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Air quality models (AQMs) are useful for studying various types of air pollutions and provide the possibility to reveal the contributors of air pollutants. Existing AQMs have been used in many scenarios having a variety of goals, e.g., focusing on some study areas and specific spatial units. Previous AQM reviews typically cover one of the forming elements of AQMs. In this review, we identify the role and relevance of every component for building AQMs, including (1) the existing techniques for building AQMs, (2) how the availability of the various types of datasets affects the performance, and (3) common validation methods. We present recommendations for building an AQM depending on the goal and the available datasets, pointing out their limitations and potentials. Based on more than 40 works on air quality, we concluded that the main utilized methods in air pollution estimation are land-use regression (LUR), machine learning, and hybrid methods. In addition, when incorporating LUR methods with traffic variables, it gives promising results; however, when using kriging or inverse distance weighting techniques, the monitoring stations measurements of air pollution data are enough to have good results. We aim to provide a short manual for people who want to build an AQM given the constraints at hands such as the availability of datasets and technical/computing resources.

Many scientific prediction problems have spatiotemporal data- and modeling-related challenges in handling complex variations in space and time using only sparse and unevenly distributed observations. This paper presents a novel deep learning architecture, Deep learning predictions for LocATion-dependent Time-sEries data (DeepLATTE), that explicitly incorporates theories of spatial statistics into neural networks to address these challenges. In addition to a feature selection module and a spatiotemporal learning module, DeepLATTE contains an autocorrelation-guided semi-supervised learning strategy to enforce both local autocorrelation patterns and global autocorrelation trends of the predictions in the learned spatiotemporal embedding space to be consistent with the observed data, overcoming the limitation of sparse and unevenly distributed observations. During the training process, both supervised and semi-supervised losses guide the updates of the entire network to: 1) prevent overfitting, 2) refine feature selection, 3) learn useful spatiotemporal representations, and 4) improve overall prediction. We conduct a demonstration of DeepLATTE using publicly available data for an important public health topic, air quality prediction, in a well-studied, complex physical environment - Los Angeles. The experiment demonstrates that the proposed approach provides accurate fine-spatial-scale air quality predictions and reveals the critical environmental factors affecting the results., Published in ICDM2020

Purpose Some predictive systems using machine learning models have been developed to predict sepsis; however, they were mostly built with a low percent of missing values, which does not correspond with the actual clinical situation. In this study, we developed a machine learning model with a high rate of missing and erroneous data to enable prediction under missing, noisy, and erroneous inputs, as in the actual clinical situation. Materials and methods The proposed artificial neural network model was implemented using the MATLAB ANN toolbox, based on stochastic gradient descent. The dataset was collected over the past decade with approval from the appropriate institutional review boards, and the sepsis status was identified and labeled using Sepsis-3 clinical criteria. The imputation method was built by last observation carried forward and mean value, aimed to simulate clinical situation. Results The mean area under the receiver operating characteristic (ROC) curve (AUC) of classifying sepsis and nonsepsis patients was 0.82 and 0.786 at 0 h and 40 h prior to onset, respectively. The highest model performance was found for one-hourly data, demonstrating that our ANN model can perform adequately with limited hourly data provided. Conclusions Our model has the moderate ability to predict sepsis up to 40 h in advance under simulated clinical situation with real-world data.

This paper proposed a statistical language models with internet based new word acquisition for spelling error detection and correction. The statistical language models are composed of off-line and online n-grams. The content in internet is regarded as a large dynamic knowledge resource that is updated by the online users over the world. Due to the word various in internet with rapid renew capability; it is useful for new lexicon learning. The proposed approach can achieve significant improvement in spelling checking. Combining these research manners, the proposed approach is able to achieve the goals of confirming, improving the detection rate of typos. For evaluation, a news dataset with ten categories is gathered as test data. Experimental results show that the proposed approach combining internet web searching outperforms traditional N-gram models, especially for the words that are out-of-vocabulary such as transliteration either in precision rate and recall rate.

Spatially explicit, fine-grained datasets describing historical urban extents are rarely available prior to the era of operational remote sensing. However, such data are necessary to better understand long-term urbanization and land development processes and for the assessment of coupled nature–human systems (e.g., the dynamics of the wildland–urban interface). Herein, we propose a framework that jointly uses remote-sensing-derived human settlement data (i.e., the Global Human Settlement Layer, GHSL) and scanned, georeferenced historical maps to automatically generate historical urban extents for the early 20th century. By applying unsupervised color space segmentation to the historical maps, spatially constrained to the urban extents derived from the GHSL, our approach generates historical settlement extents for seamless integration with the multi-temporal GHSL. We apply our method to study areas in countries across four continents, and evaluate our approach against historical building density estimates from the Historical Settlement Data Compilation for the US (HISDAC-US), and against urban area estimates from the History Database of the Global Environment (HYDE). Our results achieve Area-under-the-Curve values >0.9 when comparing to HISDAC-US and are largely in agreement with model-based urban areas from the HYDE database, demonstrating that the integration of remote-sensing-derived observations and historical cartographic data sources opens up new, promising avenues for assessing urbanization and long-term land cover change in countries where historical maps are available.

