Individual Data Values Research Articles

Automated provision of clinical routine data for a complex clinical follow-up study: A data warehouse solution.

A deep integration of routine care and research remains challenging in many respects. We aimed to show the feasibility of an automated transformation and transfer process feeding deeply structured data with a high level of granularity collected for a clinical prospective cohort study from our hospital information system to the study's electronic data capture system, while accounting for study-specific data and visits. We developed a system integrating all necessary software and organizational processes then used in the study. The process and key system components are described together with descriptive statistics to show its feasibility in general and to identify individual challenges in particular. Data of 2051 patients enrolled between 2014 and 2020 was transferred. We were able to automate the transfer of approximately 11 million individual data values, representing 95% of all entered study data. These were recorded in n = 314 variables (28% of all variables), with some variables being used multiple times for follow-up visits. Our validation approach allowed for constant good data quality over the course of the study. In conclusion, the automated transfer of multi-dimensional routine medical data from HIS to study databases using specific study data and visit structures is complex, yet viable.

A novel approach for estimation of aboveground biomass of a carbon-rich mangrove site in India

Mangroves can play a crucial part in climate change mitigation policies due to their high carbon-storing capacity. However, the carbon sequestration potential of Indian mangroves generally remained unexplored to date. In this study, multi-temporal Sentinel-1 and 2 data-derived variables were used to estimate the AGB of a tropical carbon-rich mangrove forest of India. Ensemble prediction of multiple machine learning algorithms, including Random Forest (RF), Gradient Boosted Model (GBM), and Extreme Gradient Boosting (XGB), were used for AGB prediction. The multi-temporal dataset was used in two different ways to find the most suitable method of using them. The results of the analysis showed that the modeling field measured AGB with individual date data values results in estimates with root mean square errors (RMSE) ranging from 149.242 t/ha for XGB to 151.149 t/ha for the RF. Modeling AGB with the average and percentile metrics of the multi-temporal image stack improves the prediction accuracy of AGB, with RMSE ranging from 81.882 t/ha for the XGB to 74.493 t/ha for the RF. The AGB modeling using ensemble prediction showed further improvement in accuracy with an RMSE of 72.864 t/ha and normalized RMSE of 11.38%. In this study, the intra-seasonal variation of Sentinel-1 and 2 data for mangrove ecosystems was explored for the first time. The variations in remotely sensed variables could be attributed mainly to soil moisture availability and rainfall in the mangrove ecosystem. The efficiency of Sentinel-1 and 2 data-derived variables and ensemble prediction of machine learning models for Indian mangroves were also explored for the first time. The methodologies established in this study can be used in the future for accurate prediction and repeated monitoring of AGB for mangrove ecosystems.

Competition Problems and Governance of Non-personal Agricultural Machine Data: Comparing Voluntary Initiatives in the US and EU

The arrival of digital data in agriculture opens the possibility to realise productivity gains through precision farming. It also raises questions about the distribution of these gains between farmers and agricultural service providers. Farmers’ control of the data is often perceived as a means to appropriate a larger share of these gains. We show how data-driven agricultural business models lock farm data into machines and devices that reduce competition in downstream agricultural services markets. Personal data protection regulation is not applicable to non-personal agricultural machine data. Voluntary data charters in the EU and US emulate GDPR-like principles to give farmers more control over their data but do not really change market-based outcomes due to their legal design. Third-party platforms are a necessary intermediary because farmers cannot achieve the benefits from applications that depend on economies of scale and scope in data aggregation. The low marginal value of individual farm data in such applications puts farmers in a weak bargaining position. Neutral intermediaries that are not vertically integrated into agricultural machines, inputs or services may circumvent monopolistic data lock-ins provided they can access the data. Unless they find a way to generate and monetise economies of scale and scope with their data, their business model may not be sustainable. Regulatory intervention that facilitates portability and interoperability might be useful for farmers to overcome data lock-ins, but designing data access rights is a complicated issue as many parties contribute data to the production process and may claim access rights. Minor changes in who gets access to which data under which conditions may have significant effects on stakeholders. We conclude that digital agriculture still has some way to go to reach equitable and efficient solutions to data access rights. Similar situations are likely to occur in other industries that rely on non-personal machine data.

