title: IT Governance Mechanisms: A Literature Review

abstract: Nowadays information technology (IT) is present in all organizations. This pervasive use of technology has created a critical dependency on IT that calls for a specific focus on IT Governance (ITG). Organizations with effective governance have actively adopted a set of ITG mechanisms. However, ITG mechanisms are not well defined among the literature and in some cases there are incongruities in their definition. In this paper we intend to perform a literature review (LR) in order to elicit which are the main ITG mechanisms as well as to describe them and state what they are useful for. We finish our work with conclusion, contributions, limitations and future work.


title: Canine computer interaction: towards designing a touchscreen interface for working dogs

abstract: Touchscreens can provide a way for service dogs to relay emergency information about their handlers from a home or office environment. In this paper, we build on work exploring the ability of canines to interact with touchscreen interfaces. We observe new requirements for training and explain best practices found in training techniques. Learning from previous work, we also begin to test new dog interaction techniques such as lift-off selection and sliding gestural motions. Our goal is to understand the affordances needed to make touchscreen interfaces usable for canines and help the future design of touchscreen interfaces for assistance dogs in the home.


title: Bioinformatics—an introduction for computer scientists

abstract: The article aims to introduce computer scientists to the new field of bioinformatics. This area has arisen from the needs of biologists to utilize and help interpret the vast amounts of data that are constantly being gathered in genomic research---and its more recent counterparts, proteomics and functional genomics. The ultimate goal of bioinformatics is to develop in silico models that will complement in vitro and in vivo biological experiments. The article provides a bird's eye view of the basic concepts in molecular cell biology, outlines the nature of the existing data, and describes the kind of computer algorithms and techniques that are necessary to understand cell behavior. The underlying motivation for many of the bioinformatics approaches is the evolution of organisms and the complexity of working with incomplete and noisy data. The topics covered include: descriptions of the current software especially developed for biologists, computer and mathematical cell models, and areas of computer science that play an important role in bioinformatics.


title: Learning Awareness Models

abstract: We consider the setting of an agent with a fixed body interacting with an unknown and uncertain external world. We show that by maximizing the entropy of predictions about the body---touch sensors, proprioception and vestibular information---we are able to learn dynamic models of the body that can be used for control. In spite of being trained with only internally available signals, these dynamic body models come to represent external objects through the necessity of predicting their effects on the agent's own body. Our dynamics model is able to successfully predict distributions over 132 sensor readings over 100 steps into the future. We demonstrate that even when the body is no longer in contact with an object, the latent variables of the dynamics model continue to represent its shape. That is, the model learns holistic persistent representations of objects in the world, even though the only training signals are body signals. We also collect data from a real robotic hand and show that the same models can be used to answer questions about properties of objects in the real world.


title: Improved ant colony genetic algorithm hybrid for Sudoku solving

abstract: In this paper we introduce our new improved version of ant colony optimization/genetic algorithm hybrid for Sudoku puzzle solving. Sudoku is combinatorial number puzzle that had become worldwide phenomenon in the last decade. It has also become popular mathematical test problem in order to test new optimization ideas and algorithms for combinatorial problems. In this paper we present our new ideas for populations sorting and elitism rules in order to improve our earlier evolutionary algorithm based Sudoku solvers. Experimental results show that the new ideas significantly improved the speed of Sudoku solving.


title: Contribution of Sequence Motif, Chromatin State, and DNA Structure Features to Predictive Models of Transcription Factor Binding in Yeast

abstract: Transcription factor (TF) binding is determined by the presence of specific sequence motifs (SM) and chromatin accessibility, where the latter is influenced by both chromatin state (CS) and DNA structure (DS) properties. Although SM, CS, and DS have been used to predict TF binding sites, a predictive model that jointly considers CS and DS has not been developed to predict either TF-specific binding or general binding properties of TFs. Using budding yeast as model, we found that machine learning classifiers trained with either CS or DS features alone perform better in predicting TF-specific binding compared to SM-based classifiers. In addition, simultaneously considering CS and DS further improves the accuracy of the TF binding predictions, indicating the highly complementary nature of these two properties. The contributions of SM, CS, and DS features to binding site predictions differ greatly between TFs, allowing TF-specific predictions and potentially reflecting different TF binding mechanisms. In addition, a "TF-agnostic" predictive model based on three DNA “intrinsic properties” (in silico predicted nucleosome occupancy, major groove geometry, and dinucleotide free energy) that can be calculated from genomic sequences alone has performance that rivals the model incorporating experiment-derived data. This intrinsic property model allows prediction of binding regions not only across TFs, but also across DNA-binding domain families with distinct structural folds. Furthermore, these predicted binding regions can help identify TF binding sites that have a significant impact on target gene expression. Because the intrinsic property model allows prediction of binding regions across DNA-binding domain families, it is TF agnostic and likely describes general binding potential of TFs. Thus, our findings suggest that it is feasible to establish a TF agnostic model for identifying functional regulatory regions in potentially any sequenced genome.


