title: A Unified Video Text Detection Method with Network Flow

abstract: Scene text detection in videos has many application needs but has drawn less attention than that in images. Existing methods for video text detection perform unsatisfactorily because of the insufficient utilization of spatial and temporal information. In this paper, we propose a novel video text detection method with network flow based tracking. The system first applies a newly proposed Fully Convolutional Neural Network (FCN) based scene text detection method to detect texts in individual frames and then track proposals in adjacent frames with a motion-based method. Next, the text association problem is formulated into a cost-flow network and text trajectories are derived from the network with a min-cost flow algorithm. At last, the trajectories are post-processed to improve the precision accuracy. The method can detect multi-oriented scene text in videos and incorporate spatial and temporal information efficiently. Experimental results show that the method improves the detection performance remarkably on benchmark datasets, e.g., by a 15.66% increase of ATA Average Tracking Accuracy) on ICDAR video scene text dataset.


title: Visual Turing test for computer vision systems

abstract: Today, computer vision systems are tested by their accuracy in detecting and localizing instances of objects. As an alternative, and motivated by the ability of humans to provide far richer descriptions and even tell a story about an image, we construct a “visual Turing test”: an operator-assisted device that produces a stochastic sequence of binary questions from a given test image. The query engine proposes a question; the operator either provides the correct answer or rejects the question as ambiguous; the engine proposes the next question (“just-in-time truthing”). The test is then administered to the computer-vision system, one question at a time. After the system’s answer is recorded, the system is provided the correct answer and the next question. Parsing is trivial and deterministic; the system being tested requires no natural language processing. The query engine employs statistical constraints, learned from a training set, to produce questions with essentially unpredictable answers—the answer to a question, given the history of questions and their correct answers, is nearly equally likely to be positive or negative. In this sense, the test is only about vision. The system is designed to produce streams of questions that follow natural story lines, from the instantiation of a unique object, through an exploration of its properties, and on to its relationships with other uniquely instantiated objects.


title: Optimizing the performance of sparse matrix-vector multiplication

abstract: Sparse matrix operations dominate the performance of many scientific and engineering applications. In particular, iterative methods are commonly used in algorithms for linear systems, least squares problems, and eigenvalue problems, which involve a sparse matrix-vector product in the inner loop. The performance of sparse matrix algorithms is often disappointing on modern machines because the algorithms have poor temporal and spatial locality, and are therefore limited by the speed of main memory. Unfortunately, the performance gap between memory and processing is steadily increasing, as processor performance increases by roughly 60% every year, while memory latency drops by only 7%. Performance is also highly dependent on the nonzero structure of the sparse matrix, the organization of the data and its computation, and the exact parameters of the hardware memory system. This thesis presents a toolkit called Sparsity for the automatic optimization of sparse matrix-vector multiplication. We start with an extensive study of possible memory hierarchy optimizations, in particular, reorganization of the matrix and computation around blocks of the matrix. The research demonstrates that certain kinds of blocking can be effective for both registers and caches, although the nature of that tiling is quite different due to the differences in size between typical register sets and caches. Both types of blocking are shown to be highly effective in some matrices, but ineffective in others, and the choice of block size is also shown to be highly dependent on the matrix and the machine. Thus, to automatically determine when and how the optimizations should be applied, we employ a combination of search over a set of possible optimized versions, along with newly devised performance models to eliminate or constrain the search to make it practical. We also consider a common variation of basic sparse matrix-vector multiplication in which a sparse matrix is multiplied by a set of dense vectors. This operation arises, for example, when there are multiple right-hand sides in a linear solver, or when a higher level algorithm has been blocked. The introduction of multiple vectors offers enormous optimization opportunities, effectively changing a matrix-vector operation into a matrix-matrix operation. It is well known that for dense matrices, the latter algorithm has much higher data reuse than the former, and so can achieve much better performance; the same is true in the sparse case. The Sparsity system is designed as a web service, so scientists and engineers can easily obtain highly optimized sparse matrix routines without needing to understand the specifics of the optimization techniques or how they are selected. This thesis also reports on an extensive performance study of over 40 matrices on a variety of machines. The matrices are taken from various scientific and engineering problems, as well as from linear programming and data mining. The machines include the Alpha 21164, UltraSPARC I, MIPS R10000 and PowerPC 604e. These benchmark results are useful for understanding the performance differences across application domains, the effectiveness of the optimizations, and the costs associated with evaluating our performance models in applying the optimizations. The conclusion is that Sparsity is highly effective, producing routines that are up to 3.1 times faster for a single vector and 6.2 times faster for multiple vectors.


title: Operator Scheduling in a Data Stream Manager

abstract: Many stream-based applications have sophisticated data processing requirements and real-time performance expectations that need to be met under high-volume, time-varying data streams. In order to address these challenges, we propose novel operator scheduling approaches that specify (1) which operators to schedule (2) in which order to schedule the operators, and (3) how many tuples to process at each execution step. We study our approaches in the context of the Aurora data stream manager. ::: ::: We argue that a fine-grained scheduling approach in combination with various scheduling techniques (such as batching of operators and tuples) can significantly improve system efficiency by reducing various system overheads. We also discuss application-aware extensions that make scheduling decisions according to per-application Quality of Service (QoS) specifications. Finally, we present prototype-based experimental results that characterize the efficiency and effectiveness of our approaches under various stream workloads and processing scenarios.


title: Elements of Information Theory

abstract: Preface to the Second Edition. Preface to the First Edition. Acknowledgments for the Second Edition. Acknowledgments for the First Edition. 1. Introduction and Preview. 1.1 Preview of the Book. 2. Entropy, Relative Entropy, and Mutual Information. 2.1 Entropy. 2.2 Joint Entropy and Conditional Entropy. 2.3 Relative Entropy and Mutual Information. 2.4 Relationship Between Entropy and Mutual Information. 2.5 Chain Rules for Entropy, Relative Entropy, and Mutual Information. 2.6 Jensen's Inequality and Its Consequences. 2.7 Log Sum Inequality and Its Applications. 2.8 Data-Processing Inequality. 2.9 Sufficient Statistics. 2.10 Fano's Inequality. Summary. Problems. Historical Notes. 3. Asymptotic Equipartition Property. 3.1 Asymptotic Equipartition Property Theorem. 3.2 Consequences of the AEP: Data Compression. 3.3 High-Probability Sets and the Typical Set. Summary. Problems. Historical Notes. 4. Entropy Rates of a Stochastic Process. 4.1 Markov Chains. 4.2 Entropy Rate. 4.3 Example: Entropy Rate of a Random Walk on a Weighted Graph. 4.4 Second Law of Thermodynamics. 4.5 Functions of Markov Chains. Summary. Problems. Historical Notes. 5. Data Compression. 5.1 Examples of Codes. 5.2 Kraft Inequality. 5.3 Optimal Codes. 5.4 Bounds on the Optimal Code Length. 5.5 Kraft Inequality for Uniquely Decodable Codes. 5.6 Huffman Codes. 5.7 Some Comments on Huffman Codes. 5.8 Optimality of Huffman Codes. 5.9 Shannon-Fano-Elias Coding. 5.10 Competitive Optimality of the Shannon Code. 5.11 Generation of Discrete Distributions from Fair Coins. Summary. Problems. Historical Notes. 6. Gambling and Data Compression. 6.1 The Horse Race. 6.2 Gambling and Side Information. 6.3 Dependent Horse Races and Entropy Rate. 6.4 The Entropy of English. 6.5 Data Compression and Gambling. 6.6 Gambling Estimate of the Entropy of English. Summary. Problems. Historical Notes. 7. Channel Capacity. 7.1 Examples of Channel Capacity. 7.2 Symmetric Channels. 7.3 Properties of Channel Capacity. 7.4 Preview of the Channel Coding Theorem. 7.5 Definitions. 7.6 Jointly Typical Sequences. 7.7 Channel Coding Theorem. 7.8 Zero-Error Codes. 7.9 Fano's Inequality and the Converse to the Coding Theorem. 7.10 Equality in the Converse to the Channel Coding Theorem. 7.11 Hamming Codes. 7.12 Feedback Capacity. 7.13 Source-Channel Separation Theorem. Summary. Problems. Historical Notes. 8. Differential Entropy. 8.1 Definitions. 8.2 AEP for Continuous Random Variables. 8.3 Relation of Differential Entropy to Discrete Entropy. 8.4 Joint and Conditional Differential Entropy. 8.5 Relative Entropy and Mutual Information. 8.6 Properties of Differential Entropy, Relative Entropy, and Mutual Information. Summary. Problems. Historical Notes. 9. Gaussian Channel. 9.1 Gaussian Channel: Definitions. 9.2 Converse to the Coding Theorem for Gaussian Channels. 9.3 Bandlimited Channels. 9.4 Parallel Gaussian Channels. 9.5 Channels with Colored Gaussian Noise. 9.6 Gaussian Channels with Feedback. Summary. Problems. Historical Notes. 10. Rate Distortion Theory. 10.1 Quantization. 10.2 Definitions. 10.3 Calculation of the Rate Distortion Function. 10.4 Converse to the Rate Distortion Theorem. 10.5 Achievability of the Rate Distortion Function. 10.6 Strongly Typical Sequences and Rate Distortion. 10.7 Characterization of the Rate Distortion Function. 10.8 Computation of Channel Capacity and the Rate Distortion Function. Summary. Problems. Historical Notes. 11. Information Theory and Statistics. 11.1 Method of Types. 11.2 Law of Large Numbers. 11.3 Universal Source Coding. 11.4 Large Deviation Theory. 11.5 Examples of Sanov's Theorem. 11.6 Conditional Limit Theorem. 11.7 Hypothesis Testing. 11.8 Chernoff-Stein Lemma. 11.9 Chernoff Information. 11.10 Fisher Information and the Cram-er-Rao Inequality. Summary. Problems. Historical Notes. 12. Maximum Entropy. 12.1 Maximum Entropy Distributions. 12.2 Examples. 12.3 Anomalous Maximum Entropy Problem. 12.4 Spectrum Estimation. 12.5 Entropy Rates of a Gaussian Process. 12.6 Burg's Maximum Entropy Theorem. Summary. Problems. Historical Notes. 13. Universal Source Coding. 13.1 Universal Codes and Channel Capacity. 13.2 Universal Coding for Binary Sequences. 13.3 Arithmetic Coding. 13.4 Lempel-Ziv Coding. 13.5 Optimality of Lempel-Ziv Algorithms. Compression. Summary. Problems. Historical Notes. 14. Kolmogorov Complexity. 14.1 Models of Computation. 14.2 Kolmogorov Complexity: Definitions and Examples. 14.3 Kolmogorov Complexity and Entropy. 14.4 Kolmogorov Complexity of Integers. 14.5 Algorithmically Random and Incompressible Sequences. 14.6 Universal Probability. 14.7 Kolmogorov complexity. 14.9 Universal Gambling. 14.10 Occam's Razor. 14.11 Kolmogorov Complexity and Universal Probability. 14.12 Kolmogorov Sufficient Statistic. 14.13 Minimum Description Length Principle. Summary. Problems. Historical Notes. 15. Network Information Theory. 15.1 Gaussian Multiple-User Channels. 15.2 Jointly Typical Sequences. 15.3 Multiple-Access Channel. 15.4 Encoding of Correlated Sources. 15.5 Duality Between Slepian-Wolf Encoding and Multiple-Access Channels. 15.6 Broadcast Channel. 15.7 Relay Channel. 15.8 Source Coding with Side Information. 15.9 Rate Distortion with Side Information. 15.10 General Multiterminal Networks. Summary. Problems. Historical Notes. 16. Information Theory and Portfolio Theory. 16.1 The Stock Market: Some Definitions. 16.2 Kuhn-Tucker Characterization of the Log-Optimal Portfolio. 16.3 Asymptotic Optimality of the Log-Optimal Portfolio. 16.4 Side Information and the Growth Rate. 16.5 Investment in Stationary Markets. 16.6 Competitive Optimality of the Log-Optimal Portfolio. 16.7 Universal Portfolios. 16.8 Shannon-McMillan-Breiman Theorem (General AEP). Summary. Problems. Historical Notes. 17. Inequalities in Information Theory. 17.1 Basic Inequalities of Information Theory. 17.2 Differential Entropy. 17.3 Bounds on Entropy and Relative Entropy. 17.4 Inequalities for Types. 17.5 Combinatorial Bounds on Entropy. 17.6 Entropy Rates of Subsets. 17.7 Entropy and Fisher Information. 17.8 Entropy Power Inequality and Brunn-Minkowski Inequality. 17.9 Inequalities for Determinants. 17.10 Inequalities for Ratios of Determinants. Summary. Problems. Historical Notes. Bibliography. List of Symbols. Index.


