title: The Active Layer Morphology of Organic Solar Cells Probed with Grazing Incidence Scattering Techniques

abstract: Grazing incidence X-ray scattering (GIXS) provides unique insights into the morphology of active materials and thin film layers used in organic photovoltaic devices. With grazing incidence wide angle X-ray scattering (GIWAXS) the molecular arrangement of the material is probed. GIWAXS is sensitive to the crystalline parts and allows for the determination of the crystal structure and the orientation of the crystalline regions with respect to the electrodes. With grazing incidence small angle X-ray scattering (GISAXS) the nano-scale structure inside the films is probed. As GISAXS is sensitive to length scales from nanometers to several hundred nanometers, all relevant length scales of organic solar cells are detectable. After an introduction to GISAXS and GIWAXS, selected examples for application of both techniques to active layer materials are reviewed. The particular focus is on conjugated polymers, such as poly(3-hexylthiophene) (P3HT).


title: Conditional High-Order Boltzmann Machines for Supervised Relation Learning

abstract: Relation learning is a fundamental problem in many vision tasks. Recently, high-order Boltzmann machine and its variants have shown their great potentials in learning various types of data relation in a range of tasks. But most of these models are learned in an unsupervised way, i.e., without using relation class labels, which are not very discriminative for some challenging tasks, e.g., face verification. In this paper, with the goal to perform supervised relation learning, we introduce relation class labels into conventional high-order multiplicative interactions with pairwise input samples, and propose a conditional high-order Boltzmann Machine (CHBM), which can learn to classify the data relation in a binary classification way. To be able to deal with more complex data relation, we develop two improved variants of CHBM: 1) latent CHBM, which jointly performs relation feature learning and classification, by using a set of latent variables to block the pathway from pairwise input samples to output relation labels and 2) gated CHBM, which untangles factors of variation in data relation, by exploiting a set of latent variables to multiplicatively gate the classification of CHBM. To reduce the large number of model parameters generated by the multiplicative interactions, we approximately factorize high-order parameter tensors into multiple matrices. Then, we develop efficient supervised learning algorithms, by first pretraining the models using joint likelihood to provide good parameter initialization, and then finetuning them using conditional likelihood to enhance the discriminant ability. We apply the proposed models to a series of tasks including invariant recognition, face verification, and action similarity labeling. Experimental results demonstrate that by exploiting supervised relation labels, our models can greatly improve the performance.


title: An Ironic Fist in a Velvet Glove: Creative Mis-Representation in the Construction of Ironic Similes

abstract: Irony is an effective but challenging mode of communication that allows a speaker to express viewpoints rich in sentiment with concision, sharpness and humour. Creative irony is especially common in online documents that express subjective and deeply-felt opinions, and thus represents a significant obstacle to the accurate analysis of sentiment in web texts. In this paper we look at one commonly used framing device for linguistic irony--the simile--to show how even the most creative uses of irony are often marked in ways that make them computationally feasible to detect. We conduct a very large corpus analysis of web-harvested similes to identify the most interesting characteristics of ironic comparisons, and provide an empirical evaluation of a new algorithm for separating ironic from non-ironic similes.


WeBuildAI : Participatory Framework for Fair and Efficient Algorithmic Governance


Content-Based Image Retrieval : Theory and Applications 2 Basic Concepts


title: Why is software late? An empirical study of reasons for delay in software development

abstract: A study of the reasons for delay in software development is described. The aim of the study was to gain an insight into the reasons for differences between plans and reality in development activities in order to be able to take actions for improvement. A classification was used to determine the reasons. 160 activities, comprising over 15000 hours of work, have been analyzed. The results and interpretations of the results are presented. Insight into the predominant reasons for delay enabled actions for improvements to be taken in the department concerned. Because the distribution of reasons for delay varied widely from one department to another, it is recommended that every department should gain an insight into its reasons for delay in order to be able to take adequate actions for improvement. >


title: A toolbox for the design, planning and operation of manufacturing networks in a mass customisation environment

abstract: Abstract The task of design, planning and operation of manufacturing networks is becoming more and more challenging for companies, as globalisation, mass customisation and the turbulent economic landscape create demand volatility, uncertainties and high complexity. In this context, this paper investigates the performance of decentralised manufacturing networks through a set of methods developed into a software framework in a toolbox approach. The Tabu Search and Simulated Annealing metaheuristic methods are used together with an Artificial Intelligence method, called Intelligent Search Algorithm. A multi-criteria decision making procedure is carried out for the evaluation of the quality of alternative manufacturing network configurations using multiple conflicting criteria including dynamic complexity, reliability, cost, time, quality and environmental footprint. A comparison of the performance of each method based on the quality of the solutions that it provided is carried out. The statistical design of experiments robust engineering technique is used for the calibration of the adjustable parameters of the methods. Moreover, the impact of demand fluctuation to the operational performance of the alternative networks, expressed thorough a dynamic complexity indicator, is investigated through simulation. The developed framework is validated through a real life case, with data coming from the CNC machine building industry.


Semantic Related Feature Analysis and Dynamic Evolution Based on Topic Temporal Chains under the Social Network


title: User Nutrition Modelling and Recommendation: Balancing Simplicity and Complexity

abstract: In order to use and model nutritional knowledge in a food recommender system, uncertainties regarding the users nutritional state and thus the personal health value of food items, as well as conflicting nutritional theories need to be quantified, qualified and subsumed into falsifiable models. In this paper, we reflect on different error sources with respect to nutrition and consider how such issues can be tackled in future systems. We discuss the integration of general nutritional theories into information systems as well as user specific nutritional measures and different approaches to evaluating the utility of a given nutritional model.


title: Enhancing deep learning sentiment analysis with ensemble techniques in social applications

abstract: A taxonomy that classifies ensemble models in the literature is presented.Surface and deep features integration is explored to improve classification.Several ensembles of classifiers and features are proposed and evaluated.Performance of the proposed models is evaluated on several sentiment datasets. Deep learning techniques for Sentiment Analysis have become very popular. They provide automatic feature extraction and both richer representation capabilities and better performance than traditional feature based techniques (i.e., surface methods). Traditional surface approaches are based on complex manually extracted features, and this extraction process is a fundamental question in feature driven methods. These long-established approaches can yield strong baselines, and their predictive capabilities can be used in conjunction with the arising deep learning methods. In this paper we seek to improve the performance of deep learning techniques integrating them with traditional surface approaches based on manually extracted features. The contributions of this paper are sixfold. First, we develop a deep learning based sentiment classifier using a word embeddings model and a linear machine learning algorithm. This classifier serves as a baseline to compare to subsequent results. Second, we propose two ensemble techniques which aggregate our baseline classifier with other surface classifiers widely used in Sentiment Analysis. Third, we also propose two models for combining both surface and deep features to merge information from several sources. Fourth, we introduce a taxonomy for classifying the different models found in the literature, as well as the ones we propose. Fifth, we conduct several experiments to compare the performance of these models with the deep learning baseline. For this, we use seven public datasets that were extracted from the microblogging and movie reviews domain. Finally, as a result, a statistical study confirms that the performance of these proposed models surpasses that of our original baseline on F1-Score.


title: Testing Security Policies: Going Beyond Functional Testing

abstract: While important efforts are dedicated to system functional testing, very few works study how to test specifically security mechanisms, implementing a security policy. This paper introduces security policy testing as a specific target for testing. We propose two strategies for producing security policy test cases, depending if they are built in complement of existing functional test cases or independently from them. Indeed, any security policy is strongly connected to system functionality: testing functions includes exercising many security mechanisms. However, testing functionality does not intend at putting to the test security aspects. We thus propose test selection criteria to produce tests from a security policy. To quantify the effectiveness of a set of test cases to detect security policy flaws, we adapt mutation analysis and define security policy mutation operators. A library case study, a 3-tiers architecture, is used to obtain experimental trends. Results confirm that security must become a specific target of testing to reach a satisfying level of confidence in security mechanisms.


title: On the convergence properties of a $K$-step averaging stochastic gradient descent algorithm for nonconvex optimization

abstract: Despite their popularity, the practical performance of asynchronous stochastic gradient descent methods (ASGD) for solving large scale machine learning problems are not as good as theoretical results indicate. We adopt and analyze a synchronous K-step averaging stochastic gradient descent algorithm which we call K-AVG. We establish the convergence results of K-AVG for nonconvex objectives and explain why the K-step delay is necessary and leads to better performance than traditional parallel stochastic gradient descent which is a special case of K-AVG with $K=1$. We also show that K-AVG scales better than ASGD. Another advantage of K-AVG over ASGD is that it allows larger stepsizes. On a cluster of $128$ GPUs, K-AVG is faster than ASGD implementations and achieves better accuracies and faster convergence for \cifar dataset.


title: A method of hole-filling for the depth map generated by Kinect with moving objects detection

abstract: We propose a method for filtering the occlusion areas and small holes on the depth map generated by the Kinect depth camera. This approach uses the original RGB image provided by the conventional Kinect camera and the depth map. We extract the moving body using the original image and the background differentials, then applying a 4-neighbor-pixels-interpolation to the none-body areas before filling the body areas. The proposed method can fill the occlusion areas effectively and remain the details of edges accurately, which can be used as a preprocessing stage before using the depth data of Kinect.


