title: Recurrent Neural Networks for Time Series Classification

abstract: Abstract Recurrent neural networks (RNN) are a widely used tool for the prediction of time series. In this paper we use the dynamic behaviour of the RNN to categorize input sequences into different specified classes. These two tasks do not seem to have much in common. However, the prediction task strongly supports the development of a suitable internal structure, representing the main features of the input sequence, to solve the classification problem. Therefore, the speed and success of the training as well as the generalization ability of the trained RNN are significantly improved. The trained RNN provides good classification performance and enables the user to assess efficiently the degree of reliability of the classification result.


title: A novel caching scheme for improving Internet-based . . .

abstract: Internet-based mobile ad hoc network (Imanet) is an emerging technique that combines a wired network (e.g. Internet) and a mobile ad hoc network (Manet) for developing a ubiquitous communication infrastructure. To fulfill users' demand to access various kinds of information, however, an Imanet has several limitations such as limited accessibility to the wired Internet, insufficient wireless bandwidth, and longer message latency. In this paper, we address the issues involved in information search and access in Imanets. An aggregate caching mechanism and a broadcast-based Simple Search (SS) algorithm are proposed for improving the information accessibility and reducing average communication latency in Imanets. As a part of the aggregate cache, a cache admission control policy and a cache replacement policy, called Time and Distance Sensitive (TDS) replacement, are developed to reduce the cache miss ratio and improve the information accessibility. We evaluate the impact of caching, cache management, and the number of access points that are connected to the Internet, through extensive simulation. The simulation results indicate that the proposed aggregate caching mechanism can significantly improve an Imanet performance in terms of throughput and average number of hops to access data items.


title: Being accurate is not enough: how accuracy metrics have hurt recommender systems

abstract: Recommender systems have shown great potential to help users find interesting and relevant items from within a large information space. Most research up to this point has focused on improving the accuracy of recommender systems. We believe that not only has this narrow focus been misguided, but has even been detrimental to the field. The recommendations that are most accurate according to the standard metrics are sometimes not the recommendations that are most useful to users. In this paper, we propose informal arguments that the recommender community should move beyond the conventional accuracy metrics and their associated experimental methodologies. We propose new user-centric directions for evaluating recommender systems.


Memory-Augmented Monte Carlo Tree Search


title: An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network

abstract: The stock market, which has been investigated by various researchers, is a rather complicated environment. Most research only concerned the technical indexes (quantitative factors), instead of qualitative factors, e.g., political effect. However, the latter plays a critical role in the stock market environment. Thus, this study develops a genetic algorithm based fuzzy neural network (GFNN) to formulate the knowledge base of fuzzy inference rules which can measure the qualitative effect on the stock market. Next, the effect is further integrated with the technical indexes through the artificial neural network (ANN). An example based on the Taiwan stock market is utilized to assess the proposed intelligent system. Evaluation results indicate that the neural network considering both the quantitative and qualitative factors excels the neural network considering only the quantitative factors both in the clarity of buying-selling points and buying-selling performance.


title: Understanding contention-based channels and using them for defense

abstract: Microarchitectural resources such as caches and predictors can be used to leak information across security domains. Significant prior work has demonstrated attacks and defenses for specific types of such microarchitectural side and covert channels. In this paper, we introduce a general mathematical study of microarchitectural channels using information theory. Our conceptual contribution is a simple mathematical abstraction that captures the common characteristics of all microarchitectural channels. We call this the Bucket model and it reveals that microarchitectural channels are fundamentally different from side and covert channels in networking. We then quantify the communication capacity of several microarchitectural covert channels (including channels that rely on performance counters, AES hardware and memory buses) and measure bandwidths across both KVM based heavy-weight virtualization and light-weight operating-system level isolation. We demonstrate channel capacities that are orders of magnitude higher compared to what was previously considered possible. Finally, we introduce a novel way of detecting intelligent adversaries that try to hide while running covert channel eavesdropping attacks. Our method generalizes a prior detection scheme (that modeled static adversaries) by introducing noise that hides the detection process from an intelligent eavesdropper.


title: Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields

abstract: In this article, we tackle the problem of depth estimation from single monocular images. Compared with depth estimation using multiple images such as stereo depth perception, depth from monocular images is much more challenging. Prior work typically focuses on exploiting geometric priors or additional sources of information, most using hand-crafted features. Recently, there is mounting evidence that features from deep convolutional neural networks (CNN) set new records for various vision applications. On the other hand, considering the continuous characteristic of the depth values, depth estimation can be naturally formulated as a continuous conditional random field (CRF) learning problem. Therefore, here we present a deep convolutional neural field model for estimating depths from single monocular images, aiming to jointly explore the capacity of deep CNN and continuous CRF. In particular, we propose a deep structured learning scheme which learns the unary and pairwise potentials of continuous CRF in a unified deep CNN framework. We then further propose an equally effective model based on fully convolutional networks and a novel superpixel pooling method, which is about 10 times faster, to speedup the patch-wise convolutions in the deep model. With this more efficient model, we are able to design deeper networks to pursue better performance. Our proposed method can be used for depth estimation of general scenes with no geometric priors nor any extra information injected. In our case, the integral of the partition function can be calculated in a closed form such that we can exactly solve the log-likelihood maximization. Moreover, solving the inference problem for predicting depths of a test image is highly efficient as closed-form solutions exist. Experiments on both indoor and outdoor scene datasets demonstrate that the proposed method outperforms state-of-the-art depth estimation approaches.


title: The Effect of Mindfulness-Based Therapy on Anxiety and Depression: A Meta-Analytic Review

abstract: Objective:Although mindfulness-based therapy has become a popular treatment, little is known about its efficacy. Therefore, our objective was to conduct an effect size analysis of this popular intervention for anxiety and mood symptoms in clinical samples.Method:We conducted a literature search using PubMed, PsycINFO, the Cochrane Library, and manual searches. Our meta-analysis was based on 39 studies totaling 1,140 participants receiving mindfulness-based therapy for a range of conditions, including cancer, generalized anxiety disorder, depression, and other psychiatric or medical conditions. Results:Effect size estimates suggest that mindfulness-based therapy was moderately effective for improving anxiety (Hedges’sg!0.63) and mood symptoms (Hedges’sg!0.59) from pre- to posttreatment in the overall sample. In patients with anxiety and mood disorders, this intervention was associated with effect sizes (Hedges’sg) of 0.97 and 0.95 for improving anxiety and mood symptoms, respectively. These effect sizes were robust, were unrelated to publication year or number of treatment sessions, and were maintained over follow-up.Conclusions:These results suggest that mindfulnessbased therapy is a promising intervention for treating anxiety and mood problems in clinical populations.


title: Supporting Domain Experts to Select and Configure Precise Compliance Rules

abstract: Compliance specifications concisely describe selected aspects of what a business operation should adhere to. To enable automated techniques for compliance checking, it is important that these requirements are specified correctly and precisely, describing exactly the behavior intended. Although there are rigorous mathematical formalisms for representing compliance rules, these are often perceived to be difficult to use for business users. Regardless of notation, however, there are often subtle but important details in compliance requirements that need to be considered. The main challenge in compliance checking is to bridge the gap between informal description and a precise specification of all requirements. In this paper, we present an approach which aims to facilitate creating and understanding formal compliance requirements by providing configurable templates that capture these details as options for commonly-required compliance requirements. These options are configured interactively with end-users, using question trees and natural language. The approach is implemented in the Process Mining Toolkit ProM.


title: Slightly Frequency-Shifted Reference Ultra-Wideband (UWB) Radio

abstract: A promising ultra-wideband (UWB) radio technique being widely considered for low-data-rate applications, such as those often encountered in sensor networks, is the transmitted reference (TR) UWB scheme. However, the standard TR-UWB scheme, while often motivated by the simplicity of its receiver, is still dogged by implementation concerns. In particular, the receiver requires an extremely wideband delay element, which is difficult to incorporate into low-power integrated systems. In this paper, a TR scheme is proposed in which the separation between the data and reference signals, rather than being a time delay, is a slow rotation over the symbol interval. This provides a (slightly) frequency-shifted reference that, while orthogonal to the data-bearing pulse, still goes through a nearly equal channel. A detailed analysis of the proposed scheme is provided. Simulation results demonstrate the expected result that frequency shifting of the reference in the proposed manner is not effective for high-data-rate systems that experience appreciable intersymbol interference. However, for the targeted low-to-moderate data-rate applications, numerical results demonstrate that the proposed system not only achieves the primary goal of providing a much simpler receiver architecture, but also that it outperforms the standard TR-UWB system


title: Blanket execution: dynamic similarity testing for program binaries and components

abstract: Matching function binaries--the process of identifying similar functions among binary executables--is a challenge that underlies many security applications such as malware analysis and patch-based exploit generation. Recent work tries to establish semantic similarity based on static analysis methods. Unfortunately, these methods do not perform well if the compared binaries are produced by different compiler toolchains or optimization levels. In this work, we propose blanket execution, a novel dynamic equivalence testing primitive that achieves complete coverage by overriding the intended program logic. Blanket execution collects the side effects of functions during execution under a controlled randomized environment. Two functions are deemed similar, if their corresponding side effects, as observed under the same environment, are similar too. ::: ::: We implement our blanket execution technique in a system called BLEX. We evaluate BLEX rigorously against the state of the art binary comparison tool BinDiff. When comparing optimized and un-optimized executables from the popular GNU coreutils package, BLEX outperforms BinDiff by up to 3.5 times in correctly identifying similar functions. BLEX also outperforms BinDiff if the binaries have been compiled by different compilers. Using the functionality in BLEX, we have also built a binary search engine that identifies similar functions across optimization boundaries. Averaged over all indexed functions, our search engine ranks the correct matches among the top ten results 77% of the time.


