Development of Event Segmentation in Language and Cognition: Evidence From Dwell Times and Eye Movements.

Network traffic classification is a fundamental process in network management and security. It allows network administrators to classify traffic based on various levels of classification granularity such as the source type or application. Existing literature focuses on analyzing the entire network traffic classification process with emphasis on the classification techniques. However, besides classification techniques, the literature lacks coverage on classification granularity, which deserves proper attention due to its increasing application in modern networks. Understanding the various levels of classification granularity and their use cases allow for more optimized traffic classification. As such, this paper aims to explore the different levels of classification granularity and their use cases. We studied papers published between 2013 and 2019 in order to investigate the different levels of granularity and use cases in the literature. As a result, this paper groups the classification granularity into a systematic multilevel taxonomy to assist in attaining a deeper understanding of their applications. Finally, to motivate future research, we elaborated on the current challenges and future directions for network traffic classification.

Named Entities and Their Role in Creating Context Information

We address the hierarchical (vertical) decomposition, or abstract implementation, of object specification in temporal logic. Whereas previous approaches to refinement in the context of temporal logic such as those developed by Lamport and by Barringer, Kuiper and Pnueli are based on a single logic that accommodates different levels of action granularity, our approach is based on relating different logics corresponding to different levels of granularity. More precisely, we map abstract actions (propositions) to concrete objects (theories) and, through inference rules that relate the different logics, derive properties of the abstracted actions from the behaviour of the corresponding objects. In this way, we keep a tighter control of action granularity and interference, enabling us to maintain the use of the “next” operator and make the development of reactive systems more tractable.

In this work we present an approach based on image texture analysis to obtain a description of oocyte cytoplasm which could aid the medical expert in the selection of oocytes to be used for assisted insemination. More specifically, we describe some characteristics such as different levels of uniformity and/or granularity in the oocyte cytoplasm, using multiresolution texture analysis applied to light microscope images. To this aim, we evaluate some statistical measures in the wavelet transform domain of image regions and classify them according to different levels of granularity. Preliminary experimental results on a collection of light microscope images of oocytes are reported to show the effectiveness of the proposed approach.

All system development projects involve analysis of the system architecture. However, it has been assumed thus far that there is some correct system decomposition that can be used in the architectural analysis. The sensitivity of the results to the chosen level of decomposition has not been considered. We represent forty eight idealized system architectures and a real complex system as a Design Structure Matrix at two different levels of decomposition. We analyze these architectures for their degree of modularity. We find that the degree of modularity can vary for the same system when the system is represented at the two different levels of granularity. For example, the printing system used in the case study is considered slightly integral at a higher level of decomposition and quite modular at a lower level of decomposition. We further find that even though the overall results can be different depending on the level of decomposition, the direction of change toward more modular or more integral can be calculated the same regardless of the level of decomposition. Level of decomposition can distort the results of architectural analysis and care must be taken in defining the system decomposition for any analysis.

All complex system development projects involve analysis of the system architecture. Thus far it has been assumed that there is some correct system decomposition that can be used in the architectural analysis without consideration of the sensitivity of the results to the chosen level of decomposition. We represent 88 idealized system architectures and a real complex system as a design structure matrix at two different levels of decomposition. We analyze these architectures for their degree of modularity. We find that the degree of modularity can vary for the same system when the system is represented at the two different levels of granularity. For example, the printing system used in the case study is considered slightly integral at a higher level of decomposition and quite modular at a lower level of decomposition. We further find that even though the overall results can be different depending on the level of decomposition, the direction of change toward more modular or more integral can be calculated the same regardless of the level of decomposition. We conclude that the level of decomposition can distort the results of architectural analysis and care must be taken in defining the system decomposition for any analysis.

Introduction: The need to rapidly collect, integrate, and share data on COVID-19 patients with cancer at scale has given rise to multiple internal and cross-institutional research registries. These registries support use cases that require data at different levels of granularity and are built using mixed standards. Ensuring semantic interoperability and quality of this data is critical for generating reliable and reproducible evidence. At MSK, we created a framework that enabled the rapid development of semantically compatible COVID and cancer registries and data exchange. Background: Handling and harmonizing real-world data for COVID and cancer research presented with typical challenges: maintenance of complex patient cohorts; reconciling different levels of temporal and semantic granularity; supporting crosswalks between different representations without information loss; and sharing it internally and with research consortia. Solving these challenges for COVID and cancer studies necessitated advanced infrastructure and harmonization solutions. Methods: We used MSK Extract, our research platform, to create an integrated COVID and cancer data research framework. It included a library of reusable standardized REDCap used in multiple RedCap instances supporting individual research studies; PostgreSQL database containing patient cohorts and data from Electronic Health Records (EHR) standardized to OMOP; and ETL pipelines. Our approach to the REDCap design and data management allowed for combined sets of detailed, atomic, and aggregate-level data through a combination of abstraction, curation, and extraction of data from different sources. We developed reconciliation methodology between initial curation, available raw data, and the subsequent abstraction. We enforced consistent temporal constraints on data extraction and curation. We used the OMOP vocabulary for semantic harmonization, mapping metadata from internal and external registries to OMOP concepts. We linked procedure and medication codes to high-level treatment groups leveraging classifications available in the OMOP vocabulary. Results: Our approach to the REDCap design supported various analytical use cases and enabled data sharing between different investigators and registries. Reuse of the data that was previously abstracted complemented with the data extracted from EHR allowed investigators and their teams to quickly review, validate, and update the prior curation. Explicit temporal constraints supported alignment between different registries. Using the OMOP standards and high-level treatment classifications supported data conversion between various registries and integration of the data collected via REDCap and sourced from EHR. Conclusion: Using real-world data for observational COVID and cancer research presented us with opportunities to improve and mature our evolving research infrastructure and better support internal and distributed research, and highlighted the need for uniform data standards in the cancer domain. Citation Format: Rimma Belenkaya, Adam Watson, Shantha Bethusamy, Meera Patel, Tatyana Sandler, Julian Schwartz, James Park, Maggie Dobbins, Molly Maloy, Michael Lam, Nadia Bahadur, John Philip. Data harmonization for COVID-19 and cancer research registries [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2020 Jul 20-22. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(18_Suppl):Abstract nr PO-061.

