated via analysis of an online teaching tool

Abstract Bayesian methods provide a more general approach to statistical analysis that mathematically includes Null Hypothesis Significance Testing (NHST) and classical statistical modelling as special cases. This expanded, Bayesian, approach provides several benefits, which we illustrate using a case study about decision-making by teachers. We focus on a relatively unexplored topic: the way in which a Bayesian approach provides a “bridge” between qual/quant methods. We highlight five bridges, illustrated using the case study: (1) visualization of the conceptual framework, (2) generalization via randomization and alternatives, (3) stories for interpretation, (4) computation that is flexible, and (5) continual learning, through priors. This work illustrates these bridges using a case study on a digital tool that wove together: a behavioural study to investigate decision-making, with an inbuilt perceptual component to probe rationale for specific decisions, and an interview component. A mixed method was therefore a natural choice for integrating learnings across these data sources, collected using a single online tool. Thus, the digital learning sphere provides a context for raising awareness of the potential that the Bayesian statistical paradigm offers researchers who wish to connect qual/quant methods. In conclusion, mixing-in Bayesian with qualitative not only innovates on methodology. It also reshapes the ontology, epistemiology and axiology: providing a common ground for qual/quant methods, as a basis for better communication; redefining what quantitative method is, what it can achieve, and how it is done – particularly within a mixed method framework.
Data Representation in Machine Learning Methods with its Application to Compilation Optimization and Epitope Prediction

In this dissertation we explore the application of machine learning algorithms to compilation phase order optimization, and epitope prediction. The common thread running through these two disparate domains is the type of data being dealt with. In both problem domains we are dealing with categorical data, with its representation playing a significant role in the performance of classification algorithms. We first present a neuroevolutionary approach which orders optimization phases to generate compiled programs with performance superior to those compiled using LLVM’s -O3 optimization level. Performance improvements calculated as the speed of the compiled program’s execution ranged from 27% for the ccbench program, to 40.8% for bzip2. This dissertation then explores the problem of data representation of 3D biological data, such as amino acids. A new approach for distributed representation of 3D biological data through the process of embedding is proposed and explored. Analogously to word embedding, we developed a system that uses atomic and residue coordinates to generate distributed representation for residues, which we call 3D Residue BioVectors. Preliminary results are presented which demonstrate that even the low dimensional 3D Residue BioVectors can be used to predict conformational epitopes and protein-protein interactions, with promising proficiency. The generation of such 3D BioVectors, and the proposed methodology, opens the door for substantial future improvements, and application domains. The dissertation then explores the problem domain of linear B-Cell epitope prediction. This problem domain deals with predicting epitopes based strictly on the protein sequence. We present the DRREP system, which demonstrates how an ensemble of shallow neural networks can be combined with string kernels and analytical learning algorithm to produce state of the art epitope iii prediction results. DRREP was tested on the SARS subsequence, the HIV, Pellequer, AntiJen datasets, and the standard SEQ194 test dataset. AUC improvements achieved over the state of the art ranged from 3% to 8%. Finally, we present the SEEP epitope classifier, which is a multi-resolution SMV ensemble based classifier which uses conjoint triad feature representation, and produces state of the art classification results. SEEP leverages the domain specific knowledge based protein sequence encoding developed within the protein-protein interaction research domain. Using an ensemble of multiresolution SVMs, and a sliding window based pre and post processing pipeline, SEEP achieves an AUC of 91.2 on the standard SEQ194 test dataset, a 24% improvement over the state of the art.
A Deep learning toolkit for high dimensional sequential data

Deep learning is a more recent form of machine learning based on a set of algorithms that attempt to learn using a deep graph with multiple processing layers, where layers are composed of multiple linear and non-linear transformational nodes. While research in this area has shown to improve the predictive accuracy in a number of domains, deep learning systems are highly complex and experiments can be hard to manage. In this dissertation, we present a deep learning system, built from scratch, which enables fully configurable deep learning experiments. By configurable, we mean selecting the overall learning algorithm, the number of layers within the deep network, the nodes within network layers and the propagation functions deployed at each node. We use a range of deep network configurations together with different datasets to illustrate the potential of this system but also to highlight the difficulties in tuning the model and hyper-parameters to maximise accuracy. Our research also provides a conceptual data model to capture all aspects of deep learning experiments. By specifying a conceptual model, it provides a platform for the storage and management of experimental snapshots, a key support for experiment and parameter optimisation and analysis. In addition, we developed a toolkit which supports the management and analysis of deep learning experiments and provides a new method for pausing and calibrating experiments. It also offers possibilities for interchanging experiment setup and results between deep learning researchers. Our validation takes the form of a series of case studies built from the requirements of end users and demonstrates the effectiveness of our toolkit in building deep learning algorithms.
L-DOPA-Induced Disinhibition in Parkinson Disease: More than Agonistic Behavior

