Let ohm be a Stein space with a compact smooth strongly pseudo-convex boundary. We prove that the boundary is spherical if its Bergman metric over Reg(ohm) is Kahler-Einstein

—Rapid prototyping is an effective and efficient way of requirements validation to avoid introducing errors in the early stage of software development. However, manually developing a prototype of a software system requires additional efforts, which would increase the overall cost of software development. This half-day tutorial introduces attendees to an approach with a CASE tool named RM2PT, which can be used for requirements modeling and analysis in UML and automatically generating MVC prototypes from requirements models. By investigating the executions of use cases in the generated prototypes, the stakeholders can easily check whether the requirements reflect their real needs. Moreover, requirements inconsistency can be automatically detected and further fixed through the provided features of the generated prototype. Participants will be given the chance to use RM2PT with the selected case studies, and learn how RM2PT has been and can be applied to real-world projects.

This study examines the main and interactive relations of stressors and social support with Chinese college students' psychological symptoms (e.g., anxiety, depression) during the COVID-19 pandemic. All the constructs are assessed by self-report in an anonymous survey during the pandemic outbreak. The results show that the number of stressors has a positive relation with psychological symptoms, and social support has a negative relation with psychological symptoms. In addition, social support serves as a buffer against the negative impact of stressors. These findings hold implications for university counseling services during times of acute, large-scale stressors. Specifically, effective screening procedures should be developed to identify students who experience large number of stressors and provide suitable psychological intervention for them.

BACKGROUNDPharmacokinetic evaluation is one of the key processes in drug discovery and development. However, current absorption, distribution, metabolism, and excretion prediction models still have limited accuracy.AIMThis study aims to construct an integrated transfer learning and multitask learning approach for developing quantitative structure-activity relationship models to predict four human pharmacokinetic parameters.METHODSA pharmacokinetic data set included 1104 U.S. FDA approved small molecule drugs. The data set included four human pharmacokinetic parameter subsets (oral bioavailability, plasma protein binding rate, apparent volume of distribution at steady-state, and elimination half-life). The pretrained model was trained on over 30 million bioactivity data entries. An integrated transfer learning and multitask learning approach was established to enhance the model generalization.RESULTSThe pharmacokinetic data set was split into three parts (60:20:20) for training, validation, and testing by the improved maximum dissimilarity algorithm with the representative initial set selection algorithm and the weighted distance function. The multitask learning techniques enhanced the model predictive ability. The integrated transfer learning and multitask learning model demonstrated the best accuracies, because deep neural networks have the general feature extraction ability; transfer learning and multitask learning improve the model generalization.CONCLUSIONSThe integrated transfer learning and multitask learning approach with the improved data set splitting algorithm was first introduced to predict the pharmacokinetic parameters. This method can be further employed in drug discovery and development.

Prototyping is an effective and efficient way of requirement validation to avoid introducing errors in the early stage of software development. However, manually developing a prototype of a software system requires additional efforts, which would increase the overall cost of software development. Based on our proposed approach, we develop RM2PT: a tool for generating prototypes from requirements models automatically. A requirements model consists of a use case diagram, a conceptual class diagram, system sequence diagrams for use cases, and the formal contracts of their system operations in OCL (Object Constraint Language). RM2PT can generate executable MVC (Model View Controller) prototypes from requirements models automatically. We evaluate the tool with four case studies. 93.65% of requirement specifications can be generated to the executable Java source code successfully, and only 6.35% are non-executable for our current provided generation algorithm such as sorting and event-call, which can be implemented by developers manually or invoking the APIs of advanced algorithms in Java library. The tool is efficient that the one second generated prototype of a case study requires approximate nine hours manual implementation by skilled programmers.

Prototyping is an effective and efficient way of requirements validation to avoid introducing errors in the early stage of software development. Our previous work presents a tool RM2PT to automatically generate prototypes from requirements models. The stakeholders can easily check whether the requirements reflect their real needs by investigating the executions of use cases in the generated prototypes. However, the conflict and contradictory of the requirements are hard to be discovered. In this paper, we enhance RM2PT by introducing consistency checking and state observations in the generated prototypes. Requirements inconsistency can be automatically detected and further fixed through carefully analyzing the contracts of system operations and system state observations. We have conducted four case studies with over 50 use cases. The experimental result shows that 107 requirements inconsistency are founded in requirements validations. Overall, the result is satisfiable, and the enhanced RM2PT can be further applied to the software industry for requirements validation. The tool can be downloaded at http://rm2pt.mydreamy.net and a demo video casting its features is at https://youtu.be/Y7GNa57WGfA

Model checking as a computer-assisted verification method is widely used in many fields to verify whether a design model satisfies the requirements specifications of the target system. In practice, it is difficult to design a system without the sophisticated requirements analysis. Unlike other model checking tools, the labeled transition system analyzer (LTSA) not only can specify the property specifications of the target system but also provides a structure diagram to specify the system architecture of the requirements model, which can be further used to design the target system. In this paper, we demonstrate the abilities of LTSA shipped with the classic case study of the steam boiler system. In the requirements analysis, the LTSA can specify the cyber and physical components of the target system and interactions between the components and the safety properties of the target system. In system design, the LTSA can automatically generate a start-up design model as the finite state process from the requirements model, and then a design model can be further accomplished by system architects and developers. Finally, the LTSA can automatically verify whether the design model meets the requirements specifications. Our work demonstrates the potential power of model checking tools can be applied and useful in software engineering for requirements analysis, system design, and verification.