Information extraction from historical maps represents a persistent challenge due to inferior graphical quality and the large data volume of digital map archives, which can hold thousands of digitized map sheets. Traditional map processing techniques typically rely on manually collected templates of the symbol of interest, and thus are not suitable for large-scale information extraction. In order to digitally preserve such large amounts of valuable retrospective geographic information, high levels of automation are required. Herein, we propose an automated machine-learning based framework to extract human settlement symbols, such as buildings and urban areas from historical topographic maps in the absence of training data, employing contemporary geospatial data as ancillary data to guide the collection of training samples. These samples are then used to train a convolutional neural network for semantic image segmentation, allowing for the extraction of human settlement patterns in an analysis-ready geospatial vector data format. We test our method on United States Geological Survey historical topographic maps published between 1893 and 1954. The results are promising, indicating high degrees of completeness in the extracted settlement features (i.e., recall of up to 0.96, F-measure of up to 0.79) and will guide the next steps to provide a fully automated operational approach for large-scale geographic feature extraction from a variety of historical map series. Moreover, the proposed framework provides a robust approach for the recognition of objects which are small in size, generalizable to many kinds of visual documents.

Spatially explicit, fine-grained datasets describing historical urban extents are rarely available prior to the era of operational remote sensing. However, such data are necessary to better understand long-term urbanization and land development processes and for the assessment of coupled nature–human systems (e.g., the dynamics of the wildland–urban interface). Herein, we propose a framework that jointly uses remote-sensing-derived human settlement data (i.e., the Global Human Settlement Layer, GHSL) and scanned, georeferenced historical maps to automatically generate historical urban extents for the early 20th century. By applying unsupervised color space segmentation to the historical maps, spatially constrained to the urban extents derived from the GHSL, our approach generates historical settlement extents for seamless integration with the multi-temporal GHSL. We apply our method to study areas in countries across four continents, and evaluate our approach against historical building density estimates from the Historical Settlement Data Compilation for the US (HISDAC-US), and against urban area estimates from the History Database of the Global Environment (HYDE). Our results achieve Area-under-the-Curve values >0.9 when comparing to HISDAC-US and are largely in agreement with model-based urban areas from the HYDE database, demonstrating that the integration of remote-sensing-derived observations and historical cartographic data sources opens up new, promising avenues for assessing urbanization and long-term land cover change in countries where historical maps are available.

Many domains including policymaking, urban design, and geospatial intelligence benefit from understanding people’s mobility behaviors (e.g., work commute, shopping), which can be achieved by clustering massive trajectories using the geo-context around the visiting locations (e.g., sequence of vectors, each describing the geographic environment near a visited location). However, existing clustering approaches on sequential data are not effective for clustering these context sequences based on the contexts’ transition patterns. They either rely on traditional pre-defined similarities for specific application requirements or utilize a two-phase autoencoder-based deep learning process, which is not robust to training variations. Thus, we propose a variational approach named VAMBC for clustering context sequences that simultaneously learns the self-supervision and cluster assignments in a single phase to infer moving behaviors from context transitions in trajectories. Our experiments show that VAMBC significantly outperforms the state-of-the-art approaches in robustness and accuracy of clustering mobility behaviors in trajectories.

Historical maps contain detailed geographic information difficult to find elsewhere covering long-periods of time (e.g., 125 years for the historical topographic maps in the US). However, these maps typically exist as scanned images without searchable metadata. Existing approaches making historical maps searchable rely on tedious manual work (including crowd-sourcing) to generate the metadata (e.g., geolocations and keywords). Optical character recognition (OCR) software could alleviate the required manual work, but the recognition results are individual words instead of location phrases (e.g., "Black" and "Mountain" vs. "Black Mountain"). This paper presents an end-to-end approach to address the real-world problem of finding and indexing historical map images. This approach automatically processes historical map images to extract their text content and generates a set of metadata that is linked to large external geospatial knowledge bases. The linked metadata in the RDF (Resource Description Framework) format support complex queries for finding and indexing historical maps, such as retrieving all historical maps covering mountain peaks higher than 1,000 meters in California. We have implemented the approach in a system called mapKurator. We have evaluated mapKurator using historical maps from several sources with various map styles, scales, and coverage. Our results show significant improvement over the state-of-the-art methods. The code has been made publicly available as modules of the Kartta Labs project at https://github.com/kartta-labs/Project., Comment: 10.1145/3394486.3403381

By comparing the predicted number of defects with the number found in crowdsourced test in real time, people can dynamically assess the progress of crowdsourced test tasks. In this paper, we propose a cross-project dynamic defect prediction model (CPDDPM) for crowdsourced test to predict the number of defects in real time. In the construction of training dataset, we use density-based clustering method to select instances from the multiple source project datasets and build the initial training dataset. In the dynamic correction, CPDDPM iteratively corrects the prediction model using crowdsourced test reports and ability attributes of the crowdsourced testers until the predicted results converge. We collected project defect datasets on the crowdsourced test platform, and evaluated prediction accuracy of CPDDPM by using relative error and prediction at level l. The results show that CPDDPM can greatly improve the prediction performance of defect number.

