Query: Secure Wallet-Assisted Offline Bitcoin Payments with Double-Spender Revocation

Relevant passages:
Bitcoin and Beyond: A Technical Survey on Decentralized Digital Currencies <=SEP=> Besides attracting a billion dollar economy, Bitcoin revolutionized the field of digital currencies and influenced many adjacent areas. This also induced significant scientific interest. In this survey, we unroll and structure the manyfold results and research directions. We start by introducing the Bitcoin protocol and its building blocks. From there we continue to explore the design space by discussing existing contributions and results. In the process, we deduce the fundamental structures and insights at the core of the Bitcoin protocol and its applications. As we show and discuss, many key ideas are likewise applicable in various other fields, so that their impact reaches far beyond Bitcoin itself.

Bitter to Better - How to Make Bitcoin a Better Currency <=SEP=> Bitcoin is a distributed digital currency which has attracted a substantial number of users. We perform an in-depth investigation to understand what made Bitcoin so successful, while decades of research on cryptographic e-cash has not lead to a large-scale deployment. We ask also how Bitcoin could become a good candidate for a long-lived stable currency. In doing so, we identify several issues and attacks of Bitcoin, and propose suitable techniques to address them.

Zerocash: Decentralized Anonymous Payments from Bitcoin <=SEP=> Bit coin is the first digital currency to see widespread adoption. While payments are conducted between pseudonyms, Bit coin cannot offer strong privacy guarantees: payment transactions are recorded in a public decentralized ledger, from which much information can be deduced. Zero coin (Miers et al., IEEE S&P 2013) tackles some of these privacy issues by unlinking transactions from the payment's origin. Yet, it still reveals payments' destinations and amounts, and is limited in functionality. In this paper, we construct a full-fledged ledger-based digital currency with strong privacy guarantees. Our results leverage recent advances in zero-knowledge Succinct Non-interactive Arguments of Knowledge (zk-SNARKs). First, we formulate and construct decentralized anonymous payment schemes (DAP schemes). A DAP scheme enables users to directly pay each other privately: the corresponding transaction hides the payment's origin, destination, and transferred amount. We provide formal definitions and proofs of the construction's security. Second, we build Zero cash, a practical instantiation of our DAP scheme construction. In Zero cash, transactions are less than 1 kB and take under 6 ms to verify - orders of magnitude more efficient than the less-anonymous Zero coin and competitive with plain Bit coin.

SoK: Research Perspectives and Challenges for Bitcoin and Cryptocurrencies <=SEP=> Bit coin has emerged as the most successful cryptographic currency in history. Within two years of its quiet launch in 2009, Bit coin grew to comprise billions of dollars of economic value despite only cursory analysis of the system's design. Since then a growing literature has identified hidden-but-important properties of the system, discovered attacks, proposed promising alternatives, and singled out difficult future challenges. Meanwhile a large and vibrant open-source community has proposed and deployed numerous modifications and extensions. We provide the first systematic exposition Bit coin and the many related crypto currencies or 'altcoins.' Drawing from a scattered body of knowledge, we identify three key components of Bit coin's design that can be decoupled. This enables a more insightful analysis of Bit coin's properties and future stability. We map the design space for numerous proposed modifications, providing comparative analyses for alternative consensus mechanisms, currency allocation mechanisms, computational puzzles, and key management tools. We survey anonymity issues in Bit coin and provide an evaluation framework for analyzing a variety of privacy-enhancing proposals. Finally we provide new insights on what we term disinter mediation protocols, which absolve the need for trusted intermediaries in an interesting set of applications. We identify three general disinter mediation strategies and provide a detailed comparison.

Majority Is Not Enough: Bitcoin Mining Is Vulnerable <=SEP=> 

Metadata Embeddings for User and Item Cold-start Recommendations <=SEP=> I present a hybrid matrix factorisation model representing users and items as linear combinations of their content features’ latent factors. The model outperforms both collaborative and content-based models in cold-start or sparse interaction data scenarios (using both user and item metadata), and performs at least as well as a pure collaborative matrix factorisation model where interaction data is abundant. Additionally, feature embeddings produced by the model encode semantic information in a way reminiscent of word embedding approaches, making them useful for a range of related tasks such as tag recommendations.