A comparison of conventional and wavelet transform based methods for streamflow record extension

Sufficient hydrological data, such as streamflow, are important to represent the long-term characteristics of a watershed in order to support decision-making, policy and management. Lack of data remains one of the main challenges of hydrological analyses. Therefore, the main goal of this study is to extend streamflow records using a proposed approach where the wavelet transform (WT) is incorporated as a pre-processing method into eight existing record extension techniques, namely the ordinary least-square regression (OLS), maintenance of variance (MOVE) types 1–4, Kendall-Theil robust line (KTRL), KTRL2 and robust line of organic correlation (RLOC). The performance of the WT-based methods in estimating individual data values, means and standard deviations of the extended records, and a series of percentiles of the extended records was then compared with that of the respective conventional methods (i.e., without the WT). The data used in the analysis consisted of 67 pairs of target-index stations that were obtained from Canada’s Reference Hydrometric Basin Network database, all of which contain outliers. As such, the results and discussions obtained are applicable for cases where the data contain outliers. The WT-based methods (particularly the KTRL-WT, KTRL2-WT and RLOC-WT) demonstrated consistent improvements in precision and accuracy compared with their conventional counterparts, especially in estimating the means and standard deviations of the extended records. In estimating individual data values, the WT-based methods showed inconsistent improvements. Finally, in terms of percentiles, greater improvements were seen in the estimation of higher percentiles, more specifically for the MOVE1-WT, MOVE2-WT, KTRL2-WT and RLOC-WT compared with their conventional counterparts.

Measures of the Benefit of Direct Encoding of Data Deltas for Data Pair Relation Perception.

The power of data visualization is not to convey absolute values of individual data points, but to allow the exploration of relations (increases or decreases in a data value) among them. One approach to highlighting these relations is to explicitly encode the numeric differences (deltas) between data values. Because this approach removes the context of the individual data values, it is important to measure how much of a performance improvement it actually offers, especially across differences in encodings and tasks, to ensure that it is worth adding to a visualization design. Across 3 different tasks, we measured the increase in visual processing efficiency for judging the relations between pairs of data values, from when only the values were shown, to when the deltas between the values were explicitly encoded, across position and length visual feature encodings (and slope encodings in Experiments 1 & 2). In Experiment 1, the participant's task was to locate a pair of data values with a given relation (e.g., Find the 'small bar to the left of a tall bar' pair) among pairs of the opposite relation, and we measured processing efficiency from the increase in response times as the number of pairs increased. In Experiment 2, the task was to judge which of two relation types was more prevalent in a briefly presented display of 10 data pairs (e.g., Are there more 'small bar to the left of a tall bar' pairs or more 'tall bar to the left of a small bar' pairs?). In the final experiment, the task was to estimate the average delta within a briefly presented display of 6 data pairs (e.g., What is the average bar height difference across all 'small bar to the left of a tall bar' pairs?). Across all three experiments, visual processing of relations between data value pairs was significantly better when directly encoded as deltas rather than implicitly between individual data points, and varied substantially depending on the task (improvement ranged from 25% to 95%). Considering the ubiquity of bar charts and dot plots, relation perception for individual data values is highly inefficient, and confirms the need for alternative designs that provide not only absolute values, but also direct encoding of critical relationships between those values.

Triggered or routine site monitoring visits for randomised controlled trials: results of TEMPER, a prospective, matched-pair study.

