The aim of this study is to identify potential biomarkers for early diagnosis of bladder cancer to improve survival. Bladder cancer is a common cancer disease worldwide, which a large proportion of types is urothelial cells. The purpose of our research is to use bioinformatics to find out the feature genes that cause urothelial carcinoma. In our study, we download the BLCA gene expression profile from the cancer genome atlas (TCGA). According to a screening of differentially expressed analysis, the 5035 Differentially Expressed Genes were selected (DEGs) associating with the urothelial carcinoma to construct gene co-expression networks. With weighted correlation network analysis (WGCNA), the study selected the most key module which had a similar expression for analysis. After that, we obtained hub genes that were highly related to Urothelial Carcinoma (UC) and identified the significant enrichment pathways of genes. Based on Least Absolute Selection and Shrinkage Operator (LASSO) regression model, hub genes that had less influence decaying to zero. Lastly, two feature genes (STIL, GINS1) were screened. To validate the clinical outcomes of the feature genes, different types of data sets were used to analyze the overall survival of the genes. Gene STIL was significantly related to patient prognosis. This research will help in the development of Urothelial Carcinoma, this gene can be directed to explore new treatment strategies or innovative diagnostic methods.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal carcinoma and patients at advanced stage showed poor survival rate. Despite microRNAs (miRNAs) are used as potential biomarkers in many cancers, miRNA biomarkers for predicting the tumor stage of ccRCC are still limitedly identified. Therefore, we proposed a new integrated machine learning (ML) strategy to identify a novel miRNA signature related to tumor stage and prognosis of ccRCC patients using miRNA expression profiles. A multivariate Cox regression model with three hybrid penalties including Least absolute shrinkage and selection operator (Lasso), Adaptive lasso and Elastic net algorithms was used to screen relevant prognostic related miRNAs. The best subset regression (BSR) model was used to identify optimal prognostic model. Five ML algorithms were used to develop stage classification models. The biological significance of the miRNA signature was analyzed by utilizing DIANA-mirPath. RESULTS: A four-miRNA signature associated with survival was identified and the expression of this signature was strongly correlated with high risk patients. The high risk patients had unfavorable overall survival compared with the low risk group (HR = 4.523, P-value = 2.86e-08). Univariate and multivariate analyses confirmed independent and translational value of this predictive model. A combined ML algorithm identified six miRNA signatures for cancer staging prediction. After using the data balancing algorithm SMOTE, the Support Vector Machine (SVM) algorithm achieved the best classification performance (accuracy = 0.923, sensitivity = 0.927, specificity = 0.919, MCC = 0.843) when compared with other classifiers. Furthermore, enrichment analysis indicated that the identified miRNA signature involved in cancer-associated pathways. CONCLUSIONS: A novel miRNA classification model using the identified prognostic and tumor stage associated miRNA signature will be useful for risk and stage stratification for clinical practice, and the identified miRNA signature can provide promising insight to understand the progression mechanism of ccRCC.

Health Information Exchange (HIE) exhibits remarkable benefits for patient care such as improving healthcare quality and expediting coordinated care. The Office of the National Coordinator (ONC) for Health Information Technology is seeking patient-centric HIE designs that shift data ownership from providers to patients. There are multiple barriers to patient-centric HIE in the current system, such as security and privacy concerns, data inconsistency, timely access to the right records across multiple healthcare facilities. After investigating the current workflow of HIE, this paper provides a feasible solution to these challenges by utilizing the unique features of blockchain, a distributed ledger technology which is considered "unhackable". Utilizing the smart contract feature, which is a programmable self-executing protocol running on a blockchain, we developed a blockchain model to protect data security and patients' privacy, ensure data provenance, and provide patients full control of their health records. By personalizing data segmentation and an "allowed list" for clinicians to access their data, this design achieves patient-centric HIE. We conducted a large-scale simulation of this patient-centric HIE process and quantitatively evaluated the model's feasibility, stability, security, and robustness.

An interdisciplinary effort is needed for solving the fake news crisis, because the solutions depend not only on AI, but also on social mechanisms. In this paper, we propose an AI blockchain platform to build a strong collaboration among AI blockchain researchers and news media to advance the research fighting against fake news. This platform will provide journalists with blockchain crowd-sourced and AI validated factual data on emerging news. This platform will gather blockchain traced data and AI tools that can provide pointers to the original data sources, news propagation path, AI analyzed experts to consult on a given topic. This will provide journalists with cheaper and reliable sources of information in the Internet social media age. So that factual-sourced reporting can outpace the spread of fake news on social media which will encourage factual news sources as a way to value and promote truth for society. The technical contributions of this paper are (1) mechanism building the factual news database, (2) mechanism generating the news blockchain supply chain graph, and (3) AI blockchain based crowd sourcing fake news ranking mechanisms (4) AI blockchain platform for trusting news ecosystem. (5) reviewing the state of fake news research from the technology and social aspects, and providing list of key research issues and technical challenges.

We propose and analyze a 3-tier cloud-cloudlet-device hierarchical trust-based service management protocol called IoT-HiTrust for large-scale mobile cloud Internet of Things (IoT) systems. Our mobile cloud hierarchical service management protocol allows an IoT customer to report its service experiences and query its subjective service trust score toward an IoT service provider following a scalable report-and-query design. We conduct a formal scalability analysis along with an ns-3 simulation performance analysis demonstrating that IoT-HiTrust not only achieves scalability without compromising accuracy, convergence, and resiliency properties against malicious attacks but also outperforms contemporary distributed and centralized IoT trust management protocols. We test the feasibility by applying IoT-HiTrust to two case studies: 1) a smart city travel service composition and binding application and 2) an air pollution detection and response application. The results demonstrate that IoT-HiTrust outperforms contemporary distributed and centralized trust-based IoT service management protocols in selecting trustworthy nodes to maximize application performance, while achieving scalability.

The gene BRCA1 is a human tumor suppressor gene found in all humans, and it was reported that around 5-10% of people with breast cancer have a mutation of their BRCA1 genes. In this study, we investigated potential long non-coding RNA biomarkers for BRCA1 mutation carriers breast cancer cases. From differential expression analysis of RNA-Seq data, we identified 6 significant long non-coding genes (p-value < 0.05). There are three long non-coding genes which were reported as potential biomarkers in breast cancer: AC008268.1, AC091013.1 and AL021395.1. Then the other three genes (AC008592.5, AC090204.1, LINC02570) are novel candidates reported in this study.

Cancer is a fatal disease. It is worth noting that the treatment of cancer still lacks effective drugs for cancer resistance. The development of multi-target drugs is an important direction in the future. Human epidermal growth factor receptor (EGFR and HER2) and Heat shock protein 90 (HSP90) have been proven to be useful targets in various cancer cell lines. To develop dual-target drugs for these two proteins may be more effective in cancer treatment. We performed ligand-based QSAR modeling to select potential TCM candidate compounds for HER2/HSP90 inhibition. The results show that cyclokoreanine B, dehydropodophyllotoxin, alloimperatorine, wanpeinine A, zierin, N-demethylnoracronycine, desacetyleupaserrin, dianthramine, gnoscopine, and formononetin might have the potential for HER2/HSP90 inhibition.

MicroRNAs as biomarkers play an important role in the oncogenesis process, including ovarian cancer. The objective of this study is to evaluate the miRNAs overexpression association with survival of ovarian cancer patients. MiRNA expression levels between tumor and normal samples were compared using t-test. Differentially expressed miRNAs were selected (p-value ≤ 0.001) and only 195 up-regulated miRNAs for 565 ovarian cancer samples were further analyzed using multivariate Cox regression and survival random forest. The median survival time for ovarian cancer patient was 33.64 months. The result of survival random forest and multivariate Cox regression showed that high level expression of nine miRNAs were associated with shorten survival of ovarian cancer patients; whereas high level expression of hsa-miR-154* was significantly correlated with a prolonged overall survival ovarian cancer patients. These nine aberrantly overexpressed miRNAs that resulted shorter survival time may play important roles in oncogenesis, growth, and metastasis of ovarian cancer. Hence, these findings may be used as novel prognostic biomarkers and therapeutic targets for ovarian cancer patients.

Gut microbiome plays an important role on our health and disease development. In the recent decade, many research papers reported the correlation between alternations of microbiome pattern and the occurrence/severity of diseases. However, the human microbiome is very complex and divergent between individuals. Little is known about the whole spectrum of healthy human microbiome. Using data from human microbiome project (HMP) database (n= 325), we 1) classify sample by hierarchical clustering; 2) Identification the core taxes of each class and the differential microbe cross classes; 3) examine and compare the within-sample microbial diversity (alpha-diversity) and between-person diversity (beta-diversity); 4) built a SVM-based classifier for stool microbiome classification. The results showed that 1) healthy stool microbiome can be classified into 4 classes; 2) Firmicutes and Bacteroidete are the two dominant phyla, and Bacteroides and Prevotella are the most predominant genera. Alistipes, Oscillibacter and Ruminococcus were the major taxa in certain cases; 3) Classes were differed in their microbial composition and complexity; 4) SVM-based gut microbiome classifier yield a very good classification accuracy, sensitivity and specificity.

