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This pilot study provides a conceptual framework for the application of the anthropological analysis of skeletal features and surgical interventions for the purpose of identification in cases of unknown deceased individuals with unavailable fingerprint, genetic or odontological antemortem data. The study sample includes 276 individuals with known demographic and clinical information from the Italian CAL Milano Cemetery Skeletal Collection. In the sample, 124 (45%) individuals showed one or more skeletal features that may be potentially individualizing. Of these, 79% showed two and more features, which occurred in a multitude of different combinations. Skeletal findings may provide useful postmortem information that can be compared with antemortem witness statements and clinical imaging. However, more research into the utility of dry bone findings and the availability of comparative material, including imaging, and epidemiological data needs to be undertaken before skeletal features can be implemented into identification protocols and databases.

To utilize optical techniques for bio-medical applications, e.g. disease diagnostics, the data needs to be analyzed using machine learning. This translation requires automatic data pipelines presented in this contribution for selected application.

As soon as you start thinking about saving data, you should be thinking about the security of that data. Every step of the process from deciding what data to store and what rules to implement for safeguarding that data needs to be considered. This chapter focuses on these issues.

Storing data in the blockchain brings many benefits. The block-and-chain structure protects the data stored in the blockchain from unauthorised changes. The data will also be duplicated to all nodes which maintain the blockchain. These features guarantee the data immutability and availability. However, such operation is expensive when large size data needs to be stored. We propose Senarai, a protocol which provides an affordable blockchain data storage. Our protocol utilises the fee-less transaction feature provided by Tron. By using this feature, storing an arbitrary size data can be done cheaply. We show that our protocol is sustainable such that after initialisation phase, the permanent data storage capacity is provided for free on daily basis.

This paper contains a review of modern intelligent methods’ applications for digital forensics. One of the main problems of digital forensics, which was investigated by authors, is related to the fact that a huge volume of data needs to be analysed for evidence of crime. The primary aim of this work is to improve this challenging forensic process through application of intelligent methods for analysis of digital evidences. The desired outcome of this work is to encourage advancing these methods in a forensic science discipline.

This chapter provides an overview of environmental restoration efforts involving the application of NZVI and NZVI composite materials. The chapter focuses on the novel application techniques aimed at improving the delivery, characterization and effectiveness of particles, drawing on direct field experience of the authors as well as review of over two decades of peer-reviewed literature. Stressing a base of knowledge through detailed site characterization towards a conceptual site model, this chapter’s main emphasis is on delivery techniques, options for particle formulation and challenges associated with different site conditions that would influence particle transport to the target contaminated areas, as well as monitoring requirements to evaluate treatment effectiveness. Particle types and injection characteristics are covered along with an evaluation of geological, hydrogeological, geochemical and microbiological conditions favourable for NZVI injection. The synergy between NZVI and bioremediation treatments is discussed along with observed changes in microbiological activity following particle treatments. This chapter also provides highlights of laboratory data needs, general permitting requirements and health and safety issues related to NZVI. The chapter concludes with a discussion of potential technology improvements that will likely improve the effectiveness and implementability of NZVI treatments.

This chapter describes the substantial improvements made to the Nunaliit Cybercartographic Atlas Framework since 2014. Three major challenges were identified at that time: different project data needs; local Internet availability; and the complexity of devices, formats and tools for contributing content. Considerable progress has been made in the response to these ongoing challenges. A number of recent developments in the platform are described including model-driven maps and styling, the integration of Garmin In Reach GPS devices, new semantic relationships, and D3js integration. The chapter concludes with a consideration of future developments of Nunaliit.

The emergence of more complex, data-intensive applications motivates a high demand of effective data modeling for graph databases to support efficient query answering. In this paper, we develop an intuitive graph data model for dynamic taxi ride sharing. We argue that our proposed data model meets the data needs imposed by three fundamental tasks associated with taxi ride sharing. An experiment consisting of a taxi ride sharing simulation with real-world data demonstrates the effectiveness of our modelling approach.

The Common Agricultural Policy post 2020 will address an increasing set of objectives, including contributing to the sustainable development goals and the Paris climate agreement. Current monitoring systems are focussed on structural and economic data and hardly cover performance indicators on the sustainability performance of farms. The EU financed FLINT project demonstrated that it is feasible to collect these sustainability data at farm level and illustrated the added value of having this type of data in a range of case studies. In this paper the costs and benefits of collecting sustainability indicators are described. Collecting the sustainability data on all farms included in FADN would increase the costs with about 40%. Large differences between countries can be observed depending on the current costs of data collection and the expected additional work to include sustainability indicators. Given the pressing need for these data a scenario is developed where sustainability data is collected on a sub-sample of 15.000 farms. This can be achieved within current budget limits if the current FADN sample would be reduced from 85 to 75 thousand farms.