Extracting residential areas from historical raster topographic maps benefits to analyze land type change. The existing algorithms have the shortcomings including easily misidentifying objects and low positional accuracy of the identified boundary, so we have presented a new automatic recognition method based on Gabor filter for extracting residential areas from historical raster topographic maps. First, the method detected the hatched areas using Gabor filter, Gaussian smoothing, binarization, erosion, and an operation. Afterward, an endpoint of a hatched line on the top of the hatched areas was taken as the starting point, then tracing a boundary point of the residential area along a hatched line toward northeast 45 degrees direction using dynamic filling strategy. Second, an adjacent boundary point was traced according to the pixel value relation of eight neighborhoods by taking the first found boundary point as a starting point. In the tracking process, removing the noises using the pixel value relation of the neighboring pixels and the designed strip detector. Ultimately, the residential boundary was obtained. The experiments were carried out on the samples of three typical areas. The results showed that our method was effective and practical, and outperformed the previous methods in integrity and positional accuracy of the residential boundary.

Geospatial artificial intelligence (geoAI) is an emerging scientific discipline that combines innovations in spatial science, artificial intelligence methods in machine learning (e.g., deep learning), data mining, and high-performance computing to extract knowledge from spatial big data. In environmental epidemiology, exposure modeling is a commonly used approach to conduct exposure assessment to determine the distribution of exposures in study populations. geoAI technologies provide important advantages for exposure modeling in environmental epidemiology, including the ability to incorporate large amounts of big spatial and temporal data in a variety of formats; computational efficiency; flexibility in algorithms and workflows to accommodate relevant characteristics of spatial (environmental) processes including spatial nonstationarity; and scalability to model other environmental exposures across different geographic areas. The objectives of this commentary are to provide an overview of key concepts surrounding the evolving and interdisciplinary field of geoAI including spatial data science, machine learning, deep learning, and data mining; recent geoAI applications in research; and potential future directions for geoAI in environmental epidemiology.

Historical maps provide a rich source of information for researchers in the social and natural sciences. These maps contain detailed documentation of a wide variety of natural and human-made features and their changes over time, such as the changes in the transportation networks and the decline of wetlands. It can be labor-intensive for a scientist to analyze changes across space and time in such maps, even after they have been digitized and converted to a vector format. In this paper, we present an unsupervised approach that converts vector data of geographic features extracted from multiple historical maps into linked spatio-temporal data. The resulting graphs can be easily queried and visualized to understand the changes in specific regions over time. We evaluate our technique on railroad network data extracted from USGS historical topographic maps for several regions over multiple map sheets and demonstrate how the automatically constructed linked geospatial data enables effective querying of the changes over different time periods.

Identifying mobility behaviors in rich trajectory data is of great economic and social interest to various applications including urban planning, marketing and intelligence. Existing work on trajectory clustering often relies on similarity measurements that utilize raw spatial and/or temporal information of trajectories. These measures are incapable of identifying similar moving behaviors that exhibit varying spatio-temporal scales of movement. In addition, the expense of labeling massive trajectory data is a barrier to supervised learning models. To address these challenges, we propose an unsupervised neural approach for mobility behavior clustering, called the Deep Embedded TrajEctory ClusTering network (DETECT). DETECT operates in three parts: first it transforms the trajectories by summarizing their critical parts and augmenting them with context derived from their geographical locality (e.g., using POIs from gazetteers). In the second part, it learns a powerful representation of trajectories in the latent space of behaviors, thus enabling a clustering function (such as $k$-means) to be applied. Finally, a clustering oriented loss is directly built on the embedded features to jointly perform feature refinement and cluster assignment, thus improving separability between mobility behaviors. Exhaustive quantitative and qualitative experiments on two real-world datasets demonstrate the effectiveness of our approach for mobility behavior analyses., Published as a conference paper at BigData 2019

Historical geographic data are essential for a variety of studies of cancer and environmental epidemiology, urbanization, and landscape ecology. However, existing data sources typically contain only contemporary information. Historical maps hold a great deal of detailed geographic information at various times in the past. Yet, finding relevant maps is difficult, and the map content is not machine-readable. This chapter presents the challenges and trends in building a map processing, modeling, linking, and publishing framework. The framework will enable querying historical map collections as a unified and structured spatiotemporal source in which individual geographic phenomena (extracted from maps) are modeled (described) with semantic descriptions and linked to other data sources (e.g., DBpedia). This framework will allow making use of historical geographic datasets from a variety of maps, efficiently, over large geographic extents. Realizing such a framework poses significant research challenges in multiple fields in computer science including digital map processing, data integration, and the Semantic Web technologies, and other disciplines such as spatial, social, and health sciences. Tackling these challenges will not only advance research in computer science and geographic information science but also present a unique opportunity for interdisciplinary research.

