title: Multithreaded Asynchronous Graph Traversal for In-Memory and Semi-External Memory

abstract: Processing large graphs is becoming increasingly important for many domains such as social networks, bioinformatics, etc. Unfortunately, many algorithms and implementations do not scale with increasing graph sizes. As a result, researchers have attempted to meet the growing data demands using parallel and external memory techniques. We present a novel asynchronous approach to compute Breadth-First-Search (BFS), Single-Source-Shortest-Paths, and Connected Components for large graphs in shared memory. Our highly parallel asynchronous approach hides data latency due to both poor locality and delays in the underlying graph data storage. We present an experimental study applying our technique to both In-Memory and Semi-External Memory graphs utilizing multi-core processors and solid-state memory devices. Our experiments using synthetic and real-world datasets show that our asynchronous approach is able to overcome data latencies and provide significant speedup over alternative approaches. For example, on billion vertex graphs our asynchronous BFS scales up to 14x on 16-cores.


title: BROCCOLI: Software for fast fMRI analysis on many-core CPUs and GPUs

abstract: Analysis of functional magnetic resonance imaging (fMRI) data is becoming ever more computationally demanding as temporal and spatial resolutions improve, and large, publicly available data sets proliferate. Moreover, methodological improvements in the neuroimaging pipeline, such as non-linear spatial normalization, non-parametric permutation tests and Bayesian Markov Chain Monte Carlo approaches, can dramatically increase the computational burden. Despite these challenges, there do not yet exist any fMRI software packages which leverage inexpensive and powerful graphics processing units (GPUs) to perform these analyses. Here, we therefore present BROCCOLI, a free software package written in OpenCL (Open Computing Language) that can be used for parallel analysis of fMRI data on a large variety of hardware configurations. BROCCOLI has, for example, been tested with an Intel CPU, an Nvidia GPU, and an AMD GPU. These tests show that parallel processing of fMRI data can lead to significantly faster analysis pipelines. This speedup can be achieved on relatively standard hardware, but further, dramatic speed improvements require only a modest investment in GPU hardware. BROCCOLI (running on a GPU) can perform non-linear spatial normalization to a 1 mm3 brain template in 4–6 s, and run a second level permutation test with 10,000 permutations in about a minute. These non-parametric tests are generally more robust than their parametric counterparts, and can also enable more sophisticated analyses by estimating complicated null distributions. Additionally, BROCCOLI includes support for Bayesian first-level fMRI analysis using a Gibbs sampler. The new software is freely available under GNU GPL3 and can be downloaded from github (https://github.com/wanderine/BROCCOLI/).


title: Insight Problem Solving: A Critical Examination of the Possibility of Formal Theory

abstract: This paper provides a critical examination of the current state and future possibility of formal cognitive theory for insight problem solving and its associated “aha!” experience. Insight problems are contrasted with move problems, which have been formally defined and studied extensively by cognitive psychologists since the pioneering work of Alan Newell and Herbert Simon. To facilitate our discussion, a number of classical brainteasers are presented along with their solutions and some conclusions derived from observing the behavior of many students trying to solve them. Some of these problems are interesting in their own right, and many of them have not been discussed before in the psychological literature. The main purpose of presenting the brainteasers is to assist in discussing the status of formal cognitive theory for insight problem solving, which is argued to be considerably weaker than that found in other areas of higher cognition such as human memory, decision-making, categorization, and perception. We discuss theoretical barriers that have plagued the development of successful formal theory for insight problem solving. A few suggestions are made that might serve to advance the field.


title: Gibbs Sampling for the Uninitiated

abstract: This document is intended for computer scientists who would like to try out a Markov Chain Monte Carlo (MCMC) technique, particularly in order to do inference with Bayesian models on problems related to text processing. We try to keep theory to the absolute minimum needed, though we work through the details much more explicitly than you usually see even in \introductory" explanations. That means we’ve attempted to be ridiculously explicit in our exposition and notation. After providing the reasons and reasoning behind Gibbs sampling (and at least nodding our heads in the direction of theory), we work through an example application in detail|the derivation of a Gibbs sampler for


title: SparkNet: Training Deep Networks in Spark

abstract: Training deep networks is a time-consuming process, with networks for object recognition often requiring multiple days to train. For this reason, leveraging the resources of a cluster to speed up training is an important area of work. However, widely-popular batch-processing computational frameworks like MapReduce and Spark were not designed to support the asynchronous and communication-intensive workloads of existing distributed deep learning systems. We introduce SparkNet, a framework for training deep networks in Spark. Our implementation includes a convenient interface for reading data from Spark RDDs, a Scala interface to the Caffe deep learning framework, and a lightweight multi-dimensional tensor library. Using a simple parallelization scheme for stochastic gradient descent, SparkNet scales well with the cluster size and tolerates very high-latency communication. Furthermore, it is easy to deploy and use with no parameter tuning, and it is compatible with existing Caffe models. We quantify the dependence of the speedup obtained by SparkNet on the number of machines, the communication frequency, and the cluster's communication overhead, and we benchmark our system's performance on the ImageNet dataset.


title: Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language

abstract: This article presents a measure of semantic similarity in an IS-A taxonomy based on the notion of shared information content. Experimental evaluation against a benchmark set of human similarity judgments demonstrates that the measure performs better than the traditional edge-counting approach. The article presents algorithms that take advantage of taxonomic similarity in resolving syntactic and semantic ambiguity, along with experimental results demonstrating their effectiveness.


title: Classifying Political Orientation on Twitter: It’s Not Easy!

abstract: Numerous papers have reported great success at inferring the political orientation of Twitter users. This paper has some unfortunate news to deliver: while past work has been sound and often methodologically novel, we have discovered that reported accuracies have been systemically overoptimistic due to the way in which validation datasets have been collected, reporting accuracy levels nearly 30% higher than can be expected in populations of general Twitter users. Using careful and novel data collection and annotation techniques, we collected three different sets of Twitter users, each characterizing a different degree of political engagement on Twitter - from politicians (highly politically vocal) to "normal" users (those who rarely discuss politics). Applying standard techniques for inferring political orientation, we show that methods which previously reported greater than 90% inference accuracy, actually achieve barely 65% accuracy on normal users. We also show that classifiers cannot be used to classify users outside the narrow range of political orientation on which they were trained. While a sobering finding, our results quantify and call attention to overlooked problems in the latent attribute inference literature that, no doubt, extend beyond political orientation inference: the way in which datasets are assembled and the transferability of classifiers.


title: Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents

abstract: Evolution strategies (ES) are a family of black-box optimization algorithms able to train deep neural networks roughly as well as Q-learning and policy gradient methods on challenging deep reinforcement learning (RL) problems, but are much faster (e.g. hours vs. days) because they parallelize better. However, many RL problems require directed exploration because they have reward functions that are sparse or deceptive (i.e. contain local optima), and it is unknown how to encourage such exploration with ES. Here we show that algorithms that have been invented to promote directed exploration in small-scale evolved neural networks via populations of exploring agents, specifically novelty search (NS) and quality diversity (QD) algorithms, can be hybridized with ES to improve its performance on sparse or deceptive deep RL tasks, while retaining scalability. Our experiments confirm that the resultant new algorithms, NS-ES and two QD algorithms, NSR-ES and NSRA-ES, avoid local optima encountered by ES to achieve higher performance on Atari and simulated robots learning to walk around a deceptive trap. This paper thus introduces a family of fast, scalable algorithms for reinforcement learning that are capable of directed exploration. It also adds this new family of exploration algorithms to the RL toolbox and raises the interesting possibility that analogous algorithms with multiple simultaneous paths of exploration might also combine well with existing RL algorithms outside ES.


title: Demo: BlueMountain: An Architecture to Customize Data Management on Mobile Systems

abstract: BlueMountain is a system that enables building pluggable data management solutions which can be linked with any Android app at runtime, without requiring any modifications to the Android platform. BlueMountain simplifies the app development, provides flexibility to end users, and works with existing apps.


title: Multiscale Approaches To Music Audio Feature Learning

abstract: Content-based music information retrieval tasks are typically solved with a two-stage approach: features are extracted from music audio signals, and are then used as input to a regressor or classifier. These features can be engineered or learned from data. Although the former approach was dominant in the past, feature learning has started to receive more attention from the MIR community in recent years. Recent results in feature learning indicate that simple algorithms such as K-means can be very effective, sometimes surpassing more complicated approaches based on restricted Boltzmann machines, autoencoders or sparse coding. Furthermore, there has been increased interest in multiscale representations of music audio recently. Such representations are more versatile because music audio exhibits structure on multiple timescales, which are relevant for different MIR tasks to varying degrees. We develop and compare three approaches to multiscale audio feature learning using the spherical K-means algorithm. We evaluate them in an automatic tagging task and a similarity metric learning task on the Magnatagatune dataset.