Media is an indispensable source of information and opinion, shaping the beliefs and attitudes of our society. Obviously, media portals can also provide overly biased content, e.g., by reporting on political events in a selective or incomplete manner. A relevant question hence is whether and how such a form of unfair news coverage can be exposed. This paper addresses the automatic detection of bias, but it goes one step further in that it explores how political bias and unfairness are manifested linguistically. We utilize a new corpus of 6964 news articles with labels derived from adfontesmedia.com to develop a neural model for bias assessment. Analyzing the model on article excerpts, we find insightful bias patterns at different levels of text granularity, from single words to the whole article discourse.

The purpose of this paper is to develop a diagnostic tool that can analyze light microscope images of human oocytes and derive a description of the oocyte cytoplasm that is useful for quality assessment in assisted insemination. The proposed approach includes three main phases: 1) segmentation; 2) feature extraction; and 3) clustering. In the segmentation phase, a region of interest inside the cytoplasm is extracted through morphological operators and the Hough transform. In the second phase, regions that result from segmentation are processed through a multiresolution texture analysis to extract a set of features that describe different levels of cytoplasm granularity. To this aim, we evaluate some statistics in the Haar wavelet transform domain. Finally, the extracted features are used to cluster oocytes according to different levels of granularity. This approach is made by fuzzy clustering. Experimental results on a collection of microscope images of oocytes are reported to show the effectiveness of the proposed approach. In addition, comparison with alternative methods for feature extraction and clustering is performed.

Multiword units have experienced renewed interest in recent research due to their prominent role in usage-based approaches to general linguistics, as well as in work on bilingualism and second language acquisition. While work in the last few decades focused on figurative multiword units (i.e., idioms), a growing number of studies have more recently focused on non-figurative units (collocations in particular, but also binomials or lexical bundles, for example). This work has highlighted not only the relevance of multiword units in language, but also the particular challenges that arise for non-native speakers acquiring conventional units in a second language. Despite important findings across linguistics, psycholinguistics and psychology, the sources of L2 difficulties have not been adequately and systematically investigated. The present paper brings together insights from different strands of the literature to review difficulties at three distinct loci, namely, input exposure, processing and retrieval.

