title: Joint Distribution Optimal Transportation for Domain Adaptation

abstract: This paper deals with the unsupervised domain adaptation problem, where one wants to estimate a prediction function $f$ in a given target domain without any labeled sample by exploiting the knowledge available from a source domain where labels are known. Our work makes the following assumption: there exists a non-linear transformation between the joint feature/label space distributions of the two domain $\mathcal{P}_s$ and $\mathcal{P}_t$. We propose a solution of this problem with optimal transport, that allows to recover an estimated target $\mathcal{P}^f_t=(X,f(X))$ by optimizing simultaneously the optimal coupling and $f$. We show that our method corresponds to the minimization of a bound on the target error, and provide an efficient algorithmic solution, for which convergence is proved. The versatility of our approach, both in terms of class of hypothesis or loss functions is demonstrated with real world classification and regression problems, for which we reach or surpass state-of-the-art results.


title: Redesigning biodiversity conservation projects for climate change: examples from the field

abstract: Few conservation projects consider climate impacts or have a process for developing adaptation strategies. To advance climate adaptation for biodiversity conservation, we tested a step-by-step approach to developing adaptation strategies with 20 projects from diverse geographies. Project teams assessed likely climate impacts using historical climate data, future climate predictions, expert input, and scientific literature. They then developed adaptation strategies that considered ecosystems and species of concern, project goals, climate impacts, and indicators of progress. Project teams identified 176 likely climate impacts and developed adaptation strategies to address 42 of these impacts. The most common impacts were to habitat quantity or quality, and to hydrologic regimes. Nearly half of expected impacts were temperature-mediated. Twelve projects indicated that the project focus, either focal ecosystems and species or project boundaries, need to change as a result of considering climate impacts. More than half of the adaptation strategies were resistance strategies aimed at preserving the status quo. The rest aimed to make ecosystems and species more resilient in the face of expected changes. All projects altered strategies in some way, either by adding new actions, or by adjusting existing actions. Habitat restoration and enactment of policies and regulations were the most frequently prescribed, though every adaptation strategy required a unique combination of actions. While the effectiveness of these adaptation strategies remains to be evaluated, the application of consistent guidance has yielded important early lessons about how, when, and how often conservation projects may need to be modified to adapt to climate change.


title: Tests Enhance the Transfer of Learning

abstract: Numerous learning studies have shown that if the period of time devoted to studying information (e.g., casa-house) includes at least 1 test (casa-?), performance on a final test is improved—a finding known as the testing effect. In most of these studies, however, the final test is identical to the initial test. If the final test requires a novel demonstration of learning (i.e., transfer), prior studies suggest that a greater degree of transfer reduces the size of the testing effect. The authors tested this conjecture. In 2 experiments, 4th- or 5th-grade students learned to assign regions or cities to map locations and returned 1 day later for 2 kinds of final tests. One final test required exactly the same task seen during the learning session, and the other final test consisted of novel, more challenging questions. In both experiments, testing effects were found for both kinds of final tests, and the testing effect was no smaller, and actually slightly larger, for the final test requiring transfer.


title: The Changing Structure Of The Pharmaceutical Industry

abstract: Rising research and development (R&D) expenditures by pharmaceutical companies are, in part, a consequence of changing industry structure, particularly the rise of the biotechnology sector. The creation of a market for biomedical science and increased vertical competition within the industry are likely to spur innovation and raise productivity, but they also could induce socially wasteful spending and weaken academic science. With innovation increasingly dependent on financially vulnerable firms and complex contractual arrangements, R&D investment might be becoming more sensitive to price controls or other cost containment measures.


title: Pattern-Reconfigurable Antenna With Five Switchable Beams in Elevation Plane

abstract: Pattern-reconfigurable antennas with multiple switchable beams, especially with both boresight and endfire directions, are highly desired for wireless communications. In this letter, a novel pattern-reconfigurable antenna is proposed that provides an efficient solution. By reconfiguring parasitic striplines placed around a radiating dipole and reflecting metal pieces under the dipole using p-i-n diodes, the antenna main beam can be switched to five directions in the elevation plane, approximately from $-90^{\circ }$ (left endfire), $-45^{\circ }$ , 0 $^{\circ }$ (boresight), $+45^{\circ }$ , to $+90^{\circ }$ (right endfire). The proposed antenna operates at 2.45 GHz with dimensions of about 0.57 $\lambda$ $\times\, 0.45\lambda$ $\times\, 0.28\lambda$ . An antenna prototype is fabricated and measured. For all five directional beams, the measured $|S_{11}|$ values are below $-$ 13 dB, and the measured realized gains range from 5.2 to 6.5 dBi. They agree reasonably well with the simulated ones.


title: Hierarchical cluster analysis of labour market regulations and population health: a taxonomy of low- and middle-income countries

abstract: An important contribution of the social determinants of health perspective has been to inquire about non-medical determinants of population health. Among these, labour market regulations are of vital significance. In this study, we investigate the labour market regulations among low- and middle-income countries (LMICs) and propose a labour market taxonomy to further understand population health in a global context. Using Gross National Product per capita, we classify 113 countries into either low-income (n = 71) or middle-income (n = 42) strata. Principal component analysis of three standardized indicators of labour market inequality and poverty is used to construct 2 factor scores. Factor score reliability is evaluated with Cronbach's alpha. Using these scores, we conduct a hierarchical cluster analysis to produce a labour market taxonomy, conduct zero-order correlations, and create box plots to test their associations with adult mortality, healthy life expectancy, infant mortality, maternal mortality, neonatal mortality, under-5 mortality, and years of life lost to communicable and non-communicable diseases. Labour market and health data are retrieved from the International Labour Organization's Key Indicators of Labour Markets and World Health Organization's Statistical Information System. Six labour market clusters emerged: Residual (n = 16), Emerging (n = 16), Informal (n = 10), Post-Communist (n = 18), Less Successful Informal (n = 22), and Insecure (n = 31). Primary findings indicate: (i) labour market poverty and population health is correlated in both LMICs; (ii) association between labour market inequality and health indicators is significant only in low-income countries; (iii) Emerging (e.g., East Asian and Eastern European countries) and Insecure (e.g., sub-Saharan African nations) clusters are the most advantaged and disadvantaged, respectively, with the remaining clusters experiencing levels of population health consistent with their labour market characteristics. The labour market regulations of LMICs appear to be important social determinant of population health. This study demonstrates the heuristic value of understanding the labour markets of LMICs and their health effects using exploratory taxonomy approaches.


title: Comparative Analysis of Control Plane Security of SDN and Conventional Networks

abstract: Software defined networking implements the network control plane in an external entity, rather than in each individual device as in conventional networks. This architectural difference implies a different design for control functions necessary for essential network properties, e.g., loop prevention and link redundancy. We explore how such differences redefine the security weaknesses in the SDN control plane and provide a framework for comparative analysis which focuses on essential network properties required by typical production networks. This enables analysis of how these properties are delivered by the control planes of SDN and conventional networks, and to compare security risks and mitigations. Despite the architectural difference, we find similar, but not identical, exposures in control plane security if both network paradigms provide the same network properties and are analyzed under the same threat model. However, defenses vary; SDN cannot depend on edge based filtering to protect its control plane, while this is arguably the primary defense in conventional networks. Our concrete security analysis suggests that a distributed SDN architecture that supports fault tolerance and consistency checks is important for SDN control plane security. Our analysis methodology may be of independent interest for future security analysis of SDN and conventional networks.


title: Conceptualizing, Measuring, and Managing Customer-Based Brand Equity:

abstract: The author presents a conceptual model of brand equity from the perspective of the individual consumer. Customer-based brand equity is defined as the differential effect of brand knowledge on consu...


title: Why Do Deep Neural Networks Still Not Recognize These Images?: A Qualitative Analysis on Failure Cases of ImageNet Classification

abstract: In a recent decade, ImageNet has become the most notable and powerful benchmark database in computer vision and machine learning community. As ImageNet has emerged as a representative benchmark for evaluating the performance of novel deep learning models, its evaluation tends to include only quantitative measures such as error rate, rather than qualitative analysis. Thus, there are few studies that analyze the failure cases of deep learning models in ImageNet, though there are numerous works analyzing the networks themselves and visualizing them. In this abstract, we qualitatively analyze the failure cases of ImageNet classification results from recent deep learning model, and categorize these cases according to the certain image patterns. Through this failure analysis, we believe that it can be discovered what the final challenges are in ImageNet database, which the current deep learning model is still vulnerable to.


