Query: Anno : A Graphical Tool for the Transcription and Onthe-Fly Annotation of Handwritten Documents

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The ASK Corpus - a Language Learner Corpus of Norwegian as a Second Language <=SEP=> In our paper we present the design and interface of ASK, a language learner corpus of Norwegian as a second language which contains essays collected from language tests on two different proficiency levels as well as personal data from the test takers. In addition, the corpus also contains texts and relevant personal data from native Norwegians as control data. The texts as well as the personal data are marked up in XML according to the TEI Guidelines. In order to be able to classify “errors” in the texts, we have introduced new attributes to the TEI corr and sic tags. For each error tag, a correct form is also in the text annotation. Finally, we employ an automatic tagger developed for standard Norwegian, the “Oslo-Bergen Tagger”, together with a facility for manual tag correction. As corpus query system, we are using the Corpus Workbench developed at the University of Stuttgart together with a web search interface developed at Aksis, University of Bergen. The system allows for searching for combinations of words, error types, grammatical annotation and personal data.

WordFreak: An Open Tool for Linguistic Annotation <=SEP=> WordFreak is a natural language annotation tool that has been designed to be easy to extend to new domains and tasks. Specifically, a plug-in architecture has been developed which allows components to be added to WordFreak for customized visualization, annotation specification, and automatic annotation, without re-compilation. The APIs for these plug-ins provide mechanisms to allow automatic annotators or taggers to guide future annotation to supports active learning. At present WordFreak can be used to annotate a number of different types of annotation in English, Chinese, and Arabic including: constituent parse structure and dependent annotations, and ACE named-entity and coreference annotation. The Java source code for WordFreak is distributed under the Mozilla Public License 1.1 via SourceForge at: http://wordfreak.sourceforge.net. This site also provides screenshots, and a web deployable version of WordFreak.

Comparison between JSON and XML in Applications Based on AJAX <=SEP=> As the core technology of Web 2.0, Ajax has caught more and more attention. Xml, as the traditional data load format, needs to be resolved by DOM (Document Object Model ) both in client-side and server-side, which wastes system resource and makes a great reduction of user-friendliness. In this paper, a light-weightdata-interchanging format-JSON (Java Script Object Notation) will be introduced, which provides a higher level of flexibility and efficiency. We make a comparison between JSON and XML through expriment, then use JSON as data-transfering format in an actual project. Results show that JSON is more suitable as a data-loading tool for Ajax applications.

