title: A comparison of trading algorithms based on machine learning classifiers : application on the S & P 500

text: In this paper we analyse whether different machine learning classifiers can be useful when it comes to financial data prediction. We first measure the accuracy and the volatility of our algorithm on the stocks included in the S&P 500 stock index. We then back-test it against the same benchmark for two different periods. Overall, our results show that an algorithm that trades just according to the predictions of the classifiers, is not profitable even if the accuracy obtained by the algorithm is higher than the accuracy that should be obtained according to the random walk theory. However, it can boost the results obtained by other strategies, maintaining the same level of volatility. JEL classification: C4, C8, G14.


title: Visual Perception of Parallel Coordinate Visualizations

text: Parallel coordinates is a visualization technique that provides an unbiased representation of high-dimensional data. The parallel configuration of axes treats data dimensions uniformly and is well suited for exploratory visualization. However, first-time users of parallel coordinate visualizations can find the representation confusing and difficult to understand.We used eye tracking to study how parallel coordinate visualizations are perceived, and compared the results to the optimal visual scan path required to complete the tasks. The results indicate that even first-time users quickly learn how to use parallel coordinate visualizations, pay attention to the correct task-specific areas in the visualization, and become rapidly proficient with it.


title: Attentive Explanations: Justifying Decisions and Pointing to the Evidence

text: Deep models are the defacto standard in visual decision problems due to their impressive performance on a wide array of visual tasks. However, they are frequently seen as opaque and are unable to explain their decisions. In contrast, humans can justify their decisions with natural language and point to the evidence in the visual world which supports their decisions. We propose a method which incorporates a novel explanation attention mechanism; our model is trained using textual rationals, and infers latent attention to visually ground explanations. We collect two novel datasets in domains where it is interesting and challenging to explain decisions. First, we extend the visual question answering task to not only provide an answer but also visual and natural language explanations for the answer. Second, we focus on explaining human activities in a contemporary activity recognition dataset. We extensively evaluate our model, both on the justification and pointing tasks, by comparing it to prior models and ablations using both automatic and human evaluations.


title: A chaincode based scheme for fingerprint feature extraction

text: A feature extraction method using the chaincode representation of fingerprint ridge contours is presented for use by Automatic Fingerprint Identification Systems. The representation allows efficient image quality enhancement and detection of fine minutiae feature points. For image enhancement a given fingerprint image is first binarized after a quick averaging to generate its chaincode representation. The direction field is estimated from a set of selected chaincodes. The original gray scale image is then enhanced using connected component analysis and a dynamic filtering scheme that takes advantage of the knowledge gained from the estimated direction flow of the contours. For feature extraction, the enhanced fingerprint image is carefully binarized using a local-global binarization algorithm for generating the chaincode representation. The minutiae are generated using a sophisticated ridge contour following procedure. Visual inspection of the experiment images shows that the method is very effective.


title: Model-Based Stabilisation of Deep Reinforcement Learning

text: Though successful in high-dimensional domains, deep reinforcement learning exhibits high sample complexity and suffers from stability issues as reported by researchers and practitioners in the field. These problems hinder the application of such algorithms in real-world and safety-critical scenarios. In this paper, we take steps towards stable and efficient reinforcement learning by following a model-based approach that is known to reduce agent-environment interactions. Namely, our method augments deep Q-networks (DQNs) with model predictions for transitions, rewards, and termination flags. Having the model at hand, we then conduct a rigorous theoretical study of our algorithm and show, for the first time, convergence to a stationary point. En route, we provide a counterexample showing that ’vanilla’ DQNs can diverge confirming practitioners’ and researchers’ experiences. Our proof is novel in its own right and can be extended to other forms of deep reinforcement learning. In particular, we believe exploiting the relation between reinforcement (with deep function approximators) and online learning can serve as a recipe for future proofs in the domain. Finally, we validate our theoretical results in 20 games from the Atari benchmark. Our results show that following the proposed model-based learning approach not only ensures convergence but leads to a reduction in sample complexity and superior performance. Introduction Model-free deep reinforcement learning methods have recently demonstrated impressive performance on a range of complex learning tasks (Hessel et al. 2018; Lillicrap et al. 2017; Jaderberg et al. 2017). Deep Q-Networks (DQNs) (Mnih et al. 2015), in particular, stand out as a versatile and effective tool for a wide range of applications. DQNs offer an end-to-end solution for many reinforcement learning problems by generalizing tabular Q-learning to highdimensional input spaces. Unfortunately, DQNs still suffer from a number of important drawbacks that hinder wide-spread adoption. There is a general lack of theoretical understanding to guide the use of deep Q-learning algorithms. Combining non-linear function approximation with off-policy reinforcement learning is known to lead to possible divergence issues, even in very simple tasks (Boyan and Preliminary work. Moore 1995). Despite improvements introduced by deep Qnetworks (Mnih et al. 2015), Q-learning approaches based on deep neural networks still exhibit occasional instability during the learning process, see for example (van Hasselt, Guez, and Silver 2016). In this paper, we aim to address the instability of current deep RL approaches by augmenting model-free value learning with a predictive model. We propose a hybrid model and value learning objective, which stabilises the learning process and reduces sample complexity when compared to model-free algorithms. We attribute this reduction to the richer feedback signal provided by our method compared to standard deep Q-networks. In particular, our model provides feedback at every time step based on the current prediction error, which in turn eliminates one source of sample inefficiency caused by sparse rewards. These conclusions are also in accordance with previous research conducted on linear function approximation in reinforcement learning (Parr et al. 2008; Sutton et al. 2012; Song et al. 2016). While linear function approximation results provide a motivation for model-based stabilisation, such theories fail to generalise to deep non-linear architectures (Song et al. 2016). To close this gap in the literature and demonstrate stability of our method, we prove convergence in the general deep RL setting with deep value function approximators. Theoretically analysing deep RL algorithms, however, is challenging due to nonlinear functional dependencies and objective non-convexities prohibiting convergence to globally optimal policies. As such, the best one would hope for is a stationary point (e.g., a first-order one) that guarantees vanishing gradients. Even understanding gradient behavior for deep networks in RL can be difficult due to explorationbased policies and replay memory considerations. To alleviate some of these problems, we map deep reinforcement learning to a mathematical framework explicitly designed for understanding the exploration/exploitation trade-off. Namely, we formalise the problem as an online learning game the agent plays against an adversary (i.e., the environment). Such a link allows us to study a more general problem combining notions of regret, reinforcement learning, and optimisation. Given such a link, we prove that for any > 0, regret vanishes as O ( T 1−2 lnT ) with T being the total number of rounds. This, in turn, guarantees convergence to a stationary point. ar X iv :1 80 9. 01 90 6v 1 [ cs .L G ] 6 S ep 2 01 8 Guided by the above theoretical results, we validate that our algorithm leads to faster learning by conducting experiments on 20 Atari games. Due to the high computational and budgeting demands, we chose a benchmark that is closest to our setting. Concretely, we picked DQNs as our template algorithm to improve on both theoretically and empirically. It is worth noting that extending our theoretical results to more complex RL algorithms is an orthogonal future direction. Background: Reinforcement Learning We consider a discrete time, infinite horizon, discounted Markov Decision Process (MDP) 〈 S,A, P , P , γ 〉 . Here S denotes the state space, A the set of possible actions, P (R) : S × A × R → [0, 1] is the reward function, P (S) : S × A × S → [0, 1] is the state transition function, and γ ∈ (0, 1) is a discount factor. An agent, being in state St ∈ S at time step t, executes an action at ∈ A sampled from its policy π(at|St), a conditional probability distribution where π : S × A → [0, 1]. The agent’s action elicits from the environment a reward signal rt ∈ R indicating instantaneous reward, a terminal flag ft ∈ {0, 1} indicating a terminal event that restarts the environment, and a transition to a successor state St+1 ∈ S. We assume that the sets S, A, and R are discrete. The reward rt is sampled from the conditional probability distribution P (rt|St,at). Similarly, ft ∼ P (F (ft|St,at) with P (F ) : S × A × {0, 1} → [0, 1], where a terminal event (ft = 1) restarts the environment according to some initial state distribution S0 ∼ P (S) 0 (S0). The state transition to a successor state is determined by a stochastic state transition function according to St+1 ∼ P (St+1|St,at). The agent’s goal is to maximise future cumulative reward E P (S) 0 ,π,P (R),P (F ),P (S) [ ∞ ∑ t=0 ( t ∏ t′=0 (1− ft′−1) ) γrt ] with respect to the policy π. An important quantity in RL are Q-values Q(St,at), which are defined as the expected future cumulative reward value when executing action at in state St and subsequently following policy π. Qvalues enable us to conveniently phrase the RL problem as maxπ ∑ S p (S) ∑ a π(a|S)Q(S,a), where p(S) is the stationary state distribution obtained when executing π in the environment (starting from S0 ∼ P (S) 0 (S0)). Deep Q-Networks: Value-based reinforcement learning approaches identify optimal Q-values directly using parametric function approximators Qθ(S,a), where θ ∈ Θ represents the parameters (Watkins 1989; Busoniu et al. 2010). Optimal Q-value estimates then correspond to an optimal policy π(a|S) = δa,argmaxa′Qθ(S,a′). Deep Q-networks (Mnih et al. 2015) learn a deep neural network based Qvalue approximation by performing stochastic gradient descent on the following training objective: L(θ) = ES,a,r,f,S′ [( r + (1− f)γmax a′ Qθ−(S ′,a′) −Qθ(S,a) )2] . (1) The expectation ranges over transitions S,a, r, f,S′ sampled from an experience replay memory (S′ denotes the state at the next time step). Use of this replay memory, together with the use of a separate target network Qθ− (with different parameters θ− ∈ Θ) for calculating the bootstrap values maxa′ Qθ−(S ′,a′), helps stabilise the learning process. Background: Online Learning In this paper, we employ a special form of regret minimisation games that we briefly review here. A regret minimisation game is a triple 〈Θ,F , T 〉, where Θ is a non-empty decision set, F is the set of moves of the adversary which contains bounded convex functions from R to R and T is the total number of rounds. The game commences in rounds, where at round j = 1, . . . , T , the agent chooses a prediction θj ∈ Θ and the adversary a loss function Lj ∈ F . At the end of the round, the adversary reveals its choice and the agent suffers a loss Lj(θj). In this paper, we are concerned with the full-information case where the agent can observe the complete loss function Lj at the end of each round. The goal of the game is for the agent to make successive predictions to minimise cumulative regret defined as:


