title: MOOC Pedagogy: Gleaning Good Practice from Existing MOOCs

abstract: The revolutionary potential of massive open online courses (MOOCs) has been met with much skepticism, particularly in terms of the quality of learning offered. Believing that a focus on learning is more important than a focus on course completion rates, this position paper presents a pedagogical assessment of MOOCs using Chickering and Gamson's Seven Principles of Good Practice in Undergraduate Education and Bloom's taxonomy, based on the author's personal experience as a learner in four xMOOCs. Although most xMOOCs have similar characteristics, the author shows that they are not all offered in exactly the same way, and some provide more sound pedagogy that develops higher order thinking, whereas others do not. The author uses this evaluation, as well as reviews of other xMOOCs in the literature, to glean some good pedagogical practices in xMOOCs and areas for improvement.


title: A Unifying Review of Linear Gaussian Models

abstract: Factor analysis, principal component analysis, mixtures of gaussian clusters, vector quantization, Kalman filter models, and hidden Markov models can all be unified as variations of unsupervised learning under a single basic generative model. This is achieved by collecting together disparate observations and derivations made by many previous authors and introducing a new way of linking discrete and continuous state models using a simple nonlinearity. Through the use of other nonlinearities, we show how independent component analysis is also a variation of the same basic generative model.We show that factor analysis and mixtures of gaussians can be implemented in autoencoder neural networks and learned using squared error plus the same regularization term. We introduce a new model for static data, known as sensible principal component analysis, as well as a novel concept of spatially adaptive observation noise. We also review some of the literature involving global and local mixtures of the basic models and provide pseudocode for inference and learning for all the basic models.


title: A design space for effective privacy notices

abstract: Notifying users about a system's data practices is supposed to enable users to make informed privacy decisions. Yet, current notice and choice mechanisms, such as privacy policies, are often ineffective because they are neither usable nor useful, and are therefore ignored by users. Constrained interfaces on mobile devices, wearables, and smart home devices connected in an Internet of Things exacerbate the issue. Much research has studied usability issues of privacy notices and many proposals for more usable privacy notices exist. Yet, there is little guidance for designers and developers on the design aspects that can impact the effectiveness of privacy notices. In this paper, we make multiple contributions to remedy this issue. We survey the existing literature on privacy notices and identify challenges, requirements, and best practices for privacy notice design. Further, we map out the design space for privacy notices by identifying relevant dimensions. This provides a taxonomy and consistent terminology of notice approaches to foster understanding and reasoning about notice options available in the context of specific systems. Our systemization of knowledge and the developed design space can help designers, developers, and researchers identify notice and choice requirements and develop a comprehensive notice concept for their system that addresses the needs of different audiences and considers the system's limitations and opportunities for providing notice.


title: Minimally Infrequent Itemset Mining using Pattern-Growth Paradigm and Residual Trees

abstract: Itemset mining has been an active area of research due to its successful application in various data mining scenarios including finding association rules. Though most of the past work has been on finding frequent itemsets, infrequent itemset mining has demonstrated its utility in web mining, bioinformatics and other fields. In this paper, we propose a new algorithm based on the pattern-growth paradigm to find minimally infrequent itemsets. A minimally infrequent itemset has no subset which is also infrequent. We also introduce the novel concept of residual trees. We further utilize the residual trees to mine multiple level minimum support itemsets where different thresholds are used for finding frequent itemsets for different lengths of the itemset. Finally, we analyze the behavior of our algorithm with respect to different parameters and show through experiments that it outperforms the competing ones.


title: Thresholding Techniques applied for Segmentation of RGB and multispectral images

abstract: Image segmentation is a process of partitioning an image into a set of non-overlapping regions. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. In this paper we have segmented a RGB image and a multispectral image. The image has been segmented through three threshold techniques (i.e. Iterative threshold techniques, Otsu’s threshold technique, Local Threshold Technique). Thresholding techniques are computationally simple and never fails to define disjoints regions with closed boundaries. Threshold technique is one of the important techniques in image segmentation. Thresholding techniques converts a colored image or gray scale image into binary or bimodal image (foreground and background image). The advantage of obtaining binary image through Thresholding technique is that it reduces the complexity of the data and simplifies the process of recognition and classification.


title: The Effects and Interactions of Data Quality and Problem Complexity on Classification

abstract: Data quality remains a persistent problem in practice and a challenge for research. In this study we focus on the four dimensions of data quality noted as the most important to information consumers, namely accuracy, completeness, consistency, and timeliness. These dimensions are of particular concern for operational systems, and most importantly for data warehouses, which are often used as the primary data source for analyses such as classification, a general type of data mining. However, the definitions and conceptual models of these dimensions have not been collectively considered with respect to data mining in general or classification in particular. Nor have they been considered for problem complexity. Conversely, these four dimensions of data quality have only been indirectly addressed by data mining research. Using definitions and constructs of data quality dimensions, our research evaluates the effects of both data quality and problem complexity on generated data and tests the results in a real-world case. Six different classification outcomes selected from the spectrum of classification algorithms show that data quality and problem complexity have significant main and interaction effects. From the findings of significant effects, the economics of higher data quality are evaluated for a frequent application of classification and illustrated by the real-world case.


title: Resting frontal EEG asymmetry as an endophenotype for depression risk: sex-specific patterns of frontal brain asymmetry.

abstract: Resting frontal electroencephalographic (EEG) asymmetry has been hypothesized as a marker of risk for major depressive disorder (MDD), but the extant literature is based predominately on female samples. Resting frontal asymmetry was assessed on 4 occasions within a 2-week period in 306 individuals aged 18-34 (31% male) with (n = 143) and without (n = 163) lifetime MDD as defined by the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (American Psychiatric Association, 1994). Lifetime MDD was linked to relatively less left frontal activity for both sexes using a current source density (CSD) reference, findings that were not accounted for solely by current MDD status or current depression severity, suggesting that CSD-referenced EEG asymmetry is a possible endophenotype for depression. In contrast, results for average and linked mastoid references were less consistent but demonstrated a link between less left frontal activity and current depression severity in women.


title: Development of musculoskeletal spine structure that fulfills great force requirements in upper body kinematics

abstract: The main goal of this paper is to design and evaluate a spine structure which withstands various motions. The structure around the neck has a prevailing importance since it is involved in various motions of the upper half of the body. The new design method we introduce essentially shows how to design all 7 cervical vertebrae (the part of spine in the neck) in a limited space, actuated by the wires winded around the motors. Then we show the muscle arrangements around the upper half of the spine. More specifically, we make use of a so called planar muscle mechanism. An abduction experiment which requires great force around the spine is made to show its stability. Finally, we show a variable stiffness system which enables the spine to resist an impulsive force. We have tested the system in the situation of whiplash injury which is a case of extreme external forces which can occur in car crash accidents. As such we have evaluated the strength of the design and the viability of our robot to act as a human body simulator.


title: Schedulability analysis of periodic fixed priority systems

abstract: Feasibility analysis of fixed priority systems has been widely studied in the real-time literature and several acceptance tests have been proposed to guarantee a set of periodic tasks. They can be divided in two main classes: polynomial time tests and exact tests. Polynomial time tests can efficiently be used for online guarantee of real-time applications, where tasks are activated at runtime. These tests introduce a negligible overhead, when executed upon a new task arrival, however provide only a sufficient schedulability condition, which may cause a poor processor utilization. On the other hand, exact tests, which are based on response time analysis, provide a necessary and sufficient schedulability condition, but are too complex to be executed on line for large task sets. As a consequence, for large task sets, they are often executed off line. This paper proposes a novel approach for analyzing the schedulability of periodic task sets on a single processor under an arbitrary fixed priority assignment: Using this approach, we derive a new schedulability test which can be tuned through a parameter to balance complexity versus acceptance ratio, so that it can be used on line to better exploit the processor, based on the available computational power. Extensive simulations show that our test, when used in its exact form, is significantly faster than the current response time analysis methods. Moreover the proposed approach, for its elegance and compactness, offers an explanation of some known phenomena of fixed priority scheduling and could be helpful for further work on schedulability analysis.


title: Comparing Ubisense, BeSpoon, and DecaWave UWB Location Systems: Indoor Performance Analysis

abstract: Most ultrawideband (UWB) location systems already proposed for position estimation have only been individually evaluated for particular scenarios. For a fair performance comparison among different solutions, a common evaluation scenario would be desirable. In this paper, we compare three commercially available UWB systems (Ubisense, BeSpoon, and DecaWave) under the same experimental conditions, in order to do a critical performance analysis. We include the characterization of the quality of the estimated tag-to-sensor distances in an indoor industrial environment. This testing space includes areas under line-of-sight (LOS) and diverse non-LOS conditions caused by the reflection, propagation, and the diffraction of the UWB radio signals across different obstacles. The study also includes the analysis of the estimated azimuth and elevation angles for the Ubisense system, which is the only one that incorporates this feature using an array antenna at each sensor. Finally, we analyze the 3-D positioning estimation performance of the three UWB systems using a Bayesian filter implemented with a particle filter and a measurement model that takes into account bad range measurements and outliers. A final conclusion is drawn about which system performs better under these industrial conditions.