Extremely Low-Profile, Single-Arm, Wideband Spiral Antenna Radiating a Circularly Polarized Wave <=SEP=> The antenna characteristics are described of a single-arm spiral (SAS) antenna. A balun circuit, required for a conventional two-arm balanced-mode spiral (TAS), is not necessary for feeding this SAS. First, the radiation pattern of an SAS having a disc/ground-plane is investigated. When the radius of the disc is smaller than the radius of the first-mode active region on the spiral and the spacing between the disc and spiral plane is small, the SAS is found to radiate a circularly polarized (CP) bidirectional beam, whose radiation pattern is almost symmetric with respect to the antenna axis normal to the spiral plane. Second, from a practical standpoint, the CP bidirectional beam is transformed into a CP unidirectional beam by placing a conducting cavity behind an SAS with a disc. It is revealed that the SAS has a good VSWR (less than 2) and a good axial ratio (less than 3 dB) over a design frequency range of 3 GHz to 10 GHz, where a cavity/antenna height of 7 mm (0.07 wavelength at the lowest design frequency) is chosen. The frequency response of the gain for the SAS is found to be similar to that for a conventional TAS, and the radiation efficiency for the SAS is slightly larger than that for the TAS. It is concluded that the SAS with a small disc backed by a cavity realizes a circularly polarized, low-profile, wideband antenna with a simple feed system that does not require a balun circuit.

Vocabulary acquisition from extensive reading : A case study <=SEP=> A number of studies have shown that second language learners acquire vocabulary through reading, but only relatively small amounts. However, most of these studies used only short texts, measured only the acquisition of meaning, and did not credit partial learning of words. This case study of a learner of French explores whether an extensive reading program can enhance lexical knowledge. The study assessed a relatively large number of words (133), and examined whether one month of extensive reading enhanced knowledge of these target words' spelling, meaning, and grammatical characteristics. The measurement procedure was a one-on-one interview that allowed a very good indication of whether learning occurred. The study also explores how vocabulary acquisition varies according to how often words are encountered in the texts. The results showed that knowledge of 65% of the target words was enhanced in some way, for a pickup rate of about 1 of every 1.5 words tested. Spelling was strongly enhanced, even from a small number of exposures. Meaning and grammatical knowledge were also enhanced, but not to the same extent. Overall, the study indicates that more vocabulary acquisition is possible from extensive reading than previous studies have suggested.

Gated End-to-End Memory Networks <=SEP=> Machine reading using differentiable reasoning models has recently shown remarkable progress. In this context, End-to-End trainable Memory Networks (MemN2N) have demonstrated promising performance on simple natural language based reasoning tasks such as factual reasoning and basic deduction. However, other tasks, namely multi-fact questionanswering, positional reasoning or dialog related tasks, remain challenging particularly due to the necessity of more complex interactions between the memory and controller modules composing this family of models. In this paper, we introduce a novel end-to-end memory access regulation mechanism inspired by the current progress on the connection short-cutting principle in the field of computer vision. Concretely, we develop a Gated End-toEnd trainable Memory Network architecture (GMemN2N). From the machine learning perspective, this new capability is learned in an end-to-end fashion without the use of any additional supervision signal which is, as far as our knowledge goes, the first of its kind. Our experiments show significant improvements on the most challenging tasks in the 20 bAbI dataset, without the use of any domain knowledge. Then, we show improvements on the Dialog bAbI tasks including the real human-bot conversion-based Dialog State Tracking Challenge (DSTC-2) dataset. On these two datasets, our model sets the new state of the art. ∗work done as an Intern at XRCE

