Software Engineering Research, Management and Applications

The ability to detect and analyze micro motions in human body is a crucial task in surveillance systems. Although video based systems are currently available to address this problem, but they need high computational resources and under good environmental lighting condition to capture high quality images. In this paper, a novel non-parametric method is presented to detect and calculate human gait speed while analyzing human micro motions based on radar micro-Doppler signatures to classify human motions. The analysis was applied to real data captured by 10 GHz radar from real human targets in a parking lot. Each individual was asked to perform different motions like walking, running, holding a bag while running, etc. The analysis of the gathered data revealed the human motion directions, number of steps taken per second, and whether the person is swinging arms while moving or not. Based on human motion structure and limitations, motion profile of each individual was recognizable to find the combinations between walking or running, and holding an object or swinging arms. We conclude that by adopting this method we can detect human motion profiles in radar based on micro motions of arms and legs in human body for surveillance applications in adverse weather conditions.

The Mobile Ad-hoc Network (MANET) is an emerging infrastructure-free network constructed by self-organized mobile devices. In order to manage MANET, with its dynamic topology, several network management protocols have been proposed, and Network Configuration Protocol (NETCONF) is representative one. Nonetheless, the performance of these network management protocols on MANET remains unresolved. In this paper, we leverage the Common Open Research Emulator (CORE), a network emulation tool, to conduct the quantitative performance evaluation of NETCONF in an emulated MANET environment. We design a framework that captures the key characteristics of MANET (i.e., distance, mobility, and disruption), and develop subsequent emulation scenarios to perform the evaluation. Our experimental data illustrates how NETCONF performance is affected by each individual characteristic, and the results can serve as a guideline for deploying NETCONF in MANET.

UML provides different models for understanding and describing the requirements of a system. The completeness of each model with respect to other models is critical to further analysis of the requirements and design. One problem that always plagues modelers is the acquisition of requirements knowledge for building models. In this paper, we present a knowledge-based framework to drive the process of acquiring requirements for each UML model. This framework is based on a central knowledge representation, the conceptual graphs. A set of partially complete UML models is first converted to conceptual graphs to form a requirements knowledge reservoir; then this knowledge reservoir is used to generate each UML model by transforming conceptual graphs back to UML notations. This bidirectional transforming process enables the discovery of additional requirements and possible missing requirements so that eliciting more requirements knowledge from modelers is made possible.

Recently, many open source software are used for quick delivery, cost reduction, standardization. The bug tracking systems are managed by many open source projects. Then, many data sets are recorded on the bug tracking systems by many users and project members. The quality of open source software will be improved significantly if the software managers can make an effective use of these data sets on the bug tracking systems. In this paper, we propose a method of open source software reliability assessment based on the deep learning. Also, we show several numerical examples of open source software reliability assessment in the actual software projects. Moreover, we compare the methods to estimate the level of software faults based on the deep learning by using the fault data sets of actual software projects.

Monitoring target through satellite images is widely used in intelligence analysis and for anomaly detection. Meanwhile, it is also challenging due to the shooting conditions and the huge amounts of data. We propose a method for target monitoring based on deep convolutional neural networks (DCNN). The method is implemented by three procedures: (i) Label the target and generate the dataset, (ii) train a classifier, and (iii) monitor the target. First, the target area is labelled manually to form a dataset. In the second stage a classifier based on DCNN using Keras library is well-trained. In the last stage the target is monitored in the test satellite images. The method was tested on two different application scenarios. The results show that the mothed is effective.

In these days, people share their emotions, opinions, and experiences of products or services using online review services on their comments, and the people concern the reviews to make decision when buying products or services. Sentiment analysis is one of the solution to observe and summarize emotional opinions from the data. In spite of high demands for developing sentiment analysis, the development of the sentiment analysis faces some challenges to analyze the data, because the data is unstructured, unlabeled, and noisy. The aspect-based sentiment analysis approach helps for more in-depth analysis, however building aspect and emotional expression is one of the challenge for the aspect-based sentiment analysis approach. Accordingly, we propose an unsupervised system for building aspect-expressions to minimize human-coding efforts. The proposed method uses morphological sentence patterns through an aspect-expression pattern recognizer. It guarantees relatively higher accuracy. As well as, we found some characteristics for selecting patterns to extracting aspect-expressions accurately. The greatest advantage of our system is performing without any human coded train-set.