title: Visual 3-D SLAM from UAVs

abstract: The aim of the paper is to present, test and discuss the implementation of Visual SLAM techniques to images taken from Unmanned Aerial Vehicles (UAVs) outdoors, in partially structured environments. Every issue of the whole process is discussed in order to obtain more accurate localization and mapping from UAVs flights. Firstly, the issues related to the visual features of objects in the scene, their distance to the UAV, and the related image acquisition system and their calibration are evaluated for improving the whole process. Other important, considered issues are related to the image processing techniques, such as interest point detection, the matching procedure and the scaling factor. The whole system has been tested using the COLIBRI mini UAV in partially structured environments. The results that have been obtained for localization, tested against the GPS information of the flights, show that Visual SLAM delivers reliable localization and mapping that makes it suitable for some outdoors applications when flying UAVs.


title: Driving Semantic Parsing from the World’s Response

abstract: Current approaches to semantic parsing, the task of converting text to a formal meaning representation, rely on annotated training data mapping sentences to logical forms. Providing this supervision is a major bottleneck in scaling semantic parsers. This paper presents a new learning paradigm aimed at alleviating the supervision burden. We develop two novel learning algorithms capable of predicting complex structures which only rely on a binary feedback signal based on the context of an external world. In addition we reformulate the semantic parsing problem to reduce the dependency of the model on syntactic patterns, thus allowing our parser to scale better using less supervision. Our results surprisingly show that without using any annotated meaning representations learning with a weak feedback signal is capable of producing a parser that is competitive with fully supervised parsers.


title: Random feedback weights support learning in deep neural networks

abstract: The brain processes information through many layers of neurons. This deep architecture is representationally powerful, but it complicates learning by making it hard to identify the responsible neurons when a mistake is made. In machine learning, the backpropagation algorithm assigns blame to a neuron by computing exactly how it contributed to an error. To do this, it multiplies error signals by matrices consisting of all the synaptic weights on the neuron's axon and farther downstream. This operation requires a precisely choreographed transport of synaptic weight information, which is thought to be impossible in the brain. Here we present a surprisingly simple algorithm for deep learning, which assigns blame by multiplying error signals by random synaptic weights. We show that a network can learn to extract useful information from signals sent through these random feedback connections. In essence, the network learns to learn. We demonstrate that this new mechanism performs as quickly and accurately as backpropagation on a variety of problems and describe the principles which underlie its function. Our demonstration provides a plausible basis for how a neuron can be adapted using error signals generated at distal locations in the brain, and thus dispels long-held assumptions about the algorithmic constraints on learning in neural circuits.


title: Interaction effects of perceived gaze direction and dynamic facial expression: Evidence for appraisal theories of emotion.

abstract: Appraisal theorists suggest that the face expresses cognitive processes involved both in the orienting of attention (primarily gaze direction) and in the evaluation of emotion-eliciting events. Contrary to the assumption of direct emotion recognition by basic emotions theorists, this implies an interaction effect between “perceived gaze direction” and “perceived facial expression” in inferring emotion from the face. These two theoretical perspectives were comparatively tested by requesting participants to decode dynamic synthetic facial expressions of emotion presented with either an averted or a direct gaze. Confirming the interaction predicted by appraisal theories, the perceived specificity and intensity of fear and anger depended on gaze direction (direct gaze for anger and averted gaze for fear).


title: LTE in the sky: trading off propagation benefits with interference costs for aerial nodes

abstract: The popularity of unmanned aerial vehicles has exploded over the last few years, urgently demanding solutions to transfer large amounts of data from the UAV to the ground. Conversely, a control channel to the UAV is desired, in order to safely operate these vehicles remotely. This article analyzes the use of LTE for realizing this downlink data and uplink control. By means of measurements and simulations, we study the impact of interference and path loss when transmitting data to and from the UAV. Two scenarios are considered in which UAVs act as either base stations transmitting in downlink or UEs transmitting in uplink, and their impact on the respective downlink and uplink performance of an LTE ground network is analyzed. Both measurements and simulations are used to quantify such impact for a range of scenarios with varying altitude, distance from the base station, or UAV density. The measurement sets show that signal-to-interference ratio decreases up to 7 dB for UAVs at 150 m compared to ground users. Simulation results show that a UAV density of 10/km2 gives an average degradation of the signal-to-interference ratio of more than 6 dB. It is concluded that interference is going to be a major limiting factor when LTE enabled UAVs are introduced, and that strong technical solutions will have to be found.


title: RE Data Challenge: Requirements Identification with Word2Vec and TensorFlow

abstract: Since their introduction over a year ago, Google's TensorFlow package for learning with multilayer neural networks and their Word2Vec representation of words have both gained a high degree of notoriety. This paper considers the application of TensorFlow-guided learning and Word2Vec-based representations to the problems of classification in requirements documents. In this paper, we compare three categories of machine learning techniques for requirements identification for the SecReq and NFR datasets. The first category is the baseline method used in prior work: Naive Bayes over word count and TF-IDF representations of requirements. The remaining two categories of techniques are the training of TensorFlow's convolutional neural networks on random and pre-trained Word2Vec embeddings of the words found in the requirements. This paper reports on the experiments we conducted and the accuracy results we achieved.


title: Decision tree-based method for integrating gene expression, demographic, and clinical data to determine disease endotypes

abstract: BackgroundComplex diseases are often difficult to diagnose, treat and study due to the multi-factorial nature of the underlying etiology. Large data sets are now widely available that can be used to define novel, mechanistically distinct disease subtypes (endotypes) in a completely data-driven manner. However, significant challenges exist with regard to how to segregate individuals into suitable subtypes of the disease and understand the distinct biological mechanisms of each when the goal is to maximize the discovery potential of these data sets.ResultsA multi-step decision tree-based method is described for defining endotypes based on gene expression, clinical covariates, and disease indicators using childhood asthma as a case study. We attempted to use alternative approaches such as the Student’s t-test, single data domain clustering and the Modk-prototypes algorithm, which incorporates multiple data domains into a single analysis and none performed as well as the novel multi-step decision tree method. This new method gave the best segregation of asthmatics and non-asthmatics, and it provides easy access to all genes and clinical covariates that distinguish the groups.ConclusionsThe multi-step decision tree method described here will lead to better understanding of complex disease in general by allowing purely data-driven disease endotypes to facilitate the discovery of new mechanisms underlying these diseases. This application should be considered a complement to ongoing efforts to better define and diagnose known endotypes. When coupled with existing methods developed to determine the genetics of gene expression, these methods provide a mechanism for linking genetics and exposomics data and thereby accounting for both major determinants of disease.


title: A Comparison of Approaches for Measuring Cross-Lingual Similarity of Wikipedia Articles

abstract: Wikipedia has been used as a source of comparable texts for a range of tasks, such as Statistical Machine Translation and Cross-Language Information Retrieval. Articles written in different languages on the same topic are often connected through inter-language-links. However, the extent to which these articles are similar is highly variable and this may impact on the use of Wikipedia as a comparable resource. In this paper we compare various language-independent methods for measuring cross-lingual similarity: character n-grams, cognateness, word count ratio, and an approach based on outlinks. These approaches are compared against a baseline utilising MT resources. Measures are also compared to human judgements of similarity using a manually created resource containing 700 pairs of Wikipedia articles in 7 language pairs. Results indicate that a combination of language-independent models char-n-grams, outlinks and word-count ratio is highly effective for identifying cross-lingual similarity and performs comparably to language-dependent models translation and monolingual analysis.


title: Deep Reinforcement Learning for Sepsis Treatment

abstract: Sepsis is a leading cause of mortality in intensive care units and costs hospitals billions annually. Treating a septic patient is highly challenging, because individual patients respond very differently to medical interventions and there is no universally agreed-upon treatment for sepsis. In this work, we propose an approach to deduce treatment policies for septic patients by using continuous state-space models and deep reinforcement learning. Our model learns clinically interpretable treatment policies, similar in important aspects to the treatment policies of physicians. The learned policies could be used to aid intensive care clinicians in medical decision making and improve the likelihood of patient survival.


title: Yes, we GAN: Applying Adversarial Techniques for Autonomous Driving

abstract: Generative Adversarial Networks (GAN) have gained a lot of popularity from their introduction in 2014 till present. Research on GAN is rapidly growing and there are many variants of the original GAN focusing on various aspects of deep learning. GAN are perceived as the most impactful direction of machine learning in the last decade. This paper focuses on the application of GAN in autonomous driving including topics such as advanced data augmentation, loss function learning, semi-supervised learning, etc. We formalize and review key applications of adversarial techniques and discuss challenges and open problems to be addressed.