Advances in ocean observing technologies and modeling provide the capacity to revolutionize the management of living marine resources. While traditional fisheries management approaches like single-species stock assessments are still common, a global effort is underway to adopt ecosystem-based fisheries management (EBFM) approaches. These approaches consider changes in the physical environment and interactions between ecosystem elements, including human uses, holistically. For example, integrated ecosystem assessments aim to synthesize a suite of observations (physical, biological, socioeconomic) and modeling platforms [ocean circulation models, ecological models, short-term forecasts, management strategy evaluations (MSEs)] to assess the current status and recent and future trends of ecosystem components. This information provides guidance for better management strategies. A common thread in EBFM approaches is the need for high-quality observations of ocean conditions, at scales that resolve critical physical-biological processes and are timely for management needs. Here we explore options for a future observing system that meets the needs of EBFM by (i) identifying observing needs for different user groups, (ii) reviewing relevant datasets and existing technologies, (iii) showcasing regional case studies, and (iv) recommending observational approaches required to implement EBFM. We recommend linking ocean observing within the context of Global Ocean Observing System (GOOS) and other regional ocean observing efforts with fisheries observations, new forecasting methods, and capacity development, in a comprehensive ocean observing framework.

Detecting anomalies in the behavior of a computer system is crucial for determining its security. One way of detecting these anomalies is based on the assessment of the amount and sequence of system calls issued by processes. While the number of processes on a computer can become very large, the number of system calls issued during the lifespan of such a process and its subprocesses can be humongous. In order to decide whether these anomalies are due to the intended system usage or if they are caused by malicious actions, this humongous amount of data needs being analyzed. Thus, a careful analysis of the system calls’ types, their amount, and their temporal sequence requires sophisticated support. Visualization is frequently used for this type of tasks. Starting with a carefully aggregation of the data presented in an overview representation, the quest for information is supported by carefully crafted interactions. These allow filtering the tremendous amount of data, thus removing the standard behavior data and leaving the potentially suspicious one. The latter can then be investigated on increasingly finer levels. Supporting this goal-oriented analysis, we propose novel interactive visualizations implemented in the tool SyCaT-Vis. SyCaT-Vis fosters obtaining important insights into the behavior of computer systems, the processes executed, and the system call sequences issued.

Big Data and generally accepted statistical knowledge are at the center of many decisions and rulings in US courts. Even in circumstances where statistics and Big Data knowledge are generally accepted, fact finders still need to be aware of, and in some instances scrutinize, the underlying assumptions of this generally accepted knowledge. In addition, the use of Big Data has grown dramatically in the last decade and many organizations are reliant on third parties to meet their actual or perceived data needs. In these instances, a customer cannot test drive and check under the hood of a Big Data solution and is at the mercy of a software vendor’s product. This chapter illustrates the major issues with the use of Big Data.

There is an increasing demand for real-time analysis of large volumes of data streams that are produced at high velocity. The most recent data needs to be processed within a specified delay target in order for the analysis to lead to actionable result. To this end, in this paper, we present an effective solution for detecting the correlation of such data streams within a micro-batch of a fixed time interval. Our solution, coined DCS, for Detection of Correlated Data Streams, combines (1) incremental sliding-window computation of aggregates, to avoid unnecessary re-computations, (2) intelligent scheduling of computation steps and operations, driven by a utility function within a micro-batch, and (3) an exploration policy that tunes the utility function. Specifically, we propose nine policies that explore correlated pairs of live data streams across consecutive micro-batches. Our experimental evaluation on a real world dataset shows that some policies are more suitable to identifying high numbers of correlated pairs of live data streams, already known from previous micro-batches, while others are more suitable to identifying previously unseen pairs of live data streams across consecutive micro-batches.

This research’s main objective was to provide information related to the simulation of each calculation stage of the TOPSIS method used in the Discrepancy-Tat Twam Asi evaluation model. The TOPSIS method is used to find dominant indicators in the Discrepancy-Tat Twam Asi evaluation model that determines the effectiveness of blended learning in ICT Vocational Schools. This research used a quantitative approach. The questionnaires were used as a data collection tool in this study. Questionnaires were distributed to 20 respondents (teachers and students at several ICT Vocational Schools in Bali, Indonesia) for initial data needs and distributed to fourexperts to obtain data on the TOPSIS calculation effectiveness results. The analysis technique in this research was carried out by comparing the percentage of effectiveness test results with the standards. The results of this research showed that the simulation of TOPSIS method calculation in the Discrepancy-Tat Twam Asi evaluation model had run more effectively, as indicated by score was 93.13%. The simulation results showed the dominant indicator that determines the effectiveness of the blended learning implementation was I-2 (the existence of academic community support).