Feature Selection for Maximizing the Area Under the ROC Curve <=SEP=> Feature selection is an important pre-processing step for solving classification problems. A good feature selection method may not only improve the performance of the final classifier, but also reduce the computational complexity of it. Traditionally, feature selection methods were developed to maximize the classification accuracy of a classifier. Recently, both theoretical and experimental studies revealed that a classifier with the highest accuracy might not be ideal in real-world problems. Instead, the Area Under the ROC Curve (AUC) has been suggested as the alternative metric, and many existing learning algorithms have been modified in order to seek the classifier with maximum AUC. However, little work was done to develop new feature selection methods to suit the requirement of AUC maximization. To fill this gap in the literature, we propose in this paper a novel algorithm, called AUC and Rank Correlation coefficient Optimization (ARCO) algorithm. ARCO adopts the general framework of a well-known method, namely minimal redundancy- maximal-relevance (mRMR) criterion, but defines the terms ”relevance” and ”redundancy” in totally different ways. Such a modification looks trivial from the perspective of algorithmic design. Nevertheless, experimental study on four gene expression data sets showed that feature subsets obtained by ARCO resulted in classifiers with significantly larger AUC than the feature subsets obtained by mRMR. Moreover, ARCO also outperformed the Feature Assessment by Sliding Thresholds algorithm, which was recently proposed for AUC maximization, and thus the efficacy of ARCO was validated.

Formal analysis of privacy requirements specifications for multi-tier applications <=SEP=> Companies require data from multiple sources to develop new information systems, such as social networking, e-commerce and location-based services. Systems rely on complex, multi-stakeholder data supply-chains to deliver value. These data supply-chains have complex privacy requirements: privacy policies affecting multiple stakeholders (e.g. user, developer, company, government) regulate the collection, use and sharing of data over multiple jurisdictions (e.g. California, United States, Europe). Increasingly, regulators expect companies to ensure consistency between company privacy policies and company data practices. To address this problem, we propose a methodology to map policy requirements in natural language to a formal representation in Description Logic. Using the formal representation, we reason about conflicting requirements within a single policy and among multiple policies in a data supply chain. Further, we enable tracing data flows within the supply-chain. We derive our methodology from an exploratory case study of Facebook platform policy. We demonstrate the feasibility of our approach in an evaluation involving Facebook, Zynga and AOL-Advertising policies. Our results identify three conflicts that exist between Facebook and Zynga policies, and one conflict within the AOL Advertising policy.

Overconfidence and Trading Volume <=SEP=> Theoretical models predict that overconfident investors will trade more than rational investors. We directly test this hypothesis by correlating individual overconfidence scores with several measures of trading volume of individual investors. Approximately 3,000 online broker investors were asked to answer an internet questionnaire which was designed to measure various facets of overconfidence (miscalibration, volatility estimates, better than average effect). The measures of trading volume were calculated by the trades of 215 individual investors who answered the questionnaire. We find that investors who think that they are above average in terms of investment skills or past performance (but who did not have above average performance in the past) trade more. Measures of miscalibration are, contrary to theory, unrelated to measures of trading volume. This result is striking as theoretical models that incorporate overconfident investors mainly motivate this assumption by the calibration literature and model overconfidence as underestimation of the variance of signals. In connection with other recent findings, we conclude that the usual way of motivating and modeling overconfidence which is mainly based on the calibration literature has to be treated with caution. Moreover, our way of empirically evaluating behavioral finance models the correlation of economic and psychological variables and the combination of psychometric measures of judgment biases (such as overconfidence scores) and field data seems to be a promising way to better understand which psychological phenomena actually drive economic behavior.

Defining Computational Thinking for Mathematics and Science Classrooms <=SEP=> Science and mathematics are becoming computational endeavors. This fact is reflected in the recently released Next Generation Science Standards and the decision to include ‘‘computational thinking’’ as a core scientific practice. With this addition, and the increased presence of computation in mathematics and scientific contexts, a new urgency has come to the challenge of defining computational thinking and providing a theoretical grounding for what form it should take in school science and mathematics classrooms. This paper presents a response to this challenge by proposing a definition of computational thinking for mathematics and science in the form of a taxonomy consisting of four main categories: data practices, modeling and simulation practices, computational problem solving practices, and systems thinking practices. In formulating this taxonomy, we draw on the existing computational thinking literature, interviews with mathematicians and scientists, and exemplary computational thinking instructional materials. This work was undertaken as part of a larger effort to infuse computational thinking into high school science and mathematics curricular materials. In this paper, we argue for the approach of embedding computational thinking in mathematics and science contexts, present the taxonomy, and discuss how we envision the taxonomy being used to bring current educational efforts in line with the increasingly computational nature of modern science and mathematics.