title: An entropy-based distance measure for analyzing and detecting metamorphic malware

abstract: Metamorphic malware is a kind of malware which evades signature-based anti-viruses by changing its internal structure in each infection. This paper, firstly, introduces a new measure of distance between two computer programs called program dissimilarity measure based on entropy (PDME). Then, it suggests a measure for the degree of metamorphism, based on the suggested distance measure. The distance measure is defined based on the Entropy of the two malware programs. Moreover, the paper shows that the distance measure can be used for classifying metamorphic malware via K-Nearest Neighbors (KNN) method. The method is evaluated by four metamorphic malware families. The results demonstrate that the measure can indicate the degree of metamorphism efficiently, and the KNN classification method using PDME can classify the metamorphic malware with a high precision.


title: Automated microaneurysms detection in fundus images using image segmentation

abstract: Diabetic retinopathy is one of the complicated diseases which occurs in diabetic patients when the affects damage the retina. The eyes vision can lead to be lost in case of late treatment. Microaneurysms are the earliest detectable abnormalities of diabetic retinopathy, so the automated detection of the lesions is essential and useful task. This paper proposed a simple method to detect microaneurysms based on its characteristics in fundus images using some techniques in image segmentation. First, we preprocessed to reduce image noise and improve the contrast. Then we segmented them using Canny edges detection and maximum entropy thresholding. The characteristics of microaneurysms which appear as small red dots and circular shape are the specific points to discriminate them from the other lesions as well as the anatomical structures of the fundus image by applying area and eccentricity methods. Finally, the morphological operation was applied to mark out these symptoms. The results were analysis by ophthalmologist in order to define system accuracy and preciseness. According to results of comparison, we found that the accuracy is 90 % and the average processing time is 9.53 seconds per image.


title: Face recognition across pose: A review

abstract: One of the major challenges encountered by current face recognition techniques lies in the difficulties of handling varying poses, i.e., recognition of faces in arbitrary in-depth rotations. The face image differences caused by rotations are often larger than the inter-person differences used in distinguishing identities. Face recognition across pose, on the other hand, has great potentials in many applications dealing with uncooperative subjects, in which the full power of face recognition being a passive biometric technique can be implemented and utilised. Extensive efforts have been put into the research toward pose-invariant face recognition in recent years and many prominent approaches have been proposed. However, several issues in face recognition across pose still remain open, such as lack of understanding about subspaces of pose variant images, problem intractability in 3D face modelling, complex face surface reflection mechanism, etc. This paper provides a critical survey of researches on image-based face recognition across pose. The existing techniques are comprehensively reviewed and discussed. They are classified into different categories according to their methodologies in handling pose variations. Their strategies, advantages/disadvantages and performances are elaborated. By generalising different tactics in handling pose variations and evaluating their performances, several promising directions for future research have been suggested.


title: A survey on lightweight-cryptography status and future challenges

abstract: Cryptography is that the practice and study of hiding information. It is the science of reworking message to form them secure and resistant to attack. In cryptography, the original message is converted into other message at encryption side and converted into an original message at the receiver side. For constrained devices, normal cryptography algorithms can be too slow, too big or too energy-consuming. The term light-weight cryptography studies New algorithms to outdo this problem. Light-weight cryptography is generally defined as cryptography for resource- constrained devices, for which RFID tags and WSN are typically mentioned as examples. Light-weight encryption is a junction of two terms "Light and weight", and it is a sector of a classical cryptographic algorithm. Light-weight encryption and decryption are implemented on platforms as well as hardware and software. In this paper, present analyzing study on the common use of recent hardware H/W and software S/W implementations of symmetric as well as asymmetric ciphers.


title: Local Novelty Detection in Multi-class Recognition Problems

abstract: In this paper, we propose using local learning for multiclass novelty detection, a framework that we call local novelty detection. Estimating the novelty of a new sample is an extremely challenging task due to the large variability of known object categories. The features used to judge on the novelty are often very specific for the object in the image and therefore we argue that individual novelty models for each test sample are important. Similar to human experts, it seems intuitive to first look for the most related images thus filtering out unrelated data. Afterwards, the system focuses on discovering similarities and differences to those images only. Therefore, we claim that it is beneficial to solely consider training images most similar to a test sample when deciding about its novelty. Following the principle of local learning, for each test sample a local novelty detection model is learned and evaluated. Our local novelty score turns out to be a valuable indicator for deciding whether the sample belongs to a known category from the training set or to a new, unseen one. With our local novelty detection approach, we achieve state-of-the-art performance in multi-class novelty detection on two popular visual object recognition datasets, Caltech-256 and Image Net. We further show that our framework: (i) can be successfully applied to unknown face detection using the Labeled-Faces-in-the-Wild dataset and (ii) outperforms recent work on attribute-based unfamiliar class detection in fine-grained recognition of bird species on the challenging CUB-200-2011 dataset.


Mining : Techniques and its Application


title: SOCRADES : A Web Service based Shop Floor Integration Infrastructure

abstract: On the one hand, enterprises manufacturing any kinds of goods require agile production technology to be able to fully accommodate their customers' demand for flexibility. On the other hand, Smart Objects, such as networked intelligent machines or tagged raw materials, exhibit ever increasing capabilities, up to the point where they offer their smart behaviour as web services. The two trends towards higher flexibility and more capable objects will lead to a service-oriented infrastructure where complex processes will span over all types of systems -- from the backend enterprise system down to the Smart Objects. To fully support this, we present SOCRADES, an integration architecture that can serve the requirements of future manufacturing. SOCRADES provides generic components upon which sophisticated production processes can be modelled. In this paper we in particular give a list of requirements, the design, and the reference implementation of that integration architecture.


title: Animal Phylogeny and Its Evolutionary Implications

abstract: In recent years, scientists have made remarkable progress reconstructing the animal phylogeny. There is broad agreement regarding many deep animal relationships, including the monophyly of animals, Bilateria, Protostomia, Ecdysozoa, and Spiralia. This stability now allows researchers to articulate the diminishing number of remaining questions in terms of well-defined alternative hypotheses. These remaining questions include relationships at the base of the animal tree, the position of Xenacoelomorpha, and the internal relationships of Spiralia. Recent progress in the field of animal phylogeny has important implications for our understanding of the evolution of development, morphology, genomes, and other characters. A remarkable pattern emerges—there is far more homoplasy for all these characters than had previously been anticipated, even among many complex characters such as segmentation and nervous systems. The fossil record dates most deep branches of the animal tree to an evolutionary radiation in the ...


title: Increasing brand attractiveness and sales through social media comments on public displays --- evidence from a field experiment in the retail industry

abstract: Retailers and brands are just starting to utilize online social media to support their businesses. Simultaneously, public displays are becoming ubiquitous in public places, raising the question about how these two technologies could be used together to attract new and existing customers as well as strengthen the relationship toward a focal brand. Accordingly, in a field experiment we displayed brand- and product-related comments from the social network Facebook as pervasive advertising in small-space retail stores, known as kiosks. From interviews conducted with real customers during the experiment and the corresponding analysis of sales data we could conclude three findings. Showing social media comments resulted in (1) customers perceiving brands as more innovative and attractive, (2) a measurable, positive effect on sales on both the brand and the product in question and (3) customers wanting to see the comments of others, but not their own, creating a give-and-take paradox for using public displays to show social media comments.


title: Multi-modal learning for video recommendation based on mobile application usage

abstract: The increasing popularity of mobile devices has brought severe challenges to device usability and big data analysis. In this paper we investigate the intellectual recommender system on cell phones by incorporating mobile data analysis. Nowadays with the development of smart phones, more and more applications have emerged on various areas, such as entertainment, education and health care. While these applications have brought great convenience to people's daily life, they also provide tremendous opportunities for analyzing users' interests. In this work we develop an Android background service to collect the user behaviors and analyze their preferences based on their Android application usage. As one of the most intuitive media for visual representation, videos with various types of contents are recommended to users based on a proposed graphical model. The proposed model jointly utilizes the textual descriptions of Android applications and videos, as well as the extracted video content based features. Besides, by analyzing the user's habit of application usage we seamlessly integrate the user's personal interests during the recommendation. The extensive comparisons to multiple baselines reveal the superiority of the proposed model on the recommendation quality. Furthermore, we conduct experiments on personalized recommendation to demonstrate the capacity of the proposed model in effectively analyzing the user's personal interests.


title: Unstructured Light Fields

abstract: We present a system for interactively acquiring and rendering light fields using a hand-held commodity camera. The main challenge we address is assisting a user in achieving good coverage of the 4D domain despite the challenges of hand-held acquisition. We define coverage by bounding reprojection error between viewpoints, which accounts for all 4 dimensions of the light field. We use this criterion together with a recent Simultaneous Localization and Mapping technique to compute a coverage map on the space of viewpoints. We provide users with real-time feedback and direct them toward under-sampled parts of the light field. Our system is lightweight and has allowed us to capture hundreds of light fields. We further present a new rendering algorithm that is tailored to the unstructured yet dense data we capture. Our method can achieve piecewise-bicubic reconstruction using a triangulation of the captured viewpoints and subdivision rules applied to reconstruction weights. © 2012 Wiley Periodicals, Inc.