The Unified Modeling Language (UML) is the de facto standard for requirements modeling and analysis in the software industry. However, it lacks the ability of formal analysis and verification. In this paper, we propose a synthetic approach UML-SR that integrates UML with service refinement (SR) to support the formal requirements modeling and analysis as well as formal verification. The UML-SR requirements model contains a use case diagram, the system sequence diagrams of use cases, a conceptual class diagram, and the formal contracts of system interfaces. To make this integration viable, we extend service refinement with the concepts of visibility in UML. With the visibility extension, we are able to formally specify and verify both internal and external interactions of the system. To demonstrate the effectiveness of our proposed approach, we investigate a case study of an Online Shopping System. The results show that a consistent requirements model can be eventually derived through formal refinement and verification. The proposed approach is useful and can be further applied for the requirements modeling and formal verification in the software industry.

Legacy electronic health record systems were not developed with the level of connectivity expected from them nowadays. Therefore, interoperability weakness inherent in the legacy systems can result in poor patient care and waste of financial resources. Simultaneously, healthcare providers are not yet ready to dispose of them. Large hospitals are also less likely to share their data with external care providers due to economic and political reasons. To overcome the barriers in the effective medical data exchange process, we present a novel hybrid cloud called MedShare, dealing with interoperability issues among disconnected but autonomously functioning healthcare providers. The proposed system architecture and its implementation is based upon: 1) custom data extractors to extract legacy medical data from the three hemodialysis centers under consideration; 2) negotiated and converted to a common data model in each of the private cloud of a provider; 3) indexed patient information using the HashMap technique into the public cloud that operates on private clouds, called a hybrid cloud; and 4) a set of services and tools installed as a coherent environment to exchange information smoothly. This paper enables healthcare professionals to appropriately access and securely share a patient’s medical information. MedShare allows the healthcare providers and administrators to maintain the control of their patient data, which is always the primary concern in building a trustworthy environment for exchanging patient information. Medshare effectively addresses primary security and privacy concerns surrounding the deployment of data exchange process by including patient consent and a two-way authorization process.

Extended Linear Duration Invariants (ELDI), an important subset of Duration Calculus, extends well-studied Linear Duration Invariants with logical connectives and the chop modality. It is known that the model checking problem of ELDI is undecidable with both the standard continuous-time and discrete-time semantics [12, 13], but it turns out to be decidable if only bounded execution fragments of timed automata are concerned in the context of the discrete-time semantics [36]. In this paper, we prove that this problem is still decidable in the continuous-time semantics, although it is well-known that model-checking Duration Calculus with the continuous-time semantics is much more complicated than the one with the discrete-time semantics. This is achieved by reduction to the validity of Quantified Linear Real Arithmetic (QLRA). Some examples are provided to illustrate the efficiency of our approach.

Oral disintegrating tablets (ODTs) are a novel dosage form that can be dissolved on the tongue within 3 min or less especially for geriatric and pediatric patients. Current ODT formulation studies usually rely on the personal experience of pharmaceutical experts and trial-and-error in the laboratory, which is inefficient and time-consuming. The aim of current research was to establish the prediction model of ODT formulations with direct compression process by artificial neural network (ANN) and deep neural network (DNN) techniques. 145 formulation data were extracted from Web of Science. All datasets were divided into three parts: training set (105 data), validation set (20) and testing set (20). ANN and DNN were compared for the prediction of the disintegrating time. The accuracy of the ANN model have reached 85.60%, 80.00% and 75.00% on the training set, validation set and testing set respectively, whereas that of the DNN model were 85.60%, 85.00% and 80.00%, respectively. Compared with the ANN, DNN showed the better prediction for ODT formulations. It is the first time that deep neural network with the improved dataset selection algorithm is applied to formulation prediction on small data. The proposed predictive approach could evaluate the critical parameters about quality control of formulation, and guide research and process development. The implementation of this prediction model could effectively reduce drug product development timeline and material usage, and proactively facilitate the development of a robust drug product.

