title: In pursuit of rigour and accountability in participatory design

abstract: The field of Participatory Design (PD) has greatly diversified and we see a broad spectrum of approaches and methodologies emerging. However, to foster its role in designing future interactive technologies, a discussion about accountability and rigour across this spectrum is needed. Rejecting the traditional, positivistic framework, we take inspiration from related fields such as Design Research and Action Research to develop interpretations of these concepts that are rooted in PD's own belief system. We argue that unlike in other fields, accountability and rigour are nuanced concepts that are delivered through debate, critique and reflection. A key prerequisite for having such debates is the availability of a language that allows designers, researchers and practitioners to construct solid arguments about the appropriateness of their stances, choices and judgements.To this end, we propose a "tool-to-think-with" that provides such a language by guiding designers, researchers and practitioners through a process of systematic reflection and critical analysis. The tool proposes four lenses to critically reflect on the nature of a PD effort: epistemology, values, stakeholders and outcomes. In a subsequent step, the coherence between the revealed features is analysed and shows whether they pull the project in the same direction or work against each other. Regardless of the flavour of PD, we argue that this coherence of features indicates the level of internal rigour of PD work and that the process of reflection and analysis provides the language to argue for it. We envision our tool to be useful at all stages of PD work: in the planning phase, as part of a reflective practice during the work, and as a means to construct knowledge and advance the field after the fact. We ground our theoretical discussions in a specific PD experience, the ECHOES project, to motivate the tool and to illustrate its workings. Participatory Design (PD) requires nuanced concepts of accountability and rigour.Accountability and rigour are constructed through debate, critique and reflection.Our "tool-to-think-with" guides designers in systematic and critical reflection.We provide four lenses for reflection: epistemology, values, stakeholders, outcomes.The "coherence" between reflective perspectives indicates internal rigour of PD work.


title: Cryptography on FPGAs: State of the Art Implementations and Attacks

abstract: In the last decade, it has become aparent that embedded systems are integral parts of our every day lives. The wireless nature of many embedded applications as well as their omnipresence has made the need for security and privacy preserving mechanisms particularly important. Thus, as FPGAs become integral parts of embedded systems, it is imperative to consider their security as a whole. This contribution provides a state-of-the-art description of security issues on FPGAs, both from the system and implementation perspectives. We discuss the advantages of reconfigurable hardware for cryptographic applications, show potential security problems of FPGAs, and provide a list of open research problems. Moreover, we summarize both public and symmetric-key algorithm implementations on FPGAs.


title: Size Matters: Word Count as a Measure of Quality

abstract: Wikipedia, "the free encyclopedia", now contains over two million English articles, and is widely regarded as a high-quality, authoritative encyclopedia. Some Wikipedia articles, however, are of questionable quality, and it is not always apparent to the visitor which articles are good and which are bad. We propose a simple metric -- word count -- for measuring article quality. In spite of its striking simplicity, we show that this metric significantly outperforms the more complex methods described in related work.


title: An Information-Theoretic Analysis of Deep Latent-Variable Models

abstract: We present an information-theoretic framework for understanding trade-offs in unsupervised learning of deep latent-variables models using variational inference. This framework emphasizes the need to consider latent-variable models along two dimensions: the ability to reconstruct inputs (distortion) and the communication cost (rate). We derive the optimal frontier of generative models in the two-dimensional rate-distortion plane, and show how the standard evidence lower bound objective is insufficient to select between points along this frontier. However, by performing targeted optimization to learn generative models with different rates, we are able to learn many models that can achieve similar generative performance but make vastly different trade-offs in terms of the usage of the latent variable. Through experiments on MNIST and Omniglot with a variety of architectures, we show how our framework sheds light on many recent proposed extensions to the variational autoencoder family.


title: Nasal bone length, prenasal thickness, prenasal thickness-to-nasal bone length ratio and prefrontal space ratio in second- and third-trimester fetuses with Down syndrome.

abstract: OBJECTIVES ::: To evaluate nasal bone length (NBL), prenasal thickness (PT), prenasal thickness-to-nasal bone length (PT-NBL) ratio and prefrontal space ratio (PFSR) as markers for Down syndrome in the second and third trimesters. ::: ::: ::: METHODS ::: NBL, PT, PT-NBL ratio and PFSR were measured retrospectively in stored two-dimensional images or three-dimensional volumes (corrected to the mid-sagittal plane) of fetuses with Down syndrome, which were retrieved from the digital databases of participating units. Measurements were performed on the stored images and volumes by two experienced operators, and the values obtained were compared to our previously reported normal ranges for euploid fetuses in order to assess the detection rates for Down syndrome. ::: ::: ::: RESULTS ::: A total of 159 fetuses with Down syndrome were included in the analysis, six of which were excluded because of inadequate available images. Median maternal age was 36.0 years and median gestational age was 23 + 1 weeks. NBL and PT were correlated with gestational age (P < 0.001), but the PT-NBL ratio and PFSR were not. Mean NBL, PT, PT-NBL ratio and PFSR were 4.42 mm, 5.56 mm, 1.26 and 0.34, respectively. The nasal bone was absent in 23 (15.4%) cases. As a marker for Down syndrome, the PT-NBL ratio yielded the highest detection rate (86.2%), followed by PFSR (79.7%), PT (63.4%) and NBL (61.9%). All markers were abnormal in 33.6% of cases, whilst all were normal in 4.7%. At least one of the four markers was abnormal in 95.3%, and either the PT-NBL ratio or PFSR was abnormal in 93.8%. Detection rates were not related to gestational age. ::: ::: ::: CONCLUSIONS ::: The PT-NBL ratio and PFSR are robust second- and third-trimester markers for Down syndrome. Both provide high detection rates and are easy to use, as the cut-off for normality is constant throughout gestation. Ltd.


title: Road Sign Detection in Images: A Case Study

abstract: Road sign identification in images is an important issue, in particular for vehicle safety applications. It is usually tackled in three stages: detection, recognition and tracking, and evaluated as a whole. To progress towards better algorithms, we focus in this paper on the first stage of the process, namely road sign detection. More specifically, we compare, on the same ground-truth image database, results obtained by three algorithms that sample different state-of-the-art approaches. The three tested algorithms: Contour Fitting, Radial Symmetry Transform, and pair-wise voting scheme, all use color and edge information and are based on geometrical models of road signs. The test dataset is made of 847 images 960x1080 of complex urban scenes (available at www.itowns.fr/benchmarking.html). They feature 251 road signs of different shapes (circular, rectangular, triangular), sizes and types. The pros and cons of the three algorithms are discussed, allowing to draw new research perspectives.


title: New Digital Signature Scheme in Gaussian Monoid

abstract: A new digital signature scheme in non-commutative Gaussian monoid is presented. Two algebraic structures are employed: Gaussian monoid and a certain module being compatible with a monoid. For both monoid and module, presentation and action level attributes are defined. Monoid action level is defined as monoid element (word) action on module element as an operator. A module is a set of functions (elements) with special properties and could be treated as some generalization of vector space. ::: ::: Signature scheme is based on the one-way functions (OWF) design using: three recognized hard problems in monoid presentation level, one postulated hard problem in monoid action level and one provable hard problem in module action level. ::: ::: For signature creation and verification the word equivalence problem is solved in monoid action level thus avoiding solving it in monoid presentation level. Then the three recognized hard problems in monoid presentation level can be essentially as hard as possible to increase signature security. Thus they do not influence on the word problem complexity and, consequently, on the complexity of signature realization. ::: ::: The investigation of signature scheme security against four kind of attacks is presented. It is shown that the signature has a provable security property with respect to the list of attacks presented here, which are postulated to be complete.


title: Solid state circuit breaker protection devices for DC microgrid in review

abstract: The advent of alternating current (AC) is due to its ability to transmit power over long distances from transformers, does not mark the end of utilizing direct current (DC). The development of renewable energy sources is because of high usage of DC because the generated power is directed to the main grid for conversion into electric power and the source of energy cannot tolerate high voltage. Fault detection is a major problem that arises when an excess amount of current flows in the circuit leading to the necessity of having circuit breakers. Circuit breakers are made of light materials, which creates a problem for dealing with high voltage DC applications with the protection of circuit breakers have become popularized, and different papers have outlined various studies on the effectiveness of Solid State Circuit Breakers (SSCBs) protection for a direct current microgrid. This paper reviews the benefits and shortfalls of The Wide bandgap (WBG) SSCBs and its application with photovoltaic (PV) generators.


title: Skeleton-Based Dynamic Hand Gesture Recognition

abstract: In this paper, a new skeleton-based approach is proposed for 3D hand gesture recognition. Specifically, we exploit the geometric shape of the hand to extract an effective descriptor from hand skeleton connected joints returned by the Intel RealSense depth camera. Each descriptor is then encoded by a Fisher Vector representation obtained using a Gaussian Mixture Model. A multi-level representation of Fisher Vectors and other skeleton-based geometric features is guaranteed by a temporal pyramid to obtain the final feature vector, used later to achieve the classification by a linear SVM classifier. The proposed approach is evaluated on a challenging hand gesture dataset containing 14 gestures, performed by 20 participants performing the same gesture with two different numbers of fingers. Experimental results show that our skeleton-based approach consistently achieves superior performance over a depth-based approach.


