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An automatic computer vision algorithm that detects individual paper fibres from an image, assesses the possibility of grasping the detected fibres with microgrippers and detects the suitable grasping points is presented. The goal of the algorithm is to enable automatic fibre manipulation for mechanical characterisation, which has traditionally been slow manual work. The algorithm classifies the objects in images based on their morphology, and detects the proper grasp points from the individual fibres by applying given geometrical constraints. The authors test the ability of the algorithm to detect the individual fibres with 35 images containing more than 500 fibres in total, and also compare the graspability analysis and the calculated grasp points with the results of an experienced human operator with 15 images that contain a total of almost 200 fibres. The detection results are outstanding, with fewer than 1% of fibres missed. The graspability analysis gives sensitivity of 0.83 and specificity of 0.92, and the average distance between the grasp points of the human and the algorithm is 220 µm. Also, the choices made by the algorithm are much more consistent than the human choices. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI), machine learning (ML) and deep learning (DL) techniques have made a breakthrough in many different research fields, such as computer vision, natural language processing, network management and mobile communication. One of the primary goals in 5G network pursues high efficiency, higher spectrum and energy efficiency, higher system capacity and data rates, and lower latency with cost reduction. The papers on the topic of "AI-enabled intelligent networks for 5G and beyond" contain novel network architectures and mechanisms by using AI/ML/DL approaches for 5G/B5G networks. [Extracted from the article]

The challenges of Reproducibility and Replicability (R & R) in computer science experiments have become a focus of attention in the last decade, as efforts to adhere to good research practices have increased. However, experiments using Deep Learning (DL) remain difficult to reproduce due to the complexity of the techniques used. Challenges such as estimating poverty indicators (e.g., wealth index levels) from remote sensing imagery, requiring the use of huge volumes of data across different geographic locations, would be impossible without the use of DL technology. To test the reproducibility of DL experiments, we report a review of the reproducibility of three DL experiments which analyze visual indicators from satellite and street imagery. For each experiment, we identify the challenges found in the data sets, methods and workflows used. As a result of this assessment we propose a checklist incorporating relevant FAIR principles to screen an experiment for its reproducibility. Based on the lessons learned from this study, we recommend a set of actions aimed to improve the reproducibility of such experiments and reduce the likelihood of wasted effort. We believe that the target audience is broad, from researchers seeking to reproduce an experiment, authors reporting an experiment, or reviewers seeking to assess the work of others. Plain Language Summary: This paper aims to help researchers understand the challenges of reproducing Deep Learning (DL) publications, mitigate reproducibility gaps, and make their own work more reproducible. We build on the work of others and add recommendations organized by (a) the quality of the data set (and associated metadata), (b) the DL methodology, (c) the implementation methodology, and the infrastructure used. To our knowledge, this is the first initiative of its kind to address the problem of reproducibility in remote sensing imagery and DL problems for real‐world tasks. We hope this paper lowers the barrier to entry for the DL community to improve research. Following the lifecycle mantra: reproduce!, then replicate! With the goal of improving reproducibility! Key Points: We discuss the reproducibility challenges faced in research by Deep Learning approaches using Big DataWe provide advice for pre‐screening papers (before experiments) to avoid poorly invested effortWe present a recipe with a set of mitigation strategies to address common errors users (researchers, authors, reviewers) may encounter [ABSTRACT FROM AUTHOR]

In a recent study, Mahmood and Soon [26] identified and operationalized a set of variables that can be potentially impacted by information technology. The lack of factor analysis to verify the dimensionality of different variables is a notable omission in the study. This paper describes and employs the limited-information factor analysis approach and the minimum average partial criterion for testing the unidimensionality of different variables in the Mahmood and Soon study. While reconfirming the construct validity of eight variables, our results question the unidimensionality of two original variables. Further testimony is provided in favor of our results by citing previous research studies. [ABSTRACT FROM AUTHOR]

Motivation: Medical image analysis involves a series of tasks used to assist physicians in qualitative and quantitative analyses of lesions or anatomical structures which can significantly improve the accuracy and reliability of medical diagnoses and prognoses. Traditionally, these tedious tasks were finished by experienced physicians or medical physicists and were marred with two major problems, low efficiency and bias. In the past decade, many machine learning methods have been applied to accelerate and automate the image analysis process. Compared to the enormous deployments of supervised and unsupervised learning models, attempts to use reinforcement learning in medical image analysis are still scarce. We hope that this review article could serve as the stepping stone for related research in the future. Significance: We found that although reinforcement learning has gradually gained momentum in recent years, many researchers in the medical analysis field still find it hard to understand and deploy in clinical settings. One possible cause is a lack of well‐organized review articles intended for readers without professional computer science backgrounds. Rather than to provide a comprehensive list of all reinforcement learning models applied in medical image analysis, the aim of this review is to help the readers formulate and solve their medical image analysis research through the lens of reinforcement learning. Approach & Results: We selected published articles from Google Scholar and PubMed. Considering the scarcity of related articles, we also included some outstanding newest preprints. The papers were carefully reviewed and categorized according to the type of image analysis task. In this article, we first reviewed the basic concepts and popular models of reinforcement learning. Then, we explored the applications of reinforcement learning models in medical image analysis. Finally, we concluded the article by discussing the reviewed reinforcement learning approaches' limitations and possible future improvements. [ABSTRACT FROM AUTHOR]