title: Membrane Bioreactor (MBR) Technology for Wastewater Treatment and Reclamation: Membrane Fouling

text: The membrane bioreactor (MBR) has emerged as an efficient compact technology for municipal and industrial wastewater treatment. The major drawback impeding wider application of MBRs is membrane fouling, which significantly reduces membrane performance and lifespan, resulting in a significant increase in maintenance and operating costs. Finding sustainable membrane fouling mitigation strategies in MBRs has been one of the main concerns over the last two decades. This paper provides an overview of membrane fouling and studies conducted to identify mitigating strategies for fouling in MBRs. Classes of foulants, including biofoulants, organic foulants and inorganic foulants, as well as factors influencing membrane fouling are outlined. Recent research attempts on fouling control, including addition of coagulants and adsorbents, combination of aerobic granulation with MBRs, introduction of granular materials with air scouring in the MBR tank, and quorum quenching are presented. The addition of coagulants and adsorbents shows a significant membrane fouling reduction, but further research is needed to establish optimum dosages of the various coagulants/adsorbents. Similarly, the integration of aerobic granulation with MBRs, which targets biofoulants and organic foulants, shows outstanding filtration performance and a significant reduction in fouling rate, as well as excellent nutrients removal. However, further research is needed on the enhancement of long-term granule integrity. Quorum quenching also offers a strong potential for fouling control, but pilot-scale testing is required to explore the feasibility of full-scale application.


title: BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

text: We introduce BinaryNet, a method which trains DNNs with binary weights and activations when computing parameters’ gradient. We show that it is possible to train a Multi Layer Perceptron (MLP) on MNIST and ConvNets on CIFAR-10 and SVHN with BinaryNet and achieve nearly state-of-the-art results. At run-time, BinaryNet drastically reduces memory usage and replaces most multiplications by 1-bit exclusive-not-or (XNOR) operations, which might have a big impact on both general-purpose and dedicated Deep Learning hardware. We wrote a binary matrix multiplication GPU kernel with which it is possible to run our MNIST MLP 7 times faster than with an unoptimized GPU kernel, without suffering any loss in classification accuracy. The code for BinaryNet is available.


title: Lagrangian Texture Advection: Preserving both Spectrum and Velocity Field

text: Texturing an animated fluid is a useful way to augment the visual complexity of pictures without increasing the simulation time. But texturing flowing fluids is a complex issue, as it creates conflicting requirements: we want to keep the key texture properties (features, spectrum) while advecting the texture with the underlying flow-which distorts it. In this paper, we present a new, Lagrangian, method for advecting textures: the advected texture is computed only locally and follows the velocity field at each pixel. The texture retains its local properties, including its Fourier spectrum, even though it is accurately advected. Due to its Lagrangian nature, our algorithm can perform on very large, potentially infinite scenes in real time. Our experiments show that it is well suited for a wide range of input textures, including, but not limited to, noise textures.


title: Producing radiologist-quality reports for interpretable artificial intelligence

text: Current approaches to explaining the decisions of deep learning systems for medical tasks have focused on visualising the elements that have contributed to each decision. We argue that such approaches are not enough to “open the black box” of medical decision making systems because they are missing a key component that has been used as a standard communication tool between doctors for centuries: language. We propose a model-agnostic interpretability method that involves training a simple recurrent neural network model to produce descriptive sentences to clarify the decision of deep learning classifiers. We test our method on the task of detecting hip fractures from frontal pelvic x-rays. This process requires minimal additional labelling despite producing text containing elements that the original deep learning classification model was not specifically trained to detect. The experimental results show that: 1) the sentences produced by our method consistently contain the desired information, 2) the generated sentences are preferred by doctors compared to current tools that create saliency maps, and 3) the combination of visualisations and generated text is better than either alone.


title: On Divergences and Informations in Statistics and Information Theory

text: The paper deals with the f-divergences of Csiszar generalizing the discrimination information of Kullback, the total variation distance, the Hellinger divergence, and the Pearson divergence. All basic properties of f-divergences including relations to the decision errors are proved in a new manner replacing the classical Jensen inequality by a new generalized Taylor expansion of convex functions. Some new properties are proved too, e.g., relations to the statistical sufficiency and deficiency. The generalized Taylor expansion also shows very easily that all f-divergences are average statistical informations (differences between prior and posterior Bayes errors) mutually differing only in the weights imposed on various prior distributions. The statistical information introduced by De Groot and the classical information of Shannon are shown to be extremal cases corresponding to alpha=0 and alpha=1 in the class of the so-called Arimoto alpha-informations introduced in this paper for 0<alpha<1 by means of the Arimoto alpha-entropies. Some new examples of f-divergences are introduced as well, namely, the Shannon divergences and the Arimoto alpha-divergences leading for alphauarr1 to the Shannon divergences. Square roots of all these divergences are shown to be metrics satisfying the triangle inequality. The last section introduces statistical tests and estimators based on the minimal f-divergence with the empirical distribution achieved in the families of hypothetic distributions. For the Kullback divergence this leads to the classical likelihood ratio test and estimator


title: A column store engine for real-time streaming analytics

text: This paper describes novel aspects of the column store implemented in the MemSQL database engine and describes the design choices made to support real-time streaming workloads. Column stores have traditionally been restricted to data warehouse scenarios where low latency queries are a secondary goal, and where restricting data ingestion to be offline, batched, append-only, or some combination thereof is acceptable. In contrast, the MemSQL column store implementation treats low latency queries and ongoing writes as first class citizens, with a focus on avoiding interference between read, ingest, update, and storage optimization workloads through the use of fragmented snapshot transactions and optimistic storage reordering. This implementation broadens the range of serviceable column store workloads to include those with more stringent demands on query and data latency, such as those backing operational systems used by adtech, financial services, fraud detection and other real-time or data streaming applications.


title: An Improved Approach for Text-Independent Speaker Recognition

text: This paper presents new Speaker Identification and Speaker Verification systems based on the use of new feature vectors extracted from the speech signal. The proposed structure combine between the most successful Mel Frequency Cepstral Coefficients and new features which are the Short Time Zero Crossing Rate of the signal. A comparison between speaker recognition systems based on Gaussian mixture models using the well known Mel Frequency Cepstral Coefficients and the novel systems based on the use of a combination between both reduced Mel Frequency Cepstral Coefficients features vectors and Short Time Zero Crossing Rate features is given. This comparison proves that the use of the new reduced feature vectors help to improve the system’s performance and also help to reduce the time and memory complexity of the system which is required for realistic applications that suffer from computational resource limitation. The experiments were performed on speakers from TIMIT database for different training durations. The suggested systems performances are evaluated against the baseline systems. The increase of the proposed systems performances are well observed for identification experiments and the decrease of Equal Error Rates are also remarkable for verification experiments. Experimental results demonstrate the effectiveness of the new approach which avoids the use of more complex algorithms or the combination of different approaches requiring lengthy calculation. Keywords—GMM; speaker verification; speaker recognition; speaker identification


title: Simple topologies of PWM AC-AC converters

text: This letter proposes a new family of simple topologies of PWM AC-AC converters with minimal switches. With extension from the basic DC-DC converters, a series of AC-AC converters such as buck, boost, buck-boost, Cuk, and isolated converters are obtained. By PWM duty ratio control, they become a "solid-state transformer" with a continuously variable turns ratio. All the proposed AC-AC converters in this paper employ only two switches. Compared to the existing circuits that use six switches or more, they can reduce cost and improve reliability. The operating principle and control method of the proposed topologies are presented. Analysis and simulation results are given using the Cuk AC-AC converter as an example. The analysis can be easily extended to other converters of the proposed family.


title: The MOS 36-Item Short-Form Health Survey (SF-36): II. Psychometric and clinical tests of validity in measuring physical and mental health constructs.

text: Cross-sectional data from the Medical Outcomes Study (MOS) were analyzed to test the validity of the MOS 36-Item Short-Form Health Survey (SF-36) scales as measures of physical and mental health constructs. Results from traditional psychometric and clinical tests of validity were compared. Principal components analysis was used to test for hypothesized physical and mental health dimensions. For purposes of clinical tests of validity, clinical criteria defined mutually exclusive adult patient groups differing in severity of medical and psychiatric conditions. Scales shown in the components analysis to primarily measure physical health (physical functioning and role limitations-physical) best distinguished groups differing in severity of chronic medical condition and had the most pure physical health interpretation. Scales shown to primarily measure mental health (mental health and role limitations-emotional) best distinguished groups differing in the presence and severity of psychiatric disorders and had the most pure mental health interpretation. The social functioning, vitality, and general health perceptions scales measured both physical and mental health components and, thus, had the most complex interpretation. These results are useful in establishing guidelines for the interpretation of each scale and in documenting the size of differences between clinical groups that should be considered very large.