title: Wideband Double Monopole for Mobile, WLAN, and C2C Services in Vehicular Applications

abstract: In this letter, a three-dimensional antenna solution for automotive applications is presented. The proposed solution is formed by two independent antennas, perpendicularly placed inside a plastic cover with a shark-fin shape. The antennas, designed with low-cost materials, exhibit omnidirectional radiation patterns in the azimuthal plane. The first antenna is a double-shorted monopole that provides service from 698 to 960 MHz (in the LTE700, GSM850, and GSM900 bands), whereas the second antenna is a drop-shaped monopole that operates from 1.7 to 2.7 GHz (in the DCS1800, PCS1900, WCDMA2100, WLAN2400, LTE2600, WiMAX2350, and Wi-Fi at 2.4-GHz bands), and from 5.1 to 6 GHz (in the Wi-Fi at 5 GHz and C2C bands). Results show a good matching and radiation efficiency at all bands.


title: G-Spotplasty: A New Surgical Plastic Intervention—The Preliminary Study

abstract: Ostrzenski’s G-pot anatomical structure discovery has been verified by the anatomy, histology, MRI in vivo, and electrovaginography in vivo studies. The objectives of this scientific-clinical investigation were to develop a new surgical reconstructive intervention (G-spotplasty); to determine the ability of G-spotplasty surgical implementation; to observe for potential complications; and to gather initial information on whether G-spotplasty improves female sexual activity, sexual behaviors, and sexual concerns. A case series study was designed and conducted with 5-year follow-up (October 2013 and October 2017). The rehearsal of new G-spotplasty was performed on fresh female cadavers. Three consecutive live women constituted this clinical study population, and they were subjected to the newly developed G-spotplasty procedure in October 2013. Preoperatively and postoperatively, a validated, self-completion instrument of Sexual Relationships and Activities Questionnaire (SRA-Q) was used to measure female sexual activity, sexual behaviors, and sexual concerns. Three out of twelve women met inclusion criteria and were incorporated into this study. All patients were subjected to G-spotplasty, completed 5-year follow-up, and returned completed SRA-Q in a sealed envelope. New G-spotplasty was successfully implemented without surgical difficulty and without complications. All patients reported re-establishing vaginal orgasms with different degrees of difficulties, observing return of anterior vaginal wall engorgement, and were very pleased with the outcome of G-spotplasty. The G-spotplasty is a simple surgical intervention, easy to implement, and improves sexual activities, sexual behaviors, and sexual concerns. The preliminary results are very promising and paved the way for additional clinical-scientific research. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.


title: Assessing Suicide Risk and Emotional Distress in Chinese Social Media: A Text Mining and Machine Learning Study

abstract: Background: Early identification and intervention are imperative for suicide prevention. However, at-risk people often neither seek help nor take professional assessment. A tool to automatically assess their risk levels in natural settings can increase the opportunity for early intervention. Objective: The aim of this study was to explore whether computerized language analysis methods can be utilized to assess one’s suicide risk and emotional distress in Chinese social media. Methods: A Web-based survey of Chinese social media (ie, Weibo) users was conducted to measure their suicide risk factors including suicide probability, Weibo suicide communication (WSC), depression, anxiety, and stress levels. Participants’ Weibo posts published in the public domain were also downloaded with their consent. The Weibo posts were parsed and fitted into Simplified Chinese-Linguistic Inquiry and Word Count (SC-LIWC) categories. The associations between SC-LIWC features and the 5 suicide risk factors were examined by logistic regression. Furthermore, the support vector machine (SVM) model was applied based on the language features to automatically classify whether a Weibo user exhibited any of the 5 risk factors. Results: A total of 974 Weibo users participated in the survey. Those with high suicide probability were marked by a higher usage of pronoun (odds ratio, OR=1.18, P=.001), prepend words (OR=1.49, P=.02), multifunction words (OR=1.12, P=.04), a lower usage of verb (OR=0.78, P<.001), and a greater total word count (OR=1.007, P=.008). Second-person plural was positively associated with severe depression (OR=8.36, P=.01) and stress (OR=11, P=.005), whereas work-related words were negatively associated with WSC (OR=0.71, P=.008), severe depression (OR=0.56, P=.005), and anxiety (OR=0.77, P=.02). Inconsistently, third-person plural was found to be negatively associated with WSC (OR=0.02, P=.047) but positively with severe stress (OR=41.3, P=.04). Achievement-related words were positively associated with depression (OR=1.68, P=.003), whereas health- (OR=2.36, P=.004) and death-related (OR=2.60, P=.01) words positively associated with stress. The machine classifiers did not achieve satisfying performance in the full sample set but could classify high suicide probability (area under the curve, AUC=0.61, P=.04) and severe anxiety (AUC=0.75, P<.001) among those who have exhibited WSC. Conclusions: SC-LIWC is useful to examine language markers of suicide risk and emotional distress in Chinese social media and can identify characteristics different from previous findings in the English literature. Some findings are leading to new hypotheses for future verification. Machine classifiers based on SC-LIWC features are promising but still require further optimization for application in real life. [J Med Internet Res 2017;19(7):e243]


title: A short literature review on reward-based crowdfunding

abstract: In this short article, we discuss about a popular online fundraising method, named crowdfunding. One of the most useful types is reward-based crowdfunding, where the return is tangible products. We review related researches from three streams, the conceptual research, the empirical research, and the modelling research. Some possible research directions are also discussed in the paper.