Maize is a well-studied crop. It has been used as a model plant for C4 studies of photosynthesis, as its leaves possess the Kranz Structure (KS). Unfortunately, only few studies addressed the use of computational models to describe dry maize. In particular, the mechanism of KS formation remains unclear during leaf development. This study aims to develop a computational model to answer the following two questions for leaf development in dry maze: (1) How Auxin inhibits BDL, and (2) How the MP transcription activates BDL in the seed of dry maize in early stages of embryonic leaves. We first analyze dry maize based on the S-systems model and compare it with two different regulatory networks: (1) Auxin inhibits BODENLOS (BDL), and (2) MONOPTEROS (MP) activates BODENLOS (BDL). Our hypotheses are: (1) Auxin does not inhibit BDL, and (2) MP does not activate BDL. In the second stage, we compare the S-systems parameter estimation method (SPEM) and the engineering method to analyze the two regulatory networks. Our result suggests a general mechanism for studying how the transient accumulation of Auxin activates self-sustaining and how, similar to other genetic switches, it results in unequivocal developmental responses of leaves in dry maize. The MP activates BDL are very important to the Auxin signaling mediated by MP and BDL proteins which are essential for cell-fate specification events in early embryogenesis of maize.

The ECI2 gene encodes a isomerase of mammalian peroxisomes, and one of ECI2 isoforms was found as an important cancer antigen called as hepatocellular carcinoma-associated antigen 64 (HCA64). Recently, it was also found as a gene signature for the prognosis of other cancers such as breast cancer and prostate cancer. High-throughput RNA sequencing has become the state-of-the-art method for measuring the levels of gene expression. This paper studies the expression analysis of breast cancer gene signatures with both Hisat2 and RSEM programs from breast cancer RNA-Seq datasets. The results showed that the transcript of HCA64 was only expressed in only part of breast cancer samples.

This paper provides a vision and proposes mechanisms to transform the blockchain duplicated computing into distributed parallel computing architecture by transforming smart contract which features data driven from the ground up to support moving computing to native data strategy. This new distributed parallel computing architecture can be employed to build a large size of data set from various distributed hosted medical data sets which might consist of personal electronic medical record (EMR) and various medical data. This large medical data set will enable researchers to jump start the deep learning research for medical domain. Distributed data management, distributed data sharing, and distributed learning are the core mechanisms in the new architecture. The required new researches and developments to employ Google federated learning and transfer learning algorithms in this new architecture are discussed. The approach and mechanism enabled by the new architecture is illustrated to build a real world evidence of clinical trial toward personal and precision medicine. Research issues and technical challenges are provided.

We propose a context-aware trust management model called CATrust for service-oriented ad hoc networks such as peer-to-peer and Internet of Things networks wherein a node can be a service requester or a service provider. The novelty of our design lies in the use of logistic regression to dynamically estimate trustworthiness of a service provider based on its service behavior patterns in response to context environment changes. We develop a recommendation filtering mechanism to effectively screen out dishonest recommendations even in extremely hostile environments in which the majority recommenders are dishonest. We demonstrate desirable convergence, accuracy, and resiliency properties of CATrust. We also demonstrate that CATrust outperforms contemporary peer-to-peer and Internet of Things trust models in terms of service trust prediction accuracy against collusion recommendation attacks.

This paper aims to extract significant DNA sequences appearing in both the genomes of human and viruses. To extract the co-occurrences of DNA sequences as long as possible, this study adopts a scalable approach that is based on hadoop mapreduce programming model and can extract maximal repeats; meanwhile compute the class frequency distribution of these repeats. The genomes of human and all of 4,388 viruses available in NCBI are downloaded at 2017/1/14. To take the taxonomy of viruses into consideration for further observation, only the 2,712 viruses that had been named with genus are selected from those 4,388 viruses. In this study, the taxonomic level “genus” is as the units (classes) when comparing viruses and human for experiments. Experimental results show that the longest DNA sequence appearing in both human and viruses extracted in this study is 463 base pair (bp), and that sequence, consisting of tandem repeats as “(CTAACC)n”, appears in the 5th human chromosome and virus “Human herpesvirus 6B”. It may be attractive for virologists to have further research why there exists such a long DNA fragment existed in both human and that virus. Indeed, this study may provide a new direction for genomic sequences comparison across classesthat can provide clues to inspect the existence of the relationship between these DNA maximal repeats (genotypes) with biased class frequency distribution and the features of classes (phenotypes).

This paper proposes a blockchain platform architecture for clinical trial and precision medicine and discusses various design aspects and provides some insights in the technology requirements and challenges. We identify 4 new system architecture components that are required to be built on top of traditional blockchain and discuss their technology challenges in our blockchain platform: (a) a new blockchain based general distributed and parallel computing paradigm component to devise and study parallel computing methodology for big data analytics, (b) blockchain application data management component for data integrity, big data integration, and integrating disparity of medical related data, (c) verifiable anonymous identity management component for identity privacy for both person and Internet of Things (IoT) devices and secure data access to make possible of the patient centric medicine, and (d) trust data sharing management component to enable a trust medical data ecosystem for collaborative research.

We propose and analyze a trust management protocol in service-oriented mobile ad hoc networks (MANETs) populated with service providers and service requesters, and demonstrate the resiliency and convergence properties against bad-mouthing, ballot-stuffing, opportunistic service, and self-promotion attacks. To demonstrate the applicability, we consider a mission-driven service-oriented MANET that must handle dynamically arriving tasks to achieve multiple conflicting objectives. We devise a trust-based heuristic algorithm based on auctioning with local knowledge of node status to solve this node-to-task assignment problem with multiobjective optimization (MOO) requirements. Our trust-based heuristic algorithm has a polynomial runtime complexity, rather than an exponential runtime complexity as in existing work, thus allowing dynamic node-to-task assignment to be performed at runtime. It outperforms a nontrust-based counterpart using blacklisting techniques while performing close to the ideal solution quality with perfect knowledge of node status over a wide range of environmental conditions. We conduct extensive sensitivity analysis of the results with respect to key design parameters and alternative trust protocol designs. We also develop a table-lookup method to apply the best trust protocol parameter settings upon detection of dynamically changing environmental conditions to maximize MOO performance.

Piwi-interacting RNA (piRNA), with 24 to 33 nt, is the largest category of small non-coding RNA (ncRNA) molecules expressed in animal cells. piRNA complexes have been revealed to both epigenetic and post-transcriptional regulation in germ line and somatic tissues. Generally, they are distinct from microRNA (miRNA) in size and lack of sequence conservation. In this study, class frequency distribution identified the consensus patterns of piRNA across 9 species. To analyze the mechanism of these consensus patters, 4 target prediction tools were integrated. Furthermore, the GO-Term and pathway enrichment were approached for the functional annotation of these targets. The results indicated that two maximal conserved patterns of piRNA were identified in 8 species, whereas maximal consensus pattern in all of 9 species was TAGCTCAGTCGGTAGAGCA seemed to regulate many human genes which involved in binding and activity molecular function. Additionally, these target genes are associated with a number of rare diseases. We believe that this study will contribute to a better understanding of the functionality of piRNA cross species conserved sequences.

Stents are meshed tubes used to treat narrowed or weakened arteries. It is observed that the geometrical change created by a deployed stent may influence regional characteristics and blood flow. Restenosis can occur after the stent deployment due to neointimal proliferation. In this paper a new stent design based on a regular stent was proposed to modify the wall shear stress (WSS) of existing stent design. The fluid flow inside the stent region was then compensated to approach a regular level without disturbance of a stent. Simulation results indicated the possibility to reduce the restenosis of current stent designs.

Human microbiota account for 1-3 % total human body mass. The gastro-intestinal tract, especially the gut, is rich in different microorganisms, which play important role in our health and diseases. Understanding our gut microbiome may help us to increase the precision of disease prediction and treatment. Traditional culture-based methods are time-consuming, expensive and incomplete. The 16S rRNA metagenomic approach provided a simple and culture-independent way to get more information of gut microbiota. In this study, we analyzed 30 human stool microbiome samples. The hierarchical clustering method was applied to classify the enterotypes. The results showed that: (1) There is a positive correlation between the original NGS data volume, rRNA read number and microbiota diversity. (2) Some bacterial genera presented dominantly in human gut, so that a sufficient sequencing depth is important to identify the minor microbiota component. (3) Bacteroides, Lachnospiracea_incertae_sedis and Ruminococcus2 are the most frequently presented genera. Bacteroides, Prevotella and Alistipes are the most abundant genera. (4) Clustering result showed that there are four main enterotypes: Parabacteroides, Prevotella, Ruminococcus, and Bacterioides. We can still divide Parabacteroides into 3 subclasses according to the composition of bacteria.

Cardiovascular diseases are known to be a category of diseases related to heart or blood vessels and ranked the top two and three among ten leading causes of death in Taiwan in 2011, respectively. In this study, environmental and outpatient records within Taichung Area are utilized for risk analysis of four specific categories of cardiovascular diseases using deep learning approach. Autoencoder and Softmax are employed for feature extraction and classification. The output of Softmax for each sample is interpreted as the risk of these four specific categories of cardiovascular diseases. Further analysis is done to unveil the trends with respect to the factors of gender, age, region, and month.

A palindrome is a string of the form S = A1A2or S = A1aA2, where A1and A2are substrings of S and the reverse of A2exactly matches with A1. DNA palindromes show direct influence on tumerogenesis due to the formation of DNA palindromes at microRNA genes, which involve in tumor development. Furthermore, RNA palindromes play a crucial role in genomic instability and gene amplification in some human cancers. Therefore, it is important to develop effective approaches to identify and characterize biological approximate palindromes. In this paper, we developed a simple algorithm to find all the exact and approximate palindromes up to k errors (k is specified by the user) for the two different types of RNA sequence data, i.e. mRNA sequences of fusion genes and human microRNAs (miRNAs). We confirmed that the palindromes in RNA sequences are A-U rich. According to the Kolmogorov-Smirnov test, it was found that miRNA has a frequency distribution of palindromes different from the fusion genes' mRNAs. The proposed algorithm is easy to implement, which provides an effective tool for investigating the relationship between palindromes and cancer biology.