Converting geographic features (e.g., place names) in map images into a vector format is the first step for incorporating cartographic information into a geographic information system (GIS). With the advancement in computational power and algorithm design, map processing systems have been considerably improved over the last decade. However, the fundamental map processing techniques such as color image segmentation, (map) layer separation, and object recognition are sensitive to minor variations in graphical properties of the input image (e.g., scanning resolution). As a result, most map processing results would not meet user expectations if the user does not "properly" scan the map of interest, pre-process the map image (e.g., using compression or not), and train the processing system, accordingly. These issues could slow down the further advancement of map processing techniques as such unsuccessful attempts create a discouraged user community, and less sophisticated tools would be perceived as more viable solutions. Thus, it is important to understand what kinds of maps are suitable for automatic map processing and what types of results and process-related errors can be expected. In this paper, we shed light on these questions by using a typical map processing task, text recognition, to discuss a number of map instances that vary in suitability for automatic processing. We also present an extensive experiment on a diverse set of scanned historical maps to provide measures of baseline performance of a standard text recognition tool under varying map conditions (graphical quality) and text representations (that can vary even within the same map sheet). Our experimental results help the user understand what to expect when a fully or semi-automatic map processing system is used to process a scanned map with certain (varying) graphical properties and complexities in map content. We discuss the impact of graphical quality on automatic map processing.We include a comprehensive experiment covering a wide range of map products.We report our findings on the tested map products under varying image resolutions.

With a significant amount of spatial data archives online, data conflation is becoming more and more critical in the domain of Geographical Information Science (GIScience) because of its broad applications such as detecting the development of road networks and the change of river course. Existing conflation approaches usually rely on the vector data of corresponding features in multiple sources to have an approximate location. However, they commonly overlook the uncertainty produced during the vector data generation process in the data sources. In previous work, we presented a Convolutional Neural Networks (CNN) recognition system that automatically recognizes areas of geographic features from maps and then generates a centerline representation of the area feature (e.g., from pixels of road areas to a road network). In this paper, we propose a method to systematically quantify the uncertainty generated by an image recognition model and the centerline extraction process. We provide an end-to-end evaluation method that exploits the distance map to calculate the uncertainty value for centerline extraction. Compared with methods that do not consider uncertainty value, our algorithm avoids using a fixed buffer size to identify corresponding features from multiple sources and generate accurate conflation results.

Forecasting spatially correlated time series data is challenging because of the linear and non-linear dependencies in the temporal and spatial dimensions. Air quality forecasting is one canonical example of such tasks. Existing work, e.g., auto-regressive integrated moving average (ARIMA) and artificial neural network (ANN), either fails to model the non-linear temporal dependency or cannot effectively consider spatial relationships between multiple spatial time series data. In this paper, we present an approach for forecasting short-term PM2.5 concentrations using a deep learning model, the geo-context based diffusion convolutional recurrent neural network, GC-DCRNN. The model describes the spatial relationship by constructing a graph based on the similarity of the built environment between the locations of air quality sensors. The similarity is computed using the surrounding "important" geographic features regarding their impacts to air quality for each location (e.g., the area size of parks within a 1000-meter buffer, the number of factories within a 500-meter buffer). Also, the model captures the temporal dependency leveraging the sequence to sequence encoder-decoder architecture. We evaluate our model on two real-world air quality datasets and observe consistent improvement of 5%-10% over baseline approaches.

This paper proposed a spelling error detection and correction using the linguistic features and knowledge resource. The linguistic features mainly come from language model that describes the probability of a sentence. In practice, the formal document with typos is defective and fall short of the specifications, since typos and error hidden in printed document are frequent, rework will cause the waste of paper and ink. This paper proposed an approach that addresses the spelling errors and before printing. In this method, the linguistic features are used in this research to compare and increase a new feature additionally that is a function of Internet search based on knowledge bases. Combining these research manners, this paper expect to achieve the goals of confirming, improving the detection rate of typos, and reducing the waste of resources. Experimental results shows, the proposed method is practicable and efficient for users to detect the typos in the printed documents.

Supervised methods of sentiment orientation analysis have got more and more attention for which don’t require labeled corpus and can be applied to different domains. However, traditional methods mostly consider single words which ignore the dependencies between the words of each other. To solve the problem, this paper presents an unsupervised method for sentiment orientation analysis on micro-blog based on dependency parsing and HDP model. Firstly, this method uses dependency parsing to filter the words in the corpus. Secondly, the HDP model is used to mine the implicit topics in the document. Then, a sentiment dictionary is used to calculate the sentiment distributions of the topics. Finally, the sentiment orientation of the whole micro-blog is obtained on the basis of the sentiment distributions of the topics. The experimental results show that the proposed method can effectively identify the sentiment orientation of micro-blog.

Maps depict natural and human-induced changes on earth at a fine resolution for large areas and over long periods of time. In addition, maps—especially historical maps—are often the only information source about the earth as surveyed using geodetic techniques. In order to preserve these unique documents, increasing numbers of digital map archives have been established, driven by advances in software and hardware technologies. Since the early 1980s, researchers from a variety of disciplines, including computer science and geography, have been working on computational methods for the extraction and recognition of geographic features from archived images of maps (digital map processing). The typical result from map processing is geographic information that can be used in spatial and spatiotemporal analyses in a Geographic Information System environment, which benefits numerous research fields in the spatial, social, environmental, and health sciences. However, map processing literature is spread across a broad range of disciplines in which maps are included as a special type of image. This article presents an overview of existing map processing techniques, with the goal of bringing together the past and current research efforts in this interdisciplinary field, to characterize the advances that have been made, and to identify future research directions and opportunities.