title: Representation of Linguistic Form and Function in Recurrent Neural Networks

abstract: We present novel methods for analyzing the activation patterns of recurrent neural networks from a linguistic point of view and explore the types of linguistic structure they learn. As a case study, we use a standard standalone language model, and a multi-task gated recurrent network architecture consisting of two parallel pathways with shared word embeddings: The Visual pathway is trained on predicting the representations of the visual scene corresponding to an input sentence, and the Textual pathway is trained to predict the next word in the same sentence. We propose a method for estimating the amount of contribution of individual tokens in the input to the final prediction of the networks. Using this method, we show that the Visual pathway pays selective attention to lexical categories and grammatical functions that carry semantic information, and learns to treat word types differently depending on their grammatical function and their position in the sequential structure of the sentence. In contrast, the language models are comparatively more sensitive to words with a syntactic function. Further analysis of the most informative n-gram contexts for each model shows that in comparison with the Visual pathway, the language models react more strongly to abstract contexts that represent syntactic constructions.


title: A life-cycle perspective on online community success

abstract: Using the information systems lifecycle as a unifying framework, we review online communities research and propose a sequence for incorporating success conditions during initiation and development to increase their chances of becoming a successful community, one in which members participate actively and develop lasting relationships. Online communities evolve following distinctive lifecycle stages and recommendations for success are more or less relevant depending on the developmental stage of the online community. In addition, the goal of the online community under study determines the components to include in the development of a successful online community. Online community builders and researchers will benefit from this review of the conditions that help online communities succeed.


title: Cannabis in Pain Treatment: Clinical and Research Considerations.

abstract: UNLABELLED ::: Cannabinoids show promise as therapeutic agents, particularly as analgesics, but their development and clinical use has been complicated by recognition of their botanical source, cannabis, as a substance of misuse. Although research into endogenous cannabinoid systems and potential cannabinoid pharmaceuticals is slowly increasing, there has been intense societal interest in making herbal (plant) cannabis available for medicinal use; 23 U.S. States and all Canadian provinces currently permit use in some clinical contexts. Whether or not individual professionals support the clinical use of herbal cannabis, all clinicians will encounter patients who elect to use it and therefore need to be prepared to advise them on cannabis-related clinical issues despite limited evidence to guide care. Expanded research on cannabis is needed to better determine the individual and public health effects of increasing use of herbal cannabis and to advance understanding of the pharmaceutical potential of cannabinoids as medications. This article reviews clinical, research, and policy issues related to herbal cannabis to support clinicians in thoughtfully advising and caring for patients who use cannabis, and it examines obstacles and opportunities to expand research on the health effects of herbal cannabis and cannabinoids. ::: ::: ::: PERSPECTIVE ::: Herbal cannabis is increasingly available for clinical use in the United States despite continuing controversies over its efficacy and safety. This article explores important considerations in the use of plant Cannabis to better prepare clinicians to care for patients who use it, and identifies needed directions for research.


title: Writer identification using directional ink-trace width measurements

abstract: As suggested by modern paleography, the width of ink traces is a powerful source of information for off-line writer identification, particularly if combined with its direction. Such measurements can be computed using simple, fast and accurate methods based on pixel contours, the combination of which forms a powerful feature for writer identification: the Quill feature. It is a probability distribution of the relation between the ink direction and the ink width. It was tested in writer identification experiments on two datasets of challenging medieval handwriting and two datasets of modern handwriting. The feature achieved a nearest-neighbor accuracy in the range of 63-95%, which even approaches the performance of two state-of-the-art features in contemporary-writer identification (Hinge and Fraglets). The feature is intuitive and explainable and its principle is supported by a model of trace production by a quill. It illustrates that ink width patterns are valuable. A slightly more complex variant of Quill, QuillHinge, scored 70-97% writer identification accuracy. The features are already being used by domain experts using a graphical interface.


title: Incentivizing the dissemination of truth versus fake news in social networks

abstract: The concept of truth, as a public good is the production of a collective understanding, which emerges from a complex network of social interactions. The recent impact of social networks on shaping the perception of truth in political arena shows how such perception is corroborated and established by the online users, collectively. However, investigative journalism for discovering truth is a costly option, given the vast spectrum of online information. In some cases, both journalist and online users choose not to investigate the authenticity of the news they receive, because they assume other actors of the network had carried the cost of validation. Therefore, the new phenomenon of "fake news" has emerged within the context of social networks. The online social networks, similarly to System of Systems, cause emergent properties, which makes authentication processes difficult, given availability of multiple sources. In this study, we show how this conflict can be modeled as a volunteer's dilemma. We also show how the public contribution through news subscription (shared rewards) can impact the dominance of truth over fake news in the network.


title: Semi-supervised sequence tagging with bidirectional language models

abstract: Pre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks. However, in most cases, the recurrent network that operates on word-level representations to produce context sensitive representations is trained on relatively little labeled data. In this paper, we demonstrate a general semi-supervised approach for adding pre- trained context embeddings from bidirectional language models to NLP systems and apply it to sequence labeling tasks. We evaluate our model on two standard datasets for named entity recognition (NER) and chunking, and in both cases achieve state of the art results, surpassing previous systems that use other forms of transfer or joint learning with additional labeled data and task specific gazetteers.


title: ZVS Phase-Shift PWM-Controlled Single-Stage Boost Full-Bridge AC–AC Converter for High-Frequency Induction Heating Applications

abstract: This paper presents a new single-stage utility frequency ac–high-frequency ac resonant power converter for high-frequency induction heating applications. The newly proposed ac–ac converter features a boost converter and a full-bridge resonant inverter integrated circuit with a source voltage sensorless control system. The experimental results of a 3.0 kW–40 kHz prototype are demonstrated on zero-voltage soft switching and steady-state characteristics, then, the validity of the proposed ac–ac converter is revealed from a practical viewpoint.


title: Chronobiology of Bipolar Disorder: Therapeutic Implication

abstract: Multiple lines of evidence suggest that psy- chopathological symptoms of bipolar disorder arise in part from a malfunction of the circadian system, linking the disease with an abnormal internal timing. Alterations in circadian rhythms and sleep are core elements in the disorders, characterizing both mania and depression and having recently been shown during euthymia. Several human genetic studies have implicated specific genes that make up the genesis of circadian rhythms in the manifestation of mood disorders with polymorphisms in molecular clock genes not only showing an associa- tion with the disorder but having also been linked to its phenotypic particularities. Many medications used to treat the disorder, such as antidepressant and mood sta- bilizers, affect the circadian clock. Finally, circadian rhythms and sleep researches have been the starting point of the developing of chronobiological therapies. These interventions are safe, rapid and effective and they should be considered first-line strategies for bipolar depression.


title: Effective Landmark Placement for Accurate and Reliable Mobile Robot Navigation

abstract: Being able to navigate accurately is one of the fundamental capabilities of a mobile robot to effectively execute a variety of tasks including docking, transportation, and manipulation. As real-world environments often contain changing or ambiguous areas, existing features can be insufficient for mobile robots to establish a robust navigation behavior. A popular approach to overcome this problem and to achieve accurate localization is to use artificial landmarks. In this paper, we consider the problem of optimally placing such artificial landmarks for mobile robots that repeatedly have to carry out certain navigation tasks. Our method aims at finding the minimum number of landmarks for which a bound on the maximum deviation of the robot from its desired trajectory can be guaranteed with high confidence. The proposed approach incrementally places landmarks utilizing linearized versions of the system dynamics of the robot, thus allowing for an efficient computation of the deviation guarantee. We evaluate our approach in extensive experiments carried out both in simulations and with real robots. The experiments demonstrate that our method outperforms other approaches and is suitable for long-term operation of mobile robots.


title: Enzymatic function of nitric oxide synthases

abstract: Nitric oxide (NO) is synthesised from l-arginine by the enzyme NO synthase (NOS). The complex reaction involves the transfer of electrons from NADPH, via the flavins FAD and FMN in the carboxy-terminal reductase domain, to the haem in the amino-terminal oxygenase domain, where the substrate l-arginine is oxidised to l-citrulline and NO. The haem is essential for dimerisation as well as NO production. The pteridine tetrahydrobiopterin (BH4) is a key feature of NOS, affecting dimerisation and electron transfer, although its full role in catalysis remains to be determined. NOS can also catalyse superoxide anion production, depending on substrate and cofactor availability. There are three main isoforms of the enzyme, named neuronal NOS (nNOS), inducible NOS (iNOS), and endothelial NOS (eNOS), which differ in their dependence on Ca2+, as well as in their expression and activities. These unique features give rise to the distinct subcellular localisations and mechanistic features which are responsible for the physiological and pathophysiological roles of each isoform.


title: Improving Bus Ride Comfort Using GLOSA-Based Dynamic Speed Optimisation

abstract: Public transport (PT) is one of the key elements of a sustainable transport system. Improving its quality is among several measures cities are designing to raise the modal share of PT. Ride comfort is one of the key components defining the quality. In this article, we demonstrate how the comfort can be improved if buses use a Green Light Optimal Speed Advisory (GLOSA) driver assistance system. It allows a driver to adjust speed of the bus as so that it can arrive at the intersection when the light is green. It increases comfort of the passengers, reduces fuel consumption and tailpipe emissions of the bus. We use a multi segment GLOSA, i.e., several lights in a sequence on route of a bus are taken into account in order to calculate speed advice. Such approach is useful if busways are separated from traffic. The speed optimisation method is based on a genetic algorithm. In addition, performance evaluation of a single-segment GLOSA for bus operations in mixed traffic is also evaluated. Using computational experiments we demonstrate that when compared with conventional driving, where speed is not planned, the use of GLOSA results in significant improvement in ride smoothness.