Huge collections of published research documents are available in various repositories in Indian universities and research organizations. The efficient retrieval, estimation of research trends, and identification of research gaps with the global trend in different research areas, can be a great guiding tool for regulating research in the appropriate direction. This research attempts to analyse the trend of research activities carried out by Indian researchers in the discipline of Computer Science from 2010 to 19 and forecast the upcoming research trend for the years 2020–23. A repository of the abstracts based on domains given in the Computer Science Ontology (CSO) published by the Indian researchers is developed from the Scopus database. Document Index Graph document representation model is used to store the repository, shared phrases across the documents are extracted and phrase‐based similarity is computed. Combining the Single‐term and Phrase‐based similarity, the hybrid similarity is generated and similar documents are clustered using the DBSCAN clustering technique. Topics are identified for each cluster using the Latent Dirichlet Allocation algorithm and are automatically labelled using CSO. For each topic, the trend analysis and forecasting have been done using the Auto‐Regressive Integrated Moving Average. For the assessment of the forecasting performance, the dataset from 2010 to 17 is used as a training dataset and 2018–19 as a testing dataset. The average forecasting for the year 2018 for all CSO domains belongs to the Good forecasting category with Mean Absolute Percentage Error (MAPE) 18.34, and 2019 shows reasonable forecasting with MAPE 30.20 as per the MAPE interpretation given by Lewis. For each topic the average forecasting for years 2018–19 shows either Highly accurate, Good or Reasonable forecasting. As a result, the top four domains for the years 2020–23 are also identified which can help initial researchers in the identification of a relevant topic for research and exploration. [ABSTRACT FROM AUTHOR]

Remote aerial sensing provides a non‐invasive, large geographical‐scale technology for avian monitoring, but the manual processing of images limits its development and applications. Artificial Intelligence (AI) methods can be used to mitigate this manual image processing requirement. The implementation of AI methods, however, has several challenges: (1) imbalanced (i.e., long‐tailed) data distribution, (2) annotation uncertainty in categorization, and (3) dataset discrepancies across different study sites. Here we use aerial imagery data of waterbirds around Cape Cod and Lake Michigan in the United States to examine how these challenges limit avian recognition performance. We review existing solutions and demonstrate as use cases how methods like Label Distribution Aware Marginal Loss with Deferred Re‐Weighting, hierarchical classification, and FixMatch address the three challenges. We also present a new approach to tackle the annotation uncertainty challenge using a Soft‐fine Pseudo‐Label methodology. Finally, we aim with this paper to increase awareness in the ecological remote sensing community of these challenges and bridge the gap between ecological applications and state‐of‐the‐art computer science, thereby opening new doors to future research. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles in the issue on topics including the latest developments on distributed control; network linear equations; and online distributed optimal adaptive algorithm.

One of the main challenges in computer science and image processing is 2D human pose estimation. Specifically, occlusion and in particular occlusion of human joints caused by camera angle are of paramount importance. In this paper, a new highly accurate network was proposed that can estimate 2D human poses in video images using deep learning. We employ the Single Shot MultiBox Detector network to detect the centre position of each human within a video frame and then use the stacked hourglass network to estimate the 2D human pose. We approximate the human motion as a linear motion between different frames in a certain period; and optimize the human centres based on the local outlier factor and Kalman filters. The same method is applied to optimize the human pose estimations in video, which can address the inaccurate prediction caused by human joints occlusion. The proposed adaptive network is tested using the two well‐known benchmarks for human pose estimation (MPII and Joint‐annotated Human Motion Data Base datasets), and we also generate some 2D human pose estimating qualitative results of single and multiple people in Internet videos. The experimental results show that the proposed network has strong practicability and can achieve high accuracy on adaptive estimating the 2D human pose in video. [ABSTRACT FROM AUTHOR]

