In the rapidly developing e-commerce and innovative logistics sectors, the accuracy and efficiency of package dimension measurement technology are crucial for accurate shipping cost calculation. This study proposes a novel, low-cost 3D dimension measurement system based on deep learning and a dual-camera stereo vision compensation model to improve the reliability of measurement software in computer vision computing. This system uses standard camera equipment and integrates the YOLO algorithm to establish image-based object size estimation. The design concept mimics the human eye's binocular vision mechanism. It utilizes the parallax between the center points of two cameras to estimate distance, thereby optimizing computational performance and recognition speed. A mathematical parallax model is applied to improve depth accuracy. To mitigate computational errors and enhance reliability, a two stage linear regression method combined with a weighted error compensation model is used to correct for systematic deviations between depth estimates and ground truth measurements. Experimental results show that this framework achieves an average error of less than 1 cm and an overall accuracy of 89% for 3D dimension estimation of various logistics packages under uncontrolled conditions, meeting the accuracy requirements for practical logistics billing. These results demonstrate that leveraging flexible software-based stereo vision computing can significantly improve the feasibility and reliability of automated measurement solutions in real-world logistics applications.

Knowledge Graph Completion (KGC) aims to infer missing triples for a given knowledge graph, which can be adopted to various fields ranging from scientific research to real-world applications. Despite the success of numerous KGC methods, the lack of explainability remains a common drawback. However, the explanation of KGC results is crucial in many cases such as providing medical diagnosis and recommending candidates for costly experiments, which could increase the reliability of these techniques to humans. Although a few explainable KGC methods have been proposed, the explainability module is designed for the specific KGC model and cannot be utilized for state-of-the-art KGC models. In this work, we present a post-hoc generic method, namely KGC-Explainer, that can be applied to any KGC model providing triplet scores. KGC-Explainer not only incorporates the input KGC model itself, but also leverages the structural and textual information in knowledge graphs. To demonstrate KGC-Explainer achieving design goals and the superiority of it over other methods, we conduct extensive experiments on two real-world knowledge graphs with different domains and languages, one coming from medical domain in Chinese and another coming from general domain in English. Additionally, we compare the KGC explanation with human explanation, showcasing the practical significance of KGC-Explainer.

Deep learning is developing rapidly, and the emergence of many network architectures has brought significant breakthroughs to training recognition models. Due to the maturity of edge computing technology, we can perform regional image training through distributed nodes, which significantly improves the training model's accuracy while performing transfer learning to achieve better performance. In image processing technology, high-precision recognition of non-luminous images can currently be achieved by modeling, if we replace the visual recognition target with a glowing digital panel, the recognition rate cannot be the same as the static text recognition rate. This article uses Keras to build a convolutional neural networks deep learning model to identify glowing light-emitting diodes (LED) digits, incremental learning to complete transfer learning on edge computing nodes, and an integrated IoT architecture to achieve better recognition results. In the experiment, the verification results obtained from the distributed training nodes were successfully combined to model and retrain the nodes. The proposed distributed learning method can increase the accuracy from 70% to 89%. At the same time, the misclassified images can be retrained by integrating the transfer learning model with the distributed learning results, and the accuracy reaches more than 92%.

The effectiveness and robustness of medical image segmentation algorithms inextricably depends on the quality and quantity of training data. Insufficient data continually restricts model training and affects segmentation accuracy and robustness. We rigorously create a reliable image segmentation model to tackle the challenges presented by constrained medical image datasets. Our method integrates two novel components: the coordinate attention and identity (CAI) module and the Kolmogorov–Arnold and squeeze-and-excitation (KASE) module. The CAI module consists of two branches that enhance feature representation. The initial branch utilizes coordinate attention techniques to detect cross-channel dependency. This offers location-specific and directionally aware information crucial for precisely identifying target areas. The second branch utilizes features produced by convolution as identity mappings. This tackles problems such as vanishing and exploding gradients while maintaining essential features and semantic information. The KASE module comprises two branches. The initial branch improves weight calculations by extracting global context through multiscale squeeze-and-excitation methods. The other branch integrates Kolmogorov–Arnold networks with convolutional operations. This enhances the capacity of the proposed model to learn and represent complex features. These two modules enhance the robustness of the model and improves its generalization across varied input data. Finally, we train multiple state-of-the-art models on multiple datasets using 100%, 20%, 10%, and 5% of the training set in each dataset. The test results show that our model achieves competitive performance despite limited training data.

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.

Chronic Fatigue Syndrome (CFS) is a disorder with complex symptoms among patients. In most cases, CFS sufferers describe severe body weakness, poor sleep and inability to perform their usual work as their primary complaints. Symptoms worsen when the patient attempts to do similar work as tolerated. To prevent the worsening of symptoms, the patients need to be aware of what intensity of work they can manage. In this paper, we propose CFSCare, a hardware and software-based system that uses ML models to measure CFS patients' daily activity and energy expenditure objectively. Through our developed App, CFSCare submits to the user a summary of the comprehensive reports of the user's activity and sends a recommendation to the user on how they can prevent acquiring symptoms of CFS brought about by over-exertion. We use an Android smartphone and wrist sensor (MetamotionC) to monitor their leg and hand activity. We develop ML models based on SVM and DT algorithms to predict particular leg and hand activities. Among the applied ML models, SVM exhibited a brilliant performance with 98% accuracy in predicting leg activities and an average to-fold cross-validation score of 94%. For the hand activities prediction, DT recorded the best accuracy of 96%, and the average score of to cross-validations is also 96%. Since CFS patients can tire with exertion after a small amount of daily activity, CFSCare can playa vital role in preventing the patient from over-exertion through the monitoring features.

Software defect prediction (SDP) is an essential technique for identifying potential defects in software projects. Generally, SDP is mainly divided into two procedures: extracting features from source code and building a classification model using machine learning methods. However, SDP contends with specific limitations. For example, machine learning models require a fixed input size, but the size of each program is mostly inconsistent. Another limitation is that the amount of training data may be too small for machine learning, and it is extremely difficult to handle class imbalance and dataset expansion. In this study, we propose a method called spatial pyramid pooling for defect prediction (SPP-DP) that first converts all the source files into images, each of which will generate multiple images with different lengths and widths, to address the limitations of class imbalance handling and data augmentation. Second, we input these images into a convolutional neural network (CNN) to build a classifier to predict software defects. We added a spatial pyramid pooling layer (SPP-Layer) architecture to the CNN to relax the limitation of the fixed input size. Compared with different deep learning-based techniques on five datasets, the experimental results show that our proposed SPP-DP is effective, as it can balance the dataset and provide better software defect identification ability.

