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Performance testing modeling is a relative new research field. Researches investigating how to apply models to document performance testing information essentially started to be reported in the last decade. Motivated by the lack of a standard to represent performance testing information, our research group, in collaboration with an IT company, proposed a UML approach and lately a Domain-Specific Language (DSL) to support performance testing modeling. The goal of this paper is to show how we support our partner company on the decision process to replace UML by a DSL, hence we designed and conducted an experimental study to provide evidence about the benefits and drawbacks when using UML or DSL for modeling performance testing. In this paper, we report an in vitro experiment, where the subjects designed UML models and DSL models, for the purpose of evaluation with respect to the effort and suitability, from the perspective of the performance testers and the performance engineers in the context of industry and academia for modeling performance testing. Our results indicate that, for performance modeling, effort using a DSL was lower than using UML. Our statistical analysis showed that the results were valid, i.e., that to design performance testing models using our DSL is better than using UML.

This paper proposes a new method called Virtual Run for extracting the representative trajectories from a set of similar trajectories by readjusting the sequence of movements of vehicles. This method uses convex hull and Euclidean minimum spanning tree to move the trajectory sets, so that it can generate the representative trajectories. Those representative trajectories are used to generate the digital road map which is the final goal of this research. Generally, the well-known Frechet distance measure is applied to estimate the similarity between a pair of trajectories. However, in this paper, we present that our new method is more effective than the previous methods on the basis of an experiment and evaluation. Also we show the application of generating the digital road map using the presented method. For the evaluation, we collected a set of actual trajectory data from 3,000 taxis in the Gangnam and Seongnam areas. The experimental results show that our approach can solve the problems associated with the special case of loops, which are not clearly solved by the previous approaches. The results also show that our method is capable of generating a digital road map from a set of trajectories.

This paper introduces SAMSON (Self-Adaptive Middleware for wireless SensOr Networks), an instance of a Reference Architecture (RA) that was designed to manage wireless sensor networks (WSN) in an autonomic way by enabling runtime adaptation of nodes' behavior according to context changes, application requirements, and predefined adaptation policies. In this paper, we propose a process, supported by model-driven transformations to generate SAMSON from the RA specification. The model-driven transformations are used to map each element of the RA specification to software components and to generate the source code to be deployed in a target WSN platform. We evaluate our proposal by presenting four application scenarios where SAMSON was employed to optimize the network lifetime and to react to runtime events according to adaptation policies previously defined by software architects and network administrators. Evaluation results shown that SAMSON was able to dynamically adapt the behavior of sensor nodes, to efficiently use the hardware resources, and to increase the WSN lifetime.

Over the past few years, nonvolatile memory has actively been researched and developed. Therefore, studying operating system (OS) designs predicated on the main memory in the form of a nonvolatile memory and studying methods to manage persistent data in a virtual memory are crucial to encourage the widespread use of nonvolatile memory in the future. However, the main memory in most computers today is volatile, and replacing highcapacity main memory with nonvolatile memory is extremely cost-prohibitive. This paper proposes an OS structure for nonvolatile main memory. The proposed OS structure consists of three functions to study and develop OSs for nonvolatile main memory computers. First, a structure, which is called plate, is proposed whereby persistent data are managed assuming that nonvolatile main memory is present in a computer. Second, we propose a persistent-data mechanism to make a volatile memory function as nonvolatile main memory, which serves as a basis for the development of OSs for computers with nonvolatile main memory. Third, we propose a continuous operation control using the persistent-data mechanism and plates. This paper describes the design and implementation of the OS structure based on the three functions on The ENduring operating system for Distributed EnviRonment and describes the evaluation results of the proposed functions.

Context: The activities related to Requirements engineering (RE) are some of the most important steps in software development, since the requirements describe what will be provided in a software system in order to fulfill the stakeholders' needs. In this context, the ACM Symposium on Applied Computing (SAC) has been a primary gathering forum for many RE activities. When studying a research area, it is important to identify the most active groups, topics, the research trends and so forth. Objective: This study aims to investigate how the SAC RE-Track is evolving, by analyzing the papers published in its 8 previous editions. Method: We adopted a research strategy that combines scoping study and systematic review good practices. Results: We investigated the most active countries, institutions and authors, the main topics discussed, the types of the contributions, the conferences and journals that have most referenced SAC RE-Track papers, the phases of the RE process supported by the contributions, the publications with the greatest impact, and the trends in RE. Conclusions: We found 79 papers over the 8 previous SAC RE-Track editions, which were analyzed and discussed.

Enterprise applications are used for managing operational data and are aimed at improving business efficiency. Many enterprise applications have been developed over the past three decades and are often referred to as legacy systems. Usually they are monolith, inflexible, poorly documented and hard to maintain. These issues can be addressed by improving their design using decomposition. The purpose of this paper is to introduce a method for decomposition of large scale enterprise applications and to present initial practical evaluation results. The paper systematically defines the general decomposition process including its tailoring options. The general process is adapted and applied for application in the industrial case study investigated as part of the university-industry collaboration research project.

