Information Centrality Research Articles

Proof of Zero: Revisiting Aadhaar's Fundamentals

This short law and public policy note examines some of the newer challenges posed by centralisation of biometric information under India’s Aadhaar ecosystem, together with legal and regulatory challenges arising in view of evidentiary law of fingerprinting in India an elsewhere. It them makes a case for both technological and legal structure reforms, drawing inspiration from the 2012 report and the recently released 2017 white paper on data protection and privacy law in India.

Recommending Mobile Microblog Users via a Tensor Factorization Based on User Cluster Approach

User influence is a very important factor for microblog user recommendation in mobile social network. However, most existing user influence analysis works ignore user’s temporal features and fail to filter the marketing users with low influence, which limits the performance of recommendation methods. In this paper, a Tensor Factorization based User Cluster (TFUC) model is proposed. We firstly identify latent influential users by neural network clustering. Then, we construct a features tensor according to latent influential user’s opinion, activity, and network centrality information. Furthermore, user influences are predicted by the latent factors resulting from the temporal restrained CP decomposition. Finally, we recommend microblog users considering both user influence and content similarity. Our experimental results show that the proposed model significantly improves recommendation performance. Meanwhile, the mean average precision of TFUC outperforms the baselines with 3.4% at least.

Influence of Opinion Leaders on Purchase Intention Using Social Network Analysis

This article examines social network centralities to identify peer group's opinion leader with the aim of determining whether an opinion leader and perceived value influence purchase intention in the field of paid mobile apps. Social network analysis (SNA) and regression analysis are applied to examine the hypotheses within the theoretical framework. The experiment involved a peer group of college students with total of 46 subjects. Using SPSS to analyze the influences of perceived value and the group's opinion leader on purchase intention, the results showed that consumer purchase intention is positively influenced by both the perceived value of paid mobile apps and positive advices given by opinion leader. In addition, an analysis using Ucinet 6 to examine consulting network centrality, friendship network centrality, and information centrality of every member of the group revealed that based on group structure, the group member having the highest centrality has the group's potential to be the opinion leader.

A "matter of communication": A new classification to compare and evaluate telehealth and telemedicine interventions and understand their effectiveness as a communication process.

This article attempts to define functions and applications of telemedicine and telehealth in order to achieve a simplified and comprehensive taxonomy. This may be used as a tool to evaluate their efficacy and to address health policies from the perspective of the centrality of information in the healthcare. Starting from a lexical frame, telemedicine or telehealth is conceived as a communication means and their action as a communication process. As a performance, the communication is related to the health outcome. Three functions ( telemetry, telephasis, and telepraxis) and nine applications are identified. Understanding the mechanisms of telemedicine and telehealth effectiveness is crucial for a value-driven healthcare system. This new classification-focusing on the end effect of telemedicine and telehealth and on the type of interactions between involved actors-moves toward a new and simplified methodology to compare different studies and practices, design future researches, classify new technologies and guide their development, and finally address health policies and the healthcare provision.

Integrating mobile Building Information Modelling and Augmented Reality systems: An experimental study

Abstract The benefits of Building Information Modelling (BIM) have typically been tied to its capability to support information structuring and exchange through the centralization of information. Its increasing adoption and the associated ease of data acquisition has created information intensive work environments, which can result in information overload and thus negatively impact workers task efficiency during construction. Augmented Reality (AR) has been proposed as a mechanism to enhance the process of information extraction from building information models to improve the efficiency and effectiveness of workers' tasks. Yet, there is limited research that has evaluated the effectiveness and usability of AR in this domain. This research aims to address this gap and evaluate the effectiveness of BIM and AR system integration to enhance task efficiency through improving the information retrieval process during construction. To achieve this, a design science research approach was adopted that enabled the development and performance of a mobile BIM AR system (artefact) with cloud-based storage capabilities to be tested and evaluated using a portable desktop experiment. A total of 20 participants compared existing manual information retrieval methods (control group), with information retrieval through the artefact (non-control group). The results revealed that the participants using the artefact were approximately 50% faster in completing their experiment tasks, and committed less errors, when compared to the control group. This research demonstrates that a minor modification to existing information formats (2D plans) with the inclusion of Quick Response markers can significantly improve the information retrieval process and that BIM and AR integration has the potential to enhance task efficiency.