Stable isotopes (C, N, H and S) chemically record information in bone about numerous aspects of past human lives, such as diet, residence and migration. Although widely applied, few stable isotope studies have been carried out for palaeodietary research using Norwegian material, and none from the southwest coast. The first animal and human stable isotope values from the Norwegian Stavanger region are presented in this study, revealing changes in dietary habits through a period of cultural, religious and economic changes in Norway, from the 9th century AD to the pos t -reformation period (17th century/early 18th century AD). 14 C-datings of some skeletons that gave ages between AD 800–1150 (calibrated, 2 sigma) raise questions about their origin and the expansion of Christianity. The stable isotope results indicate a high protein diet with a substantial amount of marine input. Across the centuries, changing dietary patterns emerge that are probably related to changes in Christian rituals and people's lifestyle. The intensification of fishing activities that characterises medieval Europe is also visible in the isotopic record of Stavanger. The multi-proxy approach in this study proved useful as the addition of hydrogen stable isotopic data allows for improved interpretation of the palaeodiet, whereas sulphur stable isotope data needs further exploration in order to indicate possible migration.

This chapter concentrates on how to exchange data between remote nodes. It explores several possible scenarios data in terms of data collection, routing or multi‐hop communications, and the most popular routing protocols currently in use. These scenarios are differentiated based on the source and the destination of the data inside the network. Directed diffusion is a routing protocol, uses a paradigm called information interest and a publish‐subscribe mechanism to deliver data to many different interested destinations. The most widely used routing protocol is Collection Tree, which is based on the idea that data needs to be collected from all nodes in the sensor network rather than routed from a single source. It is worth exploring a standardised routing protocol such as the one used in the Zigbee standard. There are some clear advantages to using a standardized protocol because they are often readily implemented on devices and can be used out of the box.

Earth observation imagery collected by the series of Landsat satellites constitutes the longest and most consistent collection of moderate spatial resolution remotely sensed data. Feedback from the Landsat user community has played an important role in assessing its utility and making improvements to future Landsat missions. The United States Geological Survey (USGS) and the National Aeronautical and Space Administration (NASA) continue to input from user communities. This paper describes the outcome of soliciting input from experts associated with AmericaView (AV), a non-profit organization aimed at promoting remote sensing applications in the US. The AV community identified the importance of rigorously calibrated Landsat data for LULC applications at the state level and made several recommendations for future Landsat missions. Recommendations suggested by the community of AV experts provide extremely valuable insights to USGS and NASA to further their discussions about the design and operation of future Landsat missions.

The US Environmental Protection Agency Endocrine Disruptor Screening Program (EDSP) is a tiered screening approach to determine the potential for a chemical to interact with estrogen, androgen, or thyroid hormone systems and/or perturb steroidogenesis. Use of high-throughput screening (HTS) to predict hazard and exposure is shifting the EDSP approach to (1) prioritization of chemicals for further screening; and (2) targeted use of EDSP Tier 1 assays to inform specific data needs. In this work, toxicology data for three triazole fungicides (triadimefon, propiconazole, and myclobutanil) were evaluated, including HTS results, EDSP Tier 1 screening (and other scientifically relevant information), and EPA guideline mammalian toxicology study data. The endocrine-related bioactivity predictions from HTS and information that satisfied the EDSP Tier 1 requirements were qualitatively concordant. Current limitations in the available HTS battery for thyroid and steroidogenesis pathways were mitigated by inclusion of guideline toxicology studies in this analysis. Similar margins (3–5 orders of magnitude) were observed between HTS-predicted human bioactivity and exposure values and between in vivo mammalian bioactivity and EPA chronic human exposure estimates for these products’ registered uses. Combined HTS hazard and human exposure predictions suggest low priority for higher-tiered endocrine testing of these triazoles. Comparison with the mammalian toxicology database indicated that this HTS-based prioritization would have been protective for any potential in vivo effects that form the basis of current risk assessment for these chemicals. This example demonstrates an effective, human health protective roadmap for EDSP evaluation of pesticide active ingredients via prioritization using HTS and guideline toxicology information.