Text labels in maps provide valuable geographic information by associating place names with locations. This information from historical maps is especially important since historical maps are very often the only source of past information about the earth. Recognizing the text labels is challenging because heterogeneous raster maps have varying image quality and complex map contents. In addition, the labels within a map do not follow a fixed orientation and can have various font types and sizes. Previous approaches typically handle a specific type of map or require intensive manual work. This paper presents a general approach that requires a small amount of user effort to semi-automatically recognize text labels in heterogeneous raster maps. Our approach exploits a few examples of text areas to extract text pixels and employs cartographic labeling principles to locate individual text labels. Each text label is then rotated automatically to horizontal and processed by conventional OCR software for character recognition. We compared our approach to a state-of-art commercial OCR product using 15 raster maps from 10 sources. Our evaluation shows that our approach enabled the commercial OCR product to handle raster maps and together produced significant higher text recognition accuracy than using the commercial OCR alone.

Reuse of simulation models can greatly cut on the software development time and improve development efficiency. However, platform-specific models (PSM) are difficult to be reused on different platforms because their implementation depends on the support of a particular platform. To improve the reusability of the platform-specific models on various platforms, a natural idea is to transform these models into platform-independent models (PIM) by describing simulation models in platform-independent language. Among these platform-independent languages, XML is widely used by many applications like simulation reference markup language (SRML) and open source simulation modeling language (SML) for its good characteristics. Focusing on the transformation demands, we design and implement a model transformation tool which can transform the models developed in platform-specific language (C/CPP) into XML-based model description file (SRML). Our tool can also deal with the model recursive call problems and check the completeness and correctness of models. The case study illustrates that our transformation tool can transform models into XML format correctly and it shows beautiful robustness.

Digital map processing has been an interest in the image processing and pattern recognition community since the early 80s. With the exponential growth of available map scans in the archives and on the internet, a variety of disciplines in the natural and social sciences grow interests in using historical maps as a primary source of geographical and political information in their studies. Today, many organizations such as the United States Geological Survey, David Rumsey Map Collection, OldMapsOnline.org, and National Library of Scotland, store numerous historical maps in either paper or scanned format. Only a small portion of these historical maps is georeferenced, and even fewer of them have machine-readable content or comprehensive metadata. The lack of a searchable textual content including the spatial and temporal information prevents researchers from efficiently finding relevant maps for their research and using the map content in their studies. These challenges present a tremendous collaboration opportunity for the image processing and pattern recognition community to build advance map processing technologies for transforming the natural and social science studies that use historical maps. This paper presents the potentials of using historical maps in scientific research, describes the current trends and challenges in extracting and recognizing text content from historical maps, and discusses the future outlook.

Information extraction from historical maps represents a persistent challenge due to inferior graphical quality and large data volume in digital map archives, which can hold thousands of digitized map sheets. In this paper, we describe an approach to extract human settlement symbols in United States Geological Survey (USGS) historical topographic maps using contemporary building data as the contextual spatial layer. The presence of a building in the contemporary layer indicates a high probability that the same building can be found at that location on the historical map. We describe the design of an automatic sampling approach using these contemporary data to collect thousands of graphical examples for the symbol of interest. These graphical examples are then used for robust learning to then carry out feature extraction in the entire map. We employ a Convolutional Neural Network (LeNet) for the recognition task. Results are promising and will guide the next steps in this research to provide an unsupervised approach to extracting features from historical maps.

Recognizing text in historical maps is inherently difficult due to input challenges such as artifacts interfering with the text or an unpredictable rotation and orientation of the text. This paper discusses our algorithm that overcomes the limitations of the input by adding extra input consisting of multiple layers of images of the same map area but across different time periods and names of geographic entities in the United Kingdom collected from OpenStreetMap. Using our algorithm, compared to Strabo, a state-of-the-art text recognition software on maps, we obtain a 153% improvement in precision, a 31% improvement in recall, and a 75% improvement in F-score for word recognition on maps.

A domain expert can process heterogeneous data to make meaningful interpretations or predictions from the data. For example, by looking at research papers and patent records, an expert can determine the maturity of an emerging technology and predict the geographic location(s) and time (e.g., in a certain year) where and when the technology will be a success. However, this is an expert- and manual-intensive task. This paper presents an end-to-end system that integrates heterogeneous data sources into a knowledge graph in the RDF (Resource Description Framework) format using an ontology. Then the user can easily query the knowledge graph to prepare the required data for different types of predictive analysis tools. We show a case study of predicting the (geographic) center(s) of fuel cell technologies using data collected from public sources to demonstrate the feasibility of our system. The system extracts, cleanses, and augments data from public sources including research papers and patent records. Next, the system uses an ontology-based data integration method to generate knowledge graphs in the RDF format to enable users to switch quickly between machine learning models for predictive analytic tasks. We tested the system using the Support Vector Machine and Multiple Hidden Markov Models and achieved 66.7% and 83.3% accuracy on the city and year levels of spatial and temporal resolutions, respectively.