A REVIEW ON EVALUATION METRICS FOR DATA CLASSIFICATION EVALUATIONS


title: Comprehending figurative referential descriptions.

abstract: A common way of referring to people is with figurative language. People can be referred to metaphorically, as in calling a terrible boxer «a creampuff,» or metonymically, as in calling a naval admiral «the brass». The present studies investigated the anaphoric interferences that occur during comprehension of figurative referential descriptions. Subjects read short narratives, each ending in either a literal or figurative description of another person. Immediately after the last line of each text, the anaphoric antecedent for the description was presented in a probe recognition task


title: System design for a solar powered electric vehicle charging station for workplaces

abstract: This paper investigates the possibility of charging battery electric vehicles at workplace in Netherlands using solar energy. Data from the Dutch Meteorological Institute is used to determine the optimal orientation of PV panels for maximum energy yield in the Netherlands. The seasonal and diurnal variation in solar insolation is analyzed to determine the energy availability for EV charging and the necessity for grid connection. Due to relatively low solar insolation in Netherlands, it has been determined that the power rating of the PV array can be oversized by 30% with respect to power rating of the converter. Various dynamic EV charging profiles are compared with an aim to minimize the grid dependency and to maximize the usage of solar power to directly charge the EV. Two scenarios are considered – one where the EVs have to be charged only on weekdays and the second case where EV have to be charged all 7days/week. A priority mechanism is proposed to facilitate the charging of multiple EV from a single EV–PV charger. The feasibility of integrating a local storage to the EV–PV charger to make it grid independent is evaluated. The optimal storage size that reduces the grid dependency by 25% is evaluated.


title: Concept Graph Learning from Educational Data

abstract: This paper addresses an open challenge in educational data mining, i.e., the problem of using observed prerequisite relations among courses to learn a directed universal concept graph, and using the induced graph to predict unobserved prerequisite relations among a broader range of courses. This is particularly useful to induce prerequisite relations among courses from different providers (universities, MOOCs, etc.). We propose a new framework for inference within and across two graphs---at the course level and at the induced concept level---which we call Concept Graph Learning (CGL). In the training phase, our system projects the course-level links onto the concept space to induce directed concept links; in the testing phase, the concept links are used to predict (unobserved) prerequisite links for test-set courses within the same institution or across institutions. The dual mappings enable our system to perform an interlingua-style transfer learning, e.g. treating the concept graph as the interlingua, and inducing prerequisite links in a transferable manner across different universities. Experiments on our newly collected data sets of courses from MIT, Caltech, Princeton and CMU show promising results, including the viability of CGL for transfer learning.


An Overview of Predictors for Intrinsically Disordered Proteins over 2010–2014


title: Context based face spoofing detection using active near-infrared images

abstract: In this paper, with the help of controllable active near-infrared (NIR) lights, we construct near-infrared differential (NIRD) images. Based on reflection model, NIRD image is believed to contain the lighting difference between images with and without active NIR lights. Two main characteristics based on NIRD images are exploited to conduct spoofing detection. Firstly, there exist obviously spoofing media around the faces in most conditions, which reflect incident lights in almost the same way as the face areas do. We analyze the pixel consistency between face and non-face areas and employ context clues to distinguish the spoofing images. Then, lighting feature, extracted only from face areas, is utilized to detect spoofing attacks of deliberately cropped medium. Merging the two features, we present a face spoofing detection system. In several experiments on self collected datasets with different spoofing media, we demonstrate the excellent results and robustness of proposed method.


title: Kinking, coiling, and tortuosity of extracranial internal carotid artery: is it the effect of a metaplasia?

abstract: Introduction ::: Morphological anomalies of the extracranial internal carotid artery (ICA) cause symptomatic cerebrovascular insufficiency in 4–16% of the cases. The aim of the present study is to evaluate macroscopic and microscopic features of a group of extracranial ICA anomalies, specifically kinking, coiling, and tortuosity, eventually affecting the surgical approach.


title: Reasoning about Entailment with Neural Attention

abstract: While most approaches to automatically recognizing entailment relations have used classifiers employing hand engineered features derived from complex natural language processing pipelines, in practice their performance has been only slightly better than bag-of-word pair classifiers using only lexical similarity. The only attempt so far to build an end-to-end differentiable neural network for entailment failed to outperform such a simple similarity classifier. In this paper, we propose a neural model that reads two sentences to determine entailment using long short-term memory units. We extend this model with a word-by-word neural attention mechanism that encourages reasoning over entailments of pairs of words and phrases. Furthermore, we present a qualitative analysis of attention weights produced by this model, demonstrating such reasoning capabilities. On a large entailment dataset this model outperforms the previous best neural model and a classifier with engineered features by a substantial margin. It is the first generic end-to-end differentiable system that achieves state-of-the-art accuracy on a textual entailment dataset.


title: MIMO channel modeling and capacity analysis for 5G millimeter-wave wireless systems

abstract: This paper presents a 3-D statistical channel model of the impulse response with small-scale spatially correlated random coefficients for multi-element transmitter and receiver antenna arrays, derived using the physically-based time cluster - spatial lobe (TCSL) clustering scheme. The small-scale properties of multipath amplitudes are modeled based on 28 GHz outdoor millimeter-wave small-scale local area channel measurements. The wideband channel capacity is evaluated by considering measurement-based Rician-distributed voltage amplitudes, and the spatial autocorrelation of multipath amplitudes for each pair of transmitter and receiver antenna elements. Results indicate that Rician channels may exhibit equal or possibly greater capacity compared to Rayleigh channels, depending on the number of antennas.


title: A 4-DOF MEMS Energy Harvester Using Ultrasonic Excitation

abstract: This paper presents a microelectromechanical systems (MEMS)-based energy harvester that is designed to harvest electrical energy from an external source of ultrasonic waves. The harvester features a novel 4-degree-of-freedom (DOF) mechanical design that uses three translational resonance modes and one rotational mode, with electrostatic transducers being implemented to perform the conversion of mechanical to electrical energy. The 4-DOF design of the system allows energy to be harvested regardless of the device’s orientation, which holds particular benefits for potential applications that include the powering of implanted biomedical devices. Full characterization of the fabricated MEMS harvester is performed, which shows that the device is able to generate electrical power outputs of 180, 189, and 49.1 nW due to ultrasonic excitation from the $x$ -, $y$ -, and $z$ -directions, respectively.


title: Factors Leading to Consumer Perceived Value of Smartphones and its Impact on Purchase Intention

abstract: Abstract Purpose - The aim of this research is to find the impact of consumer perceived value (CPV) on smartphone purchase intention among Malaysian working professionals. The research intends to find whether or not social value, perceived usefulness, perceived ease of use, economic value and brand image have a direct or indirect effect on smartphone purchase intention. Design/methodology/approach - Data is collected through an administered online survey with convenience sampling. A total of 302 samples were collected in Kuala Lumpur area. Demographic profile classification, reliability test, sample adequacy, confirmatory factor analysis (CFA) and structural equation modeling (SEM) path analysis is conducted to analyze the data using IBM Amos statistic 22. Findings - The findings show that social value and perceived ease of use do not have a direct or indirect effect on smartphone purchase intention among Malaysian working professionals. However, perceived usefulness, economic value and brand image have an indirect effect on smartphone purchase intention. Practical implications--The research is useful for managers and marketers, to understand the consumer perceived value (CPV) of latest generation smartphone among Malaysian working professionals. These type of consumers in the smartphone industry are important and vital for smartphone companies due to their high income and purchase decision making power. Originality/value--The study proposes and validates a new model by combining Technology Acceptance Model (TAM) and selected CPV variables which may impact Smartphone purchase intention among working professionals. Keywords: Purchase Intention (PI), Consumer Perceived Value (CPV), Social Value (SV), Economic Value (EV), Perceived Usefulness (PU), Perceived Ease of Use (PEU) and Brand Image (BI) Introduction Nowadays, maximizing consumer Perceived Value (CPV) by identifying factors influencing it is one of the biggest challenges that companies are facing around the globe. They have seen their product image that is projected to the public leading to failure and brand unpopularity. Perhaps, this concern is nowhere more evident than in the smartphones Industry where the world global consumers have witnessed the failure of phone industry giants such as Nokia and Motorola due to the inconsistent analysis of consumer perceived value evolution related to new technology (Fishman, 2014; Surowiecki, 2013). A smartphone is a user friendly cellular telephone and is a revolutionized new invention with an integrated computer and other features not originally associated with the telephones such as a web browsing, an operating system and the ability to run software applications (Margaret, 2007). The smartphone has become extremely popular today and still growing, nevertheless, the notion of CPV in the smartphone industry has captured the attention of several smartphone manufacturers. Several researchers have been interested in the notion of CPV associated with purchase intention. The first author who addressed the importance of the CPV was Zeithaml (1988) as consumer's overall evaluation of the utility of product based on perceptions of what is received and what is given, Monroe (1990) established a simple concepts of the ratio between perceived benefits and perceived sacrifices which drive purchase intention, then Sheth et al (1991) introduces the five dimensions value of consumers viz. social, epistemic, conditional, functional and emotional value), Woodruff (1997) talks about the value hierarchy of the model, Holbrooks (1994) takes about the typology of costumer value, also several other theories have been developed recently such as the theory of personal related value and the perceived value purchase (Sanchez and Iniesta, 2006). According to International Data Corporation (IDC) (2015), 4.8 million units of smartphones were shipped in Malaysia in 2015. …


Efficient Multiplication and Division Using MSP430


title: Learning Named Entity Tagger using Domain-Specific Dictionary

abstract: Recent advances in deep neural models allow us to build reliable named entity recognition (NER) systems without handcrafting features. However, such methods require large amounts of manually-labeled training data. There have been efforts on replacing human annotations with distant supervision (in conjunction with external dictionaries), but the generated noisy labels pose significant challenges on learning effective neural models. Here we propose two neural models to suit noisy distant supervision from the dictionary. First, under the traditional sequence labeling framework, we propose a revised fuzzy CRF layer to handle tokens with multiple possible labels. After identifying the nature of noisy labels in distant supervision, we go beyond the traditional framework and propose a novel, more effective neural model AutoNER with a new Tie or Break scheme. In addition, we discuss how to refine distant supervision for better NER performance. Extensive experiments on three benchmark datasets demonstrate that AutoNER achieves the best performance when only using dictionaries with no additional human effort, and delivers competitive results with state-of-the-art supervised benchmarks.