Formulary management within a limited budget is critical, especially for specialty drugs, which are used for serious medical conditions and are very expensive. Despite attempts to summarize the pertinent evidence, it is uncertain whether data needs of formulary decision makers for specialty drugs are satisfied.To assess the level of satisfaction of specialty drug formulary decision makers with regards to the strength of current available data sources and unmet needs regarding clinical, economic, and unpublished evidence.This study targeted pharmacists and physicians involved with formulary decision making at health plans or pharmacy benefit management companies at the national, large regional, and local levels. 95 individuals were invited to participate (without compensation) in a 21-item, web-based survey (Qualtrics), which was open from June 14 to July 31, 2014. The responses were coded for descriptive and statistical analysis. Statistical analyses included the Kruskal-Wallis test, analysis of variance, and the Mann-Whitney-Wilcoxon test.Of 95 pharmacists or physicians, 40 respondents initiated the survey, and 33 respondents completed the survey (response rate = 34.7%). Drug formulary decision makers infrequently rated data evidence strength (17.1% "always"). Clinical data evidence strength was rated highest with published randomized controlled trials (RCTs; mean [SD] = 4.06 [0.87] of 5.0), while participant organizations' internal data were rated highest for economic data evidence strength (mean [SD] = 3.91 [1.07] of 5.0). Decision makers rated the highest unmet need as more data generated from head-to-head RCTs (mean [SD] = 2.94 [0.25] of 3.0) and cost-effectiveness analyses (mean [SD] = 2.53 [0.67] of 3.0). The participants believed manufacturers might be in the best position to satisfy their desire for head-to-head RCTs (mean [SD] = 4.31 [1.09] of 5.0).Despite a variety of data sources, drug formulary decision makers continue to rely on published RCTs or internal economic analyses as having the strongest evidence strength. The study respondents believed that pharmaceutical manufacturers would be best able to satisfy the greatest clinical data unmet need, that is, head-to-head RCTs in specialty drug formulary decisions.This study was not funded by any company or pharmaceutical manufacturer. Navarro has worked as a consultant for Biogen, Purdue Pharma, and Novartis and has offered expert testimony on behalf of AstraZeneca. The authors declare no other potential conflicts of interest. Study design was contributed primarily by Navarro, along with Choi. Choi took the lead in data collection and interpretation, assisted by Navarro. Both authors contributed equally to manuscript writing and revision.

This article presents a multistage framework for evaluating the strength of evidence of nanomaterial (NM) exposure characterization data to optimize the utility of in vitro testing strategies for human health risk assessment. This framework is intended to aid risk assessors in evaluating the relevance of data from in vitro tests and to optimize the development of new in vitro testing strategies. The initial stage frames the exposure scenarios of interest in advance of testing to incorporate aspects such as release points, route of exposure, biological and environmental transformations, dose metrics, and biological targets in subsequent stages. The second stage considers characterization in the context of a realistic exposure and the third stage involves designing a testing strategy based on expected exposure conditions. For the fourth and final stage, we propose a matrix approach to evaluate the strength of evidence obtained in the first three stages as a basis for determining the best combination of test conditions and analytical methods available to characterize and measure exposure based on the NM type. This approach can also be used to evaluate existing data for their relevance to the expected exposure scenario and to further develop and optimize in vitro testing strategies. Implementation of the proposed strategy will generate meaningful information on NM properties and their interaction with biological systems, based on realistic exposure scenarios, which will be cost effective and can be applied for assessing risk and making intelligent regulatory decisions regarding the use and disposal of NMs.

Contaminated sites in the United States undergo remediation and restoration through regulatory programs that lead the 2 processes through independent but often parallel pathways with different objectives. The objective of remediation is to reduce risk to human health and the environment, whereas that of restoration is to restore injured resources and compensate the public for lost use of the services that natural resources provide. More complex sites, such as those associated with large river systems and urban waterways, have resulted in increasingly larger-scale ecological risk assessments (ERAs) and natural resource damage assessments (NRDAs) that take many years and involve diverse practitioners including scientists, economists, and engineers. Substantial levels of effort are now frequently required, creating a need for more efficient and cost-effective approaches to data collection, analyses, and assessments. Because there are commonalities in the data needs between ERAs and NRDAs, coordination of the design and implementation of site-specific studies that meet the needs of both programs could result in increased efficiency and lower costs. The Association for Environmental Health and Sciences Foundation convened a panel of environmental practitioners from industry, consulting, and regulatory bodies to examine the benefits and challenges associated with coordinating ERA and NRDA activities in the context of a broad range of regulatory programs. This brief communication presents the opinions and conclusions of the panelists on these issues and reports 2 case studies for which coordinated ERA and NRDA activities produced a positive outcome. Integr Environ Assess Manag 2016;12:616-621. © 2015 SETAC.

