Applied Computing and Information Technology

Sustainability of ICT businesses is a timely topic in the context of the rapidly changing macro-environmental market conditions. This paper documents research that has been undertaken to define the term business sustainability by analyzing multiple definitions proposed in the existing literature. The definitions of business sustainability have undergone a process of abstractive decomposition which decomposes the definition into pieces and then constructs a new definition from the pool of decomposed phrases. Business sustainability is a multifaceted concept with different emerging perspectives that can be achieved through the concentrated efforts on organizational activities and processes. The outcomes of business sustainability could benefit both the organization as well as the environment. Well defined production methods, architectural strategy and the organizational commitment towards technological support are paramount for ensuring business sustainability. This study’s significance is the contribution of a newer and comprehensive version of the definition the term business sustainability. The main conceptual contribution of this paper is its holistic presentation of the concept of business sustainability with contextual relevance to the ICT sector.

Context: Recent studies empirically revealed a relationship between source code comments and code quality. Some studies showed well-written source code comments could be a sign of problematic methods. Other studies also show that source code files with comments confessing a technical debt (called self-admitted technical debt, SATD) could be fixed more times. The former studies only considered the amount of comments, and their findings might be due to a specific type of comments, namely, SATD comments used in the latter studies. Objective: To clarify the relationship between comments other than SATD comments and code quality. Method: Replicate a part of the latter studies with such comments of methods on four OSS projects. Results: At both the file-level and the method-level, the presence of comments could be related to more code fixings even if the comments were not SATD comments. However, SATD comments were more effective to spot fix-prone files and methods than the non-SATD comments. Conclusions: Source code comments other than SATD comments could still be a sign of problematic code. This study demonstrates a need for further analysis on the contents of comments and its relation to code quality.

Public school officials are charged with ensuring that students receive a strong fundamental education. One tool used to test school efficacy is the standardized test. In this paper, we build a predictive model as an early warning system for schools that may fall below the state average in building level average proficiency in the Michigan Educational Assessment Program (MEAP). We utilize data mining techniques to develop various decision tree models and logistic regression models, and found that the decision tree model with entropy impurity measure accurately predicts school performance.

In this paper, we present a development technique, AppSpec, for mobile applications program. The software architecture of business logic for a mobile application can be designed by three models including the application model, the graphic user interface model, and feature model. In order to describe the three models, AppSpec consists of five development phases including requirements analysis, architecture design, navigation design, page design, and implementation and testing. For each phase, we provide diagramming techniques to support the three models for the requirements analysis of the application. Our development procedure helps program developers define functional requirements and design applications architecture through its functional flows. We applied AppSpec to developing a mobile application program, and then presented the products of diagrams as the result of performing development phases of AppSpec.

In the reason of the variability which characterizes the context of such environments, it is important that mobile applications are developed so that they can dynamically adapt their extra functional behavior, in order to optimize the experience perceived by their users. In this paper we discuss some of the problems of the current mobile based human management applications and show how the introduction of adaptive CBD (Component Based Development) model provides flexible and extensible solutions to mobile applications. Mobile applications resources become encapsulated as components, with well-defined interfaces through which all interactions occur. Builders of components can inherit the interfaces and their implementations, and methods (operations) can be redefined to better suit the component. New characteristics, such as concurrency control and persistence, can be obtained by inheriting from suitable base classes, without necessarily requiring any changes to users of these resources. We describe the mobile applications frameworks and adaptive component model, which we have developed, based upon these ideas, and show, through a prototype implementation, how we have used the model to address the problems of referential integrity and transparent component (resource) migration. We will show the prototyping applications using our approaches. We also give indications of future work.

Nowadays, when many of the visually impaired people go out, they are using white cane or a guide dog. However, these walking support tools must often rely on the user’s senses and free life of the visually impaired people are not yet guaranteed completely. Therefore, in recent years, the development of the walking support device with electronics have been actively carried out. But it is not popular yet in general. One of the reasons is that navigation type walking support device is likely to be heavy equipment, hence the visually impaired people don’t use them with ease. For such reason, we conducted development and experiment of the walking support device that enable that they go out with ease by being equipped with sensors to detect obstacles and omni-wheels and navigate them in this study. This device unites the advantages of both electronic white cane and guide dog robot.

The authors develop the user evaluation prediction models based on conjoint analysis and neural networks for interactive evolutionary computation (IEC) implemented by interactive genetic algorithm and interactive differential evolution. In addition, the facial expression generation system described in this paper simulates user evaluation based on personalized models and generates images of happy faces and sad faces automatically as an example. IEC that can optimize its targets according to the user’s preference and sensibility is attracting attention as an interactive personalization method. However, IEC has the problem of user evaluation fatigue because it requires a lot of user evaluations to search the optimum solution. Therefore, interactive systems employing IEC are used with a user evaluation prediction model so that they can reduce a user’s load. The novelties of this study are combination of conjoint analysis and large scale neural networks integrated with user evaluation prediction models. Finally, the authors verify usability of the proposed models by performing user evaluation experiments. As a result, the proposed models indicate better prediction accuracy of user evaluation than a previous research using a simple neural network. Also, the personalized models can simulate user evaluation successfully.