title: Driver Modeling Based on Driving Behavior and Its Evaluation in Driver Identification

abstract: | All drivers have habits behind the wheel. Different drivers vary in how they hit the gas and brake pedals, how they turn the steering wheel, and how much following distance they keep to follow a vehicle safely and comfortably. In this paper, we model such driving behaviors as car-following and pedal operation patterns. The relationship between following distance and velocity mapped into a two-dimensional space is modeled for each driver with an optimal velocity model approximated by a nonlinear function or with a statistical method of a Gaussian mixture model (GMM). Pedal operation patterns are also modeled with GMMs that represent the distributions of raw pedal operation signals or spectral features extracted through spectral analysis of the raw pedal operation signals. The driver models are evaluated in driver identification experiments using driving signals collected in a driving simulator and in a real vehicle. Experimental results show that the driver model based on the spectral features of pedal operation signals efficiently models driver individual differences and achieves an identification rate of 76.8% for a field test with 276 drivers, resulting in a relative error reduction of 55% over driver models that use raw pedal operation signals without spectral analysis.


title: Superblock FTL: A superblock-based flash translation layer with a hybrid address translation scheme

abstract: In NAND flash-based storage systems, an intermediate software layer called a Flash Translation Layer (FTL) is usually employed to hide the erase-before-write characteristics of NAND flash memory. We propose a novel superblock-based FTL scheme, which combines a set of adjacent logical blocks into a superblock. In the proposed Superblock FTL, superblocks are mapped at coarse granularity, while pages inside the superblock are mapped freely at fine granularity to any location in several physical blocks. To reduce extra storage and flash memory operations, the fine-grain mapping information is stored in the spare area of NAND flash memory. This hybrid address translation scheme has the flexibility provided by fine-grain address translation, while reducing the memory overhead to the level of coarse-grain address translation. Our experimental results show that the proposed FTL scheme significantly outperforms previous block-mapped FTL schemes with roughly the same memory overhead.


title: Towards circadian computing: "early to bed and early to rise" makes some of us unhealthy and sleep deprived

abstract: We often think of ourselves as individuals with steady capabilities. However, converging strands of research indicate that this is not the case. Our biochemistry varies significantly over the course of a 24 hour period. Consequently our levels of alertness, productivity, physical activity, and even sensitivity to pain fluctuate throughout the day. This offers a considerable opportunity for the UbiComp community to identify novel measurements and interventions that can leverage these daily variations. To illustrate this potential, we present results from an empirical study with 9 participants over 97 days investigating whether such variations manifest in low-level smartphone use, focusing on daily rhythms related to sleep. Our findings demonstrate that phone usage patterns can be used to detect and predict individual daily variations indicative of temporal preference, sleep duration, and deprivation. We also identify opportunities and challenges for measuring and enhancing well-being using these simple and effective markers of circadian rhythms.


title: Narcissism and Social Networking Web Sites

abstract: The present research examined how narcissism is manifested on a social networking Web site (i.e., Facebook.com). Narcissistic personality self-reports were collected from social networking Web page owners. Then their Web pages were coded for both objective and subjective content features. Finally, strangers viewed the Web pages and rated their impression of the owner on agentic traits, communal traits, and narcissism. Narcissism predicted (a) higher levels of social activity in the online community and (b) more selfpromoting content in several aspects of the social networking Web pages. Strangers who viewed the Web pages judged more narcissistic Web page owners to be more narcissistic. Finally, mediational analyses revealed several Web page content features that were influential in raters’ narcissistic impressions of the owners, including quantity of social interaction, main photo selfpromotion, and main photo attractiveness. Implications of the expression of narcissism in social networking communities are discussed.


title: A General Control Strategy for Input-Series–Output-Series Modular DC–DC Converters

abstract: Input-series-output-series (ISOS) connected dc-dc converters enable the utilization of low-voltage rating switches in high-voltage-input and high-voltage-output applications that require galvanic isolation. To achieve input or output power balance among all the constituent modules, a general control strategy eliminating the use of input-voltage sharing (IVS) loops is proposed. The instability mechanism of independent output-voltage sharing control has been investigated by using the Routh-Hurwitz criterion. From the same criterion, a stability design method for the proposed control strategy has been developed. Detailed comparison of the proposed control technique, voltage-mode IVS, and current-mode IVS control methods is made. In comparison, the proposed control strategy has the same performance compared with current-mode IVS control. The control system design is simplified by avoiding mandatory IVS loops which are needed for IVS control. Both the steady-state and dynamic performance are improved with the proposed control strategy in comparison with voltage-mode IVS control. Using the proposed control strategy, excellent input and output power balance can be achieved for the ISOS connection during transient and steady-state conditions. The proposed control strategy performance is validated by experimental results of a 960-W ISOS system composed of three two-transistor forward dc-dc converters.


title: Kinematics and Dynamics of a New 16 DOF Humanoid Biped Robot with Active Toe Joint

abstract: Humanoid biped robots are typically complex in design, having numerous Degrees-of-Freedom (DOF) due to the ambitious goal of mimicking the human gait. The paper proposes a new architecture for a biped robot with seven DOF per each leg and one DOF corresponding to the toe joint. Furthermore, this work presents close equations for the forward and inverse kinematics by dividing the walking gait into the Sagittal and Frontal planes. This paper explains the mathematical model of the dynamics equations for the legs into the Sagittal and Frontal planes by further applying the principle of Lagrangian dynamics. Finally, a control approach using a PD control law with gravity compensation was recurred in order to control the desired trajectories and finding the required torque by the joints. The paper contains several simulations and numerical examples to prove the analytical results, using SimMechanics of MATLAB toolbox and SolidWorks to verify the analytical results.


title: Noise Induced Hearing Loss: An Application of the Angelic Methodology

abstract: © 2017 The authors and IOS Press. We describe the use of the ANGELIC methodology, developed to encapsulate knowledge of particular legal domains, to build a full scale practical application for internal use by a firm of legal practitioners. We describe the application, the sources used, the stages in development and the application. Some evaluation of the project and its potential for further development is given. The project represents an important step in demonstrating that academic research can prove useful to legal practitioners confronted by real legal tasks.


title: Estimation of Missing Values Using a Weighted K-Nearest Neighbors Algorithm

abstract: This paper developed a novel method to estimate the values of missing data by the use of a weighted -nearest neighbors algorithm. A weighting scheme that exploits the correlation between a “missing” dimension and available data values from other fields, which is quantified based on the support vector regression method. The proposed method has been applied to a practical case of modeling steel corrosion. Comparing with the traditional imputation algorithm, the model results demonstrate its better generalization capability.


title: Analysis of Oscillator Phase-Noise Effects on Self-Interference Cancellation in Full-Duplex OFDM Radio Transceivers

abstract: This paper addresses the analysis of oscillator phase-noise effects on the self-interference cancellation capability of full-duplex direct-conversion radio transceivers. Closed-form solutions are derived for the power of the residual self-interference stemming from phase noise in two alternative cases of having either independent oscillators or the same oscillator at the transmitter and receiver chains of the full-duplex transceiver. The results show that phase noise has a severe effect on self-interference cancellation in both of the considered cases, and that by using the common oscillator in upconversion and downconversion results in clearly lower residual self-interference levels. The results also show that it is in general vital to use high quality oscillators in full-duplex transceivers, or have some means for phase noise estimation and mitigation in order to suppress its effects. One of the main findings is that in practical scenarios the subcarrier-wise phase-noise spread of the multipath components of the self-interference channel causes most of the residual phase-noise effect when high amounts of self-interference cancellation is desired.


title: A survey of light-load efficiency improvement techniques for low-power dc-dc converters

abstract: Maintaining high efficiency at light-load conditions is extremely important in most modern power management applications, where the predominantly digital loads spend the majority of their time in idle mode. The objective of this paper is to review circuit and system-level techniques used to leverage the existing process technologies in order to achieve high efficiency over a wide load range. The main focus is on low-power dc-dc converters operating above several MHz. Existing fixed and variable frequency schemes are contrasted in this work, namely pulse-frequency modulation with fixed and adaptive on-time, burst-mode control, adaptive gate swing, segmented power-stage, resonant gate drivers and capacitive charge recovery.


title: What you can cram into a single vector: Probing sentence embeddings for linguistic properties

abstract: Although much effort has recently been devoted to training high-quality sentence embeddings, we still have a poor understanding of what they are capturing. "Downstream" tasks, often based on sentence classification, are commonly used to evaluate the quality of sentence representations. The complexity of the tasks makes it however difficult to infer what kind of information is present in the representations. We introduce here 10 probing tasks designed to capture simple linguistic features of sentences, and we use them to study embeddings generated by three different encoders trained in eight distinct ways, uncovering intriguing properties of both encoders and training methods.