title: Organismal and individualistic concepts and the choice of methods of vegetation analysis

abstract: The organismal concept (Clements, 19 i 6; Tansley, 1920) of vegetation types (communities, associations, etc.) is that they con stitute quasi-organisms whose individual members are homoge neous areas of vegetation on the ground (stands). Individual stands adjacent to one another in the field are separated by zones of rapid transition (ecotones). This view implies that interactions between species belonging to vegetation types are such that the probability of their joint occurrence is increased. Most early descriptive plant ecology in North America and Great Britain was based on this view. The individualistic concept (Gleason, 1926) of vegetation is that its constituent species are independently distributed except for rela tively few specific interactions, the presence of other species being thought of as merely another environmental variable. Discrete ho mogeneous areas of vegetation on the ground, if they exist, are caused either by sharp environmental discontinuities or by uneven ness in the distribution of the tolerance limits of species. At present many statistical ecologists claim to favour this theory of vegetation, but their choice of techniques in the analysis of variation in vege tation and its relationship with environmental variables suggest that they are still influenced by the organismal concept. Much attention has been devoted to the application of classi fication methods to data from vegetation (Goodall, 1953; Wil liams & Lambert, 1959, 1961; Williams, Lambert & Lance, 1966). Classification of vegetation is a difficult process because the distribution of species in the field is neither regularly continuous nor discontinuous (Webb, 1954); frequently the result of this is that the groups obtained are either arbitrary and not clearly dis tinct from one another, or they overlap in composition. The pro cess of classification itself may not carry any implications about the nature of vegetation but its aim in producing groups of species


title: Learning the Visual Interpretation of Sentences

abstract: Sentences that describe visual scenes contain a wide variety of information pertaining to the presence of objects, their attributes and their spatial relations. In this paper we learn the visual features that correspond to semantic phrases derived from sentences. Specifically, we extract predicate tuples that contain two nouns and a relation. The relation may take several forms, such as a verb, preposition, adjective or their combination. We model a scene using a Conditional Random Field (CRF) formulation where each node corresponds to an object, and the edges to their relations. We determine the potentials of the CRF using the tuples extracted from the sentences. We generate novel scenes depicting the sentences' visual meaning by sampling from the CRF. The CRF is also used to score a set of scenes for a text-based image retrieval task. Our results show we can generate (retrieve) scenes that convey the desired semantic meaning, even when scenes (queries) are described by multiple sentences. Significant improvement is found over several baseline approaches.


title: Optimization of Integrated Transistors for Very High Frequency DC–DC Converters

abstract: This paper presents a method to optimize integrated lateral double-diffused MOSFET transistors for use in very high frequency (VHF, 30-300 MHz) dc-dc converters. A transistor model valid at VHF switching frequencies is developed. Device parameters are related to layout geometry and the resulting layout versus loss tradeoffs are illustrated. A method of finding an optimal layout for a given converter application is developed and experimentally verified in a 50-MHz converter, resulting in a 54% reduction in power loss over a hand-optimized device. It is further demonstrated that hot-carrier limits on device safe operating area may be relaxed under soft switching, yielding significant further loss reduction. A device fabricated with 3-μm gate length in 20-V design rules is validated at 35 V, offering reduced parasitic resistance and capacitance, as compared to the 5.5-μm device. Compared to the original design, loss is up to 75% lower in the example application.


title: An IoT Application for Fault Diagnosis and Prediction

abstract: Internet of Things (IoT) has become an important topic in both industry and academia for the recent years as it offers great potentials in numerous real world applications. This paper considers the problem of fault diagnosis and prediction from IoT data collected in the process industry. We propose a solution by making use of IoT enabling technologies offered by SAP. The proposed solution first discovers the causal relationship of the physical devices by analyzing only the device sensor data without the knowledge of the physical manufacturing system. While faults of certain devices can be detected by monitoring the healthy index of these devices in real-time, possible faults of other devices can be predicted based on the causal relationship discovered in the previous step. Such prediction capability enables new breeds of predictive maintenance applications where appropriate actions can be recommended to operators of the manufacturing system in a timely manner. The viability of the proposed solution is confirmed by a real world application of IoT conducted with an industry partner.


title: Recovering 3D Planes from a Single Image via Convolutional Neural Networks

abstract: In this paper, we study the problem of recovering 3D planar surfaces from a single image of man-made environment. We show that it is possible to directly train a deep neural network to achieve this goal. A novel plane structure-induced loss is proposed to train the network to simultaneously predict a plane segmentation map and the parameters of the 3D planes. Further, to avoid the tedious manual labeling process, we show how to leverage existing large-scale RGB-D dataset to train our network without explicit 3D plane annotations, and how to take advantage of the semantic labels come with the dataset for accurate planar and non-planar classification. Experiment results demonstrate that our method significantly outperforms existing methods, both qualitatively and quantitatively. The recovered planes could potentially benefit many important visual tasks such as vision-based navigation and human-robot interaction.


title: Sputtered Nickel Oxide Films for NO2 Gas Sensors

abstract: In recent years, lot of efforts was made to develop many types of nitrogen oxide gas sensors. Among them, most interesting are WO3, V2O5, TiO2, SnO2 and NiO thin films as gas sensing layers. Relatively small effort has been done to examine nickel oxide. But reasonably good electrical properties and stability in air make it feasible for the fabrication of nickel oxide thin film based gas sensors. Hence, in the present study NiO thin films were deposited by dc reactive magnetron sputtering technique from a nickel metal target in argon and oxygen mixed atmosphere and studied its gas sensing properties towards NO2 gas. The effect of process parameters on the morphological and electrical properties of NiO was studied by scanning electron microscopy (SEM), atomic force microscopy (AFM) and Hall effect studies respectively. The films prepared at optimum conditions showed superior electrical properties and exhibited fine and uniform grains with RMS roughness 9.4 nm. These films were tested for gas sensing characteristics of NO2 gas. The sensitivity of NiO thin film was investigated in the temperature range 373 to 573 K. The dynamic response for the NiO films was observed at an operating temperature of 473 K and gas concentration of 50 ppm for NO2 gas.


title: Design and Optimization of Neodymium-Free SPOKE-Type Motor With Segmented Wing-Shaped PM

abstract: This paper proposes a new design of SPOKE-type PM brushless direct current (BLDC) motor without using neodymium PM (Nd-PM). The proposed model has an improved output characteristic as it uses the properties of the magnetic flux effect of the SPOKE-type motor with an additional pushing assistant magnet and subassistant magnet in the shape of spoke. In this paper, ferrite PM (Fe-PM) is used instead of Nd-PM. First, the air-gap flux density and backelectromotive force (BEMF) are obtained based on the field model. Second, the analytical expressions of magnet field strength and magnet flux density are obtained in the air gap produced by Fe-PM. The developed analytical model is obtained by solving the magnetic scalar potential. Finally, the air-gap field distribution and BEMF of SPOKE-type motor are analyzed. The analysis works for internal rotor motor topologies. This paper validates results of the analytical model by finite-element analysis for wing-shaped SPOKE-type BLDC motors.


title: A brief review on multi-task learning

abstract: Multi-task learning (MTL), which optimizes multiple related learning tasks at the same time, has been widely used in various applications, including natural language processing, speech recognition, computer vision, multimedia data processing, biomedical imaging, socio-biological data analysis, multi-modality data analysis, etc. MTL sometimes is also referred to as joint learning, and is closely related to other machine learning subfields like multi-class learning, transfer learning, and learning with auxiliary tasks, to name a few. In this paper, we provide a brief review on this topic, discuss the motivation behind this machine learning method, compare various MTL algorithms, review MTL methods for incomplete data, and discuss its application in deep learning. We aim to provide the readers with a simple way to understand MTL without too many complicated equations, and to help the readers to apply MTL in their applications.


title: Introduction to AI Robotics

abstract: From the Publisher: ::: This text covers all the material needed to understand the principles behind the AI approach to robotics and to program an artificially intelligent robot for applications involving sensing, navigation, planning, and uncertainty. Robin Murphy is extremely effective at combining theoretical and practical rigor with a light narrative touch. In the overview, for example, she touches upon anthropomorphic robots from classic films and science fiction stories before delving into the nuts and bolts of organizing intelligence in robots. ::: Following the overview, Murphy contrasts AI and engineering approaches and discusses what she calls the three paradigms of AI robotics: hierarchical, reactive, and hybrid deliberative/reactive. Later chapters explore multiagent scenarios, navigation and path-planning for mobile robots, and the basics of computer vision and range sensing. Each chapter includes objectives, review questions, and exercises. Many chapters contain one or more case studies showing how the concepts were implemented on real robots. Murphy, who is well known for her classroom teaching, conveys the intellectual adventure of mastering complex theoretical and technical material.


title: A novel network coded parallel transmission framework for high-speed Ethernet

abstract: Parallel transmission, as defined in high-speed Ethernet standards, enables to use less expensive optoelectronics and offers backwards compatibility with legacy Optical Transport Network (OTN) infrastructure. However, optimal parallel transmission does not scale to large networks, as it requires computationally expensive optimal multipath routing algorithms to minimize differential delay, and thus the required buffer size to ensure frame synchronization. In this paper, we propose a novel parallel transmission framework for high-speed Ethernet, which we refer to as network coded parallel transmission, capable of effective buffer management and frame synchronization without the need for complex multipath algorithms. We show that using network coding can reduce the delay caused by packet reordering at the receiver, thus requiring a smaller overall buffer size, while improving the network throughput. We design the framework in full compliance with high-speed Ethernet standards specified in IEEE802.3ba and present detailed schemes including encoding, data structure of coded parallel transmission, buffer management and decoding at the receiver side. The proposed network coded parallel transmission framework is simple to implement and presents a potential major breakthrough in the system design of future high-speed Ethernet.


title: Make it work, make it right, make it fast: building a platform-neutral whole-system dynamic binary analysis platform

abstract: Dynamic binary analysis is a prevalent and indispensable technique in program analysis. While several dynamic binary analysis tools and frameworks have been proposed, all suffer from one or more of: prohibitive performance degradation, semantic gap between the analysis code and the program being analyzed, architecture/OS specificity, being user-mode only, lacking APIs, etc. We present DECAF, a virtual machine based, multi-target, whole-system dynamic binary analysis framework built on top of QEMU. DECAF provides Just-In-Time Virtual Machine Introspection combined with a novel TCG instruction-level tainting at bit granularity, backed by a plugin based, simple-to-use event driven programming interface. DECAF exercises fine control over the TCG instructions to accomplish on-the-fly optimizations. We present 3 platform-neutral plugins - Instruction Tracer, Keylogger Detector, and API Tracer, to demonstrate the ease of use and effectiveness of DECAF in writing cross-platform and system-wide analysis tools. Implementation of DECAF consists of 9550 lines of C++ code and 10270 lines of C code and we evaluate DECAF using CPU2006 SPEC benchmarks and show average overhead of 605% for system wide tainting and 12% for VMI.