title: Millimeter Wave Hybrid Beamforming with DFT-MUB Aided Precoder Codebook Design

abstract: The millimeter wave (mmWave) frequency band offers substantial hitherto unused spectral resources for future wireless communication systems in order to meet the increasing capacity demand. However, mmWave frequencies suffer from high propagation losses, which may be mitigated by directional beamforming in addition to baseband precoding. This is usually referred to as hybrid beamforming. In this paper, we investigate the so-called discrete Fourier transform- mutually unbiased bases (DFT-MUB) aided codebook design conceived for limited-feedback mmWave systems, where the MUB aided codebook is applied in the baseband, while the DFT codebook is invoked for RF analog phase shifters. We demonstrate that our DFT- MUB codebook design performs similarly to the optimal digital precoding matrix, where the precoder is selected as the right singular matrix of the channel. However, our solution significantly reduces the search complexity in the baseband, while performing within 2.5 dB from the optimal digital precoder.


title: Self-Organization of Very Large Document Collections: State of the Art

abstract: The Self-Organizing Map (SOM) forms a nonlinear projection from a high-dimensional data manifold onto a low-dimensional grid. A representative model of some subset of data is associated with each grid point. The SOM algorithm computes an optimal collection of models that approximates the data in the sense of some error criterion and also takes into account the similarity relations of the models. The models then become ordered on the grid according to their similarity. When the SOM is used for the exploration of statistical data, the data vectors can be approximated by models of the same dimensionality. When mapping documents, one can represent them statistically by their word frequency histograms or some reduced representations of the histograms that can be regarded as data vectors. We have made SOMs of collections of over one million documents. Each document is mapped onto some grid point, with a link from this point to the document database. The documents are ordered on the grid according to their contents and neighboring documents can be browsed readily. Keywords or key texts can be used to search for the most relevant documents first. New effective coding and computing schemes of the mapping are described.


Predicting Movie Revenue from IMDb Data


title: Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach

abstract: Learning from imbalanced data sets, where the number of examples of one (majority) class is much higher than the others, presents an important challenge to the machine learning community. Traditional machine learning algorithms may be biased towards the majority class, thus producing poor predictive accuracy over the minority class. In this paper, we describe a new approach that combines boosting, an ensemble-based learning algorithm, with data generation to improve the predictive power of classifiers against imbalanced data sets consisting of two classes. In the DataBoost-IM method, hard examples from both the majority and minority classes are identified during execution of the boosting algorithm. Subsequently, the hard examples are used to separately generate synthetic examples for the majority and minority classes. The synthetic data are then added to the original training set, and the class distribution and the total weights of the different classes in the new training set are rebalanced. The DataBoost-IM method was evaluated, in terms of the F-measures, G-mean and overall accuracy, against seventeen highly and moderately imbalanced data sets using decision trees as base classifiers. Our results are promising and show that the DataBoost-IM method compares well in comparison with a base classifier, a standard benchmarking boosting algorithm and three advanced boosting-based algorithms for imbalanced data set. Results indicate that our approach does not sacrifice one class in favor of the other, but produces high predictions against both minority and majority classes.


title: Polarity Related Influence Maximization in Signed Social Networks

abstract: Influence maximization in social networks has been widely studied motivated by applications like spread of ideas or innovations in a network and viral marketing of products. Current studies focus almost exclusively on unsigned social networks containing only positive relationships (e.g. friend or trust) between users. Influence maximization in signed social networks containing both positive relationships and negative relationships (e.g. foe or distrust) between users is still a challenging problem that has not been studied. Thus, in this paper, we propose the polarity-related influence maximization (PRIM) problem which aims to find the seed node set with maximum positive influence or maximum negative influence in signed social networks. To address the PRIM problem, we first extend the standard Independent Cascade (IC) model to the signed social networks and propose a Polarity-related Independent Cascade (named IC-P) diffusion model. We prove that the influence function of the PRIM problem under the IC-P model is monotonic and submodular Thus, a greedy algorithm can be used to achieve an approximation ratio of 1-1/e for solving the PRIM problem in signed social networks. Experimental results on two signed social network datasets, Epinions and Slashdot, validate that our approximation algorithm for solving the PRIM problem outperforms state-of-the-art methods.


title: Learning latent temporal structure for complex event detection

abstract: In this paper, we tackle the problem of understanding the temporal structure of complex events in highly varying videos obtained from the Internet. Towards this goal, we utilize a conditional model trained in a max-margin framework that is able to automatically discover discriminative and interesting segments of video, while simultaneously achieving competitive accuracies on difficult detection and recognition tasks. We introduce latent variables over the frames of a video, and allow our algorithm to discover and assign sequences of states that are most discriminative for the event. Our model is based on the variable-duration hidden Markov model, and models durations of states in addition to the transitions between states. The simplicity of our model allows us to perform fast, exact inference using dynamic programming, which is extremely important when we set our sights on being able to process a very large number of videos quickly and efficiently. We show promising results on the Olympic Sports dataset [16] and the 2011 TRECVID Multimedia Event Detection task [18]. We also illustrate and visualize the semantic understanding capabilities of our model.


title: Assumptions and beliefs, dieting, and predictors of eating disorder-related symptoms in young women and young men

abstract: Differences between assumptions and beliefs related to eating disorders were investigated in young women and young men, with and without a history of dieting. The unique predictive power of these assumptions and beliefs was also assessed, compared to generic assumptions and beliefs. Both women and men with a history of dieting scored more highly than those who had never dieted on assumptions related to weight, shape, and eating, but did not differ from this group in negative self-beliefs. Assumptions and beliefs related to eating disorders, as well as female gender, but not body mass index (BMI), history of dieting, or generic assumptions and beliefs, predicted eating disorder-related symptoms. The findings add to knowledge about the relationship between assumptions and beliefs, gender, dieting, and eating disorder-related symptoms. They also support the usefulness of the Eating Disorder Belief Questionnaire (EDBQ), a new measure of eating disorder-specific assumptions and beliefs.


title: Retinal blood vessel segmentation and bifurcation detection using combined filters

abstract: In this research, retinal blood vessel fundus images will be segmented. Moreover, bifurcation point of the segmented blood vessel images will be determined. The data used for this research was obtained from fundus image database of DRIVE (Digital Retinal Images for Vessel Extraction) dataset. The preprocessing phases contain of several steps such as green channel extraction, histogram equalization and optic disc elimination. Meanwhile, the segmentation phase was performed by combining two filtering methods. These methods are median and derivative of Gaussian filter. Median filter was used to reduce the noise on retinal fundus image such as hard and soft exudates as well as dot and blot hemorrhages. Combination of these two filtering methods were also employed to determine the bifurcation point of segmented blood vessel image. The bifurcation points and the result from blood vessel segmentation were among two of the unique parameters of retinal fundus image. These parameters will be used eventually as unique features of the advanced research which is a biometric system to identify an individual based on his/her unique retinal pattern.


title: Intelligent agent-assisted adaptive order simulation system in the artificial stock market

abstract: Highlights? We proposed a novel stock market conceptual model with the financial information. ? We proposed investment strategy and learning algorithm for fundamentals investors. ? We constructed a prototype implementation for the artificial stock market. ? We done some experiments for trading mechanism innovation. Agent-based computational economics (ACE) has received increased attention and importance over recent years. Some researchers have attempted to develop an agent-based model of the stock market to investigate the behavior of investors and provide decision support for innovation of trading mechanisms. However, challenges remain regarding the design and implementation of such a model, due to the complexity of investors, financial information, policies, and so on. This paper will describe a novel architecture to model the stock market by utilizing stock agent, finance agent and investor agent. Each type of investor agent has a different investment strategy and learning method. A prototype system for supporting stock market simulation and evolution is also presented to demonstrate the practicality and feasibility of the proposed intelligent agent-based artificial stock market system architecture.


title: When Dijkstra Meets Vanishing Point: A Stereo Vision Approach for Road Detection

abstract: In this paper, we propose a vanishing-point constrained Dijkstra road model for road detection in a stereo-vision paradigm. First, the stereo-camera is used to generate the u- and v-disparity maps of road image, from which the horizon can be extracted. With the horizon and ground region constraints, we can robustly locate the vanishing point of road region. Second, a weighted graph is constructed using all pixels of the image, and the detected vanishing point is treated as the source node of the graph. By computing a vanishing-point constrained Dijkstra minimum-cost map, where both disparity and gradient of gray image are used to calculate cost between two neighbor pixels, the problem of detecting road borders in image is transformed into that of finding two shortest paths that originate from the vanishing point to two pixels in the last row of image. The proposed approach has been implemented and tested over 2600 grayscale images of different road scenes in the KITTI data set. The experimental results demonstrate that this training-free approach can detect horizon, vanishing point, and road regions very accurately and robustly. It can achieve promising performance.


title: Spatio-Temporal Super-Resolution Reconstruction of Remote-Sensing Images Based on Adaptive Multi-Scale Detail Enhancement

abstract: There are many problems in existing reconstruction-based super-resolution algorithms, such as the lack of texture-feature representation and of high-frequency details. Multi-scale detail enhancement can produce more texture information and high-frequency information. Therefore, super-resolution reconstruction of remote-sensing images based on adaptive multi-scale detail enhancement (AMDE-SR) is proposed in this paper. First, the information entropy of each remote-sensing image is calculated, and the image with the maximum entropy value is regarded as the reference image. Subsequently, spatio-temporal remote-sensing images are processed using phase normalization, which is to reduce the time phase difference of image data and enhance the complementarity of information. The multi-scale image information is then decomposed using the L0 gradient minimization model, and the non-redundant information is processed by difference calculation and expanding non-redundant layers and the redundant layer by the iterative back-projection (IBP) technique. The different-scale non-redundant information is adaptive-weighted and fused using cross-entropy. Finally, a nonlinear texture-detail-enhancement function is built to improve the scope of small details, and the peak signal-to-noise ratio (PSNR) is used as an iterative constraint. Ultimately, high-resolution remote-sensing images with abundant texture information are obtained by iterative optimization. Real results show an average gain in entropy of up to 0.42 dB for an up-scaling of 2 and a significant promotion gain in enhancement measure evaluation for an up-scaling of 2. The experimental results show that the performance of the AMED-SR method is better than existing super-resolution reconstruction methods in terms of visual and accuracy improvements.