title: E-satisfaction and e-loyalty: A contingency framework

abstract: The authors investigate the impact of satisfaction on loyalty in the context of electronic commerce. Findings of this research indicate that although e-satisfaction has an impact on e-loyalty, this relationship is moderated by (a) consumers' individual level factors and (b) firms' business level factors. Among consumer level factors, convenience motivation and purchase size were found to accentuate the impact of e-satisfaction on e-loyalty, whereas inertia suppresses the impact of e-satisfaction on e-loyalty. With respect to business level factors, both trust and perceived value, as developed by the company, significantly accentuate the impact of e-satisfaction on e-loyalty. © 2003 Wiley Periodicals, Inc.


DESIGN PRACTICE FOR PACKED LIQUID LIQUID EXTRACTION COLUMNS


title: Extracting social events for learning better information diffusion models

abstract: Learning of the information diffusion model is a fundamental problem in the study of information diffusion in social networks. Existing approaches learn the diffusion models from events in social networks. However, events in social networks may have different underlying reasons. Some of them may be caused by the social influence inside the network, while others may reflect external trends in the ``real world''. Most existing work on the learning of diffusion models does not distinguish the events caused by the social influence from those caused by external trends. In this paper, we extract social events from data streams in social networks, and then use the extracted social events to improve the learning of information diffusion models. We propose a LADP (Latent Action Diffusion Path) model to incorporate the information diffusion model with the model of external trends, and then design an EM-based algorithm to infer the diffusion probabilities, the external trends and the sources of events efficiently.


title: Intent Recognition for Human-Robot Interaction

abstract: Effective human-robot cooperation requires robotic devices that understand human goals and intentions. We frame the problem of intent recognition as one of tracking and predicting human actions within the context of plan task sequences. A hybrid mode estimation approach, which estimates both discrete operating modes and continuous state, is used to accomplish this tracking based on possibly noisy sensor input. The operating modes correspond to plan tasks, hence, the ability to estimate and predict these provides a prediction of human actions and associated needs in the plan context. The discrete and continuous estimates interact in that the discrete mode selects continous dynamic models used in the continuous estimation, and the continuous state is used to evaluate guard conditions for mode transitions. Two applications: active prosthetic devices, and cooperative assembly, are described.


title: Broadband Substrate Integrated Waveguide Cavity-Backed Bow-Tie Slot Antenna

abstract: A novel design technique for broadband substrate integrated waveguide cavity-backed slot antenna is demonstrated in this letter. Instead of using a conventional narrow rectangular slot, a bow-tie-shaped slot is implemented to get broader bandwidth performance. The modification of the slot shape helps to induce strong loading effect in the cavity and generates two closely spaced hybrid modes that help to get a broadband response. The slot antenna incorporates thin cavity backing (height <;0.03λ ::: 0 ::: ) in a single substrate and thus retains low-profile planar configuration while showing unidirectional radiation characteristics with moderate gain. A fabricated prototype is also presented that shows a bandwidth of 1.03 GHz (9.4%), a gain of 3.7 dBi over the bandwidth, 15 dB front-to-back ratio, and cross-polarization level below -18 dB.


title: A 0.951 psrms period jitter, 3.2% modulation range, DSM-free, spread-spectrum PLL

abstract: This work presents a delta-sigma modulator free in-loop-bandwidth spread-spectrum clock generator. The proposed charge-based discrete-time loop filter with a digital-to-current converter enables wide range spread-spectrum frequency modulation with significantly relaxed PVT sensitivity. A correlated double sampling technique is leveraged to minimize 1/f noise in the proposed discrete-time loop filter. This work achieves 3.2% spread-spectrum modulation range and 26.51dB spectrum attenuation at 352MHz output frequency. A 142% change in K VCO results in less than 298ppm modulation range error. Implemented in a 0.18μm CMOS, this work achieves 951fs rms period jitter while consuming 9.93mW with a 1.8V supply.


WindowGuard: Systematic Protection of GUI Security in Android


title: 2008b), "A Model of Capital and Crises

abstract: We develop a model in which the capital of the intermediary sector plays a critical role in determining asset prices. The model is cast within a dynamic general equilibrium economy, and the role for intermediation is derived endogenously based on optimal contracting considerations. Low intermediary capital reduces the risk-bearing capacity of the marginal investor. We show how this force helps to explain patterns during financial crises. The model replicates the observed rise during crises in Sharpe ratios, conditional volatility, correlation in price movements of assets held by the intermediary sector, and fall in riskless interest rates. In a dynamic context, we show that aversion to drops in intermediary capital can generate a two-factor asset pricing model with a role for both a market factor and a liquidity factor.


title: Identifying Women's Experiences With and Strategies for Mitigating Negative Effects of Online Harassment

abstract: The popularity, availability, and ubiquity of information and communication technologies create new opportunities for online harassment. The present study evaluates factors associated with young adult women's online harassment experiences through a multi-factor measure accounting for the frequency and severity of negative events. Findings from a survey of 659 undergraduate and graduate students highlight the relationship between harassment, well-being, and engagement in strategies to manage one's online identity. We further identify differences in harassment experiences across three popular social media platforms: Facebook, Twitter, and Instagram. We conclude by discussing this study's contribution to feminist theory and describing five potential design interventions derived from our data that may minimize these negative experiences, mitigate the psychological harm they cause, and provide women with more proactive ways to regain agency when using communication technologies.


title: Depth Resolution Enhancement Technique for CMOS Time-of-Flight 3-D Image Sensors

abstract: Introducing Time-of-Flight 3-D image sensors to actual engineering applications, such as pattern recognition, is constrained not only by their limited depth and lateral resolution, but also by how similar the precision of depth measurement throughout the whole pixel-matrix is. In real operating environment, an observed 3-D-scene hardly exhibits a homogeneous reflectance factor. Moreover, the light-beam (laser source) presents a nonuniform optical power distribution in space. Thus, the amount of the incident light on the sensor surface varies drastically from one pixel to another, and so does the signal-to-noise ratio. To address this problem, this paper investigates the impact of both scene and light-source non-ideal characteristics on the sensor performance. An adaptive on-pixel analog signal processing technique is also presented and applied to the design of a 32 × 32 complementary metal oxide semiconductor (CMOS) range camera, featuring an interesting cost-efficient solution.


title: The What and How of Smart Personal Assistants: Principles and Application Domains for IS Research

abstract: Digitization brings new possibilities to ease our daily life activities by the means of assistive technology. Amazon Alexa, Microsoft Cortana, Samsung Bixby, to name only a few, heralded the age of smart personal assistants (SPAs), personified agents that combine artificial intelligence, machine learning, natural language processing and various actuation mechanisms to sense and influence the environment. However, SPA research seems to be highly fragmented among different disciplines, such as computer science, human-computer-interaction and information systems, which leads to ‘reinventing the wheel approaches’ and thus impede progress and conceptual clarity. In this paper, we present an exhaustive, integrative literature review to build a solid basis for future research. We have identified five functional principles and three research domains which appear promising for future research, especially in the information systems field. Hence, we contribute by providing a consolidated, integrated view on prior research and lay the foundation for an SPA classification scheme.