In order to retrieve impressive scene from a video database, a scene retrieval method based on facial expression recognition (FER) is proposed. The proposed method will be useful to retrieve interesting scenes from lifelog videos. When an impressive event occurs, a certain facial expression will be observed in a person in the video. It is, therefore, important for the impressive scene retrieval to precisely recognize the facial expression of the person. In this paper, we try to construct accurate FER models by introducing a learning framework on the basis of multilayer convolutional network using a number of facial features defined as the positional relations between some facial feature points. The effectiveness of the proposed method is evaluated through an experiment to retrieve emotional scenes from a lifelog video database.

Contemporary security systems attempt to provide protection against distributed denial-of-service (DDoS) attacks; however, they mostly use a variety of computing and hardware resources for load distribution and request delays. As a result, ordinary users and website visitors experience timeouts, captchas, and low-speed connections. In this paper, we propose a highly inventive multilayer system for protection against DDoS in the cloud that utilizes Threat Intelligence techniques and a proactive approach to detect traffic behavior anomalies. The first layer of the model analyzes the source IP address in the header of incoming traffic packets and the second layer analyzes the speed of requests and calculates the threshold of the attack speed. If an attack remains undetected, the incoming traffic packets are analyzed against the behavior patterns in the third layer. The fourth layer reduces the traffic load by dispatching the traffic to the proxy, if required, and the fifth layer establishes the need for port hopping between the proxy and the target website if the attack targets a specific web-application. A series of experiments were performed and the results demonstrate that this multilayer approach can detect and mitigate DDoS attacks from a variety of known and unknown sources.

Memory storage technologies are emerging. Battery backed NV-DIMMs are becoming widely available, and the general availability of next generation non-volatile (NV) memory is also considered to be close. While memory storage provides much higher performance than the current block storage devices, such as SSDs and HDDs, its capacity tends to be limited. Such a limitation makes it difficult to adapt memory storage for mass storage; thus, its uses have been limited. Memory storage extension, which we call MSX, is an operating system mechanism that has a file system directly access memory storage and also that virtually extends the capacity of memory storage to that of block storage; thus, MSX effectively utilizes the high performance of memory storage by having a file system directly access memory storage through the synchronous memory interface, and also provides the large capacity by employing block storage as backing storage. MSX was implemented in the Linux operating system kernel. This paper discusses the several practical uses of MSX in cloud computing and also in the fundamental operating system architecture.

Mobile application development process can be tremendously rewarding for aspiring software creators. But without proper planning and resource allocation, it can also be extremely difficult. In fact, development teams often overcompensate in the early stages by devoting a great deal of time to ideation and design while forgetting to put an equally important amount of effort into back-end operations, including key stages of the process such as maintenance and support. By in the context of mobile applications development project, the applications quality can be explained as following. A project manager must estimate quality while a project is in procedure. The undisclosed matrixes collected individually by a software engineer are integrated in order to determine the project level. Although it is possible to measure various quality elements with merely the collected data, it is also required to integrate the data in order to determine the mobile applications project level. The key task during the project is to measure errors and defects, even though many quality measurements can be collected. The matrix drawn from these measurements offers the standard of the effectiveness of quality warranty and the control activity of individual and group software. In this paper, we identify defects to produce reliable mobile software and analyze the relationship among different defects among mobile applications. Another goal of this paper is to design a defect trigger based on the findings. So when we archive resembling project, we can forecast defect and prepare to solve defect by using defect trigger.

Firefly algorithm is a new meta-heuristic inspired by a natural phenomenon of fireflies’ flashing light. Firefly algorithm has been successfully applied to solve several optimization problems. However, It still suffers from some drawbacks such as easily getting stuck at local optima and slow speed of convergence. This paper proposes a new hybrid variant of discrete firefly algorithm, called HDFA, to solve traveling salesman problem (TSP). In the proposed improvement, the balance between intensification and diversification is achieved by utilizing the local search procedures, 2-opt and 3-opt, to improve searching performance and speed up the convergence. In addition, the genetic algorithm operators, crossover and mutation, are added to allow performance of both local and global search respectively. The validity of HDFA is verified by comparative experiments using eighteen TSP benchmark instances from TSBLIB and compared to some well-known algorithms. Results in the conducted experiments show that HDFA has significantly better performance than the performance of compared algorithms for all instances in terms of solution quality.

The arrival of Massive Open Online Course (MOOC) was hailed by many learners around the globe. More universities are willing to offer their top notch professor’s courses as MOOC. However, when utilizing the knowledge from MOOCs, learners need to go through a number of hurdles—getting course completion certification (MS Global Learning Consortum: An example of LIP accessibility information, IMS Global Learning Consortium Inc., 2001, [10]) from various providers, get recognition of the knowledge level of a MOOC in the related domain, and make it searchable for various purposes. In this paper, we propose a MOOC course certifying agency framework, which merges learners’ profiles from various MOOC providers so consolidated profiles are available in one place. Standards such as IMS LIP and Dublin Core (Feigenbaum and Prud’Hommeaux in SPARQL by example: a tutorial, Cambridge Semantics, 2011, [5]) are adopted and expanded to describe relate MOOC course profiles, learner profiles, learning goals, and related skill sets. It enables matching of qualified learner profiles for a job position and/or to identify a set of related MOOC course profiles for some learning goal. The potential employers look for a matching skill set from converged learner profiles through the agency. Each skill for the position goes through a mapping procedure with a corresponding MOOC course profile. After mapping skills to corresponding MOOCs, the framework searches for the converged profiles. The result is the list of learners who match or almost match a given job description.