The Irish National Rare Disease Office (NRDO): A national step towards improving access to health and care services for individuals and families living with Rare Diseases

Introduction: In Europe a ‘rare disease’ (RD) is defined as a life-threatening or chronically debilitating disease affecting no more than 5/10,000 people. There are an estimated 6-8,000 known RD affecting up to 6% of the total EU population, (at least 30 million Europeans), and perhaps up to 300,000 Irish people during their lives. As chronic conditions, many of which are multisystemic, accurate and timely diagnosis and access to treatment is essential for empowering patients and Health Care Providers to manage care effectively. It is recognised that poorly coordinated care and delays in diagnosis are major issues for patients and families affected by rare diseases which are noted to be more problematic in smaller countries with more limited highly specialised expertise, (Rare Diseases UK, 2015 and Irish HSE 2012 ‘Have your say’ Rare Diseases Public Consultation) and as emphasised in the recent EC Expert Group on Rare Diseases ‘Recommendations to support the incorporation of rare diseases into social services and policies’ (April 2016). Practice change and implementation: In 2014 Ireland published its first National Plan for RD (2014-2018). Establishing a National Rare Diseases Office (NRDO) was a central recommendation of the plan to improve access to reliable information and empower patients and HCPs to make informed decisions about the management of RD. Aims and theory of change: The aim of the NRDO (established in June 2015 by the Irish HSE) is to provide current and reliable information about genetic and RD to patients, families and health professionals. The functions of the office include: Centralisation of Irish RD information through Orphanet Ireland; an international online portal for RD information and resources encompassing > 6,000 RD. (www.orpha.net) Staffing a RD Information Line to provide patients, families and HCPs with information and supports relating to RD. Hosting a website with information and links to relevant RD services and organisations around Ireland and Europe www.rarediseases.ie Engaging with clinicians, expert centres and researchers to map-out Irish RD resources to act as a ‘hub’ for integrating National Centres of Expertise in the emerging European Reference Networks. Initial outcomes: In its first year of functioning, greater than 50% of calls to the information line were from patients and families, most commonly requesting signposting of medical specialists for specific RD. Conclusions and Outlook: Over time, it is proposed that the NRDO will liaise with all national RD Centres of Expertise, with the Irish national integrated care programmes, and Rare Diseases European Reference Networks to progress improved integrated care pathways for RD patients and function as a national ‘hub’ within the Irish and European contexts, in line with the EC Expert Group on Rare Diseases April 2016 Recommendations.

Information Governance in Japan: Towards a Comparative Paradigm

The history of human civilization has been about managing information, from hunting and gathering through contemporary times. In modern societies, information flows are central to how individuals and societies interact with governments, economies, and other countries. Despite this centrality of information, information governance—how information flows are managed—has not been a central concern of scholarship. We argue that it should be, especially now that digitization has dramatically altered the amount of information generated, how it can be transmitted, and how it can be used. This book examines various aspects of information governance in Japan, utilizing comparative and historical perspectives. The aim is threefold: 1) to explore Japan’s society, politics, and economy through a critical but hitherto under-examined vantage that we believe cuts to the core of what modern societies are built with—information; 2) articulate a set of components which can be used to analyze other countries from the vantage of information governance; and 3) provide frameworks of reference to analyze each component. This book is the product of a multidisciplinary, multinational collaboration between scholars based in the US and Japan. Each are experts in their own fields (economics, political science, information science, law, library science), and were brought together in two workshops to develop, explore, and analyze the conception and various of facets of information governance. This book is frontier research by proposing and taking this conception of information governance as a framework of analysis. The introduction sets up the analysis by providing background and a framework for understanding the conception of information governance. Part I focuses on the management of government-held information. Part II examines information central to economic activity. Part III explores information flows crucial to politics and social life.