We are living in a flood of information. We hear about lots of social issues such as politics and economies in every day from the mass media. Before the appearance social media, it is difficult to interact people’s opinions with the others about the social issues. However, we can analyze important social issues using big data generated from social media. We tried to apply the relationship between agenda setting theory and social media because we have received social issues from official accounts like news using social media, and then users shared social issues to other users, so we choose tweets of Baltimore Riot to analyze. We collected tweets related with Baltimore Riot, and then we extracted term keywords using text mining technologies such as TF-IDF. Actually, we analyzed tweets of 04-27-2015 Based on detected important words, we analyzed tweets at 5-min intervals, and we extracted tweets of mass media and others. Based on user’s profiles, we found relationship of mass media and social issues. About initial phase of the social issues as it happened, local mass media leaded about incidents, and tweets exchanged and shared in local area. After writing an influence Twitter user, social issues of Baltimore Riot spread to other areas. As a result, we could detect agenda setting theory in social media using big data technology. It implies that the local mass media led the social issues, the Baltimore Riot became one of major social issues to people at the end.

A study that examines the prevalence and distribution of function side effects in general-purpose software systems is presented. The study is conducted on 19 open source systems comprising over 9.8 Million lines of code (MLOC). Each system is analyzed and the number of function side effects is determined. The results show that global variables modification and parameters by reference are the most prevalent side effect types. Thus, conducting accurate program analysis or many adaptive changes processes (e.g., automatic parallelization to improve their parallelizability to better utilize multi-core architectures) becomes very costly or impractical to conduct. Analysis of the historical data over a 7-year period for 10 systems shows that there is a relatively large percentage of affected functions over the lifetime of the systems although trend is flat in general, thus posing further problems for inter-procedural analysis.

The event driven systems can be modeled and implemented using UML state chart diagrams. Code generation tools are used in the software development for making software system designs and for automatically generating skeletal source code from the system designs. Many research works concentrate on the automatic code generation from the state diagrams. Unfortunately the existing Object oriented languages do not support the direct implementation of state diagrams. We cannot find a one to one mapping between elements in the state chart diagram and the Object oriented programming constructs. The two main components of state diagram that cannot be effectively implemented in object oriented way is state hierarchy and concurrency. In this paper, we present an implementation pattern for the state diagram which includes both hierarchical and concurrent states. The state transitions of parallel states are delegated to the composite state class. We implemented the proposed approach and compared with similar tools and the result is promising.

We consider the state space explosion problem which is a fundamental obstacle in formal verification of critical systems. In this paper, we propose a fast algorithm for distributing state spaces on a network of workstations. Our solution is an improvement version of SSCGDA algorithm (for Strict Strong Coloring based Graph Distribution Algorithm) which introduced the coloring concept and dominance relation in graphs for finding the good distribution of given graphs [1]. We report on a thorough experimental study to evaluate the performance of this new algorithm. The quality of the proposed algorithm is illustrated by comparison with existing algorithms.

In this paper we propose a model to deal with flexibility in complex Web services composition (WSC). In this context, we use a model based on high level Petri nets called RECATNets, where control and data flows are easily supported. Indeed, RECATNets combine the strengths of recursive Petri nets with the expressive power of abstract data types. Since RECATNets semantics is expressed in terms of the conditional rewriting logic, one can use the Maude LTL Model-Checker to investigate several behavioral properties of Web services composition.

The PMRF (Parameterized Matching-Ranking Framework) is a highly configurable framework supporting a parameterized matching and ranking of Web services. This paper first introduces the matching and ranking algorithms supported by the PMRF. Next, it presents the architecture of the developed system and discusses some implementation issues. Then, it provides the results of performance evaluation of the PMRF. It also compares PMRF to two exiting frameworks, namely iSeM-logic-based and SPARQLent. The different matching and ranking algorithms have been evaluated using the OWLS-TC4 datasets. The evaluation has been conducted employing the SME2 (Semantic Matchmaker Evaluation Environment) tool. The results show that the algorithms behave globally well in comparison to iSeM-logic-based and SPARQLent.