Background/aimsIn multi-site clinical trials, where trial data and conduct are scrutinised centrally with pre-specified triggers for visits to sites, targeted monitoring may be an efficient way to prioritise on-site monitoring. This approach is widely used in academic trials, but has never been formally evaluated.MethodsTEMPER assessed the ability of targeted monitoring, as used in three ongoing phase III randomised multi-site oncology trials, to distinguish sites at which higher and lower rates of protocol and/or Good Clinical Practice violations would be found during site visits. Using a prospective, matched-pair design, sites that had been prioritised for visits after having activated ‘triggers’ were matched with a control (‘untriggered’) site, which would not usually have been visited at that time. The paired sites were visited within 4 weeks of each other, and visit findings are recorded and categorised according to the seriousness of the deviation. The primary outcome measure was the proportion of sites with ≥1 ‘Major’ or ‘Critical’ finding not previously identified centrally. The study was powered to detect an absolute difference of ≥30% between triggered and untriggered visits. A sensitivity analysis, recommended by the study’s blinded endpoint review committee, excluded findings related to re-consent. Additional analyses assessed the prognostic value of individual triggers and data from pre-visit questionnaires completed by site and trials unit staff.ResultsIn total, 42 matched pairs of visits took place between 2013 and 2016. In the primary analysis, 88.1% of triggered visits had ≥1 new Major/Critical finding, compared to 81.0% of untriggered visits, an absolute difference of 7.1% (95% confidence interval −8.3%, +22.5%; p = 0.365). When re-consent findings were excluded, these figures reduced to 85.7% versus 59.5%, (difference = 26.2%, 95% confidence interval 8.0%, 44.4%; p = 0.007). Individual triggers had modest prognostic value but knowledge of the trial-related activities carried out by site staff may be useful.ConclusionTriggered monitoring approaches, as used in these trials, were not sufficiently discriminatory. The rate of Major and Critical findings was higher than anticipated, but the majority related to consent and re-consent with no indication of systemic problems that would impact trial-wide safety issues or integrity of the results in any of the three trials. Sensitivity analyses suggest triggered monitoring may be of potential use, but needs improvement and investigation of further central monitoring triggers is warranted. TEMPER highlights the need to question and evaluate methods in trial conduct, and should inform further developments in this area.

A Multi-platform Mobile Application to Collect Citizen Science Data for Bignoniaceae Phenological Research

The implementation of Citizen Science in biodiversity studies has led the general public to engage in environmental actions and to contribute to the conservation of natural resources (Chandler et al. 2017). Smartphones have become part of the daily lives of millions of people, allowing the general public to collect data and conduct automatic measurements at a very low cost. Indeed, a series of Citizen Science mobile applications have allowed citizens to rapidly record specimen observations and contribute for the development of large biodiversity databases around the World. Citizen Science applications have a multitude of purposes, as well as target a variety of taxa, biological questions and geographical regions. Brazil is a megadiverse country that includes many threatened species and Biomes. Conversation efforts are urgent and the engagement of the civil society is critical. Brazilian dry and wet forests are dominated by members of the plant family Bignoniaceae, all of which are characterized by beautiful trumpet-shaped flowers and a big-bang flowering strategy. Species of the Neotropical Bignoniaceae trees are popularly known in Brazil as “Ipê” and are broadly cultivated throughout the country due to the showy flowers and strong wood. Different species have different flower colors, making its identification relatively easy. The showy and colorful flowers are extremely admired by the local population and the media. Flowering of “Ipês” is triggered by dry climate, lower temperatures and increasing day-light, making this group an excellent model for phenological and climatic studies involving Citizen Science. Here, we developed a multi-platform mobile application focused on the plant family Bignoniaceae that allows users to contribute phenological data for species from this plant family. More specifically, through this application the user is able to provide data about specimen locations, phenology and date, all of which can be validated by a photograph. This platform is based on React Native, a hybrid app framework that helps the developers to reuse the code across multiple mobile platforms, a development much more efficient and with efforts focused on the user experience. This technology uses Javascript as programming language and Facebook React as a basis for development. The system is similar to other CS apps such as iNaturalist. Namely, the overall observations improve the quality of the ranking through positive feedback from the community, strengthening the network of interactions between users and encouraging active participation. On the other hand, the application allows users to access all previously stored observations, which, in turn, can suggest improvements to that particular observation. Furthermore, observations without a correct ID can be stored until others can suggest a correct identification, maximizing the value of individual observations and data gathered. An important aspect of this mobile application is the participation of a network of experts on this plant family, allowing a rapid and accurate verification of individual observations. This team of Bignoniaceae experts is also able to make full use of the data gathered by correlating climate and phenological patterns. Results from these analyses are provided to the citizens gathering the data which will, in turn, stimulate the collection of new data, especially in poorly sampled locations. This is a very dynamic mobile application, that aims to engage the civil society with true scientific research, stimulating the management of natural resources and conservation efforts. Through this mobile app, we hope to engage the general public into biodiversity studies by improving their knowledge on an iconic group of Brazilian plants, while contributing data for scientific studies. The system is expected to be released in May and will be available at ipesdobrasil.org.br.