Multilingual Grapheme-to-Phoneme Conversion with Global Character Vectors


title: A Maximum Entropy Framework for Semisupervised and Active Learning With Unknown and Label-Scarce Classes

abstract: We investigate semisupervised learning (SL) and pool-based active learning (AL) of a classifier for domains with label-scarce (LS) and unknown categories, i.e., defined categories for which there are initially no labeled examples. This scenario manifests, e.g., when a category is rare, or expensive to label. There are several learning issues when there are unknown categories: 1) it is a priori unknown which subset of (possibly many) measured features are needed to discriminate unknown from common classes and 2) label scarcity suggests that overtraining is a concern. Our classifier exploits the inductive bias that an unknown class consists of the subset of the unlabeled pool’s samples that are atypical (relative to the common classes) with respect to certain key (albeit a priori unknown) features and feature interactions. Accordingly, we treat negative log- $p$ -values on raw features as nonnegatively weighted derived feature inputs to our class posterior, with zero weights identifying irrelevant features. Through a hierarchical class posterior, our model accommodates multiple common classes, multiple LS classes, and unknown classes. For learning, we propose a novel semisupervised objective customized for the LS/unknown category scenarios. While several works minimize class decision uncertainty on unlabeled samples, we instead preserve this uncertainty [maximum entropy (maxEnt)] to avoid overtraining. Our experiments on a variety of UCI Machine learning (ML) domains show: 1) the use of $p$ -value features coupled with weight constraints leads to sparse solutions and gives significant improvement over the use of raw features and 2) for LS SL and AL, unlabeled samples are helpful, and should be used to preserve decision uncertainty (maxEnt), rather than to minimize it, especially during the early stages of AL. Our AL system, leveraging a novel sample-selection scheme, discovers unknown classes and discriminates LS classes from common ones, with sparing use of oracle labeling.


title: Analysis of Blockage Effects on Urban Cellular Networks

abstract: Large-scale blockages like buildings affect the performance of urban cellular networks, especially at higher frequencies. Unfortunately, such blockage effects are either neglected or characterized by oversimplified models in the analysis of cellular networks. Leveraging concepts from random shape theory, this paper proposes a mathematical framework to model random blockages and analyze their impact on cellular network performance. Random buildings are modeled as a process of rectangles with random sizes and orientations whose centers form a Poisson point process on the plane. The distribution of the number of blockages in a link is proven to be Poisson random variable with parameter dependent on the length of the link. A path loss model that incorporates the blockage effects is proposed, which matches experimental trends observed in prior work. The model is applied to analyze the performance of cellular networks in urban areas with the presence of buildings, in terms of connectivity, coverage probability, and average rate. Analytic results show while buildings may block the desired signal, they may still have a positive impact on network performance since they can block significantly more interference.


title: Analysis and design of periodic leaky-wave antennas for the millimeter waveband in hybrid waveguide-planar technology

abstract: This work presents a full-wave integral equation approach specifically conceived for the analysis and design of laterally-shielded rectangular dielectric waveguides, periodically loaded with planar perturbations of rectangular shape. This type of open periodic waveguide supports the propagation of leaky-wave modes, which can be used to build leaky-wave antennas which exhibit many desirable features for millimeter waveband applications. The particularities of the leaky-mode analysis theory are described in this paper, and comparisons with other methods are presented for validation purposes. Using this leaky-mode analysis method, a novel periodic leaky-wave antenna is presented and designed. This novel antenna shows some important improvements with respect to the features of previously proposed antennas. The results of the designed radiation patterns are validated with three-dimensional electromagnetic analysis using commercial software.


title: A High Torque Density Vernier PM Machines for Hybrid Electric Vehicle Applications

abstract: This paper proposes a novel vernier permanent magnet (VPM) machine for hybrid electric vehicle (HEV) applications. The main features of the proposed machine are concentrated armature windings with short and non- overlapping end-winding, and the permanent magnets (PMs) are on both sides of rotor and stator. Unlike conventional motors, this machine operates with flux modulation principles, and the electromagnetic torque is produced by the interaction between the armature magneto motive force (MMF) and the exciting fields by stator and rotor PMs. In this paper, the electromagnetic performance is simulated by the finite element analysis (FEA). The results show that the torque density is about 33% higher than an existing interior permanent magnet (IPM) machines.


title: Effect of quantity of martensitic transformation on fatigue behavior in type 304 stainless steel

abstract: Abstract The effects of strain-induced martensitic transformation on the fatigue behavior of type 304 stainless steel were studied. Rotating bending fatigue tests have been conducted in laboratory air and in 3%NaCl solution using specimens subjected to tensile-prestrains at ambient temperature and -25°C. Martensitic transformation developed more extensively at -25°C than at ambient temperature. In laboratory air, the fatigue strengths of the prestrained specimens increased with increasing prestrain and the specimens subjected to the same prestrains at each temperature exhibited similar fatigue strength in spite of the different amount of martensitic phase. This suggests that the increase in fatigue strength of the prestrained specimens is primarily attributed to work hardening. In 3%NaCl solution, the fatigue strengths of the prestrained specimens at -25°C decreased significantly compared to those in laboratory air. EBSD analysis revealed that the strain-induced martensitic transformation took place within slip bands but the crack initiated in austenitic phase in laboratory air. On the other hand, in 3%NaCl solution, corrosion pits generated at slip bands, from which crack initiation took place. Based on these results, it was confirmed that the strain-induced martensitic transformation exerted a large influence on the fatigue behavior in 3%NaCl solution.


title: AVATAR: A Variable-Retention-Time (VRT) Aware Refresh for DRAM Systems

abstract: Multirate refresh techniques exploit the non-uniformity in retention times of DRAM cells to reduce the DRAM refresh overheads. Such techniques rely on accurate profiling of retention times of cells, and perform faster refresh only for a few rows which have cells with low retention times. Unfortunately, retention times of some cells can change at runtime due to Variable Retention Time (VRT), which makes it impractical to reliably deploy multirate refresh. Based on experimental data from 24 DRAM chips, we develop architecture-level models for analyzing the impact of VRT. We show that simply relying on ECC DIMMs to correct VRT failures is unusable as it causes a data error once every few months. We propose AVATAR, a VRT-aware multirate refresh scheme that adaptively changes the refresh rate for different rows at runtime based on current VRT failures. AVATAR provides a time to failure in the regime of several tens of years while reducing refresh operations by 62%-72%.


title: BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits

abstract: We present efficient algorithms for the problem of contextual bandits with i.i.d. covariates, an arbitrary sequence of rewards, and an arbitrary class of policies. Our algorithm BISTRO requires d calls to the empirical risk minimization (ERM) oracle per round, where d is the number of actions. The method uses unlabeled data to make the problem computationally simple. When the ERM problem itself is computationally hard, we extend the approach by employing multiplicative approximation algorithms for the ERM. The integrality gap of the relaxation only enters in the regret bound rather than the benchmark. Finally, we show that the adversarial version of the contextual bandit problem is learnable (and efficient) whenever the full-information supervised online learning problem has a non-trivial regret guarantee (and efficient).


title: A smart bus tracking system based on location-aware services and QR codes

abstract: When it comes to taking the public transportation, time and patience are of essence. In other words, many people using public transport buses have experienced time loss because of waiting at the bus stops. In this paper, we proposed smart bus tracking system that any passenger with a smart phone or mobile device with the QR (Quick Response) code reader can scan QR codes placed at bus stops to view estimated bus arrival times, buses' current locations, and bus routes on a map. Anyone can access these maps and have the option to sign up to receive free alerts about expected bus arrival times for the interested buses and related routes via SMS and e-mails. We used C4.5 (a statistical classifier) algorithm for the estimation of bus arrival times to minimize the passengers waiting time. GPS (Global Positioning System) and Google Maps are used for navigation and display services, respectively.


title: Improving Chinese SRL with Heterogeneous Annotations

abstract: Previous studies on Chinese semantic role labeling (SRL) have concentrated on single semantically annotated corpus. But the training data of single corpus is often limited. Meanwhile, there usually exists other semantically annotated corpora for Chinese SRL scattered across different annotation frameworks. Data sparsity remains a bottleneck. This situation calls for larger training datasets, or effective approaches which can take advantage of highly heterogeneous data. In these papers, we focus mainly on the latter, that is, to improve Chinese SRL by using heterogeneous corpora together. We propose a novel progressive learning model which augments the Progressive Neural Network with Gated Recurrent Adapters. The model can accommodate heterogeneous inputs and effectively transfer knowledge between them. We also release a new corpus, Chinese SemBank, for Chinese SRL. Experiments on CPB 1.0 show that ours model outperforms state-of-the-art methods.


