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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.

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.

Novel technologies represent a promising way for boosting learning quality in scientific sectors, by engaging students more effectively into experimental activities. Mobile devices allow increasing learning effectiveness significantly, thanks to their diffusion, especially amongst youngsters. Built-in sensors can enable easily-deployable, low-cost and large-scale sensing networks, which exploit the Mobile Crowd-Sensing (MCS) paradigm and involve their owners directly into scientific monitoring activities. For such reasons, in this paper we present a MCS-based noise-sensing platform for engaging students in learning acoustics and sound metering. The proposed platform can be included into traditional didactic activities about those topics. This capability is fostered also by the Bring Your Own Device (BYOD) approach, which further eases the platform diffusion across students. Moreover, BYOD paves the way for the platform application as a MOOC (Massive Online Open Course) enabler. The paper presents the adopted design and implementation methodologies, as well as the platform prototype and its technical validation. We believe that our approach offers students new possibilities for learning how to use professional sound level meters profitably and how to perform measurements correctly.

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.

International audience; The paper is devoted to effective check of the existence and derivation of adaptive distinguishing sequences (distinguishing test cases) for possibly nondeterministic partial Finite State Machines (FSMs). The complexity of these problems for nondeterministic FSMs remains unknown, however the length of the corresponding test case is shown to be exponential. In this paper, we address FSM classes that allow to derive specific FSM projections for which the problem of checking whether a given FSM is adaptively distinguishing or not can be performed in polynomial time. In order to estimate the length of distinguishing test cases for FSMs of these classes we improve the upper bound on the length of an adaptive distinguishing test case for partial deterministic FSMs. The listed contributions make it possible to apply the proposed techniques for testing 'real' technical systems which behavior is decribed by (partial) nondeterministic FSMs

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.

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

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.

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

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.

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.

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.

This paper tackles the problem of estimating the volume of social media posts (e.g., tweets) that pertain to a particular topic. This task differs from related filtering and event detection applications in that the filtered content isn't meant for direct human consumption, but rather we are primarily interested in estimating the cardinality of relevant posts. We present a simple yet effective technique for generating and curating keywords to create what we call "overlap filters", which can be applied to a stream of social media posts. Our approach leverages human labeling and thus a crucial element of the work involves minimizing the cost of human computation. On top of a "day zero" cold start algorithm, we describe a number of optimizations that take advantage of history to further reduce labeling costs. Experimental results show that our overlap filters produce accurate volume estimates at low costs, and our method is simple enough to deploy in practice.

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.

To cope with large data sets, distributed data stores partition their data across servers. However, real-world systems usually do not provide useful transactional semantics for operations accessing multiple partitions due to the delays involved in achieving multi-partition consistency. Read Atomic Multi-Partition (RAMP) transactions have recently been proposed as efficient light-weight multi-partition transactions that guarantee read atomicity: either all updates or no updates of a transaction are visible to other transactions. In this paper we formalize RAMP transactions in rewriting logic and perform model checking verification of key properties using the Maude tool. In particular, we develop detailed formal models---and formally analyze---a number of extensions and optimizations of RAMP that are only briefly mentioned by the RAMP developers.

International audience; Analysis of floating-point programs is a topic that received an increasing attention the past few years. However, only very few works have been done regarding their termination analysis. We address that problem in this paper. We presenta technique that takes advantage of the already existing works on termination analysis of rational programs. Our approach consists in translating the floating-point programs into rational ones by means of sound approximations. Weapproximate the floating-point expressions using piecewise linear functions. Our approximation differs from the already existing ones in the sense that it can be as precise as needed.

Software Defined Network(SDN) is a promising technological advancement in the networking world. It is still evolving and security is a major concern for SDN. In this paper we proposed policy based security architecture for securing the SDN domains. Our architecture enables the administrator to enforce different types of policies such as based on the devices, users, location and path for securing the communication in SDN domain. Our architecture is developed as an application that can be run on any of the SDN Controllers. We have implemented our architecture using the POX Controller and Raspberry Pi 2 switches. We will present different case scenarios to demonstrate fine granular security policy enforcement with our architecture.

Image captions and keywords are the semantic descriptions of the dominant visual content features in a targeted visual scene. Traditional image keywords extraction processes involves intensive data- and knowledge-level operations by using computer vision and machine learning techniques. However, recent studies have shown that the gap between pixel-level processing and the semantic definition of an image is difficult to bridge by counting only the visual features. In this paper, augmented image semantic information has been introduced through harnessing functions of online image search engines. A graphical model named as the “Head-words Relationship Network” (HWRN) has been devised for tackling the aforementioned problems. The proposed algorithm starts from retrieving online images of similarly visual features from the input image, the text content of their hosting webpages are then extracted, classified and analysed for semantic clues. The relationships of those “head-words” from relevant webpages can then be modelled and quantified using linguistic tools. Experiments on the prototype system have proven the effectiveness of this novel approach. Performance evaluation over benchmarking state-of-the-art approaches has also shown satisfactory results and promising future applications.

