title: Creation of favorable user perceptions: exploring the role of intrinsic motivation

abstract: A key issue facing information systems researchers and practitioners has been the difficulty in creating favorable user reactions to new technologies. Insufficient or ineffective training has been identified as one of the key factors underlying this disappointing reality. Among the various enhancements to training being examined in research, the role of intrinsic motivation as a lever to create favorable user perceptions has not been sufficiently exploited. In this research, two studies were conducted to compare a traditional training method with a training method that included a component aimed at enhancing intrinsic motivation. The results strongly favored the use of an intrinsic motivator during training. Key implications for theory and practice are discussed.


title: Stochastic triplet embedding

abstract: This paper considers the problem of learning an embedding of data based on similarity triplets of the form “A is more similar to B than to C”. This learning setting is of relevance to scenarios in which we wish to model human judgements on the similarity of objects. We argue that in order to obtain a truthful embedding of the underlying data, it is insufficient for the embedding to satisfy the constraints encoded by the similarity triplets. In particular, we introduce a new technique called t-Distributed Stochastic Triplet Embedding (t-STE) that collapses similar points and repels dissimilar points in the embedding — even when all triplet constraints are satisfied. Our experimental evaluation on three data sets shows that as a result, t-STE is much better than existing techniques at revealing the underlying data structure.


title: Multicriteria User Modeling in Recommender Systems

abstract: The paper mentions that a hybrid recommender systems framework creates user-profile groups before applying a collaborative-filtering algorithm by incorporating techniques from the multiple-criteria decision-analysis (MCDA) field.


title: Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning

abstract: Random Matrix Theory (RMT) is applied to analyze weight matrices of Deep Neural Networks (DNNs), including both production quality, pre-trained models such as AlexNet and Inception, and smaller models trained from scratch, such as LeNet5 and a miniature-AlexNet. Empirical and theoretical results clearly indicate that the DNN training process itself implicitly implements a form of Self-Regularization. The empirical spectral density (ESD) of DNN layer matrices displays signatures of traditionally-regularized statistical models, even in the absence of exogenously specifying traditional forms of explicit regularization. Building on relatively recent results in RMT, most notably its extension to Universality classes of Heavy-Tailed matrices, we develop a theory to identify 5+1 Phases of Training, corresponding to increasing amounts of Implicit Self-Regularization. These phases can be observed during the training process as well as in the final learned DNNs. For smaller and/or older DNNs, this Implicit Self-Regularization is like traditional Tikhonov regularization, in that there is a "size scale" separating signal from noise. For state-of-the-art DNNs, however, we identify a novel form of Heavy-Tailed Self-Regularization, similar to the self-organization seen in the statistical physics of disordered systems. This results from correlations arising at all size scales, which arises implicitly due to the training process itself. This implicit Self-Regularization can depend strongly on the many knobs of the training process. By exploiting the generalization gap phenomena, we demonstrate that we can cause a small model to exhibit all 5+1 phases of training simply by changing the batch size. This demonstrates that---all else being equal---DNN optimization with larger batch sizes leads to less-well implicitly-regularized models, and it provides an explanation for the generalization gap phenomena.


title: Miniaturized shark-fin rooftop antenna with integrated DSRC communication module for connected vehicles

abstract: This work presents a compact shark-fin rooftop antenna combined with the dedicated short-range communication (DSRC) module for automotive applications. The proposed antenna is constructed by two short tapered monopoles and one cylindrical cavity resonator to achieve small size, light weight and low profile characteristics. Good impedance matching and omni-directional radiating properties of the proposed antenna are verified in the DSRC frequency band. The measured peak gain and efficiency of the antenna mounted on a large metallic plane are 4.5 dBi and 50% at 5.875 GHz. The proposed antenna is connected to the DSRC communication module consisting of transceiver, power amplifier, low-noise amplifier, and single-pole double-throw switch. The holistic DSRC communication system is embedded into the shark-fin radome and mounted on the rooftop of a vehicle to experimentally validate the usefulness of the connected vehicle application.


title: The Future of Human-AI Collaboration: A Taxonomy of Design Knowledge for Hybrid Intelligence Systems

abstract: Recent technological advances, especially in the field of machine learning, provide astonishing progress on the road towards artificial general intelligence. However, tasks in current real-world business applications cannot yet be solved by machines alone. We, therefore, identify the need for developing socio-technological ensembles of humans and machines. Such systems possess the ability to accomplish complex goals by combining human and artificial intelligence to collectively achieve superior results and continuously improve by learning from each other. Thus, the need for structured design knowledge for those systems arises. Following a taxonomy development method, this article provides three main contributions: First, we present a structured overview of interdisciplinary research on the role of humans in the machine learning pipeline. Second, we envision hybrid intelligence systems and conceptualize the relevant dimensions for system design for the first time. Finally, we offer useful guidance for system developers during the implementation of such applications.


2000, 'Artificial Neural Networks in Accounting and Finance: Modelling Issues


title: A dual-mode deblocking filter design for HEVC and H.264/AVC

abstract: SUMMARY As the successive video compression standard of H.264/AVC, High E fficiency Video Codec (HEVC) will play an important role in video coding area. In the deblocking filter part, HEVC inherits the basic property of H.264/AVC and gives some new features. Based on this variation, this paper introduces a novel dual-mode deblocking filter architecture which could support both of the HEVC and H.264/AVC standards. For HEVC standard, the proposed symmetric unified-cross unit (SUCU) based filtering scheme greatly reduces the design complexity. As a result, processing a 16×16 block needs 24 clock cycles. For H.264/AVC standard, it takes 48 clock cycles for a 16 × 16 macro-block (MB). In synthesis result, the proposed architecture occupies 41.6k equivalent gate count at frequency of 200 MHz in SMIC 65 nm library, which could satisfy the throughput requirement of super hi-vision (SHV) on 60 fps. With filter reusing scheme, the universal design for the two standards saves 30% gate counts than the dedicated ones in filter part. In addition, the total power consumption could be reduced by 57.2% with skipping mode when the edges need not be fil


title: 3D Printing of Interdigitated Li-Ion Microbattery Architectures

abstract: 3D interdigitated microbattery architectures (3D-IMA) are fabricated by printing concentrated lithium oxide-based inks. The microbatteries are composed of interdigitated, high-aspect ratio cathode and anode structures. Our 3D-IMA, which exhibit high areal energy and power densities, may find potential application in autonomously powered microdevices.


title: Factors affecting taste scores of early season seedless table grape cv. Mystery and Prime.

abstract: Table grapes of cv. Mystery and Prime were harvested from 10 farms in two growing areas of Israel over two seasons. The grapes were separated on the basis of sucrose solutions from 12 to 18%; soluble solids content (SSC), titratable acidity (TA), and pH were determined; and taste tests were conducted. SSC gave the best correlation with taste tests, and multiple regression of SSC, TA, and pH improved the correlation. There were both seasonal and regional differences in the measured maturity parameters. Lower TA and higher pH were found in grapes from the Jordan Valley. Volatiles were predominantly C(6) compounds hexanal and 2-hexanal, contributing a fresh aroma to the grapes. It is concluded that Mystery and Prime grapes have good organoleptic quality if harvested at SSC levels of >14%.


title: Nanoemulsion containing dapsone for topical administration: a study of in vitro release and epidermal permeation

abstract: Background ::: Topical administration of dapsone can be an alternative route for treatment of leprosy and can also provide new therapeutic applications for an established drug. However, the physicochemical properties of dapsone make it difficult to incorporate into conventional formulations. The current study was directed toward developing a stable nanoemulsion that contains dapsone which can be adapted for topical use.


title: Brief Announcement: Compact Self-Stabilizing Leader Election in Arbitrary Graphs

abstract: We present the first self-stabilizing algorithm for leader election in arbitrary topologies whose space complexity is O(max{log Delta, log log n}) bits per node, where n is the network size and Delta its degree. This complexity is sub-logarithmic in n when Delta = n^o(1).


title: Crowdsourcing step-by-step information extraction to enhance existing how-to videos

abstract: Millions of learners today use how-to videos to master new skills in a variety of domains. But browsing such videos is often tedious and inefficient because video player interfaces are not optimized for the unique step-by-step structure of such videos. This research aims to improve the learning experience of existing how-to videos with step-by-step annotations. We first performed a formative study to verify that annotations are actually useful to learners. We created ToolScape, an interactive video player that displays step descriptions and intermediate result thumbnails in the video timeline. Learners in our study performed better and gained more self-efficacy using ToolScape versus a traditional video player. To add the needed step annotations to existing how-to videos at scale, we introduce a novel crowdsourcing workflow. It extracts step-by-step structure from an existing video, including step times, descriptions, and before and after images. We introduce the Find-Verify-Expand design pattern for temporal and visual annotation, which applies clustering, text processing, and visual analysis algorithms to merge crowd output. The workflow does not rely on domain-specific customization, works on top of existing videos, and recruits untrained crowd workers. We evaluated the workflow with Mechanical Turk, using 75 cooking, makeup, and Photoshop videos on YouTube. Results show that our workflow can extract steps with a quality comparable to that of trained annotators across all three domains with 77% precision and 81% recall.