Cognitive diagnosis models (CDMs) estimate student ability profiles using latent attributes. Model fit to the data needs to be ascertained in order to determine whether inferences from CDMs are valid. This study investigated the usefulness of some popular model fit statistics to detect CDM fit including relative fit indices (AIC, BIC, and CAIC), and absolute fit indices (RMSEA2, ABS(fcor) and MAX(χ2jj′)). These fit indices were assessed under different CDM settings with respect to Q-matrix misspecification and CDM misspecification. Results showed that relative fit indices selected the correct DINA model most of the times and selected the correct G-DINA model well across most conditions. Absolute fit indices rejected the true DINA model if the Q-matrix was misspecified in any way. Absolute fit indices rejected the true G-DINA model whenever the Q-matrix was under-specified. RMSEA2 could be artificially low when the Q-matrix was over-specified.

Theta join is one of the essential and important types of queries in database systems. As the amount of data needs to be processed increases, processing theta joins with a single machine becomes impractical. Therefore, theta join algorithms using distributed computing frameworks have been studied widely. Although one of the state-of-the-art theta-join algorithms uses M-Bucket-I heuristic, it is hard to use since running time of M-Bucket-I heuristic, which computes a mapping from a record to a reducer (i.e., reducer mapping), is O(n) where n is the size of input data. In this paper, we propose MBI-I algorithm which reduces the running time of M-Bucket-I heuristic to and gives the same result as M-Bucket-I heuristic does. We also conducted several experiments to show algorithm and confirmed that our algorithm can improve the performance of a theta join by 10%.

Many chemicals currently used are known to elicit nervous system effects. In addition, approximately 2000 new chemicals introduced annually have not yet undergone neurotoxicity testing. This review concentrated on motor development effects associated with exposure to environmental neurotoxicants to help identify critical windows of exposure and begin to assess data needs based on a subset of chemicals thoroughly reviewed by the Agency for Toxic Substances and Disease Registry (ATSDR) in Toxicological Profiles and Addenda. Multiple windows of sensitivity were identified that differed based on the maturity level of the neurological system at the time of exposure, as well as dose and exposure duration. Similar but distinct windows were found for both motor activity (GD 8–17 [rats], GD 12–14 and PND 3–10 [mice]) and motor function performance (insufficient data for rats, GD 12–17 [mice]). Identifying specific windows of sensitivity in animal studies was hampered by study designs oriented towards detection of neurotoxicity that occurred at any time throughout the developmental process. In conclusion, while this investigation identified some critical exposure windows for motor development effects, it demonstrates a need for more acute duration exposure studies based on neurodevelopmental windows, particularly during the exposure periods identified in this review.

The fit of cognitive diagnostic models (CDMs) to response data needs to be evaluated, since CDMs might yield misleading results when they do not fit the data well. Limited-information statistic M2 and the associated root mean square error of approximation (RMSEA2) in item factor analysis were extended to evaluate the fit of CDMs. The findings suggested that the M2 statistic has proper empirical Type I error rates and good statistical power, and it could be used as a general statistical tool. More importantly, we found that there was a strong linear relationship between mean marginal misclassification rates and RMSEA2 when there was model–data misfit. The evidence demonstrated that .030 and .045 could be reasonable thresholds for excellent and good fit, respectively, under the saturated log-linear cognitive diagnosis model.

High-throughput technologies present immense opportunities to characterize brain cancer biology at a systems level. However, proteomic studies of brain cancers are still relatively scarce. Here we discuss the latest proteomic technologies, their application to profiling and quantitation of brain proteomes and how we expect these technologies will be applied to study brain cancer proteomes in the future. Mass spectrometry based proteomics with increased specificity, coverage and throughput will be pervasive in proteomics investigations of brain. Generated data needs to be captured by the curation of databases, and application of creative data analysis strategies is needed to provide meaningful insights into brain functions and associated pathologies. Overall, proteomics applications to brain cancers are in the earliest stages and the expanded use of these technologies holds enormous potential to improve our understanding of brain functions and pathologies.

Modern IT systems collect an increasing wealth of data from network gear, operating systems, applications, and other components. This data needs to be analyzed to derive vital information about the user experience and business performance. For instance, faults need to be detected, service quality needs to be measured and resource usage of the next days and month needs to be forecast.

In this paper we assess differential exposure to flooding in two coastal megacities, New York and Mumbai, both of which suffered major flood-related disasters in the past decade. Specifically, we examine whether the most exposed populations are also the most socially vulnerable. First, we developed Social Vulnerability Indices (SoVIs) for each city with census data. We then overlaid the SoVI scores onto flood extent maps for Hurricane Sandy (New York, October 2012) and the Mumbai flash floods (July 2005), as well as for the evacuation zones for New York, to examine patterns of differential exposure. Our results suggest a degree of differential exposure in New York, especially in the highest flood risk areas, and provide fairly clear evidence for differential exposure in Mumbai. However, differences in the input resolution and confidence in the datasets for Mumbai make the results more uncertain. The paper concludes with a discussion of the policy implications and the data needs for urban spatial vulnerability assessments.