Research on the information transfer characteristics of dimensions in the product variant design process

AbstractProduct variant design, as one of the key enabling technologies of mass customization, is the transfer of variant information among mating parts from the perspective of informatics. A dimension constraint network (DCN) among mating parts carries on the task of transferring variant information. What are the information transfer characteristics of dimensions in a constraint network is a fundamental issue to plan the product variant design process reasonably. We begin by showing the natural dynamics of the DCN from two aspects: structure and uncertainty. The information efficiency of the DCN was proposed based on its simple path to specify the information transfer capability of the network. Based on this, the information centrality of the dimension was developed by measuring the efficiency loss of the DCN after the removal of a dimension from the network, which describes the information transfer capability of this dimension. Further, the information centrality of a part was derived. Using a spherical valve subassembly, we calculated the information centrality of the dimensions in a constraint network. We determined that the information centrality of dimension is highly correlated to its out-degree. An approach to plan the sequence of the part variant design according to its information centrality was proposed. We calculated the uncertainties of the DCN and its cumulative uncertainties under different sequences of the part variant design. Results indicate that part variant design under the descending information centrality of the parts minimizes the uncertainty of the DCN. This suggests a new method of planning the sequence of part variant design.

Global temporal dynamic landscape of pathogen-mediated subversion of Arabidopsis innate immunity

The universal nature of networks’ structural and physical properties across diverse systems offers a better prospect to elucidate the interplay between a system and its environment. In the last decade, several large-scale transcriptome and interactome studies were conducted to understand the complex and dynamic nature of interactions between Arabidopsis and its bacterial pathogen, Pseudomonas syringae pv. tomato DC3000. We took advantage of these publicly available datasets and performed “-omics”-based integrative, and network topology analyses to decipher the transcriptional and protein-protein interaction activities of effector targets. We demonstrated that effector targets exhibit shorter distance to differentially expressed genes (DEGs) and possess increased information centrality. Intriguingly, effector targets are differentially expressed in a sequential manner and make for 1% of the total DEGs at any time point of infection with virulent or defense-inducing DC3000 strains. We revealed that DC3000 significantly alters the expression levels of 71% effector targets and their downstream physical interacting proteins in Arabidopsis interactome. Our integrative “-omics”-–based analyses identified dynamic complexes associated with MTI and disease susceptibility. Finally, we discovered five novel plant defense players using a systems biology-fueled top-to-bottom approach and demonstrated immune-related functions for them, further validating the power and resolution of our network analyses.

Big data management challenges in health research-a literature review.

Big data management for information centralization (i.e. making data of interest findable) and integration (i.e. making related data connectable) in health research is a defining challenge in biomedical informatics. While essential to create a foundation for knowledge discovery, optimized solutions to deliver high-quality and easy-to-use information resources are not thoroughly explored. In this review, we identify the gaps between current data management approaches and the need for new capacity to manage big data generated in advanced health research. Focusing on these unmet needs and well-recognized problems, we introduce state-of-the-art concepts, approaches and technologies for data management from computing academia and industry to explore improvement solutions. We explain the potential and significance of these advances for biomedical informatics. In addition, we discuss specific issues that have a great impact on technical solutions for developing the next generation of digital products (tools and data) to facilitate the raw-data-to-knowledge process in health research.

ICT and Green Sustainability Research and Teaching

The centrality of Information and Communications Technology (ICT) in modern life means that ICT plays a part in many aspects of human activity. The search for ways to mitigate the effects of climate change is no exception, and the many potential roles which ICT can play, including the ability to enable data gathering for monitoring purposes; to deliver alternative ways of working; to change supply chains and to control transport and energy supply are well understood. Indeed, examples like these are widely used in predictions of our future. However, ICT has a dual role: as an enabler of change it can support changes to societal behaviour which can reduce the environmental impact of life, housing, work and leisure activities, but as a significant user of resources, it is a contributor to the adverse effects. In order to fully reap the benefits of ICT’s contribution while limiting the resource requirements, we require education and research. Meeting the separate but related challenges of delivering sustainability by the intelligent use of ICT, whilst maximizing ICT’s own sustainability is not a simple task. Education is necessary to make all those involved – general public and ICT professionals – aware of the situation and to give them the understanding necessary to make changes, to conduct research to explore the potential of new methods and processes, and to verify that they can make a positive difference. This paper considers the state of ICT education and identifies that sustainability is not in the forefront of consideration, it is suggested that much of the research that is being carried out is often not defined as sustainability or green IT research, and discusses ways in which these issues might be addressed.