How Complex Is a Fractal? Head/tail Breaks and Fractional Hierarchy

A fractal bears a complex structure that is reflected in a scaling hierarchy, indicating that there are far more small things than large ones. This scaling hierarchy can be effectively derived using head/tail breaks—a clustering and visualization tool for data with a heavy-tailed distribution—and quantified by a head/tail breaks-induced integer, called ht-index, indicating the number of clusters or hierarchical levels. However, this integral ht-index has been found to be less precise for many fractals at their different phrases of development. This paper refines the ht-index as a fraction to measure the scaling hierarchy of a fractal more precisely within a coherent whole and further assigns a fractional ht-index—the fht-index—to an individual data value of a data series that represents the fractal. We developed two case studies to demonstrate the advantages of the fht-index, in comparison with the ht-index. We found that the fractional ht-index or fractional hierarchy in general can help characterize a fractal set or pattern in a much more precise manner. The index may help create intermediate map scales between two consecutive map scales.

Particle Swarm Intelligence and Impact Factor-Based Privacy Preserving Association Rule Mining for Balancing Data Utility and Knowledge Privacy

Organizations generally prefer data or knowledge sharing with others to obtain mutual benefits. The major issue in sharing the data or knowledge is data owners privacy requirements. Privacy preserving association rule mining is an area in which data owner can protect private association rules (sensitive knowledge) from disclosure while sharing the data. To safeguard sensitive association rules, individual data values of a database must be altered. Therefore, privacy concerns must not compromise data utility. A methodology that optimally selects and alters the transactions of the database is required to balance privacy and utility. Particle swarm optimization is a meta-heuristic technique used for optimization. Hence, an approach with particle swarm intelligence is developed to select a set of database transactions for alterations to minimize the number of non-sensitive association rules that are lost and to maintain high utility of the sanitized database without compromising on privacy concerns. The projected method for hiding association rules was assessed based on some performance parameters including utility of the transformed database. Experiments have revealed that the proposed method accomplished a good balance between privacy and utility by minimizing difference between original and transformed databases.

Big data analytics sentiment: US-China reaction to data collection by business and government

As society continues its rapid change to a digitized individual, corporate, and government environment it is prudent for researchers to investigate the zeitgeist of the global citizenry. The technological changes brought about by big data analytics are changing the way we gather and view data. This big data analytics sentiment research examines how Chinese and American respondents may view big data collection and analytics differently. The paper follows with an analysis of reported attitudes toward possible viewpoints from each country on various big data analytics topics ranging from individual to business and governmental foci. Hofstede's cultural dimensions are used to inform and frame our research hypotheses. Findings suggest that Chinese and American perspectives differ on individual data values, with the Chinese being more open to data collection and analytic techniques targeted toward individuals. Furthermore, support is found that US respondents have a more favorable view of businesses' use of data analytics. Finally, there is a strong difference in the attitudes toward governmental use of data, where US respondents do not favor governmental big data analytics usage and the Chinese respondents indicated a greater acceptance of governmental data usage. These findings are helpful in better understanding appropriate technological change and adoption from a societal perspective. Specifically, this research provides insights for corporate business and government entities suggesting how they might adjust their approach to big data collection and management in order to better support and sustain their organization's services and products.