Learning Fair Representations <=SEP=> We propose a learning algorithm for fair classification that achieves both group fairness (the proportion of members in a protected group receiving positive classification is identical to the proportion in the population as a whole), and individual fairness (similar individuals should be treated similarly). We formulate fairness as an optimization problem of finding a good representation of the data with two competing goals: to encode the data as well as possible, while simultaneously obfuscating any information about membership in the protected group. We show positive results of our algorithm relative to other known techniques, on three datasets. Moreover, we demonstrate several advantages to our approach. First, our intermediate representation can be used for other classification tasks (i.e., transfer learning is possible); secondly, we take a step toward learning a distance metric which can find important dimensions of the data for classification.

Flexible Learning on the Sphere Via Adaptive Needlet Shrinkage and Selection <=SEP=> This paper introduces an approach for flexible, robust Bayesian modeling of structure in spherical data sets. Our method is based upon a recent construction called the needlet, which is a particular form of spherical wavelet with many favorable statistical and computational properties. We perform shrinkage and selection of needlet coefficients, focusing on two main alternatives: empirical-Bayes thresholding for selection, and the horseshoe prior for shrinkage. We study the performance of the proposed methodology both on simulated data and on a real data set involving the cosmic microwave background radiation. Horseshoe shrinkage of needlet coefficients is shown to yield the best overall performance against some common benchmarks.

A privacy-compliant fingerprint recognition system based on homomorphic encryption and Fingercode templates <=SEP=> The privacy protection of the biometric data is an important research topic, especially in the case of distributed biometric systems. In this scenario, it is very important to guarantee that biometric data cannot be steeled by anyone, and that the biometric clients are unable to gather any information different from the single user verification/identification. In a biométrie system with high level of privacy compliance, also the server that processes the biométrie matching should not learn anything on the database and it should be impossible for the server to exploit the resulting matching values in order to extract any knowledge about the user presence or behavior. Within this conceptual framework, in this paper we propose a novel complete demonstrator based on a distributed biométrie system that is capable to protect the privacy of the individuals by exploiting cryptosystems. The implemented system computes the matching task in the encrypted domain by exploiting homomorphic encryption and using Fingercode templates. The paper describes the design methodology of the demonstrator and the obtained results. The demonstrator has been fully implemented and tested in real applicative conditions. Experimental results show that this method is feasible in the cases where the privacy of the data is more important than the accuracy of the system and the obtained computational time is satisfactory.

Extended Kalman filter based grid synchronization in the presence of voltage unbalance for smart grid <=SEP=> In this paper, grid synchronization for gird-connected power generation systems in the presence of voltage unbalance and frequency variation is considered. A new extended Kalman filter based synchronization algorithm is proposed to track the phase angle of the utility network. Instead of processing the three-phase voltage signal in the abc natural reference frame and resorting to the symmetrical component transformation as in the traditional way, the proposed algorithm separates the positive and negative sequences in the transformed αβ stationary reference frame. Based on the obtained expressions in αβ domain, an EKF is developed to track both the in-phase and quadrature sinusoidal signals together with the unknown frequency. An estimate of the phase angle of the positive sequence is then obtained. As a by-product, estimates of phase angle of negative sequence and grid frequency are also computed. Compared to the commonly used scheme, the proposed algorithm has a simplified structure. The good performance is supported by computer simulations.

Frequency split metal artifact reduction (FSMAR) in computed tomography. <=SEP=> PURPOSE
The problem of metal artifact reduction (MAR) is almost as old as the clinical use of computed tomography itself. When metal implants are present in the field of measurement, severe artifacts degrade the image quality and the diagnostic value of CT images. Up to now, no generally accepted solution to this issue has been found. In this work, a method based on a new MAR concept is presented: frequency split metal artifact reduction (FSMAR). It ensures efficient reduction of metal artifacts at high image quality with enhanced preservation of details close to metal implants.