Similarity measurements in images have always been a challenging task in the field of pattern recognition techniques. Shape based features is a widely adopted method in Content Based Image Retrieval (CBIR) for similarity measurement. In this paper we proposed an enhanced version of Mesh Distance Fourier Descriptor (MDFD) previously developed in our lab for the similarity measurement. Two extra levels of filters have been added to the output of MDFD so that the final output is more refined and the most similar image to the query image is selected from the database. The first level filter includes processing of images retrieved based on ratio of area of the image to the area of minimum bounding rectangle enclosing that image. The second level filtering includes calculation of average of absolute difference of global features like eccentricity, convexity and solidity of the query image and retrieved image. Adding these two extra filtering levels, the matching ratio has been increased from 84% to 88% which shows adding filters enhances the results of MDFD. In this paper we have used binary images extracted from region of interest (ROI) of mammogram which are classified into single objects using known classification methods such as K-means and SVM algorithms.

Designing modern web applications involves a wide spectrum of choices when it comes to deciding where the different tiers of application and framework code that constitute these distributed applications should be placed. These system design choices affect programmer productivity, ease of deployment, security, and performance, particularly with respect to latency and scalability. In this paper, we propose and evaluate a design choice in which not only all application logic executes server-side, but most presentation logic as well. The client browser is reduced to a rendering and I/O engine, similar to a "thin client" or "dumb terminal," but retains the full expressiveness of rich, modern Internet applications. We have developed CloudBrowser 2.0, a system that implements this distribution model using a scalable multiprocess approach. In this paper, we perform an evaluation of the benefits and costs of this approach when compared to both more traditional approaches as well as emerging alternatives. We focus on programmability and systems aspects including performance and latency.

A fundamental challenge in robotics is to reason with incomplete domain knowledge to explain unexpected observations, and partial descriptions of domain objects and events extracted from sensor observations. Existing explanation generation systems are based on ideas drawn from two broad classes of systems, and do not support all the desired explanation generation capabilities for robots. The objective of this paper is to first compare the explanation generation capabilities of a state of the art system from each of these two classes, using execution scenarios of a robot waiter assisting in a restaurant. Specifically, we investigate KRASP, a system based on the declarative language Answer Set Prolog, which uses an elaborate system description and observations of system behavior to explain unexpected observations and partial descriptions. We also explore UMBRA, an architecture that provides explanations using a weaker system description, a heuristic representation of past experience, and other heuristics for selectively and incrementally searching through relevant ground literals. Based on this study, this paper identifies some key criteria, and provides some recommendations, for developing an explanation generation system for robots that exploits the complementary strengths of the two classes of explanation generation systems.

Multiple bio-inspired approaches have been developed aiming at achieving adaptive and autonomous behavior in the cloud. However, no such effort was invested in studying biomorphic-inspired methods. In this paper, biomorphism is presented as a central evolutionary processes where cellular differentiation take place. To the best of our knowledge, the only state-of-the-art biomorphic model applied to cloud computing was reported in our previous work [28]. The biomorphic model devised in [28] and called timing algorithm was simplistic and aimed to illustrate the concept of biomorphism for cloud scaling scenarios. In this respect, one of its major disadvantage is the timer randomness which could lead to long reaction time, possibly spawning additional unnecessary VMs. In this paper, we present an enhanced biomorphic algorithm which builds on the concept of fixed timer intervals and random seeds so that to coordinate the transformation procedure. The advantage of using a different method allowed us to achieve instant reactions and a more predictive nature of the system due to the fixed time spawning intervals.

In recent years, crowdsourcing has been actively used for many people to promote their products in online social networks (OSN), e.g., Twitter. The promotion activity in Twitter is typically performed by retweeting tweets by some accounts, called workers who earn monetary profits by deliberately promoting products for a requester. In this process, a fundamental question is whether the promotions by workers are really effective or not. To answer this question, in this paper, we analyze topological characteristics for the networks of workers, and evaluate the effect of promotions. Our empirical study demonstrates that the network of workers tends to be connected as a small-world network and their promotions show low impacts in the network of workers.

Cloud computing is rapidly reshaping the server administration landscape. The widespread use of virtualization and the increasingly high server consolidation ratios, in particular, have introduced unprecedented security challenges for users, increasing the exposure to intrusions and opening up new opportunities for attacks. Deploying security mechanisms in the hypervisor to detect and stop intrusion attempts is a promising strategy to address this problem. Existing hypervisor-based solutions, however, are typically limited to very specific classes of attacks and introduce exceedingly high performance overhead for production use.In this paper, we present Slick (Storage-Level Intrusion ChecKer), an intrusion detection system (IDS) for virtualized storage devices. Slick detects intrusion attempts by efficiently and transparently monitoring write accesses to critical regions on storage devices. The low-overhead monitoring component operates entirely inside the hypervisor, with no introspection or modifications required in the guest VMs. Using Slick, users can deploy generic IDS rules to detect a broad range of real-world intrusions in a flexible and practical way. Experimental results confirm that Slick is effective at enhancing the security of virtualized servers, while imposing less than 5% overhead in production.