Societal expectations about agricultural production are changing. There are increased demands on issues such as food safety, animal welfare and the impact of agriculture on the environment (land, water and air). These changes have been refl ected in the European Union’s (EU) Common Agricultural Policy (CAP), but information on these issues is lacking and this complicates the required evaluation of policies. The EU Framework 7 project FLINT tries to close this gap by analysing the feasibility of collecting data on these new topics. FLINT has established a data infrastructure with up-to-date farm-level indicators for the monitoring and evaluation of the CAP. The project created a pilot network of more than 1,000 farms to collect a set of sustainability indicators at farm level. The pilot represents farm diversity at EU level, including the different administrative environments in the Member States. This paper sets out the context and the main contributions of the project. Societal expectations about agricultural production are changing. There are increased demands on issues such as food safety, animal welfare and the impact of agriculture on the environment (land, water and air). These changes have been refl ected in the European Union’s (EU) Common Agricultural Policy (CAP), but information on these issues is lacking and this complicates the required evaluation of policies. The EU Framework 7 project FLINT tries to close this gap by analysing the feasibility of collecting data on these new topics. FLINT has established a data infrastructure with up-to-date farm-level indicators for the monitoring and evaluation of the CAP. The project created a pilot network of more than 1,000 farms to collect a set of sustainability indicators at farm level. The pilot represents farm diversity at EU level, including the different administrative environments in the Member States. This paper sets out the context and the main contributions of the project.

What causes cities to incorporate data and analytics into their operations? So far, this question has been left largely unanswered. Research on smart cities and data needs a more holistic approach to better capture the separate and joint effects of certain causal (“condition”) variables (e.g., structures, processes) on cities' use of data and analytics (the “outcome variable”). This study adopts the Qualitative Comparative Analysis (QCA) research approach to investigate the condition variables' influence on the outcome variable to systematically detect the various causal relationships (“recipes”). The study identifies the existence of several different plausible causal pathways and a set of necessary and sufficient values of the condition variables to enhance cities' utilization of data and analytics. Building on these initial results, we offer recommendations for future research on the drivers of data and analytics utilization in cities.

The quasi-circular vegetation patches (QVPs) are mainly composed of suaeda salsa, Tamarix chinensis, and Phragmites australis in the Yellow River Delta, China. The previous studies indicate that the shrubs within a vegetation patch play an important role in the establishment and disappearance of vegetation patch. Therefore, in this work, the method based on the tasselled cap brightness and greenness components derived from the April image and the August GF-2 image acquired after water replenishment with the decision tree classifier was developed for quickly detecting the Tamarix chinensis community. The detection rate was 73.5%, and the misclassification rate was 12.1%. In the future, more samples of plant community and multi-seasonal images such as the late fall-early winter leaf-off data needs to be applied to further improve the detection accuracy.

Agent modeling has rightfully garnered much attention in the design and study of autonomous agents that interact with other agents. However, despite substantial progress to date, existing agent-modeling methods too often (a) have unrealistic computational requirements and data needs; (b) fail to properly generalize across environments, tasks, and associates; and (c) guide behavior toward inefficient (myopic) solutions. Can these challenges be overcome? Or are they just inherent to a very complex problem? In this reflection, I argue that some of these challenges may be reduced by, first, modeling alternative processes than what is often modeled by existing algorithms and, second, considering more deeply the role of non-binding communication signals. Additionally, I believe that progress in developing autonomous agents that effectively interact with other agents will be enhanced as we develop and utilize a more comprehensive set of measurement tools and benchmarks. I believe that further development of these areas is critical to creating autonomous agents that effectively model and interact with other agents.

Data is Information that has not been processed or manipulated, which is not enough for some users. So that the info is useful, the data needs to be prepared and integrated first to become Information. Universitas Muhammadiyah Yogyakarta has a tool to monitor data on Alumni, namely Tracer Study. The data currently available is still not integrated and processed correctly. Now there is a solution for processing data and data by making base data. The purpose of creating this database is to facilitate users in checking data, data that will be stored neatly and integrated into the computer. The method in this study uses the Single DDS method. Methodology This research starts from determining the subject of investigation, primary needs, creating baseline data, analyzing results. The backstage of need is carried out with two data analyzes, and interviews with LPKA (Lembaga Pengelola Kemahasiswaan dan Alumni) or Students and Alumni Management Institutions. Baseline data making in this study is single DDS, which has two data stores, namely Stage and DDS. Data analysis is done by creating reports with Microsoft Office Excel according to user needs. The testing phase is carried out with two processes, namely: ETL Testing and Functional Testing.

