This work investigates the problem of a non-linear divisible load distribution on a homogeneous linear network. A novel computational model of non-linear loads that includes complete steps for processing them, is proposed. This model solves the problem of the classical model, whose performance degrades by separating the load. This work also presents an algorithm S (Single-installment) that uses single-installment processing to distribute a non-linear divisible load on a homogeneous linear network. An algorithm M (Multi-installment) that applies multi-installment processing to reduce the initial distribution time for load is also proposed. Closed-form expressions for the parallel processing time and speed-up of the proposed algorithms are derived. The speed-up of algorithm S is much better than that of the classical algorithm that is based on the classical model. Algorithm M outperforms algorithm S in terms of speed-up when the load to be processed is very large or when the start-up costs are small.

This work investigates divisible load distribution using multi-installment processing on completeb-ary tree networks. Classic methods of distributing a divisible load divide the computation and communication processes into multiple time intervals in a pipelined fashion. The algorithm$\mathbb {M}$(multi-installment) herein uses multi-installment processing with pipelined communication to reduce the initial distribution time and to improve the performance. Closed-form expressions for the parallel processing time and speed-up are derived. This work reveals that the asymptotic speed-up of the proposed algorithm is$b\beta +1$where$\beta$is the computation-to-communication ratio of a node in the system. Algorithm$\mathbb {M}$outperforms the classic algorithm in all cases. The algorithm$\mathbb {S}$(start-up cost) that is developed herein includes the computation and communication start-up costs. Finally, two algorithms$\mathbb {M}$and$\mathbb {S}$are combined to form algorithm$\mathbb {MS}$with even better performance than each.

Model Driven Architecture (MDA) is a new software development framework. This paper presents a model-driven approach to the development of Web applications by combining Conallen's web applications design concept and Kleppe's MDA process. We use the UML extension mechanism, i.e. stereotypes, to define the various web elements, and use the Robustness diagram to represent MVC 2 structure for Web application. After required analysis, we start by using a use case diagram as CIM, and then transform CIM to PIM, and PIM to PSM. We propose mapping rules for model-to-model transformation. Finally, we develop a tool named WebPSM2Code, which can automatically transform PSM diagram to Web application code, such as Java, JSP, HTML, Servlet, Javascript, as well as deployment descriptor file. All the files can automatically address to the correct directory structure for JSP Web application, and the transformation rate is about 39% of the whole system. Using this methodology, systems can be analyzed, designed, and generated more easily and systematically. Thereby, the time that Web programmers spend on coding can be reduced.

Examination is a traditional way to assess learners’ learning status, progress and performance after a learning activity. Except the test grade, a test sheet hides some implicit information such as test concepts, their relationships, importance, and prerequisite. The implicit information can be extracted and constructed a concept map for considering (1) the test concepts covered in the same question means these test concepts have strong relationships, and (2) questions in the same test sheet means the test concepts are relative. Concept map has been successfully employed in many researches to help instructors and learners organize relationships among concepts. However, concept map construction depends on experts who need to take effort and time for the organization of the domain knowledge. In addition, the previous researches regarding to automatic concept map construction are limited to consider all learners of a class, which have not considered personalized learning. To cope with this problem, this paper proposes a new approach to automatically extract and construct concept map based on implicit information in a test sheet. Furthermore, the proposed approach also can help learner for self-assessment and self-diagnosis. Finally, an example is given to depict the effectiveness of proposed approach.

A root cause is a source of software defect, whose removal decreases or removes the defect. A root cause of software defect is injected into the software by software engineers during the development process. One of the main concerns of the software team leader, such as the project manager, is to determine who injected various root causes of the defects into the software and when these have been injected. In this paper, a cost-benefit scheme is presented, which allows a software team to determine skill weakness and improve team capability. The scheme provides effective in-process feedback based on the causal analysis of software defects. The proposed analysis scheme includes orthogonal root cause definitions, role-based root cause types, and gradational correction actions. In the experiment, the projects of a distributed health-care system are used to verify the efficiency of the proposed scheme. The results show that the root cause ratios (RCR) are 33.8%, 30.6%, 21.9%, 10.7%, and 3.0% in design, implementation, analysis, business and deployment, respectively. The defects in the projects mainly occurred during the design and implementation phases of the projects. Correction activities to enhance the designers’ skills, such as exception handling (40.5%) and DB/data schema (25.0%), are the top priorities that must be addressed by the software team. The findings can help the team leader to determine methods to improve these weaknesses.

