Multiple Levels Of Granularity Research Articles

Levels and building blocks\u2014toward a domain granularity framework for the life sciences

BackgroundWith the emergence of high-throughput technologies, Big Data and eScience, the use of online data repositories and the establishment of new data standards that require data to be computer-parsable become increasingly important. As a consequence, there is an increasing need for an integrated system of hierarchies of levels of different types of material entities that helps with organizing, structuring and integrating data from disparate sources to facilitate data exploration, data comparison and analysis. Theories of granularity provide such integrated systems.ResultsOn the basis of formal approaches to theories of granularity authored by information scientists and ontology researchers, I discuss the shortcomings of some applications of the concept of levels and argue that the general theory of granularity proposed by Keet circumvents these problems. I introduce the concept of building blocks, which gives rise to a hierarchy of levels that can be formally characterized by Keet’s theory. This hierarchy functions as an organizational backbone for integrating various other hierarchies that I briefly discuss, resulting in a domain granularity framework for the life sciences. I also discuss the consequences of this granularity framework for the structure of the top-level category of ‘material entity’ in Basic Formal Ontology.ConclusionsThe domain granularity framework suggested here is meant to provide the basis on which a more comprehensive information framework for the life sciences can be developed, which would provide the much needed conceptual framework for representing domains that cover multiple granularity levels. This framework can be used for intuitively structuring data in the life sciences, facilitating data exploration, and it can be employed for reasoning over different granularity levels across different hierarchies. It would provide a methodological basis for establishing comparability between data sets and for quantitatively measuring their degree of semantic similarity.

Multigranulation sequential three-way decisions based on multiple thresholds

Three-way decisions have become a representative of trisecting-and-acting model for uncertainty data analysis. With granular computing point of view, a more reasonable decision-making model should process data from multiview and multilevel. Classical sequential three-way decisions are based on a granular structure with a single threshold under multiple levels of granularity, which make the traditional models incapable of adapting to the case of multiview granular structures with multiple thresholds. To overcome this disadvantage, we propose a generalized multigranulation sequential three-way decision model based on multiple different thresholds. By controlling the number of multiview granular structures satisfying the corresponding two tolerance thresholds, we further adopt the optimistic, pessimistic, variable, weighted and weighted arithmetic mean aggregation strategies to construct five kinds of multigranulation sequential three-way decision models from the quantitative point of view. The corresponding relationships and the uncertainty measures of these multigranulation sequential three-way decisions are discussed. Finally, the experimental results demonstrate that different multigranulation sequential three-way decisions can solve the problem under multiple granular structures and have more flexible fault tolerance under different levels of granularity. This study will enrich and prompt the development of the multigranulation three-way decisions.

ViSeq: Visual Analytics of Learning Sequence in Massive Open Online Courses.

The research on massive open online courses (MOOCs) data analytics has mushroomed recently because of the rapid development of MOOCs. The MOOC data not only contains learner profiles and learning outcomes, but also sequential information about when and which type of learning activities each learner performs, such as reviewing a lecture video before undertaking an assignment. Learning sequence analytics could help understand the correlations between learning sequences and performances, which further characterize different learner groups. However, few works have explored the sequence of learning activities, which have mostly been considered aggregated events. A visual analytics system called ViSeq is introduced to resolve the loss of sequential information, to visualize the learning sequence of different learner groups, and to help better understand the reasons behind the learning behaviors. The system facilitates users in exploring learning sequences from multiple levels of granularity. ViSeq incorporates four linked views: the projection view to identify learner groups, the pattern view to exhibit overall sequential patterns within a selected group, the sequence view to illustrate the transitions between consecutive events, and the individual view with an augmented sequence chain to compare selected personal learning sequences. Case studies and expert interviews were conducted to evaluate the system.

Sequential three-way classifier with justifiable granularity

Sequential three-way decisions approach has been demonstrated as an effective methodology of human problem solving with the aid of multiple levels of granularity. Searching an appropriate information granularity for decision or classification is a crucial issue. In this paper, inspired by the principle of justifiable granularity, we investigate a novel classification algorithm, called sequential three-way classifier with justifiable subspace. The major contribution of this study is threefold. First, in training model, with an investigation of the essence of information granularity in rough sets theory, the justifiable attribute subspace is located in an interval with local and global notions. Second, in light of the advantages of attribute reduction technology, the local and global attribute subspaces are determined by core and reduct, respectively. Third, a novel dynamic tri-partition-based predicting strategy is presented with the aid of neighborhood monotonic property. Finally, several experiments are undertaken to verify the effectiveness of the proposed method. Compared with several state-of-the-art classifiers, the proposed algorithm generally exhibits a better classification performance involving fewer attributes.