title: Computational prediction of chemical reactions: current status and outlook.

abstract: Over the past few decades, various computational methods have become increasingly important for discovering and developing novel drugs. Computational prediction of chemical reactions is a key part of an efficient drug discovery process. In this review, we discuss important parts of this field, with a focus on utilizing reaction data to build predictive models, the existing programs for synthesis prediction, and usage of quantum mechanics and molecular mechanics (QM/MM) to explore chemical reactions. We also outline potential future developments with an emphasis on pre-competitive collaboration opportunities.


title: Hough technique for bar charts detection and recognition in document images

abstract: Charts are common graphic representation for scientific data in technical and business papers. We present a robust system for detecting and recognizing bar charts. The system includes three stages, preprocessing, detection and recognition. The kernel algorithm in detection is newly developed modified probabilistic Hough transform algorithm for parallel lines clusters detection. The main algorithms in recognition are bar pattern reconstruction and text primitives grouping in the Hough space which are also original. The experiments show the system can also recognize slant bar charts, or even hand-drawn charts.


title: A threat in the air: How stereotypes shape intellectual identity and performance

abstract: A general theory of domain identification is used to de scribe achievement barriers still faced by women in ad vanced quantitative areas and by African Americans in school. The theory assumes that sustained school success requires identification with school and its subdomains; that societal pressures on these groups (e.g., economic disadvantage, gender roles) can frustrate this identifica tion; and that in school domains where these groups are negatively stereotyped, those who have become domain identified face the further barrier of stereotype threat, the threat that others' judgments or their own actions will negatively stereotype them in the domain. Research shows that this threat dramatically depresses the stan dardized test performance ofwomen and African Ameri cans who are in the academic vanguard of their groups (offering a new interpretation of group differences in standardized test performance), that it causes disidenti fication with school, and that practices that reduce this threat can reduce these negative effects.


title: An Agile Systems Engineering Process: The Missing Link?

abstract: Abstract : Today's systems are increasingly threatened by unanticipated change arising from volatility in user requirements, Information Technology (IT) refresh rates, and responses to security vulnerabilities. With the rapidly changing world of IT, long static development cycles of a Software Intensive System (SIS), a system in which software represents the largest segment in one or more of the following criteria: system development cost, system development risk, system functionality, or development time [1] may doom the system before development begins.


title: Single-View Metrology: Algorithms and Applications

abstract: This paper addresses the problem of extracting three-dimensional geometric information from a single, uncalibrated image of a scene.This work, building upon [7], is divided into two parts. The first part describes, in simple steps, the basic algorithms to obtain partial or complete geometric reconstruction from single perspective images of a scene. The second part presents a panorama of applications of single-view metrology and discusses its relationship with different disciplines such as architecture, history of art and forensic science. Furthermore, techniques for increasing the level of automation of the reconstruction process are herein described.Several examples on photographs and historical paintings demonstrate the power and flexibility of the proposed techniques.


title: Partially observable Markov decision processes with continuous observations for dialogue management

abstract: Partially Observable Markov Decision Processes for Spoken Dialogue Management Jason D. Williams The design of robust spoken dialog systems is a significant research challenge. Speech recognition errors are common and hence the state of the conversation can never be known with certainty, and users can react in a variety of ways making deterministic forward planning impossible. This thesis argues that a partially observable Markov decision process (POMDP) provides a principled formalism for modelling human-machine conversation. Further, this thesis introduces the SDS-POMDP framework which enables statistical models of users’ behavior and the speech recognition process to be combined with handcrafted heuristics into a single framework that supports global optimization. A combination of theoretical and empirical studies confirm that the SDS-POMDP framework unifies and extends existing techniques, such as local use of confidence score, maintaining parallel dialog hypotheses, and automated planning. Despite its potential, the SDS-POMDP model faces important scalability challenges, and this thesis next presents two methods for scaling up the SDS-POMDP model to realistically sized spoken dialog systems. First, summary point-based value iteration (SPBVI) enables a single slot (a dialog variable such as a date, time, or location) to take on an arbitrary number of values by restricting the planner to consider only the likelihood of the best hypothesis. Second, composite SPBVI (CSPBVI) enables dialog managers consisting of many slots to be created by planning locally within each slot, and combining these local plans into a global plan using a simple heuristic. Results from dialog simulation show that these techniques enable the SDS-POMDP model to handle real-world dialog problems while continuing to out-perform established techniques and hand-crafted dialog managers. Finally, application to a real spoken dialog system is demonstrated.


title: Run-time power estimation for mobile and embedded asymmetric multi-core CPUs

abstract: Significant energy savings can be made in mobile devices using intelligent run-time management software (RTM) that can e↵ectively trade o↵ performance and energy. For the RTM to make informed decisions, it needs accurate and responsive run-time knowledge of the power consumption. For desktop and server systems, hardware performance monitoring counters (PMCs) have been successfully used for runtime power estimation. However, for mobile devices there is little research on the topic. This paper presents a PMCbased power model for mobile devices and the associated methodology for identifying the best PMCs and their relationship with power consumption. However, for the majority of mobile devices experimented with, PMCs were di cult or impossible to obtain and so a utilisation-based power model was proposed as a more practical alternative. Both these models are validated, respectively, on an ARM Cortex-A8 and an asymmetric ARM big.LITTLE architecture, which can be found in recent tablets and smartphones, such as the Samsung Galaxy S5. A key feature of the utilisationbased model is that it can derive the power consumed per core and per individual task. Furthermore, the model can predict the likely power consumption if a task currently executing on a particular core/frequency (e.g. Cortex-A7 at 400 MHz) were to be migrated to a di↵erent core/frequency (e.g. Cortex-A15 at 1200 MHz). This allows an RTM to explore the consequences of changing to any frequency/core combination in advance. By comparing the two models, it was found that while the PMC model achieves greater accuracy (3.2%), the utilisation model still achieves an impressive accuracy of 5.6% and 7.2% on a Cortex-A7 and Cortex-A15, respectively. As it has a high accuracy and can be easily implemented on all mobile devices, we propose that the utilisation model represents an attractive choice for guiding an RTM to improved energy e ciency.


title: An Energy-Efficient Digital ReRAM-Crossbar-Based CNN With Bitwise Parallelism

abstract: There is great attention to develop hardware accelerator with better energy efficiency, as well as throughput, than GPUs for convolutional neural network (CNN). The existing solutions have relatively limited parallelism as well as large power consumption (including leakage power). In this paper, we present a resistive random access memory (ReRAM)-accelerated CNN that can achieve significantly higher throughput and energy efficiency when the CNN is trained with binary constraints on both weights and activations, and is further mapped on a digital ReRAM-crossbar. We propose an optimized accelerator architecture tailored for bitwise convolution that features massive parallelism with high energy efficiency. Numerical experiment results show that the binary CNN accelerator on a digital ReRAM-crossbar achieves a peak throughput of 792 GOPS at the power consumption of 4.5 mW, which is 1.61 times faster and 296 times more energy-efficient than a high-end GPU.


title: A graphical sensitivity analysis for clinical trials with non-ignorable missing binary outcome.

abstract: Many clinical trials are analysed using an intention-to-treat (ITT) approach. A full application of the ITT approach is only possible when complete outcome data are available for all randomized subjects. In a recent survey of clinical trial reports including an ITT analysis, complete case analysis (excluding all patients with a missing response) was common. This does not comply with the basic principles of ITT since not all randomized subjects are included in the analysis. Analyses of data with missing values are based on untestable assumptions, and so sensitivity analysis presenting a range of estimates under alternative assumptions about the missing-data mechanism is recommended. For binary outcome, extreme case analysis has been suggested as a simple form of sensitivity analysis, but this is rarely conclusive. A graphical sensitivity analysis is proposed which displays the results of all possible allocations of cases with missing binary outcome. Extension to allow binomial variation in outcome is also considered. The display is based on easily interpretable parameters and allows informal examination of the effects of varying prior beliefs.


title: Health Neuroscience Defining a New Field

abstract: Health neuroscience is a new field that is at the interface of health psychology and neuroscience. It is concerned with the interplay between the brain and physical health over the life span. A chief goal of health neuroscience is to characterize bidirectional and dynamic brain-behavior and brain-physiology relationships that are determinants, markers, and consequences of physical-health states across the life span. The motivation behind this goal is that a better understanding of these relationships will provide mechanistic insights into how the brain links multilevel genetic, biological, psychological, behavioral, social, and environmental factors with physical health—especially vulnerability to and resilience against clinical illnesses. This review provides a conceptual introduction to health neuroscience, focusing on its major themes, representative studies, methodologies, and future directions.


title: Distributed optimization in multi-agent robotics for industry 4.0 warehouses

abstract: Robotic automation is being increasingly proselytized in the industrial and manufacturing sectors to increase production efficiency. Typically, complex industrial tasks cannot be satisfied by individual robots, rather coordination and information sharing is required. Centralized robotic control and coordination is ill-advised in such settings, due to high failure probabilities, inefficient overheads and lack of scalability. In this paper, we model the interactions among robotic units using intelligent agent based interactions. The autonomous behavior of these agents requires task/resource allocation to be performed via distributed algorithms. We use the motivating example of warehouse inventory automation to optimally allocate and distribute delivery tasks among multiple robotic agents. The optimization is decomposed using primal and dual decomposition techniques to operate in minimal latency, minimal battery usage or maximal utilization scenarios. These techniques may be applied to multiple deployments involving coordination and task allocation between autonomous agents.