Abstract With the expected rise of patients with osteoporosis-induced fractures, it has become increasingly urgent to design and use specialized health education materials to ease pain and improve bodily functions. This study designed web-based app for pain relief instructions for women with spinal compression fractures. An observational study was conducted at an educational hospital in northern Taiwan between October 2018 and September 2019. Using random assignment based on their presentation order, we divided patients into an experimental (n = 87) and control (n = 84) group. The experimental and control groups received web app healthcare instructions for relieving back pain and regular nursing care instructions, respectively. Taiwanese versions of the Brief Pain Inventory and 36-Item Short Form Health Survey were used to collect data. We collected patients’ pain rating data the day before, one month after, and three months after providing them with the instructions. Pain intensity differed significantly between the two groups, and it interacted with time. Pain impact did not significantly differ between groups; however, it interacted with time. Relieving back pain is a primary concern for those affected by it. This study presents a consistent, easy-to-use instructional healthcare web-based app that may provide knowledge of pain relief and reduce mobility impairments. Trial registration first registration on 18/05/2022, NCT05386212.

Blockchain-based smart legal contract, as a legally binding executable contract, has attracted extensive attentions in trade finance. However, since the contract is deployed on open and transparent blockchain network, all transaction data are publicly visible, which brings to privacy disclosure problem. Aiming at this problem, we introduce an improved architecture of smart legal contract with privacy protection, involving contract development, deployment, and execution. In this architecture, the sensitive data of transaction are declared and protected in the form of contract terms. These terms allow the compiler to link predefined cryptographic algorithms into smart contract programs, and then to generate executable contract code. Furthermore, as predefined cryptographic algorithms, we construct a new dual-mode identity-based broadcast encryption (DM-IBBE) scheme to meet specific-purpose or generic-purpose privacy by using selective encryption mode or exclusive encryption mode, respectively. We proved that our DM-IBBE scheme is semantically secure under the decisional Diffie–Hellman assumption. In addition, our experimental results show that the proposed scheme can satisfy the privacy requirements of transaction, and it is practicable and easy-to-develop for introducing privacy preserving mechanisms into smart legal contract languages.

Through big data analytics and deep learning, users can uncover unseen hidden information from personal data in the cloud and derive health improvements from it - including genomic, microbial, medical history, comprehensive blood analysis chemistry, proteins and metabolites, and daily data from exercise devices and scales. The analysis and integration of this data has the potential to improve health and prevent disease. In the research, a set of tools to collect, compute and analyze data through the middle layer, combined with alliance learning and deep learning technologies to conduct sampling comparison training on medical data, while combining edge computing nodes and data hidden features of alliance learning for data construction and model verification. The outcomes can provide early warning of disease agents through data analysis, early symptom detection, and long-term monitoring before the disease becomes widespread. Daily behavior and health can be improved by supporting the P4 medical model, and by integrating these capabilities, many chronic diseases can be prevented from further devastating patients' health. In the experiments, we propose a framework for designing proxies through system construction, data quantification, description and analysis of patient-specific chronic disease risk, and data learning to validate the model construction.

Public bicycle systems (PBSs) that connect end users’ houses to public mass transportation are typically viewed as the "last-mile" of public transportation. Because of the limited capacity of stations in a PBS, the PBS operator must dispatch bicycles between stations to ensure that there are always bicycles/spaces available for bicycle borrowing/returning. However, because the variances in flows among stations are large, bicycle dispatch is difficult without a precise flow forecasting approach. In this paper, we propose an innovative approach to forecast bicycle flow on the basis of a graph neural network (GNN). Instead of processing the temporal information using RNN, or 1D-CNN, our approach integrates both spatial and temporal information into graphs, and analyzes them using graph convolution. Our approach works well on NYCitibike open dataset in terms of prediction accuracy. From the experiment, our approach shows it capability in accurate forecasting of peak flows and self-adjustment while perceiving abnormal flows caused by sporadic situations.

Computer-based visual recognition technology combined with deep learning enables accurate image matching through interactive, multi-level comparison. Currently, popular search methods such as RNN, Faster RNN, etc. are used in computer vision to compute image information by performing hierarchical comparison of image objects. In today’s machine learning is used to construct training curves to predict corresponding results, but the accuracy rate will cause some data distortion due to overformation. Therefore, to solve the problem of interference is now physical, the abandonment method is developed to extract certain neurons and reduce the number of feature values. In this study, a visual image recognition framework is designed that uses advanced computer technology to mark and compare images, and to mark and eliminate blurred images. The experimental method successfully improves the prediction of accuracy after a judgment error by comparing the results of training with the results of deep learning verification. The accuracy of matrix formation and result prediction can reach 0.9812 without eliminating the image produced by the light-emitting elements. We remove noisy images based on the same sampling information. After re-training and re-predicting, the accuracy can reach 0.9847.

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.

The decentralization mechanism provides manufacturers and distributors with greater customization and flexibility they need through Internet of Things (IoT)-based industrial collaboration systems (IoT-ICS), but it has brought forward security concerns about the shared data-processing tasks and IoT-based access to services and resources. To address them, we propose a practical blockchain solution to achieve decentralized policy management and evaluation on attribute-based access control (ABAC). By offloading the responsibility of ABAC policy administration and decision making to blockchain nodes, a blockchain-based access control framework, called Policychain, is presented to ensure policy with high availability, autonomy, and traceability. To deliver a solid design, we first present a transaction-oriented policy expression scheme with a well-defined syntax and semantics. The scheme can translate ABAC policies into the blockchain transactions with JavaScript object notation (JSON) syntax and script-based logical expression. We further realize a script-driven policy evaluation by extending blockchain inherent scripting instructions to support attribute acquisition of ABAC entities. Furthermore, we propose a policy lifecycle management scheme from policy creation, renovation, to revocation, in which policies are verified by three validation principles at the transaction level. Finally, we provide sophisticated analysis and experiments to show that our framework is secure and practical for decentralized policy management on ABAC in IoT-ICS.

In recent years, advanced smart contract languages (ASCLs) have been proposed to solve the problem of difficult reading, comprehension, and collaboration when writing smart legal contracts among people in different fields. However, this kind of languages are still hard to put into practice due to the lack of an effective conversion method from the ASCLs to executable smart contract programs. Aiming at this problem, we take SPESC as example to explore how to design conversion rules from the contract in it to the target programming language in Solidity, and to propose a three-layer smart contract framework, including advanced smart-contract layer, general smart-contract layer, and executable machine-code layer. These rules provide an approach to convert the definition of SPESC contracting parties into party-contracts on target language, as well as to produce SPESC contract terms into main-contract on target language. Moreover, the proposed framework specifies not only program architecture and storage structure on general smart-contract layer, but also important mechanisms, including personnel management, timing control, exception handling, etc., which can assist programmers to write smart contract programs. Furthermore, taking four SPESC contracts as testing objects, we provide the whole process of converting from SPESC contracts to Solidity programs by the SPESC-Translator, and verify the efficiency and security of the conversion process, including coding, deploying, running, and testing through Ethereum. The instance results show that the conversion rules and the three-layer framework can simplify the writing of smart contracts, standardize the program structure, and help programmers to verify the correctness of the contract programs.

Hypotheses are the most important part of medical research. If we have a good hypothesis, we can design experiments and verify it. Therefore, we use the associations generated by association rule as hypotheses in clinical medicine research. We hope this method can help physicians quickly and correctly find research hypotheses. This experiment was divided into two parts. In the first part, we used the Apriori algorithm to find associations between cancer and other catastrophic illnesses. In the second part, we used these associations as medical-research hypotheses and designed cohort studies to verify them. In this study, we proved that the association-rules method could help clinical physicians quickly and correctly obtain clinical-medicine hypotheses.

