Hidden Structure Research Articles

Hidden Structure in the Study of Papuanistiecs and Melanesianology

Development until the late 1980s and early 1990s on embodied the initiative of a typical science as the study of Papua titled Papuanistiecs as a course of study at the Department of Southeast Asia and Oceania, Faculty of Arts, University of Leiden-The Netherlands, while Melanesianology lead anthropology has been developed since 1896 when Boas carry out research works in the region of South Pacific and the Southwest Pacific. When Papuanistiecs sounds as a specific branch of the broader Melanesianology include anthropology, the course is not closed for any other specific subject areas, especially on the natural environment as physical nature (tangible) as well as the philosophy as the inner nature (intangible). A concept of intangible or philosophy of Papua-Melanesian on Socio-Cultural structure is what being proposed in this writing work as a "hidden-structure” or ‘covered structures'. The term or this formula was by the author uses to accommodate referrals various scholars who call the social structure of Papua-Melanesian as "loosely structure" or a missing structure and also as "confusing diversity" or as confuse or chaos diverse. That, "hidden structure" is said to be so because it is hidden to those outside the system which understanding as ethics while looking for people in the system who view of understanding the relationship of emics accused loose or off and confuse or confusing is very well lightly acknowledge. Melanesianology and Papuanistiecs very well coordinated by the Sydney University and the Australian National University in Canberra by enabling the University of Papua New Guinea/UPNG in Port Moresby PNG and the University of the South Pacific/USP, in Suva-Fiji. In addition to its general purpose by making Papuanistiecs and Melanesianology as a discipline of area regional study, the special purpose of the author also is about to raise these matters in connection with the State University of Cenderawasih who have declared itself as Anthropological Study Base, then the function of Papuanistiec and Melanesianology may be of the major studies take precedence in all disciplines.

The hidden structure in coral reefs

The hidden structure in coral reefs

Le quasi-réalisme de Dworkin et la responsabilité de juger

Dworkin invented a fictional character: Hercules. Super-judge he has the capacity to reveal the hidden structure of judgments. In his famous judgment Solomon’s wisdom is recognized as divine. It is no longer sufficient for a secularized philosophical reflection. However, Dworkin’s Hercules is endowed with a capacity of unconventional coherence, which allows him to overcome the judge’s instinct. It is somehow in the position of a god. Salomon, who is called wise, has undoubtedly invented an unexpected resolution in his judgment, which is tested here in the light of the richness of the meaning of responsibility. For Salomon, as for Dworkin, responsibility is a rock. - This chapter examines in-depth his latest book, Justice for Hedgehogs, from a moral realism perspective, in order to critically analyse his narrow conception of moral realism and the various opponents of this meta-ethical theory as powerful as it is diverse.

Symmetrization of principal minors and cycle-sums

We solve the Symmetrized Principal Minor Assignment Problem, that is we show how to determine if for a given vector there is an matrix that has all principal minors equal to . We use a special isomorphism (a non-linear change of coordinates to cycle-sums) that simplifies computation and reveals hidden structure. We use the symmetries that preserve symmetrized principal minors and cycle-sums to treat three cases: symmetric, skew-symmetric and general square matrices. We describe the matrices that have such symmetrized principal minors as well as the ideal of relations among symmetrized principal minors / cycle-sums. We also connect the resulting algebraic varieties of symmetrized principal minors to tangential and secant varieties, and Eulerian polynomials.

Multitask TSK Fuzzy System Modeling by Mining Intertask Common Hidden Structure

Multitask TSK Fuzzy System Modeling by Mining Intertask Common Hidden Structure

Marine Engine Operating Regions under Principal Component Analysis to evaluate Ship Performance and Navigation Behavior

Abstract: Marine engine operating regions under principal component analysis (PCA) to evaluate ship performance and navigation behavior are presented in this study. A data set with ship performance and navigation information (i.e. a selected vessel) is considered to identify its hidden structure with respect to a selected operating region of the marine engine. Firstly, the data set is classified with respect to the engine operating points (i.e. operating modes), identifying three operating regions for the main engine. Secondly, one engine operating region (i.e. a data cluster) is analyzed to calculate the respective principal components (PCs). These PCs represent various relationships among ship performance and navigation parameters of the vessel and those relationships with respect to the marine engine operating region are used to evaluate ship performance and navigation behavior. Furthermore, such knowledge (i.e. PCs and parameter behavior) can also be used for sensor fault identification and data compression/expansion types of applications as a big data solution in shipping.