Scheduling malleable tasks under general precedence constraints involves finding a minimum makespan (maximum completion time) by a feasible allotment. Based on the monotonous penalty assumptions of Blayo et al. [2], this work defines two assumptions concerning malleable tasks: the processing time of a malleable task is nonincreasing in the number of processors, while the work of a malleable task is nondecreasing in the number of processors. Additionally, the work function is assumed herein to be convex in the processing time. The proposed algorithm reformulates the linear program of [11], and this algorithm and associated proofs are inspired by the ones of [11]. This work describes a novel polynomial-time approximation algorithm that is capable of achieving an approximation ratio of 2+√2≈3.4142. This work further demonstrates that the proposed algorithm can yield an approximation ratio of 2.9549 when the processing time is strictly decreasing in the number of the processors allocated to the task. This finding represents an improvement upon the previous best approximation ratio of 100/63+100(√6469+137)/5481≈3.2920 [12] achieved under the same assumptions.

Service systems used for various applications in home automation and security require estimating the locations precisely using certain sensors. Serving a mobile user automatically by sensing his/her locations in an indoor environment is considered as a challenge. However, indoor localization cannot be carried out effectively using the Global Positioning System (GPS). In recent years, the use of Wireless Sensor Networks (WSNs) in locating a mobile object in an indoor environment has become popular. Some physical features have also been discussed to solve localization in WSNs. In this paper, we inquire into received signal strength indication (RSSI)-based solutions and propose a new localization scheme called the closer tracking algorithm (CTA) for indoor localization. Under the proposed CTA, a mechanism on mode-change is designed to switch automatically between the optimal approximately closer approach (ACA) and the real-time tracking (RTT) method according to pre-tuned thresholds. Furthermore, we design a mechanism to move reference nodes dynamically to reduce the uncovered area of the ACA for increasing the estimation accuracy. We evaluate the proposed CTA using ZigBee CC2431 modules. The experimental results show that the proposed CTA can determine the position accurately with an error distance less than 0.9 m. At the same time, the CTA scheme has at least 87% precision when the distance is less than 0.9 m. The proposed CTA can select an adaptive mode properly to improve the localization accuracy with high confidence. Moreover, the experimental results also show that the accuracy can be improved by the deployment and movement of reference nodes.

Reducing the variance between expectation and execution of software processes is an essential activity for software development, in which the Causal Analysis is a conventional means of detecting problems in the software process. However, significant effort may be required to identify the problems of software development. Defect prevention prevents the problems from occurring, thus lowering the effort required in defect detection and correction. The prediction model is a conventional means of predicting the problems of subsequent process actions, where the prediction model can be built from the performed actions. This study proposes a novel approach that applies the Intertransaction Association Rule Mining techniques to the records of performed actions in order to discover the patterns that are likely to cause high severity defects. The discovered patterns can then be applied to predict the subsequent actions that may result in high severity defects.

As Internet use has proliferated, Web-based learning systems have become more and more popular. Numerous researchers have spent a great deal of effort to facilitate the promotion of high quality Web-based learning environments, such as intelligent Web-based learning systems and adaptive learning. To facilitate such researches, students' behavioral patterns must be observed and experimentally analyzed. However, building a Web-based learning system and the requisite collecting of behavioral patterns usually takes a great deal of time and effort. To solve this problem, this paper proposes a learning behavioral model based on Colored Petri Nets (CPN) to model and generate students' behavioral patterns. To verify the viability of the proposed model, this paper compares actual data collected from elementary school students with the behavioral pattern generated by the proposed model. The results prove: (1) The generated behavioral pattern approaches actual student behavior; (2) The generated behavioral pattern serves as adequate test data to test whether the predicted learning content of an intelligent e-learning system is appropriate; and (3) The proposed model is capable of recommending the appropriate learning content for students utilizing e-learning systems.