title: Every continuous piecewise affine function can be obtained by solving a parametric linear program

abstract: It is well-known that solutions to parametric linear or quadratic programs are continuous piecewise affine functions of the parameter. In this paper we prove the converse, i.e. that every continuous piecewise affine function can be identified with the solution to a parametric linear program. In particular, we provide a constructive proof that every piecewise affine function can be expressed as the linear mapping of the solution to a parametric linear program with at most twice as many variables as the dimension of the image of the piecewise affine function. Our method is illustrated via two small numerical examples.


title: On the Vertical Extrapolation of Mean Wind Power Density

abstract: Abstract A simple method of estimating the height variation of the mean wind power density is presented which accounts for the variation of the exponent p of the wind speed power law, with stability and roughness. Measurements are used to compare the new method to two previously suggested methods. The results indicate that the presently suggested method gives the most accurate estimate of the extrapolated mean wind power densities.


title: The Impact of Whatsapp Messenger Usage on Students Performance in Tertiary Institutions in Ghana

abstract: The perceived high level of usage of social networking applications amongst students of tertiary institutions in Ghana is inevitable. However, little is understood from empirical viewpoint about the intensity, of usage of whatsapp messenger and its impact on the academic performance of students in tertiary institutions. This study seeks to empirically identify the impact of social network (whatsapp messenger) on the performance of tertiary students in Ghana from the perspective of the students. To achieve this, 50 students from five tertiary institutions were interviewed and 500 questionnaires were administered to students from same institutions. The study revealed that, whatsapp instead of making communication easier and faster thereby enhancing effective flow of information and idea sharing among students, rather has impacted negatively on the performance of tertiary students in Ghana .The study among other things unveiled the following: whatsapp takes much of students study time , results in procrastination related problems, destroys students’ spellings and grammatical construction of sentences, leads to lack of concentration during lectures, results in difficulty in balancing online activities (whatsapp) and academic preparation and distracts students from completing their assignments and adhering to their private studies time table. Key w ords : Whatsapp Messenger, Impact, Students Performance, Tertiary institutions, Ghana.


Learning Visual Sentiment Distributions via Augmented Conditional Probability Neural Network.


title: Informal Identification of Outliers in Medical Data

abstract: Informal box plot identification of outliers in realworld medical data was studied. Box plots were used to detect univariate outliers directly whereas the box plotted Mahalanobis distances identified multivariate outliers. Vertigo and female urinary incontinence data were used in the tests. The removal of outliers increased the descriptive classification accuracy of discriminant analysis functions and nearest neighbour method, while the predictive ability of these methods reduced somewhat. Outliers were also evaluated subjectively by expert physicians, who found most of the multivariate outliers to truly be outliers in their area. The experts sometimes disagreed with the method on univariate outliers. This happened, for example, in heterogeneous diagnostic groups where also extreme values are natural. The informal method may be used for straightforward identification of suspicious data or as a tool to collect abnormal cases for an in-depth analysis.


title: Exploiting the Past and the Future in Protein Secondary Structure Prediction

abstract: Motivation: Predicting the secondary structure of a protein (alpha-helix, beta-sheet, coil) is an important step towards elucidating its three-dimensional structure, as well as its function. Presently, the best predictors are based on machine learning approaches, in particular neural network architectures with a fixed, and relatively short, input window of amino acids, centered at the prediction site. Although a fixed small window avoids overfitting problems, it does not permit capturing variable long-rang information. Results: We introduce a family of novel architectures which can learn to make predictions based on variable ranges of dependencies. These architectures extend recurrent neural networks, introducing non-causal bidirectional dynamics to capture both upstream and downstream information. The prediction algorithm is completed by the use of mixtures of estimators that leverage evolutionary information, expressed in terms of multiple alignments, both at the input and output levels. While our system currently achieves an overall performance close to 76% correct prediction ‐ at least comparable to the best existing systems ‐ the main emphasis here is on the development of new algorithmic


title: Training deep neural networks on imbalanced data sets

abstract: Deep learning has become increasingly popular in both academic and industrial areas in the past years. Various domains including pattern recognition, computer vision, and natural language processing have witnessed the great power of deep networks. However, current studies on deep learning mainly focus on data sets with balanced class labels, while its performance on imbalanced data is not well examined. Imbalanced data sets exist widely in real world and they have been providing great challenges for classification tasks. In this paper, we focus on the problem of classification using deep network on imbalanced data sets. Specifically, a novel loss function called mean false error together with its improved version mean squared false error are proposed for the training of deep networks on imbalanced data sets. The proposed method can effectively capture classification errors from both majority class and minority class equally. Experiments and comparisons demonstrate the superiority of the proposed approach compared with conventional methods in classifying imbalanced data sets on deep neural networks.


Machine learning techniques for brain-computer interfaces


title: Analysis and Prediction of Question Topic Popularity in Community Q&A Sites: A Case Study of Quora

abstract: In the past few years, Quora a community-driven social platform for question and answering, has grown exponentially from a small community of users into one of the largest and reliable source of QA users can follow each other, follow question, topics etc. Apart from the social connections that Quora provides, it has developed a knowledge base nicely organized via hierarchy and relatedness of topics. In this paper, we consider a massive dataset of more than four years and analyze the dynamics of topical growth over time; how various factors affect the popularity of a topic or its acceptance in Q&A community. We also propose a regression model to predict the popularity of the topics and discuss the important discriminating features. We achieve a high prediction accuracy (correlation coefficient ~0.773) with low root mean square error (~1.065). We further categorize thetopics into a few broad classes by implementing a simple Latent Dirichlet Allocation (LDA) model on the question texts associated with the topics. In comparison to the data sample with no categorization, this stratification of the topics enhances the prediction accuracies for several categories. However, for certain categories there seems to a slight decrease in the accuracy values and we present an in-depth discussion analyzing the cause for the same pointing out potential ways for improvement. We believe that this thorough measurement study will have a direct application to a service like recommending trending topics in Quora.


title: Balancing fairness and efficiency in tiered storage systems with bottleneck-aware allocation

abstract: Multi-tiered storage made up of heterogeneous devices are raising new challenges in allocating throughput fairly among concurrent clients. The fundamental problem is finding an appropriate balance between fairness to the clients and maximizing system utilization. ::: ::: In this paper we cast the problem within the broader framework of fair allocation for multiple resources. We present a new allocation model BAA based on the notion of per-device bottleneck sets. Clients bottlenecked on the same device receive throughputs in proportion to their fair shares, while allocation ratios between clients in different bottleneck sets are chosen to maximize system utilization. We show formally that BAA satisfies fairness properties of Envy Freedom and Sharing Incentive. We evaluated the performance of our method using both simulation and implementation on a Linux platform. The experimental results show that our method can provide both high efficiency and fairness.


title: Applying Innovation Resistance Theory to Understand Consumer Resistance of Using Online Travel in Thailand

abstract: Base on innovation resistance theory, this research builds the model of factors affecting consumers' resistance in using online travel in Thailand. Through the questionnaires and the SEM methods, empirical analysis results show that functional barriers are even greater sources of resistance to online travel website than psychological barriers. Online experience and independent travel experience have significantly influenced on consumer innovation resistance. Social influence plays an important role in this research.


Exploiting regularity without development


title: A new Molyneux's problem: Sounds, shapes and arbitrary crossmodal correspondences

abstract: Several studies in cognitive sciences have highlighted the existence of privileged and universal psychological associations between shape attributes, such as angularity, and auditory dimensions, such as pitch. These results add a new puzzle to the list of arbitrary-looking crossmodal matching tendencies whose origin is hard to explain. The puzzle is all the more general in the case of shape that the shapes-sounds correspondences have a wide set of documented effects on perception and behaviour: Sounds can for instance influence the way a certain shape is perceived (Sweeny et al., 2012). In this talk, we suggest that the study of these crossmodal correspondences can be related to the more classical cases of crossmodal transfer of shape between vision and touch documented as part of Molyneux’s question, and reveal the role that movement plays as an amodal invariant in explaining the variety of multimodal associations around shape.


title: Fog Computing with Go: A Comparative Study

abstract: The Internet of Things is a recent computing paradigm, defined by networks of highly connected things – sensors, actuators and smart objects – communicating across networks of homes, buildings, vehicles, and even people. The Internet of Things brings with it a host of new problems, from managing security on constrained devices to processing never before seen amounts of data. While cloud computing might be able to keep up with current data processing and computational demands, it is unclear whether it can be extended to the requirements brought forth by Internet of Things. Fog computing provides an architectural solution to address some of these problems by providing a layer of intermediary nodes within what is called an edge network, separating the local object networks and the Cloud. These edge nodes provide interoperability, real-time interaction, routing, and, if necessary, computational delegation to the Cloud. This paper attempts to evaluate Go, a distributed systems language developed by Google, in the context of requirements set forth by Fog computing. Similar methodologies of previous literature are simulated and benchmarked against in order to assess the viability of Go in the edge nodes of Fog computing architecture.