Interpreting the ambulatory glucose profile

A systematic approach to interpreting dense glucose data from continuous glucose monitoring (CGM) systems is important for dealing with the daunting amount of data that these systems provide. Moreover, a consensus approach would be useful to reduce differences in interpretation of similar data between physicians. The ambulatory glucose profile (AGP), now available for the FreeStyle Libre flash glucose monitoring system and some CGM systems, allows simpler and more confident identification of patterns, by averaging data for the modal day (itself derived from several daily CGM traces). Its output provides a single curve representing the average, with interquartile and interdecile ranges. Even scattered individual data values without discernible pattern can be averaged and produce useful clinically relevant information, especially where there is considerable glycaemic variability. Consensus recommendations on the use of the AGP build on an understanding of the user's daily routine, followed by examination of the AGP for periods of hypoglycaemic risk, i.e. when the 10th centile line for glucose approaches or enters the hypoglcycaemic range. These analyses are supported by the glucose pattern insights analysis available from the FreeStyle Libre flash glucose monitoring system, which flags up the likelihood of low glucose, the proximity of the median glucose to target, and the degree of variability below the median at various times of day. Where hypoglycaemia patterns are not apparent, the consensus guidance recommends assessing the periods around overnight, morning meal, midday meal, and evening meal in turn. In these ways, the consensus guideline provides the systematic approach to interpretation which is the cornerstone of using the AGP.

The impact of adopting EHRs: how losing connectivity affects clinical reasoning.

As electronic health records (EHRs) are adopted by teaching hospitals, educators must examine how this change impacts trainee development. We investigate this influence by studying clinician experiences of a hospital's move from paper charts to an EHR. We ask: how does each chart modality present conceptions of time and data interconnections? How do these conceptions affect clinical reasoning? This two-phase, longitudinal study employed constructivist grounded theory. Data were collected at a paediatric teaching hospital before (Phase 1), during and after (Phase 2) the transition from a paper chart to an EHR system. Data collection consisted of field observations (146 hours involving 300 health care providers, 22 patients and 32 patient family members), think-aloud (n = 13) and think-after (n = 11) sessions, interviews (n = 39) and document retrieval (n = 392). Theories of rhetorical genre studies and visual rhetoric informed analysis. In the paper flowsheet, clinicians recorded and viewed patient data in chronologically organised displays that emphasised data interconnections. In the EHR flowsheet, clinicians viewed and recorded individual data points that were largely chronologically and contextually isolated. Clinicians reported that this change resulted in: (i) not knowing the patient's evolving status; (ii) increased cognitive workload, and (iii) loss of clinical reasoning support mechanisms. Understanding how patient data are interconnected is essential to clinical reasoning. The use of EHRs supports this goal because the EHR is a tool for collecting dispersed data; however, these collections often deconstruct data interconnections. Where the paper flowsheet emphasises chronology and interconnectedness, the EHR flowsheet emphasises individual data values that are largely independent of time and other patient data. To prepare trainees to work with EHRs, the ways of thinking and acting that were implicitly learned through the use of paper charts must be made explicit. To support clinical reasoning, medical educators should provide lessons in connectivity – the chronologically framed data interconnections upon which clinicians rely to provide patient care.

Data seen through different lenses

Statistical reasoning focuses on properties that belong not to individual data values but to the entire aggregate. We analyze students’ statements from three different sources to explore possible building blocks of the idea of data as aggregate and speculate on how young students go about putting these ideas together. We identify four general perspectives that students use in working with data, which in addition to an aggregate perspective include regarding data as pointers, as case values, and as classifiers. Some students seem inclined to view data from only one of these three alternative perspectives, which then influences the types of questions they ask, the data representations they generate or prefer, the interpretations they give to notions such as the average, and the conclusions they draw from the data.