PIWI-interacting RNAs are a class of small RNA (sRNA) distinct from microRNA (miRNA) in sequence length and conservation structure. The recently report indicated that human piRNAs not only expressed in germline but also detected in somatic tissues. They interact with piwi-clade Argonauts (Ago), as well as sequences complement with transposons and target genes to effect epigenetic and post-transcriptional regulation. Additionally, piRNAs are involved in DNA methylation and cancer development. Investigation into the mechanism of piRNAs is an important and rapidly growing issue of research. In this study, we constructed an integrated database, piRNAtarget, for studying the functionality of piRNA and its targets. This database includes a variety of information, such as annotations, sequences, parental genes, targets, expression, mutation and methylation profiles in human piRNAs. We believe that piRNAtarget will contribute to a better understanding of the human piRNA functions. piRNAtarget is freely accessible and available at: http://120.108.102.11/~sophia/piRNAtarget.

The construction and understanding of Gene Regulatory Networks (GRNs) are among the hardest tasks faced by systems biology. To infer gene regulatory networks from gene expression data has been a vigorous research area. It aims to constitute an intermediate step from exploratory to gene expression analysis. In recent years, many reverse engineering methods have been proposed. In practice, different model approaches will generate different network structures. Therefore, it is very important for users to assess the performance of these algorithms. We present a comparative study with three different reverse engineering methods, including the S-system Parameter Estimation Method (SPEM), the Graphical Gaussian Model (GGM) and the TimeDelay-ARACNE. Our approach consists of the analysis of real gene expression data with the different methods, and the assessment of algorithmic performances by sensitivity, specificity, precision and F-score.

Cardiovascular disease (CVD) is group of diseases of the heart and blood vessels and a major global cause of death, with more people dying every year from CVDs than from any other cause. Therefore, controlling and preventing CVDs and their complex pathogenesis (i.e., influenced by genetic and lifestyle factors) has gained considerable attention. In this study, we use the LSA algorithm to cluster highly related CVD association genes. The LSA can further explore each functional gene cluster including listing of the consensus terms shared by the genes in the cluster. A comprehensive network biology analysis can integrates clustered genes in the CVD genes with pathway information by building a network of interconnected pathways. Based on LSA, the CVD association genes from the GAD database are divided into 5 clusters (k=5). The gene set enrichment analysis reveals 25 significantly pathways. Further analysis to integrate pathway analysis and to combine miRNA and Drugbank data to gain more insights in the interplay can be useful to suggest drug repositioning.

The upstream of genes are expected to contain many still unknown regulatory regions that can increase or decrease the expression of specific genes. The processes of mining distinctive patterns (region) are to extract maximal repeats (patterns) from the upstream DNA sequences of human genes, and then filter out the patterns whose class frequency distribution can fit in with that is specified by domain experts; the class frequency distribution of one pattern is the frequencies of that pattern appearing in each of classes. The computation of extracting maximal repeats and meanwhile computing their class frequency distribution can be done by a scalable approach based on a previous work via MapReduce programming model. Experimental resources include the DNA sequences extracted from the upstream 5, 000 bp DNA sequences of 49, 267 human coding&non-coding genes. The classes of human genes are divided into four classes as “non-cancer related protein-coding gene”, “oncogene”, “tumor suppressor gene” and “non-coding genes”(RNA). Experimental results show that 17 distinctive patterns selected as core patters whose length is longer than 36 bp and, appear in more than 3, 000 genes and in all of four classes. To have more specific observation, there are 22 distinctive patterns selected that appear in at least 10 genes and whose lengths are greater than 15 bp and, most of all, just happen in two classes, “oncogene” and “tumor suppressor gene”. It is very attractive and expected to extend this approach to mine for another types of distinctive patterns, e.g. biomarkers, via this approach based on class frequency distribution of selected patterns if the targeted resources of genomic sequences, containing “genotypes”, are available and each of these sequences is labeled precisely according to the features, e.g. “phenotypes”, specified by domain experts in the future.

A framework to adjust sitting postures based on musculoskeletal models is presented in this paper. After the analysis of muscles and strength, some "comfortable" postures are specified as candidate postures. An optimization algorithm is then applied to choose from these candidates, according to current conditions of muscles and the durations one needs, and form a sequence of ordered postures for the sitters. The sitters shift the postures dynamically before they feel uncomfortable, therefore they can maintain a seated posture for extended durations. The ergonomists can also benefit from the information of intermittent postures and use it as a guideline for a new chair/desk design.

Different Tremella fuciformis Berk (TFB) cultivars have different morphology and yield potential. For the identification of interspecies, the internal transcript spacer (ITS) sequences were generated by primers set BM36 and BM37 then sequenced. The results showed different bands at 570 bp for Auricularia auricula, 470 bp for Tremella fuciformis and 860 bp for Hypoxylon archeri. Among seven cultivars of Agaricus Blazei or Grifola frondosa which we collected from different area, they show same molecular bands by ITS method, 710 bp for all seven varieties Agaricus Blazei and 630 bp for all seven varieties of Grifola frondosa. The results showed that ITS can be used as an interspecies molecular marker while not for intraspecies. After sequence those bands generated by Tremella fuciformis cultivars, 99% similarity were found, and most of the different are single nucleotide polymorphism (SNP), very few molecular marker can be found by ITS method. A dual-suppression PCR method combine with adaptorligation PCR and suppression PCR were developed by this research to generate the primers set for SSR (simple sequence repeat). By using this method, the SSR can be detected by sequence the PCR products when using the special primers set design by this method. Currently, we have three primers sets can be used as molecular marker for Tremella fuciformis cultivars identification and the results had been confirmed by sequencing. The result shows that BM66 and BM93 primers set can generate SSR sequence: (AG)14for LT1, LT2, LT4, LT6 and SSR sequence: (AG)13for LT7, LT12. The primers set BM67 and BM92 generated SSR sequence: (C)14(T)18for LT1 and LT4, (C)15(T)16for LT2, (C)13(T)19for LT6, (C)14(T)16for LT7 and (C)12(T)19for LT12. The result of primers set BM99 and BM102 showed two groups of the cultivar of Tremella fuciformis used in this research can be distinguished. The LT1, LT2 and LT7 belong to one group by DNA sequence tgt instead of gtctccatttgt which is replace in group LT4, LT6 and LT12. The result shows even it is very tiny different in intraspecies of Tremella fuciformis, but the methods which we used in this research can distinguish those cultivars.

For the purpose of promoting the utilization of T-DNA mutant seeds generated by the Taiwan Rice Insertional Mutagenesis (TRIM) program for academic research, Academia Sinica contracted Asia University to establish a T-DNA Tagged Rice Service Center (TTRSC) to handle the management, conservation and distribution of mutant rice seeds inventory. To also efficiently handle the huge amount of genomic data generated from TRIM project for those mutant seeds, a cloud computing based platform named T-DNA Tagged Rice Service Information System (TTRSIS, http:[email protected]), was built within the Asia Academic Cloud to accomplish this task. Currently, four functional modules had been established in TTRSIS: Align module took in PCR sequence, used BLASTN to search and align with Rice coding sequence (CDS) database to find the insertion point in the Rice genome. Rice gene functional characterization module kept functional characterizations of Rice genome as currently available in academic publications. Then Search module provided TTRSIS users with search options of gene names, gene id, or mutant line numbers, while Physical distribution module provided mutant line rice seed inventory and distribution for academic research. An example of using OsGA20ox1 as gene name to identify is demonstrated. TTRSIS supports researcher to query interested genes located 21 kb upstream or downstream of T-DNA insertion sites, and to rapidly search candidate mutant lines in the inventory of TTRSC. With this platform, TTRSIS offers a convenient approach for researches to use the resources in TTRSC.

Molecular modeling and in silico docking experiments were performed in order to study the mechanism of action of AmpC beta-Lactamase repression via AmpR binding of UDP-MurNAc-L-Ala-D-gamma-Glu-Meso-2,6-Diaminopimelete-D-Ala-D-Ala.

Biomedical imaging is an important technique for preclinical diagnosis, medical research and biological studies. Nowadays, the development of different tools for acquiring images yields an explosion in the amount of images, which are difficult to be analyzed in detail. There is hence an increasing necessity to develop new tools for processing, analyzing and utilizing medical and biological images. In this paper, a high-level programming framework for biomedical image analysis is proposed. Example scenarios are provided to demonstrate its usefulness.

A strategy to gain a better understanding of disease proteins is to consider the interactions of these proteins by making use of the protein-protein interaction (PPI) data. It is known that proteins are composed of multiple functional domains. In this article, domain information is introduced to study two biomedical problems. For the first problem, a one to one domain-domain interaction (DDI) model is proposed to obtain specific sets of DDI for onco-proteins and tumor suppressor proteins respectively. Three specific sets of DDI, i.e. oncoprotein and oncoprotein, tumor suppressor protein and tumor suppressor protein, and oncoprotein and tumor suppressor protein, are derived from their PPI. Cross-validation test is conducted to benchmark the prediction sensitivity, specificity and F1 measure. It is found that the oncoprotein and cancer protein DDI set achieved a 74% and 84% F1 measure respectively. This indicates the feasibility of applying DDI model for cancer protein PPI studies, which can contribute to biomedical study Secondly, it is suggested that PPI, which is mediated by DDI, may be affected due to domain removal through the alternative splicing mechanism. Domain removal effects on liver cancer isoforms' PPI are studied. It is found that certain liver cancer-related isoforms mediated differential PPI. Information are integrated from three pieces of information, i.e. Gene Onotology (GO), gene expression level and PubMed, to provide supporting details for the findings. The Jaccard index is used to rank statistical significant DDI with the above combinations. Finally, web-based platforms have been set up to display the results of the two studied biomedical problems, i.e. http://ppi.bioinfo.asia.edu.tw/TsgOcgppi/ and http://ppi.bioinfo.asia.edu.tw/hccas/search.