Estimating an individual's environmental exposure is a complicated problem that depends on the amount of time of the individual's exposure, the uncertain location of the individual, and the uncertainty in the levels of environmental factors based on available localized measurements. This problem is critical in the applications of environmental science and public health. In this paper we study the fundamental issues related to spatio-temporal uncertainty of human trajectories and environmental measurements and define a model of exposure uncertainty. We adopt a geometric data structure called the Voronoi diagram to interpolate environmental data, and utilize it in our proposed method to efficiently solve this problem. We evaluate the performance of the proposed method through experiments on both synthetic and real road networks. The experimental results show that our solution based on probabilistic routing aggregation is an efficient and extensible method for environmental exposure time estimation.

Hardware failures are a major cause of computer system crashes. To improve system reliability, making device drivers sufficiently robust to tolerate hardware failures is important. Unfortunately, testing device drivers against hardware failures is not easy because hardware failures must be artificially produced in a controlled manner. Prior work has required source code, OS-dependent implementation efforts, or special virtual environments for testing in which it is difficult to find problems that only occur in real environments. This paper presents FaultVisor, a bare-metal hypervisor specialized for testing device drivers. FaultVisor injects pseudo hardware failures by modifying access to real hardware using the hypervisor layer, allowing closed-source and OS-independent driver testing in real environments, and easy insertion of test systems into existing systems. In our evaluation, FaultVisor discovered 41 fault patterns that disclose problems in device drivers, 30 of which led to critical system failures.

Can we use information from social media and crowdsourced images to detect smoke and assist rescue forces? While there are computer vision methods for detecting smoke, they require movement information extracted from video data. In this paper we propose SmokeBlock: a method that is able to segment and detect smoke in still images. SmokeBlock uses superpixel segmentation and extracts local color and texture features from images to spot smoke. We used real data from Flickr and compared SmokeBlock against state-of-the-art methods for feature extraction. Our method achieved performance superior than the competitors, for the task of smoke detection. Our findings shall support further investigations in the field of image analysis, in particular, concerning images captured with mobile devices.

Classifying tweets is an intrinsically hard task as tweets are short messages which makes traditional bags of words based approach ine cient. In fact, bags of words approaches ig- nores relationships between important terms that do not co-occur literally. In this paper we resort to word-word co-occurence informa- tion from a large corpus to expand the vocabulary of another corpus consisting of tweets. Our results show that we are able to reduce the number of erroneous classi cations by 14% using co-occurence information.

International audience; When integrating hard, soft and non-real-time tasks in general purpose operating systems, it is necessary to provide temporal isolation so that the timing properties of one task do not depend on the behaviour of the others. However, strict budget enforcement can lead to inefficient use of the computational resources in the presence of tasks with variable workload. Many resource reclaiming algorithms have been proposed in the literature for single processor scheduling , but not enough work exists for global scheduling in mul-tiprocessor systems. In this paper we propose two reclaiming algorithms for multiprocessor global scheduling and we prove their correctness. We also present their implementation in the Linux kernel and we compare their performance on synthetic experiments.

Real-time interactive application workloads (e.g. web search, social networking, etc.) are composed of a remarkably large number of mini request partitions that require stringent delayminimal aggregation of responses and their delivery back to the user. This paper presents a novel Time-Deadline-AwarepFabric (TDA-pFabric) being a data transport design whose objectives are two-fold: i) reducing the mean Flow Completion Time (FCT), the deadline mismatch and blocking probabilities as well as ii) improving the mean application throughput. A stochastic queueing model is proposed herein to capture TDA-pFabric's dynamics and evaluate its performance. The model is validated through extensive simulations whose results also show that TDA-pFabric exhibits significant improvements over existing designs in terms of the above-listed quality-of-service metrics. Scopus

This paper presents a multi-objective optimization framework for the environmental economic dispatch problem in microgrids. Besides classic constraints, also prohibited operating zones and ramp-rate limits of the generators are here considered. Pareto-optimal solutions are generated through the NSGA-II algorithm with customized constraint handling. The optimal solution is selected with TOPSIS. Simulations carried out on a prototype microgrid showed the effectiveness of the proposed framework in handling scenarios with Pareto fronts having up to four discontinuities.