Historical maps are important resources for various kinds of studies, providing insights for natural science and social science studies such as biology, landscape changes, and history [1]. However, text recognition on maps remains a challenging task because map usually has a complex background in which textual content appears in numerous colors, fonts, sizes, and orientations. Even if we were able to acquire perfectly recognized words and characters automatically, it is still difficult to generate useful information because individual words are not meaningful. For example, a typical result from OCR scanning or manual map digitization is that each recognized bounding box only contains a single word (Figure 1).

Raster maps are easily accessible and contain rich road information; however, converting the road information to vector format is challenging because of varying image quality, overlapping features, and typical lack of metadata (e.g., map geocoordinates). Previous road vectorization approaches for raster maps typically handle a specific map series and require significant user effort. In this paper, we present a general road vectorization approach that exploits common geometric properties of roads in maps for processing heterogeneous raster maps while requiring minimal user intervention. In our experiments, we compared our approach to a widely used commercial product using 40 raster maps from 11 sources. We showed that overall our approach generated high-quality results with low redundancy with considerably less user input compared with competing approaches.

Since maps are widely available for many areas around the globe, they provide a valuable resource to help understand other geospatial sources such as to identify roads or to annotate buildings in imagery. To utilize the maps for understanding other geospatial sources, one of the most valuable types of information we need from the map is the road network, because the roads are common features used across different geospatial data sets. Specifically, the set of road intersections of the map provides key information about the road network, which includes the location of the road junctions, the number of roads that meet at the intersections (i.e., connectivity), and the orientations of these roads. The set of road intersections helps to identify roads on imagery by serving as initial seed templates to locate road pixels. Moreover, a conflation system can use the road intersections as reference features (i.e., control point set) to align the map with other geospatial sources, such as aerial imagery or vector data. In this paper, we present a framework for automatically and accurately extracting road intersections from raster maps. Identifying the road intersections is difficult because raster maps typically contain much information such as roads, symbols, characters, or even contour lines. We combine a variety of image processing and graphics recognition methods to automatically separate roads from the raster map and then extract the road intersections. The extracted information includes a set of road intersection positions, the road connectivity, and road orientations. For the problem of road intersection extraction, our approach achieves over 95% precision (correctness) with over 75% recall (completeness) on average on a set of 70 raster maps from a variety of sources.

Along with continuing advancement of network and chip technology, vast amount of new router designs will be designed and utilized in differentiated Internet-connected systems, on-chip interconnection in SoC, and evolving software-defined networks. In this research, from an analysis of current and future router designs, we observe that router designs share a common composition structure. We thus devised synthesis techniques based on the common composition structure. It allows designers specifying high-level and detailed router design decisions. Together with extensible parts of router designs organized in the common structure, it can produce desirable synthesizable Verilog router designs. We implemented the synthesis design that is based on a number of synthesis techniques and HDL configuration generation method. Experimental results show that our approach can effectively produce desirable synthesizable Verilog router designs.

Historical spatiotemporal datasets are important for a variety of studies such as cancer and environmental epidemiology, urbanization, and landscape ecology. However, existing data sources typically contain only contemporary datasets. Historical maps hold a great deal of detailed geographic information at various times in the past. Yet, finding relevant maps is difficult and the map content are not machine-readable. I envision a map processing, modeling, linking, and publishing framework that allows querying historical map collections as a unified and structured spatiotemporal source in which individual geographic phenomena (extracted from maps) are modeled with semantic descriptions and linked to other data sources (e.g., DBpedia). This framework will make it possible to efficiently study historical spatiotemporal datasets on a large scale. Realizing such a framework poses significant research challenges in multiple fields in computer science including digital map processing, data integration, and the Semantic Web technologies, and other disciplines such as spatial, earth, social, and health sciences. Tackling these challenges will not only advance research in computer science but also present a unique opportunity for interdisciplinary research.

Role-based access control (RBAC) model is the most widely used access control model in software, but for today's software are becoming more and more mobility, intelligent, distributed and complexity features, RBAC model show its low capability in formalize environment, context factors. To solve the issues above, this paper proposed a Multidimensional Space Access Control (MDSAC) Model, which puts every factor (role, permission, object, environment, context) into multiple space dimensions. MDSAC formalize access control politics though coloring (black and white) those space dimensions, on behalf of white representing "Allowed" and black representing "Refused". MDSAC model can also greatly strengthen its description ability by space dimensional expansion.

Data mining tasks typically require significant effort in data preparation to find, transform, integrate and prepare the data for the relevant data mining tools. In addition, the work performed in data preparation is often not recorded and is difficult to reproduce from the raw data. In this paper we present an integrated approach to data preparation and data mining that combines the two steps into a single integrated process and maintains detailed metadata about the data sources, the steps in the process, and the resulting learned classifier produced from data mining algorithms. We present results on an example scenario, which shows that our approach provides significant reduction in the time in takes to perform a data mining task.