Smart contract is a set of digital executable protocols intended to make contractual clauses partially or fully self-executing, self-enforcing, or both. As the second-generation blockchain technology, smart contracts have greatly enriched the functional expression of blockchain to make application development more convenient. All related data of smart contracts, including program codes, intermediate states, and executed results, will be stored in blockchain to ensure that these data are not tampered with. Also, the consensus protocols of blockchain verify the correctness of the running process by executing the smart contract with the same input at all nodes. Therefore, the blockchain’s security mechanism with tamper-proof and traceable makes it possible for smart contracts to be recognized by law.

Aiming at insufficient situation to express and operate assets in smart contracts, in this article we attempt to add a new asset model into smart contract language (such as SPESC) through combing method of asset's expressions and transactions in real-world contracts. Moreover, a translation mechanism can be set up to accomplish a conversion from the asset model to an executable contract program. On this basis, we propose a new language design toward asset-driven specific smart contracts, called TA-SPESC. This language complies with the structure of real-world contracts and supports a formal definition composed of four modules: Party, asset, term, and contract attribute. This asset model on it can be used to define various types of rights (including the right of ownership, use, possession, usufruct, and disposition of assets), as well as five asset operations (including asset registration, deposit, withdrawal, transfer, and cancellation) to effectively support asset transaction. More important, a series of generation rules are proposed to translate the TA-SPESC contract to an executable contract program. Moreover, taking house rental contract as an example, we provide a TA-SPESC instance and its specific description of translation process according to the generation rules, which supports a semiautomatic generation to executable programs. Finally, the Solidity codes derived from TA-SPESC contracts are run and tested, and the experiment and comparison results indicate that TA-SPESC contracts have high abstraction and low complexity, as well as versatility and convenience of asset transaction, which lead to more reliable software with less errors and fewer misunderstanding.

Health care is becoming imperative in normal life because threats from several aspects could have negative influences on people's health. Along with machine learning development, some application software can be used for evaluating health level and provide health care reports for health supervising and management. However, it is rarely being used in common life for health managing and supervising. Meanwhile, most of the health data that are collected or processed can rarely be reused in any of applied systems. This paper is used for applying machine learning in health care management and supervision. The threats from circumstance could be detected by processing static data and dynamic data. Entire system is equipped with wearable equipment, such as cloth and shoes, which can be used to achieve real time supervising threats for the users. Machine learning can be used to achieve accurate calculation for the health data. Health management and threats supervision performance can be improved effectively. Distinguished subjects are combined to achieve new methods and integrated knowledge is used for achieving novel theory to support health care system establishment. Creative computing methods, knowledge combination is used for generating such integrated methods.

According to the 2018 annual report of US Department of Kidney Data System (USRDS), Taiwan's dialysis rate and prevalence rate are the highest in the world due to population aging, diabetes and progresses in cardiovascular care. With the rise of artificial intelligence deep learning in recent years, various analytical software resources have gradually become easier to obtain. At the same time, wearable cyber physical sensors are becoming more and more popular. Measurements on vital signs such as heartbeat, electrocardiogram, and blood oxygenation blood pressure values are ubiquitous. We propose an integrated system that combines dialysis big data deep learning analysis with cross platform physiological sensing. We specifically tackle the early warning of dialysis discomfort such as hypotension, hypertension, cramps, etc., this requires a large amount of data collection, related training, data sources including dialysis treatment process and home physiological data. Although the Dialysis machine is able to produce huge amount of IoT data, the usable data for early warning system training is not as huge due to the limited physician labors devoted for labeling questionable samples. This generally leads to low accuracy for regular CNN training methods. We enhance the AI training performance via a transfer learning technique. The AI training accuracy reaches the value of 99% with the help of transfer learning, while that of an original CNN process on the HD data bears a low 60% accuracy. Given the high prediction accuracy of our AI engine, we are able to integrate the real time measurements from Dialysis machine with wearable devices such as ECG sensors and wrist health watches, and make precision prediction of incoming discomfort during the HD treatments. The ECG signal of the same group patients are also analyzed with the same technique. The same accuracy enhancement are also observed.

With the advent of the big data era, information systems have exhibited some new features, including boundary obfuscation, system virtualization, unstructured and diversification of data types, and low coupling among function and data. These features not only lead to a big difference between big data technology (DT) and information technology (IT), but also promote the upgrading and evolution of network security technology. In response to these changes, in this paper we compare the characteristics between IT era and DT era, and then propose four DT security principles: privacy, integrity, traceability, and controllability, as well as active and dynamic defense strategy based on "propagation prediction, audit prediction, dynamic management and control". We further discuss the security challenges faced by DT and the corresponding assurance strategies. On this basis, the big data security technologies can be divided into four levels: elimination, continuation, improvement, and innovation. These technologies are analyzed, combed and explained according to six categories: access control, identification and authentication, data encryption, data privacy, intrusion prevention, security audit and disaster recovery. The results will support the evolution of security technologies in the DT era, the construction of big data platforms, the designation of security assurance strategies, and security technology choices suitable for big data.

Ischemic stroke has been known to convert to Hemorrhagic stroke with ease. It is crucial to identify high risk patients with relevant diseases. This study uses the Taiwan National Health Insurance database to collate and analyze the relevant diseases leading to Hemorrhagic from Ischemic stroke. We propose several novel machine learning based algorithms and indexing methods for disease transformation prediction and accuracy enhancements. They are a modified swarm algorithm, partial least square(PLS) algorithm and genetic algorithms. From the petition application files accumulated from 2006 to 2013 within the National Health Insurance Database, 8,483 patients with ischemic stroke is collected. Among them, the 1,145 patients with both ischemic stroke and hemorrhagic stroke were screened according to the ICD-9-CM diagnostic code. The disease history of each patient is vectorized and stacked into a matrix for analysis. The PLS/GA process is then applied on the disease history matrix, trying to filter out the candidate diseases leading to such transformation. A total of 750 diseases were found to be associated with ischemic stroke and hemorrhagic stroke through the PLS/GA process. A modified PSO algorithm is developed to further weight these selected diseases. A quartile rule is then applied to filter these weighted diseases to ten most influential diseases which are dizziness, constipation, chronic renal failure, hypertension, diabetes, hyperlipidemia, anxiety, muscle pain, prostatic hypertrophy, etc. In addition to normally known diseases to such conversion in the literature such as hypertension and diabetes, we also discovered more potential diseases. The initial analysis accuracy of our proposed methods reaches 86% on average, while that of the traditional Neural Network algorithm utilizing the same training data was only 49.5%.