title: A Bayesian Approach to Multipath Mitigation in GNSS Receivers

abstract: Multipath is known to be one of the most dominant sources of accuracy degradation in satellite-based navigation systems. Multipath may cause biased position estimates that could jeopardize high-precision applications. This paper considers the problem of tracking the time-variant synchronization parameters of both the line-of-sight signal (LOSS) and its multipath replicas. In particular, the proposed algorithm tracks time-delays, amplitudes, phases and proposes a procedure to extract Doppler shifts from complex amplitudes. However, the interest is focused on LOSS time-delay estimates, since those provide the means to compute user's position. The undertaken Bayesian approach is implemented by a particle filter. The selection of the importance density function, from which particles are generated, is performed using a Gaussian approximation of the posterior function. This selection provides a particle generating function close to the optimal, which yields to an efficient usage of particles. The complex-linear part of the model, i.e., complex amplitudes, is tackled by a Rao-Blackwellization procedure that implements a complex Kalman filter for each generated particle, thus reducing the computational load. Computer simulation results are compared to other Bayesian filtering alternatives (namely, the extended Kalman filter, the unscented Kalman filter and the sequential importance resampling algorithms) and the posterior Cramer-Rao bound.


title: Managing and Mining Graph Data

abstract: Managing and Mining Graph Data is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by leading researchers, and provide a broad perspective of the area. This is the first comprehensive survey book in the emerging topic of graph data processing. Managing and Mining Graph Data is designed for a varied audience composed of professors, researchers and practitioners in industry. This volume is also suitable as a reference book for advanced-level database students in computer science. About the Editors:Charu C. Aggarwal obtained his B.Tech in Computer Science from IIT Kanpur in 1993 and Ph.D. from MIT in 1996. He has worked as a researcher at IBM since then, and has published over 130 papers in major data mining conferences and journals. He has applied for or been granted over 70 US and International patents, and has thrice been designated a Master Inventor at IBM. He has received an IBM Corporate award for his work on data stream analytics, and an IBM Outstanding Innovation Award for his work on privacy technology. He has served on the executive committees of most major data mining conferences. He has served as an associate editor of the IEEE TKDE, as an associate editor of the ACM SIGKDD Explorations, and as an action editor of the DMKD Journal. He is a fellow of the IEEE, and a life-member of the ACM. Haixun Wang is currently a researcher at Microsoft Research Asia. He received the B.S. and the M.S. degree, both in computer science, from Shanghai Jiao Tong University in 1994 and 1996. He received the Ph.D. degree in computer science from the University of California, Los Angeles in 2000. He subsequently worked as a researcher at IBMuntil 2009. His main research interest is database language and systems, data mining, and information retrieval. He has published more than 100 research papers in referred international journals and conference proceedings. He serves as an associate editor of the IEEE TKDE, and has served as a reviewer and program committee member of leading database conferences and journals.


title: On GPU Pass-Through Performance for Cloud Gaming: Experiments and Analysis

abstract: Cloud Gaming renders interactive gaming applications remotely in the cloud and streams the scenes back to the local console over the Internet. Virtualization plays a key role in modern cloud computing platforms, allowing multiple users and applications to share a physical machine while maintaining isolation and performance guarantees. Yet the Graphical Processing Unit (GPU), which advanced game engines heavily rely upon, is known to be difficult to virtualize. Recent advances have enabled virtual machines to directly access physical GPUs and exploit their hardware's acceleration. This paper presents a experimental study on the performance of real world gaming applications as well as ray-tracing applications with GPUs. Despite the fact that the VMs are accelerated with dedicated physical GPUs, we find that the gaming applications perform poorly when virtualized, as compared to non-virtualized bare-metal base-line. For example, experiments with the Unigine gaming benchmark run at 85 FPS on our bare-metal hardware, however, when the same benchmark is run within a Xen or KVM based virtual machine the performance drops to less than 51 FPS. In contrast, ray-tracing application fares much better. Our detailed performance analysis using hardware profiling on KVM further reveals the memory bottleneck in the pass through access, particularly for real-time gaming applications.


title: Biometric Antispoofing Methods: A Survey in Face Recognition

abstract: In recent decades, we have witnessed the evolution of biometric technology from the first pioneering works in face and voice recognition to the current state of development wherein a wide spectrum of highly accurate systems may be found, ranging from largely deployed modalities, such as fingerprint, face, or iris, to more marginal ones, such as signature or hand. This path of technological evolution has naturally led to a critical issue that has only started to be addressed recently: the resistance of this rapidly emerging technology to external attacks and, in particular, to spoofing. Spoofing, referred to by the term presentation attack in current standards, is a purely biometric vulnerability that is not shared with other IT security solutions. It refers to the ability to fool a biometric system into recognizing an illegitimate user as a genuine one by means of presenting a synthetic forged version of the original biometric trait to the sensor. The entire biometric community, including researchers, developers, standardizing bodies, and vendors, has thrown itself into the challenging task of proposing and developing efficient protection methods against this threat. The goal of this paper is to provide a comprehensive overview on the work that has been carried out over the last decade in the emerging field of antispoofing, with special attention to the mature and largely deployed face modality. The work covers theories, methodologies, state-of-the-art techniques, and evaluation databases and also aims at providing an outlook into the future of this very active field of research.


title: A CONCEPTUAL FRAMEWORK OF E-FRAUD CONTROL IN AN INTEGRATED SUPPLY CHAIN

abstract: The integration of supply chains offers many benefits; yet, it may also render organisations more vulnerable to electronic fraud (e-fraud). E-fraud can drain on organisations’ financial resources, and can have a significant adverse effect on the ability to achieve their strategic objectives. Therefore, efraud control should be part of corporate board-level due diligence, and should be integrated into organisations’ practices and business plans. Management is responsible for taking into consideration the relevant cultural, strategic and implementation elements that inter-relate with each other and to coordinating the human, technological and financial resources necessary to designing and implementing policies and procedures for controlling e-fraud. Due to the characteristics of integrated supply chains, a move from the traditional vertical approach to a systemic, horizontal-vertical approach is necessary. Although the e-fraud risk cannot be eliminated, risk mitigation policies and processes tailored to an organisation’s particular vulnerabilities can significantly reduce the risk and may even preclude certain classes of frauds. In this paper, a conceptual framework of e-fraud control in an integrated supply chain is proposed. The proposed conceptual framework can help managers and practitioners better understand the issues and plan the activities involved in a systemic, horizontal-vertical approach to e-fraud control in an integrated supply chain, and can be a basis upon which empirical studies can be build.


title: A novel wavelet transform technique for on-line partial discharge measurements. 1. WT de-noising algorithm

abstract: Medium and high voltage power cables are widely used in the electrical industry with substantial growth over the last 20-30 years ago, particular in the use of XLPE insulated systems. Ageing of the cable insulation is becoming an increasing problem that requires development of reliable methods for on-line condition assessment. For insulation condition assessment of MV and HV cables, partial discharge (PD) monitoring is one of the most effective techniques. However on-site and on-line PD measurements are affected by electromagnetic interference (EMI) that makes sensitive PD detection very difficult, if not impossible. This paper describes implementation of wavelet transform techniques to reject noise from on-line partial discharge measurements on cables. A new wavelet threshold determination method is proposed with the technique. With implementation of this novel de-noising method, PD measurement sensitivity has been greatly improved. In addition, a full AC cycle data recovery can be achieved instead of focusing only on recovering individual PD pulses. Other wavelet threshold de-noising methods are discussed and examined under a noisy environment to compare their performance with the new method proposed here. The method described here has been found to be superior to the other wavelet-based methods


title: iSAX: indexing and mining terabyte sized time series

abstract: Current research in indexing and mining time series data has produced many interesting algorithms and representations. However, the algorithms and the size of data considered have generally not been representative of the increasingly massive datasets encountered in science, engineering, and business domains. In this work, we show how a novel multi-resolution symbolic representation can be used to index datasets which are several orders of magnitude larger than anything else considered in the literature. Our approach allows both fast exact search and ultra fast approximate search. We show how to exploit the combination of both types of search as sub-routines in data mining algorithms, allowing for the exact mining of truly massive real world datasets, containing millions of time series.


title: Processing of affective pictures modulates right-hemispheric gamma band EEG activity

abstract: Abstract The present study was designed to test differential hemispheric activation induced by emotional stimuli in the gamma band range (30–90 Hz). Subjects viewed slides with differing emotional content (from the International Affective Picture System). A significant valence by hemisphere interaction emerged in the gamma band from 30–50 Hz. Other bands, including alpha and beta, did not show such an interaction. Previous hypotheses suggested that the left hemisphere is more involved in positive affective processing as compared to the right hemisphere, while the latter dominates during negative emotions. Contrary to this expectation, the 30–50 Hz band showed relatively more power for negative valence over the left temporal region as compared to the right and a laterality shift towards the right hemisphere for positive valence. In addition, emotional processing enhanced gamma band power at right frontal electrodes regardless of the particular valence as compared to processing neutral pictures. The extended distribution of specific activity in the gamma band may be the signature of cell assemblies with members in limbic, temporal and frontal neocortical structures that differ in spatial distribution depending on the particular type of emotional processing.