Processing Techniques for Reduction of Cyanogenic Glycosides from Bamboo Shoots


title: Specialization of reach function in human posterior parietal cortex

abstract: Posterior parietal cortex (PPC) plays an important role in the planning and control of goal-directed action. Single-unit studies in monkeys have identified reach-specific areas in the PPC, but the degree of effector and computational specificity for reach in the corresponding human regions is still under debate. Here, we review converging evidence spanning functional neuroimaging, parietal patient and transcranial magnetic stimulation studies in humans that suggests a functional topography for reach within human PPC. We contrast reach to saccade and grasp regions to distinguish functional specificity and also to understand how these different goal-directed actions might be coordinated at the cortical level. First, we present the current evidence for reach specificity in distinct modules in PPC, namely superior parietal occipital cortex, midposterior intraparietal cortex and angular gyrus, compared to saccade and grasp. Second, we review the evidence for hemispheric lateralization (both for hand and visual hemifield) in these reach representations. Third, we review evidence for computational reach specificity in these regions and finally propose a functional framework for these human PPC reach modules that includes (1) a distinction between the encoding of reach goals in posterior–medial PPC as opposed to reach movement vectors in more anterior–lateral PPC regions, and (2) their integration within a broader cortical framework for reach, grasp and eye–hand coordination. These findings represent both a confirmation and extension of findings that were previously reported for the monkey.


HRM: an assessment of the Kenyan case


title: Optimization of GaN-based ultra-low power boost converter in far-field energy harvesting

abstract: This paper presents a method of optimizing low power boost converters for use in far-field wireless energy harvesting systems. This method uses a database of manufacturer-provided device characteristics of both Silicon and GaN FETs to construct Figures of Merit (FOMs) used for evaluating device technologies for power loss based on boost converter parameters. A loss model is constructed for predicting device power losses and system efficiency over a wide range of operating points. Using the analysis framework, an asynchronous boost converter is constructed and experimentally verified with operation as low as 10 μW with a peak efficiency of 74% at an input power of 300 μW.


title: Functional expression of the yeast FLP/FRT site-specific recombination system in Nicotiana tabacum

abstract: The FLP/FRT site-specific recombination system of Saccharomyces cerevisiae was expressed in stably transformed tobacco plants. The FLP protein efficiently catalyzes recombination between two directly repeated FLP recombination target (FRT) sites, deleting the sequence between them. In the constructs tested here, this deletion places the CaMV 35S promoter adjacent to a hygromycin resistance marker; transcriptional activation of the marker allows direct selection of recombination events. After crossing plants containing an integrated FLP expression construct with plants containing a FLP substrate, Fl seedlings can be selected directly for hygromycin resistance, indicating that recombination occurs at, or very early after zygote formation. Molecular analysis confirmed the expected recombination product.


title: Blocking-aware private record linkage

abstract: In this paper, the problem of quickly matching records (i.e., record linkage problem) from two autonomous sources without revealing privacy to the other parties is considered. In particular, our focus is to devise secure blocking scheme to improve the performance of record linkage significantly while being secure. Although there have been works on private record linkage, none has considered adopting the blocking framework. Therefore, our proposed blocking-aware private record linkage can perform large-scale record linkage without revealing privacy. Preliminary experimental results showing the potential of the proposal are reported.


title: Automated Semantic Knowledge Acquisition From Sensor Data

abstract: The gathering of real-world data is facilitated by many pervasive data sources such as sensor devices and smartphones. The abundance of the sensory data raises the need to make the data easily available and understandable for the potential users and applications. Using semantic enhancements is one approach to structure and organize the data and to make it processable and interoperable by machines. In particular, ontologies are used to represent information and their relations in machine interpretable forms. In this context, a significant amount of work has been done to create real-world data description ontologies and data description models; however, little effort has been done in creating and constructing meaningful topical ontologies from a vast amount of sensory data by automated processes. Topical ontologies represent the knowledge from a certain domain providing a basic understanding of the concepts that serve as building blocks for further processing. There is a lack of solution that construct the structure and relations of ontologies based on real-world data. To address this challenge, we introduce a knowledge acquisition method that processes real-world data to automatically create and evolve topical ontologies based on rules that are automatically extracted from external sources. We use an extended $k$ - means clustering method and apply a statistic model to extract and link relevant concepts from the raw sensor data and represent them in the form of a topical ontology. We use a rule-based system to label the concepts and make them understandable for the human user or semantic analysis and reasoning tools and software. The evaluation of our work shows that the construction of a topological ontology from raw sensor data is achievable with only small construction errors.


title: Efficient revocation and threshold pairing based cryptosystems

abstract: Boneh, Ding, Tsudik and Wong recently proposed a way for obtaining fast revocation of RSA keys. Their method consists in using security mediators that keep a piece of each user's private key in such a way that every decrytion or signature operation requires the help of the mediator for the user. Revocation is achieved by instructing the mediator to stop helping the user to sign or decrypt messages. This security architecture, called SEM, gave rise to an identity based mediated RSA scheme (IB-mRSA) that combines the advantages of fast revocation and identity based public keys. We show that, in opposition to what was stated in [9], this revocation method can be applied to several existing public key encryption and signature schemes (all those for which a secure practical threshold adaptation exists) including the Boneh-Franklin identity based encryption scheme and a pairing based digital signature schemes. We first describe a threshold adaptation of the Boneh-Franklin identity based encryption scheme and, then, we compare the mediated versions of these schemes with IB-mRSA from security and efficiency points of view.


title: Deep Belief Networks Based Voice Activity Detection

abstract: Fusing the advantages of multiple acoustic features is important for the robustness of voice activity detection (VAD). Recently, the machine-learning-based VADs have shown a superiority to traditional VADs on multiple feature fusion tasks. However, existing machine-learning-based VADs only utilize shallow models, which cannot explore the underlying manifold of the features. In this paper, we propose to fuse multiple features via a deep model, called deep belief network (DBN). DBN is a powerful hierarchical generative model for feature extraction. It can describe highly variant functions and discover the manifold of the features. We take the multiple serially-concatenated features as the input layer of DBN, and then extract a new feature by transferring these features through multiple nonlinear hidden layers. Finally, we predict the class of the new feature by a linear classifier. We further analyze that even a single-hidden-layer-based belief network is as powerful as the state-of-the-art models in the machine-learning-based VADs. In our empirical comparison, ten common features are used for performance analysis. Extensive experimental results on the AURORA2 corpus show that the DBN-based VAD not only outperforms eleven referenced VADs, but also can meet the real-time detection demand of VAD. The results also show that the DBN-based VAD can fuse the advantages of multiple features effectively.


title: The PHQ-PD as a Screening Tool for Panic Disorder in the Primary Care Setting in Spain

abstract: INTRODUCTION ::: Panic disorder is a common anxiety disorder and is highly prevalent in Spanish primary care centres. The use of validated tools can improve the detection of panic disorder in primary care populations, thus enabling referral for specialized treatment. The aim of this study is to determine the accuracy of the Patient Health Questionnaire-Panic Disorder (PHQ-PD) as a screening and diagnostic tool for panic disorder in Spanish primary care centres. ::: ::: ::: METHOD ::: We compared the psychometric properties of the PHQ-PD to the reference standard, the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I) interview. General practitioners referred 178 patients who completed the entire PHQ test, including the PHQ-PD, to undergo the SCID-I. The sensitivity, specificity, positive and negative predictive values and positive and negative likelihood ratios of the PHQ-PD were assessed. ::: ::: ::: RESULTS ::: The operating characteristics of the PHQ-PD are moderate. The best cut-off score was 5 (sensitivity .77, specificity .72). Modifications to the questionnaire's algorithms improved test characteristics (sensitivity .77, specificity .72) compared to the original algorithm. The screening question alone yielded the highest sensitivity score (.83). ::: ::: ::: CONCLUSION ::: Although the modified algorithm of the PHQ-PD only yielded moderate results as a diagnostic test for panic disorder, it was better than the original. Using only the first question of the PHQ-PD showed the best psychometric properties (sensitivity). Based on these findings, we suggest the use of the screening questions for screening purposes and the modified algorithm for diagnostic purposes.


Low Cost Eye Tracking Technique For Visual Scan Path Detection in the diagnosis of Special Learning Disorders in Children


title: Image understanding and the web: a state-of-the-art review

abstract: The contextual information of Web images is investigated to address the issue of characterizing their content with semantic descriptors and therefore bridge the semantic gap, i.e. the gap between their automated low-level representation in terms of colors, textures, shapes?and their semantic interpretation. Such characterization allows for understanding the image content and is crucial in important Web-based tasks such as image indexing and retrieval. Although we are highly motivated by the availability of rich knowledge on the Web and the relative success achieved by commercial search engines in automatically characterizing the image content using contextual information in Web pages, we are aware that the unpredictable quality of the contextual information is a major limiting factor. Among the reasons explaining the difficulty to leverage on the image contextual information, some problems are related to the characterization and extraction of this information. Indeed, the first issue is the lack of large-scale studies to highlight what is considered the relevant contextual information of an image, where it is located in a Web page and whether it is consistent across Web pages of different types, content layouts and domains. Also, the matter related to the extraction of this contextual information is topical as state-of-the-art automated extraction tools are unable to handle the heterogeneous Web. As far as the processing of the contextual information is concerned, problems linked to the syntactic and semantic characterizations of the textual components are important to address in order to tackle the semantic gap. Furthermore, questions pertaining to the organization of these textual components into coherent structures that are usable in image indexing and retrieval frameworks shall arise. To address these issues, we lay down the anatomy of a generic context-based Web image understanding framework and propose its stage-based decomposition, covering topical issues from information indexing and retrieval, image description models, natural language processing, webpage segmentation and automated information extraction. For each of the identified stages, we review state-of-the-art solutions in the literature categorized and analyzed under the light of the techniques used.