This article aims to establish a cryptographic solution to improve security and reliability of the National Institute of Standards and Technology's attribute-based access control (ABAC) model. By breaking down the existing structure of attribute-based encryption, we propose a new cryptographic ABAC (C-ABAC) framework with dynamic policy authorization and real-time attribute credentials. Moreover, a practical C-ABAC construction is proposed to support provable policy decision making and verifiable attribute Tokens among multiple distributed authorities. In this construction, we develop a concrete approach of generating a cryptographic policy from access control markup language. We also prove that attribute Token has existential unforgeability under chosen-attribute and chosen-nonce attacks, and the cryptographic policy is existentially unforgeable under chosen-object attack. In addition, our C-ABAC construction provides semantic security against chosen-plaintext attack with Token and policy queries under the extended general Diffie-Hellman exponent assumption. Finally, we evaluate the performance of the C-ABAC system according to complexity analysis and experimental results. The results show that the C-ABAC system is reliable and easy to implement.

In this paper we focus on a new generation of secure resource sharing platform in a decentralized blockchain environment with flexible and diverse permission management, as well as verifiable and transparent access process. To do it, we present a new Transaction-based Access Control (TBAC) platform which integrates the standard attribute-based access control (ABAC) model and the blockchain system. In this platform, four types of transactions and Bitcoin-type cryptographic scripts are presented to describe the TBAC access control procedure corresponding to subject registration, object escrowing and publication, access request and grant. We also present a cryptosystem associated with TBAC (CryptoTBAC) for ensuring secure attribute-exchanging and decision-making of dynamic policy. We evaluate the security of CryptoTBAC from three aspects: transaction, authorization, and decision-making security.

As smartphones become more popular, applications are being developed with new and innovative ways to solve problems in the day-to-day lives of users. One area of smartphone technology that has been developed in recent years is human activity recognition (HAR). This technology uses various sensors that are built into the smartphone to sense a person's activity in real time. Applications that incorporate HAR can be used to track a person's movements and are very useful in areas such as health care. We use this type of motion sensing technology, specifically, using data collected from the accelerometer sensor. The purpose of this study is to study and estimate the person who may become lost in a crowded area. The application is capable of estimating the movements of people in a crowded area, and whether or not the person is lost in a crowded area based on his/her movements as detected by the smartphone. This will be a great benefit to anyone interested in crowd management strategies. In this paper, we review related literature and research that has given us the basis for our own research. We also detail research on lost person behavior. We looked at the typical movements a person will likely make when he/she is lost and used these movements to indicate lost person behavior. We then evaluate and describe the creation of the application, all of its components, and the testing process.

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 this paper we address the problem of reliability and security in an open-access data sharing system. We propose a new framework, called cryptographic attribute-based access control (CABAC), in consistent with the standard ABAC model. Moreover, two new mechanisms, real-time Tokens and secure policy decision-making, are introduced for ensuring secure attribute authorization and verifiable policy decision-making. More important, we present a practical CABAC system to support adaptability and flexibility using dynamically chosen policy and real-time attribute acquisition. We prove that our CABAC system is provably secure in four aspects: the attribute Tokens are existentially unforgeable against chosen-time and chosen-attribute attacks, respectively; the secure policy is existentially unforgeable against chosen-object attack under eBDH assumption; and our entire system is semantically secure against chosen-plaintext attack with Token and policy queries under eGDHE assumption.

Hybrid systems, especially in the form of cyber physical systems, have become ubiquitous and are playing critical roles in the functioning of society, however their design and implementation are extremely difficulty, especially regarding their dependability.In this paper, we propose a hybrid high level Petri net formalism, hybrid predicate transition nets (HPrTNs), for modeling and analyzing hybrid systems.We discuss some critical concepts and features of HPrTNs.We demonstrate the applicability of HPrTNs through several well-known benchmark hybrid systems and compare our results with other relevant methods.HPrTNs are fully supported in the tool environment PIPE+.

Hemoglobin level detection is necessary for evaluating health condition in the human. In the laboratory setting, it is detected by shining light through a small volume of blood and using a colorimetric electronic particle counting algorithm. This invasive process requires time, blood specimens, laboratory equipment, and facilities. There are also many studies on non-invasive hemoglobin level detection. Existing solutions are expensive and require buying additional devices. In this paper, we present a smartphone-based non-invasive hemoglobin detection method. It uses the video images collected from the fingertip of a person. We hypothesized that there is a significant relation between the fingertip mini-video images and the hemoglobin level by laboratory "gold standard." We also discussed other non-invasive methods and compared with our model. Finally, we described our findings and discussed future works.

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.

BACKGROUND: Few studies have investigated prognostic biomarkers of distant metastases of lung cancer. One of the central difficulties in identifying biomarkers from microarray data is the availability of only a small number of samples, which results overtraining. Recently obtained evidence reveals that epithelial-mesenchymal transition (EMT) of tumor cells causes metastasis, which is detrimental to patients' survival. RESULTS: This work proposes a novel optimization approach to discovering EMT-related prognostic biomarkers to predict the distant metastasis of lung cancer using both microarray and survival data. This weighted objective function maximizes both the accuracy of prediction of distant metastasis and the area between the disease-free survival curves of the non-distant and distant metastases. Seventy-eight patients with lung cancer and a follow-up time of 120 months are used to identify a set of gene markers and an independent cohort of 26 patients is used to evaluate the identified biomarkers. The medical records of the 78 patients show a significant difference between the disease-free survival times of the 37 non-distant- and the 41 distant-metastasis patients. The experimental results thus obtained are as follows. 1) The use of disease-free survival curves can compensate for the shortcoming of insufficient samples and greatly increase the test accuracy by 11.10%; and 2) the support vector machine with a set of 17 transcripts, such as CCL16 and CDKN2AIP, can yield a leave-one-out cross-validation accuracy of 93.59%, a test accuracy of 76.92%, a large disease-free survival area of 74.81%, and a mean survival prediction error of 3.99 months. The identified putative biomarkers are examined using related studies and signaling pathways to reveal the potential effectiveness of the biomarkers in prospective confirmatory studies. CONCLUSIONS: The proposed new optimization approach to identifying prognostic biomarkers by combining multiple sources of data (microarray and survival) can facilitate the accurate selection of biomarkers that are most relevant to the disease while solving the problem of insufficient samples.

Today the quick development of computers and networks not only enriches our everyday life, but also provides us with a convenient living environment. However, hackers are everywhere in the world. Hence, our sensitive data need to be securely protected. Currently, AES and DES as the most widely used block ciphering mechanisms are popularly employed to encrypt important data. But DES was publicly cracked several years ago. On the other hand, due to the fast computer processing speed and processing power, currently no one dare to say that AES is still secure. It may be cracked someday. Therefore, in this paper, we propose a secure encryption method, called the Random Wrapping Encryption Method (RWEM for short), which is developed by employing information of current time as a part of the parameters to wrap up ciphertext in its encryption process. If a file is encrypted at different time points, the content and the length of the wrapped ciphertext file will not be the same. Thus, it is hard for hackers to analyze and crack the ciphertext. We also analyze the security of the DES, AES and RWEM, and explain why the RWEM can effectively protect some specific attacks, and why it is more secure than the DES and AES.