Abstract PD4-2: Whole exome and transcriptome sequencing of 120 primary breast cancer to discover novel therapeutic target

Abstract Introduction: Many somatic mutations, structural alterations, and gene expression changes are causally implicated in oncogenesis and tumor progression, and as a result, affect clinical outcome. Although majority of breast cancer patients have benefits from therapeutics targeting tumor biology, such as estrogen receptor and HER-2, still many patients suffer from disease recurrence and metastasis. More kind of specific target therapies are needed, especially for hormone-resistant tumor and triple-negative breast cancer. Materials and Method: To find novel therapeutic target in breast cancer, here we examine the both whole exome and whole transcriptome of fresh-frozen primary breast cancer tissues from 120 patients whose clinical, pathological, and survival data are available. Patients with Stage IV disease or who received neoadjuvant chemotherapy were excluded. 36 patients had distant metastasis within 5 years from surgery, and 84 patients were NED at least 5 years. RNA and DNA were extracted and qualities were assessed in all samples. Exome and transcriptome sequencing were done using NGS technology (Illumina HiSeq 2000). As a control, exome sequencing was done for 93 normal DNA from matched patients. Single nucleotide variations (SNV) identified in cancer samples on exonic region, nonsynonymous SNV or stop gain/loss, whose quality ≥20, and not found in 93 normal samples were included. SNVs registered in dbSNP135_common or 1000 genome allele frequency >0.001 were excluded.Results and Discussion: We identified 11,684 putative somatic mutations in 7,373 genes. Of them, 6,547 were deleterious or damaging mutation by Provean or SIFT analysis. Mutations were found in potential drug target genes, such as PIK3CA(25), PTEN(3), AKT1(3), ALK(3), ROS1(2), FGFR4(3), FGFR3(2), ERBB2(2), and IDH1(1) etc. In a pathway analysis, mutations in insulin signaling pathway were most dominant. We hypothesized that driver gene and therapeutic target has to have recurrent mutation and gene expression at least more than average expression. We calculated expression “Volume” according to the median normalized FPKM value of individual gene's RNA-seq data. With a cut-off of 3 or more mutations in each gene, 1,116 genes were selected. After the filtering of Volume<0.3, 696 genes were selected. Finally, 55 genes were selected which are druggable or potentially druggable using drug database (DrugBank, TOCRIS, Ingenuity) and Pubmed. DriverNet analysis result was also considered for the selection. All 342 tumor suppressor genes were filtered out. Interestingly, 18 of the 55 were genes involved in metabolism (fatty acid, glucose, amino acids). 12 were kinases and 4 were involved in insulin pathway. Excluding the previously confirmed therapeutic target, PIK3CA, AKT1, and NOTCHs, and considering the patients’ clinical data, our primary candidates for hormone-resistant breast cancer were NQO2, CELSR1, GLUD2, MYH9, PSMD2, NADK, IRS2, MAP3K5, and for triple-negative breast cancer were HSPG2, PHGDH, MYLK, etc. Validation with Sanger sequencing and functional study is on-going. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr PD4-2.

Political Campaigns and Big Data

Modern campaigns develop databases of detailed information about citizens to inform electoral strategy and to guide tactical efforts. Despite sensational reports about the value of individual consumer data, the most valuable information campaigns acquire comes from the behaviors and direct responses provided by citizens themselves. Campaign data analysts develop models using this information to produce individual-level predictions about citizens' likelihoods of performing certain political behaviors, of supporting candidates and issues, and of changing their support conditional on being targeted with specific campaign interventions. The use of these predictive scores has increased dramatically since 2004, and their use could yield sizable gains to campaigns that harness them. At the same time, their widespread use effectively creates a coordination game with incomplete information between allied organizations. As such, organizations would benefit from partitioning the electorate to not duplicate efforts, but legal and political constraints preclude that possibility.

Political Campaigns and Big Data

Modern campaigns develop databases of detailed information about citizens to inform electoral strategy and to guide tactical efforts. Despite sensational reports about the value of individual consumer data, the most valuable information campaigns acquire comes from the behaviors and direct responses provided by citizens themselves. Campaign data analysts develop models using this information to produce individual-level predictions about citizens’ likelihoods of performing certain political behaviors, of supporting candidates and issues, and of changing their support conditional on being targeted with specific campaign interventions. The use of these predictive scores has increased dramatically since 2004, and their use could yield sizable gains to campaigns that harness them. At the same time, their widespread use effectively creates a coordination game with incomplete information between allied organizations. As such, organizations would benefit from partitioning the electorate to not duplicate efforts, but legal and political constraints preclude that possibility.