Imbalanced classification by learning hidden data structure

ABSTRACTApproaches to solve the imbalanced classification problem usually focus on rebalancing the class sizes, neglecting the effect of the hidden structure within the majority class. The purpose of this article is to first highlight the effect of sub-clusters within the majority class on the detection of the minority instances and then handle the imbalanced classification problem by learning the structure in the data. We propose a decomposition-based approach to a two-class imbalanced classification problem. This approach works by first learning the hidden structure of the majority class using an unsupervised learning algorithm and thus transforming the classification problem into several classification sub-problems. The base classifier is constructed on each sub-problem. The ensemble is tuned to increase its sensitivity toward the minority class. We also provide a metric for selecting the clustering algorithm by comparing estimates of the stability of the decomposition, which appears necessary for good classifier performance. We demonstrate the performance of the proposed approach through various real data sets.

Reducing fingerprint collection for indoor localization

A typical WiFi-based indoor localization technique estimates a device’s location by comparing received signal strength indicator (RSSI) against stored fingerprints and finding the closest matches. However, the collection of fingerprints is notoriously laborious and costly. It is challenging to reduce fingerprint collection and recover missing data without introducing significant errors. In this article, a novel approach based on compressive sensing is presented for recovering absent fingerprints. The hidden structure and redundancy characteristics of fingerprints are revealed in a merging matrix. The spatial and temporal correlations of fingerprints result in a small rank of the merging matrix. The Sparsity Rank Singular Value Decomposition (SRSVD) method is used to effectively reduce the interference caused by the multipath effect of the WiFi signal. We further propose to combine SRSVD with the K-Nearest Neighbor (KNN) algorithm to deal with missing columns or rows in the matrix. Experimental results show that with only half of the fingerprints, our approach can recover all the fingerprint information with error rate below 6.6%. Even with only 5% of the data, the approach can recover the information with error rate below 14%, without loss of localization accuracy.

Generating Searchable Public-Key Ciphertexts With Hidden Structures for Fast Keyword Search

Existing semantically secure public-key searchable encryption schemes take search time linear with the total number of the ciphertexts. This makes retrieval from large-scale databases prohibitive. To alleviate this problem, this paper proposes Searchable Public-Key Ciphertexts with Hidden Structures (SPCHS) for keyword search as fast as possible without sacrificing semantic security of the encrypted keywords. In SPCHS, all keyword-searchable ciphertexts are structured by hidden relations, and with the search trapdoor corresponding to a keyword, the minimum information of therelations is disclosed to a search algorithm as the guidance to find all matching ciphertexts efficiently. We construct a simple SPCHS scheme from scratch in which the ciphertexts have a hidden star-like structure. We prove our scheme to be semantically secure based on the decisional bilinear Diffie-Hellman assumption in the Random Oracle (RO) model. The search complexity of our scheme is dependent on the actual number of the ciphertexts containing the queried keyword, rather than the number of all ciphertexts. Finally, we present a generic SPCHS construction from anonymous identity-based encryption and collision-free full-identity malleable Identity-Based Key Encapsulation Mechanism (IBKEM) with anonymity. We illustrate two collision-free full-identity malleable IBKEM instances, which are semantically secure and anonymous, respectively, in the RO and standard models. The latter instance enables us to construct an SPCHS scheme with semantic security in the standard model.

Variational Infinite Hidden Conditional Random Fields.

Hidden conditional random fields (HCRFs) are discriminative latent variable models which have been shown to successfully learn the hidden structure of a given classification problem. An Infinite hidden conditional random field is a hidden conditional random field with a countably infinite number of hidden states, which rids us not only of the necessity to specify a priori a fixed number of hidden states available but also of the problem of overfitting. Markov chain Monte Carlo (MCMC) sampling algorithms are often employed for inference in such models. However, convergence of such algorithms is rather difficult to verify, and as the complexity of the task at hand increases the computational cost of such algorithms often becomes prohibitive. These limitations can be overcome by variational techniques. In this paper, we present a generalized framework for infinite HCRF models, and a novel variational inference approach on a model based on coupled Dirichlet Process Mixtures, the HCRF-DPM. We show that the variational HCRF-DPM is able to converge to a correct number of represented hidden states, and performs as well as the best parametric HCRFs-chosen via cross-validation-for the difficult tasks of recognizing instances of agreement, disagreement, and pain in audiovisual sequences.

Semi-Supervised Fuzzy Clustering with Feature Discrimination.