title: Arrangements for Information Technology Governance: A Theory of Multiple Contingencies

text: Business world today identifies a need of IT use. It is clear that IT is transforming the business. Since IT offers both opportunities and threats, effective IT governance is required to deliver IT benefits. Enterprises have to understand this issue deeply in wider business perspective in order to successfully implement IT. Effective IT governance can be achieved through enterprise’s ability in identifying, analyzing and dealing with all related factors influencing IT governance arrangements. According to theory of multiple contingencies, an enterprise is subject to impact of some contingency forces by strengthening, weakening, or overriding their mutual effects on the IT governance mode. This paper is a critique about Sambamurthy and Zmud’s research to examine IT governance mode based on this theory in relation to effective IT governance and management. They believe that this approach can be used to explain many different IT governance mode phenomena.


title: Examining the effects of mass media campaign exposure and interpersonal discussions on youth's drug use: the mediating role of visiting pro-drug websites.

text: To extend past research on interpersonal communication and campaign effects, we hypothesized that anti-drug mass media campaign message exposure indirectly affects visiting anti- and pro-drug websites through targeted parent-child and friend-to-friend communication against drugs, as well as through having drug-related discussions during organized group activities. Second, we posited that engaging in anti-drug interpersonal communication indirectly affects adolescents' drug use through two intervening variables: visiting anti-drug websites and visiting pro-drug websites. Using self-reported longitudinal data from 2,749 youth, we found that as youth reported higher levels of anti-drug mass media campaign message exposure, they were more likely to talk to friends about the bad consequences of drugs, how to avoid drugs, and anti-drug ads. In turn, however, they were more likely to visit pro-drug websites, and subsequently, to smoke cigarettes.


title: Experimental results for artificial intelligence-based self-organized 5G networks

text: Until now, mobile networks are still managed in a manual and semi-automatic manner, which are costly and time-consuming. For the forthcoming Fifth Generation (5G) system, its large-scale, heterogeneous, software-defined and virtualized infrastructure simply become unmanageable if no innovative managing paradigm is applied. Recently, artificial intelligence is proposed to be applied in the 5G system to realize intelligent management and highly self-organized networks. In this paper, proof-of-concept experiments, including the setup of an intelligent 5G testbed, its closed-loop control and enabling algorithms, are presented. The experimental results reveal that applying artificial intelligence to wireless network management is both feasible and effective.


title: Programming algorithms for multilevel phase-change memory

text: Phase-change memory (PCM) has emerged as one among the most promising technologies for next-generation nonvolatile solid-state memory. Multilevel storage, namely storage of non-binary information in a memory cell, is a key factor for reducing the total cost-per-bit and thus increasing the competitiveness of PCM technology in the nonvolatile memory market. In this paper, we present a family of advanced programming schemes for multilevel storage in PCM. The proposed schemes are based on iterative write-and-verify algorithms that exploit the unique programming characteristics of PCM in order to achieve significant improvements in resistance-level packing density, robustness to cell variability, programming latency, energy-per-bit and cell storage capacity. Experimental results from PCM test-arrays are presented to validate the proposed programming schemes. In addition, the reliability issues of multilevel PCM in terms of resistance drift and read noise are discussed.


title: Analysis of Human Electrocardiogram for Biometric Recognition

text: Security concerns increase as the technology for falsification advances. There are strong evidences that a difficult to falsify biometric trait, the human heartbeat, can be used for identity recognition. Existing solutions for biometric recognition from electrocardiogram (ECG) signals are based on temporal and amplitude distances between detected fiducial points. Such methods rely heavily on the accuracy of fiducial detection, which is still an open problem due to the difficulty in exact localization of wave boundaries. This paper presents a systematic analysis for human identification from ECG data. A fiducial-detection-based framework that incorporates analytic and appearance attributes is first introduced. The appearance-based approach needs detection of one fiducial point only. Further, to completely relax the detection of fiducial points, a new approach based on autocorrelation (AC) in conjunction with discrete cosine transform (DCT) is proposed. Experimentation demonstrates that the AC/DCT method produces comparable recognition accuracy with the fiducial-detection-based approach.


title: Within-class covariance normalization for SVM-based speaker recognition

text: This paper extends the within-class covariance normalizat ion (WCCN) technique described in [1, 2] for training generaliz ed linear kernels. We describe a practical procedure for apply ing WCCN to an SVM-based speaker recognition system where the input feature vectors reside in a high-dimensional space. O ur approach involves using principal component analysis (PCA) t o split the original feature space into two subspaces: a low-dimens ional “PCA space” and a high-dimensional “PCA-complement space. ” After performing WCCN in the PCA space, we concatenate the resulting feature vectors with a weighted version of their P CAcomplements. When applied to a state-of-the-art MLLR-SVM speaker recognition system, this approach achieves improv ements of up to 22% in EER and 28% in minimum decision cost function (DCF) over our previous baseline. We also achieve substanti al improvements over an MLLR-SVM system that performs WCCN in the PCA space but discards the PCA-complement.


title: ECG Biometric Recognition: A Comparative Analysis

text: The electrocardiogram (ECG) is an emerging biometric modality that has seen about 13 years of development in peer-reviewed literature, and as such deserves a systematic review and discussion of the associated methods and findings. In this paper, we review most of the techniques that have been applied to the use of the electrocardiogram for biometric recognition. In particular, we categorize the methodologies based on the features and the classification schemes. Finally, a comparative analysis of the authentication performance of a few of the ECG biometric systems is presented, using our inhouse database. The comparative study includes the cases where training and testing data come from the same and different sessions (days). The authentication results show that most of the algorithms that have been proposed for ECG-based biometrics perform well when the training and testing data come from the same session. However, when training and testing data come from different sessions, a performance degradation occurs. Multiple training sessions were incorporated to diminish the loss in performance. That notwithstanding, only a few of the proposed ECG recognition algorithms appear to be able to support performance improvement due to multiple training sessions. Only three of these algorithms produced equal error rates (EERs) in the single digits, including an EER of 5.5% using a method proposed by us.


title: ECG to identify individuals

text: The electrocardiogram (ECG also called EKG) trace expresses cardiac features that are unique to an individual. The ECG processing followed a logical series of experiments with quantifiable metrics. Data filters were designed based upon the observed noise sources. Fiducial points were identified on the filtered data and extracted digitally for each heartbeat. From the fiducial points, stable features were computed that characterize the uniqueness of an individual. The tests show that the extracted features are independent of sensor location, invariant to the individual’s state of anxiety, and unique to an individual. 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.


title: ECG analysis: a new approach in human identification

text: In this paper, a new approach in human identification is investigated. For this purpose, a standard 12-lead electrocardiogram (ECG) recorded during rest is used. Selected features extracted from the ECG are used to identify a person in a predetermined group. Multivariate analysis is used for the identification task. Experiments show that it is possible to identify a person by features extracted from one lead only. Hence, only three electrodes have to be attached on the person to be identified. This makes the method applicable without too much effort.


title: Development of an Airborne Ground Penetrating Radar System: Antenna Design, Laboratory Experiment, and Numerical Simulation

text: A stepped-frequency continuous wave (SFCW) airborne ground penetrating radar (GPR) system was built using a vector network analyzer (VNA), an optical-to-electric ( \mmb O/\mmb E) converter, an electric-to-optical ( \mmb E/\mmb O) converter, and the resistively loaded planer dipole antennas. The resistively loaded antennas are based on the Altshuler discrete resistive loading approach. The distance from the loading point to the feeding point and the resistance has been optimized; in addition, a balun at the feeding point was introduced to convert the unbalanced signal from the coax to balanced signal. Test results show that the manufactured antennas have a wide bandwidth frequency range from 55 to 275 MHz. A laboratory experiment using the developed system was conducted in a building with high ceilings. The results registered all basic features including the ground, sub-ground surface, and the lamp hanging from the ceiling. Numerical modeling by finite-difference time-domain (FDTD) method using two-dimensional (2-D) transverse electric (TE) verifies the presence of these registered features in the measured airborne data. A bedrock outcrop in ice sheet model is reconstructed based on a piece of real airborne GPR data, the modeling profile results show excellent consistent with the observed data. The capability of airborne GPR detection of subsurface features is clearly validated.