UIMA: an architectural approach to unstructured information processing in the corporate research environment <=SEP=> IBM Research has over 200 people working on Unstructured Information Management (UIM) technologies with a strong focus on Natural Language Processing (NLP). These researchers are engaged in activities ranging from natural language dialog, information retrieval, topic-tracking, named-entity detection, document classification and machine translation to bioinformatics and open-domain question answering. An analysis of these activities strongly suggested that improving the organization’s ability to quickly discover each other's results and rapidly combine different technologies and approaches would accelerate scientific advance. Furthermore, the ability to reuse and combine results through a common architecture and a robust software framework would accelerate the transfer of research results in NLP into IBM’s product platforms. Market analyses indicating a growing need to process unstructured information, specifically multilingual, natural language text, coupled with IBM Research’s investment in NLP, led to the development of middleware architecture for processing unstructured information dubbed UIMA. At the heart of UIMA are powerful search capabilities and a data-driven framework for the development, composition and distributed deployment of analysis engines. In this paper we give a general introduction to UIMA focusing on the design points of its analysis engine architecture and we discuss how UIMA is helping to accelerate research and technology transfer. 1 Motivation and Objectives We characterize a UIM application as a software system that analyzes large volumes of unstructured information while consulting structured information sources to discover, organize and present knowledge relevant to the application’s end-user. We define structured information as information whose intended meaning is unambiguous and explicitly represented in the structure or format of the data. The canonical example of structured information is a relational database table. We define unstructured information as information whose intended meaning is only loosely implied by its form. The canonical example is a natural language document; other examples include voice, audio, image and video. To appear in a special issue of the Journal of Natural Language Engineering 2004 2 Ferrucci and Lally Do Not Distirubte without written permission from authors Market indicators suggest a rapid increase in the use of text and voice analytics to a growing class of commercially viable applications (Roush 2003). National security interests are driving applications to involve an even broader set of UIM technologies including image and video analytics. Emerging and commercially important UIM application areas include life sciences, e-commerce, technical support, advanced search, and national and business intelligence. In six major labs spread out over the globe, IBM Research has over 200 people working on Unstructured Information Management (UIM) technologies with a primary focus on Natural Language Processing (NLP). These researchers are engaged in activities ranging from natural language dialog, information retrieval, topic-tracking, named-entity detection, document classification and machine translation to bioinformatics and open-domain question answering. Each group is developing different technical and engineering approaches to process unstructured information in pursuit of their specific research agenda in the UIM space. While IBM Research’s independent UIM technologies fare well in scientific venues, accelerated development of integrated, robust applications is becoming increasingly important to IBM’s commercial interests. Furthermore the rapid integration of different alogorthims and techniques is also a means to advance scientific results. For example, experiments in statistical name-entity detection or machine translation may benefit from using a deep parser as a feature generator. Facilitating the rapid integration of an independently produced deep parser with statistical algorithm implementations can accelerate the research cycle time as well as the time to product. Some of the challenges that face a large research organization in efficiently leveraging and integrating its (and third-party) UIM assets include: • Organizational Structure. IBM Research is largely organized in three to five person teams geographically dispersed among different labs throughout world. This structure makes it more difficult to jointly develop and reuse technologies. • Skills Alignment. There are inefficiencies in having PhDs trained and specialized in, for example, computational linguistics or statistical machine learning, develop and apply the requisite systems expertise to deliver their work into product architectures and/or make it easily usable by other researchers. To appear in a special issue of the Journal of Natural Language Engineering 2004 3 Ferrucci and Lally Do Not Distirubte without written permission from authors • Development Inefficiencies. Research results are delivered in a myriad of interfaces and technologies. Reuse and integration often requires too much effort dedicated to technical adaptation. Too often work is replicated. • Speed to product. Software Products introduce their own architectures, interfaces and implementation technologies. Technology transfer from the lab to the products often becomes a significant engineering or rewrite effort for researchers. A common framework and engineering discipline could help address the challenges listed above. The Unstructured Information Management Architecture (UIMA) project was initiated within IBM Research based on the premise that a common software architecture for developing, composing and delivering UIM technologies, if adopted by the organization, would facilitate technology reuse and integration, enable quicker scientific experimentation and speed the path to product. IBM’s Unstructured Information Management Architecture (UIMA) high-level objectives are two fold: 1) Accelerate scientific advances by enabling the rapid combination of UIM technologies (e.g., natural language processing, audio and video analysis, information retrieval, etc.). 2) Accelerate transfer of UIM technologies to product by providing an architecture and associated framework implementations that promote reuse and support flexible deployment options. 2 The Unstructured Information Management Architecture (UIMA) UIMA is a software architecture for developing UIM applications. The UIMA high-level architecture, illustrated in Figure 1, defines the roles, interfaces and communications of large-grained components essential for UIM applications. These include components capable of analyzing unstructured artifacts, integrating and accessing structured sources and storing, indexing and searching for artifacts based on discovered semantic content resulting from analyses. A primary design point for UIMA is that it should admit the implementation of middleware frameworks that provide a component-based infrastructure for developing UIM applications suitable for vastly different deployment environments. These should range from lightweight and embeddable implementations to highly scalable implementations that are meant to exploit clusters of machines and provide high throughput, high availability serviced-based offerings. While the primary focus of current UIMA framework implementations is squarely on natural language To appear in a special issue of the Journal of Natural Language Engineering 2004 4 Ferrucci and Lally Do Not Distirubte without written permission from authors UIM Application Analysis Collection Level Analysis Document, Collection & Metadata Store Knowledge Source Access Collection Processing Manager (CPM) Knowledge & Data Bases Unstructured Information (Text) Analysis Engines Crawlers Analysis Engine Directory Acquisition Unstructured Information Analysis Structured Information Access Semantic Search Engine Indices Indices Documents Collections Metadata Knowledge Source Adapter Directory Access