title: Face recognition from a single training image under arbitrary unknown lighting using spherical harmonics

abstract: In this paper, we propose two novel methods for face recognition under arbitrary unknown lighting by using spherical harmonics illumination representation, which require only one training image per subject and no 3D shape information. Our methods are based on the result which demonstrated that the set of images of a convex Lambertian object obtained under a wide variety of lighting conditions can be approximated accurately by a low-dimensional linear subspace. We provide two methods to estimate the spherical harmonic basis images spanning this space from just one image. Our first method builds the statistical model based on a collection of 2D basis images. We demonstrate that, by using the learned statistics, we can estimate the spherical harmonic basis images from just one image taken under arbitrary illumination conditions if there is no pose variation. Compared to the first method, the second method builds the statistical models directly in 3D spaces by combining the spherical harmonic illumination representation and a 3D morphable model of human faces to recover basis images from images across both poses and illuminations. After estimating the basis images, we use the same recognition scheme for both methods: we recognize the face for which there exists a weighted combination of basis images that is the closest to the test face image. We provide a series of experiments that achieve high recognition rates, under a wide range of illumination conditions, including multiple sources of illumination. Our methods achieve comparable levels of accuracy with methods that have much more onerous training data requirements. Comparison of the two methods is also provided.


title: Optimal Marketing Strategies over Social Networks

abstract: We discuss the use of social networks in implementing viral marketing strategies. While influence maximization has been studied in this context (see Chapter 24 of [10]), we study revenue maximization, arguably, a more natural objective. In our model, a buyer's decision to buy an item is influenced by the set of other buyers that own the item and the price at which the item is offered. We focus on algorithmic question of finding revenue maximizing marketing strategies. When the buyers are completely symmetric, we can find the optimal marketing strategy in polynomial time. In the general case, motivated by hardness results, we investigate approximation algorithms for this problem. We identify a family of strategies called influence-and-exploit strategies that are based on the following idea: Initially influence the population by giving the item for free to carefully a chosen set of buyers. Then extract revenue from the remaining buyers using a 'greedy' pricing strategy. We first argue why such strategies are reasonable and then show how to use recently developed set-function maximization techniques to find the right set of buyers to influence.


Motor prediction


title: The representation and processing of coreference in discourse

abstract: A model is presented that addresses both the distribution and comprehension of different forms of referring expressions in language. This model is expressed in a formalism (Kamp & Reyle, 1993) that uses interpretive rules to map syntactic representations onto representations of discourse. Basic interpretive rules are developed for names, pronouns, definite descriptions, and quantified descriptions. These rules are triggered by syntactic input and interact dynamically with representations of discourse to establish reference and coreference. This interaction determines the ease with which coreference can be established for different linguistic forms given the existing discourse context. The performance of the model approximates that observed in studies of intuitive judgments of grammaticality and studies using online measures of language comprehension. The model uses the same basic interpretive mechanisms for coreference within and between sentences, thereby linking the domain traditionally studied by generative linguists to domains that have been of concern primarily to psychologists and computational linguists.


An Improved One-Time Digital Signature Scheme Based on One-Way Function.


Exercise-associated hyponatraemia or hypo-osmolarity?


CD-REST: a system for extracting chemical-induced disease relation in literature


title: Inhibitory effect of a mixture of herbal drugs (TJ-960, SK) on pentylenetetrazol-induced convulsions in El mice

abstract: Abstract TJ-960 is a spray-dried mixture of 9 herbal drugs. The convulsions of El mice induced by pentylenetetrazol (18 mg/kg) were completely inhibited by p.o. administration of TJ-960 at a daily dose of 1.0 g/kg both in 8-week-old and 4-week-old El mice. These findings suggest that TJ-960 has an inhibitory effect on the convulsions of this hereditary animal model of epilepsy.


title: From Search Session Detection to Search Mission Detection

abstract: Search mission detection aims at identifying those queries a user submits for the same information need. Such knowledge offers interesting insights into behavioral usage patterns and often can help to better support a user. However, most existing query log studies focus on search sessions only (consecutive queries for the same need) and ignore multitasking behavior (interleaved information needs) as well as hierarchies of short-term search goals in multiple sessions that form a long-term search task such as vacation planning. To better understand the dialog between user and search engine we distinguish between (1) physical search sessions, characterized by the time gap between queries, (2) logical search sessions, characterized by consecutive queries for the same information need within a physical session, and (3) search missions, characterized by logical sessions, multitasking behavior, and hierarchical goals. ::: ::: Our contributions are threefold. First, we present a new algorithm for logical session detection, which follows the state-of-the-art cascading method's rationale of combining effectiveness with efficiency. Our approach is applicable within the time-critical online scenario, where a search engine tries to support users by incorporating knowledge about their search history on the fly, as well as within the offline scenario, where the objective is to accurately partition a collected log. We improve several steps of the cascading method, among others by exploiting Linked Open Data information. Second, we demonstrate our new algorithm's applicability to accurately detect search missions. Third, we introduce a new publicly available corpus of 8800 queries labeled with session and mission information.


title: Parameter-free probabilistic API mining across GitHub

abstract: Existing API mining algorithms can be difficult to use as they require expensive parameter tuning and the returned set of API calls can be large, highly redundant and difficult to understand. To address this, we present PAM (Probabilistic API Miner), a near parameter-free probabilistic algorithm for mining the most interesting API call patterns. We show that PAM significantly outperforms both MAPO and UPMiner, achieving 69% test-set precision, at retrieving relevant API call sequences from GitHub. Moreover, we focus on libraries for which the developers have explicitly provided code examples, yielding over 300,000 LOC of hand-written API example code from the 967 client projects in the data set. This evaluation suggests that the hand-written examples actually have limited coverage of real API usages.


title: A Framework for Extracting Semantic Guarantees from Privacy

abstract: Statistical privacy views privacy definitions as contracts that guide the behavior of algorithms that take in sensitive data and produce sanitized data. For most existing privacy definitions, it is not clear what they actually guarantee. ::: In this paper, we propose the first (to the best of our knowledge) framework for extracting semantic guarantees from privacy definitions. That is, instead of answering narrow questions such as "does privacy definition Y protect X?" the goal is to answer the more general question "what does privacy definition Y protect?" ::: The privacy guarantees we can extract are Bayesian in nature and deal with changes in an attacker's beliefs. The key to our framework is an object we call the row cone. Every privacy definition has a row cone, which is a convex set that describes all the ways an attacker's prior beliefs can be turned into posterior beliefs after observing an output of an algorithm satisfying that privacy definition. ::: The framework can be applied to privacy definitions or even to individual algorithms to identify the types of inferences they defend against. We illustrate the use of our framework with analyses of several definitions and algorithms for which we can derive previously unknown semantics. These include randomized response, FRAPP, and several algorithms that add integer-valued noise to their inputs.


title: Detection and Discrimination of Open-Phase Fault in Permanent Magnet Synchronous Motor Drive System

abstract: Open-phase fault in permanent magnet synchronous motor (PMSM) drive system occurs as the phase winding is disconnected or one leg of the inverter bridge fails. It may generate large electromagnetic torque ripple and serious mechanical vibration. Therefore, a rapid fault detection method is greatly required to identify this fault at early stage and prevent damage to the system. This paper develops a method of the open-phase fault detection and discrimination for the PMSM drive system based on the zero-sequence voltage components, in which the discrimination of the fault types, namely internal stator winding failure and switches failure of the inverter is realized. Then, appropriate fault-tolerant measures may be taken according to the different fault types. The experimental platform is established, and the experimental results verify the effectiveness of the proposed method, showing that not only the open-phase fault can be rapidly detected, but also the fault type can be effectively discriminated.


title: Classifying Objectionable Websites Based on Image Content

abstract: This paper describes IBCOW (Image-based Classification of Objectionable Websites), a system capable of classifying a website as objectionable or benign based on image content. The system uses WIPEℳ (Wavelet Image Pornography Elimination) and statistics to provide robust classification of on-line objectionable World Wide Web sites. Semantically-meaningful feature vector matching is carried out so that comparisons between a given on-line image and images marked as ”objectionable” and ”benign” in a training set can be performed efficiently and effectively in the WIPE module. If more than a certain number of images sampled from a site is found to be objectionable, then the site is considered to be objectionable. The statistical analysis for determining the size of the image sample and the threshold number of objectionable images is given in this paper. The system is practical for real-world applications, classifying a Web site at a speed of less than 2 minutes each, including the time to compute the feature vector for the images downloaded from the site, on a Pentium Pro PC. Besides its exceptional speed, it has demonstrated 97% sensitivity and 97% specificity in classifying a Web site based solely on images. Both the sensitivity and the specificity in real-world applications is expected to be higher because our performance evaluation is relatively conservative and surrounding text can be used to assist the classification process.