title: Efficient planar heterojunction perovskite solar cells by vapour deposition

abstract: Nanostructured solid-state solar cells that incorporate organometallic halide perovskites as the light-absorbing material have recently attained levels of power conversion efficiency that are attractive for practical applications. Now Mingzhen Liu et al. show that, when using vapour deposition rather than the usual solution processing to deposit the perovskite, high efficiencies can be achieved with these materials in even simpler device structures. By removing the need for complex nanostructures altogether, the prospects for large-scale manufacture are potentially enhanced.


title: A Novel Islanding Detection Technique for Distributed Generation (DG) Units in Power System

abstract: Distributed generation is a form of electric power privatization. Consumers install their own power station to supply the local loads and/or share in the utility loads. Many protective problems have been developed due to the existence of these distributed generations. However, a protective strategy should be developed to protect the system and the generator itself from different hazards. One problem with such generators is an unwanted islanding phenomenon. This paper introduces a hybrid passive method for islanding detection to minimize the nondetective zone. This method based on composed of rate of change of frequency over power under each event and rate of change of DG reactive power under each event (ROCORP). Simulation results which are carried out on software PSCAD/EMTDC shows good performance of this method.


title: Degradation of mechanical properties of class-H winding insulation

abstract: Thermal ageing is one of the key factors that result in insulation degradation leading to lower dielectric strength and thereafter occurrence of partial discharge in high voltage machines. However, in low voltage high current machines, the occurrence of partial discharge is minimal and degradation of dielectric strength alone will not cause the breakdown of the insulation. This paper hypothesizes that the breakdown of insulation in low voltage/high current machines occur as a result of insulation cracking among other factors. In this paper, it is shown that thermal degradation leads to degradation of mechanical properties of Polyamide-imide insulation, particularly reduction of the ultimate tensile strength and leads to enhanced brittleness. It follows that once a crack in the insulation is formed, the breakdown strength at the point of cracking will be lower. Multiple such cracks may lead to the formation of a breakdown path thereafter leading to aggravated heating and further accelerated deterioration of the insulation. In this paper, the methodology for testing the mechanical properties of insulation material is presented. A model to account for the variation of such properties due to thermal degradation is developed.


title: Computational International Relations: What Can Programming, Coding and Internet Research Do for the Discipline?

abstract: Computational Social Science emerged as a highly technical and popular discipline in the last few years, owing to the substantial advances in communication technology and daily production of vast quantities of personal data. As per capita data production significantly increased in the last decade, both in terms of its size, bytes, as well as its detail, heart rate monitors, Internet connected appliances, smartphones, social scientists ability to extract meaningful social, political and demographic information from digital data also increased. A vast methodological gap exists in computational international relations, or ComInt, which refers to the use of one or a combination of tools such as data mining, natural language processing, automated text analysis, web scraping, geospatial analysis and machine learning to provide larger and better organized data to test more advanced theories of IR. After providing an overview of the potentials of computational IR and how an IR scholar can establish technical proficiency in computer science, such as starting with Python, R, QGis, ArcGIS or Github, this paper will focus on some of the author's works in providing an idea for IR students on how to think about computational IR. The paper argues that computational methods transcend the methodological schism between qualitative and quantitative approaches and form a solid foundation for building truly multi method research design.


title: Blockchain Platform for Industrial Internet of Things

abstract: Internet of Things (IoT) are being adopted for industrial and manufacturing applications such as manufacturing automation, remote machine diagnostics, prognostic health management of industrial machines and supply chain management. Cloud-Based Manufacturing is a recent on-demand model of manufacturing that is leveraging IoT technologies. While Cloud-Based Manufacturing enables on-demand access to manufacturing resources, a trusted intermediary is required for transactions between the users who wish to avail manufacturing services. We present a decentralized, peer-to-peer platform called BPIIoT for Industrial Internet of Things based on the Block chain technology. With the use of Blockchain technology, the BPIIoT platform enables peers in a decentralized, trustless, peer-to-peer network to interact with each other without the need for a trusted intermediary.


title: Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilization

abstract: Humans and most animals can learn new tasks without forgetting old ones. However, training artificial neural networks (ANNs) on new tasks typically causes them to forget previously learned tasks. This phenomenon is the result of "catastrophic forgetting," in which training an ANN disrupts connection weights that were important for solving previous tasks, degrading task performance. Several recent studies have proposed methods to stabilize connection weights of ANNs that are deemed most important for solving a task, which helps alleviate catastrophic forgetting. Here, drawing inspiration from algorithms that are believed to be implemented in vivo, we propose a complementary method: adding a context-dependent gating signal, such that only sparse, mostly nonoverlapping patterns of units are active for any one task. This method is easy to implement, requires little computational overhead, and allows ANNs to maintain high performance across large numbers of sequentially presented tasks, particularly when combined with weight stabilization. We show that this method works for both feedforward and recurrent network architectures, trained using either supervised or reinforcement-based learning. This suggests that using multiple, complementary methods, akin to what is believed to occur in the brain, can be a highly effective strategy to support continual learning.


Collocations


title: Hash function requirements for Schnorr signatures

abstract: We provide two necessary conditions on hash functions for the Schnorr signature scheme to be secure, assuming compact group representations such as those which occur in elliptic curve groups. We also show, via an argument in the generic group model, that these conditions are sufficient. Our hash function security requirements are variants of the standard notions of preimage and second preimage resistance. One of them is in fact equivalent to the Nostradamus attack by Kelsey and Kohno (Eurocrypt 2006), and, when considering keyed compression functions, both are closely related to the ePre and eSec notions by Rogaway and Shrimpton (FSE 2004). Our results have a number of interesting implications in practice. First, since security does not rely on the hash function being collision resistant, Schnorr signatures can still be securely instantiated with SHA-1/SHA256, unlike DSA signatures. Second, we conjecture that our properties require O(2) work to solve for a hash function with n-bit output, thereby allowing the use of shorter hashes and saving twenty-five percent in signature size. And third, our analysis does not reveal any significant difference in hardness between forging signatures and computing discrete logarithms, which plays down the importance of the loose reductions in existing random-oracle proofs, and seems to support the use of “normalsize” groups.


title: Bicycle injuries and helmet use: a systematic review and meta-analysis.

abstract: In the originally published version of this article, in Table 1, the study size for Otte and Wiese should have been given as 4245 instead of 245. Additionally, the last Dinh et al reference (#91) should have been 254 for the study size. This has now been corrected. © The Author 2017; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association Language: en Original Abstract Unaffected by the above correction of typographical erreo: Background: The research literature was systematically reviewed and results were summarized from studies assessing bicycle helmet effectiveness to mitigate head, serious head, face, neck and fatal head injury in a crash or fall. Methods: Four electronic databases (MEDLINE, EMBASE, COMPENDEX and SCOPUS) were searched for relevant, peer-reviewed articles in English. Included studies reported medically diagnosed head, face and neck injuries where helmet use was known. Non-approved helmets were excluded where possible. Summary odds ratios (OR) were obtained using multivariate meta-regression models stratified by injury type and severity. Time trends and publication bias were assessed. Results: A total of 43 studies met inclusion criteria and 40 studies were included in the meta-analysis with data from over 64 000 injured cyclists. For cyclists involved in a crash or fall, helmet use was associated with odds reductions for head [OR = 0.49, 95% confidence interval (CI): 0.42–0.57), serious head (OR = 0.31, 95% CI: 0.25–0.37), face (OR = 0.67, 95% CI: 0.56–0.81) and fatal head injury (OR = 0.35, 95% CI: 0.14–0.88). No clear evidence of an association between helmet use and neck injury was found (OR = 0.96, 95% CI: 0.74–1.25). There was no evidence of time trends or publication bias. Conclusions: Bicycle helmet use was associated with reduced odds of head injury, serious head injury, facial injury and fatal head injury. The reduction was greater for serious or fatal head injury. Neck injury was rare and not associated with helmet use. These results support the use of strategies to increase the uptake of bicycle helmets as part of a comprehensive cycling safety plan.


title: Design and implementation of real time tracking system based on Arduino Intel Galileo

abstract: Tracking and monitoring vehicles are coming vastly utilized based on Global Positioning System (GPS). In this paper a real time tracking system is proposed. The proposed framework would make great utilization of new innovation that basis on embedded board denotation Arduino Intel Galileo. This system acts on Global System for Mobile Communication (GSM), Global Positioning System (GPS) and General Packet Radio System (GPRS) which are utilized for vehicle tracking and monitoring. The SIM908 Module is applied which incorporates three technics to be specific GPS, GPRS and GSM. GPS gives the vehicle location coordinates, GPRS transmits these data to the server and finally the GSM transmits warning message to the vehicle owner phone. This paper exhibit the evolution of the vehicle tracking system prototype which is used in the vehicle. In particular, the framework will use GPS to acquire a vehicle location coordinates and send it utilizing GSM modem to the owner phone and to the web server. After that, the browser can carry on the PHP site page that utilizes Google maps to display the place in a real time. To define the location accuracy of the suggested system, we compared the system proposed results with the different commercial GPS devices.