title: Small Antennas in Wireless Communications

abstract: The objective of this paper is to provide the context, physical insight, and perspective on antennas in wireless communications. Although it does not mean to be comprehensive, the four key technologies related to small antenna designs to be reviewed and discussed, including multiband planar inverted-F antennas, broadband folded patch antennas, compact differentially fed antennas, and miniature circularly polarized patch antennas, would cover a wide range of topical interests and practical applications. A brief overview of computer software for analyzing these antennas is also provided. Hopefully, this paper will be beneficial for the diverse engineering readership of the IEEE.


title: A Multi-task Selected Learning Approach for Solving New Type 3D Bin Packing Problem

abstract: This paper studies a new type of 3D bin packing problem (BPP), in which a number of cuboid-shaped items must be put into a bin one by one orthogonally. The objective is to find a way to place these items that can minimize the surface area of the bin. This problem is based on the fact that there is no fixed-sized bin in many real business scenarios and the cost of a bin is proportional to its surface area. Based on previous research on 3D BPP, the surface area is determined by the sequence, spatial locations and orientations of items. It is a new NP-hard combinatorial optimization problem on unfixed-sized bin packing, for which we propose a multi-task framework based on Selected Learning, generating the sequence and orientations of items packed into the bin simultaneously. During training steps, Selected Learning chooses one of loss functions derived from Deep Reinforcement Learning and Supervised Learning corresponding to the training procedure. Numerical results show that the method proposed significantly outperforms Lego baselines by a substantial gain of 7.52%. Moreover, we produce large scale 3D Bin Packing order data set for studying bin packing problems and will release it to the research community.


title: EMM: Energy-Aware Mobility Management for Mobile Edge Computing in Ultra Dense Networks

abstract: Merging mobile edge computing (MEC) functionality with the dense deployment of base stations (BSs) provides enormous benefits such as a real proximity, low latency access to computing resources. However, the envisioned integration creates many new challenges, among which mobility management (MM) is a critical one. Simply applying existing radio access oriented MM schemes leads to poor performance mainly due to the co-provisioning of radio access and computing services of the MEC-enabled BSs. In this paper, we develop a novel user-centric energy-aware mobility management (EMM) scheme, in order to optimize the delay due to both radio access and computation, under the long-term energy consumption constraint of the user. Based on Lyapunov optimization and multi-armed bandit theories, EMM works in an online fashion without future system state information, and effectively handles the imperfect system state information. Theoretical analysis explicitly takes radio handover and computation migration cost into consideration and proves a bounded deviation on both the delay performance and energy consumption compared to the oracle solution with exact and complete future system information. The proposed algorithm also effectively handles the scenario in which candidate BSs randomly switch on/off during the offloading process of a task. Simulations show that the proposed algorithms can achieve close-to-optimal delay performance while satisfying the user energy consumption constraint.


title: Recovering traceability links in software artifact management systems using information retrieval methods

abstract: The main drawback of existing software artifact management systems is the lack of automatic or semi-automatic traceability link generation and maintenance. We have improved an artifact management system with a traceability recovery tool based on Latent Semantic Indexing (LSI), an information retrieval technique. We have assessed LSI to identify strengths and limitations of using information retrieval techniques for traceability recovery and devised the need for an incremental approach. The method and the tool have been evaluated during the development of seventeen software projects involving about 150 students. We observed that although tools based on information retrieval provide a useful support for the identification of traceability links during software development, they are still far to support a complete semi-automatic recovery of all links. The results of our experience have also shown that such tools can help to identify quality problems in the textual description of traced artifacts.


title: Tuned and GPU-accelerated parallel data mining from comparable corpora

abstract: The multilingual nature of the world makes translation a crucial requirement today. Parallel dictionaries constructed by humans are a widely-available resource, but they are limited and do not provide enough coverage for good quality translation purposes, due to out-of-vocabulary words and neologisms. This motivates the use of statistical translation systems, which are unfortunately dependent on the quantity and quality of training data. Such has a very limited availability especially for some languages and very narrow text domains. Is this research we present our improvements to Yalign mining methodology by reimplementing the comparison algorithm, introducing a tuning scripts and by improving performance using GPU computing acceleration. The experiments are conducted on various text domains and bi-data is extracted from the Wikipedia dumps.


title: Minimum energy mobile wireless networks

abstract: We describe a distributed position-based network protocol optimized for minimum energy consumption in mobile wireless networks that support peer-to-peer communications. Given any number of randomly deployed nodes over an area, we illustrate that a simple local optimization scheme executed at each node guarantees strong connectivity of the entire network and attains the global minimum energy solution for stationary networks. Due to its localized nature, this protocol proves to be self-reconfiguring and stays close to the minimum energy solution when applied to mobile networks. Simulation results are used to verify the performance of the protocol.


title: An Optical Brain Imaging Study on the Improvements in Mathematical Fluency from Game-based Learning

abstract: In this study we examined the effectiveness of game-based learning in improving math fluency compared to a conventional drill and practice approach. An optical brain imaging method called functional near-infrared spectroscopy (fNIR) was utilized to assess changes in brain activation in prefrontal cortex related to cognitive load and working memory functions, so that the improvement gained by the increased attentional and cognitive training involved in a mobile game called MathDash could be examined in terms of how and why game-based learning can be effective. Overall, our experiment with college students indicated that Math Dash was equally effective in terms of improving computational fluency in comparison to the drill and practice approach.


title: Privacy, Anonymity, and Perceived Risk in Open Collaboration: A Study of Tor Users and Wikipedians

abstract: This qualitative study examines privacy practices and concerns among contributors to open collaboration projects. We collected interview data from people who use the anonymity network Tor who also contribute to online projects and from Wikipedia editors who are concerned about their privacy to better understand how privacy concerns impact participation in open collaboration projects. We found that risks perceived by contributors to open collaboration projects include threats of surveillance, violence, harassment, opportunity loss, reputation loss, and fear for loved ones. We explain participants' operational and technical strategies for mitigating these risks and how these strategies affect their contributions. Finally, we discuss chilling effects associated with privacy loss, the need for open collaboration projects to go beyond attracting and educating participants to consider their privacy, and some of the social and technical approaches that could be explored to mitigate risk at a project or community level.


title: Strategic play for a pool-playing robot

abstract: Playing pool is not a trivial task for an autonomous robotic system: In order to excel, the tight coupling of accurate perception, planning and highly dynamic while precise manipulation is required. To investigate scientific challenges arising from this tight coupling requirement an anthropomorphic robotic system has been developed, which is capable of playing pool. Focusing in this paper on the planning part, a well-calibrated pool simulator is developed in order to predict the outcome of possible strokes. An optimization based framework applying a discounted return term is presented for suggesting an optimized next stroke by planning several strokes ahead. Different to existing planners so far, our planner considers the player's and opponent's skill. Therefore, strategic considerations (e.g. safety shots) can now be evaluated depending on the outcome of the next strokes for both player and opponent.


title: Cyber–Physical Device Authentication for the Smart Grid Electric Vehicle Ecosystem

abstract: Entity authentication and related key management is an active research topic in smart grid security. However, existing works seem to have overlooked the significance that the smart grid is a cyber-physical system, which entails more considerations in the integration of its cyber and physical domains. Ignoring this could possibly undermine security since the effects of cyber authorization in the smart grid are usually extended into the physical domain. The substitution attack, a kind of the man-in-the-middle attack, has been demonstrated using this gap. This paper proposes a two-factor cyber-physical device authentication protocol to defend against coordinated cyber-physical attacks in the smart grid. The idea is to combine a novel contextual factor based on physical connectivity in the power grid with the conventional authentication factor in the challenge-response protocol, widely used in cybersecurity. The resulting protocol provides assurance on not only the digital identity of a device but also the device's controllability in the physical domain. While the design is for the electric vehicle ecosystem, the framework could be readily extended to other smart grid subsystems.


title: Understanding critical health literacy: a concept analysis

abstract: Interest in and debates around health literacy have grown over the last two decades and key to the discussions has been the distinction made between basic functional health literacy, communicative/interactive health literacy and critical health literacy. Of these, critical health literacy is the least well developed and differing interpretations of its constituents and relevance exist. The aim of this study is to rigorously analyse the concept of critical health literacy in order to offer some clarity of definition upon which appropriate theory, well grounded practice and potential measurement tools can be based. The study uses a theoretical and colloquial evolutionary concept analysis method to systematically identify the features associated with this concept. A unique characteristic of this method is that it practically combines an analysis of the literature with in depth interviews undertaken with practitioners and policy makers who have an interest in the field. The study also analyses how the concept is understood across the contexts of time, place, discipline and use by health professionals, policy makers and academics. Findings revealed a distinct set of characteristics of advanced personal skills, health knowledge, information skills, effective interaction between service providers and users, informed decision making and empowerment including political action as key features of critical health literacy. The potential consequences of critical health literacy identified are in improving health outcomes, creating more effective use of health services and reducing inequalities in health thus demonstrating the relevance of this concept to public health and health promotion. While critical health literacy is shown to be a unique concept, there remain significant contextual variations in understanding particularly between academics, practitioners and policy makers. Key attributes presented as part of this concept when it was first introduced in the literature, particularly those around empowerment, social and political action and the existence of the concept at both an individual and population level, have been lost in more recent representations. This has resulted in critical health literacy becoming restricted to a higher order cognitive individual skill rather than a driver for political and social change. The paper argues that in order to retain the uniqueness and usefulness of the concept in practice efforts should be made to avoid this dilution of meaning.


