As the COVID-19 pandemic continues, a growing number of recovered patients report persistent symptoms such as fatigue, muscle weakness, sleep issues, anxiety, and depression, lasting months or even over a year. Severe cases often show significant lung damage and sometimes reduced kidney function. This study examines a dataset from recovered COVID-19 patients, using machine learning to assess the likelihood of developing Post-COVID-19 conditions. We applied several models, including XGBoost, Decision Trees, and Random Forest, to predict outcomes based on data from a specific hospital. Our approach included detailed data preprocessing-filling in missing values, feature engineering, and standardizing data to improve model accuracy and applicability. Results showed the Random Forest model as the most accurate, demonstrating the power of machine learning in making precise predictions from complex health data. Feature importance analysis revealed critical factors predicting Post-COVID-19 conditions, offering vital guidance for healthcare professionals in managing recovered patients.

Falls are consistently the top cause of death among seniors. At a time when the global population is getting older and fewer births. The shortage of nursing staff seriously affects the health care of the elderly. If information and communication technology can be used, automatic detection and identification the elderly fall, we believe it can reduce the injury of the elderly due to falls. This paper proposes a method different from the previous wearable sensing device, which is based on the displacement of human relative positional parameters in the image to identify the occurrence of human fall. We implemented a system based on OpenPose and combined with the deep learning neural network model LSTM with time series, the image recognition is carried out, the human joint parameters of human posture falling and falling in the image are captured, and the identified parameters are simply filtered, and then the filtered parameters are used for model training.

Recently, Deep Learning has been applied to many medical domains, such as medical image analysis, bioinformatic, biochemistry, drug design, and so forth, to improve the performance that is superior to traditional computational approaches; especially in computer-aided drug design. Many AI-driven drug discovery startups have utilized deep learning methodology to achieve the significant improvement of searching candidate compounds, predicting functions, and so forth. Therefore, using AI to facilitate drug design is the trend in the coming future. In this study, a comprehensive review of the current state-of-the-art in Computer-Aided Drug Design using deep learning methods is presented. Meanwhile, the challenges and potential of these methods are also highlighted.

With the flourishing of Internet of Things (IoT) technology, ubiquitous power data can be linked to the Internet and be analyzed for real-time monitoring requirement. Numerous power data would be accumulated to even Terabyte level as the time goes. To approach a real-time power monitoring platform on them, an efficient and novel implementation techniques has been developed and formed to be the kernel material of this thesis. The generic power-data source is provided by the so-called smart meters equipped inside factories located in an enterprise practically. The data collection and storage are handled by the Hadoop subsystem and the data ingestion to Hive data warehouse is conducted by the Spark unit. On the aspect of system verification, under single record query, these software modules: Hive, Spark, and Impala had been tested in terms of query-response efficiency. And for the performance exploration on the statistical query function and data ETL processing. The kernel contributions of this research work can be highlighted by two parts: (1) Multi-layer software modules are adopted to design and implement the real-time power-monitoring platform embedded with some excellent characteristics of high efficiency, high feasibility and low cost. (2) The rudimental experiments are conducted to verify the query-response efficiency, and performance evaluations for the proposed real-time power monitoring platform, which reveals the high feasibility for the target research goals.

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

In recent years, high availability shared storage will become a popular information technology industry development orientation. Currently, information technology industries emphasize to reduce high risk data requirements and improve read and write performance of data storage. Therefore, the main purpose of this work is to improve read and write performance with the best way on Ceph Storage Cluster. In this system, the data is stored on Hadoop Distributed File System (HDFS), and the data stored in-memory virtual distributed store system that mentioned as Alluxio automatically. Then, the data would be processed through Hadoop Map Reduce method and the output would be inserted into Hadoop Distributed File System and Alluxio environment. The first experiment is to use S3 as application program interface that will connect to RADOS Gateway stored data into Object Storage Daemon (OSD). The second experiment is based on the first out experiment would be through Ceph File System (CephFS) connected to Object Storage Daemon directly. The data is saved in Ceph environment more secure than in Alluxio as in-memory storage system because OSD can be used for data backup based on object storage level. We can use S3 browser (GUI) to maintain data like grant access, maintain folders maintenance, create user accounts, move data location etc. The last one, we used Inkscope monitors all system. If there is any problem, system will give warning or error responds to users automatically.