title: Activity recognition based on inertial sensors for Ambient Assisted Living

abstract: Ambient Assisted Living (AAL) aims to create innovative technical solutions and services to support independent living among older adults, improve their quality of life and reduce the costs associated with health and social care. AAL systems provide health monitoring through sensor based technologies to preserve health and functional ability and facilitate social support for the ageing population. Human activity recognition (HAR) is an enabler for the development of robust AAL solutions, especially in safety critical environments. Therefore, HAR models applied within this domain (e.g. for fall detection or for providing contextual information to caregivers) need to be accurate to assist in developing reliable support systems. In this paper, we evaluate three machine learning algorithms, namely Support Vector Machine (SVM), a hybrid of Hidden Markov Models (HMM) and SVM (SVM-HMM) and Artificial Neural Networks (ANNs) applied on a dataset collected between the elderly and their caregiver counterparts. Detected activities will later serve as inputs to a bidirectional activity awareness system for increasing social connectedness. Results show high classification performances for all three algorithms. Specifically, the SVM-HMM hybrid demonstrates the best classification performance. In addition to this, we make our dataset publicly available for use by the machine learning community.


title: Improving network security monitoring for industrial control systems

abstract: Programmable Logic Controller (PLC) technology plays an important role in the automation architectures of several critical infrastructures such as Industrial Control Systems (ICS), controlling equipment in contexts such as chemical processes, factory lines, power production plants or power distribution grids, just to mention a few examples. Despite their importance, PLCs constitute one of the weakest links in ICS security, frequently due to reasons such as the absence of secure communication mechanisms, authenticated access or system integrity checks. While events such as the Stuxnet worm have raised awareness for this problem, industry has slowly reacted, either due to reliability or cost concerns. This paper introduces the Shadow Security Unit, a low-cost device deployed in parallel with a PLC or Remote Terminal Unit (RTU), being capable of transparently intercepting its communications control channels and physical process I/O lines to continuously assess its security and operational status. The proposed device does not require significant changes to the existing control network, being able to work in standalone or integrated within an ICS protection framework.


title: Collaborative Working Architecture for IoT-Based Applications†

abstract: The new sensing applications need enhanced computing capabilities to handle the requirements of complex and huge data processing. The Internet of Things (IoT) concept brings processing and communication features to devices. In addition, the Cloud Computing paradigm provides resources and infrastructures for performing the computations and outsourcing the work from the IoT devices. This scenario opens new opportunities for designing advanced IoT-based applications, however, there is still much research to be done to properly gear all the systems for working together. This work proposes a collaborative model and an architecture to take advantage of the available computing resources. The resulting architecture involves a novel network design with different levels which combines sensing and processing capabilities based on the Mobile Cloud Computing (MCC) paradigm. An experiment is included to demonstrate that this approach can be used in diverse real applications. The results show the flexibility of the architecture to perform complex computational tasks of advanced applications.


title: Music classification as a new approach for malware detection

abstract: Each year, a huge number of malicious programs are released which causes malware detection to become a critical task in computer security. Antiviruses use various methods for detecting malware, such as signature-based and heuristic-based techniques. Polymorphic and metamorphic malwares employ obfuscation techniques to bypass traditional detection methods used by antiviruses. Recently, the number of these malware has increased dramatically. Most of the previously proposed methods to detect malware are based on high-level features such as opcodes, function calls or program’s control flow graph (CFG). Due to new obfuscation techniques, extracting high-level features is tough, fallible and time-consuming; hence approaches using program’s bytes are quicker and more accurate. In this paper, a novel byte-level method for detecting malware by audio signal processing techniques is presented. In our proposed method, program’s bytes are converted to a meaningful audio signal, then Music Information Retrieval (MIR) techniques are employed to construct a machine learning music classification model from audio signals to detect new and unseen instances. Experiments evaluate the influence of different strategies converting bytes to audio signals and the effectiveness of the method.


title: Decisions as statistical evidence and Birnbaum's ‘confidence concept’

abstract: ConclusionTo whatever extent the use of a behavioral, not an evidential, interpretation of decisions in the Lindley-Savage argument for Bayesian theory undermines its cogency as a criticism of typical standard practice, it also undermines the Neyman-Pearson theory as a support for typical standard practice. This leaves standard practice with far less theoretical support than Bayesian methods. It does nothing to resolve the anomalies and paradoxes of standard methods. (Similar statements apply to the common protestation that the models are not real anyway.) The appropriate interpretation of tests as evidence, if possible at all, is difficult and counterintuitive. Any attempt to support tests as more than rules of thumb is doomed to failure.


A Cone Complementarity Linearization Algorithm for Static Output-Feedback and Related Problems


title: Novelty or Surprise?

abstract: Novelty and surprise play significant roles in animal behavior and in attempts to understand the neural mechanisms underlying it. They also play important roles in technology, where detecting observations that are novel or surprising is central to many applications, such as medical diagnosis, text processing, surveillance, and security. Theories of motivation, particularly of intrinsic motivation, place novelty and surprise among the primary factors that arouse interest, motivate exploratory or avoidance behavior, and drive learning. In many of these studies, novelty and surprise are not distinguished from one another: the words are used more-or-less interchangeably. However, while undeniably closely related, novelty and surprise are very different. The purpose of this article is first to highlight the differences between novelty and surprise and to discuss how they are related by presenting an extensive review of mathematical and computational proposals related to them, and then to explore the implications of this for understanding behavioral and neuroscience data. We argue that opportunities for improved understanding of behavior and its neural basis are likely being missed by failing to distinguish between novelty and surprise.


title: Cloud-Trust—a Security Assessment Model for Infrastructure as a Service (IaaS) Clouds

abstract: The vulnerability of cloud computing systems (CCSs) to advanced persistent threats (APTs) is a significant concern to government and industry. We present a cloud architecture reference model that incorporates a wide range of security controls and best practices, and a cloud security assessment model—Cloud-Trust—that estimates high level security metrics to quantify the degree of confidentiality and integrity offered by a CCS or cloud service provider (CSP). Cloud-Trust is used to assess the security level of four multi-tenant IaaS cloud architectures equipped with alternative cloud security controls. Results show the probability of CCS penetration (high value data compromise) is high if a minimal set of security controls are implemented. CCS penetration probability drops substantially if a cloud defense in depth security architecture is adopted that protects virtual machine (VM) images at rest, strengthens CSP and cloud tenant system administrator access controls, and which employs other network security controls to minimize cloud network surveillance and discovery of live VMs.


title: Deep features for breast cancer histopathological image classification

abstract: Breast cancer (BC) is a deadly disease, killing millions of people every year. Developing automated malignant BC detection system applied on patient's imagery can help dealing with this problem more efficiently, making diagnosis more scalable and less prone to errors. Not less importantly, such kind of research can be extended to other types of cancer, making even more impact to help saving lives. Recent results on BC recognition show that Convolution Neural Networks (CNN) can achieve higher recognition rates than hand-crafted feature descriptors, but the price to pay is an increase in complexity to develop the system, requiring longer training time and specific expertise to fine-tune the architecture of the CNN. DeCAF (or deep) features consist of an in-between solution it is based on reusing a previously trained CNN only as feature vectors, which is then used as input for a classifier trained only for the new classification task. In the light of this, we present an evaluation of DeCaf features for BC recognition, in order to better understand how they compare to the other approaches. The experimental evaluation shows that these features can be a viable alternative to fast development of high-accuracy BC recognition systems, generally achieving better results than traditional hand-crafted textural descriptors and outperforming task-specific CNNs in some cases.


title: Posterior predictive checks can and should be Bayesian : Comment on Gelman and Shalizi , ‘ Philosophy and the practice of Bayesian statistics

abstract: Bayesian inference is conditional on the space of models assumed by the analyst. The posterior distribution indicates only which of the available parameter values are less bad than the others, without indicating whether the best available parameter values really fit the data well. A posterior predictive check is important to assess whether the posterior predictions of the least bad parameters are discrepant from the actual data in systematic ways. Gelman and Shalizi (2012a) assert that the posterior predictive check, whether done qualitatively or quantitatively, is non-Bayesian. I suggest that the qualitative posterior predictive check might be Bayesian, and the quantitative posterior predictive check should be Bayesian. In particular, I show that the 'Bayesian p-value', from which an analyst attempts to reject a model without recourse to an alternative model, is ambiguous and inconclusive. Instead, the posterior predictive check, whether qualitative or quantitative, should be consummated with Bayesian estimation of an expanded model. The conclusion agrees with Gelman and Shalizi regarding the importance of the posterior predictive check for breaking out of an initially assumed space of models. Philosophically, the conclusion allows the liberation to be completely Bayesian instead of relying on a non-Bayesian deus ex machina. Practically, the conclusion cautions against use of the Bayesian p-value in favour of direct model expansion and Bayesian evaluation.