title: Differentially Private Distributed Online Learning

abstract: In the big data era, the generation of data presents some new characteristics, including wide distribution, high velocity, high dimensionality, and privacy concern. To address these challenges for big data analytics, we develop a privacy-preserving distributed online learning framework on the data collected from distributed data sources. Specifically, each node (i.e., data source) has the capacity of learning a model from its local dataset, and exchanges intermediate parameters with a random part of their own neighboring (logically connected) nodes. Hence, the topology of the communications in our distributed computing framework is unfixed in practice. As online learning always performs on the sensitive data, we introduce the notion of differential privacy (DP) into our distributed online learning algorithm (DOLA) to protect the data privacy during the learning, which prevents an adversary from inferring any significant sensitive information. Our model is of general value for big data analytics in the distributed setting, because it can provide rigorous and scalable privacy proof and have much less computational complexity when compared to classic schemes, e.g., secure multiparty computation (SMC). To tackle high-dimensional incoming data entries, we study a sparse version of the DOLA with novel DP techniques to save the computing resources and improve the utility. Furthermore, we present two modified private DOLAs to meet the need of practical applications. One is to convert the DOLA to distributed stochastic optimization in an offline setting, the other is to use the mini-batches approach to reduce the amount of the perturbation noise and improve the utility. We conduct experiments on real datasets in a configured distributed platform. Numerical experiment results validate the feasibility of our private DOLAs.


title: Qualitative Process Theory

abstract: Abstract Objects move, collide, flow, bend, heat up, cool down, stretch, compress, and boil. These and other things that cause changes in objects over time are intuitively characterized as processes . To understand commonsense physical reasoning and make programs that interact with the physical world as well as people do we must understand qualitative reasoning about processes, when they will occur, their effects, and when they will stop. Qualitative process theory defines a simple notion of physical process that appears useful as a language in which to write dynamical theories. Reasoning about processes also motivates a new qualitative representation for quantity in terms of inequalities, called the quantity space . This paper describes the basic concepts of qualitative process theory, several different kinds of reasoning that can be performed with them, and discusses its implications for causal reasoning. Several extended examples illustrate the utility of the theory, including figuring out that a boiler can blow up, that an oscillator with friction will eventually stop, and how to say that you can pull with a string, but not push with it.


title: Cryptographic puzzles and DoS resilience, revisited

abstract: Cryptographic puzzles (or client puzzles) are moderately difficult problems that can be solved by investing non-trivial amounts of computation and/or storage. Devising models for cryptographic puzzles has only recently started to receive attention from the cryptographic community as a first step toward rigorous models and proofs of security of applications that employ them (e.g. Denial-of-Service (DoS) resistance). Unfortunately, the subtle interaction between the complex scenarios for which cryptographic puzzles are intended and typical difficulties associated with defining concrete security easily leads to flaws in definitions and proofs. Indeed, as a first contribution we exhibit shortcomings of the state-of-the-art definition of security of cryptographic puzzles and point out some flaws in existing security proofs. The main contribution of this paper are new security definitions for puzzle difficulty. We distinguish and formalize two distinct flavors of puzzle security which we call optimality and fairness and in addition, properly define the relation between solving one puzzle versus solving multiple ones. We demonstrate the applicability of our notions by analyzing the security of two popular puzzle constructions. We briefly investigate existing definitions for the related notion of security against DoS attacks. We demonstrate that the only rigorous security notion proposed to date is not sufficiently demanding (as it allows to prove secure protocols that are clearly not DoS resistant) and suggest an alternative definition. Our results are not only of theoretical interest: the better characterization of hardness for puzzles and DoS resilience allows establishing formal bounds on the effectiveness of client puzzles which confirm previous empirical observations. We also underline clear practical limitations for the effectiveness of puzzles against DoS attacks by providing simple rules of thumb that can be easily used to discard puzzles as a valid countermeasure for certain scenarios.


title: Ciphertext-Policy Attribute-Based Encryption With Delegated Equality Test in Cloud Computing

abstract: Public key encryption supporting equality test (referred to as PKE-ET) provides the capability of testing the equivalence between two messages encrypted under different public keys. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising primitive to achieve versatile and secure data sharing in the cloud computing by providing flexible one-to-many encryption. In this paper, we first initialize the concept of CP-ABE with equality test (CP-ABE-ET) by combining the notions of PKE-ET and CP-ABE. Using ABE-ET primitive, the receiver can delegate a cloud server to perform an equivalence test between two messages, which are encrypted under different access policies. During the delegated equivalence test, the cloud server is unable to obtain any knowledge of the message encrypted under either access policy. We propose a concrete CP-ABE-ET scheme using bilinear pairing and Viete’s formulas, and give the security proof of the proposed scheme formally in the standard model. Moreover, the theoretic analysis and experimental simulation reveal that the proposed scheme is efficient and practical.


title: An Extended Mathematical Programming Framework

abstract: Abstract Extended mathematical programs are collections of functions and variables joined together using specific optimization and complementarity primitives. This paper outlines a mechanism to describe such an extended mathematical program by means of annotating the existing relationships within a model to facilitate higher level structure identification. The structures, which often involve constraints on the solution sets of other models or complementarity relationships, can be exploited by modern large scale mathematical programming algorithms for efficient solution. A specific implementation of this framework is outlined that communicates structure from the GAMS modeling system to appropriate solvers in a computationally beneficial manner. Example applications are taken from chemical engineering.


title: Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT

abstract: In practical applications of tomographic imaging, there are often challenges for image reconstruction due to under-sampling and insufficient data. In computed tomography (CT), for example, image reconstruction from few views would enable rapid scanning with a reduced x-ray dose delivered to the patient. Limited-angle problems are also of practical significance in CT. In this work, we develop and investigate an iterative image reconstruction algorithm based on the minimization of the image total variation (TV) that applies to divergent-beam CT. Numerical demonstrations of our TV algorithm are performed with various insufficient data problems in fan-beam CT. The TV algorithm can be generalized to cone-beam CT as well as other tomographic imaging modalities.


DEWE: a framework for distributed elastic scientific workflow execution


title: Region Formation for Efficient Offline Location Prediction

abstract: As low-power devices continue to be integrated into daily life, we are presented with the challenge of deciding when it's beneficial for these devices to interrupt us. Predicting a user's movements provides valuable insight to solve this problem. Here, the authors present a new approach for offline location prediction for low-power devices, representing a user's mobility patterns as an optimal set of geographical regions. Their approach yielded a 27 percent increase in precision and a 13 percent increase in recall over standard time- and place-based approaches against GPS data for hundreds of users. Their approach requires minimal additional cost and opens up potential for further development.


title: Attentional Correlation Filter Network for Adaptive Visual Tracking

abstract: We propose a new tracking framework with an attentional mechanism that chooses a subset of the associated correlation filters for increased robustness and computational efficiency. The subset of filters is adaptively selected by a deep attentional network according to the dynamic properties of the tracking target. Our contributions are manifold, and are summarised as follows: (i) Introducing the Attentional Correlation Filter Network which allows adaptive tracking of dynamic targets. (ii) Utilising an attentional network which shifts the attention to the best candidate modules, as well as predicting the estimated accuracy of currently inactive modules. (iii) Enlarging the variety of correlation filters which cover target drift, blurriness, occlusion, scale changes, and flexible aspect ratio. (iv) Validating the robustness and efficiency of the attentional mechanism for visual tracking through a number of experiments. Our method achieves similar performance to non real-time trackers, and state-of-the-art performance amongst real-time trackers.


title: The Kernel Least-Mean-Square Algorithm

abstract: The combination of the famed kernel trick and the least-mean-square (LMS) algorithm provides an interesting sample-by-sample update for an adaptive filter in reproducing kernel Hilbert spaces (RKHS), which is named in this paper the KLMS. Unlike the accepted view in kernel methods, this paper shows that in the finite training data case, the KLMS algorithm is well posed in RKHS without the addition of an extra regularization term to penalize solution norms as was suggested by Kivinen [Kivinen, Smola and Williamson, ldquoOnline Learning With Kernels,rdquo IEEE Transactions on Signal Processing, vol. 52, no. 8, pp. 2165-2176, Aug. 2004] and Smale [Smale and Yao, ldquoOnline Learning Algorithms,rdquo Foundations in Computational Mathematics, vol. 6, no. 2, pp. 145-176, 2006]. This result is the main contribution of the paper and enhances the present understanding of the LMS algorithm with a machine learning perspective. The effect of the KLMS step size is also studied from the viewpoint of regularization. Two experiments are presented to support our conclusion that with finite data the KLMS algorithm can be readily used in high dimensional spaces and particularly in RKHS to derive nonlinear, stable algorithms with comparable performance to batch, regularized solutions.


title: Diffusion LMS Strategies for Distributed Estimation

abstract: We consider the problem of distributed estimation, where a set of nodes is required to collectively estimate some parameter of interest from noisy measurements. The problem is useful in several contexts including wireless and sensor networks, where scalability, robustness, and low power consumption are desirable features. Diffusion cooperation schemes have been shown to provide good performance, robustness to node and link failure, and are amenable to distributed implementations. In this work we focus on diffusion-based adaptive solutions of the LMS type. We motivate and propose new versions of the diffusion LMS algorithm that outperform previous solutions. We provide performance and convergence analysis of the proposed algorithms, together with simulation results comparing with existing techniques. We also discuss optimization schemes to design the diffusion LMS weights.