Stack Overflow has been providing question and answering service for 7 years. It has become a tremendous knowledge repository for developers' thoughts and practices. Hyperlinks in discussion threads of Stack Overflow are essential knowledge entities for programming on the Web, such as a software library, an API documentation, a code example, or a tutorial. Tens of millions of hyperlinks are disseminated in Stack Overflow, while wisdom on what web resources have been highly recognized by the community is implicit in millions of discussion threads. In this paper, we develop the WisLinker framework that extracts knowledge from discussion, then turns knowledge into wisdom by learning through the knowledge dissemination history. With this wisdom, for a specific hyperlink that users are concerned with, WisLinker can recommend web resources highly recognized by the Stack Overflow community. We evaluate the validity of WisLinker in an open-ended setting using Stack Overflow data dump. We also implement a browser extension for live recommendation of web resources while users browse web pages. WisLinker could enable more efficient exploratory search and information discovery of programming-related web resources.

In cloud computing, multi-tenancy is concomitant with scalability in the sense that sharing a single deployment instance between many customer organizations (tenants) maximizes the utilization of the available resources. However, this also introduces the need to customize the application to the (slightly) different requirements of different tenants. In the context of workflow-based SaaS offerings, this is not straightforward to accomplish without compromising scalability or manageability of the offering. In this paper, we present a middleware for multi-tenant customization of workflows that enables software providers (i) to decrease coupling of multi-tenant customization concerns and workflow design for better manageability, (ii) to activate tenant preferences at runtime, but (iii) without incurring a scalability penalty. We validate a prototype implementation of our middleware in the context of a realistic SaaS application, evaluate its scalability, and extensively compare the results with related work confirming that the proposed middleware indeed supports customization of workflows in a significantly more scalable fashion. ispartof: pages:432-439 ispartof: Proceedings of the 2016 ACM Symposium on Applied Computing vol:04-08-April-2016 pages:432-439 ispartof: ACM Symposium on Applied Computing location:Pisa, Italy date:4 Apr - 8 Apr 2016 status: published

A major challenge of the existing attack detection approaches is the identification of relevant information to a particular situation, and the use of such information to perform multi-evidence intrusion detection. Addressing such a limitation requires integrating several aspects of context to better predict, avoid and respond to impending attacks. The quality and adequacy of contextual information is important to decrease uncertainty and correctly identify potential cyber-attacks. In this paper, a systematic methodology has been used to identify contextual dimensions that improve the effectiveness of detecting cyber-attacks. This methodology combines graph, probability, and information theories to create several context-based attack prediction models that analyze data at a high- and low-level. An extensive validation of our approach has been performed using a prototype system and several benchmark intrusion detection datasets yielding very promising results.

Electroencephalogram (EEG) signals play an important role in e-healthcare systems, especially to recognise the mental state of patients that could need a special care. This paper presents an EEG-based emotion recognition approach to detect the emotional state of patients for Ambient Assisted Living (AAL). The proposed approach combines wavelet energy, modified energy and wavelet entropy features to classify four different classes of emotions. Three different classifiers are used (quadratic discriminant analysis (QDA), k-nearest neighbor, and support vector machines) to recognise the emotion of patients robustly. The new approach is tested using the EEG database "DEAP" using four electrodes. It shows high performance compared to existing algorithms. An overall classification accuracy of 83.2% is obtained.

In this paper we take an important step towards better understanding the existence and extent of entity-centric language-specific bias in multilingual Wikipedia, and any deviation from its targeted neutral point of view. We propose a methodology using sentiment analysis techniques to systematically extract the variations in sentiments associated with real-world entities in different language editions of Wikipedia, illustrated with a case study of five Wikipedia language editions and a set of target entities from four categories.

Parsing Expression Grammars (PEGs) are a formalism for specifying the syntax of programming languages which can be viewed as a description of a recursive descent parser for the language it describes. An issue that users of PEG-based parsers face is poor reporting of syntax errors on the part of PEG-based parsers. Labeled failures are an extension to PEGs that aims to address this issue by annotating a PEG with labels corresponding to syntax errors, improving the quality of error messages generated by a PEG-based parser. An alternative formulation of PEGs gives the meaning of a PEG by translation into a program of a virtual parsing machine. In this paper, we present an updated semantics for PEGs with labeled failures, and we show how to extend the parsing machine to support them. We also prove the correctness of the translation from PEGs with labels to programs of this extended parsing machine, and we evaluate the use of labeled failures on PEG-based parsers which implement their own ad-hoc error reporting strategy.