title: A Linear Differential Transimpedance Amplifier for 100-Gb/s Integrated Coherent Optical Fiber Receivers

abstract: This paper presents the design and measurements of a 32-Gb/s differential-input differential-output transimpedance amplifier (TIA) employed in dual polarization integrated coherent receivers for 100-Gb Ethernet. A circuit technique is shown that uses a replica TIA to stabilize the operating point of the two shunt-feedback input stages as well as to cancel the dc part of the two complementary input currents and balances their offset. The TIA can be operated in two modes, an automatic gain control mode to retain a good total harmonic distortion (THD) over a wide dynamic range and a manual gain control mode. Electrical as well as optical-electrical characterization of the TIA are presented. It achieves a maximum differential transimpedance of 74 dB $\Omega $ , 33 GHz of 3-dB bandwidth, 12.2 pA/ $\sqrt {\text {Hz}}$ of average input-referred noise current density with the photodiode, 900 mVpp of maximum differential output swing, less than 1% of THD for 600 mVpp differential output swing, and 500 $\mu \text{A}_{\text {pp}}$ differential input current. The linearity of the TIA is furthermore demonstrated with PAM4 measurements at 25 Gbaud. The dual TIA chip is fabricated in a 0.13- $\mu \text{m}$ SiGe:C BiCMOS technology, dissipates 436 mW of power and occupies 2 mm2 of area.


title: Review: Pulse coupled neural networks and its applications

abstract: This paper surveys the extensive usage of pulse coupled neural networks. The visual cortex system of mammalians was the backbone for the development of pulse coupled neural network. PCNN (Pulse Coupled Neural Networks) is unique from other techniques due to its synchronous pulsed output, adjustable threshold and controllable parameters. is Hence the uniqueness of this network utilized in the fields of image processing. The basic model of PCNN and the consecutive changes implemented, to strengthen the pulse coupled neural network are discussed initially. Then the applications of PCNN are broadly discussed. The other miscellaneous applications utilizing pulse coupled neural networks are thrown light in the last section.


title: Simultaneous Localization and Planning for Physical Mobile Robots via Enabling Dynamic Replanning in Belief Space

abstract: Simultaneous planning while localizing is a crucial ability for an autonomous robot operating under uncertainty. This paper addresses this problem by designing methods to dynamically replan while the localization uncertainty or environment map is updated. In particular, relying on sampling-based methods, the proposed online planning scheme can cope with challenging situations, including when the localization update or changes in the environment alter the homotopy class of trajectories, in which the optimal plan resides. The proposed algorithm eliminates the need for stabilization in the state-of-the-art FIRM (Feedback-based Information RoadMap) method, and outperforms its performance and success probability. Applying belief space planning to physical systems brings with it a plethora of challenges. Thus, designing computationally tractable algorithms, a key focus and contribution of this paper is to implement the proposed planner on a physical robot and show the SLAP (simultaneous localization and planning) performance under uncertainty, in changing environments, and in the presence of large disturbances such as kidnapped robot situation.


title: Hybrid discrete-parametric optimization for trajectory planning in on-road driving scenarios

abstract: Dynamic on-road driving scenarios require robust methods for planning a safe and feasible vehicle motion coping with both static and dynamic obstacles. Many of the different approaches which have been proposed to tackle this challenge are based on optimal control and employ local continuous or discrete optimization schemes. While discrete methods possess the ability to find reasonable solutions in a search space incorporating local minima, they tend to sacrifice optimality for real-time performance. On the other hand, local continuous methods require suitable initialization to handle the combinatorial nature of on-road driving scenarios but are capable of quickly returning an optimized solution. The presented work proposes a hybrid solution which embraces both strategies to unite their distinct advantages. A discrete optimization scheme is augmented by parametric optimization to achieve both low planning times as well as robust (re-)initialization.


title: Usability testing of smart mobile walker: A pilot study

abstract: Various robotic systems have been proposed to provide assistance in activities related to daily living and in physical therapy. Our consortium developed a prototype assistive robotic module, called the "smart mobile walker," which is a motorized walker. This robotic device was developed to assist with the mobility of senior citizens and persons with disabilities such as stroke survivors and people with spinal cord injuries. A pilot study was performed with four senior citizens to evaluate the usability of the system relative to a commercial walker. The participants performed a "10-meter walk test" (10MWT) with either the smart mobile walker or a commercial walker on flat, upward-sloping, and downward-sloping surfaces, indoors over five days. An electromyography (EMG) signal and the heart rate were recorded, and the gait parameters including the speed, cadence, stride length, and gait cycle duration were measured to evaluate the system usability. The participants performed better with the commercial walker on the flat and upward-sloping surfaces owing to the unit's light weight. On the downward-sloping surface, however, the speed control offered by the smart mobile walker was found to outweigh the advantage of the light weight of the commercial walker.


title: Design and implementation of cybersecurity testbed for industrial IoT systems

abstract: In the era of Industry 4.0, information and communication technology (ICT) has been applied to various critical infrastructures, such as power plants, smart factories, and financial networks, to ensure and automate industrial systems. In particular, in the field of power control systems, ICT technology such as industrial internet of things (IoT) is applied for efficient remote measurement. Therefore, legacy systems that were previously operated as standalone now have contact points with the external networks. In this trend, security vulnerabilities from legacy ICT have been inherited by power control systems. Therefore, various security technologies are being researched and developed to cope with cyber vulnerabilities and threats. However, it is risky to apply novel security technologies that are not verified as secure, to power control systems, the availability of which must be guaranteed to provide electricity consistently. Thus, verifying the effectiveness and stability of new security technologies is necessary to apply the technologies to power control systems. In this paper, we analyze the configuration and characteristics of the power control systems network, which is an area where industrial IoT technology is applied. We also build a testbed environment that can verify the security technology and conduct experiments to confirm the security technology for the power control system and the suitability of the testbed. The proposed testbed will be able to stably incorporate new security technologies into the critical industrial infrastructure. Further, it is also expected that the security and stability of the system will be enhanced.