title: ASP.NET and JSP Frameworks in Model View Controller Implementation

abstract: The Model View Controller (MVC) is a fundamental design pattern for the separation between user interface logic and business logic. The Model-View Controller (MVC) pattern is a method of sorting an application into three distinct components; the Model, the View, and the Controller. In this paper, we implement MVC method in two different frameworks; the ASP. Net framework and Java Server Pages (JSP) framework. Our findings -in this study- show that implementing the ASP. et framework is easier than JSP framework to achieve the MVC major concept.


title: Development of a 10 kW high power density three-phase ac-dc-ac converter using SiC devices

abstract: This paper presents the development and experimental performance of a 10 kW high power density three-phase ac-dc-ac converter. The converter consists of a Vienna-type rectifier front-end and a two-level voltage source inverter (VSI). In order to reduce the switching loss and achieve a high operating junction temperature, a SiC JFET and SiC Schottky diode are utilized. Design considerations for the phase-leg units, gate drivers, integrated input filter — combining EMI and boost inductor stages — and the system protection are described in full detail. Experiments are carried out under different operating conditions, and the results obtained verify the performance and feasibility of the proposed converter system.


title: Personalization in Goal-Oriented Dialog

abstract: The main goal of modeling human conversation is to create agents which can interact with people in both open-ended and goal-oriented scenarios. End-to-end trained neural dialog systems are an important line of research for such generalized dialog models as they do not resort to any situation-specific handcrafting of rules. However, incorporating personalization into such systems is a largely unexplored topic as there are no existing corpora to facilitate such work. In this paper, we present a new dataset of goal-oriented dialogs which are influenced by speaker profiles attached to them. We analyze the shortcomings of an existing end-to-end dialog system based on Memory Networks and propose modifications to the architecture which enable personalization. We also investigate personalization in dialog as a multi-task learning problem, and show that a single model which shares features among various profiles outperforms separate models for each profile.


title: Electronic monitoring of orthopedic brace compliance

abstract: PurposeBrace compliance measurement in adolescent idiopathic scoliosis (AIS) has been the subject of a few recent studies. Various sensors have been developed to measure compliance. We have developed a temperature-based data logger—the Cricket—specifically for scoliosis braces, with associated custom software, that is embedded directly in the brace. The purpose of this study was to analyze patterns of brace wear and patient compliance among children with AIS using the Cricket.MethodsFifty-five AIS patients prescribed various brace-time regimens were monitored using the Cricket. All subjects were treated with the Wilmington brace. The compliance rate for each group was determined.ResultsOverall compliance among subjects was 69.9 ± 31.5 %. Only 14.5 % met or exceeded prescribed brace time. This is consistent with previous compliance monitoring results.ConclusionThe results of this study objectively show the difference between prescribed and actual brace wear time and reaffirm the Cricket sensor as an accurate and comfortable brace-monitoring device.


title: USE: A UML-based specification environment for validating UML and OCL

abstract: The Unified Modeling Language (UML) is accepted today as an important standard for developing software. UML tools however provide little support for validating and checking models in early development phases. There is also no substantial support for the Object Constraint Language (OCL). We present an approach for the validation of UML models and OCL constraints based on animation and certification. The USE tool (UML-based Specification Environment) supports analysts, designers and developers in executing UML models and checking OCL constraints and thus enables them to employ model-driven techniques for software production.


title: Practical Aspects of the Bitcoin System

abstract: Digital payment schemes show an ever increasing importance. Out of the countless different schemes available this article focuses on the popular Bitcoin system. The authors provide a description of Bitcoin's unique technological basis and its accompanying ecosystem of users, miners, trading platforms and vendors. Furthermore, this article discusses Bitcoin's currency-like features and the first regulatory actions take in the European Union and in the United States of America.


title: A weight initialization method for improving training speed in feedforward neural network

abstract: Abstract An algorithm for determining the optimal initial weights of feedforward neural networks based on the Cauchy's inequality and a linear algebraic method is developed. The algorithm is computational efficient. The proposed method ensures that the outputs of neurons are in the active region and increases the rate of convergence. With the optimal initial weights determined, the initial error is substantially smaller and the number of iterations required to achieve the error criterion is significantly reduced. Extensive tests were performed to compare the proposed algorithm with other algorithms. In the case of the sunspots prediction, the number of iterations required for the network initialized with the proposed method was only 3.03% of those started with the next best weight initialization algorithm.


title: A Hybrid User Model for News Story Classification

abstract: We present an intelligent agent designed to compile a daily news program for individual users. Based on feedback from the user, the system automatically adapts to the user’s preferences and interests. In this paper we focus on the system’s user modeling component. First, we motivate the use of a multi-strategy machine learning approach that allows for the induction of user models that consist of separate models for long-term and short-term interests. Second, we investigate the utility of explicitly modeling information that the system has already presented to the user. This allows us to address an important issue that has thus far received virtually no attention in the Information Retrieval community: the fact that a user’s information need changes as a direct result of interaction with information. We evaluate the proposed algorithms on user data collected with a prototype of our system, and assess the individual performance contributions of both model components.


title: Business Model Canvas for Smart Tailor Platform

abstract: A Smart Tailor Platform is proposed as a venue to ::: integrate various players in garment industry, such as tailors, designers, customers, and other relevant stakeholders to automate its business processes. In, Malaysia, currently the processes are conducted manually which consume too much time in fulfilling its supply and demand for the industry. To facilitate this process, a study was conducted to understand the main components of the business operation. The components will be represented using a strategic management tool namely the Business Model Canvas (BMC). The inception phase of the Rational Unified Process (RUP) was employed to construct the BMC. The phase began by determining the basic idea and structure of the business process. The information gathered was classified into nine related dimensions and documented in accordance with the BMC. The ::: generated BMC depicts the relationship of all the nine dimensions for the garment industry, and thus ::: represents an integrated business model of smart ::: tailor.This smart platform allows the players in the industry to promote, manage and fulfill supply and demands of their product electronically. In addition, the BMC can be used to assist developers in designing and developing the smart tailor platform.


Awkwardly : A Response Suggester CS 224 N Final Project


title: Efficient Semantic Scene Completion Network with Spatial Group Convolution

abstract: We introduce Spatial Group Convolution (SGC) for accelerating the computation of 3D dense prediction tasks. SGC is orthogonal to group convolution, which works on spatial dimensions rather than feature channel dimension. It divides input voxels into different groups, then conducts 3D sparse convolution on these separated groups. As only valid voxels are considered when performing convolution, computation can be significantly reduced with a slight loss of accuracy. The proposed operations are validated on semantic scene completion task, which aims to predict a complete 3D volume with semantic labels from a single depth image. With SGC, we further present an efficient 3D sparse convolutional network, which harnesses a multiscale architecture and a coarse-to-fine prediction strategy. Evaluations are conducted on the SUNCG dataset, achieving state-of-the-art performance and fast speed. Code is available at this https URL


title: Dynamic Oracles for Top-Down and In-Order Shift-Reduce Constituent Parsing

abstract: We introduce novel dynamic oracles for training two of the most accurate known shift-reduce algorithms for constituent parsing: the top-down and in-order transition-based parsers. In both cases, the dynamic oracles manage to notably increase their accuracy, in comparison to that obtained by performing classic static training. In addition, by improving the performance of the state-of-the-art in-order shift-reduce parser, we achieve the best accuracy to date (92.0 F1) obtained by a fully-supervised single-model greedy shift-reduce constituent parser on the WSJ benchmark.


title: Social engineering attack examples, templates and scenarios

abstract: The field of information security is a fast-growing discipline. Even though the effectiveness of security measures to protect sensitive information is increasing, people remain susceptible to manipulation and thus the human element remains a weak link. A social engineering attack targets this weakness by using various manipulation techniques to elicit sensitive information. The field of social engineering is still in its early stages with regard to formal definitions, attack frameworks and templates of attacks. This paper proposes detailed social engineering attack templates that are derived from real-world social engineering examples. Current documented examples of social engineering attacks do not include all the attack steps and phases. The proposed social engineering attack templates attempt to alleviate the problem of limited documented literature on social engineering attacks by mapping the real-world examples to the social engineering attack framework. Mapping several similar real-world examples to the social engineering attack framework allows one to establish a detailed flow of the attack whilst abstracting subjects and objects. This mapping is then utilised to propose the generalised social engineering attack templates that are representative of real-world examples, whilst still being general enough to encompass several different real-world examples. The proposed social engineering attack templates cover all three types of communication, namely bidirectional communication, unidirectional communication and indirect communication. In order to perform comparative studies of different social engineering models, processes and frameworks, it is necessary to have a formalised set of social engineering attack scenarios that are fully detailed in every phase and step of the process. The social engineering attack templates are converted to social engineering attack scenarios by populating the template with both subjects and objects from real-world examples whilst still maintaining the detailed flow of the attack as provided in the template. Furthermore, this paper illustrates how the social engineering attack scenarios are applied to verify a social engineering attack detection model. These templates and scenarios can be used by other researchers to either expand on, use for comparative measures, create additional examples or evaluate models for completeness. Additionally, the proposed social engineering attack templates can also be used to develop social engineering awareness material.


title: Draft Genome Sequence of Bacillus sp. Strain NSP2.1, a Nonhalophilic Bacterium Isolated from the Salt Marsh of the Great Rann of Kutch, India

abstract: The 5.52-Mbp draft genome sequence of Bacillus sp. strain NSP2.1, a nonhalophilic bacterium isolated from the salt marsh of the Great Rann of Kutch, India, is reported here. An analysis of the genome of this organism will facilitate the understanding of its survival in the salt marsh.