title: Improving b2c e-commerce trust through social presence factors

abstract: In e-Commerce field, there are many researchers conducted on different properties of trust but lack of attention has been given to interpersonal based trust. This has lead to limitations for e-Commerce growth where social events have not yet fully duplicate into digital form especially in e-Commerce environment. Taking the advantage of rapid growth of social network nowadays, the objective of this study is to improve current e-Commerce implementation through social presence. In order to achieve this objective, this study has explored e-Commerce trust properties with its attributes as suggested by previous researchers. Trust factors based on social presence attributes also have been identified. This research hopes to provide a guideline to online businesses in order to develop a trusted website based on updated trend of e- Commerce which is more customer-oriented.


title: OpenStack Neat: a framework for dynamic and energy-efficient consolidation of virtual machines in OpenStack clouds

abstract: Dynamic consolidation of virtual machines VMs is an efficient approach for improving the utilization of physical resources and reducing energy consumption in cloud data centers. Despite the large volume of research published on this topic, there are very few open-source software systems implementing dynamic VM consolidation. In this paper, we propose an architecture and open-source implementation of OpenStack Neat, a framework for dynamic VM consolidation in OpenStack clouds. OpenStack Neat can be configured to use custom VM consolidation algorithms and transparently integrates with existing OpenStack deployments without the necessity of modifying their configuration. In addition, to foster and encourage further research efforts in the area of dynamic VM consolidation, we propose a benchmark suite for evaluating and comparing dynamic VM consolidation algorithms. The proposed benchmark suite comprises OpenStack Neat as the base software framework, a set of real-world workload traces, performance metrics and evaluation methodology. As an application of the proposed benchmark suite, we conduct an experimental evaluation of OpenStack Neat and several dynamic VM consolidation algorithms on a five-node testbed, which shows significant benefits of dynamic VM consolidation resulting in up to 33% energy savings. Copyright © 2014 John Wiley & Sons, Ltd.


title: Likely to stop? Predicting Stopout in Massive Open Online Courses

abstract: Understanding why students stopout will help in understanding how students learn in MOOCs. In this report, part of a 3 unit compendium, we describe how we build accurate predictive models of MOOC student stopout. We document a scalable, stopout prediction methodology, end to end, from raw source data to model analysis. We attempted to predict stopout for the Fall 2012 offering of 6.002x. This involved the meticulous and crowd-sourced engineering of over 25 predictive features extracted for thousands of students, the creation of temporal and non-temporal data representations for use in predictive modeling, the derivation of over 10 thousand models with a variety of state-of-the-art machine learning techniques and the analysis of feature importance by examining over 70000 models. We found that stop out prediction is a tractable problem. Our models achieved an AUC (receiver operating characteristic area-under-the-curve) as high as 0.95 (and generally 0.88) when predicting one week in advance. Even with more difficult prediction problems, such as predicting stop out at the end of the course with only one weeks' data, the models attained AUCs of 0.7.


title: An Iterative Algorithm for Camera Pose Estimation with Lie Group Representation

abstract: For solving camera pose problem, a novel accurate and fast pose estimation algorithm based on Lie group representation is proposed. Firstly, object function based on total space error of all feature points is built. Secondly, object function is linearized based on Lie group representation. The Jacobian matrix of camera pose is deduced and pose parameters are updated. Finally, the computation efficiency of algorithm is promoted by using Kronecker product to optimize the iteration process. Simulation and real experiments show that the proposed algorithm is able to obtain camera pose with high precision, but consumes less time compared with the existing algorithms. The proposed algorithm is capable of estimating camera pose with high precision and efficiency.


HumorHawk at SemEval-2017 Task 6: Mixing Meaning and Sound for Humor Recognition


title: A Novel Vision-Based Tracking Algorithm for a Human-Following Mobile Robot

abstract: The ability to follow a human is an important requirement for a service robot designed to work along side humans in homes or in work places. This paper describes the development and implementation of a novel robust visual controller for the human-following robot. This visual controller consists of two parts: 1) a robust algorithm that tracks a human visible in its camera view and 2) a servo controller that generates necessary motion commands so that the robot can follow the target human. The tracking algorithm uses point-based features, like speeded up robust feature, to detect human under challenging conditions, such as, variation in illumination, pose change, full or partial occlusion, and abrupt camera motion. The novel contributions in the tracking algorithm include the following: 1) a dynamic object model that evolves over time to deal with short-term changes, while maintaining stability over long run; 2) an online K-D tree-based classifier along with a Kalman filter is used to differentiate a case of pose change from a case of partial or full occlusion; and 3) a method is proposed to detect pose change due to out-of-plane rotations , which is a difficult problem that leads to frequent tracking failures in a human following robot. An improved version of a visual servo controller is proposed that uses feedback linearization to overcome the chattering phenomenon present in sliding mode-based controllers used previously. The efficacy of the proposed approach is demonstrated through various simulations and real-life experiments with an actual mobile robot platform.


Detection of Malicious Scripting Code Through Discriminant and Adversary-Aware API Analysis


title: Sampling rate synchronisation in acoustic sensor networks with a pre-trained clock skew error model

abstract: In this paper we present a combined hardware/software approach for synchronizing the sampling clocks of an acoustic sensor network. A first clock frequency offset estimate is obtained by a time stamp exchange protocol with a low data rate and computational requirements. The estimate is then postprocessed by a Kalman filter which exploits the specific properties of the statistics of the frequency offset estimation error. In long term experiments the deviation between the sampling oscillators of two sensor nodes never exceeded half a sample with a wired and with a wireless link between the nodes. The achieved precision enables the estimation of time difference of arrival values across different hardware devices without sharing a common sampling hardware.


title: Nonnegative Matrix Factorization for Interactive Topic Modeling and Document Clustering

abstract: Nonnegative matrix factorization (NMF) approximates a nonnegative matrix by the product of two low-rank nonnegative matrices. Since it gives semantically meaningful result that is easily interpretable in clustering applications, NMF has been widely used as a clustering method especially for document data, and as a topic modeling method.We describe several fundamental facts of NMF and introduce its optimization framework called block coordinate descent. In the context of clustering, our framework provides a flexible way to extend NMF such as the sparse NMF and the weakly-supervised NMF. The former provides succinct representations for better interpretations while the latter flexibly incorporate extra information and user feedback in NMF, which effectively works as the basis for the visual analytic topic modeling system that we present.Using real-world text data sets, we present quantitative experimental results showing the superiority of our framework from the following aspects: fast convergence, high clustering accuracy, sparse representation, consistent output, and user interactivity. In addition, we present a visual analytic system called UTOPIAN (User-driven Topic modeling based on Interactive NMF) and show several usage scenarios.Overall, our book chapter cover the broad spectrum of NMF in the context of clustering and topic modeling, from fundamental algorithmic behaviors to practical visual analytics systems.


title: Visual categorization with bags of keypoints

abstract: We present a novel method for generic visual categorization: the problem of identifying the object content of natural images while generalizing across variations inherent to the object class. This bag of keypoints method is based on vector quantization of affine invariant descriptors of image patches. We propose and compare two alternative implementations using different classifiers: Naive Bayes and SVM. The main advantages of the method are that it is simple, computationally efficient and intrinsically invariant. We present results for simultaneously classifying seven semantic visual categories. These results clearly demonstrate that the method is robust to background clutter and produces good categorization accuracy even without exploiting geometric information.


title: Tet Proteins Connect the O-Linked N-acetylglucosamine Transferase Ogt to Chromatin in Embryonic Stem Cells

abstract: O-linked N-acetylglucosamine (O-GlcNAc) transferase (Ogt) activity is essential for embryonic stem cell (ESC) viability and mouse development. Ogt is present both in the cytoplasm and the nucleus of different cell types and catalyzes serine and threonine glycosylation. We have characterized the biochemical features of nuclear Ogt and identified the ten-eleven translocation (TET) proteins Tet1 and Tet2 as stable partners of Ogt in the nucleus of ESCs. We show at a genome-wide level that Ogt preferentially associates with Tet1 to genes promoters in close proximity of CpG-rich transcription start sites. These regions are characterized by low levels of DNA modification, suggesting a link between Tet1 and Ogt activities in regulating CpG island methylation. Finally, we show that Tet1 is required for binding of Ogt to chromatin affecting Tet1 activity. Taken together, our data characterize how O-GlcNAcylation is recruited to chromatin and interacts with the activity of 5-methylcytosine hydroxylases.


title: Monitoring latency with OpenFlow

abstract: Software Defined Networking, especially through protocols like OpenFlow, is becoming more and more present in networks. It aims at separating the data plane from the control plane for more network programmability, serviceability, heterogeneity and maintainability. Even if mobile applications and multimedia are often pointed at to show the demise of current network architectures, there are currently no ways to efficiently dynamically obtain the latency in an OpenFlow network to efficiently apply QoS policies. In this paper, we propose a mechanism to measure link latencies from an OpenFlow controller with high accuracy and a low footprint. We implemented it and present the performance evaluation. A monitoring packet consumes only 24 Bytes, which is 81% less than the ping utility, for an average accuracy of 99.25% compared to the ping values.


title: Pairwise Document Similarity in Large Collections with MapReduce

abstract: This paper presents a MapReduce algorithm for computing pairwise document similarity in large document collections. MapReduce is an attractive framework because it allows us to decompose the inner products involved in computing document similarity into separate multiplication and summation stages in a way that is well matched to efficient disk access patterns across several machines. On a collection consisting of approximately 900,000 newswire articles, our algorithm exhibits linear growth in running time and space in terms of the number of documents.