Many user studies of software development use screen-capture software to record developers' behavior for post-mortem analysis. However, extracting behavioral patterns from screencaptured videos requires manual transcription and coding of videos, which is often tedious and error-prone. Automatically extracting Human-Computer Interaction (HCI) data from screen-captured videos and systematically analyzing behavioral data will help researchers analyze developers' behavior in software development more effectively and efficiently. In this paper, we present BPMiner, a novel behavior analysis approach to mine developers' behavior patterns from screencaptured videos using computer vision techniques and exploratory sequential pattern analysis. We have implemented a proof-of-concept prototype of BPMiner, and applied the BPMiner prototype to study the developers' online search behavior during software development. Our study suggests that the BPMiner approach can open up new ways to study developers' behavior in software development.

Recent computing paradigms like cloud computing and big data have become very appealing to outsource computation and storage, making it easier to realize personalized and patient centric healthcare through real-time analytics on user data. Although these technologies can significantly complement resource constrained mobile and wearable devices to store and process personal health information, privacy concerns are keeping patients from reaping the full benefits. In this paper, we present and evaluate a practical smartwatch based lifelog application for diabetics that leverages the cloud and homomorphic encryption for caregivers to analyze blood glucose, insulin values, and other parameters in a privacy friendly manner to ensure confidentiality such that even a curious cloud service provider remains oblivious of sensitive health data. ispartof: pages:119-124 ispartof: Proceedings of the 31st Annual ACM Symposium on Applied Computing vol:1 pages:119-124 ispartof: Symposium on Applied Computing location:Pisa, Italy date:4 Apr - 8 Apr 2016 status: published

While search engines sometimes return different documents containing contradictory answers, little is known about how users handle inconsistent information. This paper investigates the effect of search expertise (defined as specialized knowledge on the internal workings of search engines) on search behavior and satisfaction criteria of users. We selected four tasks comprising factoid questions with inconsistent answers, extracted answers that 30 study participants had found in these tasks, and analyzed their answer-finding behavior in terms of the presence or absence of search expertise. Our main findings are as follows: (1) finding inconsistent answers causes users with search expertise (search experts) to feel dissatisfied, while effort in searching for answers is the dominant factor in task satisfaction for those without search expertise (search non-experts); (2) search experts tend to spend longer completing tasks than search non-experts even after finding possible answers; and (3) search experts narrow down the scope of searches to promising answers as time passes as opposed to search non-experts, who search for any answers even in the closing stage of task sessions. These findings suggest that search non-experts tend to be less concerned about the consistency in their found answers, on the basis of which we discuss the design implications for making search non-experts aware of the existence of inconsistent answers and helping them to search for supporting evidence for answers.

In this paper, we present NCaaS (Network Configuration as a Service), a centralized cloud service for creating Virtual Networks (VNets) in SDN-based cloud architectures. NCaaS relieves tenants from the burdens and complexities of VNet creation and management by supporting dynamic provisioning operations based on Quality of Service (QoS), pricing, privacy, reliability, and energy constraints set by the tenant. NCaaS provides a unified interface through which tenants' network specifications and constraints are fed into the service provisioning algorithms. These algorithms, in turn, handle the negotiation with the different SDN Network as a Service (NaaS) providers and dynamically apply the necessary VNet creation, partitioning, and migration mechanisms to ensure the satisfaction of the tenants' preferences. To the best of our knowledge, NCaaS is the first centralized cloud service for creating, managing, and provisioning VNets dynamically on behalf of tenants and based on their requirements. A proof of concept test bed implementation of NCaaS is provided on top of the OpenVirteX network virtualization platform.

During software development, maintenance and evolution, we can assess the quality of code, design and architecture to capture hints of possible problems and face them as soon as possible. We can capture signals of architecture erosion in different ways by exploiting several tools with the aim to identify architectural violations, architectural smells or other relevant features. In this paper, we provide our experience report in using three known tools to capture information that can be useful to identify and evaluate the architectural debt of an application. We outline the main differences among these tools and the results they produce.

The teaching and learning of operating systems are hampered due to the complex nature of concepts involved. It is necessary not only theoretical studies, but importantly the laboratory practices, which are not always possible due to the lack of appropriate software instruments. This paper presents the NKE, a Nanokernel-based OS developed for ARM microprocessors and oriented to the teaching support for practical activities in operating systems classes. The NKE is developed in C language and has been successfully used in laboratory practices as part of OS and embedded systems classes. The results obtained so far are very exciting, given that the NKE promoted richer teaching and learning experiences, providing to the students a real environment for embedded OS development.

To cluster a data stream is a more challenging task than its regular batch version, having stricter performance constraints. In this paper an approach to this problem is presented, based on WiSARD, a memory-based artificial neural network (ANN) model. This model functioning was reviewed and improved, in order to adapt it to this task. The experimental results obtained support the use of this system for the analysis of data streams in an informative way.