Scalable Analysis for Multi-Scale Dataflow Models

Multi-scale dataflow models have actors acting at multiple granularity levels, e.g., a dataflow model of a video processing application with operations on frame, line, and pixel level. The state of the art timing analysis methods for both static and dynamic dataflow types aggregate the behaviours across all granularity levels into one, often large iteration, which is repeated without exploiting the structure within such an iteration. This poses scalability issues to dataflow analysis, because behaviour of the large iteration is analysed by some form of simulation that involves a large number of actor firings. We take a fresh perspective of what is happening inside the large iteration. We take advantage of the fact that the iteration is a sequence of smaller behaviours, each captured in a scenario, that are typically repeated many times. We use the (max ,+) linear model of dataflow to represent each of the scenarios with a matrix. This allows a compositional worst-case throughput analysis of the repeated scenarios by raising the matrices to the power of the number of repetitions, which scales logarithmically with the number of repetitions, whereas the existing throughput analysis scales linearly. We moreover provide the first exact worst-case latency analysis for scenario-aware dataflow. This compositional latency analysis also scales logarithmically when applied to multi-scale dataflow models. We apply our new throughput and latency analysis to several realistic applications. The results confirm that our approach provides a fast and accurate analysis.

Accidental Gaps and Surface-Based Phonotactic Learning: A Case Study of South Bolivian Quechua

The lexicon of a natural language does not contain all of the phonological structures that are grammatical. This presents a fundamental challenge to the learner, who must distinguish linguistically significant restrictions from accidental gaps ( Fischer-Jørgensen 1952 , Halle 1962 , Chomsky and Halle 1965 , Pierrehumbert 1994 , Frisch and Zawaydeh 2001 , Iverson and Salmons 2005 , Gorman 2013 , Hayes and White 2013 ). The severity of the challenge depends on the size of the lexicon ( Pierrehumbert 2001 ), the number of sounds and their frequency distribution ( Sigurd 1968 , Tambovtsev and Martindale 2007 ), and the complexity of the generalizations that learners must entertain ( Pierrehumbert 1994 , Hayes and Wilson 2008 , Kager and Pater 2012 , Jardine and Heinz 2016 ). In this squib, we consider the problem that accidental gaps pose for learning phonotactic grammars stated on a single, surface level of representation. While the monostratal approach to phonology has considerable theoretical and computational appeal ( Ellison 1993 , Bird and Ellison 1994 , Scobbie, Coleman, and Bird 1996 , Burzio 2002 ), little previous research has investigated how purely surface-based phonotactic grammars can be learned from natural lexicons (but cf. Hayes and Wilson 2008 , Hayes and White 2013 ). The empirical basis of our study is the sound pattern of South Bolivian Quechua, with particular focus on the allophonic distribution of high and mid vowels. We show that, in characterizing the vowel distribution, a surface-based analysis must resort to generalizations of greater complexity than are needed in traditional accounts that derive outputs from underlying forms. This exacerbates the learning problem, because complex constraints are more likely to be surface-true by chance (i.e., the structures they prohibit are more likely to be accidentally absent from the lexicon). A comprehensive quantitative analysis of the Quechua lexicon and phonotactic system establishes that many accidental gaps of the relevant complexity level do indeed exist. We propose that, to overcome this problem, surface-based phonotactic models should have two related properties: they should use distinctive features to state constraints at multiple levels of granularity, and they should select constraints of appropriate granularity by statistical comparison of observed and expected frequency distributions. The central idea is that actual gaps typically belong to statistically robust feature-based classes, whereas accidental gaps are more likely to be featurally isolated and to contain independently rare sounds. A maximum-entropy learning model that incorporates these two properties is shown to be effective at distinguishing systematic and accidental gaps in a whole-language phonotactic analysis of Quechua, outperforming minimally different models that lack features or perform nonstatistical induction.