Various graphic techniques have been developed to analyze structural errors in rule-based systems that utilize inference (propositional) logic rules. Four typical errors in rule-based systems are: redundancy (numerous rule sets resulting in the same conclusion); circularity (a rule leading back to itself); incompleteness (deadends or a rule set conclusion leading to unreachable goals); and inconsistency (rules conflicting with each other). This study presents a new DNA-based computing algorithm mainly based upon Adleman's DNA operations. It can be used to detect such errors. There are three phases to this molecular solution: rule-to-DNA transformation design, solution space generation, and rule verification. We first encode individual rules using relatively short DNA strands, and then generate all possible rule paths by the directed joining of such short strands to form longer strands. We then conduct the verification algorithm to detect errors. The potential of applying this proposed DNA computation algorithm to rule verification is promising given the operational time complexity of O(n*q), in which n denotes the number of fact clauses in the rule base and q is the number of rules with longest inference chain.

This work proposes efficient data aggregation algorithms based on a virtual grid for range query in wireless sensor networks. A sensor node is selected to be a manager, called head, in each grid. The responsibilities of head are to detect generated event, announce to all other heads, and respond to a moving user. A user obtains the occurred event information from its grid head. If a user is interested in an event, it issues a query to acquire data from a specified regular-shape or spreading irregular-shape ranges. Users can oversee the spreading event via querying the incurred irregular-shape range. In addition, this work proposes efficient approaches to gather data from sensor networks while voids exist. Finally, experimental results show that the proposed approaches are more energy-efficiency than the existing approach.

In the divisible load distribution, the classic methods on linear arrays divide the computation and communication processes into multiple time intervals in a pipelined fashion. Li (2003) has proposed a set of improved algorithms for linear arrays which can be generalized to k-dimensional meshes. In this paper, we first propose the algorithm M (multi-installment) that employs the multi-installment technique to improve the best algorithm Q proposed by Li. Second, we propose the algorithm S (start-up cost) that includes the computation and communication start-up costs in the design. While the asymptotic speedups of our algorithms M and S derived from the closed-form solutions are the same as algorithm Q, our algorithms approach the optimal speedups considerably faster than algorithm Q as the number of processors increases. Finally, we combine algorithms M and S and propose the algorithm MS. While algorithm MS has the same the asymptotic performance as algorithms Q and S, it achieves a better speedup when the load to be processed is very large and the number of processors is fixed or when the load to be processed is fixed and the number of processors is small.

Defect prediction is an important process in the evaluation of software quality. To accurately predict the rate of software defects can not only facilitate software review decisions, but can also improve software quality. In this paper, we have provided a defect estimation approach, which uses defective data from sequential inspections to increase the accuracy of estimating defects. To demonstrate potential improvements, the results of our approach were compared to those of two other popular estimation approaches, the capture-recapture model and the re-inspection model. By using the proposed approach, software organizations may increase the accuracy of their defect predictions and reduce the effort of subsequent inspections.

A mobile ad hoc network (MANET) is characterized by multihop wireless links in the absence of cellular infrastructure and frequent host mobility. In this paper, a new on-demand routing protocol is proposed, named on-demand routing protocol with backtracking (ORB), for multihop mobile ad hoc networks. Suppose a source host wants to deliver a message to a destination in a MANET. First, the source host issues a request to search the destination. When a node receives the request packet, it gets a hop count value which can point the way to the source. This node broadcasts the request packet to its neighbors. When a destination receives this request, it issues a reply back to the source. We proposed a scoped flooding approach which is applied to reply information to the source. This approach will find out multipath from a source to a destination Every node in the MANET maintains some information for this source-destination pair in its own route table. Each node in the forwarding route uses this information to select a group of nodes, named checkpoint nodes, which may have multipath maintained to that destination. The checkpoint node characteristic is such that a route can be recovered instantly without the need for extra control packets. When a delivering node is aware that the forwarding route is broken, it transmits an error packet back to the source along the return path. When a checkpoint receives the error packet that is backtracked to the source, it has backup paths to recover the broken route. This new route can be used immediately The main advantage of our ORB is to reduce the flooding search time and cost when a route has been broken. We show that the proposed scheme outperforms the on-demand routing protocol existing in mobile ad hoc networks.