title: On How to Design Dataflow FPGA-Based Accelerators for Convolutional Neural Networks

abstract: In the past few years we have experienced an extremely rapid growth of modern applications based on deep learning algorithms such as Convolutional Neural Network (CNN), and consequently, an intensification of academic and industrial research focused on the optimization of their imple- mentation. Among the different alternatives that have been ex- plored, FPGAs seems to be one of the most attractive, as they are able to deliver high performance and energy-efficiency, thanks to their inherent parallelism and direct hardware execution, while retaining extreme flexibility due to their reconfigurability.In this paper we present a design methodology of a dataflow accelerator for the implementation of CNNs on FPGAs, that ensures scalability – and achieve a higher degree of parallelism as the size of the CNN increases – and an efficient exploitation of the available resources. Furthermore, we analyze resource consumption of the layers of the CNN as well as latency in relation to the implementation's hyperparameters. Finally, we show that the proposed design implements a high-level pipeline between the different network layers, and as a result, we can improve the latency to process an image by feeding the CNN with batches of multiple images.


title: An Autonomous

abstract: Cloud computing paradigm allows subscription-based access to computing and storages services over the Internet. Since with advances of Cloud technology, operations such as discovery, scaling, and monitoring are accomplished automatically, negotiation between Cloud service requesters and providers can be a bottleneck if it is carried out by humans. Therefore, our objective is to offer a state-of-the-art solution to automate the negotiation process in Cloud environments. In previous works in the SLA negotiation area, requesters trust whatever QoS criteria values providers offer in the process of negotiation. However, the proposed negotiation strategy for requesters in this work is capable of assessing reliability of offers received from Cloud providers. In addition, our proposed negotiation strategy for Cloud providers considers utilization of resources when it generates new offers during negotiation and concedes more on the price of less utilized resources. The experimental results show that our strategy helps Cloud providers to increase their profits when they are participating in parallel negotiation with multiple requesters.


title: pTCP: an end-to-end transport layer protocol for striped connections

abstract: The TCP transport layer protocol is designed for connections that traverse a single path between the sender and receiver. However there are several environments in which multiple paths can be used by a connection simultaneously. We consider the problem of supporting striped connections that operate over multiple paths. We propose an end-to-end transport layer protocol called pTCP (parallel TCP) that allows connections to enjoy the aggregate bandwidths offered by the multiple paths, irrespective of the individual characteristics of the paths. We show that pTCP can have a varied range of applications through instantiations in three different environments: (a) bandwidth aggregation on multi-homed mobile hosts; (b) service differentiation using purely end-to-end mechanisms; (c) end-systems based network striping. In each of the applications, we demonstrate the applicability of pTCP and how its efficacy compares with existing approaches through simulation results.


title: Chlamydia trachomatis in non-specific urethritis.

abstract: Chlamydia trachomatis was isolated from 58.5% of 159 patients with non-specific urethritis (NSU) using irradiated McCoy cell cultures. Patients with persistent Chlamydia-positive NSU remained Chlamydia-positive each time they were examined before treatment and patients with Chlamydia-negative NSU remained Chlamydia-negative during the course of the illness. Neither the duration of symptoms of urethritis nor a history of previous urethritis affected the chlamydial isolation rate significantly. Of 40 patients with severe discharge 30 (75%) harboured C. trachomatis. One-third of the Chlamydia-positive patients had a severe urethral discharge, while this was present in only 15% of Chlamydia-negative patients. Complications--such as conjunctivitis, arthritis, and epididymitis--were more severe in men with Chlamdia-positive NSU than in those with Chlamydia-negative NSU. Of 64 men matched for sexual promiscuity but without urethritis, none harboured C. trachomatis in his urethra. This differs significantly (P less than 0.001) when compared with patients with NSU. C. trachomatis was isolated from the urogenital tract in 24 (42%) out of 57 female sexual contacts of patients with NSU. The presence of C. trachomatis in the women correlated significantly (P less than 0.001) with the isolation of the agent from their male contacts. These findings give further evidence for the aetiological role of C. trachomatis in non-specific urethritis and its sexual transmission.


title: A Novel Mechatronic Body Weight Support System

abstract: A novel mechatronic body weight support (BWS) system has been developed to provide precise body weight unloading for patients with neurological or other impairments during treadmill training. The system is composed of a passive elastic spring element to take over the main unloading force and an active closed-loop controlled electric drive to generate the exact desired force. Both force generating units, the passive spring and the active electric drive, act on the patient via a polyester rope connected to a harness worn by the patient. The length of the rope can be adjusted with an electric winch to adapt the system to different patient sizes. The system is fully computer controlled. At unloading loads of up to 60 kg and walking speeds of up to 3.2 km/h, the mean unloading error and the maximum unloading error of the presented BWS system was less than 1 and 3 kg, respectively. The performance was compared with those of two purely passive BWS systems currently being used by most other rehabilitation groups. This comprised counterweight systems and static BWS systems with fixed rope lengths. Counterweight systems reached mean and maximum unloading errors of up to 5.34 and 16.22 kg, respectively. The values for the static BWS were 11.02 kg and 27.67 kg, respectively. The novel mechatronic BWS system presented in this study adjusts desired unloading changes of up to 20 kg within less than 100 ms. Thus, not only constant BWS, but also gait cycle dependent or time variant oscillations of the desired force can be realized with high accuracy. Precise and constant unloading force is believed to be an important prerequisite for BWS gait therapy, where it is important to generate physiologically correct segmental dynamics and ground reaction forces. Thus, the novel BWS system presented in this paper is an important contribution to maximize the therapeutic outcome of human gait rehabilitation