title: Neural substrates of awakening probed with optogenetic control of hypocretin neurons

abstract: A paper published in Nature in April raised the intriguing possibility that optical therapies might be developed to treat neurological disorders. That work, in tissue slices and in C. elegans roundworms, showed that brain cells can be genetically engineered to alter their activity in response to pulses of different colours of light. A follow-up study now shows that behaviour can be modified in a living mammal by similar means. Hypocretin (Hcrt)-producing neurons in the hypothalamus are active during transitions from sleep to waking states. Optical stimulation of mouse Hcrt neurons engineered to respond to light increases the likelihood of transition from sleep to wakefulness, with higher frequencies causing more abrupt awakening. As Hcrt deficiency is linked to narcolepsy, these results may provide insights into sleep disorders. The neural underpinnings of sleep involve interactions between sleep-promoting areas such as the anterior hypothalamus, and arousal systems located in the posterior hypothalamus, the basal forebrain and the brainstem1,2. Hypocretin3 (Hcrt, also known as orexin4)-producing neurons in the lateral hypothalamus5 are important for arousal stability2, and loss of Hcrt function has been linked to narcolepsy6,7,8,9. However, it is unknown whether electrical activity arising from Hcrt neurons is sufficient to drive awakening from sleep states or is simply correlated with it. Here we directly probed the impact of Hcrt neuron activity on sleep state transitions with in vivo neural photostimulation10,11,12,13,14,15,16,17,18, genetically targeting channelrhodopsin-2 to Hcrt cells and using an optical fibre to deliver light deep in the brain, directly into the lateral hypothalamus, of freely moving mice. We found that direct, selective, optogenetic photostimulation of Hcrt neurons increased the probability of transition to wakefulness from either slow wave sleep or rapid eye movement sleep. Notably, photostimulation using 5–30 Hz light pulse trains reduced latency to wakefulness, whereas 1 Hz trains did not. This study establishes a causal relationship between frequency-dependent activity of a genetically defined neural cell type and a specific mammalian behaviour central to clinical conditions and neurobehavioural physiology.


title: Security-Aware and Privacy-Preserving D2D Communications in 5G

abstract: Device-to-device communication is a promising technology that can offload heavy traffic in 5G networks. However, due to its open nature, there are several security issues. The LTE-A standard introduces security and protection mechanisms, which include mutual authentication between the UEs and the eNB. Unfortunately, D2D communications still face various threats: jamming, data modification, free-riding, and privacy violation. In this article, we first give an architecture for an LTE-D2D system and its application scenarios with various security threats and requirements. Then we investigate privacy concerns in terms of location privacy, identity privacy, and data privacy. Afterward, we propose security solutions from aspects of application-layer security and physical-layer security, respectively. Further, we propose two frameworks for cross-application-physical-layer security methods. Finally, the challenges and future research directions are presented.


title: Video recommendation using crowdsourced time-sync comments

abstract: Most existing work on video recommendation focuses on recommending a video as a whole, largely due to the unavailability of semantic information on video shot-level. Recently a new type of video comments has emerged, called time-sync comments, that are posted by users in real playtime of a video, thus each has a timestamp relative to the video playtime. In the present paper, we propose to utilize time-sync comments for three research tasks that are infeasible or difficult to tackle in the past, namely (1) video clustering based on temporal user emotional/topic trajectory inside a video; (2) video highlight shots recommendation unsupervisedly; (3) personalized video shot recommendation tailored to user moods. We analyze characteristics of time-sync comments, and propose feasible solutions for each research task. For task (1), we propose a deep recurrent auto-encoder framework coupled with dictionary learning to model user emotional/topical trajectories in a video. For task (2), we propose a scoring method based on emotional/topic concentration in time-sync comments for candidate highlight shot ranking. For task (3), we propose a joint deep collaborative filtering network that optimizes ranking loss and classification loss simultaneously. Evaluation methods and preliminary experimental results are also reported. We plan to further refine our models for task (1) and (3) as our next step.


title: Deep Neural Network for Real-Time Autonomous Indoor Navigation

abstract: Autonomous indoor navigation of Micro Aerial Vehicles (MAVs) possesses many challenges. One main reason is that GPS has limited precision in indoor environments. The additional fact that MAVs are not able to carry heavy weight or power consuming sensors, such as range finders, makes indoor autonomous navigation a challenging task. In this paper, we propose a practical system in which a quadcopter autonomously navigates indoors and finds a specific target, i.e., a book bag, by using a single camera. A deep learning model, Convolutional Neural Network (ConvNet), is used to learn a controller strategy that mimics an expert pilot's choice of action. We show our system's performance through real-time experiments in diverse indoor locations. To understand more about our trained network, we use several visualization techniques.


title: A Complete Bidirectional Planner

abstract: The PRODIGY system is based on bidirectional planning, which is a combination of goal-directed backward chaining with simulation of plan execution. Experiments have demonstrated that it is an efficient technique, a fair match to other successful planning systems. The question of completeness of bidirectional planning, however, has remained unanswered. ::: ::: We show that PRODIGY is not complete and discuss the advantages and drawbacks of its incompleteness. We then develop a complete bidirectional planner and compare it experimentally with PRODIGY. We demonstrate that the complete planner is almost as efficient as PRODIGY and solves a wider range of problems.


title: A Neural Network Linking Process for Insurance Claims

abstract: A novel method of integrating multiple neural networks into one large network via a linking process is used to combine neural networks for the purpose of Insurance Claim Reservation. Neural networks are commonly trained to solve a specific problem for an encapsulated problem domain. Simple problems can be solved by a single network, whereby more complicated problems can be solved by sub-networks. These sub-networks then re-combined via a linking process forming a combined network, which has the ability to solve the entire problem. Insurance companies require monetary reserves for accounting, calculation of premium, reinsurance and asset liability management, which includes claims payable. Since the business of insurance companies lies in the future, accurate claims estimation for future financial years will increase profitability and ensure their solvency. A fine balance between under-reserving and over-reserving must be found because under-reserving can threaten solvency and over-reserving reduces profitability. Therefore this novel method for claims estimation with neural network linking has been developed.


title: Picbreeder: A Case Study in Collaborative Evolutionary Exploration of Design Space

abstract: For domains in which fitness is subjective or difficult to express formally, interactive evolutionary computation (IEC) is a natural choice. It is possible that a collaborative process combining feedback from multiple users can improve the quality and quantity of generated artifacts. Picbreeder, a large-scale online experiment in collaborative interactive evolution (CIE), explores this potential. Picbreeder is an online community in which users can evolve and share images, and most importantly, continue evolving others' images. Through this process of branching from other images, and through continually increasing image complexity made possible by the underlying neuroevolution of augmenting topologies (NEAT) algorithm, evolved images proliferate unlike in any other current IEC system. This paper discusses not only the strengths of the Picbreeder approach, but its challenges and shortcomings as well, in the hope that lessons learned will inform the design of future CIE systems.


title: Massive Exploration of Neural Machine Translation Architectures

abstract: Neural Machine Translation (NMT) has shown remarkable progress over the past few years with production systems now being deployed to end-users. One major drawback of current architectures is that they are expensive to train, typically requiring days to weeks of GPU time to converge. This makes exhaustive hyperparameter search, as is commonly done with other neural network architectures, prohibitively expensive. In this work, we present the first large-scale analysis of NMT architecture hyperparameters. We report empirical results and variance numbers for several hundred experimental runs, corresponding to over 250,000 GPU hours on the standard WMT English to German translation task. Our experiments lead to novel insights and practical advice for building and extending NMT architectures. As part of this contribution, we release an open-source NMT framework that enables researchers to easily experiment with novel techniques and reproduce state of the art results.


title: Locating the fovea center position in digital fundus images using thresholding and feature extraction techniques

abstract: A new methodology for detecting the fovea center position in digital retinal images is presented in this paper. A pixel is firstly searched for within the foveal region according to its known anatomical position relative to the optic disc and vascular tree. Then, this pixel is used to extract a fovea-containing subimage on which thresholding and feature extraction techniques are applied so as to find fovea center. The methodology was evaluated on 1200 fundus images from the publicly available MESSIDOR database, 660 of which present signs of diabetic retinopathy. In 93.92% of these images, the distance between the methodology-provided and actual fovea center position remained below 1/4 of one standard optic disc radius (i.e., 17, 26, and 27 pixels for MESSIDOR retinas of 910, 1380 and 1455 pixels in size, respectively). These results outperform all the reviewed methodologies available in literature. Its effectiveness and robustness with different illness conditions makes this proposal suitable for retinal image computer analyses such as automated screening for early diabetic retinopathy detection.


title: SCAN++: Efficient Algorithm for Finding Clusters, Hubs and Outliers on Large-scale Graphs

abstract: Graph clustering is one of the key techniques for understanding the structures present in graphs. Besides cluster detection, identifying hubs and outliers is also a key task, since they have important roles to play in graph data mining. The structural clustering algorithm SCAN, proposed by Xu et al., is successfully used in many application because it not only detects densely connected nodes as clusters but also identifies sparsely connected nodes as hubs or outliers. However, it is difficult to apply SCAN to large-scale graphs due to its high time complexity. This is because it evaluates the density for all adjacent nodes included in the given graphs. In this paper, we propose a novel graph clustering algorithm named SCAN++. In order to reduce time complexity, we introduce new data structure of directly two-hop-away reachable node set (DTAR). DTAR is the set of two-hop-away nodes from a given node that are likely to be in the same cluster as the given node. SCAN++ employs two approaches for efficient clustering by using DTARs without sacrificing clustering quality. First, it reduces the number of the density evaluations by computing the density only for the adjacent nodes such as indicated by DTARs. Second, by sharing a part of the density evaluations for DTARs, it offers efficient density evaluations of adjacent nodes. As a result, SCAN++ detects exactly the same clusters, hubs, and outliers from large-scale graphs as SCAN with much shorter computation time. Extensive experiments on both real-world and synthetic graphs demonstrate the performance superiority of SCAN++ over existing approaches.