Business process modelling is an important part of system design. When designing or redesigning a business process, stakeholders specify, negotiate, and agree on business requirements to be satisfied, including non-functional requirements that concern the quality of the business process. This thesis addresses the question of how to specify and compute the quality of a business process, given the model that stakeholders use. The motivation for this thesis is the increasing importance of the quality of business processes. Knowing the quality of specific business processes enables stakeholders to judge if these processes need improvement. Knowing the quality of the constructs of those processes (viz., events, inputs, activities, and outputs) and the way they are structured enables a more detailed analysis of their shortcomings and provides a basis for the design of improvements. The research challenge of this thesis is grounded in the assumption that: “Organisations need an appropriate means to effectively compute achievement of their goals and objectives by their business processes.” Given this challenge, the main research question on which this thesis focuses is: “Can the quality of a business process be computed quantitatively at different levels of granularity?” The research objective is: “To develop frameworks, factors, and metrics for computing non-functional requirements (quality) of business processes quantitatively at different levels of granularity.” The outcomes of this thesis are: 1) BPIMM, a language-independent business process integrating meta-model, based on the concepts of seven mainstream business process modelling languages: BPMN, EPC, RAD, UML AD, SADT, IDEF0, and IDEF3. 2) BPC-QC (Business Process Concept - Quality Computation), an approach to quality computation at the lowest level of granularity of a business process. The approach consists of: i. BPC-QEF (Business Process Concept - Quality Evaluation Framework), a language-independent generic framework and algorithm to compute the quality of the constructs of a business process: event, input, activity, and output. ii. A set of business process quality dimensions and factors. The following quality dimensions are distinguished: performance, efficiency, reliability, recoverability, permissibility, and availability. Each dimension categorises different quality aspects in terms of factors. A non-exhaustive set of sixteen quantitative factors is provided. iii. Quality metrics for each of the quality factors, to facilitate a quantitative computation of the quality of a specific construct of a business process. 3) BP-QC (Business Process - Quality Computation), an approach to compute the quality at the highest level of granularity of a business process. The approach consists of: i. BP-CQCF (Business Process - Compositional Quality Computation Framework), a language-independent generic framework and algorithm to compute the quality of a business process as a whole, given the quality of its constructs. ii. A set of generic business process modelling patterns to decompose a business process into more succinct parts, namely: sequential, parallel with synchronisation, exclusive, inclusive, simple loop, and complex loop. iii. A set of over one hundred computational formulae. For each combination of modelling pattern and a quality factor, there is a formula to compute the quality. 4) AAV (Approach to Application and Validation), an evaluation plan to evaluate BPIMM, BPC-QC and BP-QC in practice, together with expert stakeholders. The plan consists of the units of measure, a measurement model, and a case study procedure. To evaluate the applicability of the contributions of this thesis to real world business needs, four case studies have been conducted in different environments: a Dutch educational institution, a global financial institution, an international financial service provider, and a Dutch research project on crisis management. Each of these case studies concerns a different, single business process. This thesis shows that: 1) A quality computation approach can be adopted independent of a business process modelling language. 2) Quantitative quality factors can be introduced specifically for the constructs of a business process. 3) Quantitative metrics and computational formulae can be developed for specific quality factors, allowing the computation of different aspects of the quality of a business process quantitatively at different levels of granularity. 4) An evaluation plan can be developed to evaluate the applicability of the contributions of this thesis (viz., BPIMM, BPC-QC, and BP-QC). The contributions of this thesis are designed to be beneficial to the areas of business and management, requirements engineering, software engineering, and business process modelling. In the areas of requirements engineering and software engineering, these contributions are intended to help practitioners to consider non-functional requirements at the earliest stage. In the area of business process modelling, information systems, service computing, and cloud computing, the contributions can be used for quality-driven modelling, design, and redesign. To conclude, knowing the quality value of a business process at different levels of granularity provides a basis for its improvement.

Datasets in many applications can be viewed at different levels of granularity. Depending on the level of granularity, data mining techniques can produce different results. Correlating results from different levels of granularity can improve the quality of analysis. This paper proposes a process and measures for comparing clustering results from two levels of granularity for a mobile call dataset. The clustering is applied to the phone calls as well as phone numbers, where phone calls are finer granules while phone numbers are coarser granules. The coarse granular clustering is then expanded to a finer level and finer granular clustering is contracted to the coarser granularity for additional qualitative analysis. The paper uses a popular cluster quality measure called Davies-Bouldin index as well as a proposal for transforming clustering schemes between different levels of granularity.

The appearance of patterns could be found in different modalities of a domain, where the different modalities refer to the data sources that constitute different aspects of a domain. Particularly, the domain of our discussion refers to crime and the different modalities refer to the different data sources such as offender data, weapon data, etc. in crime domain. In addition, patterns also exist in different levels of granularity for each modality. In order to have a thorough understanding a domain, it is important to reveal the hidden patterns through the data explorations at different levels of granularity and for each modality. Therefore, this paper presents a new model for identifying patterns that exist in different levels of granularity for different modes of crime data. A hierarchical clustering approach - growing self organising maps (GSOM) has been deployed. Furthermore, the model is enhanced with experiments that exhibit the significance of exploring data at different granularities.

Spatialization is a special kind of visualization that projects multidimensional data into low-dimensional representational spaces by making use of spatial metaphors. Spatialization methods face a dual challenge: on the one hand, to apply dimension reduction techniques in order to overcome the limitations of the representational space, and on the other hand, to provide a metaphoric framework for the visualization of information at different levels of granularity. This paper investigates how granularity is modeled and visualized by the existing spatialization methods, and introduces a new approach based on kernel density estimation and landscape metaphor. According to our approach, clusters of multidimensional data are revealed by landscape "relief", and are hierarchically organized into different levels of granularity through landscape "smoothness." In addition, it is demonstrated, herein, how the exploration of information at different levels of granularity is supported by appropriate operations in the framework of an interactive spatialization environment prototype. © 2010 SPIE-IS&T.

Recently, the expertise accumulated in the field of geovisualization has found application in the visualization of abstract multidimensional data, on the basis of methods called spatialization methods. Spatialization methods aim at visualizing multidimensional data into low-dimensional representational spaces by making use of spatial metaphors and applying dimension reduction techniques. Spatial metaphors are able to provide a metaphoric framework for the visualization of information at different levels of granularity. The present paper makes an investigation on how the issue of granularity is handled in the context of representative examples of spatialization methods. Furthermore, this paper introduces the prototyping tool Geo-Scape, which provides an interactive spatialization environment for representing and exploring multidimensional data at different levels of granularity, by making use of a kernel density estimation technique and on the landscape “smoothness” metaphor. A demonstration scenario is presented next to show how Geo-Scape helps to discover knowledge into a large set of data, by grouping them into meaningful clusters on the basis of a similarity measure and organizing them at different levels of granularity.