title: Summarizing customer review based on product feature and opinion

abstract: Opinion or sentiment analysis has risen to extract useful information from a lot of unstructured text data, in the form of customer reviews on different products and their features or online SNS data respectively. Customer reviews are not only helpful for potential customers, but also are helpful for the manufacturers of the products to raise their products and services. The reviews conciseness takes the attention of the customers rather than long reviews. Opinion Mining is playing a major role to summarize customer reviews and make it easy for online customers to determine whether to purchase the products or not. In this paper, we propose a supervised lazy learning model utilizing syntactic rules for the product features and opinion words extraction in subjective review sentences. In our lazy learning algorithm, i.e. K-NN with k=3 is used for the review sentences' classification into two classes (subjective, objective). Our experiment shows that proposed method can improve the performance of existing work in terms of average precision, recall and f-score for the extraction of opinion sentences and product features.


title: The modifiable areal unit problem and implications for landscape ecology

abstract: Landscape ecologists often deal with aggregated data and multiscaled spatial phenomena. Recognizing the sensitivity of the results of spatial analyses to the definition of units for which data are collected is critical to characterizing landscapes with minimal bias and avoidance of spurious relationships. We introduce and examine the effect of data aggregation on analysis of landscape structure as exemplified through what has become known, in the statistical and geographical literature, as the Modifiable Areal Unit Problem (MAUP). The MAUP applies to two separate, but interrelated, problems with spatial data analysis. The first is the “scale problem”, where the same set of areal data is aggregated into several sets of larger areal units, with each combination leading to different data values and inferences. The second aspect of the MAUP is the “zoning problem”, where a given set of areal units is recombined into zones that are of the same size but located differently, again resulting in variation in data values and, consequently, different conclusions. We conduct a series of spatial autocorrelation analyses based on NDVI (Normalized Difference Vegetation Index) to demonstrate how the MAUP may affect the results of landscape analysis. We conclude with a discussion of the broaderscale implications for the MAUP in landscape ecology and suggest approaches for dealing with this issue.


title: Locally optimized ransac

abstract: A new enhancement of RANSAC, the locally optimized RANSAC (LO-RANSAC), is introduced. It has been observed that, to find an optimal solution (with a given probability), the number of samples drawn in RANSAC is significantly higher than predicted from the mathematical model. This is due to the incorrect assumption, that a model with. parameters computed from an outlier-free sample is consistent with all inliers. The assumption rarely holds in practice. The locally optimized RANSAC makes no new assumptions about the data, on the contrary - it makes the above-mentioned assumption valid by applying local optimization to the solution estimated from the random sample. The performance of the improved RANSAC is evaluated in a number of epipolar geometry and homography estimation experiments. Compared with standard RANSAC, the speed-up achieved is two to three fold and the quality of the solution (measured by the number of inliers) is increased by 10-20%. The number of samples drawn is in good agreement with theoretical predictions.


Decision Tree-Based Data Characterization for Meta- Learning


title: Deep Extreme Multi-label Learning

abstract: Extreme multi-label learning (XML) or classification has been a practical and important problem since the boom of big data. The main challenge lies in the exponential label space which involves 2L possible label sets especially when the label dimension L is huge, e.g., in millions for Wikipedia labels. This paper is motivated to better explore the label space by originally establishing an explicit label graph. In the meanwhile, deep learning has been widely studied and used in various classification problems including multi-label classification, however it has not been properly introduced to XML, where the label space can be as large as in millions. In this paper, we propose a practical deep embedding method for extreme multi-label classification, which harvests the ideas of non-linear embedding and graph priors-based label space modeling simultaneously. Extensive experiments on public datasets for XML show that our method performs competitive against state-of-the-art result.


Brace : Assertion-Driven Development of Cyber-Physical Systems Applications TR-ARiSE-2013-001


title: Extracting Emerging Knowledge from Social Media

abstract: Massive data integration technologies have been recently used to produce very large ontologies. However, knowledge in the world continuously evolves, and ontologies are largely incomplete for what concerns low-frequency data, belonging to the so-called long tail. Socially produced content is an excellent source for discovering emerging knowledge: it is huge, and immediately reflects the relevant changes which hide emerging entities. Thus, we propose a method for discovering emerging entities by extracting them from social content. ::: ::: Once instrumented by experts through very simple initialization, the method is capable of finding emerging entities; we use a purely syntactic method as a baseline, and we propose several semantics-based variants. The method uses seeds, i.e. prototypes of emerging entities provided by experts, for generating candidates; then, it associates candidates to feature vectors, built by using terms occurring in their social content, and then ranks the candidates by using their distance from the centroid of seeds, returning the top candidates as result. The method can be continuously or periodically iterated, using the results as new seeds. We validate our method by applying it to a set of diverse domain-specific application scenarios, spanning fashion, literature, and exhibitions.