title: Design of 128x8 Pixel Array Fingerprint Sensor with a CapacitiveSensing Circuit Technique for Image Enhancement

abstract: This paper proposes 128x8 array swipe type fingerprint sensor with a capacitive-sensing technique for image enhancement. The circuit of one pixel includes a pixel level chargesharing and charge pump to replace an ADC. The circuit also adopts the circuit technique that improves quality of dry finger image captured with capacitive fingerprint sensor LSIs. The proper operation is validated by HSPICE for one-pixel and RTL simulation including logic synthesis for a full chip design on condition of 0.35μm typical CMOS process and 3.3V power. The layout is performed by full custom flow for one-pixel and auto P&R for a full chip. The area of a full chip is 0.161mm 2 (9013μm x 1781μm) and the gate count is 303,329. The area of one-pixel is 58 x 58 μm 2 . Pitch is 60 μm and image resolution is 423dpi.


title: Distance Weighted Cosine Similarity Measure for Text Classification

abstract: In Vector Space Model, Cosine is widely used to measure the similarity between two vectors. Its calculation is very efficient, especially for sparse vectors, as only the non-zero dimensions need to be considered. As a fundamental component, cosine similarity has been applied in solving different text mining problems, such as text classification, text summarization, information retrieval, question answering, and so on. Although it is popular, the cosine similarity does have some problems. Starting with a few synthetic samples, we demonstrate some problems of cosine similarity: it is overly biased by features of higher values and does not care much about how many features two vectors share. A distance weighted cosine similarity metric is thus proposed. Extensive experiments on text classification exhibit the effectiveness of the proposed metric.


title: Sparsely sampled Fourier ptychography

abstract: Fourier ptychography (FP) is an imaging technique that applies angular diversity functions for high-resolution complex image recovery. The FP recovery routine switches between two working domains: the spectral and spatial domains. In this paper, we investigate the spectral-spatial data redundancy requirement of the FP recovery process. We report a sparsely sampled FP scheme by exploring the sampling interplay between these two domains. We demonstrate the use of the reported scheme for bypassing the high-dynamic-range combination step in the original FP recovery routine. As such, it is able to shorten the acquisition time of the FP platform by ~50%. As a special case of the sparsely sample FP, we also discuss a sub-sampled scheme and demonstrate its application in solving the pixel aliasing problem plagued in the original FP algorithm. We validate the reported schemes with both simulations and experiments. This paper provides insights for the development of the FP approach.


title: Sparsity-based DOA estimation using co-prime arrays

abstract: In this paper, we propose co-prime arrays for effective direction-of-arrival (DOA) estimation. To fully utilize the virtual aperture achieved in the difference co-array constructed from a co-prime array structure, sparsity-based spatial spectrum estimation technique is exploited. Compared to existing techniques, the proposed technique achieves better utilization of the co-array aperture and thus results in increased degrees-of-freedom as well as improved DOA estimation performance.


title: Fractal basins of attraction associated with a damped Newton's method

abstract: An intriguing and unexpected result for students learning numerical analysis is that Newton's method, applied to the simple polynomial z 3 - 1 = 0 in the complex plane, leads to intricately interwoven basins of attraction of the roots. As an example of an interesting open question that may help to stimulate student interest in numerical analysis, we investigate the question of whether a damping method, which is designed to increase the likelihood of convergence for Newton's method, modifies the fractal structure of the basin boundaries. The overlap of the frontiers of numerical analysis and nonlinear dynamics provides many other problems that can help to make numerical analysis courses interesting.


title: A pitch extraction algorithm tuned for automatic speech recognition

abstract: In this paper we present an algorithm that produces pitch and probability-of-voicing estimates for use as features in automatic speech recognition systems. These features give large performance improvements on tonal languages for ASR systems, and even substantial improvements for non-tonal languages. Our method, which we are calling the Kaldi pitch tracker (because we are adding it to the Kaldi ASR toolkit), is a highly modified version of the getf0 (RAPT) algorithm. Unlike the original getf0 we do not make a hard decision whether any given frame is voiced or unvoiced; instead, we assign a pitch even to unvoiced frames while constraining the pitch trajectory to be continuous. Our algorithm also produces a quantity that can be used as a probability of voicing measure; it is based on the normalized autocorrelation measure that our pitch extractor uses. We present results on data from various languages in the BABEL project, and show a large improvement over systems without tonal features and systems where pitch and POV information was obtained from SAcC or getf0.


title: Parallel Replication across Formats in SAP HANA for Scaling Out Mixed OLTP/OLAP Workloads

abstract: Modern in-memory database systems are facing the need of efficiently supporting mixed workloads of OLTP and OLAP. A conventional approach to this requirement is to rely on ETL-style, application-driven data replication between two very different OLTP and OLAP systems, sacrificing real-time reporting on operational data. An alternative approach is to run OLTP and OLAP workloads in a single machine, which eventually limits the maximum scalability of OLAP query performance. In order to tackle this challenging problem, we propose a novel database replication architecture called Asynchronous Parallel Table Replication (ATR). ATR supports OLTP workloads in one primary machine, while it supports heavy OLAP workloads in replicas. Here, row-store formats can be used for OLTP transactions at the primary, while column-store formats are used for OLAP analytical queries at the replicas. ATR is designed to support elastic scalability of OLAP query performance while it minimizes the overhead for transaction processing at the primary and minimizes CPU consumption for replayed transactions at the replicas. ATR employs a novel optimistic lock-free parallel log replay scheme which exploits characteristics of multi-version concurrency control (MVCC) in order to enable real-time reporting by minimizing the propagation delay between the primary and replicas. Through extensive experiments with a concrete implementation available in a commercial database system, we demonstrate that ATR achieves sub-second visibility delay even for update-intensive workloads, providing scalable OLAP performance without notable overhead to the primary.


title: A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations

abstract: Abstract Kuipers, B. and Byun, Y.T., A robot exploration and mapping strategy based on a sematic hierarchy of spatial representations, Robotics and Autonomous System, 8 (1991) 47–63. We have developed a robust qualitative method for robot exploration, mapping, and navigation in large-scale spatial environments. Experiments with a simulated robot in a variety of complex 2D environments have demonstrated that our qualitative method can build an accurate map of a previously unkown environment in spite of substantial random and systematic sensorimotor error. Most current approaches to robot exploration and mapping analyze sensor input to build a geometrically precise map of the environment, then extract topological structure from the geometric description. Our approach recognizes and exploits qualitative properties of large-scale before relatively error-prone geometrical properties. [ sensorimotor ↔ control ] → topology → geometry At the control level, distinctive places and distinctive travel edges are identified based on the interaction between the robot's control strategies, its sensorimotor system, and the world. A distinctive place is defined as the local maximum of a distinctiveness measure appropriate to its immediate neighborhood, and is found by a hill-climbing control strategy. A distinctive travel edge, similarly, is defined by a suitable measure and a path-following control strategy. The topological network description is created by linking the distinctive places and travel edges.Metrical information is then incrementally assimilated into localgeometric descriptions of places and edges, and finally merged into a global geometric map. Topological ambiguity arising from sensorily indistinguishable places can be resolved at the topological level by the exploration strategy. With this representation, successful navigation is not critically dependent on the accuracy, or even the existence, of the geometrical description. We present examples demonstrating the process by which the robot explores and builds a map of a complex environment, including the effect of sensory errors. We also discuss new research directions that are suggested by this approach.


title: Primary Dysmenorrhea Advances in Pathogenesis and Management

abstract: Primary dysmenorrhea is painful menstrual cramps without any evident pathology to account for them, and it occurs in up to 50% of menstruating females and causes significant disruption in quality of life and absenteeism. Current understanding implicates an excessive or imbalanced amount of prostanoids and possibly eicosanoids released from the endometrium during menstruation. The uterus is induced to contract frequently and dysrhythmically, with increased basal tone and increased active pressure. Uterine hypercontractility, reduced uterine blood flow, and increased peripheral nerve hypersensitivity induce pain. Diagnosis rests on a good history with negative pelvic evaluation findings. Evidence-based data support the efficacy of cyclooxygenase inhibitors, such as ibuprofen, naproxen sodium, and ketoprofen, and estrogen-progestin oral contraceptive pills (OCPs). Cyclooxygenase inhibitors reduce the amount of menstrual prostanoids released, with concomitant reduction in uterine hypercontractility, while OCPs inhibit endometrial development and decrease menstrual prostanoids. An algorithm is provided for a simple approach to the management of primary dysmenorrhea.