In this paper, the movement of bacteria, i.e. E. coli, is simulated based on the run and tumble model. The random walk parameters, such as the speed, tumbling frequency, run duration, and the turn angle between two successive runs were taken from experimental measurements, and use them to simulate the bacteria movement in cases of three different uniform chemical concentration distributions. The motility coefficient is computed to characterize the migration responses. Furthermore, a case of chemical attractant gradient distribution in the environment is designed to validate the run and tumble model. It is found that bacteria move with higher motility coefficient in higher chemical concentrations. Simulation results suggested that bacterial run and tumble model can be used to describe real bacteria movement.

The problem of identifying and counting blood cells within the blood smear is of both theoretical and practical interest. The differential counting of blood cells provides invaluable information to pathologist for diagnosis and treatment of many diseases. In this paper we propose an efficient hierarchical blood cell image identification and classification method based on multi-class support vector machine. In this automated process, segmentation and classification of blood cells are the most important stages. We segment the stained blood cells in digital microscopic images and extract the geometric features for each segment to identify and classify the different types of blood cells. The experimental results are compared with the manual results obtained by the pathologist, and demonstrate the effectiveness of the proposed method.

Mitochondria have been known as power sources for energy transduction in eukaryotic cells. Hence, mitochondrial genomes and proteins have been used widely for phylogenetic analysis. However, nuclear-encoded mitochondrial proteins are considered in this study to construct phylogenetic relationship among species. An intuitive yet effective and efficient approach to solving relative positions of species is proposed. The derived phylogenetic map demonstrates a satisfactory result, and may indicate an alternative way for phylogenetic analysis. A more complete data set is nevertheless a need.

Repetitive sequences of DNA are important to a number of regulatory functions; hence, to find repetitive DNA sequences is of considerable importance and worth to study. The purpose of this paper is to look for all possible repetitive sequences of DNA in a great deal and then to provide the biologists the repetitive sequences to do further work. Our approach first predicts the possible lengths of repetitive sequences based on the Fourier transform, and then proposes a so-called motif-identification algorithm (MIA) to find the repetitive patterns of the corresponding motif lengths. Our method compares favorably against some common motif-finding approaches, such as Spectral Repeat Finder and Gibbs sampler in all cases, and performs very well for the synthetic and real datasets.

Semantic Capability Description Language (SCDL) plays an important role as a common language and provides a generic format for problem formalization. In this paper we extend SCDL to include typical applications in drug discovery and development that include similarity and substructure searches, quantitative structure activity relationship modeling and in silico docking experiments.

The transcriptional regulation of gene expression has been known to be a key mechanism in the functioning of the cell and the gene expression is influenced by the transcriptional regulatory strengths. In this paper we extend the function of a former proposed gene expression analysis tool named Gene Expression Explorer to include the gene regulatory network reconstruction based on microarray data. The regulatory strengths between transcription factors and genes are implemented by a modified network component analysis method where the genes expression relationships between genes vs. tissues and transcription factors vs. tissues are adopted to explore the regulatory strengths between genes and various transcription factors. Visualized presentation of the proposed tool clearly illustrates the regulatory patterns of transcription factors for each related gene. The biologists could benefit from using this tool to analyze the gene regulatory network based on microarray data.

Semantic capability description language (SCDL) plays an important role as a common language and provides a generic format for problem formalization. In this paper we extend SCDL to include dynamic biological systems. A new process description language SPDL (semantic process description language) is presented and several queries as well as their corresponding SPDL descriptions are provided that are relevant to biological research. These include metabolic pathway searches and modeling of enzyme kinetics.

Cognitive informatics (CI) is the transdisciplinary enquiry of cognitive and information sciences that investigates into the internal information processing mechanisms and processes of the brain and natural intelligence, and their engineering applications via an interdisciplinary approach. CI develops a coherent set of fundamental theories and denotational mathematics, which form the foundation for most information and knowledge based science and engineering disciplines such as computer science, cognitive science, neuropsychology, systems science, cybernetics, software engineering, knowledge engineering, and computational intelligence. This paper reviews the central doctrine of CI and its applications. The theoretical framework of CI is described on the architecture of CI and its denotational mathematic means. A set of theories and formal models of CI is presented in order to explore the natural and computational intelligence. A wide range of applications of CI are described in the areas of cognitive computers, cognitive properties of knowledge, simulations of human cognitive behaviors, cognitive complexity of software, autonomous agent systems, and computational intelligence.

This paper presents our design and development of a context-driven content adaptation planner, which dynamically transforms requested Web content into a proper format conforming to receiving contexts (e.g., access condition, network connection, and receiving device). Aiming to establish a semantic foundation for content adaptation, we apply description logics (DLs) to formally define context profiles and requirements and automate content adaptation decision. In addition, the computational overhead caused by content adaptation can be moderately decreased through the reduction of the size of adapted content.

Machine learning deals with the issue of how to build programs that improve their performance at some task through experience. Machine learning algorithms have proven to be of great practical value in a variety of application domains. They are particularly useful for (a) poorly understood problem domains where little knowledge exists for the humans to develop effective algorithms; (b) domains where there are large databases containing valuable implicit regularities to be discovered; or (c) domains where programs must adapt to changing conditions. Not surprisingly, the field of Cyber space turns out to be a fertile ground where many software security problems could be formulated as learning problems and approached in terms of learning algorithms. This paper deals with the subject of applying machine learning in extraction detection. In the paper, we present our research work on design and implementation of an extrusion detection system for information security of big companies. The result shows a potential in real-world applications.

An intrusion detection system (IDS) is a security layer to detect ongoing intrusive activities in computer systems and networks. Current IDS have two main problems: The first problem is that typically so many alarms are generated as to overwhelm the system operator, many of these being false alarms. The second problem is that continuous tuning of the intrusion detection model is required in order to maintain sufficient performance due to the dynamically changing nature of the monitored system. This manual tuning process relies on the system operators to work out the updated tuning solution and to integrate it into the detection model. In this article, we present an automatically tuning intrusion detection system, which controls the number of alarms output to the system operator and tunes the detection model on the fly according to feedback provided by the system operator when false predictions are identified. This system adapts its behavior (i) by throttling the volume of alarms output to the operator in response to the ability of the operator to respond to these alarms, and (ii) by deciding how aggressively the detection model should be tuned based on the accuracy of earlier predictions. We evaluated our system using the KDDCup'99 intrusion detection dataset. Our results show that an adaptive, automatically tuning intrustion detection system will be both practical and efficient.

As a recently emerging distributed computing paradigm, mobile-agent technology attracts great interests because of its salient merits. However, it also brings significant security concerns, among which the security problems between a mobile agent and its platforms are of primary importance. While protecting a platform (platform or host security) can benefit from the security measures in a traditional client-server system, protecting a mobile agent (mobile-agent or code security) has not been met in traditional client-server systems and is a new area emerging with mobile-agent technology. Mobile-agent security is also believed to be the most difficult in the security areas of a mobile-agent system. Several methods are proposed to provide protection or detection mechanisms for mobile-agent security. However, many of them either lack an intuitive formal approach to formally model and analyze the system or lack security consideration for mobility—the most distinct characteristic of a mobile agent. In this paper, we extended the original elementary object system (EOS), which applies object-oriented technology to Petri nets, in several aspects because the original EOS cannot fully support the features of mobile-agent system and security modeling. Based on the extended EOS (EEOS), we developed a formal model for a generic secure mobile-agent system. This model supports not only strong mobility but also secure mobility of a mobile agent. Mutual authentication between a mobile agent and its hosting platform is accomplished in this model. Meanwhile, a security mechanism is presented for the detection of malicious platform attacks to mobile-agent code or execution flow during the mobile-agent execution. Using an intuitively graphical formal method to model, simulate, and analyze a secure mobile-agent system distinguishes this paper from other works on mobile-agent security. This paper also introduces how to translate our EEOS model to a colored Petri net (CPN) mo

One of the main characteristics of open distributed embedded systems is that the involved entities are often very dynamic—different individual entities may join or leave the systems frequently. Therefore, systems built of these dynamic entities must be runtime reconfigurable. In addition, large classes of open embedded systems often have high availability and dependability requirements. However, the openness makes these requirements more difficult to achieve and the system more vulnerable to attacks. This article presents a coordination model, the Actor, Role and Coordinator (ARC) model, that aims to support reconfigurability and fault localization for open distributed embedded software systems. In particular, the actor model is used to model concurrent embedded entities, while the system's reconfigurability and dependability requirements are encapsulated within coordination objects: roles and coordinators, and are achieved through coordination among the actors. Roles, as a key thrust in the ARC model not only represent an abstraction for a set of behaviors shared by a group of actors so that reconfiguration within the roles becomes transparent to entities outside the roles, but also assume coordination responsibilities among the member actors. The article also argues from both analytical and empirical perspectives that with the support of the role, faults can be localized within actors, and actor level reconfiguration becomes transparent to the system.

introduction Share on Introduction: Special issue of the IEEE SUTC'06 Editors: Jeffrey J. P. Tsai View Profile , Mukesh Singhal View Profile Authors Info & Claims ACM Transactions on Autonomous and Adaptive SystemsVolume 2Issue 3September 2007 pp 7–eshttps://doi.org/10.1145/1278460.1278461Online:01 September 2007Publication History 1citation385DownloadsMetricsTotal Citations1Total Downloads385Last 12 Months2Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

An intrusion detection system (IDS) is a security layer used to detect ongoing intrusive activities in information systems. Traditionally, intrusion detection relies on extensive knowledge of security experts, in particular, on their familiarity with the computer system to be protected. To reduce this dependence, various data-mining and machine learning techniques have been deployed for intrusion detection. An IDS is usually working in a dynamically changing environment, which forces continuous tuning of the intrusion detection model, in order to maintain sufficient performance. The manual tuning process required by current systems depends on the system operators in working out the tuning solution and in integrating it into the detection model. In this paper, an automatically tuning IDS (ATIDS) is presented. The proposed system will automatically tune the detection model on-the-fly according to the feedback provided by the system operator when false predictions are encountered. The system is evaluated using the KDDCup'99 intrusion detection dataset. Experimental results show that the system achieves up to 35% improvement in terms of misclassification cost when compared with a system lacking the tuning feature. If only 10% false predictions are used to tune the model, the system still achieves about 30% improvement. Moreover, when tuning is not delayed too long, the system can achieve about 20% improvement, with only 1.3% of the false predictions used to tune the model. The results of the experiments show that a practical system can be built based on ATIDS: system operators can focus on verification of predictions with low confidence, as only those predictions determined to be false will be used to tune the detection model.