Summary In the past, the tenacious semiconductor problems of operating temperature and power consumption limited the performance growth for single‐core microprocessors. Microprocessor vendors hence adopt the multicore chip organizations with parallel processing because the new technology promises faster and lower power needed. In a short time, this trend floods first the development of CPU, then also the other peripherals like GPU. Modern GPUs are very efficient in manipulating computer graphics, and their highly parallel structure makes them even more effective than general‐purpose CPUs for a range of graphical complex algorithms. However, technology of multicore processor brought revolution and unavoidable collision to the programming personnel. Multicore processor has high performance; however, parallel processing brings not only the opportunity but also a challenge. The issue of efficiency and the way how programmer or compiler parallelizes the software explicitly are the keys that enhance the performance on multicore chip. In this paper, we propose a parallel programming approach using hybrid CUDA, OpenMP, and MPI programming. There would be two verificational experiments presented in the paper. In the first, we would verify the availability and correctness of the auto‐parallel tools, and discuss the performance issues on CPU, GPU, and embedded system. In the second, we would verify how the hybrid programming could surely improve performance. Copyright © 2016 John Wiley & Sons, Ltd.

Among the various steps in the life cycle of software development, system requirement management is an essential but often neglected step. Comprehensive requirement management can not only help developers to work on a system to meet the requirements of a project, but can also play a vital role in the communications among stakeholders. In general, natural languages are often used to describe and record user requirements; however, this results in ambiguity, inconsistency, imprecision and incompleteness. To increase the accuracy of requirement modeling and analysis, it is important to have appropriate management methods and tools such that the requirement engineering process can be supported within the project. In this work, we propose a System Modeling Language (SysML)-based requirement management methodology to assist in the collection and the modeling of user requirements. We also provide a convenient procedure and a prototype tool to model, analyze, validate and verify the recorded system requirements, and consequently to ensure that the system can satisfy users’ requirements.

Modern computer software usually consists of a number of interacting components. Defects pertaining to component interactions may not be visible until the components are integrated. To exercise component interactions, we propose an integration testing approach based on the core concept of contracts (i.e., preconditions and postconditions of component calls in first-order logic). To enable correctness analysis and generation of coverage-based tests (including state coverage, transition or component coverage, and reachability coverage) of a contract-based test model, we transform the test model into an operational model. We have implemented this approach into a framework that has the capacity to generate executable test code in a large collection of programming languages and to run the generated test code in a rich variety of test execution environments. To demonstrate the cost-effectiveness of our approach, we have conducted a series of experiments in controlled environments to measure the performance of test generation and the fault detection capabilities of coverage-based tests. The results have clearly demonstrated that: 1) our approach can quickly generate coverage-based tests when there is enough memory for dealing with the state space of the given test models; 2) reachability coverage is more capable of detecting faults than state coverage and transition coverage; 3) postcondition-based test oracles are more capable of detecting faults than state-based test oracles; and 4) robustness tests, which exercise the situations when the contracts are broken, are important for improving fault detection capability.

This paper focuses on the deep and shallow architecture of multilayer neural networks (MNNs). The demonstration of whether or not an MNN can be replaced by another MNN with fewer layers is equivalent to studying the topological conjugacy of its hidden layers. This paper provides a systematic methodology to indicate when two hidden spaces are topologically conjugated. Furthermore, some criteria are presented for some specific cases.

Along with the growing attention, cloud computing is not only changing the computing paradigm, but also the style of computing in which dynamically scalable and virtualized resources are provided as services over the Internet. Cloud computing introduces a lot of chances, as well as, challenges in the new trend. Taiwan has been well known as a major Information Communication Technology (ICT) manufacturing traditionally. Many Taiwan's hardware products have been top ranked in the global market. Taiwan has realized the trend that Cloud Computing is leading to and has reacted to integrate its resources from the government, industry, academia, and research centers, by initiating strong incentive programs for cloud computing, with an intension to transform an ICT Island into a Cloud Village. In this article, we describe the status of the development of cloud computing technologies and its future challenges for Taiwan.