title: Designing for small display screens

abstract: Wireless access to the Internet via PDAs (personal digital assistants) provides Web type services in the mobile world. What we are lacking are design guidelines for such PDA services. For Web publishing, however, there are many resources to look for guidelines. The guidelines can be classified according to which aspect of the Web media they are related: software/hardware, content and its organization, or aesthetics and layout. In order to be applicable to PDA services, these guidelines have to be modified. In this paper we analyze the main characteristics of PDAs and their influence to the guidelines.


title: The cognitive underpinnings of effective teamwork: A meta-analysis.

abstract: Major theories of team effectiveness position emergent collective cognitive processes as central drivers of team performance. We meta-analytically cumulated 231 correlations culled from 65 independent studies of team cognition and its relations to teamwork processes, motivational states, and performance outcomes. We examined both broad relationships among cognition, behavior, motivation, and performance, as well as 3 underpinnings of team cognition as potential moderators of these relationships. Findings reveal there is indeed a cognitive foundation to teamwork; team cognition has strong positive relationships to team behavioral process, motivational states, and team performance. Meta-analytic regressions further indicate that team cognition explains significant incremental variance in team performance after the effects of behavioral and motivational dynamics have been controlled. The nature of emergence, form of cognition, and content of cognition moderate relationships among cognition, process, and performance, as do task interdependence and team type. Taken together, these findings not only cumulate extant research on team cognition but also provide a new interpretation of the impact of underlying dimensions of cognition as a way to frame and extend future research.


title: Emerging phishing trends and effectiveness of the anti-phishing landing page

abstract: Each month, more attacks are launched with the aim of making web users believe that they are communicating with a trusted entity which compels them to share their personal, financial information. Acquired sensitive information is then used for personal benefits, like, gain access to money of the individuals from whom the information was taken. Phishing costs Internet users billions of dollars every year. A recent report highlighted phishing loss of around $448 million to organizations in April 2014. Researchers at Carnegie Mellon University (CMU) created an anti-phishing landing page supported by Anti-Phishing Working Group (APWG) with the aim to train users on how to prevent themselves from phishing attacks. It is used by financial institutions, phish site take down vendors, government organizations, and online merchants. When a potential victim clicks on a phishing link that has been taken down, he / she is redirected to the landing page. In this paper, we present the comparative analysis on two datasets that we obtained from APWG's landing page log files; one, from September 7, 2008 – November 11, 2009, and other from January 1, 2014 – April 30, 2014. We found that the landing page has been successful in training users against phishing. Forty six percent users clicked lesser number of phishing URLs from January 2014 to April 2014 which shows that training from the landing page helped users not to fall for phishing attacks. Our analysis shows that phishers have started to modify their techniques by creating more legitimate looking URLs and buying large number of domains to increase their activity. We observed that phishers are exploiting Internet Corporation for Assigned Names and Numbers (ICANN) accredited registrars to launch their attacks even after strict surveillance. We saw that phishers are trying to exploit free subdomain registration services to carry out attacks. In this paper, we also compared the phishing e-mails used by phishers to lure victims in 2008 and 2014. We found that the phishing e-mails have changed considerably over time. Phishers have adopted new techniques like sending promotional e-mails and emotionally targeting users in clicking phishing URLs.


title: Lessons learned from the deployment of a smartphone-based access-control system

abstract: Grey is a smartphone-based system by which a user can exercise her authority to gain access to rooms in our university building, and by which she can delegate that authority to other users. We present findings from a trial of Grey, with emphasis on how common usability principles manifest themselves in a smartphone-based security application. In particular, we demonstrate aspects of the system that gave rise to failures, misunderstandings, misperceptions, and unintended uses; network effects and new flexibility enabled by Grey; and the implications of these for user behavior. We argue that the manner in which usability principles emerged in the context of Grey can inform the design of other such applications.


Evaluating credit card transactions in the frequency domain for a proactive fraud detection approach


title: On weight initialization in deep neural networks

abstract: A proper initialization of the weights in a neural network is critical to its convergence. Current insights into weight initialization come primarily from linear activation functions. In this paper, I develop a theory for weight initializations with non-linear activations. First, I derive a general weight initialization strategy for any neural network using activation functions differentiable at 0. Next, I derive the weight initialization strategy for the Rectified Linear Unit (RELU), and provide theoretical insights into why the Xavier initialization is a poor choice with RELU activations. My analysis provides a clear demonstration of the role of non-linearities in determining the proper weight initializations.


title: Open source seismic visualization package on OpenNebula cloud management system

abstract: Currently, the exploration of oil and gas have become more complex due to the oil depletion. The increasing difficulties in discovering new hydrocarbon reservoirs using existing technologies have caused an exceptional rise in the cost and risk to discover hydrocarbons. Seismic interpretation is one of the main element in oil exploration. Seismic interpretation software is vital to assist geophysicists to find hydrocarbons trap. However, specialized commercial seismic visualization package is highly priced. Hence, open source seismic visualization package is introduced as an alternative to minimize the cost in oil exploration. The goal of this research is to demonstrate through comparative study of computing functionalities between two different types of open source seismic visualization packages in order to find a preferable one to integrate in cloud infrastructure, OpenNebula cloud. This paper addresses how the author chooses OpendTect over ParaViewGeo as the preferable open source seismic visualization packages by comparing the functionality of the software packages. In addition, a research study on OpenNebula also has been done to understand the working principle of this cloud management system. Furthermore, an experiment has been conducted to test whether or not the preferable open source seismic visualization package is able to be integrated correctly in OpenNebula cloud with high scalability and good performance.


title: Autonomous multisensor calibration and closed-loop fusion for SLAM

abstract: The vast majority of current robot mapping and navigation systems require specific well-characterized sensors that may require human-supervised calibration and are applicable only in one type of environment. Furthermore, if a sensor degrades in performance, either through damage to itself or changes in environmental conditions, the effect on the mapping system is usually catastrophic. In contrast, the natural world presents robust, reasonably well-characterized solutions to these problems. Using simple movement behaviors and neural learning mechanisms, rats calibrate their sensors for mapping and navigation in an incredibly diverse range of environments and then go on to adapt to sensor damage and changes in the environment over the course of their lifetimes. In this paper, we introduce similar movement-based autonomous calibration techniques that calibrate place recognition and self-motion processes as well as methods for online multisensor weighting and fusion. We present calibration and mapping results from multiple robot platforms and multisensory configurations in an office building, university campus, and forest. With moderate assumptions and almost no prior knowledge of the robot, sensor suite, or environment, the methods enable the bio-inspired RatSLAM system to generate topologically correct maps in the majority of experiments.


title: A lexicon model for deep sentiment analysis and opinion mining applications

abstract: This paper presents a lexicon model for the description of verbs, nouns and adjectives to be used in applications like sentiment analysis and opinion mining. The model aims to describe the detailed subjectivity relations that exist between the actors in a sentence expressing separate attitudes for each actor. Subjectivity relations that exist between the different actors are labeled with information concerning both the identity of the attitude holder and the orientation (positive vs. negative) of the attitude. The model includes a categorization into semantic categories relevant to opinion mining and sentiment analysis and provides means for the identification of the attitude holder and the polarity of the attitude and for the description of the emotions and sentiments of the different actors involved in the text. Special attention is paid to the role of the speaker/writer of the text whose perspective is expressed and whose views on what is happening are conveyed in the text. Finally, validation is provided by an annotation study that shows that these subtle subjectivity relations are reliably identifiable by human annotators.


title: Comparison of photocatalytic activity of zinc stannate particles and zinc stannate/zinc oxide composites for the removal of phenol from water, and a study on the effect of pH on photocatalytic efficiency

abstract: Zinc stannate and composite zinc stannate/zinc oxide particles were synthesized by a precipitation method in aqueous media at room temperature. As synthesized particles were characterized by SEM, TEM and XRD. Photocatalytic degradation of 50 ppm phenol in water in the presence of as synthesized particles was studied using the fluorescence decay. Experiments were carried out in both acidic and basic conditions to assess the stability of the catalyst particles and the role of pH on the photocatalytic degradation efficiency. Composite material showed enhanced activity in slightly acidic conditions. Efficiency of photodegradation was observed to be closely related to the isoelectric point of zinc stannate/zinc oxide composites.


title: The most probable database problem

abstract: This paper proposes a novel inference task for probabilistic databases: the most probable database (MPD) problem. The MPD is the most probable deterministic database where a given query or constraint is true. We highlight two distinctive applications, in database repair of key and dependency constraints, and in finding most probable explanations in statistical relational learning. The MPD problem raises new theoretical questions, such as the possibility of a dichotomy theorem for MPD, classifying queries as being either PTIME or NP-hard. We show that such a dichotomy would diverge from dichotomies for other inference tasks. We then prove a dichotomy for queries that represent unary functional dependency constraints. Finally, we discuss symmetric probabilities and the opportunities for lifted inference.