Querying Word Embeddings for Similarity and Relatedness


title: Metacognitive awareness of learning strategies in undergraduates

abstract: Two studies examined undergraduates’ metacognitive awareness of six empirically-supported learning strategies. Study 1 results overall suggested an inability to predict the learning outcomes of educational scenarios describing the strategies of dual-coding, static-media presentations, low-interest extraneous details, testing, and spacing; there was, however, weak endorsement of the strategy of generating one’s own study materials. In addition, an independent measure of metacognitive self-regulation was correlated with scenario performance. Study 2 demonstrated higher prediction accuracy for students who had received targeted instruction on applied memory topics in their psychology courses, and the best performance for those students directly exposed to the original empirical studies from which the scenarios were derived. In sum, this research suggests that undergraduates are largely unaware of several specific strategies that could benefit memory for course information; further, training in applied learning and memory topics has the potential to improve metacognitive judgments in these domains.


title: Detecting selective forwarding attacks in wireless sensor networks

abstract: Selective forwarding attacks may corrupt some mission-critical applications such as military surveillance and forest fire monitoring. In these attacks, malicious nodes behave like normal nodes in most time but selectively drop sensitive packets, such as a packet reporting the movement of the opposing forces. Such selective dropping is hard to detect. In this paper, we propose a lightweight security scheme for detecting selective forwarding attacks. The detection scheme uses a multi-hop acknowledgement technique to launch alarms by obtaining responses from intermediate nodes. This scheme is efficient and reliable in the sense that an intermediate node reports any abnormal packet loss and suspect nodes to both the base station and the source node. To the best of our knowledge, this is the first paper that presents a detailed scheme for detecting selective forwarding attacks in the environment of sensor networks. The simulation results show that even when the channel error rate is 15%, simulating very harsh radio conditions, the detection accuracy of the proposed scheme is over 95%.


title: Keyword Search over Distributed Graphs with Compressed Signature

abstract: Graph keyword search has drawn many research interests, since graph models can generally represent both structured and unstructured databases and keyword searches can extract valuable information for users without the knowledge of the underlying schema and query language. In practice, data graphs can be extremely large, e.g., a Web-scale graph containing billions of vertices. The state-of-the-art approaches employ centralized algorithms to process graph keyword searches, and thus they are infeasible for such large graphs, due to the limited computational power and storage space of a centralized server. To address this problem, we investigate keyword search for Web-scale graphs deployed in a distributed environment. We first give a naive search algorithm to answer the query efficiently. However, the naive search algorithm uses a flooding search strategy that incurs large time and network overhead. To remedy this shortcoming, we then propose a signature-based search algorithm. Specifically, we design a vertex signature that encodes the shortest-path distance from a vertex to any given keyword in the graph. As a result, we can find query answers by exploring fewer paths, so that the time and communication costs are low. Moreover, we reorganize the graph data in the cluster after its initial random partitioning so that the signature-based techniques are more effective. Finally, our experimental results demonstrate the feasibility of our proposed approach in performing keyword searches over Web-scale graph data.


title: Adaptive, Model-driven Autoscaling for Cloud Applications

abstract: Applications with a dynamic workload demand need access to a flexible infrastructure to meet performance guarantees and minimize resource costs. While cloud computing provides the elasticity to scale the infrastructure on demand, cloud service providers lack control and visibility of user space applications, making it difficult to accurately scale the underlying infrastructure. Thus, the burden of scaling falls on the user. In this paper, we propose a new cloud service, Dependable Compute Cloud (DC2), that automatically scales the infrastructure to meet the user-specified performance requirements. DC2 employs Kalman filtering to automatically learn the (possibly changing) system parameters for each application, allowing it to proactively scale the infrastructure to meet performance guarantees. DC2 is designed for the cloud it is application-agnostic and does not require any offline application profiling or benchmarking. Our implementation results on OpenStack using a multi-tier application under a range of workload traces demonstrate the robustness and superiority of DC2 over existing rule-based approaches.


title: Generative convolutional networks for latent fingerprint reconstruction

abstract: Performance of fingerprint recognition depends heavily on the extraction of minutiae points. Enhancement of the fingerprint ridge pattern is thus an essential pre-processing step that noticeably reduces false positive and negative detection rates. A particularly challenging setting is when the fingerprint images are corrupted or partially missing. In this work, we apply generative convolutional networks to denoise visible minutiae and predict the missing parts of the ridge pattern. The proposed enhancement approach is tested as a pre-processing step in combination with several standard feature extraction methods such as MINDTCT, followed by biometric comparison using MCC and BOZORTH3. We evaluate our method on several publicly available latent fingerprint datasets captured using different sensors.


The Comforter: Providing Personal Advice and Emotional Support to Generations in the Family


title: Visual Techniques in Measuring Attitudes Towards Migrants

abstract: To overcome one of the central problems in measuring sensitive issues - socially desired responses, experimental (visual) techniques have lately been introduced into social science research. The study at hand examines attitudes towards migrants and groups of migrants in Germany by means of morphing technology. In a computer based survey, faces of women and men with different ethnical origins - Spanish, Polish, Kuwaiti and Turkish - are visually manipulated to a brighter or darker skin colour. Together with the graphical display, bibliographic notes of the fictitious person are provided in a factorial design: Information concerning educational, professional and family status are altered. The respondent gives a personal view whether a work or stay permit shall be granted to the displayed person. Using this visual technique, (marginal) effects of skin colour can be exactly detected while keeping textual information constant. Further, this approach provides us with an opportunity to determine the most crucial personal features - visual or non-visual - that account for the attitude towards a migrant. Most relevantly, we expect German respondents to assess more positively morphed faces with brighter skin colour as well as female Muslims not wearing headscarves.


title: Ultradense Word Embeddings by Orthogonal Transformation

abstract: Embeddings are generic representations that are useful for many NLP tasks. In this paper, we introduce DENSIFIER, a method that learns an orthogonal transformation of the embedding space that focuses the information relevant for a task in an ultradense subspace of a dimensionality that is smaller by a factor of 100 than the original space. We show that ultradense embeddings generated by DENSIFIER reach state of the art on a lexicon creation task in which words are annotated with three types of lexical information - sentiment, concreteness and frequency. On the SemEval2015 10B sentiment analysis task we show that no information is lost when the ultradense subspace is used, but training is an order of magnitude more efficient due to the compactness of the ultradense space.


title: Softprop: softmax neural network backpropagation learning

abstract: Multi-layer backpropagation, like many learning algorithms that can create complex decision surfaces, is prone to overfitting. Softprop is a novel learning approach presented here that is reminiscent of the softmax explore-exploit Q-learning search heuristic. It fits the problem while delaying settling into error minima to achieve better generalization and more robust learning. This is accomplished by blending standard SSE optimization with lazy training, a new objective function well suited to learning classification tasks, to form a more stable learning model. Over several machine learning data sets, softprop reduces classification error by 17.1 percent and the variance in results by 38.6 percent over standard SSE minimization.


title: Are We There Yet ? Challenging SeqSLAM on a 3000 km Journey Across All Four Seasons

abstract: When operating over extended periods of time, an autonomous system will inevitably be faced with severe changes in the appearance of its environment. Coping with such changes is more and more in the focus of current robotics research. In this paper, we foster the development of robust place recognition algorithms in changing environments by describing a new dataset that was recorded during a 728 km long journey in spring, summer, fall, and winter. Approximately 40 hours of full-HD video cover extreme seasonal changes over almost 3000 km in both natural and man-made environ- ments. Furthermore, accurate ground truth information are provided. To our knowledge, this is by far the largest SLAM dataset available at the moment. In addition, we introduce an open source Matlab implementation of the recently published SeqSLAM algorithm and make it available to the community. We benchmark SeqSLAM using the novel dataset and analyse the influence of important parameters and algorithmic steps.


title: Effects of mindfulness-based stress reduction on emotional experience and expression: a randomized controlled

abstract: Objectives: Mindfulness-based stress reduction (MBSR) has been found to reduce psychological distress and improve psychological adjustment in medical, psychiatric, and nonclinical samples. We examined its effects on several processes, attitudes, and behavior patterns related to emotion regulation. Design: Fifty-six adults were randomly assigned to MBSR or to a waiting list (WL). Results: Compared with WL completers (n = 21), MBSR completers (n = 20) reported significantly greater increases in trait mindfulness and decreases in absent-mindedness, greater increases in self-compassion, and decreases in fear of emotions, suppression of anger, aggressive anger expression, worry, and difficulties regulating emotions. The WL group subsequently received MBSR, and the two groups combined showed significant changes on all of these variables from pre-MBSR to post-MBSR, and on all except the 2 anger variables from pre-test to 2-month follow-up, as well as significant reductions in rumination. Conclusion: An 8-week mindfulness training program might increase mindful awareness in daily life and have beneficial impact on clinically relevant emotion regulation processes. © 2011 Wiley Periodicals, Inc. J Clin Psychol 68:1–15, 2011.