Snorkel MeTaL: Weak Supervision for Multi-Task Learning.

Many real-world machine learning problems are challenging to tackle for two reasons: (i) they involve multiple sub-tasks at different levels of granularity; and (ii) they require large volumes of labeled training data. We propose Snorkel MeTaL, an end-to-end system for multi-task learning that leverages weak supervision provided at multiple levels of granularity by domain expert users. In MeTaL, a user specifies a problem consisting of multiple, hierarchically-related sub-tasks-for example, classifying a document at multiple levels of granularity-and then provides labeling functions for each sub-task as weak supervision. MeTaL learns a re-weighted model of these labeling functions, and uses the combined signal to train a hierarchical multi-task network which is automatically compiled from the structure of the sub-tasks. Using MeTaL on a radiology report triage task and a fine-grained news classification task, we achieve average gains of 11.2 accuracy points over a baseline supervised approach and 9.5 accuracy points over the predictions of the user-provided labeling functions.

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI-Application in Premanifest Huntington's Disease.

Recent advances in MRI offer a variety of useful markers to identify neurodegenerative diseases. In Huntington's disease (HD), regional brain atrophy begins many years prior to the motor onset (during the "premanifest" period), but the spatiotemporal pattern of regional atrophy across the brain has not been fully characterized. Here we demonstrate an online cloud-computing platform, "MRICloud", which provides atlas-based whole-brain segmentation of T1-weighted images at multiple granularity levels, and thereby, enables us to access the regional features of brain anatomy. We then describe a regression model that detects statistically significant inflection points, at which regional brain atrophy starts to be noticeable, i.e. the "change-point", with respect to a disease progression index. We used the CAG-age product (CAP) score to index the disease progression in HD patients. Change-point analysis of the volumetric measurements from the segmentation pipeline, therefore, provides important information of the order and pattern of structural atrophy across the brain. The paper illustrates the use of these techniques on T1-weighted MRI data of premanifest HD subjects from a large multicenter PREDICT-HD study. This design potentially has wide applications in a range of neurodegenerative diseases to investigate the dynamic changes of brain anatomy.

Multi-granular electricity consumer load profiling for smart homes using a scalable big data algorithm

With rising electricity prices, there is a need to give consumers greater control over their energy consumption. It is anticipated that such informed consumers in control of their consumption patterns will contribute to reduced energy usage and thus a sustainable environment. Smart meter technology in smart homes provides real-time information to customers through devices such as in-home displays and web portals, and provide half-hourly consumption data to electricity distributors and retailers. Such data enables the profiling of consumers making it possible to understand different life styles and electricity usage behaviours to provide customised electricity billing. To obtain the anticipated benefit from such highly granular and high frequency data, it is essential to have big data technologies which can process such volumes of data in near real time. The research described in this paper focus on addressing the key requirements of large volume data processing and making use of the highly granular nature of the data. Adapting a new scalable algorithm introduced by the authors for big data processing, this work demonstrates the practicality of processing large volumes of data at multiple levels of granularity. The faster processing capacity makes it possible to continuously analyse consumption data at frequent intervals as they are collected and at a highly granular level thus providing a practical solution as a smart home application. The advantages of the technique is demonstrated using electricity consumption data for 10,000 households for a year from an Australian electricity retailer.

A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring Systems

Temporal analyses are critical to understanding learning processes, yet understudied in education research. Data from different sources are often collected at different grain sizes, which are difficult to integrate. Making sense of data at many levels of analysis, including the most detailed levels, is highly time-consuming. In this paper, we describe a generalizable approach for more efficient yet rich sensemaking of temporal data during student use of intelligent tutoring systems. This multi-step approach involves using coarse-grain temporality — learning trajectories across knowledge components — to identify and further explore “focal” moments worthy of more fine-grain, context-rich analysis. We discuss the application of this approach to data collected from a classroom study in which students engaged in a Chemistry Virtual Lab tutoring system. We show that the application of this multi-step approach efficiently led to interpretable and actionable insights while making use of the richness of the available data. This method is generalizable to many types of datasets and can help handle large volumes of rich data at multiple levels of granularity. We argue that it can be a valuable approach to tackling some of the most prohibitive methodological challenges involved in temporal learning analytics

