Search

The recognition that restructuring care processes is central to effective and efficient health care will result in the emergence of process-oriented electronic patient records (EPRs). How will these technologies come into being? Within informatics, it is often stated that to informate something, we should first model it. This paper queries whether a detailed modeling of work processes and data flows is the primary step that needs to be completed before such EPRs can be developed or tailored. Building upon a sociotechnical understanding of ICT development, we argue for a reinterpretation of ‘models’ in such development processes. We do so through a reverse engineering of parts of the paper-based medical record, which has received little attention in medical informatics. In process-oriented EPR design, we argue, modeling should not be conceived as the crucial first step in this design, but rather as an intervention in the organizational change-processes that constitute proper ICT development. [Copyright &y& Elsevier]

With the development of modern science, the adjustment of knowledge structure and the deepening of knowledge, the mode of single discipline research has been gradually broken, and interdisciplinary research has become a necessary way. All the information science and library science articles collected in the core collection of web of science from 1982 to 2020 are used for research and analysis. Through the research of interdisciplinary measurement index, this paper improves the existing interdisciplinary measurement index, uses Excel, Python and other tools to carry out data mining, cleaning, processing, analysis, and determines the interdisciplinary literature data defined in this paper. Finally, with vosviewer and other visual tools, this paper uses the citation network of bibliometrics, keywords co-occurrence and other ways to summarize interdisciplinary science's law of knowledge sharing and provides suggestions and directions for future research. [ABSTRACT FROM AUTHOR]

As the unique academic culture in Chinese philosophy and social sciences, researches on method framework provide an opportunity for understanding the thinking model and value orientation of the ancient eastern civilization. The field of information science has achieved fruitful results in the method framework research closely related to the unique discipline history and academic mission. The paper reviews information science method frameworks in China and presents their academic features from three aspects: 1. levels of the framework, 2. research strategies, and 3. essential techniques. At the same time, we summarize the value of this Chinese academic wisdom and the practical experience of information science in China to promote this research. [ABSTRACT FROM AUTHOR]

Abstract In order to reflect the internal motion characteristics of entire vector fields, texture visualization methods based on line integral convolution are usually adopted. However, the visualization results obtained in this way have low image quality. To solve this problem, this paper suggests using line integral convolution to optimize two specific aspects - texture enhancement and color enhancement – to provide an enhanced vector field visualization model. Existing texture enhancement algorithms can create texture aliasing. Based on an analysis of the relationship between vector angles, sampling distance and texture aliasing, the paper puts forward a texture enhancement algorithm that uses the vector angle to adjust the sampling distance of a high-pass filter. This greatly reduces the presence of texture aliasing. For color enhancement, a linear algorithm is usually used that adds vector size information to the vector field. However, the resulting image has a problem of color concentration. In view of this, the distribution characteristics of the vector field are analyzed using a histogram and a dynamic nonlinear color enhancement algorithm is proposed. This noticeably improves the color distribution of the resulting image and improves the overall visual quality of the result. [ABSTRACT FROM AUTHOR]

Research methods play an extremely important role in studies. Statistical methods are fundamental and vital for quantitative research. The authors of this paper investigated the research papers that used statistical methods including parametric inferential statistical methods, nonparametric inferential statistical methods, predictive statistical correlation methods, and predictive statistical regression methods in library and information science and examined the connections and interactions between statistical methods and their application areas including information creation, information selection and control, information organization, information retrieval, information dissemination, and information use. Both an inferential statistical method and graphic clustering visualization method were employed to explore the relationships between statistical methods and application areas and reveal the hidden interaction patterns. As a result, 1821 research papers employing statistical methods were identified among the papers published in six major library and information science journals from 1999 to 2017. The findings showed that application areas affected the types of statistical methods utilized. Studies in information organization and information retrieval tended to employ parametric and nonparametric inferential methods, while correlation and regression methods were applied more in studies in information use, information dissemination, information creation, and information selection and control field. These findings help researchers better understand the statistical method orientation of library and information science studies and assist educators in the field to develop applicable quantitative research methodology courses. [ABSTRACT FROM AUTHOR]