title: A Theoretical Model of the Antecedents of Educational Goal Commitment

abstract: The transition from high school to college is a key point in students’ educational route. During this transition, the ability to formulate an educational goal and the will to actively engage to this goal are supposed to lead to favorable academic outcomes. However, students differ in their commitment to their educational goal, which may translate into differences in goal implementation. How can we explain such differences? A theoretical model of the factors influencing students’ commitment to their educational goal is proposed. This model is composed of two proximal antecedents—goal importance and expectancy of goal achievement— and two distal antecedents—goal abstraction and goal integration. The proximal antecedents are mainly based on the expectancy-value framework (Eccles & Wigfield, 2002), and the distal antecedents on the assumptions relative to the hierarchical goal structure (Carver & Scheier, 1998).


title: Setting Up Pepper For Autonomous Navigation And Personalized Interaction With Users

abstract: In this paper we present our work with the Pepper robot, a service robot from SoftBank Robotics. We had two main goals in this work: improving the autonomy of this robot by increasing its awareness of the environment; and enhance the robot ability to interact with its users. To achieve this goals, we used ROS, a modern open-source framework for developing robotics software, to provide Pepper with state of the art localization and navigation capabilities. Furthermore, we contribute an architecture for effective human interaction based on cloud services. Our architecture improves Pepper speech recognition capabilities by connecting it to the IBM Bluemix Speech Recognition service and enable the robot to recognize its user via an in-house face recognition web-service. We show examples of our successful integration of ROS and IBM services with Pepper's own software. As a result, we were able to make Pepper move autonomously in a environment with humans and obstacles. We were also able to have Pepper execute spoken commands from known users as well as newly introduced users that were enrolled in the robot list of trusted users via a multi-modal interface.


title: Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers

abstract: We present a unifying framework for studying the solution of multiclass categorization problems by reducing them to multiple binary problems that are then solved using a margin-based binary learning algorithm. The proposed framework unifies some of the most popular approaches in which each class is compared against all others, or in which all pairs of classes are compared to each other, or in which output codes with error-correcting properties are used. We propose a general method for combining the classifiers generated on the binary problems, and we prove a general empirical multiclass loss bound given the empirical loss of the individual binary learning algorithms. The scheme and the corresponding bounds apply to many popular classification learning algorithms including support-vector machines, AdaBoost, regression, logistic regression and decision-tree algorithms. We also give a multiclass generalization error analysis for general output codes with AdaBoost as the binary learner. Experimental results with SVM and AdaBoost show that our scheme provides a viable alternative to the most commonly used multiclass algorithms.


title: Selective deficit in personal moral judgment following damage to ventromedial prefrontal cortex

abstract: Recent fMRI evidence has detected increased medial prefrontal activation during contemplation of personal moral dilemmas compared to impersonal ones, which suggests that this cortical region plays a role in personal moral judgment. However, functional imaging results cannot definitively establish that a brain area is necessary for a particular cognitive process. This requires evidence from lesion techniques, such as studies of human patients with focal brain damage. Here, we tested 7 patients with lesions in the ventromedial prefrontal cortex and 12 healthy individuals in personal moral dilemmas, impersonal moral dilemmas and non-moral dilemmas. Compared to normal controls, patients were more willing to judge personal moral violations as acceptable behaviors in personal moral dilemmas, and they did so more quickly. In contrast, their performance in impersonal and non-moral dilemmas was comparable to that of controls. These results indicate that the ventromedial prefrontal cortex is necessary to oppose personal moral violations, possibly by mediating anticipatory, self-focused, emotional reactions that may exert strong influence on moral choice and behavior.


title: Webcrow: A web-based system for crossword solving

abstract: Language games represent one of the most fascinating challenges of research in artificial intelligence. In this paper we give an overview of WebCrow, a system that tackles crosswords using the Web as a knowledge base. This appears to be a novel approach with respect to the available literature. It is also the first solver for non-English crosswords and it has been designed to be potentially multilingual. Although WebCrow has been implemented only in a preliminary version, it already displays very interesting results reaching the performance of a human beginner: crosswords that are "easy" for expert humans are solved, within competition time limits, with 80% of correct words and over 90% of correct letters.


title: Learning Plannable Representations with Causal InfoGAN

abstract: In recent years, deep generative models have been shown to 'imagine' convincing high-dimensional observations such as images, audio, and even video, learning directly from raw data. In this work, we ask how to imagine goal-directed visual plans -- a plausible sequence of observations that transition a dynamical system from its current configuration to a desired goal state, which can later be used as a reference trajectory for control. We focus on systems with high-dimensional observations, such as images, and propose an approach that naturally combines representation learning and planning. Our framework learns a generative model of sequential observations, where the generative process is induced by a transition in a low-dimensional planning model, and an additional noise. By maximizing the mutual information between the generated observations and the transition in the planning model, we obtain a low-dimensional representation that best explains the causal nature of the data. We structure the planning model to be compatible with efficient planning algorithms, and we propose several such models based on either discrete or continuous states. Finally, to generate a visual plan, we project the current and goal observations onto their respective states in the planning model, plan a trajectory, and then use the generative model to transform the trajectory to a sequence of observations. We demonstrate our method on imagining plausible visual plans of rope manipulation.


title: Early Diagnosis and Prediction of Sepsis Shock by Combining Static and Dynamic Information Using Convolutional-LSTM

abstract: Deep neural network models, especially Long Short Term Memory (LSTM), have shown great success in analyzing Electronic Health Records (EHRs) due to their ability to capture temporal dependencies in time series data. In this paper, we proposed a general deep neural network framework which incorporates two additional components with the aim of improving LSTM. The first component, a Convolutional Neural Network (CNN), is added before LSTM to obtain local characteristics of EHRs. The second component, a fully connected neural network (FC), introduces static information (e.g., age) to LSTM, which is applied to handle dynamic information (e.g., lab result). The medical condition we aim to predict is septic shock – it is the most advanced complication of sepsis and is due to severe abnormalities in circulation and/or cellular metabolism. Our proposed framework was evaluated for two experimental tasks: visit level early diagnosis (left align) and event level early prediction (right align). Our results show that for visit level early diagnosis, by incorporating both CNN and static information, our framework consistently outperforms the original LSTM. For event level early prediction, the same outcome is observed when predicting < 5 hours into the future, however, when predicting ≥ 5 hours into the future, the addition of the CNN component alone obtains the best results.


title: Towards multi-cue urban curb recognition

abstract: This paper presents a multi-cue approach to curb recognition in urban traffic. We propose a novel texture-based curb classifier using local receptive field (LRF) features in conjunction with a multi-layer neural network. This classification module operates on both intensity images and on three-dimensional height profile data derived from stereo vision. We integrate the proposed multi-cue curb classifier as an additional measurement module into a state-of-the-art Kaiman filter-based urban lane recognition system. Our experiments involve a challenging real-world dataset captured in urban traffic with manually labeled ground-truth. We quantify the benefit of the proposed multi-cue curb classifier in terms of the improvement in curb localization accuracy of the integrated system. Our results indicate a 25% reduction of the average curb localization error at real-time processing speeds.


title: Multi-view common space learning for emotion recognition in the wild

abstract: It is a very challenging task to recognize emotion in the wild. Recently, combining information from various views or modalities has attracted more attention. Cross modality features and features extracted by different methods are regarded as multi-view information of the sample. In this paper, we propose a method to analyse multi-view features of emotion samples and automatically recognize the expression as part of the fourth Emotion Recognition in the Wild Challenge (EmotiW 2016). In our method, we first extract multi-view features such as BoF, CNN, LBP-TOP and audio features for each expression sample. Then we learn the corresponding projection matrices to map multi-view features into a common subspace. In the meantime, we impose l_2,1-norm penalties on projection matrices for feature selection. We apply both this method and PLSR to emotion recognition. We conduct experiments on both AFEW and HAPPEI datasets, and achieve superior performance. The best recognition accuracy of our method is 0.5531 on the AFEW dataset for video based emotion recognition in the wild. The minimum RMSE for group happiness intensity recognition is 0.9525 on HAPPEI dataset. Both of them are much better than that of the challenge baseline.


title: A Rule-Based Language for Complex Event Processing and Reasoning

abstract: Complex Event Processing (CEP) is concerned with timely detection of complex events within multiple streams of atomic occurrences. It has useful applications in areas including financial services, mobile and sensor devices, click stream analysis etc. Numerous approaches in CEP have already been proposed in the literature. Event processing systems with a logic-based representation have attracted considerable attention as (among others reasons) they feature formal semantics and offer reasoning service. However logic-based approaches are not optimized for run-time event recognition (as they are mainly query-driven systems). In this paper, we present an expressive logic-based language for specifying and combining complex events. For this language we provide both a syntax as well as a formal declarative semantics. The language enables efficient run time event recognition and supports deductive reasoning. Execution model of the language is based on a compilation strategy into Prolog. We provide an implementation of the language, and present the performance results showing the competitiveness of our approach.