Abstention Protocol for Accuracy and Speed


4. Direct Domain Decomposition using the Hierarchical Matrix Technique


title: Network-attached FPGAs for data center applications

abstract: FPGAs (Field Programmable Gate Arrays) are making their way into data centers (DC). They are used as accelerators to boost the compute power of individual server nodes and to improve the overall power efficiency. However, this approach limits the number of FPGAs per node and hinders the acceleration of large-scale distributed applications.


title: Neighbourhood NILM: A Big-data Approach to Household Energy Disaggregation

abstract: In this paper, we investigate whether "big-data" is more valuable than "precise" data for the problem of energy disaggregation: the process of breaking down aggregate energy usage on a per-appliance basis. Existing techniques for disaggregation rely on energy metering at a resolution of 1 minute or higher, but most power meters today only provide a reading once per month, and at most once every 15 minutes. In this paper, we propose a new technique called Neighbourhood NILM that leverages data from 'neighbouring' homes to disaggregate energy given only a single energy reading per month. The key intuition behind our approach is that 'similar' homes have 'similar' energy consumption on a per-appliance basis. Neighbourhood NILM matches every home with a set of 'neighbours' that have direct submetering infrastructure, i.e. power meters on individual circuits or loads. Many such homes already exist. Then, it estimates the appliance-level energy consumption of the target home to be the average of its K neighbours. We evaluate this approach using 25 homes and results show that our approach gives comparable or better disaggregation in comparison to state-of-the-art accuracy reported in the literature that depend on manual model training, high frequency power metering, or both. Results show that Neighbourhood NILM can achieve 83% and 79% accuracy disaggregating fridge and heating/cooling loads, compared to 74% and 73% for a technique called FHMM. Furthermore, it achieves up to 64% accuracy on washing machine, dryer, dishwasher, and lighting loads, which is higher than previously reported results. Many existing techniques are not able to disaggregate these loads at all. These results indicate a potentially substantial advantage to installing submetering infrastructure in a select few homes rather than installing new high-frequency smart metering infrastructure in all homes.


title: A Combined Pedestrian Detection Method Based on Haar-Like Features and HOG Features

abstract: Considering the simplicity and fast training speed of Haar-like features, the high detecting precision of HOG features, a combined method is proposed on the basis of the two features. Several rectangular features which can describe local human characteristics based on original features are added. The combined method can retain the precision of HOG features and increase the speed of detection at the same time. The experiments have shown this method can detect the pedestrians quickly and effectively with high precision.


title: The Risks of Chronic Corticosteroid Exposure.

abstract: Questions from patients about pain conditions and analgesic pharmacotherapy and responses from authors are presented to help educate patients and make them more effective self-advocates. This article focuses on the use of corticosteroids to decrease inflammation and treat painful conditions as well as their long-term side effects and risks. Their long-term use can expose the body to side effects and can cause long-term health concerns. It therefore is important to be aware of the risks of long-term corticosteroid use.


Model Compression Applied to Small-Footprint Keyword Spotting.


title: Assessing information quality of a community-based encyclopedia

abstract: Effective information quality analysis needs powerful yet easy ways to obtain metrics. The English version of Wikipedia provides an extremely interesting yet challenging case for the study of Information Quality dynamics at both macro and micro levels. We propose seven IQ metrics which can be evaluated automatically and test the set on a representative sample of Wikipedia content. The methodology of the metrics construction and the results of tests, along with a number of statistical characterizations of Wikipedia articles, their content construction, process metadata and social context are reported.


title: Blame, coercions, and threesomes, precisely

abstract: We systematically present four calculi for gradual typing: the blame calculus of Wadler and Findler (2009); a novel calculus that pinpoints blame precisely; the coercion calculus of Henglein (1994); and the threesome calculus of Siek and Wadler (2010). Threesomes are given a syntax that directly exposes their origin as coercions in normal form, a more transparent presentation than that found in Siek and Wadler (2010) or Garcia (2013).


title: A Machine Learning Approach for Phenotype Name Recognition

abstract: Extracting biomedical named entities is one of the major challenges in automatic processing of biomedical literature. This paper proposes a machine learning approach for finding phenotype names in text. Features are included in a machine learning infrastructure to implement the rules found in our previously developed rule-based system. The system also uses two available resources: MetaMap and HPO. As we are not aware of any available corpus for phenotype names, a corpus has been constructed. Since manual tagging of the corpus was not possible for us, we started tagging only HPO phenotypes in the corpus and then using a semi-supervised learning method, the tagging process improved. The evaluation results (F-Score 92.25) suggest that the system achieved good performance and it outperforms the rule-based system.


title: Computer vision for assistive technologies

abstract: An original "task oriented" way to categorize the state of the art of the AT works has been introduced.The paper concentrates on a set of cross-application Computer Vision tasks that are set as the pivots to categorize AT.The paper addresses also critical assessment of ATs considering users, medical, economical and social perspectives as well as emerging open challenges, and it tries to catch a glimpse of the possible recent advancements in Computer Vision that could allow a real improvement of the assistive outcomes. In the last decades there has been a tremendous increase in demand for Assistive Technologies (AT) useful to overcome functional limitations of individuals and to improve their quality of life. As a consequence, different research papers addressing the development of assistive technologies have appeared into the literature pushing the need to organize and categorize them taking into account the application assistive aims. Several surveys address the categorization problem for works concerning a specific need, hence giving the overview on the state of the art technologies supporting the related function for the individual. Unfortunately, this "user-need oriented" way of categorization considers each technology as a whole and then a deep and critical explanation of the technical knowledge used to build the operative tasks as well as a discussion on their cross-contextual applicability is completely missing making thus existing surveys unlikely to be technically inspiring for functional improvements and to explore new technological frontiers. To overcome this critical drawback, in this paper an original "task oriented" way to categorize the state of the art of the AT works has been introduced: it relies on the split of the final assistive goals into tasks that are then used as pointers to the works in literature in which each of them has been used as a component. In particular this paper concentrates on a set of cross-application Computer Vision tasks that are set as the pivots to establish a categorization of the AT already used to assist some of the user's needs. For each task the paper analyzes the Computer Vision algorithms recently involved in the development of AT and, finally, it tries to catch a glimpse of the possible paths in the short and medium term that could allow a real improvement of the assistive outcomes. The potential impact on the assessment of AT considering users, medical, economical and social perspective is also addressed.


Social Network Security Issues : Social Engineering and Phishing Attacks


title: Combining passive haptics with redirected walking

abstract: Two large problems faced by virtual environment designers are lack of haptic feedback and constraints imposed by limited tracker space. Passive haptic feedback has been used effectively to provide a sense of touch to users (Insko, et al., 2001). Redirected walking is a promising solution to the problem of limited tracker space (Razzaque, et al., 2001). However, these solutions to these two problems are typically mutually exclusive because their requirements conflict with one another. We introduce a method by which they can be combined to address both problems simultaneously.


title: Research in data warehouse modeling and design: dead or alive?

abstract: Multidimensional modeling requires specialized design techniques. Though a lot has been written about how a data warehouse should be designed, there is no consensus on a design method yet. This paper follows from a wide discussion that took place in Dagstuhl, during the Perspectives Workshop "Data Warehousing at the Crossroads", and is aimed at outlining some open issues in modeling and design of data warehouses. More precisely, issues regarding conceptual models, logical models, methods for design, interoperability, and design for new architectures and applications are considered.


title: Automatic detection of arguments in legal texts

abstract: This paper provides the results of experiments on the detection of arguments in texts among which are legal texts. The detection is seen as a classification problem. A classifier is trained on a set of annotated arguments. Different feature sets are evaluated involving lexical, syntactic, semantic and discourse properties of the texts. The experiments are a first step in the context of automatically classifying arguments in legal texts according to their rhetorical type and their visualization for convenient access and search.


title: Ontology Design Patterns for Semantic Web Content

abstract: The paper presents a framework for introducing design patterns that facilitate or improve the techniques used during ontology lifecycle. Some distinctions are drawn between kinds of ontology design patterns. Some content-oriented patterns are presented in order to illustrate their utility at different degrees of abstraction, and how they can be specialized or composed. The proposed framework and the initial set of patterns are designed in order to function as a pipeline connecting domain modelling, user requirements, and ontology driven tasks/queries to be executed.


title: Camera pose and calibration from 4 or 5 known 3D points

abstract: We describe two direct quasilinear methods for camera pose (absolute orientation) and calibration from a single image of 4 or 5 known 3D points. They generalize the 6 point 'Direct Linear Transform' method by incorporating partial prior camera knowledge, while still allowing some unknown calibration parameters to be recovered. Only linear algebra is required, the solution is unique in non-degenerate cases, and additional points can be included for improved stability. Both methods fail for coplanar points, but we give an experimental eigendecomposition based one that handles both planar and nonplanar cases. Our methods use recent polynomial solving technology, and we give a brief summary of this. One of our aims was to try to understand the numerical behaviour of modern polynomial solvers on some relatively simple test cases, with a view to other vision applications.