title: Performance appraisal and management: The developing research agenda

abstract: Performance appraisal has widened as a concept and as a set of practices and in the form of performance management has become part of a more strategic approach to integrating HR activities and business policies. As a result of this, the research on the subject has moved beyond the limited confines of measurement issues and accuracy of performance ratings and has begun to focus more of social and motivational aspects of appraisal. This article identifies and discusses a number of themes and trends that together make up the developing research agenda for this field. It breaks these down in terms of the nature of appraisal and the context in which it operates. The former is considered in terms of contemporary thinking on the content of appraisal (contextual performance, goal orientation and self awareness) and the process of appraisal (appraiser-appraisee interaction, and multi-source feedback). The discussion of the context of appraisal concentrates on cultural differences and the impact of new technology. In reviewing these emerging areas of research, the article seeks to explore some of the implications for appraisal practice at both organizational and individual levels.


title: Modeling the learning progressions of computational thinking of primary grade students

abstract: We introduce the Progression of Early Computational Thinking (PECT) Model, a framework for understanding and assessing computational thinking in the primary grades (Grades 1 to 6). The model synthesizes measurable evidence from student work with broader, more abstract coding design patterns, which are then mapped onto computational thinking concepts. We present the results of a pilot-test study of the PECT Model in order to demonstrate its potential efficacy in detecting both differences in computational thinking among students of various ages as well as any clear overall progressions in increasing computational sophistication. Results of this sort are vital for establishing research-based and age-appropriate curricula for students in the primary grades, i.e., developing non-trivial, challenging but not overly daunting lesson plans that utilize the cognitive development stage of each grade level most effectively.


title: A flame detection algorithm based on Bag-of-Features in the YUV color space

abstract: Computer vision-based fire detection involves flame detection and smoke detection. This paper proposes a new flame detection algorithm, which is based on a Bag-of-Features technique in the YUV color space. Inspired by that the color of flame in image and video will fall in certain regions in the color space, models of flame pixels and non-flame pixels are established based on code book in the training phase in our proposal. In the testing phase, the input image is split into some N×N blocks and each block is classified respectively. In each N×N block, the pixels values in the YUV color space are extracted as features, just as in the training phase. According to the experimental results, our proposed method can reduce the number of false alarms greatly compared with an alternative algorithm, while it also ensures the accurate classification of positive samples. The classification performance of our proposed method is better than that of alternative algorithms.


title: Automatic Pavement Crack Detection Based on Structured Prediction with the Convolutional Neural Network

abstract: Automated pavement crack detection is a challenging task that has been researched for decades due to the complicated pavement conditions in real world. In this paper, a supervised method based on deep learning is proposed, which has the capability of dealing with different pavement conditions. Specifically, a convolutional neural network (CNN) is used to learn the structure of the cracks from raw images, without any preprocessing. Small patches are extracted from crack images as inputs to generate a large training database, a CNN is trained and crack detection is modeled as a multi-label classification problem. Typically, crack pixels are much fewer than non-crack pixels. To deal with the problem with severely imbalanced data, a strategy with modifying the ratio of positive to negative samples is proposed. The method is tested on two public databases and compared with five existing methods. Experimental results show that it outperforms the other methods.


title: Benefits of using artificial intelligence in medical predictions

abstract: Medicine is a domain where predictions are very important. This paper encourages the use of artificial intelligence (AI) as technological support for decision-making in medical life. Not because the human intelligence would not be enough, but because the mechanisms of artificial intelligence have several benefits that are suitable for this area. These positive features come as a result of combining two strong qualities in the same systems: the precision of mathematics and the power of current technologies. It would be a real waste to ignore them.


Non Invasive Glucose Monitoring System Using Nanosensors


Implementation of a Restricted Boltzmann Machine in a Spiking Neural Network


title: Faceted wikipedia search

abstract: Wikipedia articles contain, besides free text, various types of structured information in the form of wiki markup. The type of wiki content that is most valuable for search are Wikipedia infoboxes, which display an article’s most relevant facts as a table of attribute-value pairs on the top right-hand side of the Wikipedia page. Infobox data is not used by Wikipedia’s own search engine. Standard Web search engines like Google or Yahoo also do not take advantage of the data. In this paper, we present Faceted Wikipedia Search, an alternative search interface for Wikipedia, which facilitates infobox data in order to enable users to ask complex questions against Wikipedia knowledge. By allowing users to query Wikipedia like a structured database, Faceted Wikipedia Search helps them to truly exploit Wikipedia’s collective intelligence.


title: A Robotic Crack Inspection and Mapping System for Bridge Deck Maintenance

abstract: One of the important tasks for bridge maintenance is bridge deck crack inspection. Traditionally, a human inspector detects cracks using his/her eyes and marks the location of cracks manually. However, the accuracy of the inspection result is low due to the subjective nature of human judgement. We propose a crack inspection system that uses a camera-equipped mobile robot to collect images on the bridge deck. In this method, the Laplacian of Gaussian (LoG) algorithm is used to detect cracks and a global crack map is obtained through camera calibration and robot localization. To ensure that the robot collects all the images on the bridge deck, a path planning algorithm based on the genetic algorithm is developed. The path planning algorithm finds a solution which minimizes the number of turns and the traveling distance. We validate our proposed system through both simulations and experiments.


title: Anomaly detection approach using hybrid algorithm of data mining technique

abstract: The excessive use of the communication networks, rising of Internet of Things leads to increases the vulnerability to the important and secret information. advance attacking techniques and number of attackers are increasing radically. Intrusion is one of the main threats to the internet. Hence security issues had been big problem, so that various techniques and approaches have been presented to address the limitations of intrusion detection system such as low accuracy, high false alarm rate, and time consuming. This paper proposes a hybrid machine learning technique for network intrusion detection based on combination of K-means clustering and Sequential Minimal Optimization (SMO) classification. It introduces hybrid approach that able to reduce the rate of false positive alarm, false negative alarm rate, to improve the detection rate and detect zero-day attackers. The NSL-KDD dataset has been used in the proposed technique.. The classification has been performed by using Sequential Minimal Optimization. After training and testing the proposed hybrid machine learning technique, the results have shown that the proposed technique (K-mean + SMO) has achieved a positive detection rate of (94.48%) and reduce the false alarm rate to (1.2%) and achieved accuracy of (97.3695%).


title: Fine tuning CNNS with scarce training data — Adapting imagenet to art epoch classification

abstract: Deep Convolutional Neural Networks (CNN) have recently been shown to outperform previous state of the art approaches for image classification. Their success must in parts be attributed to the availability of large labeled training sets such as provided by the ImageNet benchmarking initiative. When training data is scarce, however, CNNs have proven to fail to learn descriptive features. Recent research shows that supervised pre-training on external data followed by domain-specific fine-tuning yields a significant performance boost when external data and target domain show similar visual characteristics. Transfer-learning from a base task to a highly dissimilar target task, however, has not yet been fully investigated. In this paper, we analyze the performance of different feature representations for classification of paintings into art epochs. Specifically, we evaluate the impact of training set sizes on CNNs trained with and without external data and compare the obtained models to linear models based on Improved Fisher Encodings. Our results underline the superior performance of fine-tuned CNNs but likewise propose Fisher Encodings in scenarios were training data is limited.


title: A 2D-View Depth Image- and CNN-Based 3D Model Identification Method

abstract: With the rapid development of three-dimensional (3D) technology and an increase in the number of available models, issues with copyright protection of 3D models are inevitable. In this paper, we propose a 2D-view depth image- and convolutional neural network (CNN)-based 3D model identification method. To identify a 3D model, we first need an adequate number of the modified versions that could be made by copyright infringers. Then, they can be represented by a number of 2D-view depth images that are captured from evenly distributed vertices on a regular convex polyhedron. Finally, a CNN is trained by these depth images to acquire the capability of identifying the 3D model. The experiment carried out with the dataset of Shape Retrieval Contest 2015 (SHREC’15): Non-Rigid 3D Shape Retrieval shows the practicability of our method, which yields 93.5% accuracy. The effectiveness of the proposed method is demonstrated via evaluation in the latest standard benchmark SHREC’17 Deformable Shape Retrieval with Missing Parts. It clearly shows superior or comparable performance to state-of-the-art methods, shown by the fact that it is in the top three of the 11 participating methods (without counting different runs).


title: Automatic Fence Segmentation in Videos of Dynamic Scenes

abstract: We present a fully automatic approach to detect and segment fence-like occluders from a video clip. Unlike previous approaches that usually assume either static scenes or cameras, our method is capable of handling both dynamic scenes and moving cameras. Under a bottom-up framework, it first clusters pixels into coherent groups using color and motion features. These pixel groups are then analyzed in a fully connected graph, and labeled as either fence or non-fence using graph-cut optimization. Finally, we solve a dense Conditional Random Filed (CRF) constructed from multiple frames to enhance both spatial accuracy and temporal coherence of the segmentation. Once segmented, one can use existing hole-filling methods to generate a fencefree output. Extensive evaluation suggests that our method outperforms previous automatic and interactive approaches on complex examples captured by mobile devices.