Copyright: © 2013 Fritsch T, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. of the Frontal Assessment Battery (FAB) [7] were used to measure conceptualization, mental flexibility, motor programming (successful execution of actions), inhibitory control, and environmental autonomy (excessive dependence on environmental cues).
Visualizing Music Collections Based on Metadata: Concepts, User Studies and Design Implications

i Preface . iii Contents . vi List of original publications . viii 1. Introduction . 1 1.1 Background and motivation . 1 1.2 Scope and research questions of the thesis . 3 1.3 Summary of publications . 4 1.4 Main contribution of the thesis . 6 1.5 Outline of the thesis . 7 2. Music Information Retrieval . 8 2.1 Metadata in MIR . 8 2.2 Music discovery . 10 2.2.1 Music recommendation systems . 10 2.2.2 SuperMusic . 11 2.3 Interaction design in MIR . 12 2.3.1 Understanding users . 12 2.3.2 Prototype and user interface design . 13 2.3.3 User evaluations . 14 3. Information visualization . 15 3.1 Representing data . 15 3.2 Interacting with data . 18 3.3 Casual information visualization . 19 4. Visualizing musical metadata . 21 4.1 Release year . 21 4.2 Tempo . 23 4.3 Genre . 25 4.3.1 Album covers . 26 4.3.2 Colors . 27 4.3.3 Icons . 28 4.3. 4 Fonts . 29 4.3. 5 Virtual worlds and avatars . 29 4.4 Mood and emotion . 30 4.5 Summary and gaps in research . 33 5. Research approach and methods . 34 5.1 Research approach . 34 5.2 Research methods . 35
User-oriented Understanding of Descriptive, Proscriptive and Prescriptive Lexicography*

Abstract: There is much uncertainty and confusion as to the real differences between prescrip-tive and descriptive dictionaries. In general, the majority of existing accounts can be summarised as follows: Descriptive relates to the empirical basis; accordance between the empirical data and the dictionary is required. Prescriptive relates to the genuine purpose of the dictionary; the dictionary is meant to help with problems concerning text production and will thus affect usage. This asym-metrical understanding would imply prescriptive and descriptive in practice being false contrasts. In this article, a more consistent terminology is suggested which allows for both the function of the dictionary and the relation of the dictionary to the empirical basis. Keywords: DESCRIPTION, DICTIONARY FUNCTION, EMPIRICAL BASIS, EXACTLY DESCRIPTIVE DICTIONARY, EXACTLY PROSCRIPTIVE DICTIONARY, EXPLICITLY PRE-SCRIPTIVE DICTIONARY, INTROSPECTION, LINGUISTIC SURVEY, NOT EXACTLY DE-SCRIPTIVE DICTIONARY, NOT EXACTLY PROSCRIPTIVE DICTIONARY, PRESCRIPTION, PROSCRIPTION, STRONGLY DESCRIPTIVE DICTIONARY, STRONGLY PRESCRIPTIVE DIC-TIONARY, TEXT CORPUS, USER-ORIENTED, WEAKLY DESCRIPTIVE DICTIONARY, WEAK-LY PRESCRIPTIVE DICTIONARY, WEAKLY PROSCRIPTIVE DICTIONARY Opsomming: Gebruikersgerigte siening van deskriptiewe, proskriptiewe en preskriptiewe woordeboeke. Daar is baie onsekerheid en verwarring oor die werklike verskille tussen preskriptiewe en deskriptiewe woordeboeke. Oor die algemeen kan die meerder-heid bestaande verklarings soos volg saamgevat word: Deskriptief hou verband met die empiriese basis; ooreenstemming tussen die empiriese gegewens en die woordeboek is nodig. Preskriptief hou verband met die werklike doel van die woordeboek; die woordeboek is bedoel om te help met probleme betreffende teksproduksie en sal dus gebruik beinvloed. Hierdie asimmetriese siening sal impliseer dat preskriptief en deskriptief in die praktyk onjuiste teenstellings is. In hierdie artikel word 'n meer konsekwente terminologie aan die hand gedoen wat rekening hou met sowel die funksie van die woordeboek as die verhouding van die woordeboek met die empiriese basis. Sleutelwoorde: DESKRIPSIE, WOORDEBOEKFUNKSIE, EMPIRIESE BASIS, PRESIES DESKRIPTIEWE WOORDEBOEK, PRESIES PROSKRIPTIEWE WOORDEBOEK, DUIDELIK PRESKRIPTIEWE WOORDEBOEK, INTROSPEKSIE, LINGUISTIESE ONDERSOEK, NIE-PRESIES DESKRIPTIEWE WOORDEBOEK, NIE-PRESIES PROSKRIPTIEWE WOORDEBOEK, PRESKRIP-SIE, PROSKRIPSIE, STERK DESKRIPTIEWE WOORDEBOEK, STERK PRESKRIPTIEWE WOOR-DEBOEK, TEKSKORPUS, GEBRUIKERSGERIG, SWAK DESKRIPTIEWE WOORDEBOEK, SWAK PRESKRIPTIEWE WOORDEBOEK, SWAK PROSKRIPTIEWE WOORDEBOEK
What Language Is