title: The Normalization Model of Attention

abstract: Attention has been found to have a wide variety of effects on the responses of neurons in visual cortex. We describe a model of attention that exhibits each of these different forms of attentional modulation, depending on the stimulus conditions and the spread (or selectivity) of the attention field in the model. The model helps reconcile proposals that have been taken to represent alternative theories of attention. We argue that the variety and complexity of the results reported in the literature emerge from the variety of empirical protocols that were used, such that the results observed in any one experiment depended on the stimulus conditions and the subject's attentional strategy, a notion that we define precisely in terms of the attention field in the model, but that has not typically been completely under experimental control.


title: Being curious about the answers to questions: novelty search with learned attention

abstract: We investigate the use of attentional neural network layers in order to learn a ‘behavior characterization’ which can be used to drive novelty search and curiosity-based policies. The space is stru...


AUTOMATED 3D OBJECT RECONSTRUCTION VIA MULTI-IMAGE CLOSE-RANGE PHOTOGRAMMETRY


title: Social media and sales : determining the predictive power of sentiment analysis towards car sales

abstract: This paper aims at exploring the use of sentiment analysis on social media as a tool for sales forecasting in the automotive industry. Previous research on this topic has presented significant results although current literature still lacks investigation on the usefulness of this technique when it comes to more expensive items. In particular, about 500,000 social media posts and eleven car models from the Dutch market are analyzed using linear models. Furthermore, the research compares these outcomes to the predictive power of search volume by using Google Trends as an indicator. Based on variables that are assessed as strong predictors of sales a prediction model using decision tree regression is built that can potentially be used by car manufacturers as an addition to traditional forecasting methods if tested and developed further. ::: The results suggest that social media sentiments have little to no predictive power towards car sales. While search volume as well as the rate of attention a model receives on social media show significant results and can be incorporated into the prediction model, the sentiments itself only obtain weak correlations with car sales and clearly show a limitation to the technique of sentiment analysis. Although the findings cannot be generalized for other car models and markets, this research contributes to further understanding the field of sentiment analysis and explores its boundaries. It also presents a prediction model that allows to approximate car sales. It therefore has both practical and academic value.


title: Dynamic Pricing and Energy Consumption Scheduling With Reinforcement Learning

abstract: In this paper, we study a dynamic pricing and energy consumption scheduling problem in the microgrid where the service provider acts as a broker between the utility company and customers by purchasing electric energy from the utility company and selling it to the customers. For the service provider, even though dynamic pricing is an efficient tool to manage the microgrid, the implementation of dynamic pricing is highly challenging due to the lack of the customer-side information and the various types of uncertainties in the microgrid. Similarly, the customers also face challenges in scheduling their energy consumption due to the uncertainty of the retail electricity price. In order to overcome the challenges of implementing dynamic pricing and energy consumption scheduling, we develop reinforcement learning algorithms that allow each of the service provider and the customers to learn its strategy without a priori information about the microgrid. Through numerical results, we show that the proposed reinforcement learning-based dynamic pricing algorithm can effectively work without a priori information about the system dynamics and the proposed energy consumption scheduling algorithm further reduces the system cost thanks to the learning capability of each customer.


title: IoT based parking system using Google

abstract: Today's world is of internet. Internet is a thing which is making our lives easier as well as faster. The use of smart phones has tremendous applications. The IoT (Internet of things) platforms allow us to connect the sensors with the internet. Hence using this we can implement many applications. The parking system is one of them. There are many problems not only in India but also in rest of the world because of unplanned parking. Hence we are trying to introduce a parking system based on IoT to make the travelling comfortable. The android applications are very user friendly interfaces can give better results for parking places although the Google maps are the key element of this system. From these maps not only user can locate the available spaces but also can find the direction for it. This system will show the efficient place for parking and it will also direct to reach there. This can reduce accidents as well as pollution.


title: Wedding Dresses and Wanted Criminals: Pinterest.com as an Infrastructure for Repository Building

abstract: We present findings from a qualitative study of activity on Pinterest.com, in which we investigated professional and personal uses of the site using interview data and observations of online activity. We find that Pinterest serves as an infrastructure for repository building that supports a wide range of activities including: discovery, collecting, collaborating, and publishing. We discuss these concepts using the language of “boundary objects” from the sociology of science. We suggest that scale is a critical dimension of boundary objects for understanding how people make sense of Pinterest and their diverse goals for using it. Profession- als often attempt to use Pinterest to create repositories that scale to groups, organizations and societies and interface with multiple social worlds whereas personal repositories often have highly localized meanings. Our approach builds on quantitative descriptions of Pinterest to understand how the site fits into a growing ecology of social network sites.


title: Methods of data augmentation for palimpsest character recognition with Deep Neural Network

abstract: Deep Neural Networks (DNN) are now widely used in computer vision, due to their recent success in large-scale image classification. Despite the impressive performance of DNN in supervised optical character recognition (OCR) for handwritten documents, they typically have not been used for the application of reading historical manuscripts. The main reason is the lack of availability of large datasets and of understanding about the amount of data required to achieve good results in supervised character classification tasks. In this paper, we show how to train DNN with a limited amount of data for the application of OCR of historical manuscripts. We investigate different strategies for data augmentation for palimpsests and analyze the effect of different methods on DNN performance. The method was tested on the well-known Archimedes palimpsest image dataset using 30 exemplars per character. We found that using the random mask overlay method of augmentation we can achieve up to 82% accuracy for classifying characters of the Archimedes palimpsest.