title: perspectives on Bitcoin and second-generation cryptocurrencies

text: Bitcoin has emerged as the most successful cryptographic currency in history. Within two years of its quiet launch in 2009, Bitcoin grew to comprise billions of dollars of economic value, even while the body of published research and security analysis justifying the system’s design was negligible. In the ensuing years, a growing literature has identified hidden-butimportant properties of the system, discovered attacks, proposed promising alternatives, and singled out difficult future challenges. This interest has been complemented by a large and vibrant community of open-source developers who steward the system, while proposing and deploying numerous modifications and extensions. We provide the first systematic exposition of the second generation of cryptocurrencies, including Bitcoin and the many alternatives that have been implemented as alternate protocols or “altcoins.” Drawing from a scattered body of knowledge, we put forward three key components of Bitcoin’s design that can be decoupled, enabling a more insightful analysis of Bitcoin’s properties and its proposed modifications and extensions. We contextualize the literature into five central properties capturing blockchain stability. We map the design space for numerous proposed modification, providing comparative analyses for alternative consensus mechanisms, currency allocation mechanisms, computational puzzles, and key management tools. We focus on anonymity issues in Bitcoin and provide an evaluation framework for analyzing a variety of proposals for enhancing unlinkability. Finally we provide new insights on a what we term disintermediation protocols, which absolve the need for trusted intermediaries in an interesting set of applications. We identify three general disintermediation strategies and provide a detailed comparative cost analysis. I. WHY BITCOIN IS WORTHY OF RESEARCH Consider two opposing viewpoints on Bitcoin in straw-man form. The first is that “Bitcoin works in practice, but not in theory.” At times devoted members of the Bitcoin community espouse this philosophy and criticize the security research community for failing to discover Bitcoin, not immediately recognizing its novelty, and still today dismissing its importance due to a lack of rigorous theoretical foundation. A second viewpoint is that Bitcoin hopelessly relies on an unknown combination of socio-economic factors for its current stability which are intractable to model with sufficient precision, failing to yield a convincing argument for the system’s soundness. Given these difficulties, experienced security researchers may avoid Bitcoin as a topic of study, considering it prudent security engineering to only design systems with precise threat models that admit formal security proofs. We strongly dismiss both of these simplistic approaches and show where each viewpoint fails, forwarding new insights based on multiple examples of existing knowledge. To the first, we contend that while Bitcoin has worked surprisingly well in practice so far, there is an important role for research to play in identifying precisely why this has been possible, moving beyond a blind acceptance of the informal arguments presented with the system’s initial proposal. Furthermore, it is crucial to understand whether Bitcoin will still “work in practice” as practices change. We expect external political and economic factors to evolve, and the system must change if and when transaction volume scales, and the nature of the monetary rewards for Bitcoin miners will change over time as part of the system design. It is not enough to argue that Bitcoin has worked from 2009–2014 and will therefore continue likewise. We do not yet have sufficient understanding to conclude with confidence that Bitcoin will continue to work well in practice, and that is a crucial research challenge that requires insight from computer science theory. To the second viewpoint, we contend that Bitcoin is filling an important niche by providing a virtual currency system without any trusted parties and without pre-assumed identities among the participants. Within these constraints, the general problem of consensus in a distributed system is impossible [5], [81] without further assumptions like Bitcoin’s premise that rational (greedy) behavior can be modeled and incentives can be aligned to ensure secure operation of the consensus algorithm. Yet these constraints matter in practice, both philosophically and technically, and Bitcoin’s approach to consensus within this model is deeply surprising and a fundamental contribution. Bitcoin’s core consensus protocol also has profound implications for many other computer security problems beyond currency1 such as distributed naming, secure timestamping and commitment, generation of public randomness, as well as many financial problems such as self-enforcing (“smart”) contracts, decentralized markets and order books, and distributed autonomous agents. In short, even though Bitcoin is not easy to model, it is worthy of considerable research attention as it may form the basis for practical solutions to exceedingly difficult and important problems. II. OVERVIEW OF BITCOIN A. A Contextualized History We refer the interested reader to existing surveys on the “first wave” of cryptocurrency research [13], [82]. In short, cryptographic currencies date back to Chaum’s proposal for 1As we shall see, it may not be possible to remove the currency functionality and still have a working consensus system. “untraceable payments” in 1983 [22], a system involving bankissued cash in the form of blindly signed coins. Unblinded coins are transferred between users and merchants, and redeemable after the bank verifies they have not been previously redeemed. Blind signatures prevent the bank from linking users to coins, providing unlinkability akin to cash. Throughout the 1990s, many variations and extensions of this scheme were proposed. Significant contributions include: removing the need for the bank to be online at purchase time [23], allowing coins to be divided into smaller units [80] and improving efficiency [21]. Several startup companies including DigiCash [95] and Peppercoin [87] attempted to bring electronic cash protocols into practice but ultimately failed in the market. In fact, no schemes from this “first wave” of cryptocurrency research achieved significant deployment. Moderately hard “proof-of-work” puzzles were proposed in the early 1990s for combatting email spam [34] (although it was never widely deployed for this purpose [59]). Many other applications followed, including proposals for a fair lottery [41], minting coins for micropayments [88], and preventing various forms of denial-of-service and abuse in anonymous networks [8]. The latter, Hashcash, was an alternative to using digital micropayments (e.g., NetBill [98] and Karma [107]). Proof of work was also used to detect sybil nodes in distributed peer-to-peer consensus protocols [5], and is used in Bitcoin consensus for a similar reason. Another essential element of Bitcoin is the public ledger, which makes double-spending detectable. In auditable ecash [93], [94], proposed in the late 1990s, the bank maintains a public database to detect double-spending and ensure the validity of coins, however the notion of publishing the entire set of valid coins was dismissed as impractical (only a Merkle root was published instead). B-money [29], proposed in 1998, appears to be the first system where all transactions are publicly (anonymously) broadcast and stored. Proposed on the Cypherpunks mailing list, b-money received minimal attention from the academic research community. Smart contracts [102], proposed in the early 1990s, enable parties to formally specify an enforceable agreement using cryptography and scripts. This idea portends Bitcoin’s scripting capabilities. In 2008, Bitcoin was announced and a white paper penned under the pseudonym Satoshi Nakamoto was posted to the Cypherpunks mailing list [77], followed quickly the source code of the original reference client. Bitcoin’s genesis block was mined on or around January 3, 2009.2 The first use of Bitcoin as a currency is thought to be a transaction in May 2010, where one user ordered pizza delivery for another in exchange for 10 000 bitcoins. Since then, increasing number of merchants and services have incorporated Bitcoin in some way, and the price has generally risen, reaching a peak of approximately $1400USD per coin in July 2013. Bitcoin’s history has also been colored by association with 2Famously, the first block contains the string “The Times 03/Jan/2009 Chancellor on brink of second bailout for banks.” crime. Bitcoin was famously used in a black market website, Silk Road [24], which operated from Feb. 2011 until Oct. 2013 when it was seized and shut down by the FBI. Botnets have found Bitcoin mining to be a supplemental source of income [46]. A current US federal court case involves a large Bitcoin-based Ponzi scheme [97]. In 2014, a computer virus called CryptoLocker extorted millions of dollars from victims by encrypting their files and demanding a Bitcoin ransom to release the decryption key [38]. Many users’ Bitcoins have been lost due to theft [33] and collapsed exchanges [75]. B. A Technical Overview We present Bitcoin’s current operation through its three main technical components: transactions (including scripts), the consensus protocol, and the communication network. Bitcoin is exceedingly complex—our goal is to present the system with sufficient technical depth, so the extant literature on Bitcoin, reviewed and evaluated in later sections of this paper, becomes understandable. In particular, a key benefit of our three-component breakdown is that it makes evaluating and systematizing proposed changes (Sections V & VIII) insightful by “decoupling” concepts that may be changed independently. Sources of information on Bitcoin. Bitcoin can be difficult to define as there is no formal


title: Intrusion Detection in the RPL-connected 6LoWPAN Networks

text: The interconnectivity of 6LoWPAN networks with the Internet raises serious security concerns, as constrained 6LoWPAN devices are accessible anywhere from the untrusted global Internet. Also, 6LoWPAN devices are mostly deployed in unattended environments, hence easy to capture and clone. Despite that state of the art crypto solutions provide information security, IPv6 enabled smart objects are vulnerable to attacks from outside and inside 6LoWPAN networks that are aimed to disrupt networks. This paper attempts to identify intrusions aimed to disrupt the Routing Protocol for Low-Power and Lossy Networks (RPL).In order to improve the security within 6LoWPAN networks, we extend SVELTE, an intrusion detection system for the Internet of Things, with an intrusion detection module that uses the ETX (Expected Transmissions) metric. In RPL, ETX is a link reliability metric and monitoring the ETX value can prevent an intruder from actively engaging 6LoWPAN nodes in malicious activities. We also propose geographic hints to identify malicious nodes that conduct attacks against ETX-based networks. We implement these extensions in the Contiki OS and evaluate them using the Cooja simulator.


title: Shrinkable, stiffness-controllable soft manipulator based on a bio-inspired antagonistic actuation principle

text: This paper explores a new hybrid actuation principle combining pneumatic and tendon-driven actuators for a soft robotic manipulator. The fusion of these two actuation principles leads to an overall antagonistic actuation mechanism whereby pneumatic actuation opposes tendon actuation - a mechanism commonly found in animals where muscles can oppose each other to vary joint stiffness. We are taking especially inspiration from the octopus who belongs to the class of Cephalopoda; the octopus uses its longitudinal and transversal muscles in its arms to achieve varied motion patterns; activating both sets of muscles, the octopus can control the arm stiffness over a wide range. Our approach mimics this behavior and achieves comparable motion patterns, including bending, elongation and stiffening. The proposed method combines the advantages of tendon-driven and pneumatic actuated systems and goes beyond what current soft, flexible robots can achieve: because the new robot structure is effectively an inflatable, sleeve, it can be pumped up to its fully inflated volume and, also, completely deflated and shrunk. Since, in the deflated state, it comprises just its outer “skin” and tendons, the robot can be compressed to a very small size, many times smaller when compared to its fully-inflated state. In this paper, we describe the mechanical structure of the soft manipulator. Proof-of-concept experiments focus on the robot's ability to bend, to morph from completely shrunk to entirely inflated as well as to vary its stiffness.


title: Fast and Accurate Crop and Weed Identification with Summarized Train Sets for Precision Agriculture

text: In this paper we present a perception system for agriculture robotics that enables an unmanned ground vehicle (UGV) equipped with a multi spectral camera to automatically perform the crop/weed detection and classification tasks in real-time. Our approach exploits a pipeline that includes two different convolutional neural networks (CNNs) applied to the input RGB+near infra-red (NIR) images. A lightweight CNN is used to perform a fast and robust, pixelwise, binary image segmentation, in order to extract the pixels that represent projections of 3D points that belong to green vegetation. A deeper CNN is then used to classify the extracted pixels between the crop and weed classes. A further important contribution of this work is a novel unsupervised dataset summarization algorithm that automatically selects from a large dataset the most informative subsets that better describe the original one. This enables to streamline and speed-up the manual dataset labeling process, otherwise extremely time consuming, while preserving good classification performances. Experiments performed on different datasets taken from a real farm robot confirm the effectiveness of our approach.