title: Hidost: a static machine-learning-based detector of malicious files

abstract: Malicious software, i.e., malware, has been a persistent threat in the information security landscape since the early days of personal computing. The recent targeted attacks extensively use non-executable malware as a stealthy attack vector. There exists a substantial body of previous work on the detection of non-executable malware, including static, dynamic, and combined methods. While static methods perform orders of magnitude faster, their applicability has been hitherto limited to specific file formats. ::: ::: This paper introduces Hidost, the first static machine-learning-based malware detection system designed to operate on multiple file formats. Extending a previously published, highly effective method, it combines the logical structure of files with their content for even better detection accuracy. Our system has been implemented and evaluated on two formats, PDF and SWF (Flash). Thanks to its modular design and general feature set, it is extensible to other formats whose logical structure is organized as a hierarchy. Evaluated in realistic experiments on timestamped datasets comprising 440,000 PDF and 40,000 SWF files collected during several months, Hidost outperformed all antivirus engines deployed by the website VirusTotal to detect the highest number of malicious PDF files and ranked among the best on SWF malware.


title: Full Flow: Optical Flow Estimation By Global Optimization over Regular Grids

abstract: We present a global optimization approach to optical flow estimation. The approach optimizes a classical optical flow objective over the full space of mappings between discrete grids. No descriptor matching is used. The highly regular structure of the space of mappings enables optimizations that reduce the computational complexity of the algorithm's inner loop from quadratic to linear and support efficient matching of tens of thousands of nodes to tens of thousands of displacements. We show that one-shot global optimization of a classical Horn-Schunck-type objective over regular grids at a single resolution is sufficient to initialize continuous interpolation and achieve state-of-the-art performance on challenging modern benchmarks.


title: Learning to predict by the methods of temporal differences

abstract: This article introduces a class of incremental learning procedures specialized for prediction-that is, for using past experience with an incompletely known system to predict its future behavior. Whereas conventional prediction-learning methods assign credit by means of the difference between predicted and actual outcomes, the new methods assign credit by means of the difference between temporally successive predictions. Although such temporal-difference methods have been used in Samuel's checker player, Holland's bucket brigade, and the author's Adaptive Heuristic Critic, they have remained poorly understood. Here we prove their convergence and optimality for special cases and relate them to supervised-learning methods. For most real-world prediction problems, temporal-difference methods require less memory and less peak computation than conventional methods and they produce more accurate predictions. We argue that most problems to which supervised learning is currently applied are really prediction problems of the sort to which temporal-difference methods can be applied to advantage.


title: Scattering from three-dimensional objects embedded in multilayered media

abstract: This paper presents a model for computing the scattered field of a three dimensional scatterer embedded in multilayered media. The embedded object is modeled using a volume integral equation approach which allows the handling of general inhomogeneous objects. THe multilayered media is modeled using a transmission line analogy. The scattered field is obtained by obtaining the equivalent current sources in the discretization elements of the object. The convolution-form of the volume integral formulation allows the use of the conjugate gradient FFT computation method which is essential for reducing the memory storage requirements. Formulations are presented for scattering from an object in free space, a scatterer on top of a perfectly conducting surface and a scatterer embedded in multilayered media. The Green's function of the integral equation has two parts: a free space primary part and a secondary part due to the layered media. The secondary Green's function is in the form of Sommerfeld's integration which commonly has a fast oscillating integrand. Different techniques are presented to overcome this difficulty. This includes using an asymptotic solution using the stationary phase method and real axis integration. Results are presented for scattering from a sphere as an example of the object.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.


title: Methionine biosynthesis and its regulation in Corynebacterium glutamicum: parallel pathways of transsulfuration and direct sulfhydrylation

abstract: There are two alternative pathways leading to methionine synthesis in microorganisms: The transsulfuration pathway involves cystathionine as the intermediate and utilizes cysteine as the sulfur source, but the direct sulfhydrylation pathway bypasses cystathionine and uses inorganic sulfur instead. While most microorganisms synthesize methionine via either one of these pathways, Corynebacterium glutamicum utilizes both pathways, which appear to be fully functional. In C. glutamicum, each pathway is catalyzed by independent enzymes and is tightly regulated by methionine. Although the physiological significance of parallel pathways remains to be elucidated, their presence suggests metabolic flexibility and efficient adaptation of the organism to its environment.


title: Gaussian processes for machine learning

abstract: We give a basic introduction to Gaussian Process regression models. We focus on understanding the role of the stochastic process and how it is used to define a distribution over functions. We present the simple equations for incorporating training data and examine how to learn the hyperparameters using the marginal likelihood. We explain the practical advantages of Gaussian Process and end with conclusions and a look at the current trends in GP work.


title: Implementation and Comparative Analysis of the Fault Attacks on AES IAJIT First Online Publication

abstract: This research presents the survey, analysis, comparisons and implementation of the most threatening new kind of cryptographic attacks known as fault attacks or implementation attacks against Advanced Encryption Standard (AES) algorithm. AES algorithm is used in various applications and is considered the most secure against conventional cryptanalytic attacks which exploits the algebraic or mathematical weaknesses in the crypto-systems. Fault attacks are based on interrupting the execution of the algorithm in such a way that it produces faulty cipher output which can be analysed to break the algorithm. This research survey various fault attacks and provide implementation of three of them in detail for demonstration purposes. It mapped the complex mathematical analysis into programming algorithms for ease of implementation. At the end it compares various types of attacks based on our devised criteria of efficiency, flexibility and usability/flexibility of the attack methods.


Motion and Teaching of a NAO Robot


title: The use of entropy to measure structural diversity

abstract: In this paper entropy based methods are compared and used to measure structural diversity of an ensemble of 21 classifiers. This measure is mostly applied in ecology, whereby species counts are used as a measure of diversity. The measures used were Shannon entropy, Simpsons and the Berger Parker diversity indexes. As the diversity indexes increased so did the accuracy of the ensemble. An ensemble dominated by classifiers with the same structure produced poor accuracy. Uncertainty rule from information theory was also used to further define diversity. Genetic algorithms were used to find the optimal ensemble by using the diversity indices as the cost function. The method of voting was used to aggregate the decisions.


title: Sampling Variations of Lead Sheets

abstract: Machine-learning techniques have been recently used with spectacular results to generate artefacts such as music or text. However, these techniques are still unable to capture and generate artefacts that are convincingly structured. In this paper we present an approach to generate structured musical sequences. We introduce a mechanism for sampling efficiently variations of musical sequences. Given a input sequence and a statistical model, this mechanism samples a set of sequences whose distance to the input sequence is approximately within specified bounds. This mechanism is implemented as an extension of belief propagation, and uses local fields to bias the generation. We show experimentally that sampled sequences are indeed closely correlated to the standard musical similarity measure defined by Mongeau and Sankoff. We then show how this mechanism can used to implement composition strategies that enforce arbitrary structure on a musical lead sheet generation problem.


title: The Appropriate Use of Approximate Entropy and Sample Entropy with Short Data Sets

abstract: Approximate entropy (ApEn) and sample entropy (SampEn) are mathematical algorithms created to measure the repeatability or predictability within a time series. Both algorithms are extremely sensitive to their input parameters: m (length of the data segment being compared), r (similarity criterion), and N (length of data). There is no established consensus on parameter selection in short data sets, especially for biological data. Therefore, the purpose of this research was to examine the robustness of these two entropy algorithms by exploring the effect of changing parameter values on short data sets. Data with known theoretical entropy qualities as well as experimental data from both healthy young and older adults was utilized. Our results demonstrate that both ApEn and SampEn are extremely sensitive to parameter choices, especially for very short data sets, N ≤ 200. We suggest using N larger than 200, an m of 2 and examine several r values before selecting your parameters. Extreme caution should be used when choosing parameters for experimental studies with both algorithms. Based on our current findings, it appears that SampEn is more reliable for short data sets. SampEn was less sensitive to changes in data length and demonstrated fewer problems with relative consistency.


title: Accelerating Machine Learning Inference with Probabilistic Predicates

abstract: Classic query optimization techniques, including predicate pushdown, are of limited use for machine learning inference queries, because the user-defined functions (UDFs) which extract relational columns from unstructured inputs are often very expensive; query predicates will remain stuck behind these UDFs if they happen to require relational columns that are generated by the UDFs. In this work, we demonstrate constructing and applying probabilistic predicates to filter data blobs that do not satisfy the query predicate; such filtering is parametrized to different target accuracies. Furthermore, to support complex predicates and to avoid per-query training, we augment a cost-based query optimizer to choose plans with appropriate combinations of simpler probabilistic predicates. Experiments with several machine learning workloads on a big-data cluster show that query processing improves by as much as 10x.


title: Exploring Emoji Usage and Prediction Through a Temporal Variation Lens

abstract: Comunicacio presentada a l'International Workshop on Emoji Understanding and Applications in Social Media (Emoji) celebrat el 25 de juny de 2018) a Stanford, Estats Units.


title: The importance of dedicated dermoscopy training during residency: A survey of US dermatology chief residents

abstract: Background Studies have shown low satisfaction levels among dermatology residents with respect to dermoscopy training. Many desire additional instruction. Objective We surveyed graduating chief residents to assess current education practices among US dermatology training programs with respect to the role of dermoscopy as an aid in the management of pigmented lesions. Methods An online survey was sent to 139 chief residents of US dermatology training programs. Results A 59% response rate was achieved. Of responding chief residents, 94% use dermoscopy. Although 92% of chief residents received dermoscopy training, only 48% trained with a pigmented lesion specialist. Among those training without a specialist, less than half received classroom or bedside teaching compared with 77% of those who trained with a specialist. Of those who trained with a specialist, 77% were satisfied with their training compared with only 30% who trained without a specialist ( P P = .0065). Limitations Response bias and limiting the survey to chief residents potentially limits our ability to generalize these results to all US dermatology trainees. Conclusion Although many residents use dermoscopy as a diagnostic tool, the lack of dedicated dermoscopy training remains a potential barrier to increasing residents' diagnostic confidence in the management of pigmented lesions. Increasing the amount of dedicated instruction on this topic is one possible approach to enhance resident satisfaction, potentially increasing their competency in the management of atypical nevi.