MIDAS: Mining differentially activated subpaths of KEGG pathways from multi-class RNA-seq data.

http://biohealth.snu.ac.kr/software/MIDAS/.

Path intersection matrices and applications to networks

For a network G, we introduce a non-singular symmetric matrix, called a path intersection matrix, that will provide a new method for computing the ratio k(G)/k(G/ab) where k(G) is the tree-number of G and G/ab is obtained from G∪ab by contracting the new edge ab between two distinct nodes a and b. The quantities k(G)/k(G/ab) appear as invariants for various networks such as effective conductance for an electrical network and an ingredient for information centrality for a social network. We will review several examples of networks where path intersection matrices can be applied.

Joint Centrality Distinguishes Optimal Leaders in Noisy Networks

We study the performance of a network of agents tasked with tracking an external unknown signal in the presence of stochastic disturbances and under the condition that only a limited subset of agents, known as leaders, can measure the signal directly. We investigate the optimal leader selection problem for a prescribed maximum number of leaders, where the optimal leader set minimizes total system error defined as steady-state variance about the external signal. In contrast to previously established greedy algorithms for optimal leader selection, our results rely on an expression of total system error in terms of properties of the underlying network graph. We demonstrate that the performance of any given set of noise-free leaders depends on their influence as determined by a new graph measure of the centrality of a set. We define the joint centrality of a set of nodes in a network graph such that a noise-free leader set with maximal joint centrality is an optimal leader set. In the case of a single leader, we prove that the optimal leader is the node with maximal information centrality for the noise-corrupted and noise-free leader cases. In the case of multiple leaders, we show that the nodes in the optimal noise-free leader set balance high information centrality with a coverage of the graph. For special cases of graphs, we solve explicitly for optimal leader sets. Examples are used to illustrate.

Improving situation awareness in crisis response teams: An experimental analysis of enriched information and centralized coordination

In responding to an emergency, the actions of emergency response teams critically depend upon the situation awareness the team members have acquired. Situation awareness, and the design of systems to support it, has been a focus in recent emergency management research. In this paper, we introduce two interventions to the core processes of information processing and information sharing in emergency response teams to analyze their effect on the teams’ situation awareness: (1) we enrich raw incoming information by adding a summary of the information received, and (2) we channel all incoming information to a central coordinator who then decides upon further distribution within the team. The effect of both interventions is investigated through a controlled experiment with experienced professional responders. Our results show distinctly different effects for information enrichment and centralization, both for the teams and for the coordinators within the team. While the interaction effects of both conditions cannot be discerned, it is apparent that processing non-enriched information and non-centralized information sharing leads to a worse overall team situation awareness. Our work suggests several implications for the design of emergency response management information systems.

A Complex Network Theory Approach for the Spatial Distribution of Fire Breaks in Heterogeneous Forest Landscapes for the Control of Wildland Fires.

Based on complex network theory, we propose a computational methodology which addresses the spatial distribution of fuel breaks for the inhibition of the spread of wildland fires on heterogeneous landscapes. This is a two-level approach where the dynamics of fire spread are modeled as a random Markov field process on a directed network whose edge weights are determined by a Cellular Automata model that integrates detailed GIS, landscape and meteorological data. Within this framework, the spatial distribution of fuel breaks is reduced to the problem of finding network nodes (small land patches) which favour fire propagation. Here, this is accomplished by exploiting network centrality statistics. We illustrate the proposed approach through (a) an artificial forest of randomly distributed density of vegetation, and (b) a real-world case concerning the island of Rhodes in Greece whose major part of its forest was burned in 2008. Simulation results show that the proposed methodology outperforms the benchmark/conventional policy of fuel reduction as this can be realized by selective harvesting and/or prescribed burning based on the density and flammability of vegetation. Interestingly, our approach reveals that patches with sparse density of vegetation may act as hubs for the spread of the fire.