title: Coexistent cutaneous tuberous and tendinous xanthomas in traumatic regions: a case report

abstract: Xanthomas are tumors containing lipid-filled cells thatcan be seen on various regions of the body and areimportant clinical manifestations of disordered lipidmetabolism [1, 2]. They are generally classified accordingto their locations and appearances. Tuberous xanthomasare nodular, firm, painless subcutaneous swellings thatdevelop in those areas which are subjected to repeatedtrauma such as the elbows, knees, and buttocks. They mayoccur as single or multiple swellings and may vary in size,sometimes coalescing to form large tumorous swellings asshowninthiscase[3–6]. Tuberous xanthomas are usuallyassociated with raised low density lipoprotein levels [3, 5,6]. Tendinous xanthomas involve tendons or fascia mostlyseen on hands, feet, elbows, or knees and, although rare,are frequently seen in type-II hyperlipoproteinemia [7–9].Planar xanthomas, on the other hand, are yellow papularplaques seen on the head and neck region, the trunk, andextremities and occur mostly in type-II hyperlipoproteine-mia. Eruptive xanthomas, in contrast, are small, inflam-matory, red to yellow papules that appear suddenlyassociated with high serum rates of triglyceride mostlyseen in types III and IV hyperlipoproteinemia [10, 11].Xanthelasmas are plane xanthomas seen on the uppereyelids due to elevated cholesterol levels as seen in type-IIhyperlipoproteinemia.Patient and methodsThis case is a 21-year-old man who was referred to ourclinic complaining of multiple tuberous xanthomas overextensor surfaces and joints of the hands, arms, and legswith tendinous xanthomas of the extensor digits and theAchilles tendon, simultaneously. The history of thepatient revealed a diagnosis of familial hyperlipidemiatype IIa for which the patient was using medication. Hehad undergone an attack of acute rheumatic fever forwhich he was still using monthly prophylactic penicillininjections. On physical examination, he had oily skin andmultiple ulcerated xanthomas all over his body presenting asyellow pigmented and infected subcutaneousmasses over thethird metacarpophalangeal (MCP) joint of the right hand andover the extensor surface of the right and left elbows (Figs. 1,2). There was a tendinous lesion without any ulceration overthe third MCP of the left hand extending onto the proximalphalanx that was mobile with flexion and extension of thefinger and over the patella of both knees (Fig. 3). Atendinous mass was also palpated in the Achilles tendon ofthe right foot (Fig. 4).


title: Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks

abstract: In this work, we propose CLass-Enhanced Attentive Response (CLEAR): an approach to visualize and understand the decisions made by deep neural networks (DNNs) given a specific input. CLEAR facilitates the visualization of attentive regions and levels of interest of DNNs during the decision-making process. It also enables the visualization of the most dominant classes associated with these attentive regions of interest. As such, CLEAR can mitigate some of the shortcomings of heatmap-based methods associated with decision ambiguity, and allows for better insights into the decision-making process of DNNs. Quantitative and qualitative experiments across three different datasets demonstrate the efficacy of CLEAR for gaining a better understanding of the inner workings of DNNs during the decision-making process.


Social Innovation : Concepts , research fields and international trends


title: RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments

abstract: RGB-D cameras (such as the Microsoft Kinect) are novel sensing systems that capture RGB images along with per-pixel depth information. In this paper we investigate how such cameras can be used for building dense 3D maps of indoor environments. Such maps have applications in robot navigation, manipulation, semantic mapping, and telepresence. We present RGB-D Mapping, a full 3D mapping system that utilizes a novel joint optimization algorithm combining visual features and shape-based alignment. Visual and depth information are also combined for view-based loop-closure detection, followed by pose optimization to achieve globally consistent maps. We evaluate RGB-D Mapping on two large indoor environments, and show that it effectively combines the visual and shape information available from RGB-D cameras.


title: Bagging by design (on the suboptimality of bagging)

abstract: Bagging (Breiman 1996) and its variants is one of the most popular methods in aggregating classifiers and regressors. Originally, its analysis assumed that the bootstraps are built from an unlimited, independent source of samples, therefore we call this form of bagging ideal-bagging. However in the real world, base predictors are trained on data subsampled from a limited number of training samples and thus they behave very differently. We analyze the effect of intersections between bootstraps, obtained by subsampling, to train different base predictors. Most importantly, we provide an alternative subsampling method called design-bagging based on a new construction of combinatorial designs, and prove it universally better than bagging. Methodologically, we succeed at this level of generality because we compare the prediction accuracy of bagging and design-bagging relative to the accuracy ideal-bagging. This finds potential applications in more involved bagging-based methods. Our analytical results are backed up by experiments on classification and regression settings.


title: Emotional and physical health benefits of expressive writing

abstract: Writing about traumatic, stressful or emotional events has been found to result in improvements in both physical and psychological health, in non-clinical and clinical populations. In the expressive writing paradigm, participants are asked to write about such events for 15–20 minutes on 3–5 occasions. Those who do so generally have significantly better physical and psychological outcomes compared with those who write about neutral topics. Here we present an overview of the expressive writing paradigm, outline populations for which it has been found to be beneficial and discuss possible mechanisms underlying the observed health benefits. In addition, we suggest how expressive writing can be used as a therapeutic tool for survivors of trauma and in psychiatric settings.


title: A Critical Assessment of Null Hypothesis Significance Testing in Quantitative Communication Research

abstract: Null hypothesis significance testing (NHST) is the most widely accepted and frequently used approach to statistical inference in quantitative communication research. NHST, however, is highly controversial, and several serious problems with the approach have been identified. This paper reviews NHST and the controversy surrounding it. Commonly recognized problems include a sensitivity to sample size, the null is usually literally false, unacceptable Type II error rates, and misunderstanding and abuse. Problems associated with the conditional nature of NHST and the failure to distinguish statistical hypotheses from substantive hypotheses are emphasized. Recommended solutions and alternatives are addressed in a companion article.


title: EXPERIMENTAL FLUID MECHANICS OF PULSATILE ARTIFICIAL BLOOD PUMPS

abstract: The fluid mechanics of artificial blood pumps has been studied since the early 1970s in an attempt to understand and mitigate hemolysis and thrombus formation by the device. Pulsatile pumps are characterized by inlet jets that set up a rotational “washing” pattern during filling. Strong regurgitant jets through the closed artificial heart valves have Reynolds stresses on the order of 10,000 dynes/cm 2 and are the most likely cause of red blood cell damage and platelet activation. Although the flow in the pump chamber appears benign, low wall shear stresses throughout the pump cycle can lead to thrombus formation at the wall of the smaller pumps (10–50 cc). The local fluid mechanics is critical. There is a need to rapidly measure or calculate the wall shear stress throughout the device so that the results may be easily incorporated into the design process.


title: DWRank: Learning concept ranking for ontology search

abstract: With the recent growth of Linked Data on the Web there is an increased need for knowledge engineers to find ontologies to describe their data. Only limited work exists that addresses the problem of searching and ranking ontologies based on a given query term. In this paper we introduce DWRank, a two-staged bi-directional graph walk ranking algorithm for concepts in ontologies. DWRank characterises two features of a concept in an ontology to determine its rank in a corpus, the centrality of the concept to the ontology within which it is defined (HubScore) and the authoritativeness of the ontology in which it is defined (AuthorityScore). DWRank then uses a Learning to Rank approach to learn the feature weights for the two aforementioned ranking strategies. We compare DWRank with state-of-the-art ontology ranking models and traditional information retrieval algorithms. This evaluation shows that DWRank significantly outperforms the best ranking models on a benchmark ontology collection for the majority of the sample queries defined in the benchmark. In addition, we compare the eectiveness of the HubScore part of our algorithm with the state-of-the-art ranking model to determine a concept centrality and show the improved performance of DWRank in this aspect. Finally, we evaluate the eectiveness of the design decisions made for the AuthorityScore method in DWRank to find missing inter-ontology links and present a graph-based analysis of the ontology corpus that shows the increased connectivity of the ontology corpus after extraction of the implicit inter-ontology links.


title: Index-based join operations in Hive

abstract: Indexing techniques are crucial for efficiency and scalability of processing queries over big data. Hive is a batch-oriented big data management engine that is well suited for data OLAP and data analysis applications. For very “selective” queries whose output sizes are a small fraction of the contributing data, the brute-force approach suffers from poor performance due to redundant disk I/O's or initiations of extra map operations. We make a first attempt and propose an index-based join technique to speed up the process and integrate it in Hive by mapping our design to the conceptual optimization flow. To evaluate the performance, we create and evaluate test queries on datasets generated using TPC-H benchmark. Our results indicate significant performance gain over relatively large data and/or highly selective queries having a two-way join and a single join condition.


title: Smart Electricity Meter Data Intelligence for Future Energy Systems: A Survey

abstract: Smart meters have been deployed in many countries across the world since early 2000s. The smart meter as a key element for the smart grid is expected to provide economic, social, and environmental benefits for multiple stakeholders. There has been much debate over the real values of smart meters. One of the key factors that will determine the success of smart meters is smart meter data analytics, which deals with data acquisition, transmission, processing, and interpretation that bring benefits to all stakeholders. This paper presents a comprehensive survey of smart electricity meters and their utilization focusing on key aspects of the metering process, different stakeholder interests, and the technologies used to satisfy stakeholder interests. Furthermore, the paper highlights challenges as well as opportunities arising due to the advent of big data and the increasing popularity of cloud environments.


title: A 1.06-to-5.09 TOPS/W reconfigurable hybrid-neural-network processor for deep learning applications

abstract: An energy-efficient hybrid neural network (NN) processor is implemented in a 65nm technology. It has two 16×16 reconfigurable heterogeneous processing elements (PEs)arrays. To accelerate a hybrid-NN, the PE array is designed to support on demand partitioning and reconfiguration for parallel processing different NNs. To improve energy efficiency, each PE supports bit-width adaptive computing to meet variant bit-width of different neural layers. Measurement results show that this processor achieves a peak 409.6GOPS running at 200MHz and at most 5.09TOPS/W energy efficiency. This processor outperforms the state-of-the-art up to 5.2X in energy efficiency.