title: An Improved 9/12 Two-Phase E-Core Switched Reluctance Machine

abstract: There has always been a high demand for cost-efficient, reliable, low-noise, and durable low-maintenance motors, especially in high-quantity applications. A switched reluctance motor (SRM) can be a great candidate because of its rigid and simple construction. Among different SRMs, the two-phase type has the simple power converter, control unit, and geometry. In this paper, a short-flux two-phase SRM (2-phase SRM) known as E-Core 6/10 is modified to produce higher torque. The formulas for the number of rotor and stator poles have been amended, and in addition to alleviating some mechanical issues, the empty adjacent-pole spaces have been fully utilized. Comparisons are made based on simulations by 2-D finite-element analysis method and experimental results. It is obtained that for the same main dimensions, the average torque in the modified 6/10 two-phase SRM improves by about 35% that validates the new proposed motor.


title: Fire smoke detection algorithm based on motion characteristic and convolutional neural networks

abstract: It is a challenging task to recognize smoke from visual scenes due to large variations in the feature of color, texture, shapes, etc. The current detection algorithms are mainly based on single feature or fusion of multiple static features of smoke, which leads to low detection accuracy. To solve this problem, this paper proposes a smoke detection algorithm based on the motion characteristics of smoke and the convolutional neural networks (CNN). Firstly, a moving object detection algorithm based on background dynamic update and dark channel priori is proposed to detect the suspected smoke regions. Then, the features of suspected region is extracted automatically by CNN, on that the smoke identification is performed. Compared to previous work, our algorithm improves the detection accuracy, which can reach 99% in the testing sets. For the problem that the region of smoke is relatively small in the early stage of smoke generation, the strategy of implicit enlarging the suspected regions is proposed, which improves the timeliness of smoke detection. In addition a fine-tuning method is proposed to solve the problem of scarce of data in the training network. Also, the algorithm has good smoke detection performance by testing under various video scenes.


title: NID-SLAM: Robust Monocular SLAM Using Normalised Information Distance

abstract: We propose a direct monocular SLAM algorithm based on the Normalised Information Distance (NID) metric. In contrast to current state-of-the-art direct methods based on photometric error minimisation, our information-theoretic NID metric provides robustness to appearance variation due to lighting, weather and structural changes in the scene. We demonstrate successful localisation and mapping across changes in lighting with a synthetic indoor scene, and across changes in weather (direct sun, rain, snow) using real-world data collected from a vehicle-mounted camera. Our approach runs in real-time on a consumer GPU using OpenGL, and provides comparable localisation accuracy to state-of-the-art photometric methods but significantly outperforms both direct and feature-based methods in robustness to appearance changes.


title: Real-time integrated face detection and recognition on embedded GPGPUs

abstract: Both face detection and face recognition have started to be used widely these days in various applications such as biometric, surveillance, security, advertisement, entertainment, and so on. The ever increasing input image size in face detection and the large input DB in face recognition keep requiring more computational power to achieve real-time processing. Recently, embedded GPUs have started to support OpenCL and many applications can be accelerated successfully as the server GPUs have. In this paper, we propose several optimization techniques for the Local Binary Pattern (LBP) based integrated face detection and recognition algorithms, and successfully accelerated them achieving 22 fps using OpenCL on ARM Mali GPU, and 38 fps using CUDA on Tegra K1 GPU for HD inputs. This corresponds to 2.9 times and 3.7 times speedups respectively. To the best of our knowledge, it is the first paper that presents the acceleration of the face detection on embedded GPGPUs, and also that presents the performance of Tegra K1 GPU.


title: Achieving interoperability between the CARARE schema for monuments and sites and the Europeana Data Model

abstract: Mapping between different data models in a data aggregation context always presents significant interoperability challenges. In this paper, we describe the challenges faced and solutions developed when mapping the CARARE schema designed for archaeological and architectural monuments and sites to the Europeana Data Model (EDM), a model based on Linked Data principles. The purpose of this mapping was to integrate more than two million metadata records from national monument collections and databases across Europe into the Europeana digital library.


title: A Survey on Application Layer Protocols for the Internet of Things

abstract: It has been more than fifteen years since the term Internet of Things (IoT) was introduced. However, despite the efforts of research groups and innovative corporations, still today it is not possible to say that the IoT is upon us. This is mainly due to the fact that a unified IoT architecture has not yet been clearly defined and there is no common agreement in defining communication protocols and standards for all the IoT parts. The framework that current IoT platforms use consists mostly in technologies that partially fulfill the IoT requirements. While developers employ existing technologies to build the IoT, research groups are working on adapting protocols to the IoT in order to optimize communications. In this paper, we present and compare existing IoT application layer protocols as well as protocols that are utilized to connect the “things” but also end-user applications to the Internet. We highlight IETF’s CoAP, IBM’s MQTT, HTML 5’s Websocket among others, and we argue their suitability for the IoT by considering reliability, security, and energy consumption aspects. Finally, we provide our conclusions for the IoT application layer communications based on the study that we have conducted.


title: Stealth Loader: Trace-Free Program Loading for API Obfuscation

abstract: Understanding how application programming interfaces (APIs) are used in a program plays an important role in malware analysis. This, however, has resulted in an endless battle between malware authors and malware analysts around the development of API [de]obfuscation techniques over the last few decades. Our goal in this paper is to show a limit of existing API de-obfuscations. To do that, we first analyze existing API [de]obfuscation techniques and clarify an attack vector commonly existed in API de-obfuscation techniques, and then we present Stealth Loader, which is a program loader using our API obfuscation technique to bypass all existing API de-obfuscations. The core idea of this technique is to load a dynamic link library (DLL) and resolve its dependency without leaving any traces on memory to be detected. We demonstrate the effectiveness of Stealth Loader by analyzing a set of Windows executables and malware protected with Stealth Loader using major dynamic and static analysis tools and techniques. The result shows that among other obfuscation techniques, only Stealth Loader is able to successfully bypass all analysis tools and techniques.


title: Statistical modeling: the two cultures

abstract: There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated bya given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown. The statistical communityhas been committed to the almost exclusive use of data models. This commit- ment has led to irrelevant theory, questionable conclusions, and has kept statisticians from working on a large range of interesting current prob- lems. Algorithmic modeling, both in theoryand practice, has developed rapidlyin fields outside statistics. It can be used both on large complex data sets and as a more accurate and informative alternative to data modeling on smaller data sets. If our goal as a field is to use data to solve problems, then we need to move awayfrom exclusive dependence on data models and adopt a more diverse set of tools.


title: High-Dimensional Data Visualization by Interactive Construction of Low-Dimensional Parallel Coordinate Plots

abstract: Abstract Parallel coordinate plots (PCPs) are among the most useful techniques for the visualization and exploration of high-dimensional data spaces. They are especially useful for the representation of correlations among the dimensions, which identify relationships and interdependencies between variables. However, within these high-dimensional spaces, PCPs face difficulties in displaying the correlation between combinations of dimensions and generally require additional display space as the number of dimensions increases. In this paper, we present a new technique for high-dimensional data visualization in which a set of low-dimensional PCPs are interactively constructed by sampling user-selected subsets of the high-dimensional data space. In our technique, we first construct a graph visualization of sets of well-correlated dimensions. Users observe this graph and are able to interactively select the dimensions by sampling from its cliques, thereby dynamically specifying the most relevant lower dimensional data to be used for the construction of focused PCPs. Our interactive sampling overcomes the shortcomings of the PCPs by enabling the visualization of the most meaningful dimensions (i.e., the most relevant information) from high-dimensional spaces. We demonstrate the effectiveness of our technique through two case studies, where we show that the proposed interactive low-dimensional space constructions were pivotal for visualizing the high-dimensional data and discovering new patterns.


Fisher Tensor Decomposition for Unconstrained Gait Recognition


title: An analysis of various snort based techniques to detect and prevent intrusions in networks proposal with code refactoring snort tool in Kali Linux environment

abstract: Security and reliability are the major concern of our daily life usage of any network. But with the swift advancements in network technology, attacks are becoming more sophisticated than defenses. Although firewalls and router-based packet filtering are essential elements of an overall network security topology, they are not enough on their own. So, to brace the network from unauthorized access the idea of Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) is attracting security experts. This paper briefs various trends in Intrusion Detection & Prevention. To understand various techniques in IDS, this paper analyses various approaches proposed by security researchers specifically using popular open source software Snort as their IDS tool. Being an open source IDS, Snort can be easily configured and deployed in any environment. To assess the efficiency, these research papers are analyzed in various performance aspects like Detection Accuracy, Scalability and Capability of detecting unknown attacks. To overcome various challenges like low detection rate, incapable of handling huge traffic, unsupported automated tuning, etc. that are identified during literature review, this paper proposes a level based architecture. All the levels are designed as incremental i.e. capable of providing the desired functionality and also its lower levels. To prove the efficiency of the proposed architecture, it can be integrated into Snort Tool using Code Refactoring. Also proposed an environment setup to evaluate the modified Snort Tool performance in future.