title: Probabilistic Inductive Logic Programming

abstract: Probabilistic inductive logic programming aka. statistical relational learning addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with machine learning and first order and relational logic representations. A rich variety of different formalisms and learning techniques have been developed. A unifying characterization of the underlying learning settings, however, is missing so far. ::: ::: In this chapter, we start from inductive logic programming and sketch how the inductive logic programming formalisms, settings and techniques can be extended to the statistical case. More precisely, we outline three classical settings for inductive logic programming, namely learning from entailment, learning from interpretations, and learning from proofs or traces, and show how they can be adapted to cover state-of-the-art statistical relational learning approaches.


title: Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines

abstract: We present a model for building, visualizing, and interacting with multiscale representations of information visualization techniques using hierarchical aggregation. The motivation for this work is to make visual representations more visually scalable and less cluttered. The model allows for augmenting existing techniques with multiscale functionality, as well as for designing new visualization and interaction techniques that conform to this new class of visual representations. We give some examples of how to use the model for standard information visualization techniques such as scatterplots, parallel coordinates, and node-link diagrams, and discuss existing techniques that are based on hierarchical aggregation. This yields a set of design guidelines for aggregated visualizations. We also present a basic vocabulary of interaction techniques suitable for navigating these multiscale visualizations.


title: WASP: Evaluation of Different Strategies for the Automatic Generation of Spanish Verse

abstract: WASP is a forward reasoning rule-based system that takes as input data a set of words and a set of verse patterns and returns a set of verses. Using a generate and test method, guided by a set of construction heuristics obtained from formal literature on Spanish poetry, the system can operate in two modes: either generating an unrestricted set of verses, or generating a poem according to one of three predefined structures (romance, cuarteto, or terceto). Five different construction heuristics are tested over different combinations of two sets of initial data, one obtained from a classic poem and one obtained from a paragraph of a doctoral thesis in linguistics. A set of numerical parameters are extracted from each test, and evaluated in search of significant correlations. The aim is to ascertain the relative importance of size of initial vocabulary, choice of words, choice of verse patterns and construction heuristics with respect to the general acceptability of the resulting verse. 1 Programs that Write Poetry Automatically The creation of programs that write poetry automatically has been a recurring dream within the AI community, but it has always been assigned a very low priority. Practical applications in the area of natural language processing, such as natural language database interfaces, information retrieval and extraction, automatic translation, and dialogue systems provide more immediate rewards. On one hand the automatic generation of poetry involves advanced linguistic skills and common sense, two of the major challenges that face AI in general. On the other hand it involves an important amount of creativity and sensibility. These ingredients are very difficult to characterise formally, and very little is known about how they might be treated algorithmically. On the positive side, poetry has the advantage of not requiring exaggerate precision. If one accepts that the main aim of a poem is to be pleasing rather than conveying a meaningful message, the general problem becomes tractable. The present paper considers how the different parameters that can be controlled by the generating program affect the acceptability of the result. The set of parameters to be monitored are: size of initial vocabulary, choice of words, choice of verse patterns, and construction heuristics. The elusive concept of acceptability of a verse is determined by resorting to hand evaluation by a team of volunteers. By searching for correlations between the strategy and initial data used to generate a verse and the positive or negative evaluation of the resulting verse, information is obtained about the relative relevance of these parameters to the end result. 1.1 Guiding Heuristically the Random Generation of a Verse Poems written by combining randomly a given set of words rate very poorly with discerning readers. For the words to make sense together, they must be organised according to particular patterns. A possible course of action would be to provide the system with adequately rich lexicon, syntax and semantics for the language involved. Results obtained with inadequate formalisms are too rigid and tend to have a mechanical ring to them. The system presented in this paper resorts to a radical simplification of the underlying linguistic skills. The exhaustive knowledge approach is abandoned in favour of a heuristic engineering solution. Only the barest outline of a grammatical outline is provided (in the form of a verse pattern) to ensure syntactic correctness. Semantic correctness is not enforced, on the understanding that creativity in poetry relies to a certain extent on daring transgressions (such as imaginative metaphors). The aim of the paper is to establish whether acceptable verse may be obtained by controlling other parameters within these initial restrictions. The hope is to identify whether the elementary ingredients considered can be manipulated smartly enough to produce a pleasing phrase. 1.2 The Effect of the Selection of Initial Data Under these restrictions, Spanish has been chosen as a test language. The phonetics of Spanish are quite straightforward to obtain from the written word. Most letters in Spanish sound the same wherever they appear in a piece of text, so the metrics, or the syllabic division, of a verse can be worked out algorithmically (RAE , 1986). Spanish scholars have a love for rules, and there is a good set of formal rules (Quilis , 1985) describing the conditions that a poem must fulfil in order to be acceptable. Given such a set of rules, the challenge becomes a simple problem of transforming the given evaluation rules (designed to be applied to an existing poem in order to ascertain its acceptability) into the corresponding construction rules. These rules have to be applied to an initial set of data consisting of: a given vocabulary (given a set of words, the poet will choose only some of them, this process of selection must surely play a role in the quality of the final result), and a particular choice of ways of combining the chosen words (word order, frequency of adjectives, length of verse...) represented as a set of verse patterns. The selected vocabulary is a set of words that includes extra information about part of speech roles, number of syllables of each word, position of stressed syllables, and rhyme. The system cannot handle morphological variations, so it considers the singular and plural, masculine and feminine forms of a word as totally distinct (and different tenses of a verb also). This decision reduces the complexity of the generation process to pattern matching between word categories and verse patterns, but it has consequences on the quality of the resulting verses. The set of valid words is stored as facts of the form:


title: Organizational change theory: implications for health promotion practice

abstract: Sophisticated understandings of organizational dynamics and processes of organizational change are crucial for the development and success of health promotion initiatives. Theory has a valuable contribution to make in understanding organizational change, for identifying influential factors that should be the focus of change efforts and for selecting the strategies that can be applied to promote change. This article reviews select organizational change models to identify the most pertinent insights for health promotion practitioners. Theoretically derived considerations for practitioners who seek to foster organizational change include the extent to which the initiative is modifiable to fit with the internal context; the amount of time that is allocated to truly institutionalize change; the ability of the agents of change to build short-term success deliberately into their implementation plan; whether or not the shared group experience of action for change is positive or negative and the degree to which agencies that are the intended recipients of change are resourced to focus on internal factors. In reviewing theories of organizational change, the article also addresses strategies for facilitating the adoption of key theoretical insights into the design and implementation of health promotion initiatives in diverse organizational settings. If nothing else, aligning health promotion with organizational change theory promises insights into what it is that health promoters do and the time that it can take to do it effectively.


title: Harvesting vibration energy by a triple-cantilever based triboelectric nanogenerator

abstract: Triboelectric nanogenerators (TENG), a unique technology for harvesting ambient mechanical energy based on triboelectric effect, have been proven to be a cost-effective, simple and robust approach for self-powered systems. Here, we demonstrate a rationally designed triple-cantilever based TENG for harvesting vibration energy. With the assistance of nanowire arrays fabricated onto the surfaces of beryllium-copper alloy foils, the newly designed TENG produces an open-circuit voltage up to 101 V and a short-circuit current of 55.7 μA with a peak power density of 252.3 mW/m2. The TENG was systematically investigated and demonstrated as a direct power source for instantaneously lighting up 40 commercial light-emitting diodes. For the first time, a TENG device has been designed for harvesting vibration energy, especially at low frequencies, opening its application as a new energy technology. ::: Open image in new window


title: Temporal Autoencoding Improves Generative Models of Time Series

abstract: Restricted Boltzmann Machines (RBMs) are generative models which can learn useful representations from samples of a dataset in an unsupervised fashion. They have been widely employed as an unsupervised pre-training method in machine learning. RBMs have been modified to model time series in two main ways: The Temporal RBM stacks a number of RBMs laterally and introduces temporal dependencies between the hidden layer units; The Conditional RBM, on the other hand, considers past samples of the dataset as a conditional bias and learns a representation which takes these into account. Here we propose a new training method for both the TRBM and the CRBM, which enforces the dynamic structure of temporal datasets. We do so by treating the temporal models as denoising autoencoders, considering past frames of the dataset as corrupted versions of the present frame and minimizing the reconstruction error of the present data by the model. We call this approach Temporal Autoencoding. This leads to a significant improvement in the performance of both models in a filling-in-frames task across a number of datasets. The error reduction for motion capture data is 56\% for the CRBM and 80\% for the TRBM. Taking the posterior mean prediction instead of single samples further improves the model's estimates, decreasing the error by as much as 91\% for the CRBM on motion capture data. We also trained the model to perform forecasting on a large number of datasets and have found TA pretraining to consistently improve the performance of the forecasts. Furthermore, by looking at the prediction error across time, we can see that this improvement reflects a better representation of the dynamics of the data as opposed to a bias towards reconstructing the observed data on a short time scale.


title: Failure mode and effects analysis based on a novel fuzzy evidential method

abstract: Abstract Failure mode and effect analysis (FMEA) has been widely applied to examine potential failures in systems, designs, and products. The risk priority number (RPN) is the key criteria to determine the risk priorities of the failure modes. Traditionally, the determination of RPN is based on the risk factors like occurrence (O), severity (S) and detection (D), which require to be precisely evaluated. However, this method has many irrationalities and needs to be improved for more applications. To overcome the shortcomings of the traditional FMEA and better model and process uncertainties, we propose a FMEA model based on a novel fuzzy evidential method. The risks of the risk factors are evaluated by fuzzy membership degree. As a result, a comprehensive way to rank the risk of failure modes is proposed by fusing the feature information of O, S and D with Dempster–Shafer (D–S) evidence theory. The advantages of the proposed method are that it can not only cover the diversity and uncertainty of the risk assessment, but also improve the reliability of the RPN by data fusion. To validate the proposed method, a case study of a micro-electro-mechanical system (MEMS) is performed. The experimental results show that this method is reasonable and effective for real applications.