Characterization of double-layer capacitors (DLCs) for power electronics applications <=SEP=> The double-layer capacitor (DLC) for power applications is a new device. A simple resistive capacitive equivalent circuit is insufficient to characterize its terminal behaviour. Based on physical reasoning, an equivalent circuit is proposed to provide the power electronics engineer with a model for the terminal behaviour of the DLC. The equivalent model consists of three Re branches, one of them with a voltage dependent capacitor. A method to identify the circuit parameters is presented. Measurements of carbon-based DLCs for power applications are presented, analysed, and the equivalent circuit response is compared with experimental results.

A novel spring mechanism to reduce energy consumption of robotic arms <=SEP=> Most conventional robotic arms use motors to accelerate the manipulator. This leads to an unnecessary high energy consumption when performing repetitive tasks. This paper presents an approach to reduce energy consumption in robotic arms by performing its repetitive tasks with the help of a parallel spring mechanism. A special non-linear spring characteristic has been achieved by attaching a spring to two connected pulleys. This parallel spring mechanism provides for the accelerations of the manipulator without compromising its ability to vary the task parameters (the time per stroke, the displacement per stroke the grasping time and the payload). The energy consumption of the arm with the spring mechanism is compared to that of the same arm without the spring mechanism. Optimal control studies show that the robotic arm uses 22% less energy due to the spring mechanism. On the 2 DOF prototype, we achieved an energy reduction of 20%. The difference was due to model simplifications. With a spring mechanism, there is an extra energetic cost, because potential energy has to be stored into the spring during startup. This cost is equal to the total energy savings of the 2 DOF arm during 8 strokes. Next, there could have been an energetic cost to position the manipulator outside the equilibrium position. We have designed the spring mechanism in such a way that this holding cost is negligible for a range of start- and end positions. The performed experiments showed that the implementation of the proposed spring mechanism results in a reduction of the energy consumption while the arm is still able to handle varying task parameters.

A Method for Compact Image Representation Using Sparse Matrix and Tensor Projections Onto Exemplar Orthonormal Bases <=SEP=> We present a new method for compact representation of large image datasets. Our method is based on treating small patches from a 2-D image as matrices as opposed to the conventional vectorial representation, and encoding these patches as sparse projections onto a set of exemplar orthonormal bases, which are learned a priori from a training set. The end result is a low-error, highly compact image/patch representation that has significant theoretical merits and compares favorably with existing techniques (including JPEG) on experiments involving the compression of ORL and Yale face databases, as well as a database of miscellaneous natural images. In the context of learning multiple orthonormal bases, we show the easy tunability of our method to efficiently represent patches of different complexities. Furthermore, we show that our method is extensible in a theoretically sound manner to higher-order matrices (¿tensors¿). We demonstrate applications of this theory to compression of well-known color image datasets such as the GaTech and CMU-PIE face databases and show performance competitive with JPEG. Lastly, we also analyze the effect of image noise on the performance of our compression schemes.

Place-Its: A Study of Location-Based Reminders on Mobile Phones <=SEP=> Context-awareness can improve the usefulness of automated reminders. However, context-aware reminder applications have yet to be evaluated throughout a person’s daily life. Mobile phones provide a potentially convenient and truly ubiquitous platform for the detection of personal context such as location, as well as the delivery of reminders. We designed Place-Its, a location-based reminder application that runs on mobile phones, to study people using location-aware reminders throughout their daily lives. We describe the design of Place-Its and a two-week exploratory user study. The study reveals that location-based reminders are useful, in large part because people use location in

MULTISCALE ENTANGLEMENT RENORMALIZATION ANSATZ <=SEP=> The goal of this paper is to demonstrate a method for tensorizing neural networks based upon an efficient way of approximating scale invariant quantum states, the Multi-scale Entanglement Renormalization Ansatz (MERA). We employ MERA as a replacement for linear layers in a neural network and test this implementation on the CIFAR-10 dataset. The proposed method outperforms factorization using tensor trains, providing greater compression for the same level of accuracy and greater accuracy for the same level of compression. We demonstrate MERAlayers with 3900 times fewer parameters and a reduction in accuracy of less than 1% compared to the equivalent fully connected layers.