title: Effective communication skills in nursing practice

abstract: This article highlights the importance of effective communication skills for nurses. It focuses on core communication skills, their definitions and the positive outcomes that result when applied to practice. Effective communication is central to the provision of compassionate, high-quality nursing care. The article aims to refresh and develop existing knowledge and understanding of effective communication skills. Nurses reading this article will be encouraged to develop a more conscious style of communicating with patients and carers, with the aim of improving health outcomes and patient satisfaction.


title: The contribution of electrophysiology to functional connectivity mapping

abstract: Abstract A powerful way to probe brain function is to assess the relationship between simultaneous changes in activity across different parts of the brain. In recent years, the temporal activity correlation between brain areas has frequently been taken as a measure of their functional connections. Evaluating ‘functional connectivity’ in this way is particularly popular in the fMRI community, but has also drawn interest among electrophysiologists. Like hemodynamic fluctuations observed with fMRI, electrophysiological signals display significant temporal fluctuations, even in the absence of a stimulus. These neural fluctuations exhibit a correlational structure over a wide range of spatial and temporal scales. Initial evidence suggests that certain aspects of this correlational structure bear a high correspondence to so-called functional networks defined using fMRI. The growing family of methods to study activity covariation, combined with the diverse neural mechanisms that contribute to the spontaneous fluctuations, has somewhat blurred the operational concept of functional connectivity. What is clear is that spontaneous activity is a conspicuous, energy-consuming feature of the brain. Given its prominence and its practical applications for the functional connectivity mapping of brain networks, it is of increasing importance that we understand its neural origins as well as its contribution to normal brain function.


title: Origami based Mechanical Metamaterials

abstract: We describe mechanical metamaterials created by folding flat sheets in the tradition of origami, the art of paper folding, and study them in terms of their basic geometric and stiffness properties, as well as load bearing capability. A periodic Miura-ori pattern and a non-periodic Ron Resch pattern were studied. Unexceptional coexistence of positive and negative Poisson's ratio was reported for Miura-ori pattern, which are consistent with the interesting shear behavior and infinity bulk modulus of the same pattern. Unusually strong load bearing capability of the Ron Resch pattern was found and attributed to the unique way of folding. This work paves the way to the study of intriguing properties of origami structures as mechanical metamaterials.


title: Ginkgo biloba Food Supplements on the European Market - Adulteration Patterns Revealed by Quality Control of Selected Samples.

abstract: The aim of this study was to prove whether Ginkgo biloba food supplements on the European market comply with pharmaceutical quality, and whether their composition satisfies the European Pharmacopoeia criteria. Medicinal products containing a standardised Ginkgo leaf extract are used for the improvement of cognitive impairment and quality of life in mild dementia. Further, Ginkgonis folium is used for the treatment of peripheral circulation disorders. Pharmacopoeial Ginkgo dry extract contains 22.0 - 27.0% flavonoids and 5.4 - 6.6% terpene lactones (ginkgolides, bilobalide). In addition to its widespread use as an herbal medicine (herbal medicinal product), the same extract can be an ingredient in food supplements. The content of active secondary metabolites was quantified in a number of European food supplements containing Ginkgo dry extract or Ginkgo leaf. Flavonoids were quantified using a modified pharmacopoeial HPLC-UV method, and terpene lactones (ginkgolides A, B, C, and bilobalide) using LC-MS/MS. Some Ginkgo leaf supplement samples were also analysed by microscopy. The quality of food supplements on the European market is dubious. In this paper, we present selected examples of several methods of adulteration and falsification, including higher/lower doses of Ginkgo dry extract or Ginkgo leaf than declared and the addition of undeclared extraneous materials. These examples reveal several patterns in the manufacturing of adulterated products.


title: Robust Subspace Segmentation with Block-Diagonal Prior

abstract: The subspace segmentation problem is addressed in this paper by effectively constructing an exactly block-diagonal sample affinity matrix. The block-diagonal structure is heavily desired for accurate sample clustering but is rather difficult to obtain. Most current state-of-the-art subspace segmentation methods (such as SSC[4] and LRR[12]) resort to alternative structural priors (such as sparseness and low-rankness) to construct the affinity matrix. In this work, we directly pursue the block-diagonal structure by proposing a graph Laplacian constraint based formulation, and then develop an efficient stochastic subgradient algorithm for optimization. Moreover, two new subspace segmentation methods, the block-diagonal SSC and LRR, are devised in this work. To the best of our knowledge, this is the first research attempt to explicitly pursue such a block-diagonal structure. Extensive experiments on face clustering, motion segmentation and graph construction for semi-supervised learning clearly demonstrate the superiority of our novelly proposed subspace segmentation methods.


title: Anisotropic Huber-L1 Optical Flow

abstract: TV regularization is an L1 penalization of the flow gradient magnitudes, and due to the tendency of the L1 norm to favor sparse solutions (i.e. lots of ‘zeros’), the fill-in effect caused by the regularizer leads to piecewise constant solutions in weakly textured areas. This effect, known as ‘staircasing’ in a 1D setting, can be reduced significantly by using a quadratic penalization for small gradient magnitudes while sticking to linear penalization for larger magnitudes to maintain the discontinuity preserving properties known from TV. A comparison of isotropic TV and isotropic Huber regularity is shown in Fig. 1 by means of rendering the disparities u1 of the Dimetrodon dataset. The color coded flow (cf. Fig. 1(a)) is superimposed as texture. Based on the two observations that motion discontinuities often occur along object boundaries and that in turn object boundaries often coincide


title: New step-up DC-DC converters for PV power generation systems

abstract: This paper proposes new step-up DC-DC converters with high ratio capability. The proposed DC-DC converters are derived using combination of boost converters and buck-boost converters. A high ratio capability is achieved by parallel input and series output combination so that the efficiency is better than the one that is achieved by using a conventional boost converter. A method to reduce the input and output ripples are also proposed in this paper. Simulated and experimental results are included to show the validity of the proposed converters.


title: A visual attention-based method to address the midas touch problem existing in gesture-based interaction

abstract: The "Midas Touch" problem has long been a difficult problem existing in gesture-based interaction. This paper proposes a visual attention-based method to address this problem from the perspective of cognitive psychology. There are three main contributions in this paper: (1) a visual attention-based parallel perception model is constructed by combining top-down and bottom-up attention, (2) a framework is proposed for dynamic gesture spotting and recognition simultaneously, and (3) a gesture toolkit is created to facilitate gesture design and development. Experimental results show that the proposed method has a good performance for both isolated and continuous gesture recognition tasks. Finally, we highlight the implications of this work for the design and development of all gesture-based applications.


Research Methods in Psychology


title: Clues for detecting irony in user-generated contents: oh...!! it's "so easy" ;-)

abstract: We investigate the accuracy of a set of surface patterns in identifying ironic sentences in comments submitted by users to an on-line newspaper. The initial focus is on identifying irony in sentences containing positive predicates since these sentences are more exposed to irony, making their true polarity harder to recognize. We show that it is possible to find ironic sentences with relatively high precision (from 45% to 85%) by exploring certain oral or gestural clues in user comments, such as emoticons, onomatopoeic expressions for laughter, heavy punctuation marks, quotation marks and positive interjections. We also demonstrate that clues based on deeper linguistic information are relatively inefficient in capturing irony in user-generated content, which points to the need for exploring additional types of oral clues.


title: Multivariate decision trees

abstract: Unlike a univariate decision tree, a multivariate decision tree is not restricted to splits of the instance space that are orthogonal to the features' axes. This article addresses several issues for constructing multivariate decision trees: representing a multivariate test, including symbolic and numeric features, learning the coefficients of a multivariate test, selecting the features to include in a test, and pruning of multivariate decision trees. We present several new methods for forming multivariate decision trees and compare them with several well-known methods. We compare the different methods across a variety of learning tasks, in order to assess each method's ability to find concise, accurate decision trees. The results demonstrate that some multivariate methods are in general more effective than others (in the context of our experimental assumptions). In addition, the experiments confirm that allowing multivariate tests generally improves the accuracy of the resulting decision tree over a univariate tree.


title: The Folk Psychology of Free Will: Fits and Starts

abstract: According to agent-causal accounts of free will, agents have the capacity to cause actions, and for a given action, an agent could have done otherwise. This paper uses existing results and presents experimental evidence to argue that young children deploy a notion of agent-causation. If young children do have such a notion, however, it remains quite unclear how they acquire it. Several possible acquisition stories are canvassed, including the possibility that the notion of agent-causation develops from a prior notion of obligation. Finally, the paper sets out how this work might illuminate the philosophical problem of free will.


title: Comparison of Novice and Experienced Drivers Using the SEEV Model to Predict Attention Allocation at Intersections During Simulated Driving

abstract: The authors compared the eye movements of novice drivers and experienced drivers while they drove a simulated driving scenario that included a number of intersections interspersed with stretches of straight road. The intersections included non-hazard events. Cassavaugh, Bos, McDonald, Gunaratne, & Backs (2013) attempted to model attention allocation of experienced drivers using the Salience Effort Expectancy and Value (SEEV) model. Here they compared two SEEV model fits between those experienced drivers and a sample of novice drivers. The first was a simplified model and the second was a more complex intersection model. The observed eye movement data was found to be a good fit to the simplified model for both experienced (R2 = 0.88) and novice drivers (R2 = 0.30). Like the previous results of the intersection model for the experienced drivers, the fit of the observed eye movement data to the intersection model for novice drivers was poor, and was no better than fitting the data to a randomized SEEV model. The authors concluded based on the simplified SEEV model, fixation count and fixation variance that experienced drivers were found to be more efficient at distributing their visual search compared to novice drivers.