title: Visual Image Reconstruction from Human Brain Activity using a Combination of Multiscale Local Image Decoders

abstract: Perceptual experience consists of an enormous number of possible states. Previous fMRI studies have predicted a perceptual state by classifying brain activity into prespecified categories. Constraint-free visual image reconstruction is more challenging, as it is impractical to specify brain activity for all possible images. In this study, we reconstructed visual images by combining local image bases of multiple scales, whose contrasts were independently decoded from fMRI activity by automatically selecting relevant voxels and exploiting their correlated patterns. Binary-contrast, 10 x 10-patch images (2(100) possible states) were accurately reconstructed without any image prior on a single trial or volume basis by measuring brain activity only for several hundred random images. Reconstruction was also used to identify the presented image among millions of candidates. The results suggest that our approach provides an effective means to read out complex perceptual states from brain activity while discovering information representation in multivoxel patterns.


title: A Wireless System for Monitoring Leakage Current in Electrical Substation Equipment

abstract: In this paper, the design and the development of a remote system for continuous monitoring of leakage currents and ground currents in high voltage electrical substations are proposed. Based on wireless local area network technology, the system can be used to monitor continuously a variety of plants within the substation and has low power consumption with inbuilt overvoltage protection. It consists of a transmitter module equipped with a data acquisition (DAQ) system connected to leakage current and voltage sensors, and a receiver module connected to a remote controller for data processing and storage. The principle of operation and the characteristics of the various components of the system are described. Validation tests have been used to verify its performance in three different test situations: A) laboratory monitoring of the leakage current and voltage of a distribution surge arrester; B) laboratory measurement of the leakage current of an outdoor insulator; and C) field monitoring of the earth current and potential rise of high-voltage tower. The measured results are in close agreement with those recorded directly through a DAQ card with fiber-optic and coaxial cable connected systems. Data processing is carried out at the receiving end so that the monitored parameter is displayed continuously or at specified time intervals. The operation of the system has been tested and proved resilient under high-frequency interference signals such as those generated by corona and surface discharges.


The Affective Core of the Self: A Neuro-Archetypical Perspective on the Foundations of Human (and Animal) Subjectivity


title: Mechanical properties of amorphous and polycrystalline multilayer systems

abstract: Abstract Amorphous and polycrystalline multilayer structures containing materials with metallic (Cr, Cr 3 C 2 ), ionic (Al 2 O 3 ) and covalent (SiC) bonding have been prepared by magnetron sputtering and ion plating in a dual-source apparatus. Up to 1000 layers have been deposited with a constant total thickness of 2.3 μm. Below a single-layer thickness of 10–30 nm the mechanical properties stress and hardness show strong variations. On one hand it is possible that below a certain thickness the mechanical properties of a single layer change (A. Pan and J.E. Greene, Thin Solid Films, 78 (1981) 25–34). On the other hand electrical resistance and electron spin density measurements indicate that electronic effects may be involved. An attempt is made to explain the observed correlations by transport mechanisms of the electrons, by saturation of dangling bonds with delocalized electrons and by changes in the electronic band structure.


title: A Study of Network Covert Channel Detection Based on Deep Learning

abstract: I195 security has become a growing concern. Computer covert channel which is regarded as an important area of information security research gets more attention. In order to detect these covert channels, a variety of detection algorithms are proposed in the course of the research. The algorithms of machine learning type show better results in these detection algorithms. However, the common machine learning algorithms have many problems in the testing process and have great limitations. Based on the deep learning algorithm, this paper proposes a new idea of network covert channel detection and forms a new detection model. On the one hand, this algorithmic model can detect more complex covert channels and, on the other hand, greatly improve the accuracy of detection due to the use of a new deep learning model. By optimizing this test model, we can get better results on the evaluation index.


title: Dynamic modeling and design optimization of a 3-DOF spherical parallel manipulator

abstract: This paper deals with the dynamic modeling and design optimization of a three Degree-of-Freedom spherical parallel manipulator. Using the method of Lagrange multipliers, the equations of motion of the manipulator are derived by considering its motion characteristics, namely, all the components rotating about the center of rotation. Using the derived dynamic model, a multiobjective optimization problem is formulated to optimize the structural and geometric parameters of the spherical parallel manipulator. The proposed approach is illustrated with the design optimization of an unlimited-roll spherical parallel manipulator with a main objective to minimize the mechanism mass in order to enhance both kinematic and dynamic performances.


title: A modular robotic system using magnetic force effectors

abstract: One of the primary impediments to building ensembles of modular robots is the complexity and number of mechanical mechanisms used to construct the individual modules. As part of the Claytronics project - which aims to build very large ensembles of modular robots - we investigate how to simplify each module by eliminating moving parts and reducing the number of mechanical mechanisms on each robot by using force-at-a-distance actuators. Additionally, we are also investigating the feasibility of using these unary actuators to improve docking performance, implement intermodule adhesion, power transfer, communication, and sensing. In this paper we describe our most recent results in the magnetic domain, including our first design sufficiently robust to operate reliably in groups greater than two modules. Our work should be seen as an extension of systems such as Fracta [9], and a contrasting line of inquiry to several other researchers' prior efforts that have used magnetic latching to attach modules to one another but relied upon a powered hinge [10] or telescoping mechanism [12] within each module to facilitate self-reconfiguration.


title: Accurate 3D car pose estimation

abstract: We propose a new approach for accurate car pose estimation in images using only a dataset of 3D untextured models. Our algorithm detects both a car and its 3D pose. It is based on the matching of 3D models with the car in the image. With a part detector based on Convolutional Neural Networks, interest points corresponding to predefined 3D parts are extracted from the image. Then, we use the car geometry to find which parts are relevant across viewpoints. Finally, a 2D/3D pose estimator is used to recover the 3D pose of the car. The main contribution is to learn appearance and geometry models from 3D models dataset only. Experiments show that the method is very competitive for car detection and coarse viewpoint classification and improves the 3D pose estimation over the state-of-the-art methods.


title: Analysis of three-level buck-boost converter operation for improved renewable energy conversion and smart grid integration

abstract: The increased smart grid integration of renewable energy sources demands high power handling and wide controllability for the enabling power conversion technologies. The conventional energy conversion techniques are inadequate to efficiently handle the highly varying nature of renewable energy sources like wave, solar, tidal and wind. The present work examines the advantages of using a three-level buck-boost DC-DC converter to aid three-level neutral-point-clamped inverter based grid integration. There are two main reasons for using this converter. It can provide the conventional buck-boost capability at higher power levels for absorbing and conditioning the renewable source output. Besides, it can be used as a voltage balancing device to satisfy the input requirement for the three-level neutral-point-clamped inverter. The work includes complete operating range analysis of the converter for the combined buck-boost action and voltage balancing effects to understand its suitability for various applications. The converter switching modes of operation are also presented in detail along with essential example waveforms. The final results show good controllability bandwidth for the converter which makes it an attractive solution for smart grid integration of renewable energy sources.


title: Softmax Discriminant Classifier

abstract: A simple but effective classifier, which is called soft max discriminant classifier or SDC for short, is presented. Based on the soft max discriminant function, SDC assigns the label information to a new testing sample by nonlinear transformation of the distance between the testing sample and training samples. Experimental results on some well-known data sets demonstrate the feasibility and effectiveness of the proposed algorithm.


title: Improving the Robustness of Location-Based Routing for Underwater Sensor Networks

abstract: This paper investigates a fundamental networking problem in underwater sensor networks: robust and energy-efficient routing. We present an adaptive location-based routing protocol, called hop-by-hop vector-based forwarding (HH-VBF). It uses the notion of a "routing vector" (a vector from the source to the sink) acting as the axis of the "routing pipe", similar to the vector based forward (VBF) routing in the work of P. Xie, J.-H. Cui and L. Lao (VBF: Vector-Based Forwarding Protocol for Underwater Sensor Networks. Technical report, UCONN CSE Technical Report: UbiNet-TR05-03 (BECAT/CSE-TR-05-6), Feb. 2005). Unlike the original VBF approach, however, HH-VBF suggests the use of a routing vector for each individual forwarder in the network, instead of a single network-wide source-to-sink routing vector. By the creation of the hop-by-hop vectors, HH-VBF can overcome two major problems in VBF: (1) too small data delivery ratio for sparse networks; (2) too sensitive to "routing pipe" radius threshold. We conduct simulations to evaluate HH-VBF, and the results show that HH-VBF yields much better performance than VBF in sparse networks. In addition, HH-VBF is less sensitive to the routing pipe radius threshold. Furthermore, we also analyze the behavior of HH-VBF and show that assuming proper redundancy and feedback techniques, HH-VBF can facilitate the avoidance of any "void" areas in the network.


title: Off-line cursive script recognition: current advances, comparisons and remaining problems

abstract: This paper presents detailed review in the field of off-line cursive script recognition. Various methods are analyzed that have been proposed to realize the core of script recognition in a word recognition system. These methods are discussed in view of the two most important properties of such systems: size and nature of the lexicon involved and whether or not a segmentation stage is present. Script recognition techniques are classified into three categories: firstly, segmentation-free methods or holistic approaches, that compare a sequence of observations derived from whole word image with similar references of words in the small lexicon. Secondly, segmentation-based methods, that look for the best match between consecutive sequences of primitive segments and letters of a possible word similar to human-like reading technique, in which secure features found all over the word are used to boot-strap a few candidates for a final evaluation phase; thirdly, hybrid approaches. Additionally, different feature extraction techniques are elaborated in conjunction with the classification process. In this scenario, implications of single and multiple classifiers are also observed. Finally, remaining problems are highlighted along with possible suggestion and strategies to solve them.