Highlights • Smart Earth is an innovative approach to environmental monitoring. • Smart Earth combines conventional monitoring with ICT and the Internet of Things. • This paper presents a meta-review of 3187 articles over the period 1997 to 2017. • Implications for environmental governance include real-time regulation; enhanced predictive management; and citizen sensing. Abstract Environmental governance has the potential to be significantly transformed by Smart Earth technologies, which deploy enhanced environmental monitoring via combinations of information and communication technologies (ICT), conventional monitoring technologies (e.g. remote sensing), and Internet of Things (IoT) applications (e.g. Environmental Sensor Networks (ESNs)). This paper presents a systematic meta-review of Smart Earth scholarship, focusing our analysis on the potential implications and pitfalls of Smart Earth technologies for environmental governance. We present a meta-review of academic research on Smart Earth, covering 3187 across the full range of academic disciplines from 1997 to 2017, ranging from ecological informatics to the digital humanities. We then offer a critical perspective on potential pathways for evolution in environmental governance frameworks, exploring five key Smart Earth issues relevant to environmental governance: data; real-time regulation; predictive management; open source; and citizen sensing. We conclude by offering suggestions for future research directions and trans-disciplinary conversations about environmental governance in a Smart Earth world. [ABSTRACT FROM AUTHOR]

Abstract In the era of Big Data, data assets have become a strategic resource which cannot be overlooked by both society and enterprises. However, data is not equal to the assets. This paper first introduces the necessary conditions of data assetization and discriminates the concepts of data governance, data management and data asset management. Then it focuses on the unique connotation and characteristics of grid data assets. With reference to the mainstream theory of data management, the framework for the grid data asset management is set up in the combination of the characteristics of data assets, business needs and the actual situation in the power supply enterprises. Finally, this paper puts forward higher system requirements and technical requirements for China's power supply enterprises to conduct data asset management. [ABSTRACT FROM AUTHOR]

Advanced metering technologies coupled with informatics creates an opportunity to form digital multi-utility service providers. These providers will be able to concurrently collect a customers’ medium-high resolution water, electricity and gas demand data and provide user-friendly platforms to feed this information back to customers and supply/distribution utility organisations. Providers that can install low-cost integrative systems will reap the benefits of derived operational synergies and access to mass markets not bounded by historical city, state or country limits. This paper provides a vision of the required transformative process and features of an integrated multi-utility service provider covering the system architecture, opportunities and benefits, impediments and strategies, and business opportunities. The heart of the paper is focused on demonstrating data modelling processes and informatics opportunities for contemporaneously collected demand data, through illustrative examples and four informative water-energy nexus case studies. Finally, the paper provides an overview of the transformative R&D priorities to realise the vision. [ABSTRACT FROM AUTHOR]

Present study intends to explore the various definitions of information via co-words analysis procedure based on the Natural Language Process (NLP). 3593 academic papers from an academic database, LISTA, are covered. In short, present study proposes four types of information definitions based on co-words for full spectrum analysis on abstracts of scientific papers under the context of Information Science research as: information as records, information as individual experience, information as the update of data or as the foundation of knowledge and information as ability of individuals. [ABSTRACT FROM AUTHOR]

Objectives: This paper investigates the role of epistemic belief in affecting Internet users' decision to share online health rumors. To delve deeper, it examines how the characteristics of rumors-true or false, textual or pictorial, dread or wish-shape the decision-making among epistemologically naïve and robust users separately.Methods: An experiment was conducted. Responses were obtained from 110 participants, who were exposed to eight rumors. This yielded 880 cases (110 participants×8 rumors) for statistical analyses.Results: Epistemologically naive participants were more likely to share online health rumors than epistemologically robust individuals. Epistemologically robust participants were more likely to share textual rumors than pictorial ones. However, there were no differences between true and false rumors or between dread and wish rumors for either epistemologically naive or robust participants.Conclusions: This paper contributes to the understanding of users' health information sharing behavior. It encourages users to cultivate robust epistemic belief in order to improve their online health information processing skills. [ABSTRACT FROM AUTHOR]

Scientific research serves as both the root and the driving force behind technological innovation. While the transformation of science to technology is more commonly observed in hard science disciplines, the application of knowledge from soft science disciplines to technological innovation has received less attention, so is the Library and Information Science (abbreviated as LIS). This study seeks to examine the role of LIS knowledge in bridging the link between science and technology, how the LIS knowledge contributes to patented inventions, and and discover the future trend of scientific and technological innovation in LIS. The study collected LIS papers cited by patents and the citing patents' data to explore the relationship between LIS papers and their citing patents based on time, impact, and topic. Six hypotheses were developed to analyze the flow of knowledge between science and technology in LIS, providing insight into the characteristics of knowledge flow and transformation of this field into technology. The study found that the amount of LIS scientific knowledge transformed into technology applications has been increasing, and the average "paper-patent" transformation time lag per year has been getting longer, etc.. Additionally, the topics "Knowledge organization and strategic planning", "Network service and reference consultation," and "Library and public services" in the LIS have the potential to further contribute to the technology field. [ABSTRACT FROM AUTHOR]