title: ViVA: a visualization and analysis tool for distributed event-based systems

abstract: Distributed event-based (DEB) systems are characterized by highly-decoupled components that communicate by exchanging messages. This form of communication enables flexible and scalable system composition but also reduces understandability and maintainability due to the indirect manner in which DEB components communicate. To tackle this problem, we present Visualizer for eVent-based Architectures, ViVA, a tool that effectively visualizes the large number of messages and dependencies that can be exchanged between components and the order in which the exchange of messages occur. In this paper, we describe the design, implementation, and key features of ViVA. (Demo video at http://youtu.be/jHVwuR5AYgA)


title: Human Action Recognition With Video Data: Research and Evaluation Challenges

abstract: Given a video sequence, the task of action recognition is to identify the most similar action among the action sequences learned by the system. Such human action recognition is based on evidence gathered from videos. It has wide application including surveillance, video indexing, biometrics, telehealth, and human-computer interaction. Vision-based human action recognition is affected by several challenges due to view changes, occlusion, variation in execution rate, anthropometry, camera motion, and background clutter. In this survey, we provide an overview of the existing methods based on their ability to handle these challenges as well as how these methods can be generalized and their ability to detect abnormal actions. Such systematic classification will help researchers to identify the suitable methods available to address each of the challenges faced and their limitations. In addition, we also identify the publicly available datasets and the challenges posed by them. From this survey, we draw conclusions regarding how well a challenge has been solved, and we identify potential research areas that require further work.


title: Learning of Behavior Trees for Autonomous Agents

abstract: Definition of an accurate system model for Automated Planner (AP) is often impractical, especially for real-world problems. Conversely, off-the-shelf planners fail to scale up and are domain dependent. These drawbacks are inherited from conventional transition systems such as Finite State Machines (FSMs) that describes the action-plan execution generated by the AP. On the other hand, Behavior Trees (BTs) represent a valid alternative to FSMs presenting many advantages in terms of modularity, reactiveness, scalability and domain-independence. In this paper, we propose a model-free AP framework using Genetic Programming (GP) to derive an optimal BT for an autonomous agent to achieve a given goal in unknown (but fully observable) environments. We illustrate the proposed framework using experiments conducted with an open source benchmark Mario AI for automated generation of BTs that can play the game character Mario to complete a certain level at various levels of difficulty to include enemies and obstacles.


title: Segmental anchoring of pitch movements: Autosegmental association or gestural coordination?

abstract: Arvaniti, Ladd and Mennen (1998) reported a phenomenon of ‘segmental anchoring’: the beginning and end of a linguistically significant pitch movement are anchored to specific locations in segmental structure, which means that the slope and duration of the pitch movement vary according to the segmental material with which it is associated. This finding has since been replicated and extended in several languages. One possible analysis is that autosegmental tones corresponding to the beginning and end of the pitch movement show secondary association with points in structure; however, problems with this analysis have led some authors to cast doubt on the ‘hypothesis’ of segmental anchoring. I argue here that segmental anchoring is not a hypothesis expressed in terms of autosegmental phonology, but rather an empirical phonetic finding. The difficulty of describing segmental anchoring as secondary association does not disprove the ‘hypothesis’, but shows the error of using a symbolic phonological device (secondary association) to represent gradient differences of phonetic detail that should be expressed quantitatively. I propose that treating pitch movements as gestures (in the sense of Articulatory Phonology) goes some way to resolving some of the theoretical questions raised by segmental anchoring, but suggest that pitch gestures have a variety of ‘domains’ which are in need of empirical study before we can successfully integrate segmental anchoring into our understanding of speech production.


Frontal Fibrosing Alopecia-A Case Report


title: The indeterminable resilience of the fascial system.

abstract: The most recent information on fascial tissue indicates that there are not fascial layers, but polyhedral microvacuoles of connective tissue, which connect the body systems and, by hosting specialized cells, permit several functions, such as motor, nervous, vascular and visceral. These microvacuoles (a repetition of polyhedral units of connective fibrils) under internal or external tension change shape and can manage the movement variations, regulating different body functions and ensuring the maintenance of efficiency of the body systems. Their plasticity is based on perfect functional chaos: it is not possible to determine the motion vectors of the different fibrils, which differ in behavior and orientation; this strategy confers to the fascial continuum the maximum level of adaptability in response to the changing internal and external conditions of the cell. The present commentary deals with this concept, providing clinical examples of different disease patterns, providing contrary examples in which this adaptability does not occur, and lastly suggesting considerations for the approach to manipulative therapy of the fascial tissue. The fascial continuum is like a flock of birds flying together without a predetermined logic and maintaining their individuality at the same time.


title: Detecting Anomalous Trajectories and Behavior Patterns Using Hierarchical Clustering from Taxi GPS Data

abstract: Anomalous taxi trajectories are those chosen by a small number of drivers that are different from the regular choices of other drivers. These anomalous driving trajectories provide us an opportunity to extract driver or passenger behaviors and monitor adverse urban traffic events. Because various trajectory clustering methods have previously proven to be an effective means to analyze similarities and anomalies within taxi GPS trajectory data, we focus on the problem of detecting anomalous taxi trajectories, and we develop our trajectory clustering method based on the edit distance and hierarchical clustering. To achieve this objective, first, we obtain all the taxi trajectories crossing the same source–destination pairs from taxi trajectories and take these trajectories as clustering objects. Second, an edit distance algorithm is modified to measure the similarity of the trajectories. Then, we distinguish regular trajectories and anomalous trajectories by applying adaptive hierarchical clustering based on an optimal number of clusters. Moreover, we further analyze these anomalous trajectories and discover four anomalous behavior patterns to speculate on the cause of an anomaly based on statistical indicators of time and length. The experimental results show that the proposed method can effectively detect anomalous trajectories and can be used to infer clearly fraudulent driving routes and the occurrence of adverse traffic events.


title: Certificateless Public Auditing Scheme for Cloud-Assisted Wireless Body Area Networks

abstract: Wireless body area networks (WBANs) consist of many small low-power sensors, through which users could monitor the real-time parameters of patients' physiology remotely. This capability could improve medical care and the monitoring of patients. WBAN devices typically have limited computing, storage, power, and communication capabilities. These limitations restrict the applications that WBANs can support. To enhance the capabilities of WBANs, the concept of cloud-assisted WBANs has been introduced recently. By using cloud computing technologies, cloud-assisted WBANs can provide more efficient processing of patients' physiology parameters and support richer services. In cloud-assisted WBANs, the data of patients' physiology are stored in the cloud. The integrity of the data is very important because these data will be used to provide a medical diagnosis and other medical treatments. To address the issue of integrity in cloud-assisted WBANs, we propose an efficient certificateless public auditing (CLPA) scheme. A security analysis of our proposed CLPA scheme shows that it is provably secure against two types of adversaries (i.e., a type-I adversary can replace users' public keys, and a type-II adversary can access the master key) in an environment of certificateless cryptography. A detailed performance analysis demonstrates that the proposed CLPA scheme yields better performance over a previously proposed CLPA scheme.


title: Primary immunodeficiency update: Part I. Syndromes associated with eczematous dermatitis

abstract: In the past decade, the availability of powerful molecular techniques has accelerated the pace of discovery of several new primary immunodeficiencies (PIDs) and revealed the biologic basis of other established PIDs. These genetic advances, in turn, have facilitated more precise phenotyping of associated skin and systemic manifestations and provide a unique opportunity to better understand the complex human immunologic response. These continuing medical education articles will provide an update of recent advances in PIDs that may be encountered by dermatologists through their association with eczematous dermatitis, infectious, and non-infectious cutaneous manifestations. Part I will discuss new primary immunodeficiencies that have an eczematous dermatitis. Part II will focus on primary immunodeficiencies that greatly increase susceptibility to fungal infection and the noninfectious presentations of PIDs.


title: Credit-Scheduled Delay-Bounded Congestion Control for Datacenters

abstract: Small RTTs (~tens of microseconds), bursty flow arrivals, and a large number of concurrent flows (thousands) in datacenters bring fundamental challenges to congestion control as they either force a flow to send at most one packet per RTT or induce a large queue build-up. The widespread use of shallow buffered switches also makes the problem more challenging with hosts generating many flows in bursts. In addition, as link speeds increase, algorithms that gradually probe for bandwidth take a long time to reach the fair-share. An ideal datacenter congestion control must provide 1) zero data loss, 2) fast convergence, 3) low buffer occupancy, and 4) high utilization. However, these requirements present conflicting goals. This paper presents a new radical approach, called ExpressPass, an end-to-end credit-scheduled, delay-bounded congestion control for datacenters. ExpressPass uses credit packets to control congestion even before sending data packets, which enables us to achieve bounded delay and fast convergence. It gracefully handles bursty flow arrivals. We implement ExpressPass using commodity switches and provide evaluations using testbed experiments and simulations. ExpressPass converges up to 80 times faster than DCTCP in 10 Gbps links, and the gap increases as link speeds become faster. It greatly improves performance under heavy incast workloads and significantly reduces the flow completion times, especially, for small and medium size flows compared to RCP, DCTCP, HULL, and DX under realistic workloads.


title: On the Calculation of the Prevalence of Transsexualism

abstract: The most-cited estimates of the prevalence of transsexualism are based on counts of gender reassignments in European clinics many years ago. Observing that reassignments have been in a start-up transient , we extend those results by recalculating prevalence from the accumulating incidence data, taking into account birth, reassignment and death rates and then, based on age-distributions of reassignment data, we determine the inherent number of persons who at some point in life will undergo reassignment. From this reanalysis of those early reports, we determine lower -bounds on the prevalence of the underlying condition of transsexualism to be between 1:1000 and 1:2000, using those reports own data. We then present more recent incidence data and alternative methods for estimating the prevalence of transsexualism, all of which indicate that the lower bound on the prevalence of transsexualism is at least 1:500, and possibly higher.