Python is widely used for web programming and GUI development. Due to the dynamic features of Python, Python programs may contain various unlimited errors. Dynamic slicing extracts those statements from a program which affect the variables in a slicing criterion with a particular input. Dynamic slicing of Python programs is essential for program debugging and fault location. In this paper, we propose an approach of dynamic slicing for Python programs which combines static analysis and dynamic tracing of the Python byte code. It precisely handles the dynamic features of Python, such as dynamic typing of variables, heavy usage of first-class objects, and dynamic modifications of classes and instances. Finally, we evaluate our approach on several Python programs. Experimental results show that the whole dynamic slicing for each subject program spends at most about 13 seconds on the average and costs at most 7.58 mb memory space overhead. Furthermore, the average slice ratio of Python source code ranges from 9.26% to 59.42%. According to it, our dynamic slicing approach can be effectively and efficiently performed. To the best of our knowledge, it is the first one of dynamic slicing for Python programs.

The emergent prevalence of childhood and adolescent obesity remains one of the most significant health care challenges facing the United States today. On the other hand, breakthroughs in Human-Robot Interaction (HRI) research and the diminishing cost of personal robots and virtual agents along with the ever-increasing use of smart personal devices, suggests that there is room for harnessing the power of ubiquitous intelligent systems that can work in partnership to solve some of our most difficult challenges in the very near future. In this paper, we present the design and prototype implementation of a collective intelligence approach aimed at employing machine learning algorithms that work in concert to facilitate the personalization of a humanoid robot Health Coach with a focus on childhood obesity intervention through Child-Robot Interactions and other adaptive Ubiquitous Computing (UbiComp) solutions.

Currently, most health care systems used among divisions in medical centers still adopt the Excel file format for a variety of scales statistics, such as the clinical self-care ability scale for Functional Independence Measure. Although people can further analyze Excel files using other statistical analysis software, such as SAS, SPSS, and STATA, they cannot effectively share the archived data in Excel among divisions. We propose to do format conversion on these data and store them in a database. As the collection of Excel files cannot be shared with ease, we plan to use HBase, a non-relational database, to further integrate data. The purpose of this paper is to construct complete import tools and solutions based on HBase to facilitate easy access of data in HBase. Besides, a visual interface is also used to manage HBase to implement user friendly client connection tools for the HBase database.

Software quality properties, normally derived from non-functional requirements, are becoming more important for software. A main reason for software evolution is the unsatisfaction to software quality properties. When improving these properties through software evolution, it is essential to know whether software functions are affected and by how much. This paper proposes an approach to linking the functions with the quality properties of software for evolution via software architecture styles, aiming at contributing to (1) predicting evolution efforts and (2) transforming software for improving its quality.

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.

Building effective test models is critical to the applications of model-based testing. This paper presents a methodology for guiding model-based testing with function nets, which are lightweight high-level Petri nets. High-level Petri nets are traditionally used for modeling, simulation, and verification purposes. In this paper, however, function nets are test models for automated generation of test cases. The proposed methodology has three key features. First, based on an analogy between modeling and programming, it identifies the basic building blocks for composing test models. Second, it provides structured processes for building test models from workflows and from the contracts of the components under test. Third, it provides several techniques for reducing the complexity of test models and thus the number of tests. The methodology has been applied to the function testing and security testing of several industry-strength applications.

Users and systems requirements are fundamental for software development and maintenance. However, for most of existing systems, you may only find design documents without requirement specification. This paper presents an ontology-based reengineering approach to recovering requirements from existing systems. The proposed approach consists of three main parts: ontology development, ontology mapping and derivation of the requirements. Domain ontology is used to model domain specific requirements and program ontology is used to present system structure and behaviour. The algorithm of ontology mapping is developed to match domain ontology and program ontology for requirement recovery. A case study of Point of Sale Terminal (POST) system is used to illustrate the approach. Conclusions are drawn and further research directions are advocated.

With the development of electronics in governments and business, the implementation of these services are increasing demand for servers. Continued expansion of servers represents our need for more space, power, air conditioning, network, human resources and other infrastructure. Regardless of how powerful servers now become, we do not make good use of all resources and strive for the waste. In this paper, the Green Power Management (GPM) is proposed for load balancing for virtual machine management on cloud. It includes three main phrases: (1) supporting green power mechanism, (2) implementing virtual machine resource monitor onto Open Nebula with web-based interface, and (3) integrating a Dynamic Resource Allocation (DRA) and Open Nebula functions as bases instead of traditionally booting physical machines with command mode.

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.

Guest Editorial: Special Section on Service-Oriented Distributed Computing Systems

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.

The research in this paper focuses on an approach to reverse engineering Web-based legacy systems with the integration of model-driven engineering and UML. Three types of link-based models of Web-based legacy systems are presented. Web-based legacy systems are parsed to find judgement conditions of model, and UML diagrams are described based on the modelling rules.

Due to high demands of software applications world wide, software industry has drawn a great attention from information communication technologies (ICT) industry for the past decade. Taiwan has been known as one of the major hardware procurement center for global ICT companies, mainly on the basis of ODM or OEM. More than 10 products are ranked No. 1 in the world wide ICT market share. However, the development of software industry has performed not as well as hardware industry. Based on the solid foundation and great achievements of hardware manufactures, Taiwan has designed some strategies and programs to enhance and promote its software industry development in last 10 years. This position paper will describe some of our observation and suggestions to Taiwan software industry. Some projects and issues related to software maintenance and evolution in Taiwan will be discussed too.

In recent years, the influence of design patterns on software quality has attracted an increasing attention in the area of software engineering, as design patterns encapsulate valuable knowledge to resolve design problems, and more importantly to improve design quality. Basically, a design pattern is composed of an intent description and a solution model. When the problem in the intent is a quality problem, the structure model should provide a solution to improve the relevant quality. In this work we provide an approach to verify if a design pattern is well-designed, i.e. it answers the question of the proposed structural model really resolves the quality problems described in the intent. Our approach is based on a generic object-oriented quality model.

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.

Web services has become popular in modern distributed applications, and the SOAP technology currently is the most used in Web services. Recent middleware research works widely use interception approach for many problem domains, for example fault tolerance, continuous audit {CA), security etc. Instead of providing a portable interceptor as CORBA does, SOAP 1.2 provides an intermediary mechanism. Due to backward compatibility issue, we found intermediary mechanism is not a feasible solution for interception. Furthermore, our use case analysis found that CORBA Portable Interceptor functionality does not fulfill all requirements of continuous audit. This motivates us to develop a new portable interceptor mechanism (PIM) on SOAP for CA. In this paper, we propose a PIM on SOAP to meet the interception requirements for Web services. Our PIM includes portable interceptor management in SOAP engine and portable interceptor interface definitions.