In recent years the introduction and subsequent enhancements of information technology has seen the emergence of major opportunities for developing new approaches to improve information interchange. The provision of information provides opportunities to find resolutions to problem situations, and the power of providing relevant and accurate information through using information systems cannot be underestimated. However, the world of information systems thinking has, so far, experienced difficulty finding a methodology suitable for utilizing the full range of available current information technology. This situation in the real world shows itself predominantly as information starvation as current information systems struggle to bring order and structure to information technology developments. This paper will discuss the issues relating to the development of a new generic framework. The novel aspect of this generic framework for planning information systems is that it integrates Soft Systems Methodology (SSM) and Viable Systems Model (VSM) in a coherent whole, whilst embracing the concepts of self-adaptation and autonomy. Further, the generic framework integrates the spatial analysis capabilities of a Geographical Information System (GIS). The generic framework will then be tested in real-world problem situations in order to test its effectiveness, efficiency and efficacy with regards to finding suitable solutions to the problem of information starvation. The importance of focusing the framework on GIS functionality is related to the fact that in many organizations GIS is employed as the solution to information starvation because of its ability to display spatial information. This paper will demonstrate the implementation of the generic framework in a UK Police Authority, a large emergency service that suffers from information starvation. In the context of a UK Police Authority the combination of SSM and VSM being supported by GIS functionality is seen as being a significant progression in relation to crime detection and operational planning. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The article offers world news briefs related to science and technology. The journal of William Ross Ashby, a British pioneer in the fields of Cybernetics will be available on the Internet, which consists of more than one million words on 7400 pages in 25 volumes. Workshop on Intelligent Knowledge Management will be held in Chendu and Jiuzhaigou, China on June 21-26, 2009. The 3rd International Multi-Conference on Society, Cybernatics and Informatics will be held in Florida on July 10-13, 2009.

In this paper some new, conceptual ideas referred to as a complexity orientation are presented on the basis of systemic planning (SP), which has been developed as a proactive, multi-methodology approach for complex planning tasks. Specifically, the complexity orientation is compared to other systems science research orientations represented by the following labels: functionalist, interpretive, emancipatory and postmodern. After presentation and discussion of the research orientation framework, SP is compared to other current systems and management practice. Finally, the potential of a complexity orientation is treated more generally and also with a focus on its epistemological interpretation, which is carried out with a special emphasis on the work of Luhmann. General findings of the paper are that a complexity orientation ought to complement the other four research orientations to serve as a methodological platform for management practice and that Luhmann's theories ought to be addressed by the systems community as a potential for informing further development of systems science and thinking. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Metaheuristics is a powerful optimization tool that is increasingly used across disciplines to tackle general purpose optimization problems. Nature‐inspired metaheuristic algorithms is a subclass of metaheuristic algorithms and have been shown to be particularly flexible and useful in solving complicated optimization problems in computer science and engineering. A common practice with metaheuristics is to hybridize it with another suitably chosen algorithm for enhanced performance. This paper reviews metaheuristic algorithms and demonstrates some of its utility in tackling pharmacometric problems. Specifically, we provide three applications using one of its most celebrated members, particle swarm optimization (PSO), and show that PSO can effectively estimate parameters in complicated nonlinear mixed‐effects models and to gain insights into statistical identifiability issues in a complex compartment model. In the third application, we demonstrate how to hybridize PSO with sparse grid, which is an often‐used technique to evaluate high dimensional integrals, to search for D‐efficient designs for estimating parameters in nonlinear mixed‐effects models with a count outcome. We also show the proposed hybrid algorithm outperforms its competitors when sparse grid is replaced by its competitor, adaptive gaussian quadrature to approximate the integral, or when PSO is replaced by three notable nature‐inspired metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

Automation in structural health monitoring (SHM) has greatly benefited from computer science's recent advances. Unlike images, the existing datasets for other types of input, such as vibration‐based damage data, are relatively smaller, less diverse, and highly imbalanced. Therefore, the reliability of data‐driven models developed for safety‐critical assessments can be questionable. This paper proposes a dual Bayesian inference where damage predictions are accompanied by measuring the model's confidence in predictions. First, it is shown how dual classification is integrated with Bayesian inference. Later, we introduce a surrogate deep learning module to transform the raw uncertainty output into an easily interpretable prediction uncertainty index (PUI). The PUI metric can be used to alarm a decision‐maker of the potential mistakes. The proposed dual Bayesian models are investigated on a 2D structure with seven different sensor layouts. Our approach yields increased robustness for different metrics compared with the benchmark. In addition to the performance boost, PUI information paves the way for a risk‐informed implementation of deep learning models in vibration‐based damage diagnosis. [ABSTRACT FROM AUTHOR]

It has been my privilege to have been one of those on the Editorial Advisory Board of this journal for much of its existence, and especially now to be asked to contribute an editorial in this 35th anniversary year of its existence. Professor Adeli's vision and mission for creating this journal was bringing new technologies into civil engineering at the time much of civil engineering practice was somewhat resistant to new computer technology. The first journal article on civil engineering application of neural networks was published in this journal in 1989, as a precursor to the machine learning technology, currently a ubiquitous technology. [Extracted from the article]

The paper presents the pheromone-Q-learning (Phe-Q) algorithm, a variation of Q-learning. The technique was developed to allow agents to communicate and jointly learn to solve a problem. Phe-Q learning combines the standard Q-learning technique with a synthetic pheromone that acts as a communication medium speeding up the learning process of cooperating agents. The Phe-Q update equation includes a belief factor that reflects the confidence an agent has in the pheromone (the communication medium) deposited in the environment by other agents. With the Phe-Q update equation, the speed of convergence towards an optimal solution depends on a number of parameters including the number of agents solving a problem, the amount of pheromone deposit, the diffusion into neighbouring cells and the evaporation rate. The main objective of this paper is to describe and evaluate the performance of the Phe-Q algorithm. The paper demonstrates the improved performance of cooperating Phe-Q agents over non-cooperating agents. The paper also shows how Phe-Q learning can be improved by optimizing all the parameters that control the use of the synthetic pheromone. [ABSTRACT FROM AUTHOR]