Social media platforms, search engine transaction logs, Web portal transaction logs, large text corpora, and other data sources offer users a variety of invaluable data sources. Data generated based on social media platforms and other data sources have become important sources of big data for researchers in information science. Big data provide not only challenges but also opportunities for information science. Emerging big data trends inevitably have an impact on research methods in information science. The authors of this paper discuss the impact of big data on research methods in information science. This paper addresses these challenges and opportunities through the lens of research methods, ranging from data processing, to sampling, to information visualization, to temporal analysis, to sentiment analysis, to correlation, to cause-effect relationship, to data accessibility, to data privacy, and data ethics issues. The discussions on related aspects of research methods provoke a healthy reflection and debate for the information science research community, which can help researchers in the field produce sound research designs for big data-oriented research studies and assist information practitioners in solving problems they face in the big data age. Research methods are fundamental and essential for any research studies. Big data analysis has a natural relationship with research methods. The paper discusses an emerging and important research topic big data from the unique angle of research methods. [ABSTRACT FROM AUTHOR]

In medical decision support systems, both the accuracy (i.e., the ability to adequately represent the decision making processes) as well as the transparency and interpretability (i.e., the ability to provide a domain user with compact and understandable explanation and justification of the proposed decisions) play essential roles. This paper presents an approach for automatic design of fuzzy rule-based classification systems (FRBCSs) from medical data using multi-objective evolutionary optimization algorithms (MOEOAs). Our approach generates, in a single run, a collection of solutions (medical FRBCSs) characterized by various levels of accuracy-interpretability trade-off. We propose a new complexity-related interpretability measure and we address the semantics-related interpretability issue by means of efficient implementation of the so-called strong fuzzy partitions of attribute domains. We also introduce a special-coding-free representation of the rule base and original genetic operators for its processing as well as we implement our ideas in the context of well-known and one of the presently most advanced MOEOAs, i.e., Non-dominated Sorting Genetic Algorithm II (NSGA-II). An important part of the paper is devoted to a broad comparative analysis of our approach and as many as 26 alternative techniques arranged in 32 experimental set-ups and applied to three well-known benchmark medical data sets ( Breast Cancer Wisconsin (Original), Pima Indians Diabetes , and Heart Disease (Cleveland) ) available from the UCI repository of machine learning databases ( http://archive.ics.uci.edu/ml ). A number of useful in medical applications performance measures including accuracy, sensitivity, specificity, and several interpretability measures are employed. The results of such a broad comparative analysis demonstrate that our approach significantly outperforms the alternative methods in terms of the interpretability of the obtained FRBCSs while remaining either competitive or superior in terms of their accuracy. It is worth stressing that the overwhelming majority of the existing medical classification methods concentrate almost exclusively on the accuracy issues. [ABSTRACT FROM AUTHOR]

The rapid development of motion capture technologies has greatly increased the use of human motion data in many applications. This has increased the demand to have an effective means to systematically analyze those massive data in order to understand human motion variation patterns. This paper studies one typical type of motion data, which are recorded as multi-stream trajectories of human joints. Such a high dimensional multi-stream data structure makes it difficult to directly perform visual comparisons or simply apply conventional methods such as PCA to capture the variation of human motion patterns. In this paper, a high order array (tensor) is suggested for data representation, based on which the Uncorrelated Multilinear Principal Component Analysis (UMPCA) is applied to analyze the variation of human motion patterns. A simulation study is presented to show the superiority of UMPCA over PCA in preserving the cross-correlation among multi-stream trajectories. The effectiveness of UMPCA is also demonstrated using a case study for analyzing vehicle ingress test data. [ABSTRACT FROM AUTHOR]