The paper proposes a unified formal model driven approach to software evolution based on both program transformation and model transformation of legacy systems. A formal model definition ensures a consistent interpretation of the legacy system and provides a theoretical foundation for dependable software evolution. The theoretical foundation is based on the construction of a wide spectrum language for reengineering, known as WSL, which enjoys a sound formal semantics. The architecture and working flow of the approach are proposed, and the mappings between WSL and PSL in MDA provide an engaging combination of traditional program transformation and modern model transformation, which shows that the proposed approach is feasible and promising in its domain. A prototype tool is developed to test the approach and a case study is used for experiments with the proposed approach and the prototype tool. Conclusion is drawn based on analysis and further research directions are also discussed

Web services together with service-oriented architectures (SOA) are playing an important role in the future of distributed computing, significantly impacting software development and evolution. With the adoption to Web services technology, more and more existing non-service-oriented software systems turn to be legacy systems. They require a service-oriented reengineering process in order to survive in service-oriented computing environment. If the reengineering goal is to expose the services of a single object or any underlying function-oriented middleware, many problems will arise including semantic mismatches, service granularity issues and state management. Attempting to masquerade software assets from a lower level of abstraction can often cause significant mismatch and exposure problems. In this paper, by using feature analysis, an approach to supporting service-oriented reengineering is presented. Service identification and packaging process are performed and resulted into a service delegation.

One of the major obstacles in reverse engineering legacy object-oriented systems is the identification of aggregation relationships. Most reverse engineering (RE) methods for aggregation relationships are based on the lifetime dependence of an object on another object since many implementations of aggregation relationships result in such dependence. However, research literature shows that lifetime dependence is not really a primary property of the aggregation relationships. A reverse engineering approach is proposed on propagation of operations. To compare the propagation-based method with the lifetime-based method, we apply both methods to ten class libraries, collect their output, and perform statistical analysis to determine the effectiveness of the two methods. The analysis results show that the propagation-based method performs significantly better than the lifetime-based method and by combing both methods the complete aggregation relationships may be reverse engineered in most object-oriented systems.

Ontology-based information retrieval is precise and effective but suffers from the problem of ontology heterogeneity. This paper focuses on the approximate information retrieval approach to solve the heterogeneity problem of both common ontologies as well as fuzzy ontologies on the semantic Web. Approximate information retrieval needs to find approximations of concepts. However, current methods cannot find the best approximations of concepts for common ontologies, and there is still no published work for fuzzy ontologies. This paper firstly proposes a method of approximate information retrieval between common ontologies. It defines multielement least upper bounds and multielement greatest lower bounds, and then simplifies the multielement bounds to remove redundancy. It provides effective algorithms to find the simplified multielement bounds, and get the best approximations of concepts from the bounds. Then for the fuzzy ontologies, the paper defines cut concepts to transform fuzzy concepts into common concepts, and then applies the proposed method for approximate information retrieval between fuzzy ontologies. The improved algorithms for fuzzy ontologies are given to make the method more feasible and effective.

Due to the convenience of pervasive information environment, many people use various computing devices to perform plenty kinds of tasks. In the field of education, there are various applications to facilitate learner, especially for e-learning. However, some computing devices suffer from the limited resources and cannot accept rich information content. Therefore, the information content has to be tailored into different kinds of presentation depending on the types of computing devices. Context sensitivity is an application software system's ability to sense and analyze context from various sources. In this paper, we aim to customize static documents using context-sensitive middleware (CSM) to sense the computing device, and then using the agent-based parser to provide suitable content representation dynamically.

Pervasive computing will be a fertile source of challenging research problems in computer systems for many years to come. The ability to obtain services and information from an environment anywhere at anytime is part of pervasive computing. The problem is that most of existing services and applications are designed for stationary PCs. How to evolve these so called legacy system towards those mobile users in a controlled manner is vital for that pervasive computing can become more widespread. In this paper, we report our efforts on the PerEvo project. After discussing the basic features and challenges of pervasive computing, we present an agent-based Web services evolution approach, which is well suited to building software solutions for pervasive computing, and illustrate our solutions through a booking scenario.

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.

System evolution can be achieved effectively from architectural point of view, and one of the most exciting outcomes of those efforts on software architecture is OMG' Model Driven Architecture (MDA) which aims at a unified framework for system evolution targeting middleware-based modern distributed system. The advance of technologies in software design and implementation, however, cannot completely avoid the need for deep understanding of legacy systems for evolution. We are still stuck with incomplete high abstraction views when evolving the legacy systems. This paper presents an approach to bridging legacy systems to MDA, which has three contributions: a suitable architecture description language for architecture recovery, the relevant abstraction rules and the integration of reverse engineering with MDA.

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.

Web search engines are very useful information service tools in the Internet. The current Web search engines produce search results relating to the search terms and the actual information collected by them. Since the selections of the search results cannot affect the future ones, they may not cover most people's interests. In the paper, feedback information produced by the users' accessing lists is represented by a rough set and can influence the search results. Thus the search engines can provide self-adaptability.

Agent technology is becoming increasingly important because of its generality, flexibility, and modularity. Moreover, design patterns and software architectures are attracting attention in object-oriented software development. The authors propose a framework for an agent system, which is based on N-tier architecture and design patterns. A negotiation agent system is presented to show the feasibility of applying this framework.

Abstract Domain knowledge is the soul of software systems. After decades of software development, domain knowledge has reached a certain degree of saturation. The recovery of domain knowledge from source code is beneficial to many software engineering activities, in particular, software evolution. In the real world, the ambiguous appearance of domain knowledge embedded in source code constitutes the biggest barrier to recovering reliable domain knowledge. In this paper, we introduce an innovative approach to recovering domain knowledge with enhanced reliability from source code. In particular, we divide domain knowledge into interconnected knowledge slices and match these knowledge slices against the source code. Each knowledge slice has its own authenticity evaluation function which takes the belief of the evidence it needs as input and the authenticity of the knowledge slice as output. Moreover, the knowledge slices are arranged to exchange beliefs with each other through interconnections, i.e. concepts, so that a better evaluation of the authenticity of these knowledge slices can be obtained. The decision on acknowledging recovered knowledge slices can therefore be made more easily. Our approach, rooted as it is in cognitive science and social psychology, is also widely applicable to other knowledge recovery tasks. Copyright © 2001 John Wiley & Sons, Ltd.

Recovering domain knowledge from legacy code plays an important role in the new information technology era, which can be of help for program understanding, system evolution and software reuse. Traditional methods for domain knowledge recovery from source code did not sufficiently address the issue of ambiguity handling, in particular, the propagation of ambiguity among multiple domain knowledge slices recovered from source code in the software reverse engineering process. We present a novel approach to recovering unambiguous domain knowledge from legacy code, where isolated ambiguous domain knowledge slices are "fused" together in an iterative ambiguity propagation process and hence the disambiguity of these recovered knowledge slices is increased.

We propose a programming language known as ConC++ which is extended of C++ to support multithread and concurrent programming using concurrent classes and protected classes. A concurrent class has an active function, which executes immediately when an object is instantiated. A protected class encapsulates a group of data and operations on them and it is used to implement synchronization and mutual-exclusion among objects of concurrent classes, with centralized object concurrency control. The approach achieves object autonomy, decreases context switches and deadlocks and improves program performance.

Legacy systems need to be "salvaged" to prolong their life circle. One way for such a salvation is to recover and maintain domain knowledge embedded in legacy code. It is the authors' observation that existing methods or tools for domain knowledge recovery from source code do not provide maintainers with sufficient assistance to: (1) reduce the size of analysable program sections, (2) identify program sections having intensive domain knowledge, and (3) maintain the belief of a network of domain knowledge extracted from source code which can accommodate change of belief coming from a user. The authors introduce techniques which can provide software maintainers with smart assistance for the above-mentioned three issues.