Block compressed sensing (BCS) is widely used in image sampling and is an efficient, effective technique. Through the use of BCS, an image can be simultaneously compressed and encrypted. In this paper, a novel reversible data hiding (RDH) method is proposed to embed additional data into BCS images. The proposed method is the first RDH method of its kind for BCS images. Results demonstrate that our approach performs better compared with other state-of-the-art RDH methods on encrypted images. [ABSTRACT FROM AUTHOR]

Water droplets help life in nature survive, thrive, and evolve. With water droplet serving as one of the indispensable elements in the Internet of Things (IoT), many droplet‐oriented technologies, such as microfluidics, droplet manipulation, electrowetting, and energy harvesting, make rapid progress driven by material science, computer science, and medicine. Droplet‐based wearable devices are endowed with advantages such as flexibility, sensing ability, and automation for various parameter detection. Besides, the continuous exploration of droplet manipulation has led to the emergence of a wide variety of manipulation methods. Meanwhile, electrowetting that utilizes external fields modifying liquid–solid surfaces has found its applications in various areas, including droplet transportation, microfabrication, and healthcare. The energy generation from water droplets also presents exciting opportunities for the development of novel electricity generators. These approaches for droplet utilization underscore the immense potentials and versatilities of droplet‐based technologies in the IoT landscape. Hence, this mini review presents the fundamental droplet‐based technologies by summarizing their working mechanisms and methods, device structures, and applications. Given the challenges in materials, fabrication, and system integration, this review shows the overall development roadmap in terms of improved functionality and performance and highlights the opportunities toward multifunctional, self‐sustainable, and intelligent systems, which is called for IoT construction. [ABSTRACT FROM AUTHOR]

Small-screen mobile terminals have difficulty accessing existing Web resources designed for large-screen devices. This paper presents an adaptive transformation method based on webpage semantic features to solve this problem. According to the text density and link density features of the webpages, the webpages are divided into two types: index and content. Our method uses an index-based webpage transformation algorithm and a content-based webpage transformation algorithm. Experiment results demonstrate that our adaptive transformation method is not dependent on specific software and webpage templates, and it is capable of enhancing Web content adaptation on small-screen terminals. [ABSTRACT FROM AUTHOR]

This paper proposes the necessity of a walking period in footstep planning and details situations in which it should be considered. An optimization‐based fast footstep planner that takes the walking period into consideration is also presented. This footstep planner comprises three stages. A binary search is first used to determine the walking period. The front stride, side stride, and walking direction are then determined using the modified rapidly‐exploring random tree algorithm. Finally, particle swarm optimization (PSO) is performed to ensure feasibility without departing significantly from the results determined in the two stages. The parameters determined in the previous two stages are optimized together through the PSO. Fast footstep planning is essential for coping with dynamic obstacle environments; however, optimization techniques may require a large computation time. The two stages play an important role in limiting the search space in the PSO. This framework enables fast footstep planning without compromising on the benefits of a continuous optimization approach. [ABSTRACT FROM AUTHOR]

In recent years, education has undergone a profound transformation process, having gone from relying only on the traditional lecture to making full use of digital formats. This gradual process, accelerated by the COVID‐19 pandemic in the last years, has triggered innovative changes in the educational process. Within this context, many lecturers have adopted the flipped classroom instructional model, aiming to improve the motivation and involvement of the students. In this model, students must acquire certain theoretical knowledge at home, and the classes, with the help of the lecturer, are used for the more practical part. This article presents the results obtained when the flipped classroom model was implemented in the computer science degree. Specifically, during 2020–2021 and 2021–2022 courses, this instructional model was assessed in 15 subjects. Results show that the flipped classroom instructional model can be stated that it has improved the students' perception of the learning experience, students' dedication and engagement has been improved, students' perception of their understanding of the subject has improved. The faculty considers that the teaching experience has improved and is in favor of continuing with the experience in future courses. We have implemented a flipped classroom experience in the computer science degree. We have involved 11 subjects of different courses. We describe our experience and share our results that will help another universities that want to improve their education experience. [ABSTRACT FROM AUTHOR]

The article presents an introduction to articles published within this edition of the journal focusing on medical decision support systems and automated diagnostic systems. These include "Augmentation of a nearest neighbour clustering algorithm with a partial supervision strategy for biomedical data classification," by Salem et al, "Comparison of different classifer algorithms for diagnosing macular and optic nerve diseases," by Polat et al, and "Electromyography signal analysis using wavelet tranform and higher order statistics to determine muscle contraction," by Hussain et al.