title: An efficient and orderly implementation of bypass queue under bursty traffic

abstract: Abstract Sharma and Pinnu proposed an implementation of bypass queue by many FIFOs; unfortunately, the detailed procedure paid little attention to maintaining cell sequence, which is an important feature in ATM network. In this paper, we propose an improved architecture which guarantees cell sequence integrity and describe its related operating procedures.


title: Automated severity assessment of software defect reports

abstract: In mission critical systems, such as those developed by NASA, it is very important that the test engineers properly recognize the severity of each issue they identify during testing. Proper severity assessment is essential for appropriate resource allocation and planning for fixing activities and additional testing. Severity assessment is strongly influenced by the experience of the test engineers and by the time they spend on each issue. The paper presents a new and automated method named SEVERIS (severity issue assessment), which assists the test engineer in assigning severity levels to defect reports. SEVERIS is based on standard text mining and machine learning techniques applied to existing sets of defect reports. A case study on using SEVERIS with data from NASApsilas Project and Issue Tracking System (PITS) is presented in the paper. The case study results indicate that SEVERIS is a good predictor for issue severity levels, while it is easy to use and efficient.


title: The Australian Freshwater Amphipods. I. The Genus Austrochiltonia (Crustacea: Amphipoda: Hyalellidae)

abstract: The genus Austrochiltonia Hurley is rediagnosed and the two known species are fully redescribed and figured. Additional information concerning the distribution of these species is presented.


title: A Critique of the ANSI Standard on Role-Based Access Control

abstract: In 2004, the American National Standards Institute approved the Role-Based Access Control standard to fulfill "a need among government and industry purchasers of information technology products for a consistent and uniform definition of role based access control (RBAC) features". Such uniform definitions give IT product vendors and customers a common and unambiguous terminology for RBAC features, which can lead to wider adoption of RBAC and increased productivity. However, the current ANSI RBAC Standard has several limitations, design flaws, and technical errors that, it unaddressed, could lead to confusions among IT product vendors and customers and to RBAC implementations with different semantics, thus defeating the standard's purpose.


title: SIDA: Self-organized ID Assignment in Wireless Sensor Networks

abstract: Having an ID being unique within the application domain for each sensor node is the basic assumption in many WSN applications. In traditional assignments, fixed-length ID schemes, which are severely limited by scalability, flexibility and energy efficiency, are adopted. To solve these problems, we propose a novel variable-length ID scheme that allows different nodes to have different ID lengths. To realize this idea, we propose a fully localized ID assignment algorithm called emphself-organized ID assignment (SIDA). SIDA presents great potential on scalability, flexibility and energy efficiency by enabling online ID assignment and various optimizations according to different communication paradigms. Analytical and simulation results show that SIDA has a comparable static ID length while the accumulated communication overhead for multi-hop transmissions can be saved by 20%; the control overhead of the new deployment assignment can be reduced to about 30% compared with fixed-length schemes.


Predicting Stock Market Behavior using Data Mining Technique and News Sentiment Analysis


title: Optimal Heavy-Traffic Queue Length Scaling in an Incompletely Saturated Switch

abstract: We consider an input queued switch operating under the MaxWeight scheduling algorithm. This system is interesting to study because it is a model for Internet routers and data center networks. Recently, it was shown that the MaxWeight algorithm has optimal heavy-traffic queue length scaling when all ports are uniformly saturated. Here we consider the case where a fraction of the ports are saturated and others are not (which we call the incompletely saturated case), and also the case where the rates at which the ports are saturated can be different. We use a recently developed drift technique to show that the heavy-traffic queue length under the MaxWeight scheduling algorithm has optimal scaling with respect to the switch size even in these cases.


Strand spaces: proving security protocols correct


title: Measuring Patient Similarities via a Deep Architecture with Medical Concept Embedding

abstract: Evaluating the clinical similarities between pairwisepatients is a fundamental problem in healthcare informatics. Aproper patient similarity measure enables various downstreamapplications, such as cohort study and treatment comparative effectiveness research. One major carrier for conductingpatient similarity research is the Electronic Health Records(EHRs), which are usually heterogeneous, longitudinal, andsparse. Though existing studies on learning patient similarityfrom EHRs have shown being useful in solving real clinicalproblems, their applicability is limited due to the lack of medicalinterpretations. Moreover, most previous methods assume avector based representation for patients, which typically requiresaggregation of medical events over a certain time period. As aconsequence, the temporal information will be lost. In this paper, we propose a patient similarity evaluation framework based ontemporal matching of longitudinal patient EHRs. Two efficientmethods are presented, unsupervised and supervised, both ofwhich preserve the temporal properties in EHRs. The supervisedscheme takes a convolutional neural network architecture, andlearns an optimal representation of patient clinical recordswith medical concept embedding. The empirical results on real-world clinical data demonstrate substantial improvement overthe baselines.


title: Learning from Complementary Labels

abstract: Collecting labeled data is costly and thus is a critical bottleneck in real-world classification tasks. To mitigate the problem, we consider a complementary label, which specifies a class that a pattern does not belong to. Collecting complementary labels would be less laborious than ordinary labels since users do not have to carefully choose the correct class from many candidate classes. However, complementary labels are less informative than ordinary labels and thus a suitable approach is needed to better learn from complementary labels. In this paper, we show that an unbiased estimator of the classification risk can be obtained only from complementary labels, if a loss function satisfies a particular symmetric condition. We theoretically prove the estimation error bounds for the proposed method, and experimentally demonstrate the usefulness of the proposed algorithms.


GELU-Net: A Globally Encrypted, Locally Unencrypted Deep Neural Network for Privacy-Preserved Learning


Real-time virtual pipes simulation and modeling for small-scale shallow water


title: CHRONIOUS: A wearable platform for monitoring and management of patients with chronic disease

abstract: The CHRONIOUS system has been developed based on an open architecture design that consists of a set of subsystems which interact in order to provide all the needed services to the chronic disease patients. An advanced multi-parametric expert system is being implemented that fuses information effectively from various sources using intelligent techniques. Data are collected by sensors of a body network controlling vital signals while additional tools record dietary habits and plans, drug intake, environmental and biochemical parameters and activity data. The CHRONIOUS platform provides guidelines and standards for the future generations of “chronic disease management systems” and facilitates sophisticated monitoring tools. In addition, an ontological information retrieval system is being delivered satisfying the necessities for up-to-date clinical information of Chronic Obstructive pulmonary disease (COPD) and Chronic Kidney Disease (CKD). Moreover, support tools are being embedded in the system, such as the Mental Tools for the monitoring of patient mental health status. The integrated platform provides real-time patient monitoring and supervision, both indoors and outdoors and represents a generic platform for the management of various chronic diseases.


title: Progressive simplicial complexes

abstract: In this paper, we introduce the progressive simplicial complex (PSC) representation, a new format for storing and transmitting triangulated geometric models. Like the earlier progressive mesh (PM) representation, it captures a given model as a coarse base model together with a sequence of refinement transformations that progressively recover detail. The PSC representation makes use of a more general refinement transformation, allowing the given model to be an arbitrary triangulation (e.g. any dimension, non-orientable, non-manifold, non-regular), and the base model to always consist of a single vertex. Indeed, the sequence of refinement transformations encodes both the geometry and the topology of the model in a unified multiresolution framework. The PSC representation retains the advantages of PM’s. It defines a continuous sequence of approximating models for runtime level-of-detail control, allows smooth transitions between any pair of models in the sequence, supports progressive transmission, and offers a space-efficient representation. Moreover, by allowing changes to topology, the PSC sequence of approximations achieves better fidelity than the corresponding PM sequence. We develop an optimization algorithm for constructing PSC representations for graphics surface models, and demonstrate the framework on models that are both geometrically and topologically complex. CR Categories: I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling surfaces and object representations. Additional


title: Image processing on multinode hadoop cluster

abstract: In the past few years the data produced all over the internet has increased with an exponential rate. The storage costs have been rising immensely. But, in the field of Computer Science, the introduction of new technologies has resulted in reduction of storage costs. This led to a rampant rise in the data generation rates. This huge amount of data that is so vast and complex such that classical methods are insufficient for processing is termed as ‘Big Data’. There are various tools in Hadoop to analyze the textual data such as Pig, HBase, etc. But the data present on the Internet as well as the Social Networking sites comprises of unstructured media. The maximum spectrum of the media files is covered by Image data. The major concern is not about the storage of images, but the processing of images being generated with the speed of light. Every day around 350 million pictures are being uploaded on social network. Until now, over 200 billion photos have been uploaded only on Facebook. This accounts to an average of around 200 photos per user. This whole amount of data generated round the globe can be classified into three formats-Structured, Semi-Structured, and Unstructured. The Image data contains not only the pictures but also the data defining those pictures such as the resolution, source of the image, capture device, etc. To fetch all this information in a structured format HIPI (Hadoop Image Processing Interface) tools are used. In this paper image processing is performed on a MultiNode Hadoop Cluster and its performance is measured.


title: Loupe - An Online Tool for Inspecting Datasets in the Linked Data Cloud

abstract: The Linked Data initiative continues to grow making more datasets available; however, discovering the type of data contained in a dataset, its structure, and the vocabularies used still remains a challenge hindering the querying and reuse. VoID descriptions provide a starting point but a more detailed analysis is required to unveil the implicit vocabulary usage such as common data patterns. Such analysis helps the selection of datasets, the formulation of effective queries, or the identification of quality issues. Loupe is an online tool for inspecting datasets by looking at both implicit data patterns as well as explicit vocabulary definitions in data. This demo paper presents the dataset inspection capabilities of Loupe.