Today, the multi-core processor has occupied more and more market shares, and the programming personnel also must face the collision brought by the revolution of multi-core processor. Semiconductor scaling limits and associated power and thermal challenges limit performance growth for single-core microprocessors. This reason leads many microprocessor vendors to turn instead to multi-core chip organizations. So programmer or compiler explicitly parallelize the software is the key for enhance the performance on multi-core chip. At the same time, parallel processing is not only the opportunity but also a challenge. The programmer or compiler explicitly parallelize the software is the key for enhance the performance on multi-core chip. In this paper, what we want to know is there any effective way that can reduce our time on rewrite or can automatically parallel the program for multi-processing purpose and do speedup the processing. We discussed some tools that can automatically generate OpenMP directives from serial C/C++ codes, and compare them with each other include normal C/C++ code, and run on general computer and embedded system. Also we compared some tools that are specifically designed to extract the most of data parallelism from C and FORTRAN kernels and translate them into NVIDIA CUDA or OpenCL to know how mush fast after use them.

In general, a design pattern is usually documented in the form of an essay, with descriptions and rough design such as intent, motivation, structure, behavior, applicability and consequence, etc. Even though there are tools supporting pattern application, developers still may misuse patterns since misunderstanding. It may result failures of systems because of inconsistencies or design errors. In fact, the refinement process by applying a design pattern is merely the addition or removal of model elements in structure view. The refinement process for each design pattern is almost constant whenever the same pattern is applied. In this paper, we propose an approach for design pattern application and assisting the design enhancement by model transformation. Furthermore, we demonstrate our approach by a case study on a real-world multi-core embedded system PVE (Parallel Video Encoder), where a design pattern Command Pipeline is designed for the design enhancement.

Multi-core programming is no more a luxury; it is now a necessity, because even embedded processors are becoming multi-core. However, the state-of-the-art techniques such as OpenMP and the Intel Threading Building Block (TBB) library are far from user-friendly due to the tedious work needed in explicitly designing multi-core programs and debugging. At the present days, a solution for above problems will be that to enhance the abstract level of multicore embedded software design. By leveraging on the expertise gained from Verifiable Embedded Real-Time Application Framework (VERTAF), we propose a Multi-Core version of VERTAF, called VERTAF/ Multi-core (VMC in short). VMC is an integrated development environment for multi-core embedded software architecture. Developers would be able to 1. describe their system requirements with SysML by using this environment, 2. model their design with SysML standard notation, 3. automatically apply a pattern structure into their design for a high quality multicore embedded system, 4. generate source code through a well-designed model; 5. map to different hardware architecture as assigned by the model, and 6. finally we can test the code.Using the model driven architecture (MDA) design flow in SysML, we saw a significantly improvement on productivity and quality of a multicore embedded programming over traditional approach.

In this paper, we proposed a low-cost and flexible Virtual Honeynet Security Platform (VHSP) and a design methodology of the Virtual Honeypot Redirect Mechanism (VHRM), which is All-in-One virtual system architecture, design and implement such platform to verify the effectiveness of the architecture and method that designed in this paper. Moreover, the main contribution of this work is not only to propose a more optimum utilization strategies for S/W and H/W resources, but also a research platform of network security with higher flexibility, usability and this conforms to the latest concept of Green IT design as well.

Most requirement documents are written in natural languages and represented in less structured and imprecise formats. Including requirement phase. Artifacts created in phases of software life cycle are required to be modeled and integrated, so the traceability, consistency, and completeness can be ensured. Without proper assistance, extracting and integrating artifacts in requirement documents represented in editor such as MS Word requires a lot of manual effort. Model-driven architecture (MDA) development help user to generating systems from high-level system models and requirements models. In this paper, we will present a model-driven object-oriented requirement editor (MOR Editor) which supports requirement document modeling and model-driven document editing. MOR Editor supports requirement engineers objectize requirement artifacts, link related requirement artifacts, provide reusable requirement template, and create a requirement document model which can integrate with artifacts in analysis, design, and implementation phases. With the support of MOR Editor, the traceability, consistency, completeness, and maintainability of requirement documents can be enhanced. A case study of using MOR Editor will be used to demonstrate the merit of proposed requirement editor.

Most requirement documents were written in ambiguous natural languages which are less formal and imprecise. Without modeling the requirement documents, the knowledge of the requirement is hard to be kept in a way, which can be analyzed and integrated with artifacts in other phases of software life cycle, e.g. UML diagrams in analysis and design phases. Therefore, maintaining the traceability and consistency of requirement documents and software artifacts in other phases is costly and error prone. In this paper, we propose a model-based object-oriented approach to requirement engineering (MORE). Applying modeling and OO technologies to requirement phases, the domain knowledge can be captured in a well-defined model, so the completeness, consistency, traceability and reusability of requirement and its integration with the artifacts of other phases can be cost effectively improved. A case study has shown the promise of our approach.