In this paper, a model called object-oriented attribute grammar (OOAG), which combines both compositional and generative techniques, is presented to effectively construct flow-based tools that deal with fine-grained language semantics as well as a mass of graphics-drawing activities. OOAG, which consists of two interrelated parts: a model-view-shape (MVS) class framework and an AG++, an object-oriented extension to traditional AGs, is intended to preserve both advantages introduced by respective OO and AG models, such as rapid prototyping, reusability, extensibility, incrementality, and applicability. So far, a flow-based editor associated with two flow-analyzer prototypes, DU/UD tools and a program slicer, have been implemented using OOAG on the Windows environment. Our flow-based editor can be used to construct programs by specifying the associated flow information in a visual way, while (incremental) flow analyzers incorporated into the editor can help analyze incomplete program fragments to locate and inform the user of possible errors or anomalies during programming.

Most legacy software systems were developed in imperative languages with traditional design approaches. Instead of continually maintaining these legacy systems in their original architecture and design at high cost, reengineering them to new systems with good design and architecture can significantly improve their understandability, reusability and maintainability. Design patterns (DPs) combine successful established design practices and experts' experiences into a set of inter-related components that exhibit known behaviours with better flexible structures. Software development with DPs provides easier understanding and standardization that make system evolution much more effective. In this paper, we use a parallel program generation environment (PPGE) as a case study to demonstrate the reengineering of a traditional software system into a pattern-based software system. An architecture using the dynamic-packing component library (ADPCL) composed of existing well-known design patterns, and a pattern-based reengineering approach for the transformation of systems are proposed. Copyright © 2000 John Wiley & Sons, Ltd.

Software reuse has the potential to improve software quality and productivity. Software reuse covers the whole process of identification, representation, retrieval, adaptation and integration of reusable software components. Although object-oriented software has potentially high reusability, retrieving reusable object-oriented software may be difficult when the reuse library is large and inaccessibly represented. Furthermore, it is hard to check the consistency of component integration due to the lack of formal descriptions of required components. In this paper, we propose a formal approach for component retrieval and integration analysis during the reuse process. During the reuse process, software components are specified with logical predicates. A two-layer specification method is proposed. The class-layer specification is used for component retrieval and the member function-layer specification is used for consistency analysis during component integration. A formal model, predicate transition nets, is used for dynamic integration analysis. We show how to derive predicate transition nets from individual components and integrated components, and how to detect potential inconsistencies by checking the reachability tree of the predicate transition net representing an integrated program. Based on the approach proposed in this paper, a comprehensive tool can be implemented. Copyright © 2000 John Wiley & Sons, Ltd.

Most of the legacy software systems were developed in imperative languages with traditional design approaches. Instead of continually maintaining these legacy systems at high cost, re-engineering them to new systems with good design and architecture can surely improve their understandability, reusability and maintainability. Design patterns (DPs) have integrated the concept of standardization and expert experiences into a set of inter-related components that can function certain behaviors with better flexible structure. The software development with DPs provides easier understanding and standardization that makes the system evolution much more effective. We use a parallel program generation environment (PPGE) as a case study to the re-engineering of a traditional software system into a pattern based software system. An architecture with the Dynamic-Packing Component Library (ADPCL) which is composed of existing well-known design patterns, and a pattern based re-engineering approach for transformation systems are also proposed.

In the past several years, various graphical techniques were proposed to analyze various types of structural errors, including inconsistency (conflict rules), incompleteness (missing rules), redundancy (redundant rules), and circularity (circular depending rules), of rule based systems. We present a special reachability graph technique based on /spl omega/-nets (a special type of low-level Petri net) to detect all of the above types of structural errors. Our new technique is simple, efficient, and can be easily automated. We highlight the unique features of this new approach and demonstrate its application through an example.

The flexibility and rich application frameworks of the Web model make Web applications more prevalent in both Internet and intranet environments. Programmers enjoy various Web application frameworks with support ranging from simple user interactions based on the plain client-server model, to complicated distributed-object computations based on CORBA. The variety gives users the flexibility to decide a proper framework, and leads to demands for new support tools and a testing framework to test and maintain Web applications. This paper presents an architecture containing several supporting tools which enhance traditional software testing architecture to fit common Web application frameworks. The architecture suits current Web models and reuses several software patterns and architectures from traditional testing environments. In addition, a prototype Web application testing environment is constructed for demonstration.

Design with reuse has been accepted as a cost-effective way to software development. Software reuse covers the process of identification, representation, retrieval, adaptation, and integration of reusable software components. In this paper, we propose a semi-formal approach to software reuse. The approach consists of the following major steps: (1) software components are annotated with formal information, (2) the software components are then translated into predicate transition nets, and (3) consistency checking of the reusable and new components is carried out using the reachability analysis technique of predicate transition (PrT) nets. The approach is demonstrated through an example.

This paper presents our reachability tree logic (RTL) and its integration with time Petri nets to specify and verify the temporal behavior of high assurance systems. The specification phase begins with a system modeling to model system requirements into a time Petri net N and construct a reachability tree RT of N. We then use RTL to specify the desired temporal behavior as formula F. The verification phase uses a model-checking algorithm to check whether RT can satisfy F, that is to find firing sequences to satisfy F. If F is not satisfied, we then modify N into N′ and obtain a RT′ of the modified N′. The modification (refinement) continues until the modified RT′ can satisfy F. In addition, we will demonstrate how to reduce the complexity of model-checking by using our RTL-based algorithm. We have implemented a specification and verification toolkit called NCUPN (National Central University Petri Nets toolkit) using Java. NCUPN is now available on the Internet via

This paper presents a model called object-oriented attribute grammar (OOAG) that can be used to construct a toolset for software maintenance. The kernel of OOAG consists of two inter-related parts: a model-view-shape (MVS) application framework and an AG++, an object-oriented extension to traditional AGs. By combining compositional and generative techniques seamlessly, OOAG preserves both advantages introduced by respective OO and AG models, such as rapid prototyping, reusability, extensibility, and incrementality. So far, a toolset prototype consisting of a number of programming and maintenance tools were implemented using OOAG on the Windows environment. The editors developed can be used to construct programs by specifying the associated flow information in explicit (visual) or implicit (textual) ways, while the (incremental) maintenance tools, such as DU/UD tools and a program slicer, can help analyze incomplete program fragments to locate and inform the user of useful information.

CORBA is becoming the most important middleware that supports the object-oriented and client/server paradigm in distributed computing systems. However, the application systems based on CORBA are still scarce. One main reason is that only a few CORBA object services have been developed. To develop a new CORBA application, a programmer should make an effort to design a program with a CORBA interface from scratch. In our previous work (Liang et al., 1997), a re-engineering approach was proposed to convert RPC-based programs to CORBA objects, which successfully speeds up the development of CORBA applications. However, the source code is required in this approach. In many cases, software designers may not get hold of the source code. It will be significant to adapt existing PC application software in binary code mode to the object services under CORBA. Our study is addressing this problem. A graphical parking lot management system, which integrates dBase III and AutoCAD V2.6i under DOS has been implemented to demonstrate the feasibility of our approach.