Card sorts are a knowledge elicitation technique in which participants are given a collection of items and are asked to partition them into groups based on their own criteria. Information about the participant's knowledge structure is inferred from the groups formed and the names used to describe the groups through various methods ranging from simple quantitative statistical measures (e.g. co-occurrence frequencies) to complex qualitative methods (e.g. content analysis on the group names). This paper introduces a new technique for analyzing card sort data that uses quantitative measures to discover rich qualitative results. This method is based upon a distance metric between sorts that allows one to measure the similarity of groupings and then look for clusters of closely related sorts across individuals. By using software for computing these clusters, it is possible to identify common concepts across individuals, despite the use of different terminology. [ABSTRACT FROM AUTHOR]

Card sorts can be used to study the way human subjects acquire and organize conceptual knowledge. Analyses of card sorts often involve subjective examination of criteria or category names or using clustering techniques, neither of which lend themselves well to direct statistical analysis. This paper defines NMST, a quantitative measure of knowledge discrimination based on repeated, single-criterion card sorts that is independent of criteria or category names and that is particularly amenable to statistical analysis. The NMST measure is illustrated by applying it to a particular data set collected in a large multinational card sort study of subjects, where the knowledge area comes from a first-year programming course. Applied to this data set, the NMST measure is shown to distinguish, with statistical significance, between a set of subjects with only an introduction to programming and a set of subjects who have completed the equivalent of a bachelor's degree or higher in a computing-related discipline. [ABSTRACT FROM AUTHOR]

Locating p facilities to serve a number of customers is a problem in many areas of business. The problem is to determine p facility locations such that the weighted average distance traveled from all the demand points to their nearest facility sites is minimized. A variant of the p-median problem is one in which a maximum distance constraint is imposed between the demand point and its nearest facility location, also known as the p-median problem with maximum distance constraint. In this paper, we apply a fairly new methodology known as genetic algorithms to solve a relatively large sized constrained version of the p-median problem. We present our computational experience on the use of genetic algorithms for solving the constrained version of the p-median problem using two different data sets. Our comparative experimental experience shows that this solution procedure performs quite well compared with the results obtained from existing techniques. [ABSTRACT FROM AUTHOR]

A widespread assumption in the contemporary discussion of probabilistic models of cognition, often attributed to the Bayesian program, is that inference is optimal when the observer's priors match the true priors in the world-the actual 'statistics of the environment.' But in fact the idea of a 'true' prior plays no role in traditional Bayesian philosophy, which regards probability as a quantification of belief, not an objective characteristic of the world. In this paper I discuss the significance of the traditional Bayesian epistemic view of probability and its mismatch with the more objectivist assumptions about probability that are widely held in contemporary cognitive science. I then introduce a novel mathematical framework, the observer lattice, that aims to clarify this issue while avoiding philosophically tendentious assumptions. The mathematical argument shows that even if we assume that 'ground truth' probabilities actually do exist, there is no objective way to tell what they are. Different observers, conditioning on different information, will inevitably have different probability estimates, and there is no general procedure to determine which one is right. The argument sheds light on the use of probabilistic models in cognitive science, and in particular on what exactly it means for the mind to be 'tuned' to its environment. [ABSTRACT FROM AUTHOR]

When producing a description of a target referent in a visual context, speakers need to choose a set of properties that distinguish it from its distractors. Computational models of language production/generation usually model this as a search process and predict that the time taken will increase both with the number of distractors in a scene and with the number of properties required to distinguish the target. These predictions are reminiscent of classic findings in visual search; however, unlike models of reference production, visual search models also predict that search can become very efficient under certain conditions, something that reference production models do not consider. This paper investigates the predictions of these models empirically. In two experiments, we show that the time taken to plan a referring expression-as reflected by speech onset latencies-is influenced by distractor set size and by the number of properties required, but this crucially depends on the discriminability of the properties under consideration. We discuss the implications for current models of reference production and recent work on the role of salience in visual search. [ABSTRACT FROM AUTHOR]

The information technology artifact (ITA) has been suggested as the core of information systems (IS) research, and the research community has been encouraged to deeply engage with the ITA. Various studies highlight, however, that the ITA continues to receive only limited attention and thus, little foundation exists for IS researchers to delineate and theorize about the ITAs studied. In this paper, we develop a framework that can be utilized as a language for articulating and theorizing the ITA in IS research. Our framework builds on the multi-faceted theoretical paradigm of systems thinking from which we derive several concepts and appropriate them to the context at hand, resulting in a seven-dimensional framework of characteristics for ITAs. In a literature survey of research on enterprise systems and enterprise resource planning systems in top IS journals, we show how ITA characteristics are currently included to present details of the relevant ITA instance and identify theoretical relationships between ITA characteristics and outcomes. We conclude the study by demonstrating the use of the ITA framework for delineating and theorizing the ITA in IS research. [ABSTRACT FROM AUTHOR]