title: Hands in Space: Gesture Interaction with Augmented-Reality Interfaces

abstract: Researchers at the Human Interface Technology Laboratory New Zealand (HIT Lab NZ) are investigating free-hand gestures for natural interaction with augmented-reality interfaces. They've applied the results to systems for desktop computers and mobile devices.


title: Sensing and Modeling Human Behavior Using Social Media and Mobile Data

abstract: In the past years, we have witnessed the emergence of the new discipline of computational social science, which promotes a new data-driven and computation-based approach to social sciences. In this article, we discuss how the availability of new technologies such as online social media and mobile smartphones has allowed researchers to passively collect human behavioral data at a scale and a level of granularity that were just unthinkable some years ago. We also discuss how these digital traces can then be used to prove (or disprove) existing theories and develop new models of human behavior.


title: Institutional Determinants of Capital Structure Adjustment Speeds

abstract: Many authors relate a firm's performance to legal and political features and the regulatory environment in which it operates. This article compares firms' capital structure adjustments across countries and investigates whether institutional differences help explain the variance in estimated adjustment speeds. We find that legal and financial traditions significantly correlate with firm adjustment speeds. More narrowly, institutional features also relate to adjustment speeds, consistent with the hypothesis that better institutions lower the transaction costs associated with adjusting a firm's leverage. Such associations between institutional arrangements and leverage adjustment speeds are consistent with the dynamic trade-off theory of capital structure choice.


title: Research of Detection Algorithm for Time Series Abnormal Subsequence

abstract: The recent advancements in sensor technology have made it possible to collect enormous amounts of data in real time. How to find out unusual pattern from time series data plays a very important role in data mining. In this paper, we focus on the abnormal subsequence detection. The original definition of discord subsequences is defective for some kind of time series, in this paper we give a more robust definition which is based on the k nearest neighbors. We also donate a novel method for time series representation, it has better performance than traditional methods (like PAA/SAX) to represent the characteristic of some special time series. To speed up the process of abnormal subsequence detection, we used the clustering method to optimize the outer loop ordering and early abandon subsequence which is impossible to be abnormal. The experiment results validate that the algorithm is correct and has a high efficiency.


title: Vehicle Number Plate Detection System for Indian Vehicles

abstract: An exponential increase in number of vehicles necessitates the use of automated systems to maintain vehicle information. The information is highly required for both management of traffic as well as reduction of crime. Number plate recognition is an effective way for automatic vehicle identification. Some of the existing algorithms based on the principle of learning takes a lot of time and expertise before delivering satisfactory results but even then lacks in accuracy. In the proposed algorithm an efficient method for recognition for Indian vehicle number plates has been devised. The algorithm aims at addressing the problems of scaling and recognition of position of characters with a good accuracy rate of 98.07%.


title: Emotional agents: A modeling and an application

abstract: This paper proposes modeling of artificial emotions through agents based on symbolic approach. The symbolic approach utilizes symbolic emotional rule-based systems (rule base that generated emotions) with continuous interactions with environment and an internal ''thinking'' machinery that comes as a result of series of inferences, evaluation, evolution processes, adaptation, learning, and emotions. We build two models for agent based systems; one is supported with artificial emotions and the other one without emotions. We use both in solving a bench mark problem; ''The Orphanage Care Problem''. The two systems are simulated and results are compared. Our study shows that systems with proper model of emotions can perform in many cases better than systems without emotions. We try to shed the light here on how artificial emotions can be modeled in a simple rule-based agent systems and if emotions as they exist in ''real intelligence'' can be helpful for ''artificial intelligence''. Agent architectures are presented as a generic blueprint on which the design of agents can be based. Our focus is on the functional design, including flow of information and control. With this information provided, the generic blueprints of architectures should not be difficult to implement agents, thus putting these theoretical models into practice. We build the agents using this architecture, and many experiments and analysis are shown.


title: Improving Session Recommendation with Recurrent Neural Networks by Exploiting Dwell Time

abstract: Recently, Recurrent Neural Networks (RNNs) have been applied to the task of session-based recommendation. These approaches use RNNs to predict the next item in a user session based on the previ- ously visited items. While some approaches consider additional item properties, we argue that item dwell time can be used as an implicit measure of user interest to improve session-based item recommen- dations. We propose an extension to existing RNN approaches that captures user dwell time in addition to the visited items and show that recommendation performance can be improved. Additionally, we investigate the usefulness of a single validation split for model selection in the case of minor improvements and find that in our case the best model is not selected and a fold-like study with different validation sets is necessary to ensure the selection of the best model.


title: Unlocking the Value of Open Data with a Process-Based Information Platform

abstract: There has been a wide shift in the way data are managed in the public administration. The move has led to an increased adoption of the Open Data model, where public administrations freely and openly publish data gathered using citizen taxes. However, undesirable side effects include the lack of data quality, incompatible formats and access methods, and various semantic interpretations of data. As a consequence, Open Data stakeholders, such as application developers, common citizens and even government agencies themselves, are overwhelmed by the large quantity of unstructured data, unable offer citizens and business value-added applications and services. To address the issue and make Open Data actionable, this paper proposes a systematic value-creation process that helps stakeholders identify the most suitable information assets and convert them into forms that can be more consumable by users. The process is enabled by the Middleware for Open-Data Aggregation (MODA), a platform designed with four main features, i) data quality assessment, ii) data homogenization for uniform access through an universal interface, iii) data correlation and semantic adaptation, and iv) secure data access. These features maximize the return on investment in Open Data by reducing time and cost of third party application development while providing improvement feedback to data sources.


title: Leveraging Crowdsourcing: Activation-Supporting Components for IT-Based Ideas Competition

abstract: Ideas competitions appear to be a promising tool for crowdsourcing and open innovation processes, especially for business-to-business software companies. Active participation of potential lead users is the key to success. Yet a look at existing ideas competitions in the software field leads to the conclusion that many information technology (IT)-based ideas competitions fail to meet requirements upon which active participation is established. The paper describes how activation-enabling functionalities can be systematically designed and implemented in an IT-based ideas competition for enterprise resource planning software. We proceeded to evaluate the outcomes of these design measures and found that participation can be supported using a two-step model. The components of the model support incentives and motives of users. Incentives and motives of the users then support the process of activation and consequently participation throughout the ideas competition. This contributes to the successful implementation and maintenance of the ideas competition, thereby providing support for the development of promising innovative ideas. The paper concludes with a discussion of further activation-supporting components yet to be implemented and points to rich possibilities for future research in these areas.


title: Prediction of driver's drowsy and alert states from EEG signals with deep learning

abstract: We investigate in this paper deep learning (DL) solutions for prediction of driver's cognitive states (drowsy or alert) using EEG data. We discussed the novel channel-wise convolutional neural network (CCNN) and CCNN-R which is a CCNN variation that uses Restricted Boltzmann Machine in order to replace the convolutional filter. We also consider bagging classifiers based on DL hidden units as an alternative to the conventional DL solutions. To test the performance of the proposed methods, a large EEG dataset from 3 studies of driver's fatigue that includes 70 sessions from 37 subjects is assembled. All proposed methods are tested on both raw EEG and Independent Component Analysis (ICA)-transformed data for cross-session predictions. The results show that CCNN and CCNN-R outperform deep neural networks (DNN) and convolutional neural networks (CNN) as well as other non-DL algorithms and DL with raw EEG inputs achieves better performance than ICA features.


title: Using Boosted Features for the Detection of People in 2D Range Data

abstract: This paper addresses the problem of detecting people in two dimensional range scans. Previous approaches have mostly used pre-defined features for the detection and tracking of people. We propose an approach that utilizes a supervised learning technique to create a classifier that facilitates the detection of people. In particular, our approach applies AdaBoost to train a strong classifier from simple features of groups of neighboring beams corresponding to legs in range data. Experimental results carried out with laser range data illustrate the robustness of our approach even in cluttered office environments


title: Support Vector Machines Under Adversarial Label Noise

abstract: In adversarial classication tasks like spam ltering and intrusion detection, malicious adversaries may manipulate data to thwart the outcome of an automatic analysis. Thus, besides achieving good classication performances, machine learning algorithms have to be robust against adversarial data manipulation to successfully operate in these tasks. While support vector machines (SVMs) have shown to be a very successful approach in classication problems, their eectiveness in adversarial classication tasks has not been extensively investigated yet. In this paper we present a preliminary investigation of the robustness of SVMs against adversarial data manipulation. In particular, we assume that the adversary has control over some training data, and aims to subvert the SVM learning process. Within this assumption, we show that this is indeed possible, and propose a strategy to improve the robustness of SVMs to training data manipulation based on a simple kernel matrix correction.