Can you infect me now?: malware propagation in mobile phone networks <=SEP=> In this paper we evaluate the effects of malware propagating usingcommunication services in mobile phone networks. Although self-propagating malware is well understood in the Internet, mobile phone networks have very different characteristics in terms of topologies, services, provisioning and capacity, devices, and communication patterns. To investigate malware in this new environment, we have developed an event-driver simulator that captures the characteristics and constraints of mobile phone networks. In particular, the simulator models realistic topologies and provisioned capacities of the network infrastructure, as well as the contactgraphs determined by cell phone address books. We evaluate the speedand severity of random contact worms in mobile phone networks, characterize the denial-of-service effects such worms could have on the network, investigate approaches to accelerate malware propagation, and discuss the implications of defending networks against such attacks.

Parametric and nonparametric linkage analysis: a unified multipoint approach. <=SEP=> In complex disease studies, it is crucial to perform multipoint linkage analysis with many markers and to use robust nonparametric methods that take account of all pedigree information. Currently available methods fall short in both regards. In this paper, we describe how to extract complete multipoint inheritance information from general pedigrees of moderate size. This information is captured in the multipoint inheritance distribution, which provides a framework for a unified approach to both parametric and nonparametric methods of linkage analysis. Specifically, the approach includes the following: (1) Rapid exact computation of multipoint LOD scores involving dozens of highly polymorphic markers, even in the presence of loops and missing data. (2) Non-parametric linkage (NPL) analysis, a powerful new approach to pedigree analysis. We show that NPL is robust to uncertainty about mode of inheritance, is much more powerful than commonly used nonparametric methods, and loses little power relative to parametric linkage analysis. NPL thus appears to be the method of choice for pedigree studies of complex traits. (3) Information-content mapping, which measures the fraction of the total inheritance information extracted by the available marker data and points out the regions in which typing additional markers is most useful. (4) Maximum-likelihood reconstruction of many-marker haplotypes, even in pedigrees with missing data. We have implemented NPL analysis, LOD-score computation, information-content mapping, and haplotype reconstruction in a new computer package, GENEHUNTER. The package allows efficient multipoint analysis of pedigree data to be performed rapidly in a single user-friendly environment.

Extensions of Parallel Coordinates for Interactive Exploration of Large Multi-Timepoint Data Sets <=SEP=> Parallel coordinate plots (PCPs) are commonly used in information visualization to provide insight into multi-variate data. These plots help to spot correlations between variables. PCPs have been successfully applied to unstructured datasets up to a few millions of points. In this paper, we present techniques to enhance the usability of PCPs for the exploration of large, multi-timepoint volumetric data sets, containing tens of millions of points per timestep. The main difficulties that arise when applying PCPs to large numbers of data points are visual clutter and slow performance, making interactive exploration infeasible. Moreover, the spatial context of the volumetric data is usually lost. We describe techniques for preprocessing using data quantization and compression, and for fast GPU-based rendering of PCPs using joint density distributions for each pair of consecutive variables, resulting in a smooth, continuous visualization. Also, fast brushing techniques are proposed for interactive data selection in multiple linked views, including a 3D spatial volume view. These techniques have been successfully applied to three large data sets: Hurricane Isabel (Vis'04 contest), the ionization front instability data set (Vis'08 design contest), and data from a large-eddy simulation of cumulus clouds. With these data, we show how PCPs can be extended to successfully visualize and interactively explore multi-timepoint volumetric datasets with an order of magnitude more data points.