title: Rapid adaptation for deep neural networks through multi-task learning.

abstract: We propose a novel approach to addressing the adaptation effectiveness issue in parameter adaptation for deep neural network (DNN) based acoustic models for automatic speech recognition by adding one or more small auxiliary output layers modeling broad acoustic units, such as mono-phones or tied-state (often called senone) clusters. In scenarios with a limited amount of available adaptation data, most senones are usually rarely seen or not observed, and consequently the ability to model them in a new condition is often not fully exploited. With the original senone classification task as the primary task, and adding auxiliary mono-phone/senone-cluster classification as the secondary tasks, multi-task learning (MTL) is employed to adapt the DNN parameters. With the proposed MTL adaptation framework, we improve the learning ability of the original DNN structure, then enlarge the coverage of the acoustic space to deal with the unseen senone problem, and thus enhance the discrimination power of the adapted DNN models. Experimental results on the 20,000-word open vocabulary WSJ task demonstrate that the proposed framework consistently outperforms the conventional linear hidden layer adaptation schemes without MTL by providing 3.2% relative word error rate reduction (WERR) with only 1 single adaptation utterance, and 10.7% WERR with 40 adaptation utterances against the un-adapted DNN models.


title: Overview of bacterial cellulose composites: a multipurpose advanced material.

abstract: Bacterial cellulose (BC) has received substantial interest owing to its unique structural features and impressive physico-mechanical properties. BC has a variety of applications in biomedical fields, including use as biomaterial for artificial skin, artificial blood vessels, vascular grafts, scaffolds for tissue engineering, and wound dressing. However, pristine BC lacks certain properties, which limits its applications in various fields; therefore, synthesis of BC composites has been conducted to address these limitations. A variety of BC composite synthetic strategies have been developed based on the nature and relevant applications of the combined materials. BC composites are primarily synthesized through in situ addition of reinforcement materials to BC synthetic media or the ex situ penetration of such materials into BC microfibrils. Polymer blending and solution mixing are less frequently used synthetic approaches. BC composites have been synthesized using numerous materials ranging from organic polymers to inorganic nanoparticles. In medical fields, these composites are used for tissue regeneration, healing of deep wounds, enzyme immobilization, and synthesis of medical devices that could replace cardiovascular and other connective tissues. Various electrical products, including biosensors, biocatalysts, E-papers, display devices, electrical instruments, and optoelectronic devices, are prepared from BC composites with conductive materials. In this review, we compiled various synthetic approaches for BC composite synthesis, classes of BC composites, and applications of BC composites. This study will increase interest in BC composites and the development of new ideas in this field.


title: Empowering production workers with digitally facilitated knowledge processes – a conceptual framework

abstract: Recent digital advancements, including social software, mobile technologies and augmented reality, offer promising opportunities to empower knowledge workers in their production environment by leveraging their knowledge processes, decision-making skills and social interaction practices. This paper proposes a conceptual framework for empowering workers in industrial production environments with digitally facilitated knowledge management processes. The framework explores four concrete facets of digital advancements that apply to a wide range of knowledge processes and production strategies in manufacturing companies. Each of these advancements are capable of supporting one specific facet of the individual knowledge management processes of workers; knowledge transfer, discovery, acquisition and sharing. The study contributes to the production research community by aligning emerging digital technologies and current trends in advanced manufacturing environments to benefit workers and improve job satisfaction, ...


title: Response to Susilo and Duchaine : beyond neuropsychological dissociations in understanding face and word representations

abstract: We welcome the opportunity to clarify our theoretical position in light of comments by Susilo and Duchaine. Our central claim is that face and word processing are carried out by a distributed network of partially specialized cortical regions, with the degree of specialization varying across individuals (partly as a function of language lateralization). Thus, and perhaps not surprisingly, Susilo and Duchaine are adopting an overly all-or-none perspective when they mischaracterize our views as implying that cortical regions are ‘not specialized for particular categories’ or that ‘individuals with prosopagnosia will always have some deficits in word recognition while individuals with alexia will always have some deficits in face recognition’ (emphasis added). Rather, on our view, cortical regions are not dedicated to categories, and patients with severe face or word impairments will, as a population, tend to be more moderately impaired in the other domain, as well.


title: When Static Media Promote Active Learning : Annotated Illustrations Versus Narrated Animations in Multimedia Instruction

abstract: In 4 experiments, students received a lesson consisting of computer-based animation and narration or a lesson consisting of paper-based static diagrams and text. The lessons used the same words and graphics in the paper-based and computer-based versions to explain the process of lightning formation (Experiment 1), how a toilet tank works (Experiment 2), how ocean waves work (Experiment 3), and how a car’s braking system works (Experiment 4). On subsequent retention and transfer tests, the paper group performed significantly better than the computer group on 4 of 8 comparisons, and there was no significant difference on the rest. These results support the static media hypothesis, in which static illustrations with printed text reduce extraneous processing and promote germane processing as compared with narrated animations.


title: Daily Aerobic Exercise Improves Reactive Hyperemia in Patients With Essential Hypertension

abstract: Abstract —The effects of long-term aerobic exercise on endothelial function in patients with essential hypertension remain unclear. To determine whether endothelial function relating to forearm hemodynamics in these patients differs from normotensive subjects and whether endothelial function can be modified by continued physical exercise, we randomized patients with essential hypertension into a group that engaged in 30 minutes of brisk walking 5 to 7 times weekly for 12 weeks (n=20) or a group that underwent no activity modifications (control group, n=7). Forearm blood flow was measured using strain-gauge plethysmography during reactive hyperemia to test for endothelium-dependent vasodilation and after sublingual nitroglycerin administration to test endothelium-independent vasodilation. Forearm blood flow in hypertensive patients during reactive hyperemia was significantly less than that in normotensive subjects (n=17). Increases in forearm blood flow after nitroglycerin were similar between hypertensive and normotensive subjects. Exercise lowered mean blood pressure from 115.7±5.3 to 110.2±5.1 mm Hg ( P P P N G -monomethyl-L-arginine abolished the enhancement of reactive hyperemia induced by 12 weeks of exercise. These findings suggest that through increased release of nitric oxide, continued physical exercise alleviates impairment of reactive hyperemia in patients with essential hypertension.


title: i-vector based speaker recognition on short utterances

abstract: Robust speaker verification on short utterances remains a key consideration when deploying automatic speaker recognition, as many real world applications often have access to only limited duration speech data. This paper explores how the recent technologies focused around total variability modeling behave when training and testing utterance lengths are reduced. Results are presented which provide a comparison of Joint Factor Analysis (JFA) and i-vector based systems including various compensation techniques; Within-Class Covariance Normalization (WCCN), LDA, Scatter Difference Nuisance Attribute Projection (SDNAP) and Gaussian Probabilistic Linear Discriminant Analysis (GPLDA). Speaker verification performance for utterances with as little as 2 sec of data taken from the NIST Speaker Recognition Evaluations are presented to provide a clearer picture of the current performance characteristics of these techniques in short utterance conditions.


title: Multistage Cutting Stock Problems of Two and More Dimensions

abstract: In earlier papers [Opns. Res. 9, 849-859 1961, and 11, 863-888 1963] the one-dimensional cutting stock problem was discussed as a linear programming problem. There it was shown how the difficulty of the enormous number of columns occurring in the linear programming formulation could be overcome by solving a knapsack problem at every pivot step. In this paper higher dimensional cutting stock problems are discussed as linear programming problems. The corresponding difficulty of the number of columns cannot in general be overcome for there is no efficient method for solving the generalized knapsack problem of the higher dimensional problem. However a wide class of cutting stock problems of industry have restrictions that permit their generalized knapsack problem to be efficiently solved. All of the cutting stock problems that yield to this treatment are ones in which the cutting is done in stages. In treating these practical cutting problems, one often encounters additional conditions that affect the solution. An example of this occurs in the cutting of corrugated boxes, which involves an auxiliary sequencing problem. This problem is discussed in some detail, and a solution described for the sequencing problem under given simplifying assumptions.


title: High SNR capacity of millimeter wave MIMO systems with one-bit quantization

abstract: Millimeter wave (mmWave) is a viable technology for future cellular systems. With bandwidths on the order of a gigahertz, high-resolution analog-to-digital converters (ADCs) become a power consumption bottleneck. One solution is to employ very low resolution one-bit ADCs. This paper analyzes the flat fading multiple-input multiple-output (MIMO) channel with one-bit ADC. Bounds on the high signal-to-noise ratio (SNR) capacity are derived for the single-input multiple-output (SIMO) channel and the general MIMO channel. The results show how the number of paths, number of transmit antennas, and number of receive antennas impact the capacity at high SNR.