This paper presents a software architecture that ensures system dependability through external coordination. Under this architecture, a distributed system is decomposed into two types of entities, i.e., computational components and coordinators. The system's computational functionalities are carried out by computational components, whereas coordinators are to coordinate these computational components to respect the system's non-functional requirements, such as reliability constraints. Coordination in the system is done implicitly through intercepting, rerouting and suppressing communications between components. Additionally, roles are introduced into the system to bridge the connection between components and coordinators. Roles abstract the non-functional behaviors shared by computational components. Thus role-based coordination is more scalable than individual component-based coordination. Additionally, the dynamicity of components is concealed from the coordinators by the roles. A proof of concept implementation is built on top of the Actor Architecture, a middleware for distributed systems based on the Actor model by G. Aghal et al. (1997), The results show that not only such separation is feasible, but also that the architecture allows us to better control the dependability of the system. Finally, the application developed based on the proposed model is more modularized and consequently more reusable

Summary form only given. This article discusses issues, challenges, and future directions in multimedia research, along the following three scale-oriented issues plus the one which is common to them

Formal model and verification techniques can be used to design and analyze many kinds of systems. However, there are few formal models for verification and analysis of sensor networks in the literature. In this paper, we present a graphical simulation system for modeling and analysis of sensor networks. A formal model of sensor networks is proposed based on Space Time Petri Net (STPN). STPN is a formal model language which is extended from time Petri Nets (TPN) and colored Petri nets (CPN). The idea is to add space information to the places of the original TPN to model the space information in sensor networks, such as the locations of sensor nodes. And based on this modeling idea, a set of new concepts are proposed, such as space place, broadcast transition, and dynamic topology. The STPN not only can model the various characteristics of sensor networks, such as temporal and spatial information, but also can simulate the different behavior of sensor networks. A prototype system has been implemented to model and analyze the behavior of sensor networks systems.

In the era of post-genomics, almost all the genes have been sequenced and enormous amount of data have been generated. Hence, to mine useful information from these data is a very important topic. In this paper, we present a software architecture for finding motifs using genetic algorithm (GA). The new approach can find potential motifs in the regions located from the -2000 bp upstream to +1000 bp downstream of transcription start site (TSS). The mutation in the genetic algorithm is performed using position weight matrices to reserve the completely conserved positions. The crossover in the GA is implemented with specially-designed gap penalties to produce an optimal child pattern. We also present a rearrangement method based on position weight matrices to avoid the presence of a very stable local minimum that may be difficult for operators to generate the optimal pattern. The predicted results obtained from our approach are more accurate than that of Gibbs sampler and we spend less computation time than MEME.

Since the problems in bioinformatics are related to massive computing and massive data. In recent years, due to distributed computing is gaining recognition. The task originally requiring high computing power does not only rely on supercomputer. Distributed computing used off-the-shelf PC with high speed network can offer low cost and high performance computing power to handle the task. Therefore, the purpose of this paper is to implement a complete distributed computing platform based on peer-to-peer file sharing technology. The platform integrated scheduling, load balancing, file sharing, maintenance of data integrity, and user-friendly interface etc. functions. Through the platform can assist bioinformaticists in massive computing and massive data problems. Besides, the platform is easier use, more reliable, and more helpful than others for researchers to conduct bioinformatics research.

In the era of post-genomics, almost all the genes have been sequenced and enormous amounts of data have been generated. Hence, to mine useful information from these data is a very important topic. In this paper we propose a new approach for finding potential motifs in the regions located from the -2000 bp upstream to +1000 bp downstream of transcription start site (TSS). This new approach is developed based on the genetic algorithm (GA). The mutation in the GA is performed by using position weight matrices to reserve the completely conserved positions. The crossover is implemented with special-designed gap penalties to produce the optimal child pattern. We also present a rearrangement method based on position weight matrices to avoid the presence of a very stable local minimum, which may make it quite difficult for the other operators to generate the optimal pattern. Our approach shows superior results by comparing with multiple em for motif elicitation (MEME) and Gibbs sampler, which are two popular algorithms for finding motifs.

Mobile agent technology has received a large amount of attention throughout recent years because of its salient merits and promising future. But it also brings significant new security problems. Little work has been done to solve these problems through intuitive formal methods. In this paper, we proposed a new formal model of a secure mobile agent system based on an extended elementary object system (EEOS) which combines object oriented technology with Petri nets. This model has three hierarchical levels: mobile agent platform level, mobile agent level and security mechanism level. We also proposed a security mechanism - synchronous firing security mechanism to detect malicious host attacks on mobile agent code or execution flow. This paper also discusses how to translate our EEOS model to a coloured Petri Net model supported by Design/CPN and presents the simulation-based analysis of our EEOS model.

Varied data mining techniques have been developed for intrusion detection. However, it is unclear which data mining technique is most effective. In this paper, we present our research work in developing a Multi-Class SLIPPER (MC-SLIPPER) system for intrusion detection to learn whether we can get benefit from boosting based learning algorithm. The key idea is to use the available binary SLIPPER as a basic module, which is a rule learner based on confidence-rated boosting. Multiple arbitral strategies based on prediction confidence are proposed to arbitrate results from all binary SLIPPER modules. Our system is evaluated on the KDDCUP'99 intrusion detection dataset. The experimental results show that we get best performance using a 5-7-5 BP neural network; and the performance using other arbitral strategies are better than the winner of the contest does in term of misclassification cost (MC)

System assembly is one of the major issues in engineering complex component-based systems. This is especially true when heterogeneous, COTS and GOTS distributed systems, typical in industrial applications, are involved. The goal of system assembly is not only to make constituent components work together, but also to ensure that the components as a whole behave consistently and guarantee certain end-to-end properties. Despite recent advances, there is a lack of understanding about software composability, as well as theory and techniques for checking and verifying component-based systems. A theory of software system constraints about components, their environment and about system as a whole is the necessary foundation toward solid understanding of the composability of component-based systems. In this paper, we present a systematic approach for constraint specification and constraint propagation in concert with design refinement with a novel technique to ensure consistency between system-wide and component constraints in a design composition process of component-based systems. The consistent constraint propagation is used in our approach to drive progressive verification of the design. It allows us to verify overall design composition without interference of internal details of component designs. Verification is done separately at architectural and component levels without having to compose results of component analyses. A component can be safely replaced with alternative design without re-verifying the overall system composition so long as the replacement conforms to the corresponding interface and component constraint(s).

Clinically, cancer is a complex family of diseases. From the view of molecular biology, cancer is a genetic disease resulting from abnormal gene expression. This alternation of gene expression could be resulting from DNA instability, such as translocation, amplification, deletion or point mutations. A large amplification or deletion of a chromosome region can be easily detected by two methods: loss of heterozygosity (LOH) and comparative genomic hybridization (CGH). The different gene expression pattern can be monitored by high throughput microarray analysis. Enormous data accumulated by practicing these technologies and the data pool is continuing enlarging with an amazing rate. To aid investigators mining useful information in these data deposits, new data storing and analysis tools must be developed. Two value-added databases are constructed to achieve this purpose. They contain information of genes in the unstable regions of cancer cells basing on the data accumulated from LOH and CGH experiments and information of cancer cell gene expression profiles according to microarray analysis, respectively. An automatic system to retrieve interesting gene information, to compare with the known databases, to analyze and predict the protein functions, and to group the genes of the same function will be integrated into the database circuit. An automatic update system will be installed and performed after the setup of the two databases. The system keeps also the probability to modify and to accept new data obtained from any new techniques. Our goal is to help biologists to find the needles in a haystack that is, to find the real cancer-related genes (oncogenes or tumor suppressor genes) for further research purpose.

Security system architecture governs the composition of components in security systems and interactions between them. It plays a central role in the design of software security systems that ensure secure access to distributed resources in networked environment. In particular, the composition of the systems must consistently assure security policies that it is supposed to enforce. However, there is currently no rigorous and systematic way to predict and assure such critical properties in security system design. A systematic approach is introduced to address the problem. We present a methodology for modeling security system architecture and for verifying whether required security constraints are assured by the composition of the components. We introduce the concept of security constraint patterns, which formally specify the generic form of security policies that all implementations of the system architecture must enforce. The analysis of the architecture is driven by the propagation of the global security constraints onto the components in an incremental process. We show that our methodology is both flexible and scalable. It is argued that such a methodology not only ensures the integrity of critical early design decisions, but also provides a framework to guide correct implementations of the design. We demonstrate the methodology through a case study in which we model and analyze the architecture of the Resource Access Decision (RAD) Facility, an OMG standard for application-level authorization service.