Recent decades have witnessed a massive increase in the volume and quality of hydrologic data available to aid water resources decision makers, managers, and scientists. This has been accompanied by exponential growth in both desktop and cloud computing, as well as data storage capabilities. As a result, there are abundant opportunities to drastically change how water data is collected, managed, disseminated, and analyzed – which should ultimately have significant positive impacts on water science, engineering, and management. We are at the cusp of a new era in water data science which brings with it many exciting technological and scientific challenges and opportunities. Many of these challenges are alleviated and opportunities are multiplied when hydrology is viewed as a "team sport" rather than as an individual activity. These factors formed the motivation for the development of the HydroShare open-source software and the hydroshare.org operational system. This retrospective paper reviews a decade of HydroShare development and operation by presenting the general architecture, functionality, key contributions of the project to earth science and cyberinfrastructure research, current usage metrics, and future directions. • We present a review of the design and development of HydroShare for publishing water data and models. • The design and current functionality of this open source and open access software system is presented. • We review specific scientific and technical contributions of the project in both information science and modeling. • Current usage statistics and future directions of the system are discussed. [ABSTRACT FROM AUTHOR]

The paper deals with the divergence of information filter in multi sensor target tracking problem using bearing only measurements. Information filter has a number of advantages in terms of computational requirements over Kalman filter for target tracking applications. Compared to Kalman filter it also has the advantage that one can start estimation even without an initial estimate. But this filter is seen to diverge after tracking for a short period of time, even when the target is moving at a constant velocity. A technique to overcome this problem has been discussed in this paper. The information update equations of the conventional information filter are modified in terms of fuzzy function of error and change of error, and the results have been found to be encouraging. The efficacy of the technique in preventing divergence is demonstrated in the context of tracking a maneuvering target also. [ABSTRACT FROM AUTHOR]

Objective: This paper presents benchmarking results of human epithelial type 2 (HEp-2) interphase cell image classification methods on a very large dataset. The indirect immunofluorescence method applied on HEp-2 cells has been the gold standard to identify connective tissue diseases such as systemic lupus erythematosus and Sjögren's syndrome. However, the method suffers from numerous issues such as being subjective, time consuming and labor intensive. This has been the main motivation for the development of various computer-aided diagnosis systems whose main task is to automatically classify a given cell image into one of the predefined classes.Methods and Material: The benchmarking was performed in the form of an international competition held in conjunction with the International Conference of Image Processing in 2013: fourteen teams, composed of practitioners and researchers in this area, took part in the initiative. The system developed by each team was trained and tested on a very large HEp-2 cell dataset comprising over 68,000 images of HEp-2 cell. The dataset contains cells with six different staining patterns and two levels of fluorescence intensity. For each method we provide a brief description highlighting the design choices and an in-depth analysis on the benchmarking results.Results: The staining pattern recognition accuracy attained by the methods varies between 47.91% and slightly above 83.65%. However, the difference between the top performing method and the seventh ranked method is only 5%. In the paper, we also study the performance achieved by fusing the best methods, finding that a recognition rate of 85.60% is reached when the top seven methods are employed.Conclusions: We found that highest performance is obtained when using a strong classifier (typically a kernelised support vector machine) in conjunction with features extracted from local statistics. Furthermore, the misclassification profiles of the different methods highlight that some staining patterns are intrinsically more difficult to recognize. We also noted that performance is strongly affected by the fluorescence intensity level. Thus, low accuracy is to be expected when analyzing low contrasted images. [ABSTRACT FROM AUTHOR]

Finding heavy-elements (heavy-hitters) in streaming data is one of the central, and well-understood tasks. Despite the importance of this problem, when considering the sliding windows model of streaming (where elements eventually expire) the problem of finding L 2 -heavy elements has remained completely open despite multiple papers and considerable success in finding L 1 -heavy elements. Since the L 2 -heavy element problem doesn't satisfy certain conditions, existing methods for sliding windows algorithms, such as smooth histograms or exponential histograms are not directly applicable to it. In this paper, we develop the first polylogarithmic-memory algorithm for finding L 2 -heavy elements in the sliding window model. Our technique allows us not only to find L 2 -heavy elements, but also heavy elements with respect to any L p with 0 < p ≤ 2 on sliding windows. By this we completely “close the gap” and resolve the question of finding L p -heavy elements in the sliding window model with polylogarithmic memory, since it is well known that for p > 2 this task is impossible. We demonstrate a broader applicability of our method on two additional examples: we show how to obtain a sliding window approximation of the similarity of two streams, and of the fraction of elements that appear exactly a specified number of times within the window (the α -rarity problem). In these two illustrative examples of our method, we replace the current expected memory bounds with worst case bounds. [ABSTRACT FROM AUTHOR]