Data alignment that facilitates data locality so that the data access communication costs can be minimized, helps distributed memory parallel machines improve their throughput. Most data alignment methods are devised mainly to align the arrays referenced using linear subscripts or quadratic subscripts with few (one or two) loop index variables. We propose two communication-free alignment techniques to align the arrays referenced using linear subscripts or quadratic subscripts with multiple loop index variables. The experimental results from our techniques on vector loop and TRFD of the perfect benchmarks reveal that our techniques can improve the execution times of the subroutines in these benchmarks.

The star graph interconnection network has been recognized as an attractive alternative to the popular hypercube network. In this paper we first address a dual-Hamiltonian-path-based (DHPB) routing model with two virtual channels based on two Hamiltonian paths and a network partitioning strategy for wormhole-routed star graph networks. Then, we propose three efficient multicast routing schemes on basis of such a model. The proposed schemes are network-selection-based (NSB), heuristic-network-selection-based (HNSB), and two-phase heuristic-network-selection-based (TP-HNSB) dual-path routing. All of the three proposed schemes are deadlock-free. Finally, experimental results are given to show our proposed three routing schemes outperform the unicast-based, the Hamiltonian-path, and the single-Hamiltonian-path-based (SHPB) dual-path routing schemes significantly.

We address reconfigurable algorithms for managing replicated data with strict consistency requirements, that is, whenever the user performs an update operation, the update is applied to all reachable copies as part of the update protocol. A key issue of designing such algorithms is to determine how often one should detect and react to failure conditions so that reorganization operations can be performed at the appropriate time to improve the availability of replicated data. We use dynamic voting as a case study to illustrate how often such failure detection and reconfiguration activities should be performed so as to maximize the data availability. We show that there exists an optimal period at which the failure detection and reconfiguration activities should be performed to optimize the system availability. Stochastic Petri nets (SPNs) are used as a tool to facilitate our analysis.

Voting is a simple and yet effective way of managing replicated data in distributed systems. In this paper we analyze its response time behavior. We investigate a technique for obtaining the access time distribution for requests that access replicated data maintained by the distributed system. The technique is based on Petri net modeling and can be used to estimate the reliability of real-time applications which must access replicate data with a deadline requirement.

In this paper, we propose four efficient multicast routing schemes in wormhole-routed star networks with multidestination routing capability. All of the four proposed schemes are path-based and deadlock-free. The first scheme, dual-path routing, sends the message in parallel through two independent paths. The second one, shortcut-node-based datapath routing, is similar to dual-path routing except that the routing tries to find a shortcut node to route the message as soon as possible to reduce the length of transmission path. The third one, multipath routing, is a multiple dual-path routing strategy that includes source-to-relay and relay-to-destination phases. The last scheme, proximity grouping routing, is similar to multipath routing except that in the partitioning step of source and destination nodes the relation of spatial locality of nodes is also taken into account to reduce the length of transmission paths. Finally, the experimental results are given to show that the performance based on unicast-based and traditional Hamiltonian-path routing schemes can be improved significantly by the four proposed routing schemes respectively.

In this paper, WC proposed the infinity Lambda test.The infinity Lambda test can be applied IO deal with data dependence of coupled arrays with symbolic (urrknowr, at compile time) bounds.Experimental data reflecting the effect of the infinity Lambda test are also presented.

In this paper, we study the exploitation of statement-level parallelism in dependence cycles of Do-loops executed in a random (general) synchronization mode, with emphasis on the effect of statement ordering on parallelism level. For a dependence cycle of a single parallel loop, the parallelism exposed, in general, varies with the alignment of statements. Statement reordering (without modifying the semantics) relies on the compile-time prediction of execution-time of the loop. An improved timing formula and the derived algorithm of statement reordering in single Do loop with dependence cycles to improve the performance of parallel loops are proposed and discussed.

We propose an ideal development paradigm facilitating the implementation of distributed applications. In this paradigm a developer focuses his mind only on the application itself and does not need to spend time on application-unrelated activities. The programming style is nearly consistent with that of centralized system. The mechanism of the general support environment to this paradigm is described. An example explaining the implementation for an environment using the Sun RPC facility as the underlying communication component is also presented.