Today, consumers request virtual resources like CPU, RAM, disk (etc.) supplied by the service providers (like Amazon) and they pay on a "pay-as-you-go" basis. Generally, the supervisors adopt virtualization technologies, which optimize resources usage and limit the operating cost. The virtualization technologies are classified in two categories. The first one concerns the heavy virtualization. Each virtual machines (VM) emulates hardware and embeds its own operating system (OS) that is completely isolated from the host OS. The second one concerns the light virtualization, which is based on the management of containers. The containers share the host OS kernel [5] while ensuring isolation. In this paper, we benchmark the performance and the energy consumption of an infrastructure that is based on the software Hadoop regarding the two technologies of virtualization. At first, we will identify the points to be improved concerning Hadoop performances and then we will reduce the deployment cost on the cloud. Second, the Hadoop community finds an in-depth study of the resources consumption depending on the environment of deployment. Our experiments are based on the comparison of the Docker technology (light virtualization) and VMware technology® (heavy virtualization). We come to the point that in most experiments the light technology offers better performances in completion time of workloads and it is more adapted to be used with the Hadoop software.

Large set cardinality estimators and other streaming oriented operations are the cornerstone of big data processing. Cardinality estimators combined with in-memory based storage systems provide a fast framework for keeping valuable application data easily queryable and maintanable. This has a plethora of applications. For instance, a common use case is to maintain a number of counters for monitoring application statistics for real time dashboard purposes. Another such case is large set size estimation for big data systems in internal operations like counting. In this paper is addressed the issue of scaling the computation of a cardinality estimator in the presence of node failures in a distributed setting. Moreover, for the proposed estimation technique eventual consistency is proved, which is adequate for most cases in distributed applications. To the best of the authors knowledge, this functionality is not currently provided by commonly used commercial and open source systems. Additionally, the proposed approach is generic enough to be applied to other algorithms, which can help build a basic framework for more complex operations in the big data field. We demonstrate this with graph metric calculation applications in the large scale biodata mining field.

Alzheimer's disease is the most common form of dementia. It is among the major causes of death in the world. It has been proved that Alzheimer's is associated with elevated concentrations of homocysteine. Being part of a complex biological system, we can model it to study its behaviour and predict its deregulations earlier. In this sense, mathematical modelling has limitations in behaviour study. In order to respond to these limits, the adoption of computer modelling has become increasingly widespread in systems biology. In this paper, we propose to use Petri nets to model a biological complex system related to Alzheimer's.

Nowadays, the integration of wireless sensor networks with cloud computing is of great interest to healthcare research and industry. In this context, the design of sensor clouds enhances the reliability, extensibility, scalability, and availability of several WSN applications. This paper proposes a novel Sensor Cloud architecture to monitor high-risk patients. On top of the physical sensors, we build virtual sensors with respect to the medical staff's demands. These virtual sensors are dynamically created and updated based on the patient's location and health status, and also depending on the occurrence of sensitive events related to the status of the associated physical sensors. In addition to that, the configuration of virtual sensors is enriched with new functions of monitoring, maintenance, power saving and security to enhance their use. A simulation is conducted to assess the effectiveness of the proposed architecture.

Spectrum sensing plays a critical role in improving spectrum utilization for cognitive radio networks. The majority of existing sensing approaches aim to detect the existence of a signal on a busy channel without differentiating whether a signal originates from a primary user or not. In this paper, we attempt to solve this challenge by an experimental approach using GNU radio and Universal Software Radio Peripheral (USRP) board. A two-stage sensing strategy is proposed to detect channel states. The first stage detects whether a channel is busy or idle by observing both the energy level and bandwidth. Once the channel is busy, we further determine the channel is occupied by a Primary User (PU) and an Secondary User (SU). From experiments, we observe that a receiver cannot successfully receive a signal if it implements different demodulation schemes with a transmitter. For example, a Binary Phase Shift Keying (BPSK) demodulator cannot detect a Quadrature Phase Shift Keying (QPSK) signal transmitted by a transmitter. Motivated by this observation, we propose that at the second stage, PUs and SUs are negotiated to conduct different modulations. Then SUs are equipped with two demodulators that will be able to demodulate both the PUs signal and the SUs signal. Given that SU and PU perform distinct modulations, once the PU comes to its licensed channel, the SU communicating on that channel will recognize it and withdraw from the channel immediately. Extensive experiments using GNU radio and USRP boards are carried out under various scenarios to evaluate the effectiveness of the proposed approach.