As the availability of data is increasing everyday, the need to reflect on how to make these data meaningful and impactful becomes vital. Current data paradigms have provided data life cycles that often focus on data acumen and data stewardship approaches. In an effort to examine the convergence, tensions, and harmonies of these two approaches, a group of researchers participated in an interactive panel session at the Association of Information Science and Technology Annual meeting in 2019. The panel presenters described their various research activities in which they confront the challenges of the computational and social perspectives of the data continuum. This paper provides a summary of this interactive panel. [ABSTRACT FROM AUTHOR]

Computer aided engineering; design, drafting and manufacturing, software packages are widely used in modern industry. The product development cycle, from idea to manufacturing, goes through different stages using different computer aided programs. These programs exchange a lot of information in different formats. One of these formats is 2D drawing in DXF standard format. The exchanged files suffer from overlapped entities and different interpretation for entity formats. This paper emphasizes on the sources of this problem and its negative effects. Moreover, it introduces software for eliminating the overlapping entities and re-classification of them. In addition, the paper explains the algorithms, which are encapsulated in the software. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes the idea of combining “interest groups” with the practical decision information to classify the decision makers (DMs) in complex multi-attribute large-group decision-making (CMALGDM) problems in interval-valued intuitionistic fuzzy (IVIF) environment. It constructs a partial binary tree DEA-DA cyclic classification model to achieve the multiple groups’ classification of DMs. Not only does this method provide references for the classification of DMs when the decision information is known, but it also lays foundations for DMs’ effective weight determination and the aggregation of decision information. First, this paper normalizes all of the cost attributes into benefit attributes to avoid the wrong decision result. Second, it employs the C-OWA operator to transform IVIF number (IVIFN) samples into single-valued samples. With respect to this transformation, this paper provides the corresponding BUM functions of DMs according to their risk attitudes; therefore, the preference information of DMs can be more objectively aggregated. Third, this paper adopts the partial binary tree DEA-DA cyclic classification model to present an accurate classification of DMs. Thus, for each interest group, group members with different interest preferences can be distinguished and distributed to the appropriate groups. Finally, to show the feasibility and validity of the model, we give an illustrative example. [Copyright &y& Elsevier]

Abstract: There is a demand for new and evolved research practices resulting from the so called “data deluge” emerging from high volume digital collection methods. As the volume of raw data increases traditional data processing methodologies, especially those involving manual manipulation are becoming increasingly difficult to manage. This paper presents the “Semantic Reef” architecture that offers an alternative approach to the development, application and execution of observational hypotheses involving studies of coral reef ecosystems. The Semantic Reef Knowledge Representation system is an eco-informatics application designed to assist in the integration of remotely sensed data streams and historic data sets supporting flexible hypothesis design and knowledge extraction. The system is an ontology-based architecture built to allow researchers to combine disjoint data sets into a single Knowledge Base for modelling the impact of climate change on coral reef ecosystems. The Knowledge Base consists of a hierarchy of ontologies developed to maximise usability and reusability by separating data instances from the concept descriptions. The model can be effectively reused to extract or disclose phenomena of any coral reef. This paper both demonstrates and describes a performance analysis of the Semantic Reef knowledge system. [Copyright &y& Elsevier]

Abstract: Cloud computing is actually a model for enabling convenient, limitless, on demand network access to a shared pool of computing resource. This paper describes some aspect of cloud architecture including cloud based information mechanism, especially useful in information system. Paper mention about the challenges and opportunities of cloud based model. At last we mental the main SWOT in respect of information delivery mechanism. [Copyright &y& Elsevier]