title: A mathematical model of news propagation on online social network and a control strategy for rumor spreading

abstract: People of the modern world are using social network Web sites to communicate with others either known or unknown, for getting opinions of others and giving their opinions to others. The post, weblogs, effects or affects human mind, at least for some time. These posts take a part in choosing their decisions and play an important role. But the information present in the post is either information or just misinformation, i.e., just a rumor. People are confused to distinguish these posts in either a correct information or misinformation. It is important to decide whether this is information or just a rumor because it may cause a support of the wrong decision of the whole majority. In this paper, a mathematical framework is presented related to these matters. Firstly, we proposed a mathematical model of news spreading from some posts displayed in an online social network. The development of mathematical models of news propagation uses the epidemiological modeling technique. Then, we proposed criteria of rumor detection and verification for the model. In the case of rumor, a revised model is proposed with media awareness as a control strategy for reducing the rumor spreading.


title: Data-Aware Vaccine Allocation Over Large Networks

abstract: Given a graph, like a social/computer network or the blogosphere, in which an infection (or meme or virus) has been spreading for some time, how to select the k best nodes for immunization/quarantining immediately? Most previous works for controlling propagation (say via immunization) have concentrated on developing strategies for vaccination preemptively before the start of the epidemic. While very useful to provide insights in to which baseline policies can best control an infection, they may not be ideal to make real-time decisions as the infection is progressing. In this paper, we study how to immunize healthy nodes, in the presence of already infected nodes. Efficient algorithms for such a problem can help public-health experts make more informed choices, tailoring their decisions to the actual distribution of the epidemic on the ground. First we formulate the Data-Aware Vaccination problem, and prove it is NP-hard and also that it is hard to approximate. Secondly, we propose three effective polynomial-time heuristics DAVA, DAVA-prune and DAVA-fast, of varying degrees of efficiency and performance. Finally, we also demonstrate the scalability and effectiveness of our algorithms through extensive experiments on multiple real networks including large epidemiology datasets (containing millions of interactions). Our algorithms show substantial gains of up to ten times more healthy nodes at the end against many other intuitive and nontrivial competitors.


title: A Data Mining Ontology for Grid Programming

abstract: The Grid is an integrated infrastructure for coordinated resource sharing and problem solving in distributed environments. The effective and efficient use of stored data and its transformation into information and knowledge will be a main driver in Grid evolution. The use of ontologies to describe Grid resources will simplify and structure the systematic building of Grid applications through the composition and reuse of software components and the development of knowledge-based services and tools. The paper presents an ontology for the Data Mining domain that can be used to simplify the development of distributed knowledge discovery applications on the Grid, offering to a domain expert a reference model for the different kind of data mining tasks, methodologies and software available to solve a given problem, helping a user in finding the most appropriate solution. How the DAMON ontology is used to enhance the design of distributed data mining applications on the KNOWLEDGE GRID is also shown.


title: Deception Techniques in Computer Security: A Research Perspective

abstract: A recent trend both in academia and industry is to explore the use of deception techniques to achieve proactive attack detection and defense—to the point of marketing intrusion deception solutions as zero-false-positive intrusion detection. However, there is still a general lack of understanding of deception techniques from a research perspective, and it is not clear how the effectiveness of these solutions can be measured and compared with other security approaches. To shed light on this topic, we introduce a comprehensive classification of existing solutions and survey the current application of deception techniques in computer security. Furthermore, we discuss the limitations of existing solutions, and we analyze several open research directions, including the design of strategies to help defenders to design and integrate deception within a target architecture, the study of automated ways to deploy deception in complex systems, the update and re-deployment of deception, and, most importantly, the design of new techniques and experiments to evaluate the effectiveness of the existing deception techniques.


title: Faithful Multimodal Explanation for Visual Question Answering

abstract: AI systems' ability to explain their reasoning is critical to their utility and trustworthiness. Deep neural networks have enabled significant progress on many challenging problems such as visual question answering (VQA). However, most of them are opaque black boxes with limited explanatory capability. This paper presents a novel approach to developing a high-performing VQA system that can elucidate its answers with integrated textual and visual explanations that faithfully reflect important aspects of its underlying reasoning while capturing the style of comprehensible human explanations. Extensive experimental evaluation demonstrates the advantages of this approach compared to competing methods with both automatic evaluation metrics and human evaluation metrics.


title: What If We Simply Swap the Two Text Fragments? A Straightforward yet Effective Way to Test the Robustness of Methods to Confounding Signals in Nature Language Inference Tasks

abstract: Nature language inference (NLI) task is a predictive task of determining the inference relationship of a pair of natural language sentences. With the increasing popularity of NLI, many state-of-the-art predictive models have been proposed with impressive performances. However, several works have noticed the statistical irregularities in the collected NLI data set that may result in an over-estimated performance of these models and proposed remedies. In this paper, we further investigate the statistical irregularities, what we refer as confounding factors, of the NLI data sets. With the belief that some NLI labels should preserve under swapping operations, we propose a simple yet effective way (swapping the two text fragments) of evaluating the NLI predictive models that naturally mitigate the observed problems. Further, we continue to train the predictive models with our swapping manner and propose to use the deviation of the model’s evaluation performances under different percentages of training text fragments to be swapped to describe the robustness of a predictive model. Our evaluation metrics leads to some interesting understandings of recent published NLI methods. Finally, we also apply the swapping operation on NLI models to see the effectiveness of this straightforward method in mitigating the confounding factor problems in training generic sentence embeddings for other NLP transfer tasks.


title: An Energy-Balanced Routing Method Based on Forward-Aware Factor for Wireless Sensor Networks

abstract: As an important part of industrial application (IA), the wireless sensor network (WSN) has been an active research area over the past few years. Due to the limited energy and communication ability of sensor nodes, it seems especially important to design a routing protocol for WSNs so that sensing data can be transmitted to the receiver effectively. An energy-balanced routing method based on forward-aware factor (FAF-EBRM) is proposed in this paper. In FAF-EBRM, the next-hop node is selected according to the awareness of link weight and forward energy density. Furthermore, a spontaneous reconstruction mechanism for local topology is designed additionally. In the experiments, FAF-EBRM is compared with LEACH and EEUC, experimental results show that FAF-EBRM outperforms LEACH and EEUC, which balances the energy consumption, prolongs the function lifetime and guarantees high QoS of WSN.


title: Condition-based maintenance modelling

abstract: The use of condition monitoring techniques in industry to direct maintenance actions has increased rapidly over recent years to the extent that it has marked the beginning of what is likely to prove a new generation in production and maintenance management practice. There are both economic and technological reasons for this development driven by tight profit margins, high outage costs and an increase in plant complexity and automation. Technical advances in condition monitoring techniques have provided a means to achieve high availability and to reduce scheduled and unscheduled production shutdowns. In all cases, the measured condition information does, in addition to potentially improving decision making, have a value added role for a manager in that there is now a more objective means of explaining actions if challenged.


title: SCRaMbLE generates evolved yeasts with increased alkali tolerance

abstract: Background ::: Strains with increased alkali tolerance have a broad application in industrial, especially for bioremediation, biodegradation, biocontrol and production of bio-based chemicals. A novel synthetic chromosome recombination and modification by LoxP-mediated evolution (SCRaMbLE) system has been introduced in the synthetic yeast genome (Sc 2.0), which enables generation of a yeast library with massive structural variations and potentially drives phenotypic evolution. The structural variations including deletion, inversion and duplication have been detected within synthetic yeast chromosomes.


title: Privacy Aware Access Control for Big Data

abstract: Big Data is an emerging phenomenon that is rapidly changing business models and work styles 1. Big Data platforms allow the storage and analysis of high volumes of data with heterogeneous format from different sources. This integrated analysis allows the derivation of properties and correlations among data that can then be used for a variety of purposes, such as making predictions that can profitably affect decision processes. As a matter of fact, nowadays Big Data analytics are generally considered an asset for making business decisions. Big Data platforms have been specifically designed to support advanced form of analytics satisfying strict performance and scalability requirements. However, no proper consideration has been devoted so far to data protection. Indeed, although the analyzed data often include personal and sensitive information, with relevant threats to privacy implied by the analysis, so far Big Data platforms integrate quite basic form of access control, and no support for privacy policies. Although the potential benefits of data analysis are manifold, the lack of proper data protection mechanisms may prevent the adoption of Big Data analytics by several companies. This motivates the fundamental need to integrate privacy and security awareness into Big Data platforms. In this paper, we do a first step to achieve this ambitious goal, discussing research issues related to the definition of a framework that supports the integration of privacy aware access control features into existing Big Data platforms.