title: An artificial gastrocnemius for a transtibial prosthesis

abstract: A transtibial amputee does not have a functional gastrocnemius muscle, which affects the knee as well as the ankle joint. In this investigation, we developed a transtibial prosthesis comprising an artificial gastrocnemius mechanism as well as a powered ankle-foot device. A pilot study was conducted with a bilateral transtibial amputee walking at a self-selected speed. The trial compared muscle electromyography and metabolic cost data for the amputee while using the active gastrocnemius prosthesis and a conventional Flex-Foot prosthesis. The experimental data showed that the compensation for ankle-foot and gastrocnemius function offered by the active device resulted in a reduced metabolic cost for the amputee participant.


title: Foreign Diversification of Philippine Firms

abstract: The paper catalogues listed Philippine companies that have either expanded operations into foreign markets or announced plans to do so. The paper uses both newspaper accounts and company disclosures to the Philippine Stock Exchange as sources of information on foreign diversification. The paper finds that for the large majority of the companies in the study, diversification moves—both planned and actual—occurred only in the last five years. More recently, these moves are in the direction of ASEAN and neighboring East Asian countries, are in the same lines of business as in the Philippines, are undertaken by companies belonging to the largest business groups in the Philippines, and exclude banks.


title: A novel MEMS-based tunable dielectric resonator filter

abstract: This paper presents the design and implementation of a new class of high-Q tunable dielectric resonator (DR) filters based on microelectromechanical systems (MEMS) switches. The use of MEMS switches results in the compact implementation of the proposed filter with near to zero DC power consumption for tuning and high tuning speed. The filter consists of two disk shape dielectric resonators with circular holes created in the center of each resonator. An initial prototype of the filter is assembled. The filter operates in TME mode at a center frequency of 4.8 GHz with a bandwidth of 21 MHz. Measurement results of this prototype demonstrate a tuning range of 160 MHz while the quality factor ranges from 1200–510 over the tuning range. The proposed tuning approach is applicable to other modes of dielectric resonator filters.


title: Reliability evaluation of Microgrid with PV-WG hybrid system

abstract: This paper presents a research method of reliability of Microgrid. Firstly, according to different features between the traditional power sources and distributed power supply, the models of power generation reliability evaluation including wind power and solar power are established. Secondly, on the basis of the different operation modes and the importance of the loads, the reliability evaluation index of the sensitive load and non-sensitive load in Microgrid are identified in the condition of their component failure, the external power failure, the micro-source fault as well as in case of lack of micro-source and so on when the Microgrid connected to the great power network or running isolated. Finally, a case of a typical Microgrid system is exhibited. The effectiveness and feasibility of the transactions and optimization model are also overall demonstrated by this simulation.


title: Autonomous cleaning robot: Roboking system integration and overview

abstract: This paper would present the system integration and overview of the autonomous cleaning robot Roboking. Roboking is self-propelled autonomously navigating vacuum cleaning robot. It uses several sensors in order to protect indoor environments and itself while cleaning. In this paper, we would describe the principle of operation along with system structure, sensors, functions and integrated subsystems.


title: Gremlin Language for Querying the BiographDB Integrated Biological Database

abstract: In the last decade, biological tasks became much more complex and often their solution requires the simultaneous use of several different resources. Examples of typical bioinformatics scenarios are given by the gene functional studies, the study of microRNA Single Nucleotide Polymorphisms in cancer disease, or the study of protein motifs linked to specific cellular pathways. Available bioinformatics tools give a big contribute in problem solving, but they still require several and time consuming efforts. In this work, we highlight how the Gremlin graph traversal language can be also considered as lingua franca and as a valid middleware tool in the implementation, for instance, of some high-level web based tool to solve complex biological tasks. Gremlin queries are tested via a web interface to our previously developed integrated graph database, BioGraphDB.


title: Automatic Eye Detection and Its Validation

abstract: The accuracy of face alignment affects the performance of a face recognition system. Since face alignment is usually conducted using eye positions, an accurate eye localization algorithm is therefore essential for accurate face recognition. In this paper, we first study the impact of eye locations on face recognition accuracy, and then introduce an automatic technique for eye detection. The performance of our automatic eye detection technique is subsequently validated using FRGC 1.0 database. The validation shows that our eye detector has an overall 94.5% eye detection rate, with the detected eyes very close to the manually provided eye positions. In addition, the face recognition performance based on the automatic eye detection is shown to be comparable to that of using manually given eye positions.


title: Security in embedded systems: Design challenges

abstract: Many modern electronic systems---including personal computers, PDAs, cell phones, network routers, smart cards, and networked sensors to name a few---need to access, store, manipulate, or communicate sensitive information, making security a serious concern in their design. Embedded systems, which account for a wide range of products from the electronics, semiconductor, telecommunications, and networking industries, face some of the most demanding security concerns---on the one hand, they are often highly resource constrained, while on the other hand, they frequently need to operate in physically insecure environments.Security has been the subject of intensive research in the context of general-purpose computing and communications systems. However, security is often misconstrued by embedded system designers as the addition of features, such as specific cryptographic algorithms and security protocols, to the system. In reality, it is a new dimension that designers should consider throughout the design process, along with other metrics such as cost, performance, and power.The challenges unique to embedded systems require new approaches to security covering all aspects of embedded system design from architecture to implementation. Security processing, which refers to the computations that must be performed in a system for the purpose of security, can easily overwhelm the computational capabilities of processors in both low- and high-end embedded systems. This challenge, which we refer to as the "security processing gap," is compounded by increases in the amounts of data manipulated and the data rates that need to be achieved. Equally daunting is the "battery gap" in battery-powered embedded systems, which is caused by the disparity between rapidly increasing energy requirements for secure operation and slow improvements in battery technology. The final challenge is the "assurance gap," which relates to the gap between functional security measures (e.g., security services, protocols, and their constituent cryptographic algorithms) and actual secure implementations. This paper provides an introduction to the challenges involved in secure embedded system design, discusses recent advances in addressing them, and identifies opportunities for future research.


title: Surgical evacuation of acute subdural hematoma improves cerebral hemodynamics in children: a transcranial Doppler evaluation

abstract: OBJECTIVE ::: The objective was to evaluate cerebral hemodynamics in young children with acute subdural hematoma (SDH) and the impact of surgical treatment using transcranial Doppler (TCD). ::: ::: ::: DESIGN ::: The design was a prospective study of infants with SDH requiring surgical evacuation. ::: ::: ::: SETTING ::: The setting was the neuro intensive care unit of a university hospital. ::: ::: ::: INTERVENTIONS ::: Indications for surgical evacuation were based upon clinical and radiological arguments. Surgery included emergency needle aspiration followed by external or/and internal shunting as required. A TCD evaluation was performed before needle aspiration, and after each surgical drainage procedure. It included a pressure provocation test to assess cerebral compliance. Preoperative and postoperative middle cerebral artery (MCA) velocities, Gosling pulsatility (PI) and Pourcelot resistivity (RI) indexes and compliance were compared with Student's t-test, or Fisher's exact test as indicated. ::: ::: ::: MEASUREMENTS AND MAIN RESULTS ::: Out of 26 infants, 23 (88%) had injuries that had possibly been inflicted, and 3 had accidental injuries. Initial TCD evaluation demonstrated intracranial hypertension with decreased diastolic velocity, increased PI and RI, and decreased compliance. Surgical evacuation resulted in statistically significant improvement in cerebral hemodynamics (diastolic velocity: 17.2+/-10 cm/s vs. 31.1+/-10 cm/s, p<0.0015, PI: 2.5+/-1.3 vs. 1.4+/-0.8, p<0.002, RI: 0.8+/-0.2 vs. 0.6+/-0.1, p<0.005) in all but 3 infants, who eventually died. Surgical drainage (primary shunting or external drainage) was needed in 23 infants and resulted in further improvement in cerebral hemodynamics. Finally, 73% of the infants made a good recovery. ::: ::: ::: CONCLUSIONS ::: Children with acute bilateral HSD have a high incidence of increased intracranial pressure as assessed by TCD. Surgical evacuation improves cerebral hemodynamics. TCD could be used for assessing the need for, and the efficiency of surgical drainage.


title: CAME: cloud-assisted motion estimation for mobile video compression and transmission

abstract: Video streaming has become one of the most popular networked applications and, with the increased bandwidth and computation power of mobile devices, anywhere and anytime streaming has become a reality. Unfortunately, it remains a challenging task to compress high-quality video in real-time in such devices given the excessive computation and energy demands of compression. On the other hand, transmitting the raw video is simply unaffordable from both energy and bandwidth perspective. In this paper, we propose CAME, a novel cloud-assisted video compression method for mobile devices. CAME leverages the abundant cloud server resources for motion estimation, which is known to be the most computation-intensive step in video compression, accounting for over 90% of the computation time. With CAME, a mobile device selects and uploads only the key information of each picture frame to cloud servers for mesh-based motion estimation, eliminating most of the local computation operations. We develop smart algorithms to identify the key mesh nodes, resulting in minimum distortion and data volume for uploading. Our simulation results demonstrate that CAME saves almost 30% energy for video compression and transmission.


title: A Price Driven Hazard Approach to User Retention

abstract: Customer loyalty is crucial for internet services since retaining users of a service to ensure the staying time of the service is of significance for increasing revenue. It demands the retention of customers to be high enough to meet the needs for yielding profit for the internet servers. Besides, the growing of rich purchasing interaction feedback helps in uncovering the inner mechanism of purchasing intent of the customers. ::: In this work, we exploit the rich interaction data of user to build a customers retention evaluation model focusing on the return time of a user to a product. Three aspects, namely the consilience between user and product, the sensitivity of the user to price and the external influence the user might receive, are promoted to effect the purchase intents, which are jointly modeled by a probability model based on Cox's proportional hazard approach. The hazard based model provides benefits in the dynamics in user retention and it can conveniently incorporate covariates in the model. Extensive experiments on real world purchasing data have demonstrated the superiority of the proposed model over state-of-the-art algorithms.