METHODS
FSMAR combines a raw data inpainting-based MAR method with an image-based frequency split approach. Many typical methods for metal artifact reduction are inpainting-based MAR methods and simply replace unreliable parts of the projection data, for example, by linear interpolation. Frequency split approaches were used in CT, for example, by combining two reconstruction methods in order to reduce cone-beam artifacts. FSMAR combines the high frequencies of an uncorrected image, where all available data were used for the reconstruction with the more reliable low frequencies of an image which was corrected with an inpainting-based MAR method. The algorithm is tested in combination with normalized metal artifact reduction (NMAR) and with a standard inpainting-based MAR approach. NMAR is a more sophisticated inpainting-based MAR method, which introduces less new artifacts which may result from interpolation errors. A quantitative evaluation was performed using the examples of a simulation of the XCAT phantom and a scan of a spine phantom. Further evaluation includes patients with different types of metal implants: hip prostheses, dental fillings, neurocoil, and spine fixation, which were scanned with a modern clinical dual source CT scanner.


RESULTS
FSMAR ensures sharp edges and a preservation of anatomical details which is in many cases better than after applying an inpainting-based MAR method only. In contrast to other MAR methods, FSMAR yields images without the usual blurring close to implants.


CONCLUSIONS
FSMAR should be used together with NMAR, a combination which ensures an accurate correction of both high and low frequencies. The algorithm is computationally inexpensive compared to iterative methods and methods with complex inpainting schemes. No parameters were chosen manually; it is ready for an application in clinical routine.

Tagging Urdu Sentences from English POS Taggers <=SEP=> Being a global language, English has attracted a majority of researchers and academia to work on several Natural Language Processing (NLP) applications. The rest of the languages are not focused as much as English. Part-of-speech (POS) Tagging is a necessary component for several NLP applications. An accurate POS Tagger for a particular language is not easy to construct due to the diversity of that language. The global language English, POS Taggers are more focused and widely used by the researchers and academia for NLP processing. In this paper, an idea of reusing English POS Taggers for tagging non-English sentences is proposed. On exemplary basis, Urdu sentences are processed to tagged from 11 famous English POS Taggers. State-of-the-art English POS Taggers were explored from the literature, however, 11 famous POS Taggers were being input to Urdu sentences for tagging. A famous Google translator is used to translate the sentences across the languages. Data from twitter.com is extracted for evaluation perspective. Confusion matrix with kappa statistic is used to measure the accuracy of actual Vs predicted tagging. The two best English POS Taggers which tagged Urdu sentences were Stanford POS Tagger and MBSP POS Tagger with an accuracy of 96.4% and 95.7%, respectively. The system can be generalized for multilingual sentence tagging. Keywords—Standford part-of-speech (POS) tagger; Google translator; Urdu POS tagging; kappa statistic

IR sensitivity enhancement of CMOS Image Sensor with diffractive light trapping pixels <=SEP=> We report on the IR sensitivity enhancement of back-illuminated CMOS Image Sensor (BI-CIS) with 2-dimensional diffractive inverted pyramid array structure (IPA) on crystalline silicon (c-Si) and deep trench isolation (DTI). FDTD simulations of semi-infinite thick c-Si having 2D IPAs on its surface whose pitches over 400 nm shows more than 30% improvement of light absorption at λ = 850 nm and the maximum enhancement of 43% with the 540 nm pitch at the wavelength is confirmed. A prototype BI-CIS sample with pixel size of 1.2 μm square containing 400 nm pitch IPAs shows 80% sensitivity enhancement at λ = 850 nm compared to the reference sample with flat surface. This is due to diffraction with the IPA and total reflection at the pixel boundary. The NIR images taken by the demo camera equip with a C-mount lens show 75% sensitivity enhancement in the λ = 700–1200 nm wavelength range with negligible spatial resolution degradation. Light trapping CIS pixel technology promises to improve NIR sensitivity and appears to be applicable to many different image sensor applications including security camera, personal authentication, and range finding Time-of-Flight camera with IR illuminations.

The Gambler's Ruin Problem, Genetic Algorithms, and the Sizing of Populations <=SEP=> This paper presents a model to predict the convergence quality of genetic algorithms based on the size of the population. The model is based on an analogy between selection in GAs and one-dimensional random walks. Using the solution to a classic random walk problemthe gambler's ruinthe model naturally incorporates previous knowledge about the initial supply of building blocks (BBs) and correct selection of the best BB over its competitors. The result is an equation that relates the size of the population with the desired quality of the solution, as well as the problem size and difficulty. The accuracy of the model is verified with experiments using additively decomposable functions of varying difficulty. The paper demonstrates how to adjust the model to account for noise present in the fitness evaluation and for different tournament sizes.