title: Disturbed Vitamin A Metabolism in Non-Alcoholic Fatty Liver Disease (NAFLD)

abstract: Vitamin A is required for important physiological processes, including embryogenesis, vision, cell proliferation and differentiation, immune regulation, and glucose and lipid metabolism. Many of vitamin A’s functions are executed through retinoic acids that activate transcriptional networks controlled by retinoic acid receptors (RARs) and retinoid X receptors (RXRs).The liver plays a central role in vitamin A metabolism: (1) it produces bile supporting efficient intestinal absorption of fat-soluble nutrients like vitamin A; (2) it produces retinol binding protein 4 (RBP4) that distributes vitamin A, as retinol, to peripheral tissues; and (3) it harbors the largest body supply of vitamin A, mostly as retinyl esters, in hepatic stellate cells (HSCs). In times of inadequate dietary intake, the liver maintains stable circulating retinol levels of approximately 2 μmol/L, sufficient to provide the body with this vitamin for months. Liver diseases, in particular those leading to fibrosis and cirrhosis, are associated with impaired vitamin A homeostasis and may lead to vitamin A deficiency. Liver injury triggers HSCs to transdifferentiate to myofibroblasts that produce excessive amounts of extracellular matrix, leading to fibrosis. HSCs lose the retinyl ester stores in this process, ultimately leading to vitamin A deficiency. Non-alcoholic fatty liver disease (NAFLD) is the hepatic manifestation of metabolic syndrome and is a spectrum of conditions ranging from benign hepatic steatosis to non-alcoholic steatohepatitis (NASH); it may progress to cirrhosis and liver cancer. NASH is projected to be the main cause of liver failure in the near future. Retinoic acids are key regulators of glucose and lipid metabolism in the liver and adipose tissue, but it is unknown whether impaired vitamin A homeostasis contributes to or suppresses the development of NAFLD. A genetic variant of patatin-like phospholipase domain-containing 3 (PNPLA3-I148M) is the most prominent heritable factor associated with NAFLD. Interestingly, PNPLA3 harbors retinyl ester hydrolase activity and PNPLA3-I148M is associated with low serum retinol level, but enhanced retinyl esters in the liver of NAFLD patients. Low circulating retinol in NAFLD may therefore not reflect true “vitamin A deficiency”, but rather disturbed vitamin A metabolism. Here, we summarize current knowledge about vitamin A metabolism in NAFLD and its putative role in the progression of liver disease, as well as the therapeutic potential of vitamin A metabolites.


title: Multifaceted Nature of Intrinsic Motivation: The Theory of 16 Basic Desires.

abstract: R. W. White (1959) proposed that certain motives, such as curiosity, autonomy, and play (called intrinsic motives, or IMs), have common characteristics that distinguish them from drives. The evidence that mastery is common to IMs is anecdotal, not scientific. The assertion that “intrinsic enjoyment” is common to IMs exaggerates the significance of pleasure in human motivation and expresses the hedonistic fallacy of confusing consequence for cause. Nothing has been shown scientifically to be common to IMs that differentiates them from drives. An empirically testable theory of 16 basic desires is put forth based on psychometric research and subsequent behavior validation. The desires are largely unrelated to each other and may have different evolutionary histories. Motives are reasons people hold for initiating and performing voluntary behavior. They indicate the meaning of human behavior, and they may reveal a person’s values. Motives often affect a person’s perception, cognition, emotion, and behavior. A person who is highly motivated to gain social status, for example, may be observant of marks of social distinction, may think often about issues pertaining to wealth, may especially enjoy the feeling of selfimportance, and may behave in ways associated with upper-class status. 1 By defining motives as reasons, we do not imply that motives are primarily cognitive, any more than establishing a motive for a crime in a court of law requires conscious premeditation. A person can have a reason to behave, and thus a motive, without necessarily being aware of it. Aristotle (330 BCE/1953) divided motives into ends versus means on the basis of the individual’s purpose for performing the behavior. Ends are indicated when a person engages in a behavior for no apparent reason other than that is what the person desires to do. Examples include a child playing ball for physical exercise and a student reading a book out of curiosity. In each of these examples, the goal is desired for its own sake. In contrast, means are indicated when a person performs an act for its instrumental value. Examples include a professional athlete who plays ball for a salary and a student who studies to improve a grade. In each of these examples, the goal (salary, grade) is desired because it produces something else. A person


title: Enhancing the TED-LIUM Corpus with Selected Data for Language Modeling and More TED Talks

abstract: In this paper, we present improvements made to the TED-LIUM corpus we released in 2012. These enhancements fall into two categories. First, we describe how we filtered publicly available monolingual data and used it to estimate well-suited language models (LMs), using open-source tools. Then, we describe the process of selection we applied to new acoustic data from TED talks, providing additions to our previously released corpus. Finally, we report some experiments we made around these improvements.


title: Self-Transcendence and Depression in Middle-Age Adults

abstract: Self-transcendence has been found to be an important correlate of mental health in older adults and adults facing the end of life. This study extends current theory by examining the relationship of transcendence and other transcendence variables to depression in middle-age adults (N = 133). Reed’s Self-Transcendence Scale, the Center for Epidemiological Studies-Depression Scale, and measures of parenting, acceptance and spirituality were administered. Findings indicating significant inverse correlations between self-transcendence and depression, as well as between other measures of transcendence and depression support Reed’s (1991b) theory. Multiple regression analysis indicated that acceptance may be another significant correlate of depression. Significant gender differences and age-related patterns of increased levels of self-transcendence were found. Study results illuminate the need to continue research into developmentally based transcendence variables related to various experiences of health and wel...


title: A viability approach for fast recursive feasible finite horizon path planning of autonomous RC cars

abstract: We consider a viability based approach to guarantee recursive feasibility of a finite horizon path planner. The path planner is formulated as a hybrid system for which a difference inclusion reformulation is derived by exploiting the special structure of the problem. Based on this approximation, the viability kernel, which characterizes all safe states and the corresponding safe controls, can be calculated. Using the set of safe controls the computation time of the on-line path planning can be reduced, by only generating viable trajectories. Finally, a condition characterizing the unsafe set in case of on-line obstacle avoidance is derived.


Polynomial Trajectory Planning for Quadrotor Flight


title: Two-Step Moving Target Detection Algorithm for Automotive 77 GHz FMCW Radar

abstract: Today, 77GHz FMCW (Frequency Modulation Continuous Wave) radar sensors are used for automotive applications. In typical automotive radar, the target of interest is a moving target. Thus, to improve the detection probability and reduce the false alarm rate, an MTD(Moving Target Detection) algorithm should be required. This paper describes the proposed two-step MTD algorithm. The 1st MTD processing consists of a clutter cancellation step and a noise cancellation step. The two steps can cancel almost all clutter including stationary targets. However, clutter still remains among the interest beat frequencies detected during the 1st MTD and CFAR (Constant False Alarm) processing. Thus, in the 2nd MTD step, we remove the rest of the clutter with zero phase variation.


Retention and transfer of mediation set in paired-associate learning of educable retarded children.


Machine Learning Experiments for Textual Entailment


title: fTPM: A Software-Only Implementation of a {TPM} Chip

abstract: Commodity CPU architectures, such as ARM and Intel CPUs, have started to offer trusted computing features in their CPUs aimed at displacing dedicated trusted hardware. Unfortunately, these CPU architectures raise serious challenges to building trusted systems because they omit providing secure resources outside the CPU perimeter. This paper shows how to overcome these challenges to build software systems with security guarantees similar to those of dedicated trusted hardware. We present the design and implementation of a firmware-based TPM 2.0 (fTPM) leveraging ARM TrustZone. Our fTPM is the reference implementation of a TPM 2.0 used in millions of mobile devices. We also describe a set of mechanisms needed for the fTPM that can be useful for building more sophisticated trusted applications beyond just a TPM.


title: DOLLÁR, et al.: INTEGRAL CHANNEL FEATURES 1 Integral Channel Features

abstract: We study the performance of ‘integral channel features’ for image classification tasks, ::: focusing in particular on pedestrian detection. The general idea behind integral channel features is that multiple registered image channels are computed using linear and ::: non-linear transformations of the input image, and then features such as local sums, histograms, and Haar features and their various generalizations are efficiently computed ::: using integral images. Such features have been used in recent literature for a variety of ::: tasks – indeed, variations appear to have been invented independently multiple times. ::: Although integral channel features have proven effective, little effort has been devoted to ::: analyzing or optimizing the features themselves. In this work we present a unified view ::: of the relevant work in this area and perform a detailed experimental evaluation. We ::: demonstrate that when designed properly, integral channel features not only outperform ::: other features including histogram of oriented gradient (HOG), they also (1) naturally ::: integrate heterogeneous sources of information, (2) have few parameters and are insensitive to exact parameter settings, (3) allow for more accurate spatial localization during ::: detection, and (4) result in fast detectors when coupled with cascade classifiers.


title: Tandem-STRAIGHT: A temporally stable power spectral representation for periodic signals and applications to interference-free spectrum, F0, and aperiodicity estimation

abstract: A simple new method for estimating temporally stable power spectra is introduced to provide a unified basis for computing an interference-free spectrum, the fundamental frequency (F0), as well as aperiodicity estimation. F0 adaptive spectral smoothing and cepstral liftering based on consistent sampling theory are employed for interference-free spectral estimation. A perturbation spectrum, calculated from temporally stable power and interference-free spectra, provides the basis for both F0 and aperiodicity estimation. The proposed approach eliminates ad-hoc parameter tuning and the heavy demand on computational power, from which STRAIGHT has suffered in the past.


title: Designing games to motivate physical activity

abstract: Engagement with computer games causes children and adolescent users to spend a substantial amount of time at sedentary game playing activity. We hypothesise that this engagement can be leveraged to motivate users to increase their amount of physical activity. In this paper, we present a novel approach for designing computer games, according to which the users' physical activity reinforces their game character. This way the users are seamlessly motivated to perform physical activity while maintaining their enjoyment of playing the game.