Research leading to this paper has been supported by the grants ‘Language and Mental Health’, AH/L004070/1, and ‘Un-Cartesian linguistics’, AH/H50009X/1 awarded by the Arts and Humanities Research Council, UK, and the grants ‘Language, Deixis, and the Disordered Mind’ (FFI2013-40526-P) and ‘Cognitive and linguistic diversity across mental disorders’ (FFI 042177665-77665-4-16) awarded by the Ministerio de economia y competitividad, Madrid.
Spectral methods for data analysis

“Spectral methods” captures generally the class of algorithms which cast their input as a matrix and employ linear algebraic techniques, typically involving the eigenvectors or singular vectors of the matrix. Spectral techniques have had much success in a variety of data analysis domains, from text classification [26] to website ranking [59, 47]. However, little rigorous analysis has been applied to these algorithms, and we are left without a firm understanding of why these approaches work as well as they do. In this thesis, we study the application of spectral techniques to data mining, looking specifically at those problems on which spectral techniques have performed well. We will cast each problem into a common mathematical framework, giving a unified theoretical justification for the empirical success of spectral techniques in these domains. Specifically, we present models that justify the prior empirical success of spectral algorithms for tasks such as object classification, web site ranking, and graph partitioning, as well as new algorithms using these techniques for as of yet underdeveloped data mining tasks such as collaborative filtering. We will then take the understanding from this common framework and use it to unify several spectral results in the random graph literature. Finally, we will study several techniques for extending the practical applicability of these techniques, through computational acceleration, support for incremental calculation, and deployment in a completely decentralized environment.
Analysis of major research trends in artificial intelligence through analysis of thesis data

In this paper, we collected the articles related to artificial intelligence among SCI(E) journals published by Korean authors in 'Web of Science' and conducted frequency analysis and keyword network analysis. As a result of the analysis, the artificial intelligence thesis showed an average growth of about 10% per year, but the relative ratio decreased. As time went on, we could confirm that there is a lot of practical and applied research in artificial intelligence research. Unlike the US 'National Strategy for Artificial Intelligence Research and Development,' the field of research in Korea was focused on local and technical aspects. Therefore, Korea should go beyond the theoretical and technical iterations of artificial intelligence, and research should be carried out to present a comprehensive future direction.
Sistem Diagnosis Penyakit pada Kambing Menggunakan Metode Forward Chaining

Goats are ruminants and grazers. In cultivating goats, the average goat owners have less knowledge in terms of diseases that attack their domestic goats. Expert systems began to be used to help an expert or expert in diagnosing disease in goats based on existing symptoms. Objectives and benefits of Diagnosis goat disease, among others, is to facilitate and accelerate knowing the type of disease that attacks the goat. To determine the diagnosis of goat disease, researchers create expert system by applying forward chaining method. Diagnose goat disease using Forward Chaining method. In this study the types of diseases that can be diagnosed as much as 16 diseases. Testing used is the test of accuracy with test data of 16 diseases with an accuracy rate of 100%. The conclusion of the research that has been done based on the problems that have been resolved through the manufacture of this expert system is expert system to determine goat disease designed with rule base and forward chaining method. The number of rules used as many as 16 rules with 43 types of questions in accordance with the number of symptoms.