Integrated Care Search: Fast and Reliable Access to Integrated Care Research

Introduction : This workshop is an interactive demonstration of a validated search filter which has been developed to assist clinicians and managers in locating published research on integrated care. The presenters will give a brief overview of the project and the methodology used. They will demonstrate the beta version of the search filter and invite comment and feedback from the audience. Attendees are encouraged to share their experiences in locating the peer-reviewed literature on IC, including specific questions on aspects of IC for which evidence is sought. Integrated care (IC) is a prime driver of healthcare policy and practice reform internationally as governments and healthcare organisations endeavour to enhance efficiencies and patient-centred care . It is, however, a multifaceted concept, with a vast range of overlapping terminologies, and arguably lacks a unifying definition [1]. Search filters represent one innovative solution to this problem, by enabling brokered access to the underlying evidence base for rapid utilisation. Methods : The International Foundation for Integrated Care (IFIC), Central Coast Local Health District (NSW), The University of Newcastle (NSW), and Flinders Filters, Flinders University (SA)[2] collaborated to create an IC search filter (or “search strategy”) with known retrieval effectiveness in the PubMed Database, using a well-established experimental methodology[3]. Results: An IC search filter with a known level of sensitivity has been developed and validated for PubMed. This filter will be combined with a range of topics of interest to the IC community for more focused retrieval. These topics will include patient-centred care; health and social care integration; and chronic disease management. The search filter, badged as Integrated Care Search, will be made available on the IFIC website at https://integratedcarefoundation.org/ Discussions : IC is a polymorphous topic with contextually different meanings. Despite inherent complexities, under the guidance of international experts, it was possible to develop and validate a search filter which is capable of retrieving PubMed indexed IC literature at multiple levels of granularity through simple, one-click links. Integrated Care Search will improve the ease, speed, and accuracy by which IC evidence is found. End-users can rely on the fact that it comprises a range of core IC concept terms, comprehensively identified and tested in a set of literature deemed of importance by IC experts. References : 1- World Health Organization. Health Services Delivery Programme. Division of Health Systems and Public Health. Integrated care models: an overview. Copenhagen: WHO Regional Office for Europe; 2016. 2- Flinders University. Flinders Filters [internet]. Bedford Park SA: Flinders University [unknown date] [cited 2017 Sept 29]. Available from: http://www.flinders.edu.au/clinical-change/research/flinders-filters/ 3- Tieman JJ, Lawrence MA, Damarell RA, Sladek RM, Nikolof A. LIt.search: fast tracking access to Aboriginal and Torres Strait Islander health literature. Australian Health Review. 2014;38(5):541-5. doi:10.1071/ah14019.

The percentage cube

OLAP cubes provide exploratory query capabilities combining joins and aggregations at multiple granularity levels. However, cubes cannot intuitively or directly show the relationship between measures aggregated at different grouping levels. One prominent example is the percentage, which is widely used in most analytical applications. Considering this limitation, we introduce percentage cube as a generalized data cube that takes percentages as its basic measure. More precisely, a percentage cube shows the fractional relationship in every cuboid between each aggregated measure on several dimensions and its rolled-up measure aggregated by fewer dimensions. We propose the syntax and introduce query optimizations to materialize the percentage cube. We justify that percentage cubes are significantly harder to evaluate than standard data cubes because in addition to the exponential number of cuboids, there is an additional exponential number of grouping column pairs (grouping columns at the individual level and the total level) on which percentages are computed. We propose alternative methods to prune the cube to identify interesting percentages including a row count threshold, a percentage threshold, and selecting the top k percentages. We study percentage aggregations within the classification of distributive, algebraic, and holistic functions. Finally, we also consider the problem of incremental computation of percentage cube. Experiments compare our query optimizations with existing SQL functions, evaluate the impact and speed of lattice pruning methods and study the effectiveness of the incremental computation.