BACKGROUND: Protein structural data has increased exponentially, such that fast and accurate tools are necessary to access structure similarity search. To improve the search speed, several methods have been designed to reduce three-dimensional protein structures to one-dimensional text strings that are then analyzed by traditional sequence alignment methods; however, the accuracy is usually sacrificed and the speed is still unable to match sequence similarity search tools. Here, we aimed to improve the linear encoding methodology and develop efficient search tools that can rapidly retrieve structural homologs from large protein databases. RESULTS: We propose a new linear encoding method, SARST (Structural similarity search Aided by Ramachandran Sequential Transformation). SARST transforms protein structures into text strings through a Ramachandran map organized by nearest-neighbor clustering and uses a regenerative approach to produce substitution matrices. Then, classical sequence similarity search methods can be applied to the structural similarity search. Its accuracy is similar to Combinatorial Extension (CE) and works over 243,000 times faster, searching 34,000 proteins in 0.34 sec with a 3.2-GHz CPU. SARST provides statistically meaningful expectation values to assess the retrieved information. It has been implemented into a web service and a stand-alone Java program that is able to run on many different platforms. CONCLUSION: As a database search method, SARST can rapidly distinguish high from low similarities and efficiently retrieve homologous structures. It demonstrates that the easily accessible linear encoding methodology has the potential to serve as a foundation for efficient protein structural similarity search tools. These search tools are supposed applicable to automated and high-throughput functional annotations or predictions for the ever increasing number of published protein structures in this post-genomic era.

In this paper, we present an embedded system design which is cost-efficient and which considers performance improvement and power consumption based on the frequency with which instructions are executed. We use the locality of running programs to optimize the use of memory space, system performance, and power consumption; that is, we compress infrequently executed codes to save the use of memory space but compress (encode) frequently executed codes to power consumption and maximize performance. To save the number of memory access times, the frequently executed instructions are encoded as shorter code words and then the continuous code words are packed into a pseudo instruction. Once the decompression engine fetches one pseudo instruction, it can extract multiple instructions to reduce the number of memory access times. In addition, we also propose a design with multiple look-ahead tables that solves the problem which arises due to the reduction in instructions from one-time memory access which occurs because the great amount of frequently executed instructions. From our simulation results, our method with one 256-instruction look-ahead table does not increase the compression ratio, and the ratio of the power consumption can be reduced by about 47.23% than compressing all instructions. With regard to multiple look-ahead table methods, when one 512-instruction look-ahead table is used, the ratio of the power consumption is reduced by 37.25% only, however, when two look-ahead tables that contain 256 instructions in each table are used, the power consumption can be improved by 49.36%. According to the simulation results, our proposed methods based on the frequencies of executed instructions result in low power consumption, performance improvement and reduced memory space.

The processes of software engineering and the industries themselves tend to be fluctuant. There are numerous factors, such as software methodologies, technologies, supporting tools, and process managements, which may significantly affect the strategies and activities of a software development process. We improve the PIE approach and propose an XML-based meta-model for process and agent-based integrated software development environment (PRAISE). PRAISE includes both the external representation in UML and its internal representation in XML, and can be used to support the integration of software development in a global aspect.

Abstract Many disparate software analysis/design methods and tools (called paradigms in this paper) individually offer faster and more efficient software design/evolution, yet they are generally partially incompatible. Therefore, when they are used together, they suffer from a lack of communication and integration. Unanticipated and inadvertent inconsistencies arising from the implicit incompatibilities result in significantly increased costs and errors during software maintenance. To improve both software evolution and maintenance, we propose an XML‐based model that effectively unifies and integrates current software paradigms while helping reduce ripple effects and related software maintenance difficulties. Copyright © 2003 John Wiley & Sons, Ltd.

Software standards are highly recommended because they promise faster and more efficient ways for software development with proven techniques and standard notations. Designers who adopt standards like UML and design patterns to construct models and designs in the processes of development suffer from a lack of communication and integration of various models and designs. Also, the problem of implicit inconsistency caused by making changes to components of the models and designs will significantly increase the cost and error for the process of maintenance. In this paper, an XML-based unified model is proposed to solve the problems and to improve both software development and maintenance through unification and integration.