title: Visual Place Recognition: A Survey

abstract: Visual place recognition is a challenging problem due to the vast range of ways in which the appearance of real-world places can vary. In recent years, improvements in visual sensing capabilities, an ever-increasing focus on long-term mobile robot autonomy, and the ability to draw on state-of-the-art research in other disciplines—particularly recognition in computer vision and animal navigation in neuroscience—have all contributed to significant advances in visual place recognition systems. This paper presents a survey of the visual place recognition research landscape. We start by introducing the concepts behind place recognition—the role of place recognition in the animal kingdom, how a “place” is defined in a robotics context, and the major components of a place recognition system. Long-term robot operations have revealed that changing appearance can be a significant factor in visual place recognition failure; therefore, we discuss how place recognition solutions can implicitly or explicitly account for appearance change within the environment. Finally, we close with a discussion on the future of visual place recognition, in particular with respect to the rapid advances being made in the related fields of deep learning, semantic scene understanding, and video description.


title: Traffic flow prediction using neural network

abstract: Traffic flow management and analysis have become essential for both individuals to better manage and route their daily commutes, and for transportation planners to optimally schedule road infrastructure maintenance tasks. Therefore the ability to predict the nature of the traffic stream accurately is one of the most important requirements of traffic management systems. In this research, we will propose an intelligent method to predict traffic flow based on real data for the years 2016 and 2017 provided by the Moroccan center for road studies and research. The proposed solution focuses on training a neural network model to estimate future traffic flow on an hourly basis. Results determined by the simulation gave a good prediction to the traffic data.


title: The Preparation of H 3 -Vitamins D 2 and D 3 and Their Localization in the Rat *

abstract: A convenient procedure for the preparatioNonen of tritiumlabeled vitamin D/sub 2/ and D/sub 3/ is described. The provitamins were exposed to tritium gas by the procedure of Wilzbach, irradiated with ultraviolet light, and isolated as the crystalline 3,5-dinitrobenzoates. The H/sup 3/-vitamin D/sub 2/, specific activity 3.2 mC/mmole, or 450 disintegrations per minute per IU, and H/sup 3/- vitamin D/sub 3/, specific activity 7.2 mC/mmole or 1050 dpm/IU, are fully biologically active and were shown to be radiochemically pure in at least three chromatographic systems. The distribution of a physiological dose of Ha-vitamin D/sub 3/ as a function of time has been studied in the various tissues of the rat. Evidence is presented for the involvement of the ileum in the absorption of vitamin D. The liver rapidly accumulated the largest amount of radioactivity whether the dose was administered orally or intracardially. No evidence was found for the involvement of the adrenals in the metabolism of vitamin D. (auth)


title: The Weak Byzantine Generals Problem

abstract: The Byzantine Generals Problem requires processes to reach agreement upon a value even though some of them may fad. It is weakened by allowing them to agree upon an "incorrect" value if a failure occurs. The transaction eormmt problem for a distributed database Js a special case of the weaker problem. It is shown that, like the original Byzantine Generals Problem, the weak version can be solved only ff fewer than one-third of the processes may fad. Unlike the onginal problem, an approximate solution exists that can tolerate arbaranly many failures.


title: Group Optimization to Maximize Peer Assessment Accuracy Using Item Response Theory

abstract: As an assessment method based on a social constructivist approach, peer assessment has become popular in recent years. When the number of learners increases as in MOOCs, peer assessment is often conducted by dividing learners into multiple groups to reduce the learner’s assessment workload. However, in this case, a difficulty remains that the assessment accuracies of learners in each group depends on the assigned rater. To solve that problem, this study proposes a group optimization method to maximize peer assessment accuracy based on item response theory using integer programming. Experimental results, however, showed that the proposed method does not necessarily present higher accuracy than a random group formation. Therefore, we further propose an external rater selection method that assigns a few outside-group raters to each learner. Simulation and actual data experiments demonstrate that introduction of external raters using the proposed method improves the peer assessment accuracy considerably.


title: Pregelix: Big(ger) Graph Analytics on A Dataflow Engine

abstract: There is a growing need for distributed graph processing systems that are capable of gracefully scaling to very large graph datasets. Unfortunately, this challenge has not been easily met due to the intense memory pressure imposed by process-centric, message passing designs that many graph processing systems follow. Pregelix is a new open source distributed graph processing system that is based on an iterative dataflow design that is better tuned to handle both in-memory and out-of-core workloads. As such, Pregelix offers improved performance characteristics and scaling properties over current open source systems (e.g., we have seen up to 15X speedup compared to Apache Giraph and up to 35X speedup compared to distributed GraphLab), and more effective use of available machine resources to support Big(ger) Graph Analytics.


title: User Profiles and Personas in Consumer Health Technologies

abstract: Consumer health technologies (CHT) are considered important catalysts for what some are calling the "dawn of a new era - the age of the empowered health consumer" [1]. To develop effective systems, we must reach the level of personal connection to capture the essence of user groups and their issues. User profiles and personas can be used to help the development team to mentally visualize and communicate how intended users of CHT's will interact with the proposed design in a meaningful way. We use an action research approach to call attention to important health related characteristics for CHT user profiles and personas, provide persona and profile artifacts for chronic diabetics, and illustrate the process and benefit of these tools in requirements generation, design, and implementation planning.


Cooking quality of potatoes.


title: FoldMecha: Exploratory Design and Engineering of Mechanical Papercraft

abstract: We present FoldMecha, a computer-aided design (CAD) system for exploratory construction of mechanical papercraft. FoldMecha enables students to (a) design their own movements with simple mechanisms by modifying parameters and (b) build physical prototypes. This paper describes the system, as well as associated prototyping methods that make the construction process easier and more adaptable to widely different creations. The paper also discusses a week-long workshop that we held with six teenagers using FoldMecha. The teens successfully designed and built their own mechanisms, and adapted them to a variety of creations. Throughout the workshop, they progressively achieved an advanced level of skill and understanding about mechanical movements.


title: Can visual fixation patterns improve image fidelity assessment?

abstract: This paper presents the results of a computational experiment designed to investigate the extent to which metrics of image fidelity can be improved through knowledge of where humans tend to fixate in images. Five common metrics of image fidelity were augmented using two sets of fixation data, one set obtained under task-free viewing conditions and another set obtained when viewers were asked to judge image quality. The augmented metrics were then compared to subjective ratings of the images. The results show that most metrics can be improved using eye fixation data, but a greater improvement was found using fixations obtained in the task-free condition (task-free viewing).


title: Exploration of UK Lotto results classified into two periods

abstract: Abstract United Kingdom Lotto results are obtained from urn containing some numbers of which six winning numbers and one bonus are drawn at each draw event. There is always a need from prospective players for analysis that can aid them in increasing their chances of winning. In this paper, historical data of the United Kingdom Lotto results were grouped into two periods (19/11/1994–7/10/2015 and 10/10/2015–10/5/2017). The classification was as a result of increase of the lotto numbers from 49 to 59. Exploratory statistical and mathematical tools were used to obtain new patterns of winning numbers. The data can provide insights on the random nature and distribution of the winning numbers and help prospective players in increasing their chances of winning the lotto.


title: Bi-directional Attention with Agreement for Dependency Parsing

abstract: We develop a novel bi-directional attention model for dependency parsing, which learns to agree on headword predictions from the forward and backward parsing directions. The parsing procedure for each direction is formulated as sequentially querying the memory component that stores continuous headword embeddings. The proposed parser makes use of {\it soft} headword embeddings, allowing the model to implicitly capture high-order parsing history without dramatically increasing the computational complexity. We conduct experiments on English, Chinese, and 12 other languages from the CoNLL 2006 shared task, showing that the proposed model achieves state-of-the-art unlabeled attachment scores on 6 languages.


title: Telemetric intra-cranial pressure monitoring: clinical and financial considerations.

abstract: Intracranial pressure (ICP) measurement is an important diagnostic tool in Neurosurgery. Until relatively recently, conventional monitoring has required that subjects be admitted to a hospital bed and the device is only able to be left in-situ for limited periods of time. We have evaluated a Telemetric ICP monitoring system that has been proven, by several other groups worldwide, to permit rapid, repeated and prolonged ICP measurement, in multiple environments. In our unit, 4 patients have been implanted to-date, between the ages of 4 and 16, manifesting a wide range of complex neurosurgical conditions. The sensors have been left in-situ for between 460 and 632 days. There have been no clinical complications and the system has been universally well tolerated. Clinical events, costs and patient experience were all assessed prior to and following implantation. Overall, there was a significant reduction in associated admissions (44.3%), imaging requirements (72.5%) and costs (50.0%). Subjective feedback from both the patients (where possible) and their families was overwhelmingly positive, partly due to (a) the system's ease of use, (b) its ability to reduce the number of admissions/tests required and (c) the facility for rapid measurement of ICP that permitted on-the-spot reassurance of concerns. Additionally, the ability to monitor ICP at home and/or whilst ambulant, has provided measurements that were hitherto inaccessible to our team, facilitating all the potential benefits that analysis of such information would provide. Indeed, we have seen the resultant management in each case has been completely altered by the availability of this data, reaffirming that the importance of being able to obtain it should not be underestimated. The combination of both this and the ability to markedly improve patient experience, along with generating significant cost-savings, lead the authors to suggest that the implantation of this system should be strongly considered in selected individuals.