Breast cancer ranks among the most prevalent malignant tumours and is the primary contributor to cancer‐related deaths in women. Breast imaging is essential for screening, diagnosis, and therapeutic surveillance. With the increasing demand for precision medicine, the heterogeneous nature of breast cancer makes it necessary to deeply mine and rationally utilize the tremendous amount of breast imaging information. With the rapid advancement of computer science, artificial intelligence (AI) has been noted to have great advantages in processing and mining of image information. Therefore, a growing number of scholars have started to focus on and research the utility of AI in breast imaging. Here, an overview of breast imaging databases and recent advances in AI research are provided, the challenges and problems in this field are discussed, and then constructive advice is further provided for ongoing scientific developments from the perspective of the National Natural Science Foundation of China. [ABSTRACT FROM AUTHOR]

In this paper, we present a new, easy-to-generate system that is capable of creating virtual 3D tree models and simulating a variety of growth processes of a tree from a single, real tree image. We not only construct various tree models with the same trunk through our proposed digital image matting method and skeleton-based abstraction of branches, but we also animate the visual growth of the constructed 3D tree model through usage of the branch age information combined with a scaling factor. To control the simulation of a tree growth process, we consider treegrowing attributes, such as branching orders, branch width, tree size, and branch self-bending effect, at the same time. Other invisible branches and leaves are automatically attached to the tree by employing parametric branch libraries under the conventional procedural assumption of structure having a local selfsimilarity. Simulations with a real image confirm that our system makes it possible to achieve realistic tree models and growth processes with ease. [ABSTRACT FROM AUTHOR]

Collecting and managing data for clinical and translational research presents significant challenges for clinical and translational researchers, many of whom lack needed access to data management expertise, methods, and tools. At many institutions, funding constraints result in differential levels of research informatics support among investigators. In addition, the lack of widely shared models and ontologies for clinical research informatics and health information technology hampers the accurate assessment of investigators' needs and complicates the efficient allocation of crucial resources for research projects, ultimately affecting the quality and reliability of research. In this paper, we present a model for providing flexible, cost-efficient institutional support for clinical and translational research data management and informatics, the research management team, and describe our initial experiences with deploying this model at our institution. Clin Trans Sci 2012; Volume 5: 464-469 [ABSTRACT FROM AUTHOR]

A supply chain is not a linear type of inter-firm structure but is often considered as a network. Business networks are underpinned by the firms' resources, social legitimacy and associated power, which are also seen in the adoption theories of business-to-business integration (B2Bi) in the supply chain. However, there appears to be scarcity of the discussion on the theoretical relationship between them. This paper aims to enrich the previous findings of technology adoption theories in a business-to-business context by proposing a structural model and using Structural Equation Modelling approach to test it. It focuses on the integrated supply chain to test, analyse and extend the adoption factors to the use of computer-based information systems (IS). The survey data were collected in the Taiwanese Information Technology Industry. The path analyses indicate the answers for three issues raised from the research framework and confirm the associations between a firm's existing system support readiness and the network determinants outside organizational boundaries. Further, it identifies the interrelationships among these factors and indicates that some of them mediate the enterprises' behaviour on investments to increase current IS for B2Bi purposes. [ABSTRACT FROM AUTHOR]

Information systems (IS) research has been long concerned with improving task-related performance. The concept of fit is often used to explain how system design can improve performance and overall value. So far, the literature has focused mainly on performance evaluation criteria that are based on measures of task efficiency, accuracy, or productivity. However, nowadays, productivity gain is no longer the single evaluation criterion. In many instances, computer systems are expected to enhance our creativity, reveal opportunities and open new vistas of uncharted frontiers. To address this void, we introduce the concept of generativity in the context of IS design and develop two corresponding design considerations –‘generative capacity’ that refers to one's ability to produce something ingenious or at least new in a particular context, and ‘generative fit’ that refers to the extent to which an IT artefact is conducive to evoking and enhancing that generative capacity. We offer an extended view of the concept of fit and realign the prevailing approaches to human–computer interaction design with current leading-edge applications and users' expectations. Our findings guide systems designers who aim to enhance creative work, unstructured syntheses, serendipitous discoveries, and any other form of computer-aided tasks that involve unexplored outcomes or aim to enhance our ability to go boldly where no one has gone before. In this paper, we explore the underpinnings of ‘generative capacity’ and argue that it should be included in the evaluation of task-related performance. Then, we briefly explore the role of fit in IS research, position ‘generative fit’ in that context, explain its role and impact on performance, and provide key design considerations that enhance generative fit. Finally, we demonstrate our thesis with an illustrative vignette of good generative fit, and conclude with ideas for further research. [ABSTRACT FROM AUTHOR]

Telecommunication networks have evolved over time as a result of technological advances, and network topologies and equipment have become increasingly complex. Expert systems are being successfully applied to the management of telecommunication networks. However, applying expert systems to network design is another especially beneficial yet still not very common approach. In this paper we propose a rule-based expert system called Datacab. Datacab was developed at Enditel Endesa in collaboration with the Electronic Technology Department of the University of Seville, for the automatic design of hybrid fibre coax (HFC) cable networks. Using data from a geographical information system as input, it automatically generates viable HFC network designs. [ABSTRACT FROM AUTHOR]