As smartphones become more and more popular, O2O (Offline-to-Online) applications have been spotlighted. They provide users offline data using NFC, QR, and Beacon technologies. But, there are some problems such as the high cost of server maintenance and the private data spills since they are implemented on WWW technologies. This paper suggests PWW (Person Wide Web) platform that provides server-less service on smartphones. In PWW, a smartphone has a PWW browser which is composed of an anchor scanner, a web view, an API linkage library, and internal/cloud storages. When the PWW browser scans an anchor includes a URL, it opens a web document stored in the internal storage of the smartphone or its corresponding cloud. The web document contains the API linkage library to utilize the several devices like a camera, GPS, and so on in the smartphone. In order to show the usefulness of PWW platform, we described the example of handling private data such as business cards. The PWW platform could be applied to various area in real world by developing web-based mobile applications.

Dynamic Time Warping (DTW) is a widely used distance measure for time series data mining. Its quadratic complexity requires the application of various techniques (e.g. warping constraints, lower-bounds) for deployment in real-time scenarios. In this paper we propose a randomized greedy warping algorithm for finding similarity between time series instances. We show that the proposed algorithm outperforms the simple greedy approach and also provides very good time series similarity approximation consistently, as compared to DTW. We show that the Randomized Time Warping (RTW) can be used in place of DTW as a fast similarity approximation technique by trading some classification accuracy for very fast classification.

As the era of Big-data has risen, the importance of big data technologies is also increasing day by day. Especially, Hadoop has become a critical part of the overall Big-data system because of its ability to store, process, and analyze thousands of terabytes of data. A major issue for supporting high performance on Hadoop is managing the growth of data while satisfying high storage I/O request. Hadoop's overall performance is largely influenced by the storage input/output(I/O). However, storage I/O technologies are still very limited. Therefore, now more than ever, studies on improving storage I/O on a distributed file system of Hadoop(HDFS) have been gaining popularity. To this end, latest trend in storage systems is to utilize hybrid storage devices. However, it is not easy to use the information of heterogeneous storage devices in HDFS. This is because, when reading data, HDFS is unable to exploit such heterogeneous storage type information yet. In this paper, we propose a hybrid block-selection method on the HDFS, we consider the storage type such as SSD and HDD when reading data. Using this method, the Hadoop Eco System utilizes the high SSD bandwidth by priority. As a result, we certainly improve the Hadoop Eco System overall performance. In the experiments, we demonstrated that our new method efficiently reduced the execution time of select count(*) query and TPC H benchmark up to 22% and 30% on average.

This paper presents an empirical study of exception handling strategies in Java libraries. The study conducts an integrated analysis of exception flows and handler actions from Java libraries with the aim to understand the impact of adopted exception handling strategies from the perspective of libraries' users. We extended an existing static analysis tool to identify exception flows in software libraries and collected data from the eight most downloaded Java libraries in Maven repository. After that, manual analysis was performed to categorize the tailored handler actions for each exception handler. Our results show that a high number of uncaught runtime exceptions and subsumption handlers were applied in some libraries. We also investigated the community anti-patterns implemented by handler actions in exception flows. Our results reveal the need to have automated support to allow the integrated analysis of exception flows and their handler actions explicitly.

Lack of trust and transparency are among the main reasons hindering adoption of cloud computing. Users in fact can inspect neither their applications nor the treatment of their data, and have little or no guarantees about their security. In this context, there is a pressing need for assurance techniques supporting some key properties of cloud services and applications. Cloud security certification is a major assurance technique that has been proposed to increase cloud security, trust, and transparency. However, certification is a tedious, costly, and time-consuming process for the provider that wants to certify one of its services/applications. In this paper, we propose a test-based security certification framework for the cloud implementing a certification process and a cloud engineering methodology based on it, which supports providers in the design and development of ready-to-be-certified services/applications.

Documentation debt is a type of technical debt that describes problems in documentation such as missing, inadequate or incomplete artifacts. Unlike traditional methods, agile methodologies usually employ short iterative cycles and rely on tacit knowledge within a team. In particular, Agile Requirements (AR) (e.g., user stories) tend to reduce the focus on requirements specification activities. This scenario contributes to the occurrence of documentation debt. The goal of this paper is to investigate the impact that this type of debt brings to projects developed by using AR. We address this goal by performing a retrospective study in a real software project that used AR in its development. Our analysis was concentrated on data from 132 maintenance and evolution tasks. Of this total, 65 were related to the presence of documentation debt and were performed within a timeframe of 18 months. The findings indicated an extra maintenance effort of about 47% of the total effort estimated for developing the project and an extra cost of about 48% of the initial cost of the development phase.

The conventional application of learning to rank algorithms tends to use as many training queries as possible to leverage the benefit brought by a large amount of labeled data. However, the use of all training queries available may also include the low quality ones, and consequently, degrades the retrieval effectiveness, hence the need for selecting training queries. Existing training query selection approaches incorporate a variety of indirect indicators of the training queries such as the query performance predictors and the relevance scores into a classification or regression based approach. In this paper, we propose to select training queries by the direct measurement of the training query quality, namely the resulting retrieval performance on a subset of validation queries, instead of the indirect indicators that may not have strong correlations with a training query's quality. Evaluation on the standard LETOR 4.0 dataset shows that our proposed approach outperforms the state-of-the-art baselines.