title: Osmotic Heat Engine Using Thermally Responsive Ionic Liquids.

text: The osmotic heat engine (OHE) is a promising technology for converting low grade heat to electricity. Most of the existing studies have focused on thermolytic salt systems. Herein, for the first time, we proposed to use thermally responsive ionic liquids (TRIL) that have either an upper critical solution temperature (UCST) or lower critical solution temperature (LCST) type of phase behavior as novel thermolytic osmotic agents. Closed-loop TRIL-OHEs were designed based on these unique phase behaviors to convert low grade heat to work or electricity. Experimental studies using two UCST-type TRILs, protonated betaine bis(trifluoromethyl sulfonyl)imide ([Hbet][Tf2N]) and choline bis(trifluoromethylsulfonyl)imide ([choline][Tf2N]) showed that (1) the specific energy of the TRIL-OHE system could reach as high as 4.0 times that of the seawater and river water system, (2) the power density measured from a commercial FO membrane reached up to 2.3 W/m2, and (3) the overall energy efficiency reached up to 2.6% or 18% of the Carnot efficiency at no heat recovery and up to 10.5% or 71% of the Carnet efficiency at 70% heat recovery. All of these results clearly demonstrated the great potential of using TRILs as novel osmotic agents to design high efficient OHEs for recovery of low grade thermal energy to work or electricity.


title: Study of Viola-Jones Real Time Face Detector

text: Face detection has been one of the most studied topics in computer vision literature. Given an arbitrary image, the goal of face detection is to determine whether or not there are any faces in the image and, if present, return the image location and extent of each face. While this appears to be a trivial task for human beings, it is a very challenging task for computers. The difficulty associated with face detection can be attributed to many variations in scale, location, view point, illumination, occlusions, etc. Although there have been hundreds of reported approaches to face detection, if one were asked to name a single face detection algorithm that has the most impact in recent decades, it will most likely be the Viola and Jones face detection, which is capable of processing images extremely rapidly and achieve high detection rates. This project is going to study and understand the Viola-Jones algorithm by implementing the whole detection framework and based on the implementation, conduct experiment to hopefully further improve the performance.


title: Parameterized Models for Facial Animation

text: Parameterized models can produce realistic, manipulable images of human faces— with a surprisingly small number of parameters.


title: Augmented Reality through Wearable Computing

text: Wearable computing moves computation from the desktop to the user. We are forming a community of networked, wearable-computer users to explore, over a long period, the augmented realities that these systems can provide. By adapting its behavior to the user's changing environment, a body-worn computer can assist the user more intelligently, consistently, and continuously than a desktop system. A text-based augmented reality, the Remembrance Agent, is presented to illustrate this approach. Video cameras are used both to warp the visual input (mediated reality) and to sense the user's world for graphical overlay. With a camera, the computer could track the user's finger to act as the system's mouse; perform face recognition; and detect passive objects to overlay 2.5D and 3D graphics onto the real world. Additional apparatus such as audio systems, infrared beacons for sensing location, and biosensors for learning about the wearer's affect are described. With the use of input from these interface devices and sensors, a long-term goal of this project is to model the user's actions, anticipate his or her needs, and perform a seamless interaction between the virtual and physical environments.


title: Visual-Interactive Preprocessing of Time Series Data

text: Time series data is an important data type in many different application scenarios. Consequently, there are a great variety of approaches for analyzing time series data. Within these approaches different strategies for cleaning, segmenting, representing, normalizing, comparing, and aggregating time series data can be found. When combining these operations, the time series analysis preprocessing workflow has many degrees of freedom. To define an appropriate preprocessing pipeline, the knowledge of experts coming from the application domain has to be included into the design process. Unfortunately, these experts often cannot estimate the effects of the chosen preprocessing algorithms and their parameterizations on the time series. We introduce a system for the visual-interactive exploitation of the preprocessing parameter space. In contrast to ‘black box’-driven approaches designed by computer scientists based on the requirements of domain experts, our system allows these experts to visual-interactively compose time series preprocessing pipelines by themselves. Visual support is provided to choose the right order and parameterization of the preprocessing steps. We demonstrate the usability of our approach with a case study from the digital library domain, in which time-oriented scientific research data has to be preprocessed to realize a visual search and analysis application.


title: Design of fully printable chipless RFID tag on flexible substrate for secure banknote applications

text: A fully printable, passive and planar chipless RFID tag on thin flexible substrate is presented. The chipless tag encodes data into the spectral signature using a multiresonating circuit. Both amplitude and phase components of the spectral signature are used for data encoding. The tag comprises of a microstrip multiresonating circuit with 6 multi-spiral resonators and two cross-polarized UWB disc loaded monopole transmitting and receiving antennas. The chipless tag is designed to be printed on the Australian banknote and provide an extra layer of security against counterfeiting.


Active Antennas : The Next Step in Radio and Antenna Evolution


title: Computational Creativity

text: Creative thinking is one of the hallmarks of human-level competence. Although it is still a poorly understood subject speculative ideas about brain processes involved in creative thinking may be implemented in computational models. A review of different approaches to creativity, insight and intuition is presented. Two factors are essential for creativity: imagination and selection or filtering. Imagination should be constrained by experience, while filtering in the case of creative use of words may be based on semantic and phonological associations. Analysis of brain processes involved in invention of new words leads to practical algorithms that create many interesting and novel names associated with a set of keywords.


title: Robust Speaker-Adaptive HMM-Based Text-to-Speech Synthesis

text: This paper describes a speaker-adaptive HMM-based speech synthesis system. The new system, called ldquoHTS-2007,rdquo employs speaker adaptation (CSMAPLR+MAP), feature-space adaptive training, mixed-gender modeling, and full-covariance modeling using CSMAPLR transforms, in addition to several other techniques that have proved effective in our previous systems. Subjective evaluation results show that the new system generates significantly better quality synthetic speech than speaker-dependent approaches with realistic amounts of speech data, and that it bears comparison with speaker-dependent approaches even when large amounts of speech data are available. In addition, a comparison study with several speech synthesis techniques shows the new system is very robust: It is able to build voices from less-than-ideal speech data and synthesize good-quality speech even for out-of-domain sentences.


title: Dermoscopic findings in Laugier-Hunziker syndrome.

text:
BACKGROUND
Laugier-Hunziker syndrome (LHS) is a rare, acquired mucocutaneous hyperpigmentation often associated with longitudinal melanonychia. The clinical behavior of mucocutaneous pigmented lesions ranges from benign to highly malignant. Therefore, in most cases, the clinical diagnosis should be confirmed by further diagnostic methods. Dermoscopy is a noninvasive technique that has been used to make more accurate diagnoses of pigmented skin lesions. Nevertheless, to our knowledge, the dermoscopic features of the pigmented lesions in LHS have not been described previously. Herein, we report a case of LHS together with its dermoscopic features.

OBSERVATIONS
The clinical examination revealed macular hyperpigmentation on the oral and genital mucosa, conjunctiva, and palmoplantar region together with longitudinal melanonychia. Dermoscopic examination of mucosal lesions on the patient's lips and vulva revealed a parallel pattern. Longitudinal homogeneous pigmentation was observed on the toenails. The pigmented macules on the palms and the sole showed a parallel furrow pattern. A skin biopsy sample taken from the labial lesion was compatible with a diagnosis of mucosal melanosis.

CONCLUSIONS
By means of this case report, the dermoscopic features of the pigmented lesions in LHS are described for the first time, which facilitates diagnosis with a noninvasive technique. Future reports highlighting the dermoscopic features of this syndrome may simplify the diagnosis of LHS, which is thought to be underdiagnosed.


title: Current Methods in the Automatic Tissue Segmentation of 3D Magnetic Resonance Brain Images

text: Accurate segmentation of magnetic resonance (MR) images of the brain is of interest in the study of many brain disorders. In this paper, we provide a review of some of the current approaches in the tissue segmentation of MR brain images. We broadly divided current MR brain image segmentation algorithms into three categories: classificationbased, region-based, and contour-based, and discuss the advantages and disadvantages of these approaches. We also briefly review our recent work in this area. We show that by incorporating two key ideas into the conventional fuzzy cmeans clustering algorithm, we are able to take into account the local spatial context and compensate for the intensity nonuniformity (INU) artifact during the clustering process. We conclude this review by pointing to some possible future directions in this area.


title: Generalised spatial modulation with multiple active transmit antennas

text: We propose a new generalised spatial modulation (GSM) technique, which can be considered as a generalisation of the recently proposed spatial modulation (SM) technique. SM can be seen as a special case of GSM with only one active transmit antenna. In contrast to SM, GSM uses the indices of multiple transmit antennas to map information bits, and is thus able to achieve substantially increased spectral efficiency. Furthermore, selecting multiple active transmit antennas enables GSM to harvest significant transmit diversity gains in comparison to SM, because all the active antennas transmit the same information. On the other hand, inter-channel interference (ICI) is completely avoided by transmitting the same symbols through these active antennas. We present theoretical analysis using order statistics for the symbol error rate (SER) performance of GSM. The analytical results are in close agreement with our simulation results. The bit error rate performance of GSM and SM is simulated and compared, which demonstrates the superiority of GSM. Moreover, GSM systems with configurations of different transmit and receive antennas are studied. Our results suggest that using a less number of transmit antennas with a higher modulation order will lead to better BER performance.


title: Safety in shipping: the human element.

text:
INTRODUCTION
There are numerous diverse papers that have addressed issues within maritime safety; to date there has been no comprehensive review of this literature to aggregate the causal factors within accidents in shipping and surmise current knowledge.