FrameSTEP: A framework for annotating semantic trajectories based on episodes

We are witnessing an increasing usage of location data by a variety of applications. Consequently, information systems are required to deal with large datasets containing raw data to build high level abstractions. Semantic Web technologies offer powerful representation tools for pervasive applications. The convergence of location-based services and Semantic Web standards allows an easier interlinking and annotation of trajectories. However, due to the wide range of requirements on modeling mobile object trajectories, it is important to define a high-level data model for representing trajectory episodes and contextual elements with multiple levels of granularity and different options to represent spatial and temporal extents, as well as to express quantitative and qualitative semantic descriptions. In this article, we focus on modeling mobile object trajectories in the context of Semantic Web. First, we introduce a new version of the Semantic Trajectory Episodes (STEP) ontology to represent generic spatiotemporal episodes. Then, we present FrameSTEP as a new framework for annotating semantic trajectories based on episodes. As a result, we combine our ontology, which can represent spatiotemporal phenomena at different levels of granularity, with annotation algorithms, which allow to create instances of our model. The proposed spatial annotation algorithm explores the Linked Open Data cloud and OpenStreetMap tags to find relevant types of spatial features in order to describe the environment where the trajectory took place. Our framework can guide the development of future expert systems in trajectory analysis. It enables reasoning about knowledge gathered from large trajectory data and linked datasets in order to create several intelligent services.

CrowdMask: Using Crowds to Preserve Privacy in Crowd-Powered Systems via Progressive Filtering

Crowd-powered systems leverage human intelligence to go beyond the capabilities of automated systems, but also introduce privacy and security concerns because unknown people must view the data that the system processes. While automated approaches cannot robustly filter private information from these datasets, people have the ability to do so if the risk from them viewing the data can be mitigated. We present a crowd-powered approach to masking private content in data by segmenting and distributing smaller segments to crowd workers so that individual workers can identify potentially private content without being able to fully view it themselves. We introduce a novel pyramid workflow for segmentation that uses segments at multiple levels of granularity to overcome problems with fixed-sized approaches. We implement our approach in CrowdMask, a system that allows images with potentially sensitive content to be masked by appearing in progressively larger, more identifiable segments, and masking portions of the image as soon as a risk is identified. Our experiments with 4134 Mechanical Turk workers show that CrowdMask can effectively mask private content from images without revealing sensitive content to constituent workers, while still enabling future systems to use the filtered result.

Attribute reduction for sequential three-way decisions under dynamic granulation

In real-world decision making, sequential three-way decisions are an effective way of human problem solving under multiple levels of granularity. Making the right decision at the most optimal level is a crucial issue. To this end, we address the attribute reduction problem for sequential three-way decisions under dynamic granulation. By reviewing the existing definitions of attribute reducts, a new attribute reduct for sequential three-way decisions is defined, and a corresponding monotonic attribute significance measure is designed. An attribute reduction algorithm satisfying the monotonicity of the probabilistic positive region is developed. The relationships of the different attribute reducts, the probabilistic positive regions and the probabilistic positive rules for decision-theoretic rough set models are further discussed under global view, local view and sequential three-way decisions. Experimental results demonstrate that our method is effective. This study will provide a new insight into the attribute reduction problem of sequential three-way decisions.

BattRAE: Bidimensional Attention-Based Recursive Autoencoders for Learning Bilingual Phrase Embeddings

In this paper, we propose a bidimensional attention based recursiveautoencoder (BattRAE) to integrate clues and sourcetargetinteractions at multiple levels of granularity into bilingualphrase representations. We employ recursive autoencodersto generate tree structures of phrases with embeddingsat different levels of granularity (e.g., words, sub-phrases andphrases). Over these embeddings on the source and targetside, we introduce a bidimensional attention network to learntheir interactions encoded in a bidimensional attention matrix,from which we extract two soft attention weight distributionssimultaneously. These weight distributions enableBattRAE to generate compositive phrase representations viaconvolution. Based on the learned phrase representations, wefurther use a bilinear neural model, trained via a max-marginmethod, to measure bilingual semantic similarity. To evaluatethe effectiveness of BattRAE, we incorporate this semanticsimilarity as an additional feature into a state-of-the-art SMTsystem. Extensive experiments on NIST Chinese-English testsets show that our model achieves a substantial improvementof up to 1.63 BLEU points on average over the baseline.

Patterns and Sequences: Interactive Exploration of Clickstreams to Understand Common Visitor Paths.