title: Business Intelligence: Data Mining and Optimization for Decision Making

abstract: Business intelligence is a broad category of applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping enterprise users make better business decisions. The term implies having a comprehensive knowledge of all factors that affect a business, such as customers, competitors, business partners, economic environment, and internal operations, therefore enabling optimal decisions to be made. Business Intelligence provides readers with an introduction and practical guide to the mathematical models and analysis methodologies vital to business intelligence. This book: Combines detailed coverage with a practical guide to the mathematical models and analysis methodologies of business intelligence. Covers all the hot topics such as data warehousing, data mining and its applications, machine learning, classification, supply optimization models, decision support systems, and analytical methods for performance evaluation. Is made accessible to readers through the careful definition and introduction of each concept, followed by the extensive use of examples and numerous real-life case studies. Explains how to utilise mathematical models and analysis models to make effective and good quality business decisions. This book is aimed at postgraduate students following data analysis and data mining courses. Researchers looking for a systematic and broad coverage of topics in operations research and mathematical models for decision-making will find this an invaluable guide.


title: Multi-Frequency, Linear and Circular Polarized, Metamaterial-Inspired, Near-Field Resonant Parasitic Antennas

abstract: Several metamaterial-inspired, electrically small, near-field resonant parasitic antennas are presented. Both electric and magnetic couplings to the parasitic are compared and contrasted. The electric-coupled versions are shown to be more efficient and to have more bandwidth. The evolution of circular polarized designs from their linear counterparts by introducing multiple parasitics having different resonant frequencies is demonstrated. Single L1 and dual band L1/L2 GPS systems are emphasized for practical illustrations of the resulting performance characteristics. Preliminary experimental results for a dual band, circularly polarized GPS L1/L2 antenna are provided and underscore several practical aspects of these designs.


title: Structure-Aware Data Consolidation

abstract: We present a structure-aware technique to consolidate noisy data, which we use as a pre-process for standard clustering and dimensionality reduction. Our technique is related to mean shift, but instead of seeking density modes, it reveals and consolidates continuous high density structures such as curves and surface sheets in the underlying data while ignoring noise and outliers. We provide a theoretical analysis under a Gaussian noise model, and show that our approach significantly improves the performance of many non-linear dimensionality reduction and clustering algorithms in challenging scenarios.


title: Who Regulates Ethics in the Virtual World

abstract: This paper attempts to give an insight into emerging ethical issues due to the increased usage of the Internet in our lives. We discuss three main theoretical approaches relating to the ethics involved in the information technology (IT) era: first, the use of IT as a tool; second, the use of social constructivist methods; and third, the approach of phenomenologists. Certain aspects of ethics and IT have been discussed based on a phenomenological approach and moral development. Further, ethical issues related to social networking sites are discussed. A plausible way to make the virtual world ethically responsive is collective responsibility which proposes that society has the power to influence but not control behavior in the virtual world.


title: Collaborative creativity: a complex systems model with distributed affect

abstract: The study of creativity has received significant attention over the past century, with a recent increase in interest in collaborative, distributed creativity. We posit that creativity in distributed groups is fostered by software interfaces that specifically enable socio-emotional or affective communication. However, previous work on creativity and affect has primarily focused on the individual, while group creativity research has concentrated more on cognition rather than affect. In this paper we propose a new model for creativity in distributed groups, based on the theory of groups as complex systems, that includes affect as well as cognition and that explicitly calls out the interface between individuals as a key parameter of the model. We describe the model, the four stages of collaborative creativity and the causal dynamics in each stage, and demonstrate how affect and interface can facilitate the generation, selection, and amplification of ideas in the various stages of collaborative creativity. We then validate our model with data from three field sites. The data was collected from longitudinal studies of two distributed groups involved in producing creative products--astrophysicists studying supernovae and the expansion rate of the universe and children creating multimedia programming projects online-"-and interviews with staff in a multinational engineering company.


LINKING ACCOUNTING AND PROCESS-AWARE INFORMATION SYSTEMS – TOWARDS A GENERALIZED INFORMATION MODEL FOR PROCESS-ORIENTED ACCOUNTING


title: An Achilles' Heel in Signature-Based IDS: Squealing False Positives in SNORT

abstract: We report a vulnerability to network signature-based IDS which we have tested using Snort and we call “Squealing”. This vulnerability has significant implications since it can easily be generalized to any IDS. The vulnerability of signature-based IDS to high false positive rates has been welldocumented but we go further to show (at a high level) how packets can be crafted to match attack signatures such that a alarms on a target IDS can be conditioned or disabled and then exploited. This is the first academic treatment of this vulnerability that has already been reported to the CERT Coordination Center and the National Infrastructure Protection Center. Independently, other tools based on “squealing” are poised to appear that, while validating our ideas, also gives cause for concern. keywords: squealing, false positive, intrusion detection, IDS, signature-based, misuse behavior, network intrusion detection, snort


title: Decentralized learning for wireless communications and networking

abstract: This chapter deals with decentralized learning algorithms for in-network processing of graph-valued data. A generic learning problem is formulated and recast into a separable form, which is iteratively minimized using the alternating-direction method of multipliers (ADMM) so as to gain the desired degree of parallelization. Without exchanging elements from the distributed training sets and keeping inter-node communications at affordable levels, the local (per-node) learners consent to the desired quantity inferred globally, meaning the one obtained if the entire training data set were centrally available. Impact of the decentralized learning framework to contemporary wireless communications and networking tasks is illustrated through case studies including target tracking using wireless sensor networks, unveiling Internet traffic anomalies, power system state estimation, as well as spectrum cartography for wireless cognitive radio networks.


title: Deep Graphs

abstract: We propose an algorithm for deep learning on networks and graphs. It relies on the notion that many graph algorithms, such as PageRank, Weisfeiler-Lehman, or Message Passing can be expressed as iterative vertex updates. Unlike previous methods which rely on the ingenuity of the designer, Deep Graphs are adaptive to the estimation problem. Training and deployment are both efficient, since the cost is $O(|E| + |V|)$, where $E$ and $V$ are the sets of edges and vertices respectively. In short, we learn the recurrent update functions rather than positing their specific functional form. This yields an algorithm that achieves excellent accuracy on both graph labeling and regression tasks.


title: Modular Spoke-Type Permanent-Magnet Machine for In-Wheel Traction Applications

abstract: This paper proposes a novel modular spoke-type permanent-magnet machine for in-wheel traction applications. First, the topology and the operating principle are briefly introduced. Then, an analytical model of the proposed machine is deduced based on conformal mapping. A three-phase 48-slot 52-pole modular spoke-type permanent-magnet (PM) machine is designed based on the deduced mathematical model where the dimensions and power source are constrained the same as those of a commercial three-phase surface PM machine in electric motorcycles. Electromagnetic performance comparisons among the proposed machine, the commercial machine, and a conventional spoke-type PM machine are conducted based on finite-element analysis (FEA) with respect to magnetic field, back electromotive force (EMF), flux-weakening capability, torque capability, machine efficiency, etc. The results indicate that the proposed machine has an improved torque, a higher efficiency, and an extremely better flux-weakening capability than the conventional in-wheel machine. Finally, a prototype machine is manufactured and tested to verify both the analytically and FEA predicted results.


title: A branch and bound algorithm for the maximum clique problem

abstract: Abstract A method to solve the maximum clique problem based on an unconstrained quadratic zero-one programming formulation is presented. A branch and bound algorithm for unconstrained quadratic zero-one programming is given that uses a technique to dynamically select variables for the ordering of the branching tree. Dynamic variable selection is equivalent to vertex selection in a similar branch and bound algorithm for the maximum clique problem. In this paper we compare two different rules for selecting a vertex. The first rule selects a variable corresponding to a vertex with high connectivity (a greedy approach) and the second rule selects a variable corresponding to a vertex with low connectivity (a nongreedy approach). We demonstrate that the first rule discovers a maximum clique sooner but it takes significantly longer to verify optimality. Computational results for an efficient vectorizable implementation on an IBM 3090 are provided for randomly generated graphs with up to 1000 vertices and 150,000 edges.


title: Students' Syntactic Mistakes in Writing Seven Different Types of SQL Queries and its Application to Predicting Students' Success

abstract: The computing education community has studied extensively the errors of novice programmers. In contrast, little attention has been given to student's mistake in writing SQL statements. This paper represents the first large scale quantitative analysis of the student's syntactic mistakes in writing different types of SQL queries. Over 160 thousand snapshots of SQL queries were collected from over 2000 students across eight years. We describe the most common types of syntactic errors that students make. We also describe our development of an automatic classifier with an overall accuracy of 0.78 for predicting student performance in writing SQL queries.


title: Fast Multi-Column Sorting in Main-Memory Column-Stores

abstract: Sorting is a crucial operation that could be used to implement SQL operators such as GROUP BY, ORDER BY, and SQL:2003 PARTITION BY. Queries with multiple attributes in those clauses are common in real workloads. When executing queries of that kind, state-of-the-art main-memory column-stores require one round of sorting per input column. With the advent of recent fast scans and denormalization techniques, that kind of multi-column sorting could become a bottleneck. In this paper, we propose a new technique called "code massaging", which manipulates the bits across the columns so that the overall sorting time can be reduced by eliminating some rounds of sorting and/or by improving the degree of SIMD data level parallelism. Empirical results show that a main-memory column-store with code massaging can achieve speedup of up to 4.7X, 4.7X, 4X, and 3.2X on TPC-H, TPC-H skew, TPC-DS, and real workload, respectively.


title: Classification of Insider Threat Detection Techniques

abstract: Most insider attacks done by people who have the knowledge and technical know-how of launching such attacks. This topic has long been studied and many detection techniques were proposed to deal with insider threats. This short paper summarized and classified insider threat detection techniques based on strategies used for detection.