Routine Malaria surveillance data is useful for assessing incidence and trends over time and in stratification for targeting of malaria. The reporting completeness and potential bias of such data needs assessment. Approximately 52 million people (68%) live in malaria risk areas in Ethiopia, primarily at altitudes below 2,000 meters. Objective: The general objective of this analysis was to assess the trend and magnitude of Malaria surveillance data by time, place and laboratory result for the last five years in Benishangul Gumuz Region. Descriptive cross-sectional study was used to analyze Malaria surveillance data in terms of time, place and laboratory result. Data cleaning and analysis was made by using excel 2013 software. A total of 96757 expected health facilities were in the region from 2013-2017. A total of 86266 health facilities were reported in the last five years with an average completeness of 89.2%. In the last five years (2013-2017) total of 1186514 clinically and confirmed malaria cases were reported regionally with 2290606 total malaria suspected fever examined with ninety inpatient deaths. Among the total clinically and confirmed cases, 1172244 (98.8%) were outpatients, 14248 (1.2%) were inpatients and 1013046 (85.0%) cases were confirmed by laboratory. Out of the total confirmed malaria cases 811984 (80.2%) cases were due to P.falciparum malaria and 201030 (19.8%) cases were due to P.vivax malaria. There were no other species of malaria reported other than these two species. From the total expected health facilities; a total of 86266 health facilities were reported in the last five years with an average completeness of 89.2% which is above the WHO minimum requirement (80.0%). The malaria reports shows that significant improvement in cases of data quality and management from year to year and currently all zones, Woredas and health facilities including private and NGO facilities are included and reported weekly the malaria surveillance from the data report identified in this study. The average five year regional incidence of confirmed malaria (P. Falciparum and P. Vivax) was less than half the incidence of the clinical and confirmed malaria cases.

Decision making and business support is an important process in data mining and this can be achieved by means of pattern classification and extraction. Since the huge volume of data needs starving knowledge to process and organisation faces many issues in solving those issues. Clustering is an effective technology available to analyse and convert the datasets into meaningful patterns. Clustering in data mining uses various attributes to compute large dataset and meet out the real time issues. The proposed model uses Bayesian hierarchical clustering model with Dirichlet model to resolve the issues in large dataset analysis. Experimental results prove that proposed model experience better clustering efficiency than conventional complete link agglomerative clustering by achieving 92% of clustering accuracy.

Data mining is the current buzz of the data processing world. There are large number of applications which are producing the large amount of data. This data need to be processed for the extraction of the analyzed data. So that this analysis can be used in the decision making purpose in relevant organizations. But the data collected from the row sources are of imbalance in nature. This data needs to be processed to generate the balancing dataset. Because processing imbalance data will be highly in efficient process. There are various types of discrepancies lies into the dataset. In current research paper these different types of discrepancies are to be neutralized. So that processing and analysis of the dataset will become easy and more efficient. These discrepancies lies into the dataset are like small disjuncts, lack of density in data, overlapping between the classes, impact of noisy data, the borderline etc. using different techniques these problems into the dataset are being neutralized. So that balanced data can be inputted for the data mining purpose.

International audience; The chapter “Data Summaries and Representations: Definitions and Practical Use” examines data structures used to deal with complex networked data, using temporal networks as a concrete case. Complex networked data has become available in a variety of contexts, describing a variety of systems with growing abundance of details, such as, for instance, links between individuals in social networks, or the temporal evolution of these links. However, data needs to be summarized and represented in simple forms. This chapter describes several commonly used data summaries and levels of representation of temporal networks, as well as novel data representations that have been developed through the MULTIPLEX project. It focuses in particular on the case of temporal networks of contacts between individuals and shows in a series of concrete use cases how different representations can be used to characterize and compare data, or feed data-driven models of epidemic spreading processes.

When developing new indicators for policy advice, two different approaches exist and may be combined with each other. First, a data-driven, bottom-up approach determines indicators primarily by the availability of suitable data. Second, indicators can be developed by a top-down approach, on the basis of political fields of action and related normative goals. While the bottom-up approach might not meet the needs of an up-to-date policy advice, the top-down approach might lack the necessary data. To discuss these problems and possible solutions, we refer to the ongoing development of an indicator system on impacts of climate change on biodiversity in Germany, where a combination of both approaches has been successfully applied. We describe suitable indicators of this system and discuss the reasons for the remaining gaps. Both approaches, mentioned above, have advantages, constraints, and shortcomings. The scientific accuracy of the indicators, the availability of data and the purpose of policy advice have to be well-balanced while developing such indicator systems.