Deep Learning Approaches to Semantic Relevance Modeling for Chinese Question-Answer Pairs <=SEP=> The human-generated question-answer pairs in the Web social communities are of great value for the research of automatic question-answering technique. Due to the large amount of noise information involved in such corpora, it is still a problem to detect the answers even though the questions are exactly located. Quantifying the semantic relevance between questions and their candidate answers is essential to answer detection in social media corpora. Since both the questions and their answers usually contain a small number of sentences, the relevance modeling methods have to overcome the problem of word feature sparsity. In this article, the deep learning principle is introduced to address the semantic relevance modeling task. Two deep belief networks with different architectures are proposed by us to model the semantic relevance for the question-answer pairs. According to the investigation of the textual similarity between the community-driven question-answering (cQA) dataset and the forum dataset, a learning strategy is adopted to promote our models’ performance on the social community corpora without hand-annotating work. The experimental results show that our method outperforms the traditional approaches on both the cQA and the forum corpora.

JINS MEME algorithm for estimation and tracking of concentration of users <=SEP=> Activity tracking using a wearable device is an emerging research field. Large-scale studies on activity tracking performed with eyewear-type wearable devices remains a challenging area owing to the negative effect such devices have on users' looks. To cope with this challenge, JINS Inc., an eyewear retailer in Japan, has developed a state-of-the-art smart eyewear called JINS MEME. The design of JINS MEME is almost the same as that of Wellington-type eyeglasses so that people can use it in their daily lives. JINS MEME is equipped with sensors to detect a user's eye movement and head motion. In addition to these functions of JINS MEME, JINS developed an application to measure concentration levels of users. In this demonstration, users will experience wearing the JINS MEME glasses and their concentration will be measured while they perform a certain task at our booth.

A 5GS/s voltage-to-time converter in 90nm CMOS <=SEP=> A voltage-to-time converter (VTC) is presented for use in a time-based analog-to-digital converter (ADC). The converter runs with a 5 GHz input clock to provide a maximum conversion rate of 5 GS/s. A novel architecture enables the VTC to provide an adjustable linear delay versus input voltage characteristic. The circuit is realized in a 90nm CMOS process. After calibration, the converter achieves better than 3.7 effective bits for input frequencies up to 1.75 GHz, making it suitable for use in a time-based ADC with up to 4-bit resolution.

A Client-Driven Approach for Channel Management in Wireless LANs <=SEP=> We propose an efficient client-based approach for channel management (channel assignment and load balancing) in 802.11-based WLANs that lead to better usage of the wireless spectrum. This approach is based on a “conflict set coloring” formulation that jointly performs load balancing along with channel assignment. Such a formulation has a number of advantages. First, it explicitly captures interference effects at clients. Next, it intrinsically exposes opportunities for better channel re-use. Finally, algorithms based on this formulation do not depend on specific physical RF models and hence can be applied efficiently to a wide-range of inbuilding as well as outdoor scenarios. We have performed extensive packet-level simulations and measurements on a deployed wireless testbed of 70 APs to validate the performance of our proposed algorithms. We show that in addition to single network scenarios, the conflict set coloring formulation is well suited for channel assignment where multiple wireless networks share and contend for spectrum in the same physical space. Our results over a wide range of both simulated topologies and in-building testbed experiments indicate that our approach improves application level performance at the clients by upto three times (and atleast 50%) in comparison to current best-known techniques.

Meta-Learning with Hessian Free Approach in Deep Neural Nets Training <=SEP=> Meta-learning is a promising method to achieve efficient training method towards deep neural net and has been attracting increases interests in recent years. But most of the current methods are still not capable to train complex neuron net model with long-time training process. In this paper, a novel second-order meta-optimizer, named Meta-learning with Hessian-Free(MLHF) approach, is proposed based on the Hessian-Free approach. Two recurrent neural networks are established to generate the damping and the precondition matrix of this Hessian-Free framework. A series of techniques to meta-train the MLHF towards stable and reinforce the meta-training of this optimizer, including the gradient calculation of H . Numerical experiments on deep convolution neural nets, including CUDA-convnet and ResNet18(v2), with datasets of CIFAR10 and ILSVRC2012, indicate that the MLHF shows good and continuous training performance during the whole long-time training process, i.e., both the rapiddecreasing early stage and the steadily-deceasing later stage, and so is a promising meta-learning framework towards elevating the training efficiency in real-world deep neural nets.