Curved Folded Plate Timber Structures <=SEP=> Current research at the laboratory for timber construction IBOIS at EPFL focuses on shell structures. Two main families are investigated: shells made of linear elements, as timber rib shells, and shells made of surface elements, typically folded plate structures. With regard to timber rib shells, bent linear timber elements are used to create the shape of a shell. With folded plate structures, new timber derived products are used to create structures made of planar panels, where the panel simultaneously covers space and acts as load bearing element. Indeed, timber industry has developed in the last fifteen years new large-size timber panels. Composition and dimensions of these panels and the possibility of milling them with Computer Numerical Controlled machines shows great potential for folded plate structures. In the present paper we investigate to combine both approaches using bent timber panels for curved folded plate structures.

Detection of leukemia in human blood sample based on microscopic images: A study <=SEP=> At the moment, identification of blood disorders is through visual inspection of microscopic images of blood cells. From the identification of blood disor de s, it can lead to classification of certain dise ases related to blood. This paper describes a preliminar y study of developing a detection of leukemia types using microscopic blood sample images. Analyzing through images is very important as from images, diseases can be detected and diagnosed at earlier stage. Fro m there, further actions like controlling, monitori ng and prevention of diseases can be done. Images are used as they are cheap and do not require expensive tes ting and lab equipments. The system will focus on white blood cells disease, leukemia. The system will use features in microscopic images and examine changes on texture, geometry, color and statistical analysi s. Changes in these features will be used as a classif ier input. A literature review has been done and Reinforcement Learning is proposed to classify type s of leukemia. A little discussion about issues inv olved by researchers also has been prepared.

Food region segmentation in meal images using touch points <=SEP=> We propose an interactive scheme for segmenting meal images for automated dietary assessment. A smartphone user photographs a meal and marks a few touch points on the resulting image. The segmentation algorithm initializes a set of food segments with the touch points, and grows them using local image features. We evaluate the algorithm with a data set consisting of 300 manually segmented meal images. The precision of segmentation is 0.87, compared with 0.70 for fully automatic segmentation. The results show that the precision of segmentation was significantly improved by incorporating minimal user intervention.

Recurrent Capsule Network for Relations Extraction: A Practical Application to the Severity Classification of Coronary Artery Disease <=SEP=> Coronary artery disease (CAD) is one of the leading causes of cardiovascular disease deaths. CAD condition progresses rapidly, if not diagnosed and treated at an early stage may eventually lead to an irreversible state of the heart muscle death. Invasive coronary arteriography is the gold standard technique for CAD diagnosis. Coronary arteriography texts describe which part has stenosis and how much stenosis is in details. It is crucial to conduct the severity classification of CAD. In this paper, we propose a recurrent capsule network (RCN) to extract semantic relations between clinical named entities in Chinese coronary arteriography texts, through which we can automatically find out the maximal stenosis for each lumen to inference how severe CAD is according to the improved method of Gensini. Experimental results on the corpus collected from Shanghai Shuguang Hospital show that our proposed RCN model achieves a F1-score of 0.9641 in relation extraction, which outperforms the baseline methods.

A Novel User Authentication Scheme Based on QR-Code <=SEP=> User authentication is one of the fundamental procedures to ensure secure communications and share system resources over an insecure public network channel. Thus, a simple and efficient authentication mechanism is required for securing the network system in the real environment. In general, the password-based authentication mechanism provides the basic capability to prevent unauthorized access. Especially, the purpose of the one-time password is to make it more difficult to gain unauthorized access to restricted resources. Instead of using the password file as conventional authentication systems, many researchers have devoted to implement various one-time password schemes using smart cards, time-synchronized token or short message service in order to reduce the risk of tampering and maintenance cost. However, these schemes are impractical because of the far from ubiquitous hardware devices or the infrastructure requirements. To remedy these weaknesses, the attraction of the QR-code technique can be introduced into our one-time password authentication protocol. Not the same as before, the proposed scheme based on QR code not only eliminates the usage of the password verification table, but also is a cost effective solution since most internet users already have mobile phones. For this reason, instead of carrying around a separate hardware token for each security domain, the superiority of handiness benefit from the mobile phone makes our approach more practical and convenient.