Map labels provide valuable geographic information by annotating geographic phenomenona with text descriptions. However, many interesting and useful maps are only available as images and hence this information is not readily accessible in a Geographic Information System (GIS). Previous work on text recognition in maps considers maps as a special type of image to be processed using Optical Character Recognition (OCR) techniques and does not pay attention to the typical workflows in a GIS. As a result, to convert map labels into machine-readable text, a user has to switch between OCR and GIS software, transform the detected text locations from the image coordinates (in OCR) to the map coordinates (in GIS), and apply data import/export procedures. This tedious process limits the opportunity to access text information in maps. This paper presents ArcStrabo, an integration of our previous text recognition work and a GIS, which uses a GIS user interface, workflows, and data types to enable efficient training of text recognition algorithms for converting map labels to a table of geographic names. We show that ArcStrabo facilitates map digitization processes, eliminates the need for GIS users to learn additional OCR tools, and does not require manual data export/import procedures between GIS and OCR software.

A significant challenge in handling geographic datasets is that the datasets can come from heterogeneous sources with various data qualities and formats. Before these datasets can be used in a Geographic Information System (GIS) for spatial analysis or to create maps, a typical task is to clean the attribute data and transform the data into a uniform format. However, conventional GIS products focus on manipulating the spatial component of geographic features and only offer basic tools for editing the attribute data (e.g., one row at a time). This limits the capability for handling large datasets in a GIS since manually editing and transforming attribute data between different formats is not practical for thousands of geographic features. In this demo, we present ArcKarma, which is built on our previous work on data transformation, to efficiently clean and transform data attributes in a GIS. ArcKarma generates transformation programs from a few user-provided examples and applies these programs to transform individual attribute columns into the desired formats. We show that ArcKarma produces accurate results and eliminates the need for laborious manual data cleaning and scripting tasks.

Given the increasing popularity and availability of location tracking devices, large quantities of spatiotemporal data are available from many different sources. Quick interactive analysis of such data is important in order to understand the data, identify patterns, and eventually make a marketable product. Since the data do not necessarily follow the relational model and may require flexible processing possibly using advanced machine learning techniques, spatial databases or similar query tools do not make the best means for such analysis. Moreover, the high complexity of geometric operations makes the quick interactive analysis very difficult. In this paper, we present a highly flexible functional query engine that 1) works with multiple schema types, 2) provides fast response times by spatiotemporal indexing and parallelization, 3) helps understand the data using visualizations and 4) is highly extensible to easily add complex functionality. To demonstrate its usefulness, we use our tool to solve a real world problem of crime pattern analysis in Los Angeles County and compare the process with other well known tools.

Today, as more and more businesses and individuals into the study of cloud computing, data storage in the cloud platform is also growing. So how cloud environment quickly and effectively store, manage and use these data has become a very important and challenging issues. This paper mainly discusses the storage model based on Map/Reduce text categorization, at the same time combining forecasting data classification strategy, classifying the data in the cloud storage system, the hot data stored in the hot disk area, the cold disk data stored in the cold area, and neural network to predict seasonal data, to predict the temperature data in the next period of time, the data in the hot or cold area can be seasonal migration in the region. Indexing based on this model, which can improve the efficiency of huge amounts of data indexing. To a certain extent, reduce query latency and improve search efficiency.

Historical maps contain rich cartographic information, such as road networks, but this information is "locked" in images and inaccessible to a geographic information system (GIS). Manual map digitization requires intensive user effort and cannot handle a large number of maps. Previous approaches for automatic map processing generally require expert knowledge in order to fine-tune parameters of the applied graphics recognition techniques and thus are not readily usable for non-expert users. This paper presents an efficient and effective graphics recognition technique that employs interactive user intervention procedures for processing historical raster maps with limited graphical quality. The interactive procedures are performed on color-segmented preprocessing results and are based on straightforward user training processes, which minimize the required user effort for map digitization. This graphics recognition technique eliminates the need for expert users in digitizing map images and provides opportunities to derive unique data for spatiotemporal research by facilitating time-consuming map digitization efforts. The described technique generated accurate road vector data from a historical map image and reduced the time for manual map digitization by 38%.

Access control model is widely used in access control politics testing, it usually consists of three hierarchy fields: roles, permissions and contexts. But the relations information between the three fields is not taken into consideration intact when building the access control models. Because of the information is leaking, engineers has to use random testing or pair wise testing when using access control model, they has to exchange test coverage for test efficiency. Some researchers extended the original access control model with rules, priority and status in order to complementary the missing information, but due to the structure disadvantage of original access control model, few works showed promising result. This paper presents a method using labeled transition system model in formalizing software access control politics though several examples, the method can formalize key information like rules, priority and status along with roles, permissions and contexts into model. This paper also briefly introduces how to use labeled transition system model in security test cases automatic generating.