Research on semantic webs has become increasingly widespread in the computer science community. The core technology of a semantic web is an artefact called an ontology. The major problem in constructing an ontology is the long period of time required. Another problem is the large number of possible meanings for the knowledge in the ontology. In this paper, we present a novel ontology construction based on artificial neural networks and a Bayesian network. First, we collected web pages related to the problem domain using search engines. The system then used the labels of the HTML tags to select keywords, and used WordNet to determine the meaningful keywords, called terms. Next, it calculated the entropy value to determine the weight of the terms. After the above steps, the projective adaptive resonance theory neural network clustered the collected web pages and found the representative term of each cluster of web pages using the entropy value. The system then used a Bayesian network to insert the terms and complete the hierarchy of the ontology. Finally, the system used a resource description framework to store and express the ontology results. [ABSTRACT FROM AUTHOR]

Bankruptcy prediction and credit scoring are the two important problems facing financial decision support. The multilayer perceptron (MLP) network has shown its applicability to these problems and its performance is usually superior to those of other traditional statistical models. Support vector machines (SVMs) are the core machine learning techniques and have been used to compare with MLP as the benchmark. However, the performance of SVMs is not fully understood in the literature because an insufficient number of data sets is considered and different kernel functions are used to train the SVMs. In this paper, four public data sets are used. In particular, three different sizes of training and testing data in each of the four data sets are considered (i.e. 3:7, 1:1 and 7:3) in order to examine and fully understand the performance of SVMs. For SVM model construction, the linear, radial basis function and polynomial kernel functions are used to construct the SVMs. Using MLP as the benchmark, the SVM classifier only performs better in one of the four data sets. On the other hand, the prediction results of the MLP and SVM classifiers are not significantly different for the three different sizes of training and testing data. [ABSTRACT FROM AUTHOR]

An approach for managing knowledge in an organization in the new infrastructure of Semantic Web consists of building a corporate semantic web (CSW). The main components of a CSW are (i) evolving resources distributed over an intranet and indexed using (ii) semantic annotations expressed with the vocabulary provided by (iii) a shared ontology. However, changes in the operating environment may lead to some inconsistencies in the system and they result in need of modifications of the CSW components. These changes need to be evolved and well managed. In this paper we present a rule-based approach allowing us to detect and correct semantic annotation inconsistencies. This rule-based approach is implemented in the CoSWEM system enabling to manage the evolution of such a CSW, especially to address the evolution of semantic annotations when its underlying ontologies change. [ABSTRACT FROM AUTHOR]

Uniting ontologies and rules has become a central topic in the Semantic Web. Bridging the discrepancy between these two knowledge representations, this paper introduces DatalogDL as a family of hybrid languages, where Datalog rules are parameterized by various DL (description logic) languages ranging from to . Making Datalog DL a decidable system with complexity of EXPTIME, we propose independent properties in the DL body as the restriction to hybrid rules, and weaken the safeness condition to balance the trade-off between expressivity and reasoning power. Building on existing well-developed techniques, we present a principled approach to enrich (RuleML) rules with information from (OWL) ontologies, and develop a prototype system combining a rule engine (OO jDREW) with a DL reasoner (RACER). [ABSTRACT FROM AUTHOR]

Neural network ensembles (sometimes referred to as committees or classifier ensembles) are effective techniques to improve the generalization of a neural network system. Combining a set of neural network classifiers whose error distributions are diverse can generate better results than any single classifier. In this paper, some methods for creating ensembles are reviewed, including the following approaches: methods of selecting diverse training data from the original source data set, constructing different neural network models, selecting ensemble nets from ensemble candidates and combining ensemble members' results. In addition, new results on ensemble combination methods are reported. [ABSTRACT FROM AUTHOR]

This paper presents the findings of an empirical study investigating the nature of the process of information systems development. These findings are used to identify systems development approach, which extends understanding of the information systems development process beyond the levels of methodology and technical expertise. The term approach expresses the rich, varied, and complex ways in which professionals deal with development situations. The term transcends methodological components– which can be combined, tailored, or omitted altogether. Within approach, a framework for method choice, a key diagnostic scheme, and the use of a central repository reveal the significant background activity that goes into developing information systems. It is suggested that the information systems development process ultimately involves a professional using an approach to intervene in a problematic situation for developing the organization's information and, if needed, other systems. This understanding has implications for examining the field's ineffectiveness in relation to the approach adopted– rather than the methodology used. [ABSTRACT FROM AUTHOR]

How to find and detect novel or unknown network attacks is one of the most important objectives in current intrusion detection systems. In this paper, a rule evolution approach based on Genetic Programming (GP) for detecting novel attacks on networks is presented and four genetic operators, namely reproduction, mutation, crossover, and dropping condition operators, are used to evolve new rules. New rules are used to detect novel or known network attacks. A training and testing dataset proposed by DARPA is used to evolve and evaluate these new rules. The proof of concept implementation shows that a rule generated by GP has a low false positive rate (FPR), a low false negative rate and a high rate of detecting unknown attacks. Moreover, the rule base composed of new rules has high detection rate with low FPR. An alternative to the DARPA evaluation approach is also investigated. [ABSTRACT FROM AUTHOR]