The rating prediction algorithm based on matrix factorization is one of the research hotspots. When the training data is increased, all data needs to be retrained to improve recommendation results with new changes. When the data is large, the cost of calculation and time will be higher. This paper proposed an incremental recommendation algorithm based on regularized matrix factorization (IncRMF), the algorithm deals with new users and items ratings incrementally, get the updated data of training and ratings prediction by optimizing the previous result, and significantly reduce the amount of calculation in the process of training. Theoretical analysis and experimental results show that this method can guarantee the accuracy of prediction results (the difference of prediction error between IncRMF and normal regularized matrix factorization method not more than 1.36%), while the amount of calculation is significantly reduced.

To conduct risk assessments of exogenous chemicals for which there are also endogenous exposures, knowledge of the chemistry and biology of both types of exposures needs to be integrated into problem formulation and carried through to risk characterization. This issue is framed in a risk assessment context, highlighting the importance of quantifying increments of dose from all sources of the same or similar chemicals interacting with biological targets; understanding the influence of endogenous chemical concentrations on disease risk; and assessing total dose to targets in evaluating risk from incremental environmental exposures. Examples of recent assessments illustrate the importance of addressing this issue. Evaluations of data on blood or organ concentrations of ammonia, methanol, formaldehyde, acetaldehyde, and three gaseous signaling molecules (hydrogen sulfide, carbon monoxide, and nitric oxide) provide examples where current data are already informing perspectives on relative exposures at the portal of entry and systemically. To facilitate quality risk assessments of exogenous chemicals with endogenous exposures, a series of specific questions are presented that need to be addressed in systematic review to enhance problem formulation, improve the development of holistic conceptual models, and to facilitate the identification of priority data needs for improving risk assessments.

It is popular to use real-world data to evaluate data mining techniques. However, there are some disadvantages to use real-world data for such purposes. Firstly, real-world data in most domains is difficult to obtain for several reasons, such as budget, technical or ethical. Secondly, the use of many of the real-world data is restricted, those data sets do either not contain specific patterns that are easy to mine or the data needs special preparation and the algorithm needs very specific settings in order to find patterns in it. The solution to this could be the generation of synthetic, "meaningful data" (data with intrinsic patterns). This paper presents a novel approach for generating synthetic data by developing a tool, including novel algorithms for specific data mining patterns, and a user-friendly interface, which is able to create large data sets with predefined classification rules, multilinear regression patterns. A preliminary run of the prototype proves that the generation of large amounts of such "meaningful data" is possible. Also the proposed approach could be extended to a further development for generating synthetic data with other intrinsic patterns.

This survey paper looks at the open data, which is one of the most important elements of developing smart city and universal village. This paper focuses on the relationship between open data and smart city and the evaluation of open data projects. It describes the reason why data needs to be opened up, how open data should be evaluated, the current status of open data projects, and the challenges the open data developers are facing. Besides, the paper introduces the open data platform of the United Kingdom as a case study to explain open data project in the end.

International audience; Frequent Itemsets mining is a key concept in Association Rule Mining task, it aims to discover the frequent itemsets in a transactional dataset.Nowadays large amounts of data needs to be analysed, thus the use of traditional approaches for mining frequent itemsets suffer from computational time and memory requirement, beside the difficulty for the user to provide an appropriate minimum support value. For these reasons, we propose to tackle the problem of mining frequent itemsets by mean of Chemical Reaction Optimization metaheuristic without the need to specify minsup threshold . The results show that the proposed approach gives good results and it can be used as alternative to mine frequent itemsets.

The advancements in hyperspectral remote sensing are increasing continuously and recording a wealth of spatial as well as spectral information about an object, but resulting high volume of data. Analysis and classification of this high volume hyperspectral data needs a ground truth data or spectral library or image based endmembers which assist to unmix the mixed pixels and map their spatial distribution. Till date, though several hyperspectral endmember extraction algorithms have been proposed, every algorithm has its own limitations. The perfect endmember extraction algorithm would find unique spectra with no prior knowledge. This paper discusses the recent improvements and challenges in hyperspectral endmember extraction. The algorithms evaluated includes PPI, NFINDR, FIPPI and ATGP. The experiments are performed on the subset of Hyperion and AVIRIS_NG datasets.

Due to the limited computational resources of data owners and the developments in cloud computing, more and more data owners choose to store data on the cloud to reduce their own storage burden. When the data owner wants to re-select a new cloud service provider, the data needs to be transmitted from Cloud A to Cloud B. How to ensure that data has been safely transferred to Cloud B and Cloud A honestly implementing data deletion has become a hot topic for many scholars. To solve these problems, Liang et al. proposed a provable data transfer protocol based on provable data possession and deletion for secure cloud storage. However, we find a security flaw in their scheme. In this paper, we first review their scheme and then present our attack in detail. Finally, we propose an improved scheme which can resist the attack well.