METHODS
This paper reviewed the literature on safety in three key areas: common themes of accidents, the influence of human error, and interventions to make shipping safer. The review included 20 studies of seafaring across the following areas: fatigue, stress, health, situation awareness, teamwork, decision-making, communication, automation, and safety culture.

RESULTS
The review identifies the relative contributions of individual and organizational factors in shipping accidents, and also presents the methodological issues with previous research.

CONCLUSIONS
The paper concludes that monitoring and modifying the human factors issues presented in this paper could contribute to maritime safety performance.

IMPACT ON INDUSTRY
This review illustrates which human factors issues are prevalent in incidents therefore this gives shipping practitioners a focus for interventions.


title: Ranging RFID Tags With Ultrasound

text: Indoor localization and tracking of persons and assets with centimeter-level accuracy for inventory, security, medical monitoring, and training, as well as gesture interfaces for domotics, is highly desirable in the framework of the emerging IoT paradigm. Low cost, tiny, battery, or batteryless operated position sensors are required. 3-D localization can be computed by combining three or more distance measurements between sensor and reference points. Our aim is to give the capability of measuring the distance from a reference point to radio frequency identification (RFID) tags. The main challenge is in the estimation of the distances with millimeter accuracy in presence of both size and power supply strict constraints, and thus with very limited computational power. An accurate ranging technique using cross-correlation and small RFID-based sensors is proposed. Its originality resides in moving the main computational efforts from the sensor to an external processing unit with sufficient computational and supply power, thus overcoming the sensor limits. The system is composed of a beacon that emits ultrasound chirps and RF sync signals, a RFID-based distance sensor, a commercial RFID reader, and a processing unit. Main advantages are the high miniaturization and low power consumption of the remote sensor, and its compatibility with existing RFID standards.


title: Discriminant validity of well-being measures.

text: The convergent and discriminant validities of well-being concepts were examined using multitrait-multimethod matrix analyses (D. T. Campbell & D. W. Fiske, 1959) on 3 sets of data. In Study 1, participants completed measures of life satisfaction, positive affect, negative affect, self-esteem, and optimism on 2 occasions 4 weeks apart and also obtained 3 informant ratings. In Study 2, participants completed each of the 5 measures on 2 occasions 2 years apart and collected informant reports at Time 2. In Study 3, participants completed 2 different scales for each of the 5 constructs. Analyses showed that (a) life satisfaction is discriminable from positive and negative affect, (b) positive affect is discriminable from negative affect, (c) life satisfaction is discriminable from optimism and self-esteem, and (d) optimism is separable from trait measures of negative affect.


title: Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation

text: Predicting personalized sequential behavior is a key task for recommender systems. In order to predict user actions such as the next product to purchase, movie to watch, or place to visit, it is essential to take into account both long-term user preferences and sequential patterns (i.e., short-term dynamics). Matrix Factorization and Markov Chain methods have emerged as two separate but powerful paradigms for modeling the two respectively. Combining these ideas has led to unified methods that accommodate long-and short-term dynamics simultaneously by modeling pairwise user-item and item-item interactions. In spite of the success of such methods for tackling dense data, they are challenged by sparsity issues, which are prevalent in real-world datasets. In recent years, similarity-based methods have been proposed for (sequentially-unaware) item recommendation with promising results on sparse datasets. In this paper, we propose to fuse such methods with Markov Chains to make personalized sequential recommendations. We evaluate our method, Fossil, on a variety of large, real-world datasets. We show quantitatively that Fossil outperforms alternative algorithms, especially on sparse datasets, and qualitatively that it captures personalized dynamics and is able to make meaningful recommendations.


title: A New Approach for Image Steganography Using Edge Detection Method for Hiding Text in Color Images Using HSI Color Model

text: The objective of the present project is to uses a new techniques for steganography in a HSI color cover images, which hides secret message in the edges of the carrier images using 2-bit LSB substitution for embedding. To get true edges, canny edge detection technique has been used. Amount of data to be embedded plays an important role on the selection of edges. The main advantage of using HIS color mode is that it produces a image with a significantly larg file size hence we hide large amount of secrete message Experimental results have shown that the proposed technique performs better and higher embedding capacity.


title: Design and Implementation of a 40-kV, 20-kJ/s Capacitor Charger for Pulsed-Power Application

text: This paper presents the design and implementation of a 40-kV, 20-kJ/s high-voltage capacitor charger based on a series/parallel resonant converter. The inclusion of the parallel resonant capacitor component in this circuit results in the production of a trapezoidal resonant current, which reduces conduction loss. This capacitor is practically realized as a part of the balancing network of the high-voltage rectifiers in the secondary side of the circuit. Particular attention in this paper is paid to the high-voltage transformer, which must be carefully designed to provide the required functionality without negative impact on the resonant circuit. A PSpice simulation has been used to prove that the proposed control method for the circuit is valid. This is supported with the experimental results, which verify that the operation of the circuit is as expected and that the converter is able to meet all the design criteria with an efficiency of up to 96%.


title: Industrial Applications of Pulsed Power Technology

text: A review of mainly the past two years is undertaken of the industrial applications of pulsed power. Repetitively operated pulsed power generators with a moderate peak power have been developed for industrial applications. These generators are reliable and have low maintenance. Development of the pulsed power generators helps promote industrial applications of pulsed power for such things as food processing, medical treatment, water treatment, exhaust gas treatment, ozone generation, engine ignition, ion implantation and others. Here, industrial applications of pulsed power are classified by application for biological effects, for pulsed streamer discharges in gases, for pulsed discharges in liquid or liquid- mixture, and for material processing.


title: A Hybrid Symmetrical Voltage Multiplier

text: Voltage multiplier circuits are widely used in many high-voltage/low-current applications. A conventional symmetrical voltage multiplier (SVM) has much better performance, when compared with its half-wave counterpart. However, it requires a high-voltage transformer (HVT) with center-tapped secondary to perform its push-pull kind of operation. The design of an HVT with center-tapped secondary is relatively complex. This paper proposes a hybrid SVM (HSVM) for dc high-voltage applications. The multiplier is formed by cascading a diode-bridge rectifier and an SVM with diode-bridge rectifier as the first stage of multiplier. The proposed topology saves two high-voltage capacitors and requires only one secondary winding of HVT. Besides, it has lesser voltage drop and faster transient response at start-up, when compared with conventional SVM. The feasibility of the proposed HSVM is validated both by simulation and experimental results of a laboratory scaled-down prototype.


title: Solid state Marx Generator using series-connected IGBTs

text: This paper describes a newly developed novel repetitive impulse voltage generator using a boost converter array. To solve problems such as short life time, low operating frequency, and the fixed pulse width of conventional generators, the proposed generator is designed with a boost converter array that employs series-connected capacitors and insulated gate bipolar transistors. The circuit can easily obtain a high-voltage pulse without any high-voltage direct current source and pulse transformer. Thus, the proposed circuit not only allows elimination of the expensive high-frequency transformer but also allows operation at a frequency up to several kilohertz with high reliability and longer life span. To validate the proposed circuit, two pulse generators rated at 1.8 kV, 40 A and 20 kV, 300 A are implemented and tested.


title: Compact and high repetitive pulsed power modulator based on semiconductor switches

text: This paper describes the design of a compact and highly repetitive pulsed power modulator based on semiconductor switches. Output specifications of the proposed modulator are as follows: variable output pulse voltage, 1-10 kV; width, 1-10 μs; pulse repetition rate (PRR), 1-50,000 pps; and average output power, 10 kW. The proposed solid-state pulsed power modulator has two main parts: an IGBT stack that hasa gate drive circuit for the pulse output with a variable pulse width and repetition rate, and a capacitor charger that has a controllable output voltage. To connect all the storage capacitors in a series for high voltage pulses, a simple and reliable IGBT stack structure was proposed based on the Marx generator. In addition, the gate driver circuit, which supplies power and signals to all the IGBTs simultaneously, was introduced. This providesa superior protection function against the arc and the short. To charge the storage capacitors, a novel 10 kW (10 kV, 1 A) high voltage capacitor charger with the combined advantage of a series-loaded resonant converter and a ZVS (zero-voltage-switching) full-bridge pulse width modulation (PWM) converter was proposed and designed especially for the solid-state pulsed power modulator. Theexperiment results verified that the proposed scheme and structure can be used effectively for a high voltage pulsed power modulator that requires variable voltage, repetition rate, and pulse width, depending on the process of the applications.


title: Global burden of cardiovascular diseases: part I: general considerations, the epidemiologic transition, risk factors, and impact of urbanization.

text: This two-part article provides an overview of the global burden of atherothrombotic cardiovascular disease. Part I initially discusses the epidemiologic transition which has resulted in a decrease in deaths in childhood due to infections, with a concomitant increase in cardiovascular and other chronic diseases; and then provides estimates of the burden of cardiovascular (CV) diseases with specific focus on the developing countries. Next, we summarize key information on risk factors for cardiovascular disease (CVD) and indicate that their importance may have been underestimated. Then, we describe overarching factors influencing variations in CVD by ethnicity and region and the influence of urbanization. Part II of this article describes the burden of CV disease by specific region or ethnic group, the risk factors of importance, and possible strategies for prevention.


title: Does Barbie make girls want to be thin? The effect of experimental exposure to images of dolls on the body image of 5- to 8-year-old girls.