Semi-supervised clustering algorithms are increasingly employed for discovering hidden structure in data with partially labelled patterns. In order to make the clustering approach useful and acceptable to users, the information provided must be simple, natural and limited in number. To improve recognition capability, we apply an effective feature enhancement procedure to the entire data-set to obtain a single set of features or weights by weighting and discriminating the information provided by the user. By taking pairwise constraints into account, we propose a semi-supervised fuzzy clustering algorithm with feature discrimination (SFFD) incorporating a fully adaptive distance function. Experiments on several standard benchmark data sets demonstrate the effectiveness of the proposed method.

A visual tool for DNA repeats screening.

DNA sequence analysis has been developing to reveal some hidden structure, to distinguish coding from noncoding regions in DNA sequence, and to explore structural similarity among DNA sequences. DNA repeats are associated with human disease, seems to play a role in genome organization and evolution and are important in regulatory processes. A lot of the methods for finding repeated sequences use signal processing techniques which implies distances, similarities and consensus sequences to generate candidate sequences. This paper presents results obtained using a mapping algorithm and a custom dot-plot analysis combined with image processing techniques, to isolate the position of DNA patterns with different lengths.

Inversion of the seismic parabolic Radon transform and the seismic hyperbolic Radon transform

Reflection seismology is a method of exploration of the hidden structure of the earth subsurface by processing received seismograms. For a long time, this processing utilizes the so-called slant stack transform, or line Radon transform. More recently, two generalizations of the slant stack transform have been introduced to extract new features of the seismic data and to improve their treatment. These transforms are the parabolic and hyperbolic seismic Radon transforms. The first transform maps a given function to its integrals over parabolas with a fixed axis direction, whereas the second one maps a function to its integrals over hyperbolas (more generally also over ellipses and circles) of fixed axis directions. We show how they can be converted to a line Radon transform, and thereby obtain their inversion formulas. Numerical simulations for each transform were performed and commented to illustrate the suggested algorithms.

Object-Oriented Programming and Representation of Objects

Abstract In this paper, a lesson is drawn from the way class definitions are provided in object-oriented programming. The distinction is introduced between the visible structure given in a class definition and the hidden structure, and then possible connections are indicated between these two structures and the structure of an entity modeled by the class definition.

The challenge of empirically assessing the effects of constitutions

Mutually supporting methodologies are necessary for building a convincing case establishing a particular effect. Strengths and weaknesses of four empirical methods are discussed. Econometric methods quantify the relative importance of different factors and may assess the time frame over which constitutions matter, but have difficulties in dealing with nonlinear interactions among constitutional and cultural details. Cluster analysis can be a pre-requisite to other methods, and an analytic method in itself, useful for identifying the details that really matter and discovering surprising patterns in the data. I discuss the application of cluster analysis on the Comparative Constitutions Project database. Qualitative comparative analysis can reveal the hidden structure of interactions among different variables, but robustness checks are difficult to perform. Case studies are useful for distinguishing between rules-in-use and rules-in-form and for discovering important informal aspects. They can deal with complex nonlinearities well, but they are often hard to generalize.

Greedy column subset selection for large-scale data sets

In today’s information systems, the availability of massive amounts of data necessitates the development of fast and accurate algorithms to summarize these data and represent them in a succinct format. One crucial problem in big data analytics is the selection of representative instances from large and massively distributed data, which is formally known as the Column Subset Selection problem. The solution to this problem enables data analysts to understand the insights of the data and explore its hidden structure. The selected instances can also be used for data preprocessing tasks such as learning a low-dimensional embedding of the data points or computing a low-rank approximation of the corresponding matrix. This paper presents a fast and accurate greedy algorithm for large-scale column subset selection. The algorithm minimizes an objective function, which measures the reconstruction error of the data matrix based on the subset of selected columns. The paper first presents a centralized greedy algorithm for column subset selection, which depends on a novel recursive formula for calculating the reconstruction error of the data matrix. The paper then presents a MapReduce algorithm, which selects a few representative columns from a matrix whose columns are massively distributed across several commodity machines. The algorithm first learns a concise representation of all columns using random projection, and it then solves a generalized column subset selection problem at each machine in which a subset of columns are selected from the sub-matrix on that machine such that the reconstruction error of the concise representation is minimized. The paper demonstrates the effectiveness and efficiency of the proposed algorithm through an empirical evaluation on benchmark data sets.