Raster maps contain rich road information, such as the topology and names of roads, but this information is “locked” in images and inaccessible in a geographic information system (GIS). Previous approaches for road extraction from raster maps typically handle this problem as raster-to-vector conversion and hence the extracted road vector data are line segments without the knowledge of road names and where a road starts and ends. This paper presents a technique that builds on the results from our previous road vectorization and text recognition work to generate named road vector data from raster maps. This technique first segments road vectorization results using road intersections to determine the lines that represent individual roads in the map. Then the technique exploits spatial relationships between roads and recognized text labels to generate road names for individual road segments. We implemented this approach in our map processing system, called Strabo, and demonstrate that the system generates accurate named road vector data on example maps with 92.83% accuracy.

One of the main limitations for OFDM-based transmission systems is the high Peak-to-Average Power Ratio (PAPR) of the transmitted signals. In this paper, the residue number system (RNS) based OFDM transmission scheme is proposed to reduce the PAPR, which uses the features of RNS to control the dynamic range of the transmitted signals and avoid the nonlinear distortion for the PAPR reduction. Experimental results show that at least 5dB of PAPR performance is improved with the proposed method, compared with the original OFDM.

Text recognition is difficult from documents that contain multi-oriented, curved text lines of various character sizes. This is because layout analysis techniques, which most optical character recognition (OCR) approaches rely on, do not work well on unstructured documents with non-homogeneous text. Previous work on recognizing non-homogeneous text typically handles specific cases, such as horizontal and/or straight text lines and single-sized characters. In this paper, we present a general text recognition technique to handle non-homogeneous text by exploiting dynamic character grouping criteria based on the character sizes and maximum desired string curvature. This technique can be easily integrated with classic OCR approaches to recognize non-homogeneous text. In our experiments, we compared our approach to a commercial OCR product using a variety of raster maps that contain multi-oriented, curved and straight text labels of multi-sized characters. Our evaluation showed that our approach produced accurate text recognition results and outperformed the commercial product at both the word and character level accuracy.

Software Security Evaluation (SSE) is an important activity for information security assurance. While the exiting SSE is very lack of dependability information for evaluation conclusion. In this paper, confidence measure was chosen as the indicator of dependability, and a quantitative analysis method based on parametric estimation theory was proposed. First, SSE conclusion was modeled as a stochastic variable conforms to the normal distribution. Second, confidence measure computing equation by big samples was given based on traditional mathematical statistics. Finally, to improve the practicability of the analysis method, the small samples situation was considered, and the confidence measure computing method was present based on Bayes statistics. This dependability analysis method is suit for SSE system which conclusion is explicit value. And it can bring confidence information of SSE to all the stakeholders of the target software for scientific decisions.

Raster maps contain valuable road information, which is especially important for the areas where road vector data are otherwise not readily accessible. However, converting the road information in raster maps to road vector data usually requires significant user effort to achieve high accuracy. In this demo, we present Strabo, which is a system that extracts road vector data from heterogeneous raster maps. We demonstrate Strabo's fully automatic technique for extracting road vector data from raster maps with good image quality and the semi-automatic technique for handling raster maps with poor image quality. We show that Strabo requires minimal user input for extracting road vector data from raster maps with varying map complexity (i.e., overlapping features in maps) and image quality.

Text labels in raster maps provide valuable geospatial information by associating geographical names with geospatial locations. Although present commercial optical character recognition (OCR) products can achieve a high recognition rate on documents containing text lines of the same orientation, text recognition on raster maps is challenging due to the varying text orientations and the overlap of text labels. This paper presents a text recognition approach that focuses on locating individual text labels in the map and detecting their orientations to then leverage the horizontal text recognition capability of commercial OCR software. We show that our approach detects accurate string orientations and achieves 96.2% precision and 94.7% recall on character recognition and 80.6% precision and 84.1% recall on word recognition.

Maps can be a great source of information for a given geographic region, but they can be difficult to find and even harder to process. A significant problem is that many interesting and useful maps are only available in raster format, and even worse many maps have been poorly scanned and they are often compressed with lossy compression algorithms. Furthermore, for many of these maps there is no meta data providing the geographic coordinates, scale, or projection. Previous research on map processing has developed techniques that typically work on maps from a single map source. In contrast, we have developed a general approach to finding and processing street maps. This includes techniques for discovering maps online, extracting geographic and textual features from maps, using the extracted features to determine the geographic coordinates of the maps, and aligning the maps with imagery. The resulting system can find, register, and extract a variety of features from raster maps, which can then be used for various applications, such as annotating satellite imagery, creating and updating maps, or constructing detailed gazetteers.

Raster maps are an important source of road information. Because of the overlapping map features (e.g., roads and text labels) and the varying image quality, extracting road vector data from raster maps usually requires significant user input to achieve accurate results. In this paper, we present an accurate road vectorization technique that minimizes user input by combining our previous work on extracting road pixels and road-intersection templates to extract accurate road vector data from raster maps. Our approach enables GIS applications to exploit the road information in raster maps for the areas where the road vector data are otherwise not easily accessible, such as the countries of the Middle East. We show that our approach requires minimal user input and achieves an average of 93.2% completeness and 95.6% correctness in an experiment using raster maps from various sources.