Spreadsheets are nowadays used in a variety of contexts, including in in manipulatin large and complex data. This data is stored in a large unstructured matrix, which is hard to understand and to manipulate. Recent research has been done to manipulate and query such unstructured data, namely by proposing different query approaches to spreadsheets. In this paper we present an empirical study evaluating three recent query approaches to spreadsheets assessing their usage to query spreadsheets. The results of our study show that the end-users' productivity increases when using visual, model-driven queries are used., (SFRH/BPD/112733/2015)

We describe a tool-supported performance exploration approach in which we use genetic algorithms to find a potential user behavioural pattern that maximizes the resource utilization of the system under test. This work is built upon our previous work in which we generate load from workload models that describe the expected behaviour of the users. In this paper, we evolve a given probabilistic workload model (specified as a Markov Chain Model) by optimizing the probability distribution of the edges in the model and generating different solutions. During the evolution, the solutions are ranked according to their fitness values. The solutions with the highest fitness are chosen as parent solutions for generating offsprings. At the end of an experiment, we select the best solution among all the generations. We validate our approach by generating load from both the original and the best solution model, and by comparing the resource utilization they create on the system under test.

Architectural tactics are the building blocks of software architecture. They describe solutions for addressing specific quality concerns, and are prevalent across many software systems. Once a decision is made to utilize a tactic, the developer must generate a concrete plan for implementing the tactic in the code. Unfortunately, this is a non-trivial task for even experienced developers. Developers often resort to using search engines, crowd-sourcing websites, or discussion forums to find sample code snippets. A robust Tactic Search Engine can replace this manual, internet-based search process and help developers to reuse proper architectural knowledge and accurately implement tactics and patterns from a wide range of open source systems. In this paper we analyze several implementations of architectural tactics in the open source community and identify the foundation for building a practical Tactic Search Engine. We also introduce the concept of tactical-clones which may be used as the basic element of a tactic search engine.

Real-time collaboration among geographically distributed users is becoming increasingly important in our private and professional lives. Operational Transformation (OT) is a well established technique for consistency maintenance in the domain of collaborative text editing. In this paper we expand the scope of OT to support simultaneous editing of shared JSON objects. The presented approach can be used in almost any modern web framework to implement collaboration tools that are, in contrast to popular real-time collaboration tools like Google Docs, no longer restricted to text documents. Alongside with a detailed presentation of the underlying data structures and mechanics we discuss how web applications can be designed to use our extension.

This paper presents an extension to the Arduino framework that introduces multitasking support and allows running multiple concurrent tasks in addition to the single execution cycle provided by the standard Arduino framework. The extension has been implemented through the ERIKA Enterprise open-source real-time kernel, while maintaining the simplicity of the programming paradigm typical of the Arduino framework. Furthermore, a support for resource sharing has also been integrated in the external Arduino libraries to guarantee mutual exclusion in such a multi-task environment.

Data streams generated in real-time can be strongly temporally dependent. In this case, standard techniques where we suppose that class labels are not correlated may produce sub-optimal performance because the assumption is incorrect. To deal with this problem, we present in this paper a new algorithm to classify temporally correlated data based on deferral learning. This approach is suitable for learning over time-varying streams. We show how simple classifiers such as Naive Bayes can boost their performance using this new meta-learning methodology. We give an empirical validation of our new algorithm over several real and artificial datasets.

Linearizability is a widely recognised correctness criterion for concurrent objects. A number of proof methods for verifying linearizability exist. In this paper, we simplify one such method with a systematic approach for invariant generation. Although this existing refinement-based method is itself systematic and fully tool-supported, it requires the verifier to provide a specific invariant over the implementation. While a chosen invariant may suffice for some proof obligations of the method, it may not for others resulting in a new, stronger invariant to be chosen and the previously completed proof steps to be redone. Our approach avoids such wasted proof effort by generating an invariant which is guaranteed to be sufficient for all proof obligations.

This paper presents FIDE, an Integrated Development Environment (IDE) for building applications using Functional Mock-up Units (FMUs) that implement the standardized Functional Mock-up Interface (FMI). FIDE is based on the actororiented Ptolemy II framework and leverages its graphical user interface, simulation engine, and code generation feature to let a user arrange a collection of FMUs and compile them into a portable and embeddable executable that efficiently co-simulates the ensemble. The FMUs are orchestrated by a well-vetted implementation of a master algorithm (MA) that deterministically combines discrete and continuous-time dynamics. The ability to handle these interactions correctly hinges on the implementation of extensions to the FMI 2.0 standard. We explain the extensions, outline the architecture of FIDE, and show its use on a particularly challenging example that cannot be handled without the proposed extensions to FMI 2.0 for co-simulation.

When testing from a deterministic Finite State Machine specification M of an implementation N, state identification plays an essential role. In the case that M does not have a single input sequence to identify its states, state identification has to be performed by using a set of input sequences, elements of which need to be applied at the same state of N. Locating sequences have been proposed in the literature in order to guarantee that N is brought back to the same state over and over, in order to apply these input sequences. Although the lengths of locating sequences are exponential in general, there are some methods proposed in the literature to reduce their length. In this paper, we present an improved method for reducing the length of locating sequences. We give a formal description of our method and explain how the existing methods relate to our method.