The classification of multi-modal data with hidden conditional random field

The classification of multi-modal data has been an active research topic in recent years. It has been used in many applications where the processing of multi-modal data is involved. Motivated by the assumption that different modalities in multi-modal data share latent structure (topics), this paper attempts to learn the shared structure by exploiting the symbiosis of multiple-modality and therefore boost the classification of multi-modal data, we call it Multi-modal Hidden Conditional Random Field (M-HCRF). M-HCRF represents the intrinsical structure shared by different modalities as hidden variables in a undirected general graphical model. When learning the latent shared structure of the multi-modal data, M-HCRF can discover the interactions among the hidden structure and the supervised category information. The experimental results show the effectiveness of our proposed M-HCRF when applied to the classification of multi-modal data.

Determinants of social quality and their regional disparities: an integrated approach for health equity in South Korea.

Quality of life was originally included in the concept of social quality (SQ), which refers to the possibility of manifesting the life chance possessed by each individual and the consequences resulting from restricting such possibility. Social quality describes how favorable the socioenvironmental components are that impact the possibility of an individual's life. Despite the close relationship between community capacity and SQ, the components and regional disparities of SQ have not yet been examined. This study identified community-based distribution and disparities of SQ in South Korea, including health indicators. Standardized methods of SQ were used to examine the interrelationships among institutional capacity, citizen capacity, and their associations with population-based health indicators. Under the principles of conceptual suitability, reliability, clarity, comparability, and changeability, a total of 18 SQ indicators were collected, then transformed by European Social Survey standardization and Geographical Information System computation. In the results, the hidden structure that determined the distribution of the SQ indicators was the financial independence and average length of residence. Financial independence indicated the size of the budget that each local community controls was out of the total budget. The average length of residence showed a reverse-U-shape relation to the mutual supports of the residents. The regional distribution of the SQ indicators largely differed from the local economic index or health indicators. Disparities in SQ indicators are likely to arise from the degree of urbanization and the degree of citizens' cohesiveness. Therefore, it is necessary to analyze in-depth cases of both local government with high SQ indicators in all fields and those with low SQ indicators in all fields. In addition, there is a need to elucidate the structural causes and backgrounds that produce disparities in SQ, thus lowering disparities among regions, and to develop policies that will promote the overall and continuous improvement of SQ.

On the Certification of Bonded Repairs to Primary Composite Aircraft Components

Airworthiness certification is required when bonded repairs are made to primary composite structure in situations where damage has reduced or has the potential to reduce residual strength to below the design ultimate strength. Generally, certification of bonded primary structure poses many difficulties. As most repairs are one-off events meeting these certification requirements is especially challenging since demonstration by testing will generally not be possible or cost-effective. This paper discusses options for addressing the two key issues relating to certification: (a) how to validate initial and enduring bond strength of adhesive bonds, mainly given the inability of conventional non-destructive inspection to provide this assurance and (b) how to develop acceptable generic design allowables for bonded repairs which represent actual failure modes – especially for cyclic loading, since validation by testing of simulated repairs will generally be infeasible. It is concluded that proof testing of bonded repair coupons is a promising approach for validating bond strength and fatigue testing of representative bond joint specimen can provide generic allowables for patch design. For hidden structure or very high value repairs structural health monitoring of repairs based on a strain-transfer approach offers considerable promise.

Probabilistic latent semantic analysis applied to whole bacterial genomes identifies common genomic features.

The spread of drug resistance amongst clinically-important bacteria is a serious, and growing, problem [1]. However, the analysis of entire genomes requires considerable computational effort, usually including the assembly of the genome and subsequent identification of genes known to be important in pathology. An alternative approach is to use computational algorithms to identify genomic differences between pathogenic and non-pathogenic bacteria, even without knowing the biological meaning of those differences. To overcome this problem, a range of techniques for dimensionality reduction have been developed. One such approach is known as latent-variable models [2]. In latent-variable models dimensionality reduction is achieved by representing a high-dimensional data by a few hidden or latent variables, which are not directly observed but inferred from the observed variables present in the model. Probabilistic Latent Semantic Indexing (PLSA) is an extention of LSA [3]. PLSA is based on a mixture decomposition derived from a latent class model. The main objective of the algorithm, as in LSA, is to represent high-dimensional co-occurrence information in a lower-dimensional way in order to discover the hidden semantic structure of the data using a probabilistic framework. In this work we applied the PLSA approach to analyse the common genomic features in methicillin resistant Staphylococcus aureus, using tokens derived from amino acid sequences rather than DNA. We characterised genome-scale amino acid sequences in terms of their components, and then investigated the relationships between genomes and tokens and the phenotypes they generated. As a control we used the non-pathogenic model Gram-positive bacterium Bacillus subtilis.