title: Research of quickly identifying markers on Augmented Reality

abstract: For complicated calculation caused by marker unwrapping in two main visual marker tracking platforms in Augmented Reality, ARToolKit and ARTag, a new kind of markers based on ARToolKit is designed and implemented, which are called diagonal connected components markers. Those markers can be recognized without unwrapping, which just rely on numbers of white connected components of two diagonals of those markers. Experiment results show that compared with ARToolKit markers, the speed of recognition of those new markers is greatly increased.


title: A Scalable 5–15 Gbps, 14–75 mW Low-Power I/O Transceiver in 65 nm CMOS

abstract: We present a scalable low-power I/O transceiver in 65 nm CMOS, capable of 5-15 Gbps operation over single-board and backplane FR4 channels with power efficiencies between 2.8-6.5 mW/Gbps. Nonlinear power-performance tradeoff is achieved by the use of scalable transceiver circuit blocks and joint optimization of the supply voltage, bias currents and driver power with data rate. Low-power operation is enabled by passive equalization through inductive link termination, active continuous-time RX equalization, global TX/RX clock distribution with on-die transmission lines, and low-noise offset-calibrated receivers.


title: Common-duty-ratio control of input-series connected modular DC-DC converters with active input voltage and load-current sharing

abstract: This paper proposes a simple control method to achieve active sharing of input voltage and load current among modular converters that are connected in series at the input and in parallel at the output. The input-series connection enables a fully modular power-system architecture, where low voltage and low power modules can be connected in any combination at the input and/or at the output, to realize any given specifications. Further, the input-series connection enables the use of low-voltage MOSFETs that are optimized for very low RDSON , thus, resulting in lower conduction losses. In the proposed scheme, the duty ratio to all the converter modules connected in input-series and output-parallel (ISOP) configuration is made common. This scheme does not require a dedicated input-voltage or load-current-share controller. It relies on the inherent self-correcting characteristic of the ISOP connection when the duty ratio of all the converters is the same. The proposed scheme is analyzed using the average model of a forward converter. The stability and performance of the scheme are verified through numerical simulation, both in frequency domain and in time domain. The proposed control method is also validated on an experimental prototype ISOP system comprising of two forward converters


title: Exploring substance use normalization among adolescents: A multilevel study in 35 countries

abstract: The substance use normalization thesis predicts that adolescent substance users are less likely to report substance use risk factors in high than in low prevalence countries. This study tests whether national population-level alcohol, cigarette and cannabis prevalence rates moderate the strength of the relationship between individual level social and behavioral risk factors and individual level alcohol, cigarette and cannabis use. Data from 2009/2010 Health Behaviour in School-Aged Children Study (N ¼ 68,045, age ¼ 15) from 35 countries was analyzed using logistic Hierarchical Linear Modeling. As expected based on low cannabis prevalence rates in all countries studied, no evidence of normalization was found for recent cannabis use. Also in line with the normalization thesis, results show that for substance use that reaches above 40% in at least some of the countries studied (drunkenness, alcohol and cigarette use), adolescents who reported use are less likely to report social and behavioral risk factors in high prevalence countries than in low prevalence countries. However, support for the normalization thesis was only partial in that results show that in models where evidence for normalization was found, there are risk factors that predict substance use to an equal degree regardless of country level prevalence rates. The current research shows that the normalization thesis is a useful framework for understanding the contextual aspects of adolescent alcohol, tobacco, and cannabis use. The study has implications for drug prevention as it suggests that selective prevention efforts may be particularly useful in low prevalence countries where screening based on risk factors may usefully identify adolescents at most risk for developing drug use problems. This approach may be less useful in high prevalence countries where screening based on risk factors is less likely to satisfactorily identify those at risk for developing drug use problems.


title: Volume 1 – No. 22 Fuzzy Logic Based Image Edge Detection Algorithm in MATLAB

abstract: This paper reports the implementation, in MATLAB environment, of a very simple but efficient fuzzy logic based algorithm to detect the edges of an input image by scanning it throughout using a 2*2 pixel window. Also, a Graphical User Interface (GUI) in MATLAB has been designed to aid the loading of the image, and to display the resultant image at different intermediate levels of processing. Threshold level for the image can be set from the slider control of GUI. Fuzzy inference system designed has four inputs, which corresponds to four pixels of instantaneous scanning matrix, one output that tells whether the pixel under consideration is “black”, “white” or “edge” pixel. Rule base comprises of sixteen rules, which classify the target pixel. Algorithm for the noise removal has been implemented at different levels of processing. The resultant image from FIS is subjected to first and second derivative to trace the edges of the image and for their further refinement. The results of the implemented algorithm has been compared with the standard edge detection algorithm such as ‘Canny’, ‘Sobel’, ‘Prewit’ and ‘Roberts’. Main feature of the algorithm is that it has been designed by the smallest possible mask i.e. 2*2 unlike 3*3 or bigger masks found in the literature.