Thoth: Comprehensive Policy Compliance in Data Retrieval Systems <=SEP=> Data retrieval systems process data from many sources, each subject to its own data use policy. Ensuring compliance with these policies despite bugs, misconfiguration, or operator error in a large, complex, and fast evolving system is a major challenge. Thoth provides an efficient, kernel-level compliance layer for data use policies. Declarative policies are attached to the systems’ input and output files, key-value tuples, and network connections, and specify the data’s integrity and confidentiality requirements. Thoth tracks the flow of data through the system, and enforces policy regardless of bugs, misconfigurations, compromises in application code, or actions by unprivileged operators. Thoth requires minimal changes to an existing system and has modest overhead, as we show using a prototype Thoth-enabled data retrieval system based on the popular Apache Lucene.

A multiparadigm intelligent tutoring system for robotic arm training <=SEP=> To assist learners during problem-solving activities, an intelligent tutoring system (ITS) has to be equipped with domain knowledge that can support appropriate tutoring services. Providing domain knowledge is usually done by adopting one of the following paradigms: building a cognitive model, specifying constraints, integrating an expert system, and using data mining algorithms to learn domain knowledge. However, for some ill-defined domains, each single paradigm may present some advantages and limitations in terms of the required resources for deploying it, and tutoring support that can be offered. To address this issue, we propose using a multiparadigm approach. In this paper, we explain how we have applied this idea in CanadarmTutor, an ITS for learning to operate the Canadarm2 robotic arm. To support tutoring services in this ill-defined domain, we have developed a multiparadigm model combining: 1) a cognitive model to cover well-defined parts of the task and spatial reasoning, 2) a data mining approach for automatically building a task model from user solutions for ill-defined parts of the task, and 3) a 3D path-planner to cover other parts of the task for which no user data are available. The multiparadigm version of CanadarmTutor allows providing a richer set of tutoring services than what could be offered with previous single paradigm versions of CanadarmTutor.

Intellectual Capital (IC) Measurement in the Mass Media Context <=SEP=> Mass media is the key in°uencer of public opinion. The in°uence is not only limited to political and social, but also relates to organisational and economical reputation and brands. Within public opinion, organisations must manage how they are represented competitively within mass media so that they can develop their brand strategically to grow and compete in the current global knowledge economy. This is where the link to Intellectual Capital (IC) Measurement is signi ̄cant. IC, as the sum of all an organisation's intangible assets drives a company's presence and value within the media, albeit related to human, structural or relational capital attributes. The measurement, therefore, of IC in the mass media context is invaluable to understand how a company is placed strategically and competitively in the external space, and how this links to internal activities, goals and outcomes. This paper is an attempt to address some of the issues related to IC measurements in the mass media context by suggesting a framework that provides a multidisciplinary and holistic approach to the understanding and contextualising of the organisation's presence in the public space.

Orthogonal nonnegative matrix tri-factorization for co-clustering: Multiplicative updates on Stiefel manifolds <=SEP=> Matrix factorization-based methods become popular in dyadic data analysis, where a fundamental problem, for example, is to perform document clustering or co-clustering words and documents given a term-document matrix. Nonnegative matrix tri-factorization (NMTF) emerges as a promising tool for co-clustering, seeking a 3-factor decomposition X USV with all factor matrices restricted to be nonnegative, i.e., U P 0; S P 0;V P 0: In this paper we develop multiplicative updates for orthogonal NMTF where X USV is pursued with orthogonality constraints, UU 1⁄4 I; and VV 1⁄4 I, exploiting true gradients on Stiefel manifolds. Experiments on various document data sets demonstrate that our method works well for document clustering and is useful in revealing polysemous words via co-clustering words and documents. 2010 Elsevier Ltd. All rights reserved.