title: Bayesian depth estimation from monocular natural images

abstract: Estimating an accurate and naturalistic dense depth map from a single monocular photographic image is a difficult problem. Nevertheless, human observers have little difficulty understanding the depth structure implied by photographs. Two-dimensional (2D) images of the real-world environment contain significant statistical information regarding the three-dimensional (3D) structure of the world that the vision system likely exploits to compute perceived depth, monocularly as well as binocularly. Toward understanding how this might be accomplished, we propose a Bayesian model of monocular depth computation that recovers detailed 3D scene structures by extracting reliable, robust, depth-sensitive statistical features from single natural images. These features are derived using well-accepted univariate natural scene statistics (NSS) models and recent bivariate/correlation NSS models that describe the relationships between 2D photographic images and their associated depth maps. This is accomplished by building a dictionary of canonical local depth patterns from which NSS features are extracted as prior information. The dictionary is used to create a multivariate Gaussian mixture (MGM) likelihood model that associates local image features with depth patterns. A simple Bayesian predictor is then used to form spatial depth estimates. The depth results produced by the model, despite its simplicity, correlate well with ground-truth depths measured by a current-generation terrestrial light detection and ranging (LIDAR) scanner. Such a strong form of statistical depth information could be used by the visual system when creating overall estimated depth maps incorporating stereopsis, accommodation, and other conditions. Indeed, even in isolation, the Bayesian predictor delivers depth estimates that are competitive with state-of-the-art "computer vision" methods that utilize highly engineered image features and sophisticated machine learning algorithms.


title: NO$_x$ Sensor Reading Correction in Diesel Engine Selective Catalytic Reduction System Applications

abstract: In this paper, we investigate NO $_x$ sensor reading correction in diesel engine selective catalytic reduction (SCR) system applications. It is well known that the urea-based SCR system is promising to remove engine-out NO $_x$ emissions and achieve strict legislative emission regulations. However, if the urea is overdosed, the system also leads to ammonia slip in the tailpipe. In order to control the NO $_x$ emissions and constrain the ammonia slip, a feedback loop with NO $_x$ and ammonia sensors may be needed. An interesting phenomenon in the closed loop is that a well calibrated NO $_x$ sensor does not work well in the urea-based SCR systems. For this typical application, there may be several sources for the NO $_x$ sensor reading mismatching issues. Due to the existence of the gaseous ammonia, the NO $_x$ sensor reading is approximately a combination of the actual NO $_x$ concentration and the ammonia concentration. Another phenomenon is that the influence of the ammonia concentration to the NO $_x$ sensor reading is significantly different with respect to different temperatures. Based on such observations and the importance of the NO $_x$ concentration reading, we devote to develop an algorithm to correct the NO $_x$ sensor reading. With considerable experimental data which cover most of the typical SCR operating temperature range, we obtain the relationship between the cross-sensitivity factor and the temperature by employing the adaptive-network-based fuzzy inference system (ANFIS). The developed fuzzy inference system is then applied to experimental validation tests. Compared with the existing extended Kalman filter (EKF), the developed fuzzy inference system has prominent advantages on the reading correction performance.


title: Characterizing Smoking and Drinking Abstinence from Social Media

abstract: Social media has been established to bear signals relating to health and well-being states. In this paper, we investigate the potential of social media in characterizing and understanding abstinence from tobacco or alcohol use. While the link between behavior and addiction has been explored in psychology literature, the lack of longitudinal self-reported data on long-term abstinence has challenged addiction research. We leverage the activity spanning almost eight years on two prominent communities on Reddit: StopSmoking and StopDrinking. We use the self-reported "badge" information of nearly a thousand users as gold standard information on their abstinence status to characterize long-term abstinence. We build supervised learning based statistical models that use the linguistic features of the content shared by the users as well as the network structure of their social interactions. Our findings indicate that long-term abstinence from smoking or drinking (~one year) can be distinguished from short-term abstinence (~40 days) with 85% accuracy. We further show that language and interaction on social media offer powerful cues towards characterizing these addiction-related health outcomes. We discuss the implications of our findings in social media and health research, and in the role of social media as a platform for positive behavior change and therapy.


title: Virtualization vs Containerization to Support PaaS

abstract: PaaS vendors face challenges in efficiently providing services with the growth of their offerings. In this paper, we explore how PaaS vendors are using containers as a means of hosting Apps. The paper starts with a discussion of PaaS Use case and the current adoption of Container based PaaS architectures with the existing vendors. We explore various container implementations - Linux Containers, Docker, Warden Container, lmctfy and OpenVZ. We look at how each of this implementation handle Process, FileSystem and Namespace isolation. We look at some of the unique features of each container and how some of them reuse base Linux Container implementation or differ from it. We also explore how IaaSlayer itself has started providing support for container lifecycle management along with Virtual Machines. In the end, we look at factors affecting container implementation choices and some of the features missing from the existing implementations for the next generation PaaS


title: Resource Mining: Applying Process Mining to Resource-Oriented Systems

abstract: Service Oriented Architecture is an increasingly popular approach to implement complex distributed systems. It enables implementing complex functionality just by composing simple services into so called business processes. Unfortunately, such composition of services may lead to some incorrect system behavior. In order discover such depreciances and fix them, process mining methods may be used. Unfortunately, the current state of the art focuses only on SOAP-based Web Services leaving RESTful Web Service (resource-oriented) unsupported. In this article the relevance of adapting the Web Service Mining methods to new resource-oriented domain is introduced with initial work on process discovery in such systems.


title: Efficient Phrase-Based Document Similarity for Clustering

abstract: In this paper, we propose a phrase-based document similarity to compute the pair-wise similarities of documents based on the suffix tree document (STD) model. By mapping each node in the suffix tree of STD model into a unique feature term in the vector space document (VSD) model, the phrase-based document similarity naturally inherits the term tf-idf weighting scheme in computing the document similarity with phrases. We apply the phrase-based document similarity to the group-average Hierarchical Agglomerative Clustering (HAC) algorithm and develop a new document clustering approach. Our evaluation experiments indicate that, the new clustering approach is very effective on clustering the documents of two standard document benchmark corpora OHSUMED and RCV1. The quality of the clustering results significantly surpass the results of traditional single-word \textit{tf-idf} similarity measure in the same HAC algorithm, especially in large document data sets. Furthermore, by studying the property of STD model, we conclude that the feature vector of phrase terms in the STD model can be considered as an expanded feature vector of the traditional single-word terms in the VSD model. This conclusion sufficiently explains why the phrase-based document similarity works much better than the single-word tf-idf similarity measure.


title: Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing

abstract: Deep learning is a promising approach for extracting accurate information from raw sensor data from IoT devices deployed in complex environments. Because of its multilayer structure, deep learning is also appropriate for the edge computing environment. Therefore, in this article, we first introduce deep learning for IoTs into the edge computing environment. Since existing edge nodes have limited processing capability, we also design a novel offloading strategy to optimize the performance of IoT deep learning applications with edge computing. In the performance evaluation, we test the performance of executing multiple deep learning tasks in an edge computing environment with our strategy. The evaluation results show that our method outperforms other optimization solutions on deep learning for IoT.


title: CodeTopics: which topic am I coding now?

abstract: Recent studies indicated that showing the similarity between the source code being developed and related high-level artifacts (HLAs), such as requirements, helps developers improve the quality of source code identifiers. In this paper, we present CodeTopics, an Eclipse plug-in that in addition to showing the similarity between source code and HLAs also highlights to what extent the code under development covers topics described in HLAs. Such views complement information derived by showing only the similarity between source code and HLAs helping (i) developers to identify functionality that are not implemented yet or (ii) newcomers to comprehend source code artifacts by showing them the topics that these artifacts relate to.


title: Educational Process Mining: A Systematic Literature Review

abstract: Process mining (PM) is an emerging research discipline that is concerned with obtaining fact-based insights on how business processes are executed within an organization. It combines model-based and data-oriented analysis techniques to discover, monitor and improve executed processes by extracting knowledge from event logs generated and stored in information systems. When process mining techniques are applied to educational data, they are often referred to as Educational Process Mining (EPM), and in different case studies EPM results were promising. This paper presents a systematic review of the current status of research in this important area. We examined the literature on research with associated case studies conducted in the domain during the past eight years (2009-2016). Search terms identified 59 papers, but inclusion and exclusion criteria limited the key studies to 37. The research questions, methodologies and findings of these published papers were analysed carefully. This paper aims to highlight the use of process mining to improve educational processes, and to provide researchers with a comprehensive background for understanding the current work being undertaken in this field.