title: EMERGING TECHNOLOGIES ROBOT ASSISTED LANGUAGE LEARNING

abstract: Along with the rapid development of information and communication technologies, educators are trying to keep up with the dramatic changes in our electronic environment. These days mobile technology, with popular devices such as iPhones, Android phones, and iPads, is steering our learning environment towards increasingly focusing on mobile learning or m-Learning. Currently, most interfaces employ keyboards, mouse or touch technology, but some emerging input-interfaces use voiceor marker-based gesture recognition. In the future, one of the cutting-edge technologies likely to be used is robotics. Robots are already being used in some classrooms and are receiving an increasing level of attention. Robots today are developed for special purposes, quite similar to personal computers in their early days. However, in the future, when mass production lowers prices, robots will bring about big changes in our society. In this column, the author focuses on educational service robots. Educational service robots for language learning and robot-assisted language learning (RALL) will be introduced, and the hardware and software platforms for RALL will be explored, as well as implications for future research.


title: Exploiting sparsity to build efficient kernel based collaborative filtering for top-N item recommendation

abstract: Abstract The increasing availability of implicit feedback datasets has raised the interest in developing effective collaborative filtering techniques able to deal asymmetrically with unambiguous positive feedback and ambiguous negative feedback. In this paper, we propose a principled kernel-based collaborative filtering method for top-N item recommendation with implicit feedback. We present an efficient implementation using the linear kernel, and we show how to generalize it to kernels of the dot product family preserving the efficiency. We also investigate on the elements which influence the sparsity of a standard cosine kernel. This analysis shows that the sparsity of the kernel strongly depends on the properties of the dataset, in particular on the long tail distribution. We compare our method with state-of-the-art algorithms achieving good results both in terms of efficiency and effectiveness.


title: Attribute-Based Encryption with Fast Decryption

abstract: Attribute-based encryption (ABE) is a vision of public key encryption that allows users to encrypt and decrypt messages based on user attributes. This functionality comes at a cost. In a typical implementation, the size of the ciphertext is proportional to the number of attributes associated with it and the decryption time is proportional to the number of attributes used during decryption. Specifically, many practical ABE implementations require one pairing operation per attribute used during decryption.


title: A Tagged Corpus and a Tagger for Urdu

abstract: In this paper, we describe a release of a sizeable monolingual Urdu corpus automatically tagged with part-of-speech tags. We extend the work of Jawaid and Bojar (2012) who use three different taggers and then apply a voting scheme to disambiguate among the different choices suggested by each tagger. We run this complex ensemble on a large monolingual corpus and release the tagged corpus. Additionally, we use this data to train a single standalone tagger which will hopefully significantly simplify Urdu processing. The standalone tagger obtains the accuracy of 88.74% on test data.


title: Parameters Optimization of Synchronous Induction Coilgun Based on Ant Colony Algorithm

abstract: Electrical-to-kinetic energy conversion efficiency of synchronous induction coilguns (SICG) is the main limiting factor of its development. In the system of SICG, the change of any electromagnetic parameters can directly or indirectly affects the electrical-to-kinetic energy conversion efficiency. In order to improve the electrical-to-kinetic energy conversion efficiency of SICG, the electromechanical model of SICG was built at first in this paper. Then, the structural parameters of SICG with 60-mm caliber were optimized with ant colony optimization algorithm. The electrical-to-kinetic energy conversion efficiency of the system was regarded as the target function, while the center-to-center space between the drive coil and the armature, and the structural parameters of the drive coil and the armature were regarded as variables. Results of research indicated that the electrical-to-kinetic energy conversion efficiency of the system was improved through parameter optimization. In order to validate the results of parameter optimization, an experiment was carried out with the first stage of SICG.


title: Image Classification with the Fisher Vector: Theory and Practice

abstract: A standard approach to describe an image for classification and retrieval purposes is to extract a set of local patch descriptors, encode them into a high dimensional vector and pool them into an image-level signature. The most common patch encoding strategy consists in quantizing the local descriptors into a finite set of prototypical elements. This leads to the popular Bag-of-Visual words representation. In this work, we propose to use the Fisher Kernel framework as an alternative patch encoding strategy: we describe patches by their deviation from an “universal” generative Gaussian mixture model. This representation, which we call Fisher vector has many advantages: it is efficient to compute, it leads to excellent results even with efficient linear classifiers, and it can be compressed with a minimal loss of accuracy using product quantization. We report experimental results on five standard datasets—PASCAL VOC 2007, Caltech 256, SUN 397, ILSVRC 2010 and ImageNet10K—with up to 9M images and 10K classes, showing that the FV framework is a state-of-the-art patch encoding technique.


URIEL and lang2vec: Representing languages as typological, geographical, and phylogenetic vectors


title: A Mathematical Analysis on Vector Inversion Generators

abstract: This paper presents a mathematical model for a vector inversion generator, through which explicit analytical expressions for component stresses and output characteristics can be obtained. The derivation of a simplified equivalent circuit for transient analysis of the converter is presented in detail. The equivalent circuit model is used to analyze the performance of a 4-stage vector inversion generator. The results are presented and compared with simulated results to validate the model. Methods that could be used to improve some of the limitations imposed by this model are also presented and verified.


title: Synthetic aperture radar processing by a multiple scale neural system for boundary and surface representation

abstract: Abstract A neural network model of boundary segmentation and surface representation is developed to process images containing range data gathered by a synthetic aperture radar (SAR) sensor. The boundary and surface processing are accomplished by an improved Boundary Countour System (BCS) and Feature Countour System (FCS), respectively, that have been derived from analyses of perceptual and neurobiological data. BCS/FCS processing makes structures such as motor vehicles, roads, and buildings more salient and interpretable to human observers than they are in the original imagery. Early processing by ON cells and OFF cells embedded in shunting center-surround network models preprocessing by lateral geniculate nucleus (LGN). Such preprocessing compensates for illumination gradients, normalizes input dynamic range, and extracts local ratio contrasts. ON cell and OFF cell outputs are combined in the BCS to define oriental filters that model corticla simple cells. Pooling ON and OFF outputs at simple cells overcomes complementary processing deficiencies of each cell type along concave and convex contours, and enhances simpl;e cell sensitivity to image edges. Oriented filter outputs are rectified and outputs sensitive to opposite contrast polarities are pooled to define complex cells. The complex cells output to stages of short-range spatial competition (or endstopping) and orientational competition among hypercomplex cells. Hypercomplex cells activate long-range cooperative bipole cells that begin to group image boundaries. Nonlinear feedback between bipole cells and hypercomplex cell segments image regions by cooperatively completing and regularizing the most favored boundaries while suppressing image noise and weaker boundary groupings. Boundary segmentation is performed by three copies of the BCS at small, medium, and large filter scales, whose subsequent interaction distances covary with the size of the filter. Filling-in of multiple surface representations occurs within the FCS at each scale via a boundary-gated diffusion process. Diffusion is activated by the normalized LGN ON and OFF outputs within ON and OFFfilling-in domains. Diffusion is restricted to the regions defined by gating signals from the corresponding BCS boundary segmentation. The filled-in opponent ON and OFFsignals are subtracted to form double opponent surface representations. These surface representations are shown by any of three methods to be sensitive to both image ratio contrasts and background luminance. The three scales of surface representation are then added to yield a final multiple-scale output. The BCS and FCS are shown to perform favorably in comparison to several other techniques for speckle removal.


title: New Directions in Cryptography

abstract: Two kinds of contemporary developments in cryptography are examined. Widening applications of teleprocessing have given rise to a need for new types of cryptographic systems, which minimize the need for secure key distribution channels and supply the equivalent of a written signature. This paper suggests ways to solve these currently open problems. It also discusses how the theories of communication and computation are beginning to provide the tools to solve cryptographic problems of long standing.


A Comparison of Stream-Oriented High-Availability Algorithms


title: The BigGrams: the semi-supervised information extraction system from HTML: an improvement in the wrapper induction

abstract: The aim of this study is to propose an information extraction system, called BigGrams, which is able to retrieve relevant and structural information (relevant phrases, keywords) from semi-structural web pages, i.e. HTML documents. For this purpose, a novel semi-supervised wrappers induction algorithm has been developed and embedded in the BigGrams system. The wrappers induction algorithm utilizes a formal concept analysis to induce information extraction patterns. Also, in this article, the author (1) presents the impact of the configuration of the information extraction system components on information extraction results and (2) tests the boosting mode of this system. Based on empirical research, the author established that the proposed taxonomy of seeds and the HTML tags level analysis, with appropriate pre-processing, improve information extraction results. Also, the boosting mode works well when certain requirements are met, i.e. when well-diversified input data are ensured.


title: Consideration on successive interference canceller (SIC) receiver at cell-edge users for non-orthogonal multiple access (NOMA) with SU-MIMO

abstract: Non-orthogonal multiple access (NOMA) utilizing transmit power difference and advanced successive interference canceller (SIC) receivers has been considered as a candidate radio access technology for future wireless communication systems. Single-user multiple-input multiple-output (SU-MIMO) is one of the key technologies in Long-Term-Evolution (LTE) systems. Thus, it has been proved to combine with NOMA for further system performance improvement. In previous studies, SIC receiver is adopted at cell center users (UEs), while SIC processing is not taken into account at cell-edge UEs, considering the feasibility of UE receiver and control signaling overhead. However, it may put limitation on the performance of NOMA. To further enhance the performance of NOMA with SU-MIMO, advanced receiver can be considered at cell-edge UEs. In this paper, we investigate the impact of applying the SIC receiver at cell-edge UEs in downlink NOMA with SU-MIMO. The detail SIC detection schemes at cell-edge UE will be discussed, including ideal SIC processing and non-ideal SIC processing. The simulation results show that performance gains of NOMA can be improved with SIC processing at cell-edge UEs. It indicates that more advanced receiver can be considered for cell-edge UE to achieve upper-bound performance.