Abstract: Ontological analysis of modelling languages has been mainly used for evaluating quality of modelling language w.r.t. one specific upper ontology. Generally speaking this evaluation has been done by identifying the coverage of the modelling language constructs w.r.t. the ontology and vice-versa. However, a quite limited support has been developed for performing the ontological analysis task. Specifically, various ontologies used for ontological analysis are not associated to a machine readable format; the coverage of modelling language constructs is mostly provided by informal tables mapping one construct on to one ontological concept; the way in which this coverage task is undertaken is poorly specified (resulting in distinct results for distinct experts involved), and finally, preventing any ontology enrichment for dealing with some specialised language constructs. This limited support also prevents application of ontological analysis outcomes to problems and domains dealing with interoperability, integration and integrated usage of enterprise and IS models, which is today one of the key aspects for making interoperable, maintainable and evolvable inter and intra enterprise software systems. The paper provides an overview of the Unified Enterprise Modelling Language (UEML) approach, which introduces advanced support to ontological analysis of modelling languages. The paper is specifically focused on the task of ontological analysis of modelling languages (named incorporation of modelling languages) by introducing and explaining several guidelines and rules for driving the task: therefore, not all the aspects of the UEML approach will be discussed through the paper. The guidelines and rules are illustrated by incorporation of three selected modelling constructs from IDEF3, a well known language for specifying enterprise processes. [Copyright &y& Elsevier]

Abstract: Biology databases (bioDBs) play an ever-increasing role in the post-genome era for bioinformatics education. In order to handle the increasing demand for bioDBs in bioinformatics education, since Spring 2005, the College of Information Science and Technology (IST) at University of Nebraska at Omaha (UNO) has offered a course entitled “Database Search and Pattern Discovery” for seniors of our undergraduate Bioinformatics program (which is also available for graduate students majored in Compute Science program) on an annual base. This paper summarizes considerations behind development of this course, describes how the course is structured, reports our findings and observations, and raises issues of urgent tasks. The main objective of this paper is to exchange our thoughts of bioinformatics course development and solicit feedbacks and suggestions from colleagues working in bioinformatics education and related fields. [Copyright &y& Elsevier]

Abstract: The Multiple Vehicle Pickup and Delivery Problem with Time Windows (MV-PDPTWs) is an important problem in logistics and transportation. However, this problem is characterized by having a large number of constraints that are difficult to deal with in a solution algorithm. Indeed, merely constructing a feasible solution to this hard problem is a challenge in itself. In this research, we compare several construction algorithms that generate initial feasible solutions to the problem. The suggested algorithms all utilize a simple routing heuristic to create individual vehicle routes. The algorithms differ, though, in whether routes are generated sequentially or in parallel. They also have different criteria for selecting requests and the routes in which they will be inserted. Inserting a request in a route is either based on a first acceptance criterion, in which a request is inserted in the first route where a feasible insertion is found, or a best acceptance criterion, in which a request is inserted in the estimated best route for insertion. Experimental results on several benchmark problem instances indicate that the sequential construction heuristic may be the most suitable construction algorithm for this problem, in terms of simplicity of coding, solution quality as well as processing speed. [1] This paper is part of the PhD thesis of the first author (), and it is an expanded version of the MIC2009 conference paper (). 1 [Copyright &y& Elsevier]

Abstract The aim of this paper is to complement existing oscillation results for third-order neutral delay differential equations by establishing sufficient conditions for nonexistence of so-called Kneser solutions. Combining newly obtained results with existing ones, we attain oscillation of all solutions of the studied equations. [ABSTRACT FROM AUTHOR]

Abstract Zipf–Mandelbrot entropy has many applications in many applied sciences, for example, in information theory, ecology and economics, etc. The main purpose of this paper is to associate results for convex functions with Zipf–Mandelbrot entropy. First of all, we present some inequalities for convex functions and their applications for Csiszar divergence. Then we use two classes of convex functions and apply inequalities for convex functions and establish several results for Zipf–Mandelbrot entropy. Related results for Zipf–Mandelbrot entropies corresponding to different parameters have been proved. [ABSTRACT FROM AUTHOR]

Abstract: The notion of succinct indexes can be dated back from the debut of Jacobson’s thesis (1988) , and has triggered many results in the last decade. In traditional indexing, some given data are preprocessed so as to support online queries (and updates) on the data as efficiently as possible. When succinctness is involved, we are restricted to index the data using only an information–theoretically minimum number of bits. This paper concerns the succinct indexing schemes for a well-studied problem called Searchable Partial Sums (SPS). In SPS, an array of non-negative -bit integers is preprocessed so as to support online sum and search queries, and possibly update operation of individual entry. A succinct indexing scheme would allow only bits to represent the array . The only known result is that when (in this case, it is known as the Dynamic Bit Array Problem), we can support both queries in time and update in amortized time for any with . This paper shows that even for , we can index succinctly such that both query and update operations can be supported using the same time complexities. Moreover, the time for update becomes the worst-case time. Furthermore, the tradeoff between the query times and the update time is optimal as implied by Paˇtraşcu and Demaine’s lower bound result (2006) . In general when , we show a lower bound of time for the search query irrespective of the update time. This gives a tighter lower bound as compared to that of Paˇtraşcu and Demaine’s, which is a consequence of the requirement of succinctness. On the other hand, we give a succinct index that can support sum in time, search in time, and update in time, where . The query times are optimal when . This paper also extends the Searchable Partial Sums with insert and delete operations, and provides a succinct data structure for some cases. [Copyright &y& Elsevier]