This paper takes up the problem of medical resource sharing through MicroService architecture without compromising patient privacy.To achieve this goal, we suggest refactoring the legacy EHR systems into autonomous MicroServices communicating by the unified techniques such as RESTFul web service.This lets us handle clinical data queries directly and far more efficiently for both internal and external queries.The novelty of the proposed approach lies in avoiding the data de-identification process often used as a means of preserving patient privacy.The implemented toolkit combines software engineering technologies such as Java EE, RESTful web services, JSON Web Tokens to allow exchanging medical data in an unidentifiable XML and JSON format as well as restricting users to the need-to-know principle.Our technique also inhibits retrospective processing of data such as attacks by an adversary on a medical dataset using advanced computational methods to reveal Protected Health Information (PHI).The approach is validated on an endoscopic reporting application based on openEHR and MST standards.From the usability perspective, the approach can be used to query datasets by clinical researchers, governmental or non-governmental organizations in monitoring health care and medical record services to improve quality of care and treatment.

When beginners practice Chinese calligraphy, they often copy from ancient calligraphic works and try to imitate the style as closely as possible. However there are inevitably some characters whose styles are not correctly followed. Thus we are motivated to detect the style consistency of all written characters in one practice. With the styles extracted by using stacked autoencoders of deep neural network model, we discriminate correctly styled and alien styled characters using a trained one-class support vector machine. Thus we can pick out those outliers. The proposed algorithm reaches satisfactory results. The algorithm can also be applied to other image style detection problems.

目的 对智能环保产品的功能定位及其设计方法的分类进行研究.方法 结合实例及用户需求分析,基于现有技术,对智能环保产品进行创新功能设计研究.结论 通过对现有智能环保产品的功能设计进行研究,得出了3种基本功能类型,包括围绕环保大数据进行的智能终端产品及可穿戴产品设计、环保公益类型产品设计、运用新技术和新媒介进行的创新设计;并针对不同的功能类型总结出了产品的基本设计方法;概括了现阶段智能环保产品的设计机会、设计领域、可实现的功能等内容.

Media Directory; Burrelle’s Media Directory; Cabell’s Directories; Compendex (Elsevier Engineering Index); CSA Illumina; Current

We propose an interactive construction method of 3D objects from Chinese ink paintings for the challenge problem of generating the Chinese ink animation. Marching Cube method is the popular method of 3D modeling; however, it has the limitation on constructing the objects with complex shapes by lines in Chinese ink paintings. Based on our method, we develop a software system for constructing 3D objects interactively by the manual input of brush strokes from the Chinese ink paintings. And then the system can generate 3D objects with 2D surface mesh and 3D skinned mesh automatically. Finally, the system renders them with the original ink effect textures of input image. Our experiments show that the approach is suitable for converting 2D painting image into 3D objects. This work would be helpful for the problem of generating 3D animation of Chinese ink painting.

QVTR-XSLT is a tool for design and execution of transformations based on the graphical notation of QVT Relation. In this paper, we present a solution to the "FIXML to Java, C# and C++" case study of the Transformation Tool Contest (TTC) 2014 using the QVTR-XSLT tool.

Reproducing the writing process of ancient handwritten artworks is a popular way to appreciating and learning the expert skills of Chinese calligraphy. This paper presents a system for reappearing the writing processes of calligraphic characters in different styles. In order to convey the accurate brush skill inside a stroke, a calligraphic character is first decomposed into several strokes, then the writing trajectory and footprint data of each stroke are calculated based on the edge and skeleton, which reveal the relations between shape description and writing skills, and finally the character can be rendered in the oriental ink style dynamically along the trajectory using our writing rhythm and brush footprint models. Consequently, the animation of calligraphy writing can be produced with both shape and spirit features conveyed ([see PDF]), and thus provides a visual and relax way to the comprehension of the complicated and difficult techniques in Chinese calligraphy.

Notwithstanding the advancement of service computing in recent years, service composition is still main issue in this field. In this paper, the authors present an integrated framework for semantic service composition using answer set programming. Unlike the AI planning approaches of top-down workflow with nested composition and combining composition procedure into service discovery, this proposed framework integrates designed workflow with nested composition. In addition, the planning is based on interface variables with validation through pre and post conditions. Moreover, a unified implementation of service discovery, selection, composition and validation is achieved by answer set programming. Finally, the framework performance is demonstrated by a travel booking example on QWSDataset.

In model driven architecture (MDA), system requirements are first captured by UML (unified modeling language) use cases with sequence diagrams to describe their intended use and implemented by classes of objected-oriented languages in the subsequent design stages. It is important that the dynamic behavior specified by the sequence diagrams is in full compliance with the implementation classes. This paper proposes an automatic approach and tool support for generating class contracts, which define a precondition and a postcondition for each operation of the class. The former serves as a guard to ensure invocations of the operations respect the semantics introduced by the sequence diagrams, and the latter places the system in a legal state to facilitate the succeeding operation calls. The contracts can be easily mapped to code of an object-oriented language such as Java. Thus, the approach helps to bridge the gap between the requirements and design stages of system development process. We use our model transformation tool to first generate a UML protocol state machine from the sequence diagrams, and then derive the contracts for a controller class. The transformations take into account the concurrency and critical constructs of the respective UML diagrams.