Describing Linked Datasets On the Design and Usage of voiD , the “ Vocabulary Of Interlinked Datasets ”


title: Non-cytotoxic Thymus capitata extracts prevent Bovine herpesvirus-1 infection in cell cultures

abstract: BACKGROUND ::: Bovine herpes virus type 1 (BHV-1) still causes great economic loss to the livestock industry and trade because there aren't any available drugs that proved to be fully effective against it. In this study, the cytotoxicity and the antiviral activities of the Thymus capitata extracts were evaluated for the development of new, non toxic and specific anti-herpesvirus drug. Aqueous extracts (AE), ethanolic extracts (EE) and essential oil (EO) of the aerial parts of Thymus capitata were analyzed to determine their chemical compositions by gas chromatography, and high performance liquid chromatography combined with mass spectrometry. Their cytotoxicity and antiviral activities against Bovine Herpesvirus type 1 (BHV-1) were evaluated by quantifying the reduction of the viral cytopathic effect using Madin-Darby Bovine Kidney cell line with colorimetric assay. T. capitata extracts were added at different stages of the viral infection to investigate and better quantify their potential inhibitory effects. ::: ::: ::: RESULTS ::: Polyphenols and flavonoids were the major compounds found in T. capitata EO, EE and AE. The cytotoxic concentrations at 50% were 48.70, 189 and 289 μg ml(-1) for EO, EE and AE, respectively. The inhibitor concentrations at 50% for the EO, EE and AE, were 3.36, 47.80 and 164 μg ml(-1), respectively. The selectivity index anti-BHV-1 values were 14.49, 3.95 and 1.81 for EO, EE and AE, respectively. Thus, the EO extracts were the most efficient antiviral compounds. T. capitata extracts affect mainly the adsorption of BHV-1 virus to host cells. ::: ::: ::: CONCLUSION ::: T. capitata extracts inhibit the viral replication by interfering with the early stages of viral adsorption and replication. Thus, T. capitata is a potential candidate for anti-herpesvirus treatment.


title: Explicit Approximations of the Gaussian Kernel

abstract: We investigate training and using Gaussian kernel SVMs by approximating the kernel with an explicit finite- dimensional polynomial feature representation based on the Taylor expansion of the exponential. Although not as efficient as the recently-proposed random Fourier features [Rahimi and Recht, 2007] in terms of the number of features, we show how this polynomial representation can provide a better approximation in terms of the computational cost involved. This makes our "Taylor features" especially attractive for use on very large data sets, in conjunction with online or stochastic training.


title: A Hybrid Evolutionary Approach to the University Course Timetabling Problem

abstract: Combinations of evolutionary based approaches with local search have provided very good results for a variety of scheduling problems. This paper describes the development of such an algorithm for university course timetabling. This problem is concerned with the assignment of lectures to specific timeslots and rooms. For a solution to be feasible, a number of hard constraints must be satisfied. The quality of the solution is measured in terms of a penalty value which represents the degree to which various soft constraints are satisfied. This hybrid evolutionary approach is tested over established datasets and compared against state-of-the-art techniques from the literature. The results obtained confirm that the approach is able to produce solutions to the course timetabling problem which exhibit some of the lowest penalty values in the literature on these benchmark problems. It is therefore concluded that the hybrid evolutionary approach represents a particularly effective methodology for producing high quality solutions to the university course timetabling problem.


title: Determining the Epipolar Geometry and its Uncertainty: A Review

abstract: Two images of a single scene/object are related by the epipolar geometry, which can be described by a 3×3 singular matrix called the essential matrix if images‘ internal parameters are known, or the fundamental matrix otherwise. It captures all geometric information contained in two images, and its determination is very important in many applications such as scene modeling and vehicle navigation. This paper gives an introduction to the epipolar geometry, and provides a complete review of the current techniques for estimating the fundamental matrix and its uncertainty. A well-founded measure is proposed to compare these techniques. Projective reconstruction is also reviewed. The software which we have developed for this review is available on the Internet.


title: A critique of network speed in VLSI models of computation

abstract: Evaluates various proposed VLSI models of computation. While there is a consensus on the appraisal of chip area, controversy remains with regard to computation time. Thus, the authors have analyzed in detail the propagation of signals on disperse lines. The results are expressed in terms of adimensional parameters characteristic of any given fabrication technology. The conclusion is that both current and projected silicon technologies fall within the realm of the capacitive model where a dispersive line can be replaced by a capacitance proportional to its length. Diffusion phenomena therefore appear to exceed the present VLSI horizon.


title: Making the connection: the effectiveness of Internet training in small businesses

abstract: Aims to provide an analysis of the introduction of Internet‐based skills into small firms. Seeks to contribute to the wider debate on the content and style of training most appropriate for employees and managers of SMEs.


title: Solar flare M-class prediction using artificial intelligence techniques

abstract: Currently, astronomical data have increased in terms of volume and complexity. To bring out the information in order to analyze and predict, the artificial intelligence techniques are required. This paper aims to apply artificial intelligence techniques to predict M-class solar flare. Artificial neural network, support vector machine and naive bayes techniques are compared to define the best prediction performance accuracy technique. The dataset have been collected from daily data for 16 years, from 1998 to 2013. The attributes consist of solar flares data and sunspot number. The sunspots are a cooler spot on the surface of the sun, which have relation with solar flares. The Java-based machine learning WEKA is used for analysis and predicts solar flares. The best forecasted performance accuracy is achieved based on the artificial neural network method.


title: Smart inverter volt/var control functions for high penetration of PV on distribution systems

abstract: Small- and large-scale wind and solar photovoltaic (PV) generation systems are interconnecting to the distribution system at a rapid pace. Penetration limits are often imposed due to the impact these additional distributed resource can have on voltage. In order to accommodate high penetration scenarios without impacting power quality or reliability, additional control functions must be utilized. A recent industry collaborative effort has identified key functions that allow these additional resources to coordinate with grid operations and provide a more grid-friendly response. This paper addresses one of these functions, inverter volt/var control. Simulations are performed to illustrate the effectiveness of this type of control for PV deployment on the distribution system.


title: The PI3K/AKT Pathway and Renal Cell Carcinoma

abstract: Abstract The phosphatidylinositol 3 kinase (PI3K)/AKT pathway is genetically targeted in more pathway components and in more tumor types than any other growth factor signaling pathway, and thus is frequently activated as a cancer driver. More importantly, the PI3K/AKT pathway is composed of multiple bifurcating and converging kinase cascades, providing many potential targets for cancer therapy. Renal cell carcinoma (RCC) is a high-risk and high-mortality cancer that is notoriously resistant to traditional chemotherapies or radiotherapies. The PI3K/AKT pathway is modestly mutated but highly activated in RCC, representing a promising drug target. Indeed, PI3K pathway inhibitors of the rapalog family are approved for use in RCC. Recent large-scale integrated analyses of a large number of patients have provided a molecular basis for RCC, reiterating the critical role of the PI3K/AKT pathway in this cancer. In this review, we summarize the genetic alterations of the PI3K/AKT pathway in RCC as indicated in the latest large-scale genome sequencing data, as well as treatments for RCC that target the aberrant activated PI3K/AKT pathway.


title: An Efficient Similarity Measure for User-Based Collaborative Filtering Recommender Systems Inspired by the Physical Resonance Principle

abstract: User-based collaborative filtering is an important technique used in collaborative filtering recommender systems to recommend items based on the opinions of like-minded nearby users, where similarity computation is the critical component. Traditional similarity measures, such as Pearson’s correlation coefficient and cosine Similarity, mainly focus on the directions of co-related rating vectors and have inherent limitations for recommendations. In addition, CF-based recommendation systems always suffer from the cold-start problem, where users do not have enough co-related ratings for prediction. To address these problems, we propose a novel similarity measure inspired by a physical resonance phenomenon, named resonance similarity (RES). We fully consider different personalized situations in RES by mathematically modeling the consistency of users’ rating behaviors, the distances between the users’ opinions, and the Jaccard factor with both the co-related and non-related ratings. RES is a cumulative sum of the arithmetic product of these three parts and is optimized using learning parameters from data sets. Results evaluated on six real data sets show that RES is robust against the observed problems and has superior predictive accuracy compared with the state-of-the-art similarity measures on full users’, grouped users’, and cold-start users’ evaluations.


title: Automated energy optimization of HTTP requests for mobile applications

abstract: Energy is a critical resource for apps that run on mobile de- vices. Among all operations, making HTTP requests is one of the most energy consuming. Previous studies have shown that bundling smaller HTTP requests into a single larger HTTP request can be an effective way to improve energy efficiency of network communication, but have not defined an automated way to detect when apps can be bundled nor to transform the apps to do this bundling. In this paper we propose an approach to reduce the energy consumption of HTTP requests in Android apps by automatically detecting and then bundling multiple HTTP requests. Our approach first detects HTTP requests that can be bundled using static analysis, then uses a proxy based technique to bundle HTTP requests at runtime. We evaluated our approach on a set of real world marketplace Android apps. In this evaluation, our approach achieved an average energy reduction of 15% for the subject apps and did not impose a significant runtime overhead on the optimized apps.