In this paper, we present a new compositional verification methodology for efficiently verifying high-assurance properties such as reachability and deadlock freedom of real-time systems. In this methodology, each component of real-time systems is initially specified as a timed automaton and it communicates with other components via synchronous and/or asynchronous communication channels. Then, each component is analyzed by a generation of its state-space graph which is formalized as a new state-space representation model called Multiset Labeled Transition Systems (MLTSs). Afterward, the state spaces of the components are hierarchically composed and simplified through a composition algorithm and a set of condensation rules, respectively, to get a condensed state space of the system. The simplified state spaces preserve equivalence with respect to deadlock and reachable states. Such equivalence is assured by our reduction theories called IOT-failure equivalence and IOT-state equivalence. To show the performance of our methodology, we developed a verification tool RT-IOTA and carried out experiments on some benchmarks such as CSMA/CD protocol, a rail-road crossing, an alternating bit-protocol, etc. Specifically, we look at the time taken to generate the statespace, the size of the state space, and the amount of reduction achieved by our condensation rules. The results demonstrate the strength of our new technique in dealing with the state-explosion problem.

In this paper, we propose a Petri nets 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 nets first, then derive an incidence matrix from the net. In order to let fuzzy rule-based systems detect above the structural errors, we are presenting a Petri-nets-based mechanism. This mechanism consists of three phases: rule normalization, rules transformation, and rule verification. Rules will be first normalized into Horn clauses, then transform the normalized rules into a high-level Petri net, and finally we verify these normalized rules. In addition, we are presenting our approach to simulate the truth conditions which still hold after a transition firing and negation in Petri nets for rule base modeling. In this paper, we refer to fuzzy rules as the rules with certainty factors, the degree of truth is computed in an algebraic form based on state equation which can be implemented in matrix computation in Petri nets. Therefore, the fuzzy reasoning problems can be transformed as the liner equation problems that can be solved in parallel. We have implemented a Petri nets tool to realize the mechanism presented fuzzy rules in this paper.

Machine learning deals with the issue of how to build programs that improve their performance at some task through experience. Machine learning algorithms have proven to be of great practical value in a variety of application domains. Not surprisingly, the field of software engineering turns out to be a fertile ground where many software development and maintenance tasks could be formulated as learning problems and approached in terms of learning algorithms. This paper deals with the subject of applying machine learning methods to so are engineering. In the paper, we first provide the characteristics and applicability of some frequently utilized machine learning algorithms. We then summarize and analyze the existing work and discuss some general issues in this niche area. Finally we offer some guidelines on applying machine learning methods to software engineering tasks.

Many mobile and wireless devices are connecting to the Internet nowadays, among them, mobile phones and PDAs are the most popular ones. Thus, in this paper, we will focus on how to develop embedded software running on the two devices by using design patterns and Java based software components. Notably, some components may be used directly in an embedded software system, whereas most components must be specialized prior to reuse. Developers have to identify the variation points on these components. In order to develop a reusable embedded software system, we will identify several variation points with some variants, and introduce some useful design patterns for implementing variation points. Consequently, we can customize an embedded software system just through attaching variants on corresponding variation point in our system. The design patterns we are using are property container, strategy, decorator, and model-view-controller. The component technology we are using is J2ME. J2ME is a specification focused on the development of mobile applications. It provides a similar environment as standard Java environment. J2ME's components programming includes the Spotlet programming for PDA, and the MIDlet programming for Java phone. In addition, along with the XML, J2ME can also provide the XMIDlet programming for dynamic downloading and execution of XML-based applications for PDA and Java phones.

We present an approach for analysis of security system architecture. Constraint patterns are introduced to formally specify the generic form of security policies that all implementations of the system architecture must enforce. The analysis is driven by incrementally decomposing a system-wide constraint pattern into a set of constraint patterns of constituent components. Since there are potentially many ways to partition a security system, a key element of the analysis is to verify that the component constraint patterns are collectively consistent with the global constraint pattern under the given architecture. A "consistent" component constraint is then used as the basis for analyzing possible designs of the component. We show that our approach is both flexible and scalable, which not only ensures the consistency of critical early design decisions, but also provides a framework to guide correct implementations of the design.

This paper presents a methodology to reduce the high analysis complexity in modeling and verifying high-assurance properties of safety-critical systems. Our new methodology includes a state-space construction mechanism, multiset labeled transition systems, two congruence theorems and a set of condensation rules for the analysis of deadlock states and reachable states. In the new state-space construction mechanism, the state space can hierarchically be generated and simplified for both synchronous and asynchronous processes. In our multiset labeled transition systems, several actions can be performed as an atomic instruction. Our congruence theorems and condensation ules are much more powerful than current compositional-verification techniques since the high interleaved behaviors of asynchronous as well as invisible actions can be eliminated efficiently. Our experimental results on the analysis of the deadlock property show a significant improvement on many systems such as asynchronous-communication models and resource-competition problems.

We present a new net-reduction methodology to facilitate the analysis of real-time systems using Delay Time Petri Nets (DTPNs). Net reduction is one of the most important techniques for reducing the state-explosion problem of Petri nets. However, the application of net reduction to current timed-extensions of Petri nets (such as Merlin's Time PNs) is very limited due to the difficulty faced in the preservation of timing constraints. To overcome this problem, we introduce DTPNs which are inspired by Merlin's (1976) Time PNs, Senac's (1994) Hierarchical Time Stream PNs, and Little's (1991) Timed PNs. We show that DTPNs are much more suitable for net reduction. Then, we present a new set of DTPN reduction rules for the analysis of schedule and deadlock analysis. Our work is distinct from the others since our goal is to analyze real-time systems and the reduction methods we propose preserve both timing properties (schedule) and deadlock. To evaluate our framework, we have implemented an automated analysis tool whose main functions include net reduction and class-graph generation. The experimental results show that our net-reduction methodology leads to a significant contribution to the efficient analysis of real-time systems.

A variety of techniques and tools exist to parallelize software systems on different parallel architectures (SIMD, MIMD). With the advances in high-speed networks, there has been a dramatic increase in the number of client/server applications. A variety of client/server applications are deployed today, ranging from simple telnet sessions to complex electronic commerce transactions. Industry standard protocols, like Secure Socket Layer (SSL), Secure Electronic Transaction (SET), etc., are in use for ensuring privacy and integrity of data, as well as for authenticating the sender and the receiver during message passing. Consequently, a majority of applications using parallel processing techniques are becoming synchronization-centric, i.e., for every message transfer, the sender and receiver must synchronize. However, more effective techniques and tools are needed to automate the clustering of such synchronization-centric applications to extract parallelism. The authors present a new clustering algorithm to facilitate the parallelization of software systems in a multiprocessor environment. The new clustering algorithm achieves traditional clustering objectives (reduction in parallel execution time, communication cost, etc.). Additionally, our approach: 1) reduces the performance degradation caused by synchronizations, and 2) avoids deadlocks during clustering. The effectiveness of our approach is depicted with the help of simulation results.

We present an incremental algorithm for model checking the real-time systems against the requirements specified in the real-time extension of modal mu-calculus. Using this algorithm, we avoid the repeated construction and analysis of the whole state-space during the course of evolution of the system from time to time. We use a finite representation of the system, like most other algorithms on real-time systems. We construct and update a graph (called TSG) that is derived from the region graph and the formula. This allows us to halt the construction of this graph when enough nodes have been explored to determine the truth of the formula. TSG is minimal in the sense of partitioning the infinite state space into regions and it expresses a relation on the set of regions of the partition. We use the structure of the formula to derive this partition. When a change is applied to the timed automaton of the system, we find a new partition from the current partition and the TSG with minimum cost.

Despite the growing importance of multimedia applications, we still know relatively little about how to specify, design, and maintain this class of complex applications in a systematic manner. The concept of software architecture has recently emerged as a way to improve our ability to effectively construct and maintain large-scale complex software systems. Under this new paradigm, software engineers are able to do evolutionary design of complex systems through architecture specification, design rationale capture, architecture validation and verification, and architecture transformation. Several architecture description languages (ADLs), such as Wright, Rapide, UniCon, ACME, etc. have been proposed to support the architecture development under this new software paradigm. Although current ADLs more or less support certain features of object-oriented design approach, few of them are purely based on object-oriented paradigm. In this paper, we present an architecture description language — OOADL (Object-Oriented Architecture Description Language) to facilitate the architecture specification of multimedia software systems. This language takes object-oriented paradigm as its backbone, and provides formal semantics for modeling architectures of software systems. It also aims at other goals such as, support for hierarchical refinement, support for reuse of architecture styles, support for analysis, and support for exception handling. We also introduce the default architecture style which brings the features of extensibility and re-usability into the language. Finally, we use OOADL to construct part of the architecture framework of a multimedia system to illustrate the usage and modeling capability of OOADL.

The concept of knowledge-based software architecture has recently emerged as a new way to improve our ability to effectively construct and maintain complex, large-scale software systems. Under this new paradigm, software engineers are able to do evolutionary design of complex systems through architecture specification, design rationale capture, architecture validation and verification, and architecture transformation. This paper surveys some of the important techniques that have been developed to support these activities. In particular, we are interested in knowledge/requirements acquisition and analysis. We survey some tools that use the knowledge-based approach to solve these problems. We also discuss various software architecture styles, architecture description languages (ADLs) and features of ADLs that help build better software systems. We then compare various ADLs based on these features. The efficient methods that were developed for verification, validation and high assurance of architectures are also discussed. Based on our survey results, we give a basis for comparing the various knowledge-based systems and list these comparisons in the form of a table.