title: Impact of Online Consumer Reviews on Sales: The Moderating Role of Product and Consumer Characteristics

abstract: Abstract This article examines how product and consumer characteristics moderate the influence of online consumer reviews on product sales using data from the video game industry. The findings indicate that online reviews are more influential for less popular games and games whose players have greater Internet experience. The article shows differential impact of consumer reviews across products in the same product category and suggests that firms' online marketing strategies should be contingent on product and consumer characteristics. The authors discuss the implications of these results in light of the increased share of niche products in recent years.


title: PI and Sliding Mode Control of a Cuk Converter

abstract: Proportional-integral (PI) and sliding mode controls are combined to regulate a fourth-order Cuk converter in a continuous conduction mode. A closed-loop system is obtained with the aid of the equivalent control method. Based on the Routh-Hurwitz stability criterion and root locus, the appropriate PI gains are obtained and a stable and robust system suitable for large signal variations is achieved. The minimum or nonminimum phase behavior of the closed-loop system and the transients of the closed- loop system under step variations of various circuit parameters are analyzed. Under a wide range of operating points, the Cuk converter under the proposed controller has a load voltage tracking accuracy within ±0.05 V with a moderate maximum switching frequency of not greater than 100 KHz. The merits and demerits of the proposed controller are compared with some other controllers.


title: Multi-Stream Multi-Class Fusion of Deep Networks for Video Classification

abstract: This paper studies deep network architectures to address the problem of video classification. A multi-stream framework is proposed to fully utilize the rich multimodal information in videos. Specifically, we first train three Convolutional Neural Networks to model spatial, short-term motion and audio clues respectively. Long Short Term Memory networks are then adopted to explore long-term temporal dynamics. With the outputs of the individual streams on multiple classes, we propose to mine class relationships hidden in the data from the trained models. The automatically discovered relationships are then leveraged in the multi-stream multi-class fusion process as a prior, indicating which and how much information is needed from the remaining classes, to adaptively determine the optimal fusion weights for generating the final scores of each class. Our contributions are two-fold. First, the multi-stream framework is able to exploit multimodal features that are more comprehensive than those previously attempted. Second, our proposed fusion method not only learns the best weights of the multiple network streams for each class, but also takes class relationship into account, which is known as a helpful clue in multi-class visual classification tasks. Our framework produces significantly better results than the state of the arts on two popular benchmarks, 92.2% on UCF-101 (without using audio) and 84.9% on Columbia Consumer Videos.


Relationship of Gout and Hyperuricemia in Cardiovascular and Renal Morbidity: 2003 National Nutrition and Health Survey (NNHeS)


title: The min-max edge q-coloring problem

abstract: In this paper we introduce and study a new problem named \emph{min-max edge $q$-coloring} which is motivated by applications in wireless mesh networks. The input of the problem consists of an undirected graph and an integer $q$. The goal is to color the edges of the graph with as many colors as possible such that: (a) any vertex is incident to at most $q$ different colors, and (b) the maximum size of a color group (i.e. set of edges identically colored) is minimized. We show the following results: 1. Min-max edge $q$-coloring is NP-hard, for any $q \ge 2$. 2. A polynomial time exact algorithm for min-max edge $q$-coloring on trees. 3. Exact formulas of the optimal solution for cliques and almost tight bounds for bicliques and hypergraphs. 4. A non-trivial lower bound of the optimal solution with respect to the average degree of the graph. 5. An approximation algorithm for planar graphs.


title: Observations of Pair Programming: Variations in Collaboration Across Demographic Groups

abstract: Sixty-nine pairs of Latino and white middle school students were video recorded as they pair programmed for approximately 20 minutes. We quantitatively analyzed the partners' interactions specifically looking at characteristics of their collaboration. A qualitative analysis of interactions of three girl pairs was conducted to gain insight into how students' experience in diverse cultural communities may relate to their collaboration during pair programming.


title: Measurement of dietary intake in children.

abstract: When children and adolescents are the target population in dietary surveys many different respondent and observer considerations surface. The cognitive abilities required to self-report food intake include an adequately developed concept of time, a good memory and attention span, and a knowledge of the names of foods. From the age of 8 years there is a rapid increase in the ability of children to self-report food intake. However, while cognitive abilities should be fully developed by adolescence, issues of motivation and body image may hinder willingness to report. Ten validation studies of energy intake data have demonstrated that mis-reporting, usually in the direction of under-reporting, is likely. Patterns of under-reporting vary with age, and are influenced by weight status and the dietary survey method used. Furthermore, evidence for the existence of subject-specific responding in dietary assessment challenges the assumption that repeated measurements of dietary intake will eventually obtain valid data. Unfortunately, the ability to detect mis-reporters, by comparison with presumed energy requirements, is limited unless detailed activity information is available to allow the energy intake of each subject to be evaluated individually. In addition, high variability in nutrient intakes implies that, if intakes are valid, prolonged dietary recording will be required to rank children correctly for distribution analysis. Future research should focus on refining dietary survey methods to make them more sensitive to different ages and cognitive abilities. The development of improved techniques for identification of mis-reporters and investigation of the issue of differential reporting of foods should also be given priority. Childhood: Adolescents: Dietary intake


title: Information Extraction From Microblogs: A Survey

abstract: Microblogging(e.g. Twitter, http://twitter.com), as a new form of online communication in which users talk about their daily lives, publish opinions or share information by short posts, has become one of the most popular social networking services today, which makes it potentially a large information base attracting increasing attention of researchers in the field of knowledge discovery and data mining. In this paper, we conduct a survey about existing research on information extraction from microblogging services and their applications, and then address some promising future works. We specifically analyze three types of information: personal, social and travel information.


title: Classroom climate and motivated behaviour in secondary schools

abstract: For some children, school failure is attributed to a lack in motivation. This article reports a study of motivation from an ecological perspective, considering the individual in interaction with the meaningful environment. Unlike much of the motivational literature that measures motivation in terms of constructs that are assessed largely via self-report, the dependent variables used in this study were three measures of more immediate classroom behaviours: participation, self-reported engagement, and task completion. The results show that aspects of classroom climate are significantly related to all of these measures of motivation. Implications for practice are discussed.


title: Secure and Efficient Cloud Data Deduplication With Randomized Tag

abstract: Cross-client data deduplication has been widely used to eliminate redundant storage overhead in cloud storage system. Recently, Abadi et al. introduced the primitive of MLE2 with nice security properties for secure and efficient data deduplication. However, besides the computationally expensive non-interactive zero-knowledge proofs, their fully randomized scheme (R-MLE2) requires the inefficient equality-testing algorithm to identify all duplicate ciphertexts. Thus, an interesting challenging problem is how to reduce the overhead of R-MLE2 and propose an efficient construction for R-MLE2. In this paper, we introduce a new primitive called $\mu \text{R}$ -MLE2, which gives a partial positive answer for this challenging problem. We propose two schemes: static scheme and dynamic scheme, where the latter one allows tree adjustment by increasing some computation cost. Our main trick is to use the interactive protocol based on static or dynamic decision trees. The advantage gained from it is, by interacting with clients, the server will reduce the time complexity of deduplication equality test from linear time to efficient logarithmic time over the whole data items in the database. The security analysis and the performance evaluation show that our schemes are Path-PRV-CDA2 secure and achieve several orders of magnitude higher performance for data equality test than R-MLE2 scheme when the number of data items is relatively large.


title: Electron stimulated oxidation of GaAs, studied by quantitative auger electron spectroscopy

abstract: The oxidation properties of the clean polar (111) faces of GaAs, prepared by Ar+ bombardment and proper annealing, are investigated. Considering the adsorbed layer as a continuum and using empirical values for the escape depth of the Auger electrons from literature, the coverage of oxygen on these faces is quantitatively determined. For a coverage of up to 10% of a monolayer the sticking coefficients are about 10−3 for the (111) As face and about 10−4 for the (111) Ga face, respectively. They decrease rapidly with increasing coverage. The oxidation is strongly stimulated by electron irradiation causing dissociation of the oxygen which is originally adsorbed in molecular form. In this way a compact oxide layer is formed which shows As depletion as a result of sublimation of As4O6 and a chemical shift of the Ga Auger peaks is observed. The cross section for the O2 dissociation is calculated to be 1.8–2.5 A2 depending on electron energy.


title: Features selection of cotton disease leaves image based on fuzzy feature selection techniques

abstract: In the research of identifying and diagnosing cotton disease using computer vision intellectively in the agriculture, feature selection is a key question in pattern recognition and affects the design and performance of the classifier. In this paper, the fuzzy feature selection approach - fuzzy curves (FC) and surfaces (FS) - is proposed to select features of cotton disease leaves image. In order to get best information for diagnosing and identifying, a subset of independent significant features is identified exploiting the fuzzy feature selection approach. Firstly, utilize FC to automatically and quickly isolate a small set of significant features from the set of original features according to their significance and eliminate spurious features; then, use FS to get rid of the features dependent on the significant features. This approach reduces the dimensionality of the feature space so that lead to a simplified classification scheme appropriate for practical classification applications. The results show that the effectiveness of features selected by the FC and FS method is much better than that selected by human randomly or other methods.