Min Max Normalization Based Data Perturbation Method for Privacy Protection

Data mining system contain large amount of private and sensitive data such as healthcare, financial and criminal records. These private and sensitive data can not be share to every one, so privacy protection of data is required in data mining system for avoiding privacy leakage of data. Data perturbation is one of the best methods for privacy preserving. We used data perturbation method for preserving privacy as well as accuracy. In this method individual data value are distorted before data mining application. In this paper we present min max normalization transformation based data perturbation. The privacy parameters are used for measurement of privacy protection and the utility measure shows the performance of data mining technique after data distortion. We performed experiment on real life dataset and the result show that min max normalization transformation based data perturbation method is effective to protect confidential information and also maintain the performance of data mining technique after data distortion.

Extracting Visually Presented Element Relationships from Web Documents

Many documents in the World Wide Web present structured information that consists of multiple pieces of data with certain relationships among them. Although it is usually not difficult to identify the individual data values in the document text, their relationships are often not explicitly described in the document content. They are expressed by visual presentation of the document content that is expected to be interpreted by a human reader. In this paper, the authors propose a formal generic model of logical relationships in a document based on an interpretation of visual presentation patterns in the documents. The model describes the visually expressed relationships between individual parts of the contents independently of the document format and the particular way of presentation. Therefore, it can be used as an appropriate document model in many information retrieval or extraction applications. The authors formally define the model, the authors introduce a method of extracting the relationships between the content parts based on the visual presentation analysis and the authors discuss the expected applications. The authors also present a new dataset consisting of programmes of conferences and other scientific events and the authors discuss its suitability for the task in hand. Finally, the authors use the dataset to evaluate results of the implemented system.

Highly robust methods in data mining

This paper is devoted to highly robust methods for information extraction from data, with a special attention paid to methods suitable for management applications. The sensitivity of available data mining methods to the presence of outlying measurements in the observed data is discussed as a major drawback of available data mining methods. The paper proposes several newhighly robust methods for data mining, which are based on the idea of implicit weighting of individual data values. Particularly it propose a novel robust method of hierarchical cluster analysis, which is a popular data mining method of unsupervised learning. Further, a robust method for estimating parameters in the logistic regression was proposed. This idea is extended to a robust multinomial logistic classification analysis. Finally, the sensitivity of neural networks to the presence of noise and outlying measurements in the data was discussed. The method for robust training of neural networks for the task of function approximation, which has the form of a robust estimator in nonlinear regression, was proposed.

Orientation Modulation for Data Hiding in Clustered-Dot Halftone Prints

We present a new framework for data hiding in images printed with clustered dot halftones. Our application scenario, like other hardcopy embedding methods, encounters fundamental challenges due to extreme bilevel quantization inherent in halftoning, the stringent requirements of image fidelity, and other unavoidable printing and scanning distortions. To overcome these challenges, while still allowing for automated extraction of the embedded data and a high embedding capacity, we propose a number of innovations. First, we perform the embedding jointly with the halftoning by employing an analytical halftone threshold function that allows steering of the halftone spot orientation within each halftone cell based upon embedded data. In this process, image fidelity is emphasized and, if necessary, the capability to recover individual data values is sacrificed resulting in unavoidable erasures and errors. To overcome these and other sources of errors, we propose a suitable data detection and error control methodology based upon a statistical representation for the print-scan channel that effectively models the channel dependence upon the cover image gray-level. To combat the geometric distortion inherent in the print-scan process, we exploit the periodic halftone structure to recover from global scaling and rotation and propose a novel decision directed synchronization technique that counters locally varying printing distortion. Experimental results demonstrate the power of the proposed framework: we achieve high operational rates while preserving halftone image quality.