The concept of software architecture has recently emerged as a new way to improve our ability to effectively construct large scale software systems. However, there is no formal architecture specification language available to model and analyze temporal properties of complex real-time systems. In this paper, an object-oriented logic-based architecture specification language for real-time systems is discussed. Representation of the temporal properties and timing constraints, and their integration with the language to model real-time concurrent systems is given. Architecture based specification languages enable the construction of large system architectures and provide a means of testing and validation. In general, checking the timing constraints of real-time systems is done by applying model checking to the constraint expressed as a formula in temporal logic. The complexity of such a formal method depends on the size of the representation of the system. It is possible that this size could increase exponentially when the system consists of several concurrently executing real-time processes. This means that the complexity of the algorithm will be exponential in the number of processes of the system and thus the size of the system becomes a limiting factor. Such a problem has been defined in the literature as "state explosion problem". We propose a method of incremental verification of architectural specifications for real-time systems. The method has a lower complexity in a sense that it does not work on the whole state space, but only on a subset of it that is relevant to the property to be verified.

In spite of advances in various transformation systems the transformation of a nonmonotonic-logic-based requirements specification into a procedural (imperative) language program has not been investigated. This paper presents a logic-based transformation system that can transform a nonmonotonic-logic-based specification, the Frame-and-Rule Oriented Requirement Specification Language (FRORL), into procedural language programs. We discuss how to handle nonmonotonic inheritance in FRORL and then establish a matrix-based data flow and dependency analysis mechanism to find all the possible data transformation paths in a logic-based specification. Using a newly developed algorithm, we can adjust the execution sequence of a logic-based specification so that the functions included in the logic-based specification can be represented by a sequential procedural language program.

The state-explosion problem of formal verification has obstructed its application to large-scale software systems. In this article, we introduce a set of new condensation theories: IOT-failure equivalence, IOT-state equivalence, and firing-dependence theory to cope with this problem. Our condensation theories are much weaker than current theories used for the compositional verification of Petri nets. More significantly, our new condensation theories can eliminate the interleaved behaviors caused by asynchronously sending actions. Therefore, our technique provides a much more powerful means for the compositional verification of asynchronous processes. Our technique can efficiently analyze several state-based properties: boundedness, reachable markings, reachable submarkings, and deadlock states. Based on the notion of our new theories, we develop a set of condensation rules for efficient verification of large-scale software systems. The experimental results show a significant improvement in the analysis large-scale concurrent systems.

Timing analysis is essential to the development of valid computer systems especially for a distributed real-time system. We present both static and dynamic analysis procedures to verify timing properties of distributed real-time systems using timing constraint Petri nets (TCPNs). With both derived static information and collected dynamic data, we can locate a faulty task in a distributed real-time system using a timing behavior cross reference task. After a faulty task has been pinpointed the causes of a timing constraint violation in a faulty task can be identified.

Based on system execution traces, this paper presents a dynamic approach for visualizing and debugging timing constraint violations occurring in distributed real-time systems. The system execution traces used for visualization and debugging are collected during the execution of a target program in such a way that its run-time behavior is not interfered with. This is made possible by our non-interference distributed real-time monitoring system which is capable of collecting system’s run-time traces by monitoring and fetching the data passing through the internal buses of a target system. After the run-time data has been collected, the visualization and debugging activities then proceeded. The timing behavior of a target program is visualized as two graphs—the Colored Process Interaction Graph (CPIG) and the Dedicated Colored Process Interaction Graph (DCPIG). The CPIG depicts the timing behavior of a target program by graphically representing interprocess relationships during their communication and synchronization. The DCPIG can reduce visualization and debugging complexity by focusing on the portion of a target program which has direct or indirect correspondence with an imposed timing constraint. With the help of the CPIG and the DCPIG, a timing analysis method is used for computing the system-related timing statistics and analyzing the causes of timing constraint violations. A visualization and debugging system, called VDS, has been implemented using OpenWindows on Sun-4’s/UNIX workstations.

A general approach to test case generation relying on a novel testing method is presented. We also evaluate theoretically how generated test cases will contribute to, for example, the verification criterion, when attempting to establish the correctness of a program. Our testing methods have much smaller complexity when applied to well structured programs of at least moderate size, and have better conformance, as compared to existing data flow coverage and control flow coverage oriented testing methods.

In software engineering, requirements analysis is a knowledge intensive task: and it requires an expert to understand what the clients need. We introduce a method which contains different AI techniques to perform this task, and a knowledge-based requirements analysis system, RAKES is presented to explain our approach. With RAKES, not only the ordinary functional requirements are collected, but also the non-traditional information. Nonfunctional requirements include the quality of operations or the background information for constructing the requirements which are gathered through a knowledge-based support. Different kinds of information collected are stored and organized in a knowledge base and can be used as the source of the user input in the latter phases of software development. Algorithms and procedures have been developed for constructing the interface language, organizing the knowledge base, and applying the knowledge base to different tasks. RAKES is integrated to the FRORL architecture to offer a systematic way toward requirements analysis, specification production, prototype generation, specification debugging, and code transformation.

We present timing constraint Petri nets (or TCPN's for short) and describe how to use them to model a real-time system specification and determine whether the specification is schedulable with respect to imposed timing constraints. The strength of TCPN's over other time-related Petri nets is in the modeling and analysis of conflict structures. Schedulability analysis is conducted in three steps: specification modeling, reachability simulation, and timing analysis. First, we model a real-time system by transforming its system specification along with its imposed timing constraints into a TCPN; we call this net N/sub s/. Then we simulate the reachability of N/sub s/ to verify whether a marking, M/sub n/, is reachable from an initial marking, M/sub o/. It is important to note that a reachable marking in Petri nets is not necessarily reachable in TCPN's due to the imposed timing constraints, Therefore, in the timing analysis step, a reachable marking M/sub n/, found in the reachability simulation step is analyzed to verify whether M/sub n/, is reachable with the timing constraints. M/sub n/ is said to be reachable in the TCPN's if and only if we can find at least one firing sequence /spl sigma/ so that all transitions in /spl sigma/ are strongly schedulable with respect to M/sub o/ under the timing constraints. If such M/sub n/ can be found, then we can assert that the specification is schedulable under the imposed timing constraints, otherwise the system specification needs to be modified or the timing constraints need to be relaxed. We also present a synthesis method for determining the best approximation of the earliest fire beginning time (EFBT) and the latest fire ending time (LFET) of each strongly schedulable transition.>

Current requirements specifications tend to focus more on the functional aspect than the nonfunctional side of a product. Many techniques and tools have been developed to specify and evaluate functional requirements, but few of them are equipped to deal with nonfunctional requirements. In this paper we extend the formal requirements specification language, FRORL, to model nonfunctional requirements and show how these nonfunctional requirements are related to the functional requirements. We also introduce a parallel evaluation technique to evaluate the functional requirements model by satisfying the nonfunctional requirements associated to it. By examining the result from the functional model, we can see how the nonfunctional requirements are satisfied, so that we can adjust and modify the nonfunctional requirements.>

Safety-critical systems are not only difficult to build, but also difficult to debug because they often have strict timing constraints and non-deterministic behavior. A correct and precise specification reduces the effort spent in testing and debugging the implemented system. This paper presents techniques of specification debugging and issues related to it. We introduce an approach to the debugging of a specification in FRORL (Frame and Rule Oriented Requirements Language), which supports non-determinism and non-monotonicity in a system. The approach aids the user in detecting and correcting the possible faults which can arise not only when writing the specification, but also after the verification of the specification.

Nonmonotonic logic is intended to apply specifically to situations where the initial information is incomplete. Using nonmonotonic reasoning procedures we shall be able to jump to conclusions, but withdraw them later when we gain additional information. A number of nonmonotonic logics have been introduced and widely discussed. Nonmonotonic logics tend to be introduced proof theoretically, and little attention is paid to their semantic characteristics or their computational tractability. We address both of these issues by presenting a nonmonotonic logic for the Herbrand subset of first-order predicate logic. This nonmonotonic logic is shown to be both sound and complete. Theories formulated in this logic can be executed in logic programming fashion.>

A framework for developing autonomous decentralized systems (ADSs) using a frame-and-rule oriented requirements language (FRORL), is presented. FRORL is a logic-based requirements specification language and serves multiple roles for specifying users' requirements, for representing development knowledge, and for knowledge communications among decentralized autonomous agents. Using the machinery of Horn-clause logic, various properties of an autonomous decentralized system specified in FRORL can be analyzed. The specifications represented in FRORL can also be transformed into various kinds of logic-based or procedural programming languages. Through a FRORL development methodology, the design and implementation of autonomous decentralized systems can be easily achieved.>

The authors present a framework for a transformation system which can transform a nonmonotonic logic-based specification language, FRORL, into various kinds of procedural language programs. They discuss how to handle nonmonotonic inheritance in FRORL. A matrix-based data flow and dependency analysis mechanism is established to find all the possible data transformation paths in a logic-based specification. An algorithm is proposed to adjust the execution sequence of a logic-based specification so that the functions included in the logic-based specification can be represented by a procedural language program.>