title: Bell: bit-encoding online memory leak detection

abstract: Memory leaks compromise availability and security by crippling performance and crashing programs. Leaks are difficult to diagnose because they have no immediate symptoms. Online leak detection tools benefit from storing and reporting per-object sites (e.g., allocation sites) for potentially leaking objects. In programs with many small objects, per-object sites add high space overhead, limiting their use in production environments.This paper introduces Bit-Encoding Leak Location (Bell), a statistical approach that encodes per-object sites to a single bit per object. A bit loses information about a site, but given sufficient objects that use the site and a known, finite set of possible sites, Bell uses brute-force decoding to recover the site with high accuracy.We use this approach to encode object allocation and last-use sites in Sleigh, a new leak detection tool. Sleigh detects stale objects (objects unused for a long time) and uses Bell decoding to report their allocation and last-use sites. Our implementation steals four unused bits in the object header and thus incurs no per-object space overhead. Sleigh's instrumentation adds 29% execution time overhead, which adaptive profiling reduces to 11%. Sleigh's output is directly useful for finding and fixing leaks in SPEC JBB2000 and Eclipse, although sufficiently many objects must leak before Bell decoding can report sites with confidence. Bell is suitable for other leak detection approaches that store per-object sites, and for other problems amenable to statistical per-object metadata.


title: DXT: Darshan eXtended Tracing

abstract: Author(s): Xu, Cong; Snyder, Shane; Kulkarni, Omkar; Venkatesan, Vishwanath; Carns, Phillip; Byna, Surendra; Sisneros, Robert; Chadalavada, Kalyana | Abstract: As modern supercomputers evolve to exascale, their I/O subsystems are becoming increasingly complex, making optimization of I/O for scientific applications a daunting task. Although I/O profiling tools facilitate the process of optimizing application I/O performance, legacy profiling tools lack flexibility in their level of detail and ability to correlate traces with other sources of data. Additionally, a lack of robust trace analysis tools makes it difficult to derive actionable insights from large-scale I/O traces. Darshan is an HPC I/O characterization tool that records statistics in a lightweight manner that makes it appropriate for full-time production deployment. However, Darshan’s default characterization mechanism records information at a fixed granularity. We augment Darshan by proposing Darshan eXtended Tracing (DXT) for more detailed profiling of I/O software stacks. DXT enables users and administrators to vary the level of fidelity captured by Darshan at run time without modifying or recompiling applications. This capability facilitates systematic analysis on the I/O behavior of applications and can provide useful application kernel I/O traces to help advance parallel I/O research. We have demonstrated the power of DXT by obtaining a wide range of useful statistics for multiple case studies, and we further show that DXT is able to do so same with negligible overhead.


title: Real World Performance of Approximate String Comparators for use in Patient Matching

abstract: Medical record linkage is becoming increasingly important as clinical data is distributed across independent sources. To improve linkage accuracy we studied different name comparison methods that establish agreement or disagreement between corresponding names. In addition to exact raw name matching and exact phonetic name matching, we tested three approximate string comparators. The approximate comparators included the modified Jaro-Winkler method, the longest common substring, and the Levenshtein edit distance. We also calculated the combined root-mean square of all three. We tested each name comparison method using a deterministic record linkage algorithm. Results were consistent across both hospitals. At a threshold comparator score of 0.8, the Jaro-Winkler comparator achieved the highest linkage sensitivities of 97.4% and 97.7%. The combined root-mean square method achieved sensitivities higher than the Levenshtein edit distance or longest common substring while sustaining high linkage specificity. Approximate string comparators increase deterministic linkage sensitivity by up to 10% compared to exact match comparisons and represent an accurate method of linking to vital statistics data.


title: Beautiful Math, Part 2: Aesthetic Patterns Based on Fractal Tilings

abstract: A fractal tiling (f-tiling) is a tiling whose boundary is fractal. This article presents two families of rare, infinitely many f-tilings. Each f-tiling is constructed by reducing tiles by a fixed scaling factor, using a single prototile, which is a segment of a regular polygon. The authors designed invariant mappings to automatically produce appealing seamless, colored patterns from such tilings.


Detection of Algorithmically Generated Malicious Domain


abstract: Marlene Goldman suggests in her paper "Representations of Buddhism in Ondaatje's Anil's Ghost" that at first glance Ondaatje appears to promote the idea of a Sri Lankan Buddhist faith as transcending history. Ondaatje introduces the subject of Buddhism early on in the novel, emphasizing initially the devastation wrought by imperial and colonial forces. Goldman, however, argues that subsequent references to Buddhism undermine the initial portrayal of a religion besieged by external imperialist forces. For example, at one point, the character Palipana refers to the assassination of his brother, Narada, a Buddhist monk. Narada was possibly the victim of a "political killing" and rumours suggest he was killed by a novice and thus his death recalls the historical connection between the JVP (termed "the antigovernment insurgents" in the novel) and young Buddhist monks. Goldman argues that rather than offering a sanitized account that ignores Buddhism's enmeshment in politics, Ondaatje's novel addresses the complex relationship between religion, politics, and violence in Sri Lanka. Marlene Goldman, "Representations of Buddhism in Ondaatje's Anil's Ghost" page 2 of 9 CLCWeb: Comparative Literature and Culture 6.3 (2004):


title: An organizational model for digital library evaluation

abstract: Evaluation is a central digital library practice. It provides important data for managing digital libraries and informing strategic decision-making. Digital library evaluation and management are organizational as well as technical practices. What evaluation models can account for these organizational