Knowledge manipulation activities: results of a Delphi study <=SEP=> Knowledge-based organizations are hosts for multitudes of knowledge management (KM) episodes. Each episode is triggered by a knowledge need and culminates with the satisfaction of that need (or its abandonment). Within an episode, one or more of the organization’ processors (human and/or computer-based) manipulate knowledge resources in various ways in an effort to meet the need. This paper identifies and characterizes a generic set of elemental knowledge manipulation activities that can be arranged in a variety of patterns within KM episodes. It also indicates possible knowledge flows that can occur among the activities. This descriptive framework was developed using conceptual synthesis and a Delphi methodology involving an international panel of researchers and practitioners in the KM field. The framework can serve as a common language for discourse about knowledge manipulation. For researchers, it suggests issues that deserve investigation and concepts that must be considered in explorations of KM episodes. For practitioners, the framework provides a perspective on activities that need to be considered in the design, measurement, control, coordination, and support of an organization’ KM episodes. # 2002 Elsevier Science B.V. All rights reserved.

Brain activities associated with gaming urge of online gaming addiction. <=SEP=> The aim of this study was to identify the neural substrates of online gaming addiction through evaluation of the brain areas associated with the cue-induced gaming urge. Ten participants with online gaming addiction and 10 control subjects without online gaming addiction were tested. They were presented with gaming pictures and the paired mosaic pictures while undergoing functional magnetic resonance imaging (fMRI) scanning. The contrast in blood-oxygen-level dependent (BOLD) signals when viewing gaming pictures and when viewing mosaic pictures was calculated with the SPM2 software to evaluate the brain activations. Right orbitofrontal cortex, right nucleus accumbens, bilateral anterior cingulate and medial frontal cortex, right dorsolateral prefrontal cortex, and right caudate nucleus were activated in the addicted group in contrast to the control group. The activation of the region-of-interest (ROI) defined by the above brain areas was positively correlated with self-reported gaming urge and recalling of gaming experience provoked by the WOW pictures. The results demonstrate that the neural substrate of cue-induced gaming urge/craving in online gaming addiction is similar to that of the cue-induced craving in substance dependence. The above-mentioned brain regions have been reported to contribute to the craving in substance dependence, and here we show that the same areas were involved in online gaming urge/craving. Thus, the results suggest that the gaming urge/craving in online gaming addiction and craving in substance dependence might share the same neurobiological mechanism.

Enhancing well-being and alleviating depressive symptoms with positive psychology interventions: a practice-friendly meta-analysis. <=SEP=> Do positive psychology interventions-that is, treatment methods or intentional activities aimed at cultivating positive feelings, positive behaviors, or positive cognitions-enhance well-being and ameliorate depressive symptoms? A meta-analysis of 51 such interventions with 4,266 individuals was conducted to address this question and to provide practical guidance to clinicians. The results revealed that positive psychology interventions do indeed significantly enhance well-being (mean r=.29) and decrease depressive symptoms (mean r=.31). In addition, several factors were found to impact the effectiveness of positive psychology interventions, including the depression status, self-selection, and age of participants, as well as the format and duration of the interventions. Accordingly, clinicians should be encouraged to incorporate positive psychology techniques into their clinical work, particularly for treating clients who are depressed, relatively older, or highly motivated to improve. Our findings also suggest that clinicians would do well to deliver positive psychology interventions as individual (versus group) therapy and for relatively longer periods of time.

ARLearn: Augmented Reality Meets Augmented Virtuality <=SEP=> This article deals with educational opportunities for mixed reality games and related scenarios for learning. It discusses several issues and educational challenges to be tackled when linking augmented reality and augmented virtuality. Second, the paper describes the architecture of the ARLearn system which offers highly flexible support for different educational settings. Three prototypical use cases implemented based on the underlying ARLearn framework are discussed, which are a field trip system, an augmented Google StreetView client called StreetLearn, and a real time crisis intervention game. ARLearn combines real time notification and mixed reality games across Mobile Augmented Reality and Virtual Reality and the authors aim to use the underlying (open source) framework for further case studies and mixed reality applications for learning support.