WalkRanker: A Unified Pairwise Ranking Model with Multiple Relations for Item Recommendation


title: Mobile Health Apps to Facilitate Self-Care: A Qualitative Study of User Experiences

abstract: Objective ::: Consumers are living longer, creating more pressure on the health system and increasing their requirement for self-care of chronic conditions. Despite rapidly-increasing numbers of mobile health applications (‘apps’) for consumers’ self-care, there is a paucity of research into consumer engagement with electronic self-monitoring. This paper presents a qualitative exploration of how health consumers use apps for health monitoring, their perceived benefits from use of health apps, and suggestions for improvement of health apps. ::: ::: ::: Materials and Methods ::: ‘Health app’ was defined as any commercially-available health or fitness app with capacity for self-monitoring. English-speaking consumers aged 18 years and older using any health app for self-monitoring were recruited for interview from the metropolitan area of Perth, Australia. The semi-structured interview guide comprised questions based on the Technology Acceptance Model, Health Information Technology Acceptance Model, and the Mobile Application Rating Scale, and is the only study to do so. These models also facilitated deductive thematic analysis of interview transcripts. Implicit and explicit responses not aligned to these models were analyzed inductively. ::: ::: ::: Results ::: Twenty-two consumers (15 female, seven male) participated, 13 of whom were aged 26–35 years. Eighteen participants reported on apps used on iPhones. Apps were used to monitor diabetes, asthma, depression, celiac disease, blood pressure, chronic migraine, pain management, menstrual cycle irregularity, and fitness. Most were used approximately weekly for several minutes per session, and prior to meeting initial milestones, with significantly decreased usage thereafter. Deductive and inductive thematic analysis reduced the data to four dominant themes: engagement in use of the app; technical functionality of the app; ease of use and design features; and management of consumers’ data. ::: ::: ::: Conclusions ::: The semi-structured interviews provided insight into usage, benefits and challenges of health monitoring using apps. Understanding the range of consumer experiences and expectations can inform design of health apps to encourage persistence in self-monitoring.


title: Renal tumor versus renal cyst

abstract: The precise diagnosis of carcinoma of the kidney is of particular importance because renal mass lesions are frequently encountered which require nephrectomy if malignant, and renal tissue conservation if benign. The diagnostic problems include those of differentiating benign from malignant tumors and tumors from cysts. Delineation of the extent of tumors is also essential for treatment planning and prognostic implications.


title: Chaffing and Winnowing : Confidentiality without Encryption

abstract: • Encryption: transforming the message to a ciphertext such that an adversary who overhears the ciphertext can not determine the message sent. The legitimate receiver possesses a secret decryption key that allows him to reverse the encryption transformation and retrieve the message. The sender may have used the same key to encrypt the message (with symmetric encryption schemes) or used a different, but related key (with public-key schemes). DES and RSA are familiar examples of encryption schemes.


title: Low voltage low power analog circuit design OTA using signal attenuation technique in universal filter application

abstract: This paper presents a new configuration for a linear operational transconductance amplifier (OTA) using a signal attenuation technique. The OTA is designed to operate with a ±0.8V supply voltage and consumes 0.45mW power. All simulations are performed by ELDO model BSIM3v3 technology CMOS TSMC 0.18μm. The simulation results of this circuit showed a high DC gain of 73.6dB with a unity frequency of 50.19MHz and a total harmonic distortion of −60.81dB at 100 kHz for an input voltage of 1Vpp. Based on this circuit, a voltage mode universal filter has been implemented. The simulation results are in a good agreement with the theoretical calculations.


title: Digital culture clash: “massive” education in theE-learning and Digital CulturesMOOC

abstract: While education has been both open and online, the sizeable enrolment numbers associated with massive open online courses (MOOCs) are somewhat unprecedented. In order to gauge the significance of education at scale, this article analyses specific examples of massive participation derived from E-learning and Digital Cultures, a MOOC from the University of Edinburgh in partnership with Coursera. Student-created content, user statistics, and survey data are illustrated to examine the experiences and repercussions of engaging with educational activity where participants number in the tens of thousands. This activity is shown to mirror established instructionist or constructivist approaches to pedagogy. However, rather than working with “massiveness,” these positions are suggested to oppose large participant numbers. Concluding remarks propose an irreducible diversity of participation, rather than a generalised categorisation of “student,” and call for future considerations of the MOOC to move beyond individua...


title: Properties of Carbon Nanotube Antenna

abstract: Carbon nanotube as microwave antennae have expansive prospect of application, such as: nano-interconnect technology, fiber communication, aviation communication, because of the small size, light weight and good electronic properties. In this paper, we discuss some of the properties of carbon nanotube antennae, e.g., current distribution on the single antenna, re-radiation lobe pattern of single antenna, is shown.


title: Glucose and physostigmine effects on morphine- and amphetamine-induced increases in locomotor activity in mice

abstract: Recent findings indicate that glucose antagonizes several behavioral effects of cholinergic antagonists and augments those of cholinergic agonists. For example, scopolamine elicits increased locomotor activity, an action which is attenuated by glucose and by combined treatment with glucose and physostigmine at doses which are individually without effect. Opiate and catecholamine agonists, such as morphine and amphetamine, also elicit hyperactivity. The present study examined interactions of glucose and physostigmine with morphine- and amphetamine-induced hyperactivity. Mice received saline, morphine (10 mg/kg), or amphetamine (1 mg/kg) 50 min prior to testing, followed by saline, physostigmine (0.01, 0.05, 0.1, or 0.2 mg/kg), or glucose (10, 50, 100, or 500 mg/kg) administered 20 min prior to activity testing in an open field. Physostigmine significantly attenuated both morphine- and amphetamine-induced increases in activity, but higher doses were required to attenuate the effects of amphetamine. Like physostigmine, glucose significantly attenuated morphine-induced activity levels, but unlike physostigmine, glucose did not attenuate amphetamine-induced activity. Thus, the behavioral effects of morphine were more susceptible to modification by physostigmine and glucose than were the effects of amphetamine. The attenuation of morphine-induced hyperactivity demonstrates a similarity between glucose and cholinergic agonists, and also indicates that glucose may inhibit, directly or indirectly, opiate functions. More generally, these findings add to the evidence that circulating glucose levels selectively influence a growing list of behavioral and neurobiological functions.


title: Bilateral texture filtering

abstract: This paper presents a novel structure-preserving image decomposition operator called bilateral texture filter. As a simple modification of the original bilateral filter [Tomasi and Manduchi 1998], it performs local patch-based analysis of texture features and incorporates its results into the range filter kernel. The central idea to ensure proper texture/structure separation is based on patch shift that captures the texture information from the most representative texture patch clear of prominent structure edges. Our method outperforms the original bilateral filter in removing texture while preserving main image structures, at the cost of some added computation. It inherits well-known advantages of the bilateral filter, such as simplicity, local nature, ease of implementation, scalability, and adaptability to other application scenarios.


title: Exploring the effect of vibrotactile feedback through the floor on social presence in an immersive virtual environment

abstract: We investigate the effect of vibrotactile feedback delivered to one's feet in an immersive virtual environment (IVE). In our study, participants observed a virtual environment where a virtual human (VH) walked toward the participants and paced back and forth within their social space. We compared three conditions as follows: participants in the “Sound” condition heard the footsteps of the VH; participants in the “Vibration” condition experienced the vibration of the footsteps along with the sounds; while participants in the “Mute” condition were not exposed to sound nor vibrotactile feedback. We found that the participants in the “Vibration” condition felt a higher social presence with the VH compared to those who did not feel the vibration. The participants in the “Vibration” condition also exhibited greater avoidance behavior while facing the VH and when the VH invaded their personal space.


title: Can Online Peer Assessment be Trusted

abstract: Introduction Learning assessment often constitutes a problematic task for the teacher. Its repetitive nature makes the teacher's job fastidious and the grading of hundreds of exam papers is far from exciting. In addition, the educational value of the assessment methods is often very low. The post-exam phases are difficult to put into practice with a large number of students, particularly, the feedback phase. Moreover, students are not really fond of these phases. In order to improve this situation, several devices can be used depending on the aspects that need to be changed. In this study, our primary concern is not to improve the reliability or the quality of assessment (De Ketele & Gerard, 2005), but rather to reduce the teacher's workload, without affecting the quality of assessment. In order to attain this, we suggest the use of computer-assisted assessments (Davies, 2001; Stephens & Bull, 1998) and the application of online peer assessment (Topping, 1998). Our proposal could be acceptable for the formative assessment of the learning process (Perrenoud, 2001) as this assessment method does not have many direct consequences on decisions concerning access to the following year or to certification. However, what about summative evaluation? Can grading carried out by students be trusted* What are the conditions of acceptability of our proposal? Several attempts (Topping, 1998) have been made to involve more students in this kind of approach, but it seems that the use of technologies can facilitate the implementation of these solutions. What remains to be established is whether this study can show that the assessment, thus produced, will be acceptable. Our research focuses on this issue of acceptability by measuring precisely the difference between assessments carried out by peers and those carried out by the teacher. Online peer assessment The use of online peer assessment is recent. It allows the students to assess the work of their peers and even assess their own work. Unlike the self-assessment techniques, which are generally limited to basic cognitive levels (Bloom, 1956; Anderson & Krathwohl, 2001), peer assessment enables to develop learning at high cognitive levels. This is a method that involves the students in the revision, assessment, and feedback process of work online. Doiron (2003) indicates that certain authors criticize the use of information and communication technology for peer assessment, arguing that it is not as rigorous as traditional types of assessment, that it requires too much student effort by putting too much pressure on them, that it is not reliable, and that it is not necessarily fair. In response, other authors such as Bostock (2000) are convinced that the (formative or summative) assessment of other students' work by the students themselves has several advantages for the learning process, both for the assessor and the assessee. Bostock points out that peer assessment encourages the students to be independent and develops skills in high cognitive areas. He acknowledges certain weaknesses of this type of assessment, in particular the over-estimating of friends' work, but he explains that this can be avoided by setting up a system which would guarantee anonymity, multiple assessments, a great number of assessors, and moderation by the teacher. Moreover, Bostock, specifies that Internet and information and communication technologies enable an easier management of a greater number of students. Several authors (Rada & Michailidis & Wang, 1994; Fisher, 1999) explain that peer assessment may be proposed as an alternative solution to reducing teacher workload. Thus, Cho et al. (2006) explain that, despite the progress made during the last two decades in student writing skills, the courses rarely include real comprehensive writing tasks. According to them, this is due to the teachers' workload: assessing writing skills requires too much time and effort (Rada et al. …