Characterisation of the Corticospinal Tract Using Diffusion Magnetic Resonance Imaging in Unilateral and Bilateral Cerebral Palsy Patients


title: End-to-end packet delay and loss behavior in the internet

abstract: We use the measured round trip delays of small UDP probe packets sent at regular time intervals to analyze the end-to-end packet delay and loss behavior in the Internet. By varying the interval between probe packets, it is possible to study the structure of the Internet load over different time scales. In this paper, the time scales of interest range from a few milliseconds to a few minutes. Our observations agree with results obtained by others using simulation and experimental approaches. For example, our estimates of Internet workload are consistent with the hypothesis of a mix of bulk traffic with larger packet size, and interactive traffic with smaller packet size. We observe compression (or clustering) of the probe packets, rapid fluctuations of queueing delays over small intervals, etc. Our results also show interesting and less expected behavior. For example, we find that the losses of probe packets are essentially random unless the probe traffic uses a large fraction of the available bandwidth. We discuss the implications of these results on the design of control mechanisms for the Internet.


title: Reliability of a quantitative clinical posture assessment tool among persons with idiopathic scoliosis

abstract: Objective To determine overall, test–retest and inter-rater reliability of posture indices among persons with idiopathic scoliosis. Design A reliability study using two raters and two test sessions. Setting Tertiary care paediatric centre. Participants Seventy participants aged between 10 and 20 years with different types of idiopathic scoliosis (Cobb angle 15 to 60 ◦ ) were


title: An Implementation of Data Pre-Processing for Small Dataset

abstract: re-processing in data mining played essential role for enhancing data quality. The basic concept behind is that, learning with accurate and high quality data may provide more efficient classification results as compared to learning with poor quality of data. In this presented paper a pre- processing technique is implemented with slight modification which is based on the technique given in (1). In this paper a promising approach of data pre-processing is provided which utilizes a fuzzy technique in order to improve the data quality. The implementation of available technique is performed using MATLAB. Additionally, the improved fuzzy technique is also implemented with it. The results demonstrate the effectiveness in classification accuracy after implementation of both techniques. Finally, the obtained results favour the proposed model for enhancing the performance of classifiers in both manners supervised and unsupervised manner. Keywords—data mining,pre-processing, data quality enhancement, classification, performance improvement


title: Neural Image Compression for Gigapixel Histopathology Image Analysis

abstract: We propose Neural Image Compression (NIC), a two-step method to build convolutional neural networks for gigapixel image analysis solely using weak image-level labels. First, gigapixel images are compressed using a neural network trained in an unsupervised fashion, retaining high-level information while suppressing pixel-level noise. Second, a convolutional neural network (CNN) is trained on these compressed image representations to predict image-level labels, avoiding the need for fine-grained manual annotations. We compared several encoding strategies, namely reconstruction error minimization, contrastive training and adversarial feature learning, and evaluated NIC on a synthetic task and two public histopathology datasets. We found that NIC can exploit visual cues associated with image-level labels successfully, integrating both global and local visual information. Furthermore, we visualized the regions of the input gigapixel images where the CNN attended to, and confirmed that they overlapped with annotations from human experts.


title: Non-uniformity Correction of Infrared Images by Midway Equalization

abstract: The non-uniformity is a time-dependent noise caused by the lack of sensor equalization. We present here the detailed algorithm and online demo of the non-uniformity correction method by midway infrared equalization. This method was designed to suit infrared images. Nevertheless, it can be applied to images produced for example by scanners, or by push-broom satellites. This single image method works on static images, is fully automatic, has no user parameter, and requires no registration. It needs no camera motion compensation, no closed aperture sensor equalization and is able to correct for a fully non-linear non-uniformity.


title: Dry powder inhalers for pulmonary drug delivery

abstract: The pulmonary route is an interesting route for drug administration, both for effective local therapy (asthma, chronic obstructive pulmonary disease or cystic fibrosis) and for the systemic administration of drugs (e.g., peptides and proteins). Well-designed dry powder inhalers are highly efficient systems for pulmonary drug delivery. However, they are also complicated systems, the the performance of which relies on many aspects, including the design of the inhaler (e.g., resistance to air flow and the used de-agglomeration principle to generate the inhalation aerosol), the powder formulation and the air flow generated by the patient. The technical background of these aspects, and how they may be tuned in order to obtain desired performance profiles, is reviewed. In light of the technical background, new developments and possibilities for further improvements are discussed.


title: Coupled-Resonator Filters With Frequency-Dependent Couplings: Coupling Matrix Synthesis

abstract: This letter presents a novel technique for synthesis of coupled-resonator filters with inter-resonator couplings varying linearly with frequency. The values of non-zero elements of the coupling matrix are found by solving a nonlinear least squares problem involving eigenvalues of matrix pencils derived from the coupling matrix and reference zeros and poles of scattering parameters. The proposed method was verified by numerical tests carried out for various coupling schemes including triplets and quadruplets for which the frequency-dependent coupling was found to produce an extra zero.


title: Training robust models using Random Projection

abstract: Regularization plays an important role in machine learning systems. We propose a novel methodology for model regularization using random projection. We demonstrate the technique on neural networks, since such models usually comprise a very large number of parameters, calling for strong regularizers. It has been shown recently that neural networks are sensitive to two kinds of samples: (i) adversarial samples, which are generated by imperceptible perturbations of previously correctly-classified samples—yet the network will misclassify them; and (ii) fooling samples, which are completely unrecognizable, yet the network will classify them with extremely high confidence. In this paper, we show how robust neural networks can be trained using random projection. We show that while random projection acts as a strong regularizer, boosting model accuracy similar to other regularizers, such as weight decay and dropout, it is far more robust to adversarial noise and fooling samples. We further show that random projection also helps to improve the robustness of traditional classifiers, such as Random Forrest and Gradient Boosting Machines.


title: Games solved: Now and in the future

abstract: In this article we present an overview on the state of the art in games solved in the domain of two-person zero-sum games with perfect information. The results are summarized and some predictions for the near future are given. The aim of the article is to determine which game characteristics are predominant when the solution of a game is the main target. First, it is concluded that decision complexity is more important than state-space complexity as a determining factor. Second, we conclude that there is a trade-off between knowledge-based methods and brute-force methods. It is shown that knowledge-based methods are more appropriate for solving games with a low decision complexity, while brute-force methods are more appropriate for solving games with a low state-space complexity. Third, we found that there is a clear correlation between the first-player's initiative and the necessary effort to solve a game. In particular, threat-space-based search methods are sometimes able to exploit the initiative to prove a win. Finally, the most important results of the research involved, the development of new intelligent search methods, are described.


title: A Complexity-Invariant Distance Measure for Time Series Gustavo E.A.P.A. Batista 1,2

abstract: The ubiquity of time series data across almost all human endeavors has produced a great interest in time series data mining in the last decade. While there is a plethora of classification algorithms that can be applied to time series, all of the current empirical evidence suggests that simple nearest neighbor classification is exceptionally difficult to beat. The choice of distance measure used by the nearest neighbor algorithm depends on the invariances required by the domain. For example, motion capture data typically requires invariance to warping. In this work we make a surprising claim. There is an invariance that the community has missed, complexity invariance. Intuitively, the problem is that in many domains the different classes may have different complexities, and pairs of complex objects, even those which subjectively may seem very similar to the human eye, tend to be further apart under current distance measures than pairs of simple objects. This fact introduces errors in nearest neighbor classification, where complex objects are incorrectly assigned to a simpler class. We introduce the first complexity-invariant distance measure for time series, and show that it generally produces significant improvements in classification accuracy. We further show that this improvement does not compromise efficiency, since we can lower bound the measure and use a modification of triangular inequality, thus making use of most existing indexing and data mining algorithms. We evaluate our ideas with the largest and most comprehensive set of time series classification experiments ever attempted, and show that complexity-invariant distance measures can produce improvements in accuracy in the vast majority of cases.


title: A Glimpse Far into the Future: Understanding Long-term Crowd Worker Quality

abstract: Microtask crowdsourcing is increasingly critical to the creation of extremely large datasets. As a result, crowd workers spend weeks or months repeating the exact same tasks, making it necessary to understand their behavior over these long periods of time. We utilize three large, longitudinal datasets of nine million annotations collected from Amazon Mechanical Turk to examine claims that workers fatigue or satisfice over these long periods, producing lower quality work. We find that, contrary to these claims, workers are extremely stable in their quality over the entire period. To understand whether workers set their quality based on the task's requirements for acceptance, we then perform an experiment where we vary the required quality for a large crowdsourcing task. Workers did not adjust their quality based on the acceptance threshold: workers who were above the threshold continued working at their usual quality level, and workers below the threshold self-selected themselves out of the task. Capitalizing on this consistency, we demonstrate that it is possible to predict workers' long-term quality using just a glimpse of their quality on the first five tasks.