High-quality computational imaging through simple lenses <=SEP=> Modern imaging optics are highly complex systems consisting of up to two dozen individual optical elements. This complexity is required in order to compensate for the geometric and chromatic aberrations of a single lens, including geometric distortion, field curvature, wavelength-dependent blur, and color fringing.
 In this article, we propose a set of computational photography techniques that remove these artifacts, and thus allow for postcapture correction of images captured through uncompensated, simple optics which are lighter and significantly less expensive. Specifically, we estimate per-channel, spatially varying point spread functions, and perform nonblind deconvolution with a novel cross-channel term that is designed to specifically eliminate color fringing.

Feature vector selection and projection using kernels <=SEP=> This paper provides new insight into kernel methods by using data selection. The kernel trick is used to select from the data a relevant subset forming a basis in a feature space F . Thus the selected vectors de,ne a subspace in F . Then, the data is projected onto this subspace where classical algorithms are applied. We show that kernel methods like generalized discriminant analysis (Neural Comput. 12 (2000) 2385) or kernel principal component analysis (Neural Comput. 10 (1998) 1299) can be expressed more easily. Moreover, it will turn out that the size of the basis is related to the complexity of the model. Therefore, the data selection leads to a complexity control and thus to a better generalization. The approach covers a wide range of algorithms. We investigate the function approximation on real classi,cation problems and on a regression problem. c © 2003 Elsevier B.V. All rights reserved.

A case for stateful forwarding plane <=SEP=> In Named Data Networking (NDN), packets carry data names instead of source and destination addresses. This paradigm shift leads to a new network forwarding plane: data consumers send Interest packets to request desired data, routers forward Interest packets and maintain the state of all pending Interests, which is then used to guide Data packets back to the consumers. Maintaining the pending Interest state, together with the two-way Interest and Data exchange, enables NDN routers’ forwarding process to measure performance of different paths, quickly detect failures and retry alternative paths. In this paper we describe an initial design of NDN’s forwarding plane and evaluate its data delivery performance under adverse conditions. Our results show that this stateful forwarding plane can successfully circumvent prefix hijackers, avoid failed links, and utilize multiple paths to mitigate congestion. We also compare NDN’s performance with that of IP-based solutions to highlight the advantages of a stateful forwarding plane. 2013 Elsevier B.V. All rights reserved.

An ontology-based approach for the reconstruction and analysis of digital incidents timelines <=SEP=> Due to the democratisation of new technologies, computer forensics investigators have to deal with volumes of data which are becoming increasingly large and heterogeneous. Indeed, in a single machine, hundred of events occur per minute, produced and logged by the operating system and various software. Therefore, the identification of evidence, and more generally, the reconstruction of past events is a tedious and time-consuming task for the investigators. Our work aims at reconstructing and analysing automatically the events related to a digital incident, while respecting legal requirements. To tackle those three main problems (volume, heterogeneity and legal requirements), we identify seven necessary criteria that an efficient reconstruction tool must meet to address these challenges. This paper introduces an approach based on a three-layered ontology, called ORD2I, to represent any digital events. ORD2I is associated with a set of operators to analyse the resulting timeline and to ensure the reproducibility of the investigation. © 2015 Elsevier Ltd. All rights reserved.

Assessing heterogeneity in meta-analysis: Q statistic or I2 index? <=SEP=> In meta-analysis, the usual way of assessing whether a set of single studies is homogeneous is by means of the Q test. However, the Q test only informs meta-analysts about the presence versus the absence of heterogeneity, but it does not report on the extent of such heterogeneity. Recently, the I(2) index has been proposed to quantify the degree of heterogeneity in a meta-analysis. In this article, the performances of the Q test and the confidence interval around the I(2) index are compared by means of a Monte Carlo simulation. The results show the utility of the I(2) index as a complement to the Q test, although it has the same problems of power with a small number of studies.