In this paper, a formal method is presented to: (1) establish a system to visualize huge quantities of related information (2) graphically show the degree of similarity between these related documents (3) navigate between related documents so that the views are reconfigured in a dynamic and gradual way. This method uses, as input, a set of documents that have been related using any existing algorithm (relationships based on contents, on votes to contents, on social relations, on demographic information, etc.). The proposed method includes two different visualization and navigational versions related to: (a) visualization by explicit graph and (b) visualization by implicit graph. As a summary, the following characteristics are presented: (1) this method maximizes the information that is displayed at each moment, so that a balance is found between: (a) the number of relations shown, (b) the number of documents viewed, (c) the amount of information presented by each document and (d) how to show the degree of similarity between documents, (2) the method includes specific rules of interaction with the user to allow navigation in a simple, gradual and intuitive way. [ABSTRACT FROM AUTHOR]

Abstract Chronic Disease Prediction plays a pivotal role in healthcare informatics. It is crucial to diagnose the disease at an early stage. This paper presents a survey on the utilization of feature selection and classification techniques for the diagnosis and prediction of chronic diseases. Adequate selection of features plays a significant role for enhancing accuracy of classification systems. Dimensionality reduction helps in improving overall performance of machine learning algorithm. The application of classification algorithms on disease datasets yields promising results by developing adaptive, automated and intelligent diagnostic systems for chronic diseases. Parallel classification systems can be used to expedite the process and to enhance the computational efficiency of results. This work presents a comprehensive overview of various feature selection methods and their inherent pros and cons. We then analyze adaptive classification systems and parallel classification systems for chronic disease prediction. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes Macro-informatics of cognition that is the guideline for mathematical formulation of macroscopic feature. The macroscopic feature emerges from the total of shape elements, and the feature is important in the styling design. The mathematical formulation of macroscopic feature is difficult using conventional microscopic shape information such as dimension and curvature. In this paper, for formulation of macroscopic feature, the importance of “condition” that is various physical elements in the circumstance is mentioned. Moreover, this paper describes the mathematical formulation of macroscopic feature “complexity,” and its application for design. The formulation consists of curvature integration and multi-resolution representation. In application, shape generation method based on a genetic algorithm is introduced. [Copyright &y& Elsevier]

Abstract: The increasing importance of text-based information retrieval (IR) developments in the architecture, engineering, and construction industries (AEC) and the lack of sharable testing resources to support these developments call for an approach that can be used to generate domain-specific reference collections. To address this need, the authors investigated the characteristics of the testing environment in AEC and ways to adapt dominant collection preparation methods for the domain. This paper presents the authors’ collection generation approach through the preparation process of the Taiwanese National Center for Research on Earthquake Engineering (NCREE) collection. The collection’s Chinese-to-English translation instruments are also discussed as matching semantic/linguistic resources are highly valued in AEC’s text-based IR developments. The paper also includes a use case for the NCREE collection to show how a collection generated by the proposed approach could be applied to support research experiment and validation. The direct outputs, the NCREE collection and its translation instruments, are sharable and reusable testing resources, while mechanisms for seeking collections from other researchers are part of the extended research endeavors. [Copyright &y& Elsevier]

Abstract: Motif patterns consisting of sequences of intermixed solid and don’t-care characters have been introduced and studied in connection with pattern discovery problems of computational biology and other domains. In order to alleviate the exponential growth of such motifs, notions of maximal saturation and irredundancy have been formulated, whereby more or less compact subsets of the set of all motifs can be extracted, that are capable of expressing all others by suitable combinations. In this paper, we introduce the notion of maximal irredundant motifs in a two-dimensional array and develop initial properties and a combinatorial argument that poses a linear bound on the total number of such motifs. The remainder of the paper presents approaches to the discovery of irredundant motifs both by offline and incremental algorithms. [Copyright &y& Elsevier]