A formal requirement specification language, the frame-and-rule oriented requirement specification language FRORL, developed to facilitate the specification, analysis, and development of a software system is presented. The surface syntax of FRORL is based on the concepts of frames and production rules that may bear hierarchical relationships to each other, relying on multiple inheritance. To provide thorough semantic foundations, FRORL is based on a nonmonotonic variant of Horn-clause logic. Using the machinery of Horn-clause logic, various properties of a FRORL specification can be analyzed. Among the external properties of FRORL are formality, object-orientedness, and a wide spectrum of life cycle phases. Intrinsic properties are modularity, provision for incremental development, inheritance, refinement, reusability, prototyping, and executability. A software development environment based on FRORL has been implemented using the C language on a Sun workstation.>

FRORL is a requirements specification language which is proposed to exploit knowledge representation techniques as an aid in the specification, analysis and development of a software system. RT-FRORL is an extension of FRORL that inherits FRORL's basic structure but also includes those language constructs needed to support the specification of real-time systems. The syntax of RT-FRORL is based on frames and production rules, and the semantics of RT-FRORL are defined through the integration of first-order logic and temporal logic extension. Using RT-FRORL, concurrent and absolute time properties of real-time systems can be specified easily.>

A modification is presented of temporal logic-event-based real-time logic (ERL), based on an event-based conceptual model. The ERL provides a high-level framework for specifying timing properties of real-time software systems. The ERL is used to specify both expected behavior (specification) and actual behavior (execution traces) of the target systems; it is also used to verify that the program achieves the specification. A method is also described to implement the ERL using the Prolog programming language for testing and debugging real-time software systems.>

Most real-time applications require a high degree of reliability. Past research efforts have not fully addressed issues peculiar to real-time systems. Recently much attention has been focused on formal methods to verify timing properties of real-time systems. Despite the contribution of these formal methods, the dynamic method for analyzing timing properties is still necessary because of the inherent limitations of formal methods and imperfect program execution environment. The authors present a dynamic timing analysis method for identifying the causes of timing errors in real-time systems.>

The authors present an approach to fuzzy logic and reasoning using the resolution principle based on a novel operator, the fuzzy operator. After presenting the fuzzy resolution principle, a general methodology for reasoning under uncertainty, based on resolution in this fuzzy logic, is discussed. The calculus presented is referred to as fuzzy operator logic or FOL. Some simple examples illustrating the reasoning process using FOL are included.>

Testing and debugging of real-time software systems are difficult because of timing constraints imposed on them and because of their nondeterministic behavior. Conventional static analysis is not adequate to deal with the violation of timing constraints which are inherent in real-time software systems. The authors present a dynamic analysis method which uses the recorded run-time information to test and debug this kind of timing error. Based on the proposed approach, a noninterference monitoring system architecture has been developed to collect the process-level and function-level program execution data of a target real-time software system without affecting its execution. Different-leveled logical views are then reconstructed from this collected run-time information. A dynamic analysis method is then presented to analyze the timing behavior of real-time software systems.>

A noninterference monitoring and replay mechanism using the recorded execution history of a program to control the replay of the program behavior and guarantee the reproduction of its errors is presented. Based on this approach, a noninterference monitoring architecture has been developed to collect the program execution data of a target real-time software system without affecting its execution. A replay mechanism designed to control the reproduction of the program behavior as well as the examination of the states of the target system and its behavior is presented. The monitoring system has been implemented using a Motorola 68000 computer in a Unix system environment. An example is used to illustrate how the mechanism detects timing errors of real-time software systems.>

On the basis of a general mechanism for reproducing the execution behavior of real-time software systems, a new approach is suggested for a knowledge-based debugger for real-time software systems. A description is given of the use of noninterference architecture to collect the program execution history of a target system without interfering with the execution of the target system. Thus, the timing constraints of a real-time software system can be preserved. To eliminate the redundant information in the collected execution history, a postprocessing mechanism is introduced to organize the necessary information for testing and debugging. The redundant data, which are useless for debugging certain types of error, are removed, and only the required data proceed to the debugging phase. A knowledge-based debugging tool is then used to assist users in localizing errors. Examples are used to illustrate the approach in detecting synchronization and timing errors of real-time software systems.>

Exploratory prototypes allow examination and validation of the functionality of a software system under construction by observing the behavior of the system requirements brought out through an interpreter. In order to produce the exploratory prototype rapidly, a language must be available to provide freedom from implementation concerns and allow for a natural representation of the problem domain through inheritance hierarchies and exception handling mechanisms. For embedded systems the prototyping language must also allow for specification of the system as a set of concurrently executing and interacting processes. The language FRORL2, which uses frames and production rules to construct exploratory prototypes for embedded systems, is discussed.>

A replay mechanism which uses the recorded execution history to control the replay of program behavior and guarantees the reproduction of program errors is presented. Based on this approach, a non-interference monitoring architecture has been developed to collect the program execution data of a target system without affecting its execution. The replay mechanism has been designed to control the reproduction of the program behavior as well as the examination of the states and behavior of the target system.>

Presents a knowledge-based rapid prototyping system that facilitates the documentation, analysis, and development of a software system. The particular application domain to be modeled is represented in terms of objects and activities. A frame-and-rule oriented requirements language (FRORL) is developed to describe the objects and activities of the problem domain. FRORL is designed to capture most of the semantics of the application domain, thus making it possible to translate the requirements specification directly to the Prolog code. The checking for correctness of the specification developed in FRORL using a resolution strategy is also presented.>

Summary form only given. Considers the kinds of software-engineering problems that can be solved by expert systems, as well as how to acquire and represent software knowledge. Other questions addressed are: (1) can expert systems development benefit from the experience in software engineering; (2) is a special design methodology required for expert systems developed; and (3) how to apply current software engineering techniques to facilitate the development of expert systems.>

IDSCA, an intelligent system, is developed for the direction selection of controller's action in a multiloop control system. In the design of a controller, the selections of both the valve type and the controller's action direction are important tasks, which directly affects the operation and safety of production. Traditional design can hardly solve the problem. Programmed in OPS5, IDSCA can perform the heuristic inference reasoning and make the intelligent decision. A significant result from IDSCA is the fact that a new design criterion is developed, which may complement the knowledge of controller design technique. the other important investigation is that the Adaptive Feedback Testing System (AFTS) is developed to provide the high reliability of the design results. These two investigations indicate that the development of intelligent systems can stimulate and help the development of both AI and related prototype problems. Moreover, IDSCA has some additional important features: its knowledge base can be modified and new production rules can be created in the running process to solve special problems; and the hierarchy of meta-level control strategy provides the means to manage the knowledge base of IDSCA efficiently. In this article, the principle of building intelligent systems is discussed. As an example, the cascade control system of a polymerizer is applied to illustrate the use of IDSCA.

A knowledge-based system to support software design is presented. The knowledge-based system consists of a frame-and-rule oriented requirements language (FRORL), a knowledge base, an inference engine, and a front-end dialogue system. The FRORL is developed to facilitate the documentation and analysis of a software system. The software design heuristic guidelines (e.g. stepwise refinement, functional decomposition) and the domain knowledge are encoded using Prolog and are stored in a knowledge base. An inference mechanism is implemented to interpret the FRORL specification, that uses the design expertise in the knowledge base to perform a program transformation process. The front-end dialogue system is used to obtain the environment information from users to facilitate the transformation process. The knowledge-based system is implemented in Prolog on an IBM 4341 computer. Results indicate that the system can incrementally accumulate design experience and facilitate the software design process. Examples are also given to illustrate this system.>

An integrated life-cycle model is presented for use in a software maintenance environment. The model represents information about the development and maintenance of software systems, emphasizing relationships between different phases of the software life cycle. It provides the basis for automated tools to assist maintenance personnel in making changes to existing software systems. The model is independent of particular specification, design, and programming languages because it represents only certain 'basic' semantic properties of software systems: control flow, data flow, and data structure. The software development processes by which one phase of the software life cycle is derived from another are represented by graph rewriting rules, which indicate how various components of a software system have been implemented. This approach permits analysis of the basic properties of a software system throughout the software life cycle. Examples are given to illustrate the integrated software life-cycle model during evolution.>

In this paper, an intelligent navigation system for autonomous land vehicles (ALV) using hierarchical terrain representation has been developed which can successfully negotiate an obstacle and threat-laden terrain, even if nothing is known beforehand about the terrain. The ALV stores new information in its memory as it travels, has the ability to backtrack out of unexpected dead ends, and performs spontaneous decision-making in the field based on local sensor readings. The optimal global route of the ALV journey is obtained using dynamic programming, and decision-making is accomplished via a production rule-based system. Execution examples demonstrate the power of the prototype system to solving navigation problems. This establishes the feasibility of constructing a valid ALV by combining search techniques with artificial intelligence tools such as production rule-based systems.

Logic can be used to precisely express human thoughts and inferences. In this paper, an approach using first-order logic for knowledge representation of software component interconnection information to facilitate the validity and integrity checking of the interconnection among software components during software development or modification is presented. Directed graphs are first used to model the structure and behavior of a large-scale software system, and a first-order theory of directed graphs (the DG theory) is established. The interconnection behavior among software components in a large-scale software system is a directed graph which is called software component interconnection graph (CIG). The behavior of the CIG is interpreted using the DG theory and translated into logic representation. The translated logic representation is a set of logic clauses and can be considered as a set of axioms. Automated reasoning techniques based on these axioms can be used to perform the validity and integrity checking of software properties in the software development or maintenance phase.

Software design is the process which translates requirements into a detailed design representation of a software system. It is argued that good software design is the key to reliable and understandable software. Important techniques for software design, including architectural and detailed design stages, are surveyed. Recent advances in distributed software system design methodologies are also reviewed. To ensure software quality, various design verification and validation techniques are discussed. In addition, current software metrics and error-resistant software design methodologies are considered. Future research in software design is considered.