text: The ubiquitous Barbie doll was examined in the present study as a possible cause for young girls' body dissatisfaction. A total of 162 girls, from age 5 to age 8, were exposed to images of either Barbie dolls, Emme dolls (U.S. size 16), or no dolls (baseline control) and then completed assessments of body image. Girls exposed to Barbie reported lower body esteem and greater desire for a thinner body shape than girls in the other exposure conditions. However, this immediate negative impact of Barbie doll was no longer evident in the oldest girls. These findings imply that, even if dolls cease to function as aspirational role models for older girls, early exposure to dolls epitomizing an unrealistically thin body ideal may damage girls' body image, which would contribute to an increased risk of disordered eating and weight cycling.


title: Supporting requirements traceability through refactoring

text: Modern traceability tools employ information retrieval (IR) methods to generate candidate traceability links. These methods track textual signs embedded in the system to establish relationships between software artifacts. However, as software systems evolve, new and inconsistent terminology finds its way into the system's taxonomy, thus corrupting its lexical structure and distorting its traceability tracks. In this paper, we argue that the distorted lexical tracks of the system can be systematically re-established through refactoring, a set of behavior-preserving transformations for keeping the system quality under control during evolution. To test this novel hypothesis, we investigate the effect of integrating various types of refactoring on the performance of requirements-to-code automated tracing methods. In particular, we identify the problems of missing, misplaced, and duplicated signs in software artifacts, and then examine to what extent refactorings that restore, move, and remove textual information can overcome these problems respectively. We conduct our experimental analysis using three datasets from different application domains. Results show that restoring textual information in the system has a positive impact on tracing. In contrast, refactorings that remove redundant information impact tracing negatively. Refactorings that move information among the system modules are found to have no significant effect. Our findings address several issues related to code and requirements evolution, as well as refactoring as a mechanism to enhance the practicality of automated tracing tools.


title: 3D face landmark labelling

text: Most 3D face processing systems require feature detection and localisation, for example to crop, register, analyse or recognise faces. The three features often used in the literature are the tip of the nose, and the two inner corner of the eyes. Failure to localise these landmarks can cause the system to fail and they become very difficult to detect under large pose variations or when occlusion is present. In this paper, we present a proof-of-concept for a face labelling system capable of overcoming this problem, as a larger number of landmarks are employed. A set of points containing hand-placed landmarks is used as input data. The aim here is to retrieve the landmark's labels when some part of the face is missing. By using graph matching techniques to reduce the number of candidates, and translation and unit-quaternion clustering to determine a final correspondence, we evaluate the accuracy at which landmarks can be retrieved under changes in expression, orientation and in the presence of occlusions.


title: Converging on the Divergent: The History (and Future) of the International Joint Workshops in Computational Creativity

text: When heckled, professional comedians frequently lament that “everyone’s a comedian!” It’s easy to see why: professional comedians don’t possess different kinds of brains from others or engage in radically different kinds of behaviors from others, and moreover, the success of their acts is predicated on others’ shared ability to understand and reason about comic situations they describe. The difference between comedians and their audience is a matter not of kind, but of degree, a difference that is reflected in the vocational emphasis they place on humor. Researchers in the field of computational creativity find themselves in a similar situation. As a subdiscipline of artificial intelligence, computational creativity explores theories and practices that give rise to a phenomenon, creativity, that all intelligent systems, human or machine, can legitimately lay claim to. Who is to say that a given AI system is not creative, insofar as it solves nontrivial problems or generates useful outputs that are not hard wired into its programming? As with comedians’ being funny, the difference between studying computational creativity and studying artificial intelligence is one of emphasis rather than one of kind: the field of computational creativity, as typified by a long-running series of workshops at AI-related conferences, places a vocational emphasis on creativity and attempts to draw together the commonalities of what Articles


title: Instance-level Facial Attributes Transfer with Geometry-Aware Flow

text: We address the problem of instance-level facial attribute transfer without paired training data, e.g., faithfully transferring the exact mustache from a source face to a target face. This is a more challenging task than the conventional semantic-level attribute transfer, which only preserves the generic attribute style instead of instance-level traits. We propose the use of geometry-aware flow, which serves as a wellsuited representation for modeling the transformation between instance-level facial attributes. Specifically, we leverage the facial landmarks as the geometric guidance to learn the differentiable flows automatically, despite of the large pose gap existed. Geometry-aware flow is able to warp the source face attribute into the target face context and generate a warp-and-blend result. To compensate for the potential appearance gap between source and target faces, we propose a hallucination sub-network that produces an appearance residual to further refine the warp-and-blend result. Finally, a cycle-consistency framework consisting of both attribute transfer module and attribute removal module is designed, so that abundant unpaired face images can be used as training data. Extensive evaluations validate the capability of our approach in transferring instance-level facial attributes faithfully across large pose and appearance gaps. Thanks to the flow representation, our approach can readily be applied to generate realistic details on high-resolution images.


title: GPS spoofer countermeasure effectiveness based on signal strength, noise power, and C/N0 measurements

text: Spoofing sources can effectively disrupt a GPS receiver during the acquisition phase by generating multiple false correlation peaks and increasing the noise floor. Such deceptive correlation peaks can mislead the GPS receiver into acquiring the spoofer generated signals rather than the authentic signals. Also, the spoofer can increase the receiver noise floor to bury the authentic signals in the noise and at the same time generate correlation peaks with amplitudes commensurate with reasonable C/N0 expectations. The main focus of this paper is on assessment of the reduced effectiveness of the GPS spoofer countermeasure during acquisition where the GPS receiver utilizes C/N0 discrimination. As shown, whereas the C/N0 discrimination is of limited effectiveness, with a modest circuit modification, the receiver can measure the absolute power of the correlation peaks, which is an effective means of detecting and discriminating spoofer sources. It will be shown that employing absolute power monitoring technique considerably reduces the vulnerability region of the receiver compared with the C/N0 monitoring techniques. Copyright © 2012 John Wiley & Sons, Ltd.


title: Cancer statistics in China, 2015.

text: With increasing incidence and mortality, cancer is the leading cause of death in China and is a major public health problem. Because of China's massive population (1.37 billion), previous national incidence and mortality estimates have been limited to small samples of the population using data from the 1990s or based on a specific year. With high-quality data from an additional number of population-based registries now available through the National Central Cancer Registry of China, the authors analyzed data from 72 local, population-based cancer registries (2009-2011), representing 6.5% of the population, to estimate the number of new cases and cancer deaths for 2015. Data from 22 registries were used for trend analyses (2000-2011). The results indicated that an estimated 4292,000 new cancer cases and 2814,000 cancer deaths would occur in China in 2015, with lung cancer being the most common incident cancer and the leading cause of cancer death. Stomach, esophageal, and liver cancers were also commonly diagnosed and were identified as leading causes of cancer death. Residents of rural areas had significantly higher age-standardized (Segi population) incidence and mortality rates for all cancers combined than urban residents (213.6 per 100,000 vs 191.5 per 100,000 for incidence; 149.0 per 100,000 vs 109.5 per 100,000 for mortality, respectively). For all cancers combined, the incidence rates were stable during 2000 through 2011 for males (+0.2% per year; P = .1), whereas they increased significantly (+2.2% per year; P < .05) among females. In contrast, the mortality rates since 2006 have decreased significantly for both males (-1.4% per year; P < .05) and females (-1.1% per year; P < .05). Many of the estimated cancer cases and deaths can be prevented through reducing the prevalence of risk factors, while increasing the effectiveness of clinical care delivery, particularly for those living in rural areas and in disadvantaged populations.


THE ACTIVITY-BASED APPROACH


title: Spermatocele presenting as acute scrotum.

text: Corresponding Author: Atsuya Hikosaka, MD Department of Urology, Toyota Kosei Hospital, 500-1, Ibobara, Josui-cho, Toyota, Aichi 470-0396, Japan Tel: +81 565 43 5000 Fax: +81 565 43 5100 E-mail: uropatho@ybb.ne.jp INTRODUCTION Spermatocele, a retention cyst of the scrotum which is or has been in communication with the semen-carrying system,(1) is a relatively common clinical entity. It presents typically as an intrascrotal paratesticular mass, but it usually has few subjective symptoms. Here, we report a case of spermatocele manifested with acute scrotum due to its unique feature.


title: Reconstructing Indian Population History

text: India has been underrepresented in genome-wide surveys of human variation. We analyse 25 diverse groups in India to provide strong evidence for two ancient populations, genetically divergent, that are ancestral to most Indians today. One, the ‘Ancestral North Indians’ (ANI), is genetically close to Middle Easterners, Central Asians, and Europeans, whereas the other, the ‘Ancestral South Indians’ (ASI), is as distinct from ANI and East Asians as they are from each other. By introducing methods that can estimate ancestry without accurate ancestral populations, we show that ANI ancestry ranges from 39–71% in most Indian groups, and is higher in traditionally upper caste and Indo-European speakers. Groups with only ASI ancestry may no longer exist in mainland India. However, the indigenous Andaman Islanders are unique in being ASI-related groups without ANI ancestry. Allele frequency differences between groups in India are larger than in Europe, reflecting strong founder effects whose signatures have been maintained for thousands of years owing to endogamy. We therefore predict that there will be an excess of recessive diseases in India, which should be possible to screen and map genetically.


title: Maximum energy harvesting from medium voltage electric-field energy using power line insulators

text: One essential requirement for a modern Smart Grid is continuous monitoring of the grid variables. This task is efficiently achieved using self-powered electronic devices deployed throughout the grid network. Within the vicinity of the grid power lines, the high voltages of these lines can provide a source of energy for such loads using capacitive coupling. This is known as Electric-Field Energy Harvesting (EFEH). However, the reported strategies that use this principle have not explored how to maximise their power transfer, which is essential for practical systems since the available energy from EFEH is invariably quite small. In this paper, a novel approach for optimum energy harvesting is proposed using the parasitic capacitance of medium-voltage power line insulators. A follow-on power electronic conversion system is then used to maintain the load conditions that maximise the extracted energy. The system performance has been analysed using theoretical studies and computer simulations to identify the operating conditions that maximise the extracted energy. Results obtained so far suggest that up to 100 mW can be harvested from 22 kV grid feeders, using only the capacitive coupling that exists between the line insulator harvester and the feeder conductor.