The identification of relevant information in large text databases is a challenging task. One of the reasons is human beings' limitations in handling large volumes of data. A common solution for scavenging data from texts are word clouds. A word cloud illustrates word usage in a document by resizing individual words in documents proportionally to how frequently they appear. Even though word clouds are easy to understand, they are not particularly efficient, because they are static. In addition, the presented information lacks context, i.e., words are not explained and they may lead to radically erroneous interpretations. To tackle these problems we developed VCloud, a tool that allows the user to interact with word clouds, therefore allowing informative and interactive data exploration. Furthermore, our tool also allows one to compare two data sets presented as word clouds. We evaluated VCloud using real data about the evolution of gastritis research through the years. The papers indexed by Pubmed related to this medical context were selected for visualization and data analysis using VCloud. A domain expert explored these visualizations, being able to extract useful information from it. This illustrates how can VCloud be a valuable tool for visual text analytics.

In this paper, we develop a novel probabilistic hybrid inertial navigation system (INS)/pedestrian dead reckoning (PDR) measurement tracking algorithm, namely (PHIMTA), that provides high accuracy tracking in slow pedestrian scenarios. We then combine it with the latest localisation algorithms, such as grid-based belief propagation (GBP) and stop-and-go (SnG), that allow for improved accuracy in GPS-denied environments.

Planning and scheduling problems are omnipresent, not only in logistics, but also in timetabling human resources, and underneath in software platforms. These problems can have static nature, i.e. there is no change in the problem information after schedule is generated. In contrast, dynamic problems constantly need to deal with unexpected events (e.g., traffic jams, accidents, new tasks). There are two common approaches to deal with these problems: static approaches, which generate a schedule in one shot; and dynamic approaches, which keep running like an ongoing concern. With the advent of research on smart cities, smart scheduling techniques are in focus. They address the dynamic problems using smart technologies for generating and executing schedules. Without proper means to compare, it is hard, if not impossible, to select the best approach for a given problem. Due to lack of comparison between the static and the dynamic approaches, currently industry relies almost solely on the static approaches. But moving towards smart cities, with every device being interconnected and real time events required to be handled, makes it necessary to adapt to dynamic approaches. In this paper, we argue the need for comparison of the static and the dynamic planning approaches. We have identified the key challenges for realizing such comparison and propose two techniques for making such comparisons. These techniques are applicable in a wide variety of applications.

This paper presents a new data stream preprocessing approach based on texture analysis from Recurrence Plot (RP). We adopt 12 real datasets related to sound recognition, signals processing, chemistry reactions and others. We consider data streams as discrete time series with a fixed number of observations. Classifier models were created using SVM and C5.0 algorithms with features extracted from RP using texture descriptors like to Gabor filter, GLCM and DCT-2D. Our analysis shows that the influence of stochastic-deterministic rate obtained by RQA on data stream reconstructed in phase space have motivated the preprocessing step to classification, outperforming another approach applied an audio stream that uses model's coefficients as features in around 16% of average accuracy and in around 67.66% the state-of-art algorithm that uses distance as measure.

Global demographics show a steady growth in the population of cognitively impaired patients. Consequently, the aging societies are looking to adopt smart technologies in healthcare services to early detect the onset of cognitive decline. These technologies include advanced methods that enable continuous in-house monitoring of the elderly's activities through unobtrusive sensing for recognizing abnormal behaviors that may indicate cognitive deficits. In an earlier work, we proposed a technique to detect the early symptoms of cognitive impairment by continuously monitoring the daily behavior of an elderly at home to recognize fine-grained abnormal behaviors. Recognition was based on rule-based descriptions of anomalies manually defined by domain experts. However, those rules strongly depend on the specific home environment, on the used sensors, and on the particular habits of the elderly; hence, their definition is time-expensive, and rules are not seamlessly portable to different environments. In order to address this issue, in this paper we propose a method to automatically learn the rule-based definitions of behavioral anomalies. In particular, we use a rule induction algorithm to infer those rules based on a dataset of activities and anomalies. We evaluated our method using a dataset of activities and abnormal behaviors carried out in an instrumented smart home. Our method achieves high precision and recall values, around 0.97 and 0.85, respectively, which are comparable to those obtained using manually-defined rules.

SimRank is a well-known link-based similarity measure that can be applied on a citation graph to compute similarity of academic literature data. The intuition behind SimRank is that two objects are similar if they are referenced by similar objects. SimRank has attracted a growing interest in the areas of data mining and information retrieval recently. Despite of the current success of SimRank, it has some problems that negatively affect its effectiveness in similarity computation. In this paper, we discuss the three existing problems of SimRank, present SimRank variants that have been proposed to solve those problems, and evaluate the effectiveness of SimRank and its variants in similarity computation for academic literature data by conducting extensive experiments on a real-world dataset.