title: Blind Deconvolution of Medical Ultrasound Images: A Parametric Inverse Filtering Approach

abstract: The problem of reconstruction of ultrasound images by means of blind deconvolution has long been recognized as one of the central problems in medical ultrasound imaging. In this paper, this problem is addressed via proposing a blind deconvolution method which is innovative in several ways. In particular, the method is based on parametric inverse filtering, whose parameters are optimized using two-stage processing. At the first stage, some partial information on the point spread function is recovered. Subsequently, this information is used to explicitly constrain the spectral shape of the inverse filter. From this perspective, the proposed methodology can be viewed as a "hybridization" of two standard strategies in blind deconvolution, which are based on either concurrent or successive estimation of the point spread function and the image of interest. Moreover, evidence is provided that the "hybrid" approach can outperform the standard ones in a number of important practical cases. Additionally, the present study introduces a different approach to parameterizing the inverse filter. Specifically, we propose to model the inverse transfer function as a member of a principal shift-invariant subspace. It is shown that such a parameterization results in considerably more stable reconstructions as compared to standard parameterization methods. Finally, it is shown how the inverse filters designed in this way can be used to deconvolve the images in a nonblind manner so as to further improve their quality. The usefulness and practicability of all the introduced innovations are proven in a series of both in silico and in vivo experiments. Finally, it is shown that the proposed deconvolutioh algorithms are capable of improving the resolution of ultrasound images by factors of 2.24 or 6.52 (as judged by the autocorrelation criterion) depending on the type of regularization method used.


title: Basic Overview of Chemoinformatics

abstract: There is no particular point in time that determines when chemoinformatics was founded or established. It slowly evolved from several, often quite humble beginnings. Scientists in various fields of chemistry struggled with the development of computer methods which allowed them to manage the enormous amount of chemical information and to find relationships between the structure and properties of a compound. During the 1960s some early developments appeared that led to a flurry of activities in the 1970s. This review provides a general overview of basic methods in the specific fields of chemoinformatics, from encoding chemical compounds, storing and searching data in databases, to generating and analyzing these data. In addition, the chief interconnecting points of chemoinformatics applications are highlighted including the contributions of Johann Gasteiger to this field.


title: Oxidation of Fatty Acids Is the Source of Increased Mitochondrial Reactive Oxygen Species Production in Kidney Cortical Tubules in Early Diabetes

abstract: Mitochondrial reactive oxygen species (ROS) cause kidney damage in diabetes. We investigated the source and site of ROS production by kidney cortical tubule mitochondria in streptozotocin-induced type 1 diabetes in rats. In diabetic mitochondria, the increased amounts and activities of selective fatty acid oxidation enzymes is associated with increased oxidative phosphorylation and net ROS production with fatty acid substrates (by 40% and 30%, respectively), whereas pyruvate oxidation is decreased and pyruvate-supported ROS production is unchanged. Oxidation of substrates that donate electrons at specific sites in the electron transport chain (ETC) is unchanged. The increased maximal production of ROS with fatty acid oxidation is not affected by limiting the electron flow from complex I into complex III. The maximal capacity of the ubiquinol oxidation site in complex III in generating ROS does not differ between the control and diabetic mitochondria. In conclusion, the mitochondrial ETC is neither the target nor the site of ROS production in kidney tubule mitochondria in short-term diabetes. Mitochondrial fatty acid oxidation is the source of the increased net ROS production, and the site of electron leakage is located proximal to coenzyme Q at the electron transfer flavoprotein that shuttles electrons from acyl-CoA dehydrogenases to coenzyme Q.


title: Sense and avoid for Unmanned Aerial Vehicles using ADS-B

abstract: We present the design and implementation of an aircraft collision avoidance algorithm for Unmanned Aerial Vehicles (UAVs). Automatic Dependent Surveillance-Broadcast (ADS-B) is used to detect aircraft. The UAV needs to fly through pre-assigned waypoints while avoiding collisions with other aircraft. The aircraft are indifferent to the UAV. A collision with aircraft are detected by simulating the UAV's trajectory along the path of assigned waypoints using its closed-loop dynamics. A sampling based algorithm is used for collision avoidance path planning. A second collision check is performed on the generated path with the updated UAV and aircraft's states. The path will be re-planned if it leads to a collision. The algorithm was validated in Software-In-the-Loop Simulation (SITL). ADS-B data obtained from commercial aircraft flying over the Phoenix Skyharbor airport were used for simulating the collisions. The paper shows that the algorithm enables the UAV to avoid multiple aircraft with different approaching angles and speeds.


title: Automated Oracle Comparators for TestingWeb Applications

abstract: Software developers need automated techniques to maintain the correctness of complex, evolving Web applications. While there has been success in automating some of the testing process for this domain, there exists little automated support for verifying that the executed test cases produce expected results. We assist in this tedious task by presenting a suite of automated oracle comparators for testing Web applications. To effectively identify failures, each comparator is specialized to particular characteristics of the possibly nondeterministic Web applications' output in the form of HTML responses. We also describe combinations of comparators designed to achieve both high precision and recall in failure detection and a tool for helping testers to analyze the output of multiple oracles in detail. We present results from an evaluation of the effectiveness and costs of the oracle comparators. We also provide recommendations to testers on applying effective oracle comparators based on their application's characteristics.


title: The development of an Ontology for driving Context Modelling and reasoning

abstract: In order to use technology to influence human behaviour and promote safer and more fuel efficient behaviour through incentive mechanisms, an instrumented vehicle is developed. The first step is to make it “perceive” the outside world, so extracting knowledge from some data sources such as sensors is crucial. More critically, there is a fundamental need for a standard that would enable knowledge sharing/exchanging among the different entities, e.g., between on-board sensors, in-vehicle controls and traffic management agencies. This paper proposes an Ontology for Context Modelling (OCM) to be used as the world model for driving context representation and reasoning, which can enable a better understanding of traffic context and sensor capability, which is the basis for providing data source to Advanced Driver Assistance Systems (ADAS), V2X (Vehicle-to-Everything) communications and even driving decision making within autonomous vehicles. Through the experiments, we evaluate the capability of the OCM to represent the driving context and the reasoning mechanism to compensate for sensor failures and recognize lane changing and overtaking events. This methodology has significant value for creating standards in autonomous and semi-autonomous cars.


title: Methods to study splicing from high-throughput RNA Sequencing data

abstract: The development of novel high-throughput sequencing (HTS) methods for RNA (RNA-Seq) has provided a very powerful mean to study splicing under multiple conditions at unprecedented depth. However, the complexity of the information to be analyzed has turned this into a challenging task. In the last few years, a plethora of tools have been developed, allowing researchers to process RNA-Seq data to study the expression of isoforms and splicing events, and their relative changes under different conditions. We provide an overview of the methods available to study splicing from short RNA-Seq data. We group the methods according to the different questions they address: 1) Assignment of the sequencing reads to their likely gene of origin. This is addressed by methods that map reads to the genome and/or to the available gene annotations. 2) Recovering the sequence of splicing events and isoforms. This is addressed by transcript reconstruction and de novo assembly methods. 3) Quantification of events and isoforms. Either after reconstructing transcripts or using an annotation, many methods estimate the expression level or the relative usage of isoforms and/or events. 4) Providing an isoform or event view of differential splicing or expression. These include methods that compare relative event/isoform abundance or isoform expression across two or more conditions. 5) Visualizing splicing regulation. Various tools facilitate the visualization of the RNA-Seq data in the context of alternative splicing. In this review, we do not describe the specific mathematical models behind each method. Our aim is rather to provide an overview that could serve as an entry point for users who need to decide on a suitable tool for a specific analysis. We also attempt to propose a classification of the tools according to the operations they do, to facilitate the comparison and choice of methods.


title: Anxiety, Anxiety Sensitivity, and Perceived Stress as Predictors of Recent Drinking, Alcohol Craving, and Social Stress Response in Heavy Drinkers

abstract: Background ::: Stress and anxiety are widely considered to be causally related to alcohol craving and consumption, as well as development and maintenance of alcohol use disorder (AUD). However, numerous preclinical and human studies examining effects of stress or anxiety on alcohol use and alcohol-related problems have been equivocal. This study examined relationships between scores on self-report anxiety, anxiety sensitivity, and stress measures and frequency and intensity of recent drinking, alcohol craving during early withdrawal, as well as laboratory measures of alcohol craving and stress reactivity among heavy drinkers with AUD. ::: ::: Methods ::: Media-recruited, heavy drinkers with AUD (N = 87) were assessed for recent alcohol consumption. Anxiety and stress levels were characterized using paper-and-pencil measures, including the Beck Anxiety Inventory (BAI), the Anxiety Sensitivity Index-3 (ASI-3), and the Perceived Stress Scale (PSS). Eligible subjects (N = 30) underwent alcohol abstinence on the Clinical Research Unit; twice daily measures of alcohol craving were collected. On day 4, subjects participated in the Trier Social Stress Test; measures of cortisol and alcohol craving were collected. ::: ::: Results ::: In multivariate analyses, higher BAI scores were associated with lower drinking frequency and reduced drinks/drinking day; in contrast, higher ASI-3 scores were associated with higher drinking frequency. BAI anxiety symptom and ASI-3 scores also were positively related to Alcohol Use Disorders Identification Test total scores and AUD symptom and problem subscale measures. Higher BAI and ASI-3 scores but not PSS scores were related to greater self-reported alcohol craving during early alcohol abstinence. Finally, BAI scores were positively related to laboratory stress-induced cortisol and alcohol craving. In contrast, the PSS showed no relationship with most measures of alcohol craving or stress reactivity. ::: ::: Conclusions ::: Overall, clinically oriented measures of anxiety compared with perceived stress were more strongly associated with a variety of alcohol-related measures in current heavy drinkers with AUD.


title: Neuronal substrates of complex behaviors in C. elegans.