title: The Pinwheel Model of Bereavement

abstract: The Pinwheel Model of Bereavement is a process-orientated model of grief which recognizes loss as a unique lived experience. The model and relevant nursing response are described using Margaret Newman's nursing theory of health as expanding consciousness. The model is based on research by Carter (1989) and clinical experience. The contextual theme for the model is “personal history.” Six core themes are: being stopped, hurting, missing, holding, seeking, and valuing. Three meta themes are change, expectations, and inexpressibility. Capacities for “being with” the bereaved are identified for the practice of nursing


title: Addressing cold start in recommender systems: a semi-supervised co-training algorithm

abstract: Cold start is one of the most challenging problems in recommender systems. In this paper we tackle the cold-start problem by proposing a context-aware semi-supervised co-training method named CSEL. Specifically, we use a factorization model to capture fine-grained user-item context. Then, in order to build a model that is able to boost the recommendation performance by leveraging the context, we propose a semi-supervised ensemble learning algorithm. The algorithm constructs different (weak) prediction models using examples with different contexts and then employs the co-training strategy to allow each (weak) prediction model to learn from the other prediction models. The method has several distinguished advantages over the standard recommendation methods for addressing the cold-start problem. First, it defines a fine-grained context that is more accurate for modeling the user-item preference. Second, the method can naturally support supervised learning and semi-supervised learning, which provides a flexible way to incorporate the unlabeled data. The proposed algorithms are evaluated on two real-world datasets. The experimental results show that with our method the recommendation accuracy is significantly improved compared to the standard algorithms and the cold-start problem is largely alleviated.


title: Rapid Single-Cell Electroporation for Labeling Organotypic Cultures

abstract: Abstract : Single-cell electroporation is a technique for transfecting individual cells in tissue culture at relatively high efficiencies, however it is both time-consuming and low-throughput and this limits the number of different labeling agents that can be effectively introduced into a region of tissue in reasonable periods of time. A novel system that will rapidly load, clean, and accurately position a glass micropipette electrode into tissue culture for single-cell electroporation is proposed. The system will significantly increase the number of different labeling agents that can be introduced into a single tissue culture per unit time. This in turn, will provide a means for improving the study of neural anatomy at cellular resolutions in both tissue culture and in vivo environments.


title: ARM(R) Microcontroller Based Automatic Power Factor Monitoring and Control System

abstract: The project aims to build a simple, compact and energy-efficient system for automatic power factor monitoring and control. It incorporates Stellar is® ARM® Cortex-M3 based LM3S608 micro controller kit along with a zero crossing detector circuit and a relay driver circuit. Current transformer and potential transformer are used for gathering real time current and potential data. It is fed into the micro controller via a zero crossing detector built using TI OPA4277 High Precision Operational Amplifier. From the directly received phase data, micro controller calculates the phase difference and hence the power factor of the system. It is displayed on an HD44780 controlled 16x2 Liquid Crystal Display. According to the current power factor, capacitors are automatically switched into the circuit using relays to bring the power factor close to unity. The relay driver circuit is built using TI ULN2003A IC. The system has been tested on different loads. Power factor is accurately displayed and brought close to unity. The use of ARM® based 32-bit micro controller promises extension of the project to a complete power management unit. Apart from lower power consumption, it allows easy extension of functionalities from power data logging, remote monitoring and control, emergency alarm etc.


Herbal Cosmetics for Skin and Hair care


Kazuya Masu 0 Modeling of Spiral Inductors


On-Policy vs. Off-Policy Updates for Deep Reinforcement Learning


title: A Non-Invasive Multichannel Hybrid Fiber-Optic Sensor System for Vital Sign Monitoring

abstract: In this article, we briefly describe the design, construction, and functional verification of a hybrid multichannel fiber-optic sensor system for basic vital sign monitoring. This sensor uses a novel non-invasive measurement probe based on the fiber Bragg grating (FBG). The probe is composed of two FBGs encapsulated inside a polydimethylsiloxane polymer (PDMS). The PDMS is non-reactive to human skin and resistant to electromagnetic waves, UV absorption, and radiation. We emphasize the construction of the probe to be specifically used for basic vital sign monitoring such as body temperature, respiratory rate and heart rate. The proposed sensor system can continuously process incoming signals from up to 128 individuals. We first present the overall design of this novel multichannel sensor and then elaborate on how it has the potential to simplify vital sign monitoring and consequently improve the comfort level of patients in long-term health care facilities, hospitals and clinics. The reference ECG signal was acquired with the use of standard gel electrodes fixed to the monitored person's chest using a real-time monitoring system for ECG signals with virtual instrumentation. The outcomes of these experiments have unambiguously proved the functionality of the sensor system and will be used to inform our future research in this fast developing and emerging field.


title: Rapid increase of overweight and obesity among primary school-aged children in the Caribbean; high initial BMI is the most significant predictor

abstract: To examine predictors of increasing overweight among children in two developing countries. Primary school children (6–10 y at baseline, n = 336) and their caregivers. Longitudinal data were collected in 2012, with follow-up 18 months later. Data on children’s height, weight and dietary intake were collected within 8 primary public schools in Trinidad and 7 schools in St. Kitts. Caregivers’ demographic and anthropometric data were also collected. At baseline, children’s age and sex and caregivers’ BMI, age, and marital status and reported dietary intake were similar across all weight groups. The incidence of overweight and obesity among children was 8.8% and 8.1%, respectively. Dietary intake at baseline was not related to becoming overweight or obese. Similarly there were no differences in reported intake among children who became overweight or obese except that they consumed fewer fruits (0.54±0.92 vs. 0.98±1.66, p = 0.017). Misreporting of energy intake was higher among overweight/obese children as compared to those who were not overweight/obese (27% vs. 17%, p = 0.047). The baseline predictors of increasing BMI (adjusted) of the children were older age, higher baseline BMI z-score and higher height-for-age (HFA) z-score; caregiver BMI, children’s energy intake (with adjustment for misreporting) did not predict changes in children’s BMI. The increasing prevalence of overweight/obesity among children is a serious problem in the Caribbean. Heavier children are at elevated risk of continued rapid increase in their weight status, pointing to the need for early intervention.


title: Long-Range IoT Technologies: The Dawn of LoRa™

abstract: The last years have seen the widespread diffusion of novel Low Power Wide Area Network (LPWAN) technologies, which are gaining momentum and commercial interest as enabling technologies for the Internet of Things. In this paper we discuss some of the most interesting LPWAN solutions, focusing in particular on LoRa™, one of the last born and most promising technologies for the wide-area IoT.


title: SpWA: An Efficient Sparse Winograd Convolutional Neural Networks Accelerator on FPGAs

abstract: FPGAs have been an efficient accelerator for CNN inference due to its high performance, flexibility, and energy-efficiency. To improve the performance of CNNs on FPGAs, fast algorithms and sparse methods emerge as the most attractive alternatives, which can effectively reduce the complexity of CNNs. Using fast algorithms, the feature maps are transformed to special domain to reduce the arithmetic complexity. On the other hand, compressing CNN models by pruning the unimportant connections reduces both storage and arithmetic complexity. In this paper, we introduce sparse Winograd convolution accelerator (SpWA) combining these two orthogonal approaches on FPGAs. First, we employ a novel dataflow by rearranging the filter layout in Winograd convolution. Then we design an efficient architecture to implement SpWA using line buffer design and Compress-Sparse-Column (CSC) format-based processing element. Finally, we propose an efficient algorithm based on dynamic programming to balance the computation among different processing elements. Experimental results on VGG16 and YOLO network show a 2.9x∼3.1x speedup compared with state-of-the-art technique.


X Marks the Spot: Scan-Flip-Flop Clustering for Faster-than-at-Speed Test


title: Rotational propulsion enabled by inertia

abstract: The fluid mechanics of small-scale locomotion has recently attracted considerable attention, due to its importance in cell motility and the design of artificial micro-swimmers for biomedical applications. Most studies on the topic consider the ideal limit of zero Reynolds number. In this paper, we investigate a simple propulsion mechanism --an up-down asymmetric dumbbell rotating about its axis of symmetry-- unable to propel in the absence of inertia in a Newtonian fluid. Inertial forces lead to continuous propulsion for all finite values of the Reynolds number. We study computationally its propulsive characteristics as well as analytically in the small-Reynolds-number limit. We also derive the optimal dumbbell geometry. The direction of propulsion enabled by inertia is opposite to that induced by viscoelasticity.


title: BRITE: an approach to universal topology generation

abstract: Effective engineering of the Internet is predicated upon a detailed understanding of issues such as the large-scale structure of its underlying physical topology, the manner in which it evolves over time, and the way in which its constituent components contribute to its overall function. Unfortunately, developing a deep understanding of these issues has proven to be a challenging task, since it in turn involves solving difficult problems such as mapping the actual topology, characterizing it, and developing models that capture its emergent behavior. Consequently, even though there are a number of topology models, it is an open question as to how representative the generated topologies they generate are of the actual Internet. Our goal is to produce a topology generation framework which improves the state of the art and is based on the design principles of representativeness, inclusiveness, and interoperability. Representativeness leads to synthetic topologies that accurately reflect many aspects of the actual Internet topology (e.g. hierarchical structure, node degree distribution, etc.). Inclusiveness combines the strengths of as many generation models as possible in a single generation tool. Interoperability provides interfaces to widely-used simulation applications such as ns and SSF and visualization tools like otter. We call such a tool a universal topology generator.