Dataset for forensic analysis of B-tree file system


title: Deep Learning Based Recommender System: A Survey and New Perspectives

abstract: With the growing volume of online information, recommender systems have been an effective strategy to overcome information overload. The utility of recommender systems cannot be overstated, given their widespread adoption in many web applications, along with their potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also to the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. The field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning-based recommender systems. More concretely, we provide and devise a taxonomy of deep learning-based recommendation models, along with a comprehensive summary of the state of the art. Finally, we expand on current trends and provide new perspectives pertaining to this new and exciting development of the field.


title: Microscopic Nuclei Classification, Segmentation and Detection with improved Deep Convolutional Neural Network (DCNN) Approaches

abstract: Due to cellular heterogeneity, cell nuclei classification, segmentation, and detection from pathological images are challenging tasks. In the last few years, Deep Convolutional Neural Networks (DCNN) approaches have been shown state-of-the-art (SOTA) performance on histopathological imaging in different studies. In this work, we have proposed different advanced DCNN models and evaluated for nuclei classification, segmentation, and detection. First, the Densely Connected Recurrent Convolutional Network (DCRN) model is used for nuclei classification. Second, Recurrent Residual U-Net (R2U-Net) is applied for nuclei segmentation. Third, the R2U-Net regression model which is named UD-Net is used for nuclei detection from pathological images. The experiments are conducted with different datasets including Routine Colon Cancer(RCC) classification and detection dataset, and Nuclei Segmentation Challenge 2018 dataset. The experimental results show that the proposed DCNN models provide superior performance compared to the existing approaches for nuclei classification, segmentation, and detection tasks. The results are evaluated with different performance metrics including precision, recall, Dice Coefficient (DC), Means Squared Errors (MSE), F1-score, and overall accuracy. We have achieved around 3.4% and 4.5% better F-1 score for nuclei classification and detection tasks compared to recently published DCNN based method. In addition, R2U-Net shows around 92.15% testing accuracy in term of DC. These improved methods will help for pathological practices for better quantitative analysis of nuclei in Whole Slide Images(WSI) which ultimately will help for better understanding of different types of cancer in clinical workflow.


title: Bi-Directional Differentiable Input Reconstruction for Low-Resource Neural Machine Translation

abstract: We aim to better exploit the limited amounts of parallel text available in low-resource settings by introducing a differentiable reconstruction loss for neural machine translation (NMT). We reconstruct the input from sampled translations and leverage differentiable sampling and bi-directional NMT to build a compact model that can be trained end-to-end. This approach achieves small but consistent BLEU improvements on four language pairs in both translation directions, and outperforms an alternative differentiable reconstruction strategy based on hidden states.


title: Information Security Threats Classification Pyramid

abstract: Threat classification is extremely important for organizations, as it is an important step towards implementation of information security. Most of the existing threat classifications listed threats in static ways without linking threats to information system areas. The aim of this paper is to design a methodology that can classify deliberate threats in a dynamic way to represent each threat in different areas of the information system. This technique is based on the following factors: the attacker's prior knowledge (i. e. the knowledge hold by the source of the threat) about the system, loss of security information and the criticality of the area that might be affected by that threat.


title: An Analysis of Efficient Multi-Core Global Power Management Policies: Maximizing Performance for a Given Power Budget

abstract: Chip-level power and thermal implications will continue to rule as one of the primary design constraints and performance limiters. The gap between average and peak power actually widens with increased levels of core integration. As such, if per-core control of power levels (modes) is possible, a global power manager should be able to dynamically set the modes suitably. This would be done in tune with the workload characteristics, in order to always maintain a chip-level power that is below the specified budget. Furthermore, this should be possible without significant degradation of chip-level throughput performance. We analyze and validate this concept in detail in this paper. We assume a per-core DVFS (dynamic voltage and frequency scaling) knob to be available to such a conceptual global power manager. We evaluate several different policies for global multi-core power management. In this analysis, we consider various different objectives such as prioritization and optimized throughput. Overall, our results show that in the context of a workload comprised of SPEC benchmark threads, our best architected policies can come within 1% of the performance of an ideal oracle, while meeting a given chip-level power budget. Furthermore, we show that these global dynamic management policies perform significantly better than static management, even if static scheduling is given oracular knowledge.


Requirements for Domain-Specific Languages


title: Context-Dependent Word Representation for Neural Machine Translation

abstract: We first observe a potential weakness of continuous vector representations of symbols in neural machine translation. That is, the continuous vector representation, or a word embedding vector, of a symbol encodes multiple dimensions of similarity, equivalent to encoding more than one meaning of the word. This has the consequence that the encoder and decoder recurrent networks in neural machine translation need to spend substantial amount of their capacity in disambiguating source and target words based on the context which is defined by a source sentence. Based on this observation, in this paper we propose to contextualize the word embedding vectors using a nonlinear bag-of-words representation of the source sentence. Additionally, we propose to represent special tokens (such as numbers, proper nouns and acronyms) with typed symbols to facilitate translating those words that are not well-suited to be translated via continuous vectors. The experiments on En-Fr and En-De reveal that the proposed approaches of contextualization and symbolization improves the translation quality of neural machine translation systems significantly.


title: Static detection of second-order vulnerabilities in web applications

abstract: Web applications evolved in the last decades from simple scripts to multi-functional applications. Such complex web applications are prone to different types of security vulnerabilities that lead to data leakage or a compromise of the underlying web server. So called second-order vulnerabilities occur when an attack payload is first stored by the application on the web server and then later on used in a security-critical operation. ::: ::: In this paper, we introduce the first automated static code analysis approach to detect second-order vulnerabilities and related multi-step exploits in web applications. By analyzing reads and writes to memory locations of the web server, we are able to identify unsanitized data flows by connecting input and output points of data in persistent data stores such as databases or session data. As a result, we identified 159 second-order vulnerabilities in six popular web applications such as the conference management systems HotCRP and Open-Conf. Moreover, the analysis of web applications evaluated in related work revealed that we are able to detect several critical vulnerabilities previously missed.


title: Accurate depth map estimation from a lenslet light field camera

abstract: This paper introduces an algorithm that accurately estimates depth maps using a lenslet light field camera. The proposed algorithm estimates the multi-view stereo correspondences with sub-pixel accuracy using the cost volume. The foundation for constructing accurate costs is threefold. First, the sub-aperture images are displaced using the phase shift theorem. Second, the gradient costs are adaptively aggregated using the angular coordinates of the light field. Third, the feature correspondences between the sub-aperture images are used as additional constraints. With the cost volume, the multi-label optimization propagates and corrects the depth map in the weak texture regions. Finally, the local depth map is iteratively refined through fitting the local quadratic function to estimate a non-discrete depth map. Because micro-lens images contain unexpected distortions, a method is also proposed that corrects this error. The effectiveness of the proposed algorithm is demonstrated through challenging real world examples and including comparisons with the performance of advanced depth estimation algorithms.


CIRCULAR POLARIZATION DUAL FEED MICROSTRIP PATCH ANTENNA WITH 3 dB HYBRID COUPLER FOR WLAN ( 2 . 4 GHZ


title: Early detection of diabetic retinopathy from digital retinal fundus images

abstract: Diabetic retinopathy is the impairment of the retinal blood vessels due to complications of diabetes, which can subsequently lead to loss of vision. The only solution for this problem is through the use of a retinal screening system that would diagnose the retinal damage at an early stage. This paper proposes the use of morphological operations and segmentation techniques for the detection of blood vessels, exudates and microaneurysms. The retinal fundus image is partitioned into four sub images. Various features are extracted from the retinal fundus image. Haar wavelet transformations are applied on the features extracted. Principal component analysis technique is then applied for better feature selection. Back propagation neural network and one rule classifier techniques are used for the classifying the images as diabetic or non-diabetic. Experiments are performed on a publically available diabetic retinopathy data set DIARETDB1. Performance is evaluated with metrics like sensitivity, specificity and accuracy, the results obtained are encouraging.


title: Dendroid: A text mining approach to analyzing and classifying code structures in Android malware families

abstract: The rapid proliferation of smartphones over the last few years has come hand in hand with and impressive growth in the number and sophistication of malicious apps targetting smartphone users. The availability of reuse-oriented development methodologies and automated malware production tools makes exceedingly easy to produce new specimens. As a result, market operators and malware analysts are increasingly overwhelmed by the amount of newly discovered samples that must be analyzed. This situation has stimulated research in intelligent instruments to automate parts of the malware analysis process. In this paper, we introduce Dendroid, a system based on text mining and information retrieval techniques for this task. Our approach is motivated by a statistical analysis of the code structures found in a dataset of Android OS malware families, which reveals some parallelisms with classical problems in those domains. We then adapt the standard Vector Space Model and reformulate the modelling process followed in text mining applications. This enables us to measure similarity between malware samples, which is then used to automatically classify them into families. We also investigate the application of hierarchical clustering over the feature vectors obtained for each malware family. The resulting dendograms resemble the so-called phylogenetic trees for biological species, allowing us to conjecture about evolutionary relationships among families. Our experimental results suggest that the approach is remarkably accurate and deals efficiently with large databases of malware instances.