title: A hardware Gaussian noise generator using the Box-Muller method and its error analysis

abstract: We present a hardware Gaussian noise generator based on the Box-Muller method that provides highly accurate noise samples. The noise generator can be used as a key component in a hardware-based simulation system, such as for exploring channel code behavior at very low bit error rates, as low as 10-12 to 10-13. The main novelties of this work are accurate analytical error analysis and bit-width optimization for the elementary functions involved in the Box-Muller method. Two 16-bit noise samples are generated every clock cycle and, due to the accurate error analysis, every sample is analytically guaranteed to be accurate to one unit in the last place. An implementation on a Xilinx Virtex-4 XC4VLX100-12 FPGA occupies 1,452 slices, three block RAMs, and 12 DSP slices, and is capable of generating 750 million samples per second at a clock speed of 375 MHz. The performance can be improved by exploiting concurrent execution: 37 parallel instances of the noise generator at 95 MHz on a Xilinx Virtex-II Pro XC2VP100-7 FPGA generate seven billion samples per second and can run over 200 times faster than the output produced by software running on an Intel Pentium-4 3 GHz PC. The noise generator is currently being used at the Jet Propulsion Laboratory, NASA to evaluate the performance of low-density parity-check codes for deep-space communications


title: Evaluation of Near-Duplicate Image Retrieval Algorithms for the Identification of Celebrities in Web Images

abstract: Near-duplicate image retrieval is a classical research problem in computer vision, for which a large number of diverse approaches have been proposed. Recent studies have revealed that it can be used as an intermediate step to implement search-based celebrity identification given the existence of huge volume of user-tagged or text-surrounded celebrity images on the web. However, the effectiveness of existing near-duplicate image retrieval methods for such a task still remains unclear. To address this issue, this paper presents a comprehensive study of the existing near-duplicate image retrieval methods in a structural way. Four representatives of the existing methods, i.e. hash signature, mean SSIM, BoVW with SIFT features and ARG, are experimentally evaluated using a self-constructed dataset containing 24762 images of 15 top searched celebrities collected using 6 news search engines and the Google image search engine. The experimental results reveal that, compared with global feature based methods, local feature based ones are usually more appropriate for the task of celebrity identification in web images, as they can deal with partial duplicate and scene similar images better. In particular, BoVW with SIFT features is recommended as it provides the best trade-off between on-line speed and retrieval accuracy.


title: WAVES: A Wearable Asymmetric Vibration Excitation System for Presenting Three-Dimensional Translation and Rotation Cues

abstract: WAVES, a Wearable Asymmetric Vibration Excitation System, is a novel wearable haptic device for presenting three dimensions of translation and rotation guidance cues. In contrast to traditional vibration feedback, which usually requires that users learn to interpret a binary cue, asymmetric vibrations have been shown to induce a pulling sensation in a desired direction. When attached to the fingers, a single voicecoil actuator presents a translation guidance cue and a pair of voicecoil actuators presents a rotation guidance cue. The directionality of mechanoreceptors in the skin led to our choice of the location and orientation of the actuators in order to elicit very strong sensations in certain directions. For example, users distinguished a "left" cue versus a "right" cue 94.5% of the time. When presented with one of six possible direction cues, users on average correctly identified the direction of translation cues 86.1% of the time and rotation cues 69.0% of the time.


title: Deep Reinforcement Learning with Iterative Shift for Visual Tracking

abstract: Visual tracking is confronted by the dilemma to locate a target both accurately and efficiently, and make decisions online whether and how to adapt the appearance model or even restart tracking. In this paper, we propose a deep reinforcement learning with iterative shift (DRL-IS) method for single object tracking, where an actor-critic network is introduced to predict the iterative shifts of object bounding boxes, and evaluate the shifts to take actions on whether to update object models or re-initialize tracking. Since locating an object is achieved by an iterative shift process, rather than online classification on many sampled locations, the proposed method is robust to cope with large deformations and abrupt motion, and computationally efficient since finding a target takes up to 10 shifts. In offline training, the critic network guides to learn how to make decisions jointly on motion estimation and tracking status in an end-to-end manner. Experimental results on the OTB benchmarks with large deformation improve the tracking precision by 1.7% and runs about 5 times faster than the competing state-of-the-art methods.


title: An improved Ziggurat-based hardware Gaussian random number generator

abstract: The Ziggurat algorithm is an efficient way for building a Gaussian random number generator (GRNG), which is useful in many scientific and engineering applications. As the classic ziggurat-based GRNG includes nonlinear operations in judging the wedge and tail regions, which is complicated and resource consuming. An improved ziggurat algorithm is proposed by optimizing the accepting model with piecewise linear fitting, replacing the nonlinear operations with linear ones. The improved GRNG works with a 33% decrease of resource and has no significant performance loss. In addition, the uniform random number generator (URNG) in GRNG is implemented by metastability-based ring oscillators with XOR trees, which is the state-of-the-art technique for generating true random numbers in fully digital ways. FPGA results show that the proposed GRNG generates good normal distribution samples with a smooth power spectrum.


title: A fog computing solution for advanced metering infrastructure

abstract: With rapid increase in deployment of advanced metering infrastructure, the amount of the data collected increases dramatically. Incorporation of such voluminous data requires wide-ranging transformation of the existing metering infrastructure. However, as the number of smart meters increases to more than hundreds of thousands, it would become increasingly clear that the state-of-the-art centralized information processing architecture will no longer be sustainable under such big data explosion. In this short communication letter, we propose an implantable data storage-and-processing solution for improving the existing smart meter infrastructure. The practicality of the proposed solution is validated on a proof-of-concept testbed.


title: Millimeter-Wave MIMO Prototype: Measurements and Experimental Results

abstract: Millimeter-wave multi-input multi-output (mm-Wave MIMO) systems are one of the candidate schemes for 5G wireless standardization efforts. In this context, the main contributions of this article are three-fold. 1) We describe parallel sets of measurements at identical transmit-receive location pairs with 2.9, 29 and 61 GHz carrier frequencies in indoor office, shopping mall, and outdoor settings. These measurements provide insights on propagation, blockage and material penetration losses, and the key elements necessary in system design to make mm-Wave systems viable in practice. 2) One of these elements is hybrid beamforming necessary for better link margins by reaping the array gain with large antenna dimensions. From the class of fully-flexible hybrid beamformers, we describe a robust class of directional beamformers towards meeting the high data-rate requirements of mm-Wave systems. 3) Leveraging these design insights, we then describe an experimental prototype system at 28 GHz that realizes high data-rates on both the downlink and uplink and robustly maintains these rates in outdoor and indoor mobility scenarios. In addition to maintaining large signal constellation sizes in spite of radio frequency challenges, this prototype leverages the directional nature of the mm-Wave channel to perform seamless beam switching and handover across mm-Wave base-stations thereby overcoming the path losses in non-line-of-sight links and blockages encountered at mm-Wave frequencies.


title: Revisiting the Commons: Local Lessons, Global Challenges

abstract: In a seminal paper, Garrett Hardin argued in 1968 that users of a commons are caught in an inevitable process that leads to the destruction of the resources on which they depend. This article discusses new insights about such problems and the conditions most likely to favor sustainable uses of common-pool resources. Some of the most difficult challenges concern the management of large-scale resources that depend on international cooperation, such as fresh water in international basins or large marine ecosystems. Institutional diversity may be as important as biological diversity for our long-term survival.


title: Fault Diagnosis of On-Load Tap-Changer in Converter Transformer Based on Time–Frequency Vibration Analysis

abstract: The suitable condition of an on-load tap-changer (OLTC) is essential for the operation of converter transformer due to its frequent switch for the voltage regulation of power system. This paper describes a methodology to obtain the OLTC vibration characteristics in time–frequency domain. Considering the possible aliasing effect in vibration signal processing, an improved empirical mode decomposition (EMD) is proposed with masking signals of multiple frequencies added, which has obvious superiority in aliasing reduction compared with conventional methods. Then, an effective fault-diagnosis strategy based on energy distributions variations of OLTC vibration signals according to Lorentz information measure is brought up. The calculated results under normal and typical fault conditions of model and real OLTC have shown that the energy spectrums of different conditions vary significantly so that the similarity index can measure the difference degree of energy distribution. Meanwhile, the index of contact looseness is higher than the insulated panel looseness which indicates that the contact looseness fault has greater influence on switch-over process of OLTC.


title: The IoT hub: a fog node for seamless management of heterogeneous connected smart objects

abstract: The Internet of Things (IoT) will interconnect billions of devices, denoted as “smart objects,” deployed pervasively, which will be extremely heterogeneous, in terms of hardware (i.e., computational power and available memory), software (i.e., operating systems, applications), and communication interfaces. Traditional Internet actors, such as personal computers, mobile devices, and cloud servers, will also communicate with smart objects, thus creating even more complexity. The IoT has so far grown as several vertical silos, with the purpose of demonstrating the concept of the IoT, rather than focusing on the actual construction of a highly interoperable infrastructure for the development of applications. The Internet Protocol (IP) stack (in particular, HTTP and CoAP) has been foreseen as the driver for integration and interoperability among devices and basis for the evolution of the Web of Things. However, in order to manage the physical diversity of devices and to create an IP-based infrastructure, the presence of network elements able to bridge different networks to enable direct end-to-end communication is required. Moreover, effective interaction with applications might require the presence of intermediaries, such as proxies, which may optionally implement protocol and data format translation functionalities. Given the above considerations, we propose a Fog node, denoted as “IoT Hub,” placed at the edge of multiple networks, which enhances the networks capabilities by implementing the following functions: border router; cross-proxy; cache; and resource directory. An implementation of the IoT Hub is presented together with a performance evaluation in a real-world IoT testbed.