title: Recognizing Challenging Handwritten Annotations with Fully Convolutional Networks

abstract: This paper introduces a very challenging dataset of historic German documents and evaluates Fully Convolutional Neural Network (FCNN) based methods to locate handwritten annotations of any kind in these documents. The handwritten annotations can appear in form of underlines and text by using various writing instruments, e.g., the use of pencils makes the data more challenging. We train and evaluate various end-to-end semantic segmentation approaches and report the results. The task is to classify the pixels of documents into two classes: background and handwritten annotation. The best model achieves a mean Intersection over Union (IOU) score of 95.6% on the test documents of the presented dataset. We also present a comparison of different strategies used for data augmentation and training on our presented dataset. For evaluation, we use the Layout Analysis Evaluator for the ICDAR 2017 Competition on Layout Analysis for Challenging Medieval Manuscripts.


title: Stereo-Based Autonomous Navigation and Obstacle Avoidance*

abstract: Abstract This paper presents a stereo vision-based autonomous navigation system using a GPS and a modified version of the VFH algorithm. In order to obtain a high-accuracy disparity map and meet the time constraints of the real time navigation system, this work proposes the use of a semi-global stereo method. By not suffering the same issues of the regularly used local stereo methods, the employed stereo technique enables the generation of a highly dense, efficient, and accurate disparity map. Obstacles are detected using a method that checks for relative slopes and heights differences. Experimental tests using an electric vehicle in an urban environment were performed to validate the proposed approach.


title: IEEE 802.11ad: directional 60 GHz communication for multi-Gigabit-per-second Wi-Fi [Invited Paper]

abstract: With the ratification of the IEEE 802.11ad amendment to the 802.11 standard in December 2012, a major step has been taken to bring consumer wireless communication to the millimeter wave band. However, multi-gigabit-per-second throughput and small interference footprint come at the price of adverse signal propagation characteristics, and require a fundamental rethinking of Wi-Fi communication principles. This article describes the design assumptions taken into consideration for the IEEE 802.11ad standard and the novel techniques defined to overcome the challenges of mm-Wave communication. In particular, we study the transition from omnidirectional to highly directional communication and its impact on the design of IEEE 802.11ad.


title: Threaded cognition: An integrated theory of concurrent multitasking

abstract: The authors propose the idea of threaded cognition, an integrated theory of concurrent multitasking-that is, performing 2 or more tasks at once. Threaded cognition posits that streams of thought can be represented as threads of processing coordinated by a serial procedural resource and executed across other available resources (e.g., perceptual and motor resources). The theory specifies a parsimonious mechanism that allows for concurrent execution, resource acquisition, and resolution of resource conflicts, without the need for specialized executive processes. By instantiating this mechanism as a computational model, threaded cognition provides explicit predictions of how multitasking behavior can result in interference, or lack thereof, for a given set of tasks. The authors illustrate the theory in model simulations of several representative domains ranging from simple laboratory tasks such as dual-choice tasks to complex real-world domains such as driving and driver distraction.


title: Temperament and childhood obesity risk: a review of the literature.

abstract: OBJECTIVE: Temperament, or individual differences in behavioral styles, could explain why many individuals are susceptible to childhood obesity in the current environment while others are not. The objective of this article is to review the growing body of literature linking weight outcomes with 2 aspects of temperament, negativity and self-regulation. METHOD: Studies conducted through 2011 and assessing at least 1 weight outcome and 1 aspect of early negativity or self-regulation in normally developing infants and young children were included. RESULTS: Most studies suggest that greater levels of negative reactivity in early life may increase the risk of obesity, and greater self-regulation may be protective. While temperaments of individuals tend to be relatively stable over time, there is evidence that links between early temperament and weight outcomes are malleable. CONCLUSIONS: Relationships between temperament and weight outcomes are becoming well established. More research is needed to confirm the longitudinal nature of these relationships and to shed light on the mediators and moderators of these relationships and their implications for obesity interventions.


title: Multiphase Electric Machines for Variable-Speed Applications

abstract: Although the concept of variable-speed drives, based on utilization of multiphase machines, dates back to the late 1960s, it was not until the mid- to late 1990s that multiphase drives became serious contenders for various applications. These include electric ship propulsion, locomotive traction, electric and hybrid electric vehicles, ldquomore-electricrdquo aircraft, and high-power industrial applications. As a consequence, there has been a substantial increase in the interest for such drive systems worldwide, resulting in a huge volume of work published during the last ten years. An attempt is made in this paper to provide a brief review of the current state of the art in the area. After addressing the reasons for potential use of multiphase rather than three-phase drives and the available approaches to multiphase machine designs, various control schemes are surveyed. This is followed by a discussion of the multiphase voltage source inverter control. Various possibilities for the use of additional degrees of freedom that exist in multiphase machines are further elaborated. Finally, multiphase machine applications in electric energy generation are addressed.


title: Impact of peer teaching on nursing students: perceptions of learning environment, self-efficacy, and knowledge.

abstract: BACKGROUND ::: Peer teaching has been shown to enhance student learning and levels of self efficacy. ::: ::: ::: OBJECTIVES ::: The purpose of the current study was to examine the impact of peer-teaching learning experiences on nursing students in roles of tutee and tutor in a clinical lab environment. ::: ::: ::: SETTINGS ::: This study was conducted over a three-semester period at a South Central University that provides baccalaureate nursing education. ::: ::: ::: PARTICIPANTS ::: Over three semesters, 179 first year nursing students and 51 third year nursing students participated in the study. ::: ::: ::: METHODS ::: This mixed methods study, through concurrent use of a quantitative intervention design and qualitative survey data, examined differences during three semesters in perceptions of a clinical lab experience, self-efficacy beliefs, and clinical knowledge for two groups: those who received peer teaching-learning in addition to faculty instruction (intervention group) and those who received faculty instruction only (control group). Additionally, peer teachers' perceptions of the peer teaching learning experience were examined. ::: ::: ::: RESULTS ::: Results indicated positive response from the peer tutors with no statistically significant differences for knowledge acquisition and self-efficacy beliefs between the tutee intervention and control groups. In contrast to previous research, students receiving peer tutoring in conjunction with faculty instruction were statistically more anxious about performing lab skills with their peer tutor than with their instructors. Additionally, some students found instructors' feedback moderately more helpful than their peers and increased gains in knowledge and responsibility for preparation and practice with instructors than with peer tutors. ::: ::: ::: CONCLUSIONS ::: The findings in this study differ from previous research in that the use of peer tutors did not decrease anxiety in first year students, and no differences were found between the intervention and control groups related to self efficacy or cognitive improvement. These findings may indicate the need to better prepare peer tutors, and research should be conducted using more complex skills.


title: Consensus in a box: inexpensive coordination in hardware

abstract: Consensus mechanisms for ensuring consistency are some of the most expensive operations in managing large amounts of data. Often, there is a trade off that involves reducing the coordination overhead at the price of accepting possible data loss or inconsistencies. As the demand for more efficient data centers increases, it is important to provide better ways of ensuring consistency without affecting performance. ::: ::: In this paper we show that consensus (atomic broadcast) can be removed from the critical path of performance by moving it to hardware. As a proof of concept, we implement Zookeeper's atomic broadcast at the network level using an FPGA. Our design uses both TCP and an application specific network protocol. The design can be used to push more value into the network, e.g., by extending the functionality of middleboxes or adding inexpensive consensus to in-network processing nodes. ::: ::: To illustrate how this hardware consensus can be used in practical systems, we have combined it with a mainmemory key value store running on specialized microservers (built as well on FPGAs). This results in a distributed service similar to Zookeeper that exhibits high and stable performance. This work can be used as a blueprint for further specialized designs.


title: Global multi-output decision trees for interaction prediction

abstract: Interaction data are characterized by two sets of objects, each described by their own set of features. They are often modeled as networks and the values of interest are the possible interactions between two instances, represented usually as a matrix. Here, a novel global decision tree learning method is proposed, where multi-output decision trees are constructed over the global interaction setting, addressing the problem of interaction prediction as a multi-label classification task. More specifically, the tree is constructed by splitting the interaction matrix both row-wise and column-wise, incorporating this way both interaction dataset features in the learning procedure. Experiments are conducted across several heterogeneous interaction datasets from the biomedical domain. The experimental results indicate the superiority of the proposed method against other decision tree approaches in terms of predictive accuracy, model size and computational efficiency. The performance is boosted by fully exploiting the multi-output structure of the model. We conclude that the proposed method should be considered in interaction prediction tasks, especially where interpretable models are desired.


title: Comparative Analysis and Framework Evaluating Web Single Sign-On Systems

abstract: We perform a comprehensive analysis and comparison of 14 web SSO systems proposed and/or deployed within the last decade, including both federated identity and credential/password management schemes. We identify common design properties and use them to develop a taxonomy for SSO schemes, highlighting the associated trade-offs in benefits offered. We develop a framework to evaluate the schemes, in which we identify 14 security, usability, deployability, and privacy benefits. We also discuss how differences in priorities between users, SPs, and IdPs impact the design and deployment of SSO schemes.