BloomFlash: Bloom Filter on Flash-Based Storage <=SEP=> The bloom filter is a probabilistic data structure that provides a compact representation of a set of elements. To keep false positive probabilities low, the size of the bloom filter must be dimensioned a priori to be linear in the maximum number of keys inserted, with the linearity constant ranging typically from one to few bytes. A bloom filter is most commonly used as an in memory data structure, hence its size is limited by the availability of RAM space on the machine. As datasets have grown over time to Internet scale, so have the RAM space requirements of bloom filters. If sufficient RAM space is not available, we advocate that flash memory may serve as a suitable medium for storing bloom filters, since it is about one-tenth the cost of RAM per GB while still providing access times orders of magnitude faster than hard disk. We present BLOOMFLASH, a bloom filter designed for flash memory based storage, that provides a new dimension of trade off with bloom filter access times to reduce RAM space usage (and hence system cost). The simple design of a single flat bloom filter on flash suffers from many performance bottlenecks, including in-place bit updates that are inefficient on flash and multiple reads and random writes spread out across many flash pages for a single lookup or insert operation. To mitigate these performance bottlenecks, BLOOMFLASH leverages two key design innovations: (i) buffering bit updates in RAM and applying them in bulk to flash that helps to reduce random writes to flash, and (ii) a hierarchical bloom filter design consisting of component bloom filters, stored one per flash page, that helps to localize reads and writes on flash. We use two real-world data traces taken from representative bloom filter applications to drive and evaluate our design. BLOOMFLASH achieves bloom filter access times in the range of few tens of microseconds, thus allowing up to order of tens of thousands operations per sec.

Optimizing the Hadoop MapReduce Framework with high-performance storage devices <=SEP=> Solid-state drives (SSDs) are an attractive alternative to hard disk drives (HDDs) to accelerate the Hadoop MapReduce Framework. However, the SSD characteristics and today’s Hadoop framework exhibit mismatches that impede indiscriminate SSD integration. This paper explores how to optimize a Hadoop MapReduce Framework with SSDs in terms of performance, cost, and energy consumption. It identifies extensible best practices that can exploit SSD benefits within Hadoop when combined with high network bandwidth and increased parallel storage access. Our Terasort benchmark results demonstrate that Hadoop currently does not sufficiently exploit SSD throughput. Hence, using faster SSDs in Hadoop does not enhance its performance. We show that SSDs presently deliver significant efficiency when storing intermediate Hadoop data, leaving HDDs for Hadoop Distributed File System (HDFS). The proposed configuration is optimized with the JVM reuse option and frequent heartbeat interval option. Moreover, we examined the performance of a state-of-the-art non-volatile memory express interface SSD within the Hadoop MapReduce Framework. While HDFS read and write throughput increases with high-performance SSDs, achieving complete system performance improvement requires carefully balancing CPU, network, and storage resource capabilities at a system level.

HoPE: Hot-Cacheline Prediction for Dynamic Early Decompression in Compressed LLCs <=SEP=> Data compression plays a pivotal role in improving system performance and reducing energy consumption, because it increases the logical effective capacity of a compressed memory system without physically increasing the memory size. However, data compression techniques incur some cost, such as non-negligible compression and decompression overhead. This overhead becomes more severe if compression is used in the cache. In this article, we aim to minimize the read-hit decompression penalty in compressed Last-Level Caches (LLCs) by speculatively decompressing frequently used cachelines. To this end, we propose a Hot-cacheline Prediction and Early decompression (HoPE) mechanism that consists of three synergistic techniques: Hot-cacheline Prediction (HP), Early Decompression (ED), and Hit-history-based Insertion (HBI). HP and HBI efficiently identify the hot compressed cachelines, while ED selectively decompresses hot cachelines, based on their size information. Unlike previous approaches, the HoPE framework considers the performance balance/tradeoff between the increased effective cache capacity and the decompression penalty. To evaluate the effectiveness of the proposed HoPE mechanism, we run extensive simulations on memory traces obtained from multi-threaded benchmarks running on a full-system simulation framework. We observe significant performance improvements over compressed cache schemes employing the conventional Least-Recently Used (LRU) replacement policy, the Dynamic Re-Reference Interval Prediction (DRRIP) scheme, and the Effective Capacity Maximizer (ECM) compressed cache management mechanism. Specifically, HoPE exhibits system performance improvements of approximately 11%, on average, over LRU, 8% over DRRIP, and 7% over ECM by reducing the read-hit decompression penalty by around 65%, over a wide range of applications.