We propose a Petri net formalism for the verification of rule based systems. Typical structural errors in a rule based system are redundancy, inconsistency, incompleteness, and circularity. Since our verification is based on Petri nets and their incidence matrix, we need to transform rules into a Petri net first, then derive an incidence matrix from the net. In order to allow a rule based system to be immune from the above described structural errors, we have observed that for all columns in the matrix, all positive entries must be above all negative entries; and for all rows in the matrix, all positive entries must be to the right of all negative entries. If this is not the case, the rule based system may commit errors. Based on this concept, we have developed a tool consisting of the following four phases: rule normalization, rule ordering, rule-to-Petri-net transformation, and rule verification. In phase one, we normalize the rules into Horn clauses. We rearrange the ordering of these normalized rules in phase two, then transform the reordered rules into a Petri net and its corresponding incidence matrix in phase three. In phase four, we perform the rule verification based on the incidence matrix.

Discusses the current situation of formal methods and their use in the re-engineering of computing systems, especially real-time systems. Based on the analysis results, a solution which uses a consistent 4-sector Wide Spectrum Language (WSL) is proposed, which presently includes the general architecture and work flow, the structure of the Object-Action Model, the syntax and semantics of the Object-oriented Temporal Agent Model (ObTAM) and the Timed Guarded Command Language (TGCL). A small case study shows an optimistic future for our WSL technique. Further research will aim to build the complete semantic kernel of the WSL and its associated algebraic laws, including transformation rules and abstraction rules.

We propose an approach to reuse-based software development using a formal method. In our approach, each software component is annotated with a set of predicates to formally describe the component and is classified using a faceted scheme. A user may retrieve components from the library using either keywords or predicates. When a component is retrieved, it is checked to determine if it matches the requirements. Then, the component is integrated with the designed system, along its required functionalities. The integrated component/system as transformed into a Predicate/Transition net (PrT net) to perform consistency checking. If there is no inconsistency, the component may be adapted or incorporated directly. Otherwise, the conditions that cause the inconsistency will be revealed. The user may decide to search the library again by modifying the query specification and restart the whole process, or to terminate the search.

The paper presents a mechanism for normalizing fuzzy rules of a rule-based system, transforming the rules into a fuzzy Petri net, and answering queries based on the net with imprecise information. In addition, the authors present an algorithm utilizing the formalism of Petri nets to compute the degree of truth of the resulting answers to the queries. The degree of truth is computed in algebraic form based on the state equation which can be implemented in matrix computation in Petri nets.

Although metrics have been used in the whole process of software engineering with the ostensible purpose of improving the quality of software, little thought appears to have been given to the use of metrics for reverse engineering. Not surprisingly, there are remarkably few metrics tools available for this purpose. The aim of this study was to investigate the provision of metrics suitable for measuring aspects of software for the process of reverse engineering. It should be noted, however, that the use of these metrics will help not only in reverse engineering for software maintenance but also in re-engineering in software evolution

Software reuse covers the whole process of identification, representation, retrieval, adaptation and integration of reusable software components. The utilization of current library techniques is important in such a software process. However, these approaches tend to be retrieval based and offer no formal assistance to software integration in reuse. We propose a semantic interface Predicate/Transition nets based formal method for reuse. In this method, software components are selected from a reuse library and automatically translated and integrated into the net model. Thus, software integration and adaptation problems are converted into problems in the corresponding net model. With this formal method, software reuse problems such as the detection of inconsistencies in component integration can be automatically revealed.

Petri net theory allows a system to be modeled by a Petri net, an abstract formal model of information flow characterised by controls and constraints. Analysis of the Petri net can then reveal important information about the structure and dynamic behaviour of the modeled system, which can be used to evaluate the modeled system and suggest improvements or changes. This feature can be used in software maintenance. We propose a method for developing a Predicate/Transition net (PrT net) for software maintenance, whereby, software components are reverse engineered and annotated with formal predicates, transformed into a PrT net, and analysed on the PrT net. The advantage of the method is that inconsistencies of a program can be detected and revealed precisely.

Software reuse covers the whole process of identification, representation, retrieval, adaptation and integration of reusable software components. The paper discusses the Module Integration and Adaptation Tool for Ada Components (MIATAC) approach. Building a tool to identify reusable components and effectively generate formal specifications for reusable components will make the MIATAC a more effective approach to software reuse. The Maintainer's Assistant (MA) is a reverse engineering tool using formal methods. The integration of these two tools, MA-MIATAC, will offer a new approach to components reuse.

The application of reusable software has the potential to improve software quality and productivity. The utilization of current reuse library techniques is important in such a software process. However these approaches tend to be retrieval based and offer no formal assistance in the adaptation and integration of a retrieved component. As software components become larger their reuse value greatly increases and the ease of adapting and integrating the component decreases. We describe a formal notation which describes the semantics of module interfaces for complex software. We describe the Module Integration and Analysis Tool for Ada Components prototype analysis engine which is used to recognize and analyze our module interface notations and assist developers in the adaptation and integration of large Ada components.>

A reengineering approach to program translation that helps to improve the quality of translated code is described. A detailed discussion of the major components of this approach is presented. The benefits of applying reengineering technologies to existing software systems are mainly from cost savings on reuse and maintenance. For example, reusing existing C codes in new Ada systems with minimal reimplementation or porting effort yields substantial cost savings. Applying reengineering tools to convert languages to Ada automatically has been beneficial. In addition, manually managing and analyzing a software system is very difficult due to the high complexity and large scale of software systems. The reengineering tool reduces the time spent on the labor-intensive task of code searching because it automatically identifies the software components and their dependency relationships. It then enforces good software engineering practices such as information hiding, localization, and abstraction in the new representation.>

An important software design activity is the decomposition of complex systems into conceptually independent modules that cooperate to achieve a desired result.This modularization represents a significant software engineering activity that continues to receive considerable research attention.This paper illustrates how software may be modularized by automatically determining the cohesiveness of modules in the system.Module cohesion is defined to be a quality attribute that seeks to measure the singleness of purpose of a module.We propose a metric that measures the cohesion of individual subprograms of a software system as related to each other.This metric is illustrated with detailed examples and is supported with empirical evidence supporting the viability of the measure.

It is shown how the semantic basis underlying modern system structure constructs (supported in languages such as Ada and C++) can be used to simplify the understanding of software written in conventional imperative languages. The restructuring technique analyzes the input source code system into a hierarchical system structure that exploits information hiding and localization properties. This hierarchical view will help to accurately identify a component's dependencies and visibility relationships with other system subcomponents. Besides obvious understandability and maintainability benefits, this form of restructure offers a convenient framework for translating conventional languages into languages that support modularity, abstract data types, and hierarchical system structure. The current restructuring tool has been successfully applied to an antisubmarine warfare subsystem.>