Modern web clickstream data consists of long, high-dimensional sequences of multivariate events, making it difficult to analyze. Following the overarching principle that the visual interface should provide information about the dataset at multiple levels of granularity and allow users to easily navigate across these levels, we identify four levels of granularity in clickstream analysis: patterns, segments, sequences and events. We present an analytic pipeline consisting of three stages: pattern mining, pattern pruning and coordinated exploration between patterns and sequences. Based on this approach, we discuss properties of maximal sequential patterns, propose methods to reduce the number of patterns and describe design considerations for visualizing the extracted sequential patterns and the corresponding raw sequences. We demonstrate the viability of our approach through an analysis scenario and discuss the strengths and limitations of the methods based on user feedback.

A multi-level anomaly detection algorithm for time-varying graph data with interactive visualization

This work presents a modeling and analysis framework for graph sequences which addresses the challenge of detecting and contextualizing anomalies in streaming graph data. Our goal is to detect changes at multiple levels of granularity, thereby identifying specific nodes and subgraphs causing a graph to appear anomalously. In particular, the framework detects changes in community membership, density, and node degree in a sequence of graphs where these are relatively stable. In route to this end, we introduce a new graph model, a generalization of the BTER model of Seshadhri et al., by adding flexibility to community structure, and use this model to perform multi-scale graph anomaly detection. This technique provides insight into a graph’s structure and internal context that may shed light on a detected event. Additionally, this multi-scale analysis facilitates intuitive visualizations by allowing users to narrow focus from an anomalous graph to particular subgraphs or nodes causing the anomaly. For evaluation, two hierarchical anomaly detectors are tested against a baseline Gaussian method on a series of sampled graphs. We demonstrate that our graph statistics-based approach outperforms both a distribution-based detector and the baseline in a labeled setting with community structure, and it accurately detects anomalies in synthetic and real-world datasets at the node, subgraph, and graph levels. To illustrate the accessibility of information made possible via this technique, the anomaly detector and an associated interactive visualization tool are tested on NCAA football data, where teams and conferences that moved within the league are identified with perfect recall, and precision >0.786.

Bayesian Inference of Recursive Sequences of Group Activities from Tracks

We present a probabilistic generative model for inferring a description of coordinated, recursively structured group activities at multiple levels of temporal granularity based on observations of individuals’ trajectories. The model accommodates: (1) hierarchically structured groups, (2) activities that are temporally and compositionally recursive, (3) component roles assigning different subactivity dynamics to subgroups of participants, and (4) a nonparametric Gaussian Process model of trajectories. We present an MCMC sampling framework for performing joint inference over recursive activity descriptions and assignment of trajectories to groups, integrating out continuous parameters. We demonstrate the model’s expressive power in several simulated and complex real-world scenarios from the VIRAT and UCLA Aerial Event video data sets.

FCUDA-HB: Hierarchical and Scalable Bus Architecture Generation on FPGAs With the FCUDA Flow

Recent progress in high-level synthesis (HLS) has helped raise the abstraction level of hardware design. HLS flows reduce designer effort by allowing development in a high-level language, which improves debugging, code reuse and ability to explore different implementation options. However, although the HLS process is fast, implementation and performance analysis still require lengthy logic synthesis and physical design. For design optimization, HLS tools require design space exploration to obtain parallelism at multiple levels of granularity including parallelism within a single HLS-generated core and parallelism between multiple instances of cores. Core interconnect and external bandwidth limitations can significantly impact feasible options in the design space. With many dimensions in a design space exploration, it quickly becomes infeasible to perform full logic synthesis and physical design for each possible design point. However, generation and evaluation of communications infrastructure as part of the exploration is critical to determine the system performance. Thus, in this paper, we extend the prior multilevel granularity parallelism exploration in the FCUDA HLS flow, which takes CUDA code as design input and generates a corresponding field programmable gate array implementation. Our framework performs an initial characterization of the application design space, then analytically explores the design space considering parallelism, core interconnect, and external memory bandwidth, and selects a pareto-optimal set of designs. Our flow is completely automated to perform the exploration to characterize the analytical model, perform the exploration, select a solution, and integrate multiple instantiations of FCUDA cores via an advanced extensible interface bus interconnect. Our results demonstrate that this new FCUDA flow efficiently identifies and generates implementations with up to $5\times $ improved system performance compared to single-level granularity parallelism (core-level optimization).