title: Measuring People-Flow through Doorways Using Easy-to-Install IR Array Sensors

text: People counting has many applications in smart buildings. For example, adjusting HVAC systems based on the number of occupants in each room can save a significant amount of energy. In addition, security and safety of the building can be managed by determining the number and location of occupants. Different technologies and sensing platforms have proposed for accurate and efficient people counting. However, these solutions are expensive, hard to deploy, or privacy invasive. We investigate the possibility of placing an 8×8 IR array sensor at the doorways and counting the number of people inside rooms. Our solution is real-time, inexpensive, privacy preserving with much less deployment constraints compared to its competitors. The proposed solution deals with realistic and dynamic changes in the sensing environment by leveraging a combination of Otsus thresholding and modeling thermal noise distribution. We evaluated our solution via several controlled and uncontrolled real-world environments. The results show an average of 93% accuracy in estimating the number of occupants in rooms.


title: A “K Hypotheses + Other” Belief Updating Model

text: Spoken dialog systems typically rely on recognition confidence scores to guard against potential misundersta ndings. While confidence scores can provide an initial assessment for the reliability of the information obtained fro m the user, ideally systems should leverage information that is available in subsequent user responses to update and improve the accuracy of their beliefs. We present a machine-learning based solution for this problem. We use a compresse d representation of beliefs that tracks up to k hypotheses for each concept at any given time. We train a generalized l inear model to perform the updates. Experimental results show that the proposed approach significantly outperform s heuristic rules used for this task in current systems. Fu rthermore, a user study with a mixed-initiative spoken dialog sy stem shows that the approach leads to significant gains in task success and in the efficiency of the interaction, a cross a wide range of recognition error-rates.


title: An efficient selection of HOG feature for SVM classification of vehicle

text: Support Vector Machine (SVM) classifier with Histogram of Oriented Gradients (HOG) feature become one of the most popular techniques used for vehicle detection in recent years. And the computing time of SVM is a main obstacle to get real time implementation which is important for Advanced Driver Assistance Systems (ADAS) applications. One of the effective ways to reduce the computing complexity of SVM is to reduce the dimension of HOG feature. In this paper, we examine the effect of the number of HOG bins on the vehicle detection and the symmetric characteristics of HOG feature of vehicle. And we successfully demonstrate the speed-up of SVM classifier for vehicle detection by about three times while maintaining the detection performance.


title: Biomimetic design through natural language analysis to facilitate cross-domain information retrieval

text: Biomimetic, or biologically inspired, design uses analogous biological phenomena to develop solutions for engineering problems. Several instances of biomimetic design result from personal observations of biological phenomena. However, many engineers’ knowledge of biology may be limited, thus reducing the potential of biologically inspired solutions. Our approach to biomimetic design takes advantage of the large amount of biological knowledge already available in books, journals, and so forth, by performing keyword searches on these existing natural-language sources. Because of the ambiguity and imprecision of natural language, challenges inherent to natural language processing were encountered. One challenge of retrieving relevant cross-domain information involves differences in domain vocabularies, or lexicons. A keyword meaningful to biologists may not occur to engineers. For an example problem that involved cleaning, that is, removing dirt, a biochemist suggested the keyword “defend.” Defend is not an obvious keyword to most engineers for this problem, nor are the words defend and “clean0remove” directly related within lexical references. However, previous work showed that biological phenomena retrieved by the keyword defend provided useful stimuli and produced successful concepts for the clean0remove problem. In this paper, we describe a method to systematically bridge the disparate biology and engineering domains using natural language analysis. For the clean0remove example, we were able to algorithmically generate several biologically meaningful keywords, including defend, that are not obviously related to the engineering problem. We developed a method to organize and rank the set of biologically meaningful keywords identified, and confirmed that we could achieve similar results for two other examples in encapsulation and microassembly. Although we specifically address cross-domain information retrieval from biology, the bridging process presented in this paper is not limited to biology, and can be used for any other domain given the availability of appropriate domain-specific knowledge sources and references.


title: NuSMV 2: An OpenSource Tool for Symbolic Model Checking

text: Alessandro Cimatti 1, Edmund Clarke2, Enrico Giunchiglia3, Fausto Giunchiglia 4, Marco Pistore1, Marco Roveri 1, Roberto Sebastiani 4, and Armando Tacchella 3 1 ITC-IRST, Via Sommarive 18, 38050 Trento, Italy fcimatti,pistore,roverig@irst.itc.it 2 Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh (PA), USA emc@cs.cmu.edu 3 DIST – Università di Genova, Viale Causa 13, 16145 Genova, Italy fenrico,tacg@mrg.dist.unige.it 4 Università di Trento, Via Sommarive 14, 38050 Trento, Italy ffausto,rsebag@science.unitn.it


title: On the Robustness of a Neural Network

text: With the development of neural networks based machine learning and their usage in mission critical applications, voices are rising against the black box aspect of neural networks as it becomes crucial to understand their limits and capabilities. With the rise of neuromorphic hardware, it is even more critical to understand how a neural network, as a distributed system, tolerates the failures of its computing nodes, neurons, and its communication channels, synapses. Experimentally assessing the robustness of neural networks involves the quixotic venture of testing all the possible failures, on all the possible inputs, which ultimately hits a combinatorial explosion for the first, and the impossibility to gather all the possible inputs for the second.In this paper, we prove an upper bound on the expected error of the output when a subset of neurons crashes. This bound involves dependencies on the network parameters that can be seen as being too pessimistic in the average case. It involves a polynomial dependency on the Lipschitz coefficient of the neurons' activation function, and an exponential dependency on the depth of the layer where a failure occurs. We back up our theoretical results with experiments illustrating the extent to which our prediction matches the dependencies between the network parameters and robustness. Our results show that the robustness of neural networks to the average crash can be estimated without the need to neither test the network on all failure configurations, nor access the training set used to train the network, both of which are practically impossible requirements.


title: A framework for automatic question generation from text using deep reinforcement learning

text: Automatic question generation (QG) is a useful yet challenging task in NLP. Recent neural network-based approaches represent the state-of-the-art in this task, but they are not without shortcomings. Firstly, these models lack the ability to handle rare words and the word repetition problem. Moreover, all previous works optimize the cross-entropy loss, which can induce inconsistencies between training (objective) and testing (evaluation measure). In this paper, we present a novel deep reinforcement learning based framework for automatic question generation. The generator of the framework is a sequence-to-sequence model, enhanced with the copy mechanism to handle the rare-words problem and the coverage mechanism to solve the word repetition problem. The evaluator model of the framework evaluates and assigns a reward to each predicted question. The overall model is trained by learning the parameters of the generator network which maximizes the reward. Our framework allows us to directly optimize any task-specific score including evaluation measures such as BLEU, GLEU, ROUGE-L, etc., suitable for sequence to sequence tasks such as QG. Our comprehensive evaluation shows that our approach significantly outperforms state-of-the-art systems on the widely-used SQuAD benchmark in both automatic and human evaluation.


Type of thesis: Master Degree Project in Accounting, 30 credits University: University of Gothenburg, School of Business, Economics and Law Semester: Spring 2017 Author: Kevin Larsson & Philip Kaber Supervisor: Elisabeth Frisk Title: The Practical Use of BI Visualizations in Decision-making Background and problem: Due to the constantly altering business environment, firms are trying to find ways of combining information technology (IT) and decision-making in order to grasp information of the complex and dynamic business environment. As a solution to this strenuousness; business intelligence (BI) systems serve to conduct more efficient and reliable decisions by the means of visualizations. One existing gap in the research of BI concerns the sociotechnical effects involved in the decision-making, in which knowledge of how BI visualizations are used and may be used in practice to support the decision-making have remained limited. Research aim: The research aim of the study is to investigate how BI visualizations are and may be used to support strategic and operational decisions. Further, the study addresses how BI visualizations are used in various decision contexts. Research questions: The following questions are addressed in order to achieve the research aim: (1) How are and may BI visualizations be used in strategic and operational decisions? (1a) Which visual elements are used depending on the task and decision category? (2) How are BI visualizations used in different decision contexts? Research design: A qualitative study was made through semi-structured interviews. In-depth interviews with six people from the purchasing company were conducted in order to understand how BI and visualizations are and may be used. Two people from the consultancy company were interviewed in order to understand how BI and visualizations may be used. Discussion and conclusion: The primary purpose of BI systems in the purchasing company is to support decision-making from a strategic perspective by providing the business with new insights and monitor business processes. However, the findings in this study also suggest that BI visualizations are used for operational errands. Strategic decisions are primary based on graphical representations, whereas operational decisions are based on data derived from tables and additional calculations in Excel. By creating more interactive, initiative, and humanizing visualizations, the end-users may discover new patterns of information and enhance the cognitive fit without being overwhelmed by data. BI visualizations may also be used in a larger extent to detect errors in data. The usage depends on the decision situation, in which decisions made in the fact-based contexts domains tend to be based on intuitions and BI visualizations, before responding to the business problem. This differs from the pattern-based management domains, in which actions are done prior to the analysis as there is lack of data. Further the findings indicate that several people are often involved in the decisions. The mental model of the respondents is not entirely fit in a cognitive aspect.