abstract: A current challenge in neuroscience is to bridge the gaps between genes, proteins, neurons, neural circuits, and behavior in a single animal model. The nematode Caenorhabditis elegans has unique features that facilitate this synthesis. Its nervous system includes exactly 302 neurons, and their pattern of synaptic connectivity is known. With only five olfactory neurons, C. elegans can dynamically respond to dozens of attractive and repellent odors. Thermosensory neurons enable the nematode to remember its cultivation temperature and to track narrow isotherms. Polymodal sensory neurons detect a wide range of nociceptive cues and signal robust escape responses. Pairing of sensory stimuli leads to long-lived changes in behavior consistent with associative learning. Worms exhibit social behaviors and complex ultradian rhythms driven by Ca(2+) oscillators with clock-like properties. Genetic analysis has identified gene products required for nervous system function and elucidated the molecular and neural bases of behaviors.


title: 17.8 A 190nW 33kHz RC oscillator with ±0.21% temperature stability and 4ppm long-term stability

abstract: In wireless networks with a low duty cycle, the radio is operational for only a small percentage of the time. A sleep timer is used to synchronize the data transmission and reception. The total system power is then limited by the sleep power and the sleep timer frequency stability. Low-frequency crystal oscillators are a common choice for sleep timers due to their excellent long-term stability, frequency stability over temperature, and very low power consumption. However, the external crystal cost and board area are undesired. If an integrated oscillator is used as an alternative, the frequency variation must be minimized so the sleep time can be maximized.


title: Bayesian Paragraph Vectors

abstract: Word2vec (Mikolov et al., 2013) has proven to be successful in natural language processing by capturing the semantic relationships between different words. Built on top of single-word embeddings, paragraph vectors (Le and Mikolov, 2014) find fixed-length representations for pieces of text with arbitrary lengths, such as documents, paragraphs, and sentences. In this work, we propose a novel interpretation for neural-network-based paragraph vectors by developing an unsupervised generative model whose maximum likelihood solution corresponds to traditional paragraph vectors. This probabilistic formulation allows us to go beyond point estimates of parameters and to perform Bayesian posterior inference. We find that the entropy of paragraph vectors decreases with the length of documents, and that information about posterior uncertainty improves performance in supervised learning tasks such as sentiment analysis and paraphrase detection.


title: LQR control of a quad-active-bridge converter for renewable integration

abstract: In this paper, the design of a linear quadratic regulator (LQR) controller for the DC-DC stage of a Solid State Transformer (SST) based on a quad-active-bridge (QAB) to integrate both photovoltaic (PV) power generation and battery energy storage is presented. The gyrator-based average model is used for deriving the state-space representation of the plant. The dynamic performance of the designed controller is verified through extensive simulation of the switching model and validated with experimental results.


title: Nutritional Ecology of Entomophagy in Humans and Other Primates

abstract: Entomophagy is widespread among nonhuman primates and is common among many human communities. However, the extent and patterns of entomophagy vary substantially both in humans and nonhuman primates. Here we synthesize the literature to examine why humans and other primates eat insects and what accounts for the variation in the extent to which they do so. Variation in the availability of insects is clearly important, but less understood is the role of nutrients in entomophagy. We apply a multidimensional analytical approach, the right-angled mixture triangle, to published data on the macronutrient compositions of insects to address this. Results showed that insects eaten by humans spanned a wide range of protein-to-fat ratios but were generally nutrient dense, whereas insects with high protein-to-fat ratios were eaten by nonhuman primates. Although suggestive, our survey exposes a need for additional, standardized, data.


title: Governance of open innovation networks with national vs international scope

abstract: Purpose ::: ::: ::: ::: ::: The realisation of joint innovation outcomes in open innovation networks is closely related to an efficient utilisation of governance mechanisms, which coordinate joint processes (e.g. knowledge sharing) and eliminate undesired behaviours (e.g. opportunism). Hence, the purpose of this paper is to analyse the complex effects of multiple governance approaches on outcomes of open innovation networks with a national and an international scope. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: The study draws on a large-scale survey-based study of 100 mechanical engineering firms involved in open innovation networks. Hypotheses are tested by means of PLS structural equation modelling. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The evidence shows that the three governance mechanisms: transactional governance, relational governance and institutionalised governance significantly foster innovation outcomes of open innovation networks. In national open innovation networks, only relational governance exerts positive effects, internationally transactional and institutionalised governance is necessary. ::: ::: ::: ::: ::: Research limitations/implications ::: ::: ::: ::: ::: The study contributes to research in multiple ways. First, it shows that governance of open innovation networks is crucial for their innovation performance, thereby providing some explanations for the performance differences between certain networks. Second, the results indicate that the effects of governance mechanisms depend on the scope of the network. By showing that the effect of governance mechanisms varies under different contextual conditions the study also contributes to the ongoing debate on combined effects of governance mechanisms. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: The paper fills important gaps in the existing research on the link between governance and performance in open innovation networks and delineates interesting areas for further research.


title: Flow-Guided Feature Aggregation for Video Object Detection

abstract: Extending state-of-the-art object detectors from image to video is challenging. The accuracy of detection suffers from degenerated object appearances in videos, e.g., motion blur, video defocus, rare poses, etc. Existing work attempts to exploit temporal information on box level, but such methods are not trained end-to-end. We present flow-guided feature aggregation, an accurate and end-to-end learning framework for video object detection. It leverages temporal coherence on feature level instead. It improves the per-frame features by aggregation of nearby features along the motion paths, and thus improves the video recognition accuracy. Our method significantly improves upon strong single-frame baselines in ImageNet VID, especially for more challenging fast moving objects. Our framework is principled, and on par with the best engineered systems winning the ImageNet VID challenges 2016, without additional bells-and-whistles. The proposed method, together with Deep Feature Flow, powered the winning entry of ImageNet VID challenges 2017. The code is available at this https URL.


title: Cost Optimization of Elasticity Cloud Resource Subscription Policy

abstract: In cloud computing, resource subscription is an important procedure which enables customers to elastically subscribe to IT resources based on their service requirements. Resource subscription can be divided into two categories, namely long term reservation and on-demand subscription.Although customers need to pay the upfront fee for a long term reservation contract, the usage charge of reserved resources is generally much cheaper than that of the on-demand subscription. To provide a better Internet service by using cloud resource, service operators will expect to make a trade-off between the amount of long term reserved resources and that of on-demand subscribed resources. Therefore, how to properly make resource provision plans is a challenging issue. In this paper, we present a two-phase algorithm for service operators to minimize their service provision cost. In the first phase, we propose a mathematical formulae to compute the optimal amount of long-term reserved resources. In the second phase, we use the Kalman filter to predict resource demand and adaptively change the subscribed on-demand resources such that provision cost could be minimized. We evaluated our solution by using real-world data. Our numerical results indicated that the proposed mechanisms are able to significantly reduce the provision cost.


title: Effects of playing a computer game using a bright display on presleep physiological variables, sleep latency, slow wave sleep and REM sleep

abstract: Summary ::: ::: Epidemiological studies have shown that playing a computer game at night delays bedtime and shortens sleeping hours, but the effects on sleep architecture and quality have remained unclear. In the present study, the effects of playing a computer game and using a bright display on nocturnal sleep were examined in a laboratory. Seven male adults (24.7 ± 5.6 years old) played exciting computer games with a bright display (game-BD) and a dark display (game-DD) and performed simple tasks with low mental load as a control condition in front of a BD (control-BD) and DD (control-DD) between 23:00 and 1:45 hours in randomized order and then went to bed at 2:00 hours and slept until 8:00 hours. Rectal temperature, electroencephalogram (EEG), heart rate and subjective sleepiness were recorded before sleep and a polysomnogram was recorded during sleep. Heart rate was significantly higher after playing games than after the control conditions, and it was also significantly higher after using the BD than after using the DD. Subjective sleepiness and relative theta power of EEG were significantly lower after playing games than after the control conditions. Sleep latency was significantly longer after playing games than after the control conditions. REM sleep was significantly shorter after the playing games than after the control conditions. No significant effects of either computer games or BD were found on slow-wave sleep. These results suggest that playing an exciting computer game affects sleep latency and REM sleep but that a bright display does not affect sleep variables.


title: Event-Centric Clustering of News Articles

abstract: Entertainity AB plans to build a news service to provide news to end-users in an innovative way. The service must include a way to automatically group series of news from different sources and publ ...


Hand Gesture Interaction with a 3 D Virtual Environment


title: A survey on trust management for intelligent transportation system

abstract: Trust management is a fundamental and critical aspect of any serious application in ITS. However, only a few studies have addressed this important problem. In this paper, we present a survey on trust management for ITS. We first describe the properties of trust, trust metrics and potential attacks against trust management schemes. Existing related works are then reviewed based on the way in which trust management is implemented. Along with the review, we also identify some open research questions for future work, and consequently present a novel idea of trust management implementation.


title: A Crowd Monitoring Framework using Emotion Analysis of Social Media for Emergency Management in Mass Gatherings

abstract: In emergency management for mass gathering, the knowledge about crowd types can highly assist with providing timely response and effective resource allocation. Crowd monitoring can be achieved using computer vision based approaches and sensory data analysis. The emergence of social media platforms presents an opportunity to capture valuable information about how people feel and think. However, reviewing current works shows that there are a limited number of studies that use social media in crowd monitoring and/or incorporate a unified crowd model for consistency and interoperability. This presents a novel framework for crowd monitoring using social media. It includes a standard crowd model to represent different types of crowds. The proposed framework considers the effect of emotion on crowd behaviour and uses the emotion analysis of social media to identify the crowd types in an event. An experiment using historical data of a past event to validate our framework and model is described.