title: Improv: An Input Framework for Improvising Cross-Device Interaction by Demonstration

abstract: As computing devices become increasingly ubiquitous, it is now possible to combine the unique capabilities of different devices or Internet of Things to accomplish a task. However, there is currently a high technical barrier for creating cross-device interaction. This is especially challenging for end users who have limited technical expertise—end users would greatly benefit from custom cross-device interaction that best suits their needs. In this article, we present Improv, a cross-device input framework that allows a user to easily leverage the capability of additional devices to create new input methods for an existing, unmodified application, e.g., creating custom gestures on a smartphone to control a desktop presentation application. Instead of requiring developers to anticipate and program these cross-device behaviors in advance, Improv enables end users to improvise them on the fly by simple demonstration, for their particular needs and devices at hand. We showcase a range of scenarios where Improv is used to create a diverse set of useful cross-device input. Our study with 14 participants indicated that on average it took a participant 10 seconds to create a cross-device input technique. In addition, Improv achieved 93.7% accuracy in interpreting user demonstration of a target UI behavior by looking at the raw input events from a single example.


title: How good is the Electricity benchmark for evaluating concept drift adaptation

abstract: In this correspondence, we will point out a problem with testing adaptive classifiers on autocorrelated data. In such a case random change alarms may boost the accuracy figures. Hence, we cannot be sure if the adaptation is working well.


title: Thresholding of statistical maps in functional neuroimaging using the false discovery rate

abstract: Findingobjective and effective thresholds for voxelwise statistics derived from neuroimaging data has been a long-standing problem. With at least one test performed for every voxel in an image, some correction of the thresholds is needed to control the error rates, but standard procedures for multiple hypothesis testing (e.g., Bonferroni) tend to not be sensitive enough to be useful in this context. This paper introduces to the neuroscience literature statistical procedures for controlling the false discovery rate (FDR). Recent theoretical work in statistics suggests that FDR-controlling procedures will be effective for the analysis of neuroimaging data. These procedures operate simultaneously on all voxelwise test statistics to determine which tests should be considered statistically significant. The innovation of the procedures is that they control the expected proportion of the rejected hypotheses that are falsely rejected. We demonstrate this approach using both simulations and functional magnetic resonance imaging data from two simple experiments.


title: Optimizing the P300-based brain–computer interface: current status, limitations and future directions

abstract: This paper summarizes the presentations and discussions at a workshop held during the Fourth International BCI Meeting charged with reviewing and evaluating the current state, limitations and future development of P300-based brain–computer interface (P300-BCI) systems. We reviewed such issues as potential users, recording methods, stimulus presentation paradigms, feature extraction and classification algorithms, and applications. A summary of the discussions and the panel's recommendations for each of these aspects are presented.


title: Moderating variables of music training-induced neuroplasticity: a review and discussion

abstract: A large body of literature now exists to substantiate the long-held idea that musicians' brains differ structurally and functionally from nonmusicians' brains. These differences include changes in volume, morphology, density, connectivity, and function across many regions of the brain. In addition to the extensive literature that investigates these differences cross-sectionally by comparing musicians and nonmusicians, longitudinal studies have demonstrated the causal influence of music training on the brain across the lifespan. However, there is a large degree of inconsistency in the findings, with discordance between studies, laboratories, and techniques. A review of this literature highlights a number of variables that appear to moderate the relationship between music training and brain structure and function. These include age at commencement of training, sex, absolute pitch, type of training, and instrument of training. These moderating variables may account for previously unexplained discrepancies in the existing literature, and we propose that future studies carefully consider research designs and methodologies that control for these variables.


title: Maintenance and abstraction of program data using formal transformations

abstract: The authors present the DREAM (Data Reverse Engineering and Abstraction Mechanism) method for restructuring program data to meet the changing demands upon it. It is important that data changes are made correctly to avoid undesirable effects on the operation of the program and to minimise effort in maintenance. To facilitate this program transformations are used to manipulate the code, allowing the maintainer to concentrate upon the restructuring strategy without having to worry about the correctness of changes. Restructuring of programs containing discrete types is examined within the paper. This allows work to be focussed upon a small subset of commonly available data types to make development of solutions feasible and to provide a starting point for the development of a generic method which covers other types.


title: Continuous dynamic optimisation using evolutionary algorithms

abstract: Evolutionary dynamic optimisation (EDO), or the study of applying evolutionary algorithms to dynamic optimisation problems (DOPs) is the focus of this thesis. Based on two comprehensive literature reviews on existing academic EDO research and real-world DOPs, this thesis for the first time identifies some important gaps in current academic research where some common types of problems and problem characteristics have not been covered. In an attempt to close some of these gaps, the thesis makes the following contributions: First, the thesis helps to characterise DOPs better by providing a new definition framework, two new sets of benchmark problems (for certain classes of continuous DOPs) and several new sets of performance measures (for certain classes of continuous DOPs). Second, the thesis studies continuous dynamic constrained optimisation problems (DCOPs), an important and common class of DOPs that have not been studied in EDO research. Contributions include developing novel optimisation approaches (with superior results to existing methods), analysing representative characteristics of DCOPs, identifying the strengths/weaknesses of existing methods and suggesting requirements for an algorithm to solve DCOPs effectively. Third, the thesis studies dynamic time-linkage optimisation problems (DTPs), another important and common class of DOPs that have not been well-studied in EDO research. Contributions include developing a new optimisation approach (with better results than existing methods in certain classes of DTPs), analysing the characteristics of DTPs and the strengths and weaknesses of existing EDO methods in solving certain classes of DTPs.


title: Zero-Shot Recognition via Structured Prediction

abstract: We develop a novel method for zero shot learning (ZSL) based on test-time adaptation of similarity functions learned using training data. Existing methods exclusively employ source-domain side information for recognizing unseen classes during test time. We show that for batch-mode applications, accuracy can be significantly improved by adapting these predictors to the observed test-time target-domain ensemble. We develop a novel structured prediction method for maximum a posteriori (MAP) estimation, where parameters account for test-time domain shift from what is predicted primarily using source domain information. We propose a Gaussian parameterization for the MAP problem and derive an efficient structure prediction algorithm. Empirically we test our method on four popular benchmark image datasets for ZSL, and show significant improvement over the state-of-the-art, on average, by 11.50 % and 30.12 % in terms of accuracy for recognition and mean average precision (mAP) for retrieval, respectively.


title: Comparison of in-person and digital photograph assessment of stage III and IV pressure ulcers among veterans with spinal cord injuries.

abstract: Digital photographs are often used in treatment monitoring for home care of less advanced pressure ulcers. We investigated assessment agreement when stage III and IV pressure ulcers in individuals with spinal cord injury were evaluated in person and with the use of digital photographs. Two wound-care nurses assessed 31 wounds among 15 participants. One nurse assessed all wounds in person, while the other used digital photographs. Twenty-four wound description categories were applied in the nurses' assessments. Kappa statistics were calculated to investigate agreement beyond chance (p < or = 0.05). For 10 randomly selected "double-rated wounds," both nurses applied both assessment methods. Fewer categories were evaluated for the double-rated wounds, because some categories were chosen infrequently and agreement could not be measured. Interrater agreement with the two methods was observed for 12 of the 24 categories (50.0%). However, of the 12 categories with agreement beyond chance, agreement was only "slight" (kappa = 0-0.20) or "fair" (kappa = 0.21-0.40) for 6 categories. The highest agreement was found for the presence of undermining (kappa = 0.853, p < 0.001). Interrater agreement was similar to intramethod agreement (41.2% of the categories demonstrated agreement beyond chance) for the nurses' in-person assessment of the double-rated wounds. The moderate agreement observed may be attributed to variation in subjective perception of qualitative wound characteristics.


title: Reducing Versatile Bat Wing Conformations to a 1-DoF Machine

abstract: Recent works have shown success in mimicking the flapping flight of bats on the robotic platform Bat Bot (B2). This robot has only five actuators but retains the ability to flap and fold-unfold its wings in flight. However, this bat-like robot has been unable to perform folding-unfolding of its wings within the period of a wingbeat cycle, about 100 ms. The DC motors operating the spindle mechanisms cannot attain this folding speed. Biological bats rely on this periodic folding of their wings during the upstroke of the wingbeat cycle. It reduces the moment of inertia of the wings and limits the negative lift generated during the upstroke. Thus, we consider it important to achieve wing folding during the upstroke. A mechanism was designed to couple the flapping cycle to the folding cycle of the robot. We then use biological data to further optimize the mechanism such that the kinematic synergies of the robot best match those of a biological bat. This ensures that folding is performed at the correct point in the wingbeat cycle.


title: Anonymity and security in public Internet forums

abstract: In this paper, we first introduce the privacy and security requirements in public forums, which include anonymity, traceability, impossibility of impersonation, unforgeability, verifiability, fairness, easy anonymity revocation, and un-involvement of authorities. We then propose an anonymous and secure forum protocol based on a concept of alias certificate with detailed analysis to show that our scheme satisfies all these requirements. In particular, participants only need to register once with the authorities and the authorities are not involved during message postings, yet the anonymity of participants and the traceability of malicious information can be achieved. This can greatly increase the usability and efficiency as participants often post messages frequently.