A policy development model for reducing bullwhips in hybrid production-distribution systems

The study considered a hybrid production-distribution system (PDS) in which products move both downstream and upstream. The System Dynamics (SD) modeling methodology was used to examine the effects of integrating product returns and recovery options to the traditional downstream flow in the PDS. The recovery options of remanufacture, cannibalization and refurbish were found to have the most significant effects to the resulting degree of bullwhips and inventory variances. The SD model was then used to identify effective policies that could manage inventory, production and distribution flows in the PDS. Among these policies included the coupling of complementary recovery options and centralization of demand information. It was observed that these policies could actually smoothen the flow of production, which eventually leads to significant decreases in the inventory variances and amplifications in all echelons of the hybrid PDS.

Ranking novel cancer driving synthetic lethal gene pairs using TCGA data.

Synthetic lethality (SL) has emerged as a promising approach to cancer therapy. In contrast to the costly and labour-intensive genome-wide siRNA or CRISPR-based human cell line screening approaches, computational approaches to prioritize potential synthetic lethality pairs for further experimental validation represent an attractive alternative. In this study, we propose an efficient and comprehensive in-silico pipeline to rank novel SL gene pairs by mining vast amounts of accumulated tumor high-throughput sequencing data in The Cancer Genome Atlas (TCGA), coupled with other protein interaction networks and cell line information. Our pipeline integrates three significant features, including mutation coverage in TCGA, driver mutation probability and the quantified cancer network information centrality, into a ranking model for SL gene pair identification, which is presented as the first learning-based method for SL identification. As a result, 107 potential SL gene pairs were obtained from the top 10 results covering 11 cancers. Functional analysis of these genes indicated that several promising pathways were identified, including the DNA repair related Fanconi Anemia pathway and HIF-1 signaling pathway. In addition, 4 SL pairs, mTOR-TP53, VEGFR2-TP53, EGFR-TP53, ATM-PRKCA, were validated using drug sensitivity information in the cancer cell line databases CCLE or NCI60. Interestingly, significant differences in the cell growth of mTOR siRNA or EGFR siRNA knock-down were detected between cancer cells with wild type TP53 and mutant TP53. Our study indicates that the pre-screening of potential SL gene pairs based on the large genomics data repertoire of tumor tissues and cancer cell lines could substantially expedite the identification of synthetic lethal gene pairs for cancer therapy.

Social networks predict selective observation and information spread in ravens.

Animals are predicted to selectively observe and learn from the conspecifics with whom they share social connections. Yet, hardly anything is known about the role of different connections in observation and learning. To address the relationships between social connections, observation and learning, we investigated transmission of information in two raven (Corvus corax) groups. First, we quantified social connections in each group by constructing networks on affiliative interactions, aggressive interactions and proximity. We then seeded novel information by training one group member on a novel task and allowing others to observe. In each group, an observation network based on who observed whose task-solving behaviour was strongly correlated with networks based on affiliative interactions and proximity. Ravens with high social centrality (strength, eigenvector, information centrality) in the affiliative interaction network were also central in the observation network, possibly as a result of solving the task sooner. Network-based diffusion analysis revealed that the order that ravens first solved the task was best predicted by connections in the affiliative interaction network in a group of subadult ravens, and by social rank and kinship (which influenced affiliative interactions) in a group of juvenile ravens. Our results demonstrate that not all social connections are equally effective at predicting the patterns of selective observation and information transmission.

지상 무인로봇체계 네트워크를 위한 매개 중심도와 위치정보 기반 Ad Hoc 라우팅

지상 무인로봇체계 네트워크를 위한 매개 중심도와 위치정보 기반 Ad Hoc 라우팅