title: Speech feature denoising and dereverberation via deep autoencoders for noisy reverberant speech recognition

abstract: Denoising autoencoders (DAs) have shown success in generating robust features for images, but there has been limited work in applying DAs for speech. In this paper we present a deep denoising autoencoder (DDA) framework that can produce robust speech features for noisy reverberant speech recognition. The DDA is first pre-trained as restricted Boltzmann machines (RBMs) in an unsupervised fashion. Then it is unrolled to autoencoders, and fine-tuned by corresponding clean speech features to learn a nonlinear mapping from noisy to clean features. Acoustic models are re-trained using the reconstructed features from the DDA, and speech recognition is performed. The proposed approach is evaluated on the CHiME-WSJ0 corpus, and shows a 16-25% absolute improvement on the recognition accuracy under various SNRs.


title: Making Decisions about Privacy: Information Disclosure in Context-Aware Recommender Systems

abstract: Recommender systems increasingly use contextual and demographical data as a basis for recommendations. Users, however, often feel uncomfortable providing such information. In a privacy-minded design of recommenders, users are free to decide for themselves what data they want to disclose about themselves. But this decision is often complex and burdensome, because the consequences of disclosing personal information are uncertain or even unknown. Although a number of researchers have tried to analyze and facilitate such information disclosure decisions, their research results are fragmented, and they often do not hold up well across studies. This article describes a unified approach to privacy decision research that describes the cognitive processes involved in users’ “privacy calculus” in terms of system-related perceptions and experiences that act as mediating factors to information disclosure. The approach is applied in an online experiment with 493 participants using a mock-up of a context-aware recommender system. Analyzing the results with a structural linear model, we demonstrate that personal privacy concerns and disclosure justification messages affect the perception of and experience with a system, which in turn drive information disclosure decisions. Overall, disclosure justification messages do not increase disclosure. Although they are perceived to be valuable, they decrease users’ trust and satisfaction. Another result is that manipulating the order of the requests increases the disclosure of items requested early but decreases the disclosure of items requested later.


title: Synthesis of a compound T-junction for a two-way splitter with arbitrary power ratio

abstract: We have developed a simple and efficient procedure to design H-plane rectangular waveguide T-junctions for equal and unequal two-way power splitters. This synthesis procedure is scalable, renders manufacturable structures, applicable to any arbitrary power split-ratio, and can offer wide band operation. In our implementation, we utilized wedges and inductive windows (being an integral part of the T-junctions), to provide more degrees of freedom, thus, excellent match at the input port, flat power-split ratio over the band with equal phase, where phase balance is essential for various antenna feeds.


title: Convolutional deep belief networks for feature extraction of EEG signal

abstract: In recent years, deep learning approaches have been successfully used to learn hierarchical representations of image data, audio data etc. However, to our knowledge, these deep learning approaches have not been extensively studied for electroencephalographic (EEG) data. Considering the properties of EEG data, high-dimensional and multichannel, we applied convolutional deep belief networks to the feature learning of EEG data and evaluated it on the datasets from previous BCI competitions. Compared with other state-of-the-art feature extraction methods, the learned features using convolutional deep belief network have better performance.


title: Max-Cosine Matching Based Neural Models for Recognizing Textual Entailment

abstract: Recognizing textual entailment is a fundamental task in a variety of text mining or natural language processing applications. This paper proposes a simple neural model for RTE problem. It first matches each word in the hypothesis with its most-similar word in the premise, producing an augmented representation of the hypothesis conditioned on the premise as a sequence of word pairs. The LSTM model is then used to model this augmented sequence, and the final output from the LSTM is fed into a softmax layer to make the prediction. Besides the base model, in order to enhance its performance, we also proposed three techniques: the integration of multiple word-embedding library, bi-way integration, and ensemble based on model averaging. Experimental results on the SNLI dataset have shown that the three techniques are effective in boosting the predicative accuracy and that our method outperforms several state-of-the-state ones.


title: Mathematical modelling and simulation of pop-up books

abstract: Abstract The design and manufacturing of pop-up books are mainly manual at present, but a number of the processes therein can benefit from computerization and automation. This paper studies one aspect of the design of pop-up books: the mathematical modelling and simulation of the pieces popping up as a book is opened. It developes the formulae for the essential parameters in the pop-up animation. This animation enables the designer to determine on a computer if a particular set-up is appropriate to the theme which the page is designed to express, removing the need for the laborious and time-consuming task of making manual prototypes.


title: The necessities and luxuries of mate preferences: Testing the trade-offs

abstract: Social exchange and evolutionary models of mate selection incorporate economic assumptions but have not considered a key distinction between necessities and luxuries. This distinction can clarify an apparent paradox: Status and attractiveness, though emphasized by many researchers, are not typically rated highly by research participants. Three studies supported the hypothesis that women and men first ensure sufficient levels of necessities in potential mates before considering many other characteristics rated as more important in prior surveys. In Studies 1 and 2, participants designed ideal long-term mates, purchasing various characteristics with 3 different budgets. Study 3 used a mate-screening paradigm and showed that people inquire 1st about hypothesized necessities. Physical attractiveness was a necessity to men, status and resources were necessities to women, and kindness and intelligence were necessities to both. Relationship researchers adopting social exchange and evolutionary perspectives have used economic principles (e.g., Hatfield, Utne, & Traupmann, 1979; Kenrick, Groth, Trost, & Sadalla, 1993). However, a key distinction from economics has been omitted—necessities versus luxuries (e.g., Varian, 1984). Though people with high incomes may dedicate a large proportion of their income to luxury items, people with limited budgets tend to first purchase sufficient quantities of essential items before buying luxuries. Given no constraints on his or her “mating budget,” a participant in a typical mate preference study is often put in the position of someone answering a question about how to spend imaginary lottery winnings. We suggest that previous research has, by placing no constraints on participants’ mating preferences, led to some ambiguities in the field’s understanding of the differential priorities of men and women.


title: Group nearest neighbor queries

abstract: Given two sets of points P and Q, a group nearest neighbor (GNN) query retrieves the point(s) of P with the smallest sum of distances to all points in Q. Consider, for instance, three users at locations q/sub 1/ q/sub 2/ and q/sub 3/ that want to find a meeting point (e.g., a restaurant); the corresponding query returns the data point p that minimizes the sum of Euclidean distances |pq/sub i/| for 1/spl les/i/spl les/3. Assuming that Q fits in memory and P is indexed by an R-tree, we propose several algorithms for finding the group nearest neighbors efficiently. As a second step, we extend our techniques for situations where Q cannot fit in memory, covering both indexed and nonindexed query points. An experimental evaluation identifies the best alternative based on the data and query properties.


title: Fusion of IMU and Vision for Absolute Scale Estimation in Monocular SLAM

abstract: The fusion of inertial and visual data is widely used to improve an object’s pose estimation. However, this type of fusion is rarely used to estimate further unknowns in the visual framework. In this paper we present and compare two different approaches to estimate the unknown scale parameter in a monocular SLAM framework. Directly linked to the scale is the estimation of the object’s absolute velocity and position in 3D. The first approach is a spline fitting task adapted from Jung and Taylor and the second is an extended Kalman filter. Both methods have been simulated offline on arbitrary camera paths to analyze their behavior and the quality of the resulting scale estimation. We then embedded an online multi rate extended Kalman filter in the Parallel Tracking and Mapping (PTAM) algorithm of Klein and Murray together with an inertial sensor. In this inertial/monocular SLAM framework, we show a real time, robust and fast converging scale estimation. Our approach does not depend on known patterns in the vision part nor a complex temporal synchronization between the visual and inertial sensor.


title: Exercise for the Treatment of Depression and Anxiety

abstract: Depression and anxiety are the most common psychiatric conditions seen in the general medical setting, affecting millions of individuals in the United States. The treatments for depression and anxiety are multiple and have varying degrees of effectiveness. Physical activity has been shown to be associated with decreased symptoms of depression and anxiety. Physical activity has been consistently shown to be associated with improved physical health, life satisfaction, cognitive functioning, and psychological well-being. Conversely, physical inactivity appears to be associated with the development of psychological disorders. Specific studies support the use of exercise as a treatment for depression. Exercise compares favorably to antidepressant medications as a first-line treatment for mild to moderate depression and has also been shown to improve depressive symptoms when used as an adjunct to medications. While not as extensively studied, exercise has been shown to be an effective and cost-efficient treatment alternative for a variety of anxiety disorders. While effective, exercise has not been shown to reduce anxiety to the level achieved by psychopharmaceuticals.


title: Distributed LDA-based Topic Modeling and Topic Agglomeration in a Latent Space

abstract: We describe the methodology that we followed to automatically extract topics corresponding to known events provided by the SNOW 2014 challenge in the context of the SocialSensor project. A data crawling tool and selected filtering terms were provided to all the teams. The crawled data was to be divided in 96 (15-minute) timeslots spanning a 24 hour period and participants were asked to produce a fixed number of topics for the selected timeslots. Our preliminary results are obtained using a methodology that pulls strengths from several machine learning techniques, including Latent Dirichlet Allocation (LDA) for topic modeling and Non-negative Matrix Factorization (NMF) for automated hashtag annotation and for mapping the topics into a latent space where they become less fragmented and can be better related with one another. In addition, we obtain improved topic quality when Copyright c © by the paper’s authors. Copying permitted only for private and academic purposes. In: S. Papadopoulos, D. Corney, L. Aiello (eds.): Proceedings of the SNOW 2014 Data Challenge, Seoul, Korea, 08-04-2014, published at http://ceur-ws.org sentiment detection is performed to partition the tweets based on polarity, prior to topic modeling.