The large scale and dynamic behavior of cyber-physical systems (CPS) require great effort at the middleware level. Middleware lies at the architectural point where low-level communication and execution meet the horizontal control and orchestration accross nodes and systems. Most realtime research on distributed CPS concentrates on off-line schedulability algorithms for systems with static structure and make heavy assumptions on the network predictability, neglecting the role of the middleware layer. In this paper, we explore the usage of middleware technology to support dynamic and timely execution in CPS, with a middleware design that considers the time requirements of the distributed processes or units. We have carried out a specific implementation that validates or approach, showing a stable service time for large numbers of clients, over an actual network that uses Internet protocols for transport and routing.

Data reduction schemes accompany additional metadata maintenance overhead and computation overhead, they have been implemented in host systems with relatively rich amounts of resources rather than the inside of storage devices with insufficient resources. However, following the recent advent of flash memory based Solid State Drives (SSD), attempts to apply these schemes to the inside of storage devices have been active. Nevertheless, complex data reduction schemes such as compression and deduplication cannot still be easily utilized in the inside of SSD without being accelerated by additional hardware. In this paper, we develop a zerofree FTL (zf-FTL), an FTL which equips with the zero page elimination functionality. The zf-FTL effectively reduces the amount of data that will be actually written to the flash memory without requiring any additional data structure or dedicated hardware.

In this paper, we investigate an optimization scheme for extreme learning machine (ELM) regression, named OELR, to overcome the limitation of ELM that it may lead to overfitting on large training data sets. OELR amounts to minimization of e-insensitive loss and minimization of the norm of the output weights of single hidden layer feedforward networks (SLFNs). Compared to support vector regression (SVR), OELR has less optimization constraints. Empirical results on the benchmark data sets show that the competitive performance of the OELR over the state-ofthe-art regression learning algorithms.

In this paper, we explore how legacy software logic can be re-used in a new context-of-use. In effect, oftentimes existing software is required to evolve or to be integrated into a new complex environment. Technological improvements, new usages, regulations or financial constraints are such reasons for evolution. Parts of the legacy functions are still useful and it is certainly challenging to integrate such code, as legacy systems were designed and implemented for a specific purpose in the past. Nevertheless, they are an expensive investment and transforming them to allow modification and integration with other actors would not only save high investment, but also enable future modifications. We consider that an adequate technique to drive the integration of legacy systems into self-adaptive systems is to transform first the legacy code into a multi-agent system (MAS), from which adaptation within the system and communication with external actors is easier to attain. We refer to this process as "adaptization". We use as an adaptation proxy conceptually intelligent agents conforming to the Belief, Desire, Intention (BDI) model, one of the tools of Agent-Oriented Software Engineering (AOSE). This proxy permits to understand the legacy system in terms of goals to achieve and to address adaptiveness as a goal selection problem at run-time (based on context), thus abstracting away the current implementation to a high level goal-achievement mechanism. We propose KOALA, a novel stepwise methodology towards adaptization.

Today's Web Applications (WAs) are complex multi-user and multi-tenant software systems, used by users with different roles and often developed to support and manage complex business processes. Due to the changing nature of such processes, WAs need to be easily and quickly modified, to be adapted and aligned to the processes they support. In recent years, Model Driven Engineering (MDE) approaches have been used to support the development and the evolution of WAs. However, the definition of appropriate MDE approaches for the development of flexible process-centric WAs is still limited. In particular, flexible workflow models have never been integrated with the models used in MDE approaches to develop this type of applications. This paper proposes an MDE approach for the development and the evolution of flexible process-centric WAs that integrates four MDE metamodels used to represent the main components of a WA with the metamodel of Declare, a declarative language to model business processes. A case study, carried out to validate the feasibility, the correctness and the effectiveness of the proposed approach is presented and discussed.

This paper presents the application of shape distributions to diagnose lung nodules in computerized tomography images. Our study uses the nodule surface and also interior surfaces generated by their voxel's joint attributes and distribution. The surfaces are captured using 3D Alpha Shapes algorithm. All surfaces are characterized using shape distributions D1, D2, D3, D4 and A3 in order to represent shape behavior based on statistical distribution of their points in a 3D contour. We evaluate experiments with these features using Support Vector Machines and the combination of all descriptors proving above of 90% of accuracy. This indicates a promising feature to distinguish malignant from benign nodules for lung cancer diagnosis.

This paper presents a head-tracking system based on an inertial sensor that sends rotation data to an Android application in charge of recording and visualizing data in real time. Data are sent via wireless channel using Bluetooth Low Energy communication and are processed to provide a realistic real-time 3D animation of the user head.