This paper describes an evolutionary algorithm, GenD, conceived by Buscema in 1998 at the Centro Ricerche di Scienze della Comunicazione – Semeion in Rome, where it is still successfully used and has been further developed. Unlike classic genetic algorithms, the GenD system maintains an inner instability during evolution, presenting a continuous evolution of the evolution and a natural increase in biodiversity during the progress of the algorithm. The theory which leads to defining the GenD system is outlined. Specific characteristics of GenD, such as the definition of a species-health aware evolutionary law, the use of genetic operators and the adoption of a structured organization of individuals (tribes), are described. In order to measure GenD capabilities, we investigated also different problems, such as that known as the travelling sales person problem, which belongs to the class of full NP problems. [ABSTRACT FROM AUTHOR]

Choice problems as a class of decision problems have attracted great attention for the last couple of decades. Among the frameworks and supporting theories used in their study, two have had the greatest impact: bounded rationality and cost–benefit. Both theories could find support from past empirical studies under different conditions or problem environments. In the past studies, problem size has been shown to play an important role in decision-making. As problem size increases, a decision process may be detoured and the decision outcome may be different. In this paper we investigate the impact of problem size on three important aspects of the computer-aided decision process – strategy selection, decision time/effort, and decision quality – through very large choice problems. [ABSTRACT FROM AUTHOR]

The paper presents a methodology for building sequential decision support systems based on decision theory using value of information (for short, DT-VOI based SDSSs). DT-VOI based SDSSs support decision-makers in difficult problems of sequential decision-making. In particular we consider the problem of building DT-VOI based SDSSs which are capable of supporting decisions in critical situations where (1) making a decision entails knowing the states of some critical hypotheses, and such knowledge is acquired by performing suitable tests; (2) test outcomes are uncertain; (3) performing a test entails, in general, some drawbacks, so that a trade-off exists between such drawbacks and the value of the information provided by the test; (4) performing a test has the side-effect that it changes the expected benefit from performing other tests; (5) exceptional situations alter probability and utility default values. [ABSTRACT FROM AUTHOR]

Determining and assessing the requisite skills of information technology (IT) personnel have become critical as the value of IT has risen in modern organizations. In addition to technical skills traditionally expected of IT personnel, softer skills like managerial, business, and interpersonal skills have been increasingly cited in previous studies as mandatory for these employees. This paper uses a typology of IT personnel skills -- technology management skills, business functional skills, interpersonal skills, and technical skills -- and investigates their relationships to two information systems (IS) success variables, IS infrastructure flexibility and the competitive advantage provided by IS. The study investigates these relationships using the perceptions of chief information officers (CIOs) from mostly Fortune 2000 companies. The contributions of this study are: IT personnel skills do affect IS success, technical skills are viewed as the most important skill set in affecting IS infrastructure flexibility and competitive advantage, and modularity is viewed as more valuable to competitive advantage than integration. Several explanations are offered for the lack of positive relationships between the softer IT personnel skills and the dimensions of IS success used in this study. [ABSTRACT FROM AUTHOR]

This research explores how systems science and an object-oriented methodology can be used together to increase the effectiveness of organizational requirements analysis for information systems (IS) development. Organizational failure of IS development is defined as a gap between what the users expect from an information provision system and how well these expectations are met by the perceived performance of the delivered system. Problems leading to this expectation–perception gap are identified and modelled as five interrelated discrepancies or gaps throughout the process of IS development. These gaps could be bridged by using systems science methods and object-oriented analysis. A wider framework which incorporates elements of both methodologies is formulated and applied to a real-world case. Lessons are generated from reflections upon what has been done and how it was done. Possible ways to integrate object-oriented analysis with systems science methods are suggested. Copyright © 2000 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

3D printing is a state‐of‐the‐art technology for the fabrication of biomaterials with myriad applications in translational medicine. After stimuli‐responsive properties were introduced to 3D printing (known as 4D printing), intelligent biomaterials with shape configuration time‐dependent character have been developed. Polysaccharides are biodegradable polymers sensitive to several physical, chemical, and biological stimuli, suited for 3D and 4D printing. On the other hand, engineering of mechanical strength and printability of polysaccharide‐based scaffolds along with their aneural, avascular, and poor metabolic characteristics need to be optimized varying printing parameters. Multiple disciplines such as biomedicine, chemistry, materials, and computer sciences should be integrated to achieve multipurpose printable biomaterials. In this work, 3D and 4D printing technologies are briefly compared, summarizing the literature on biomaterials engineering though printing techniques, and highlighting different challenges associated with 3D/4D printing, as well as the role of polysaccharides in the technological shift from 3D to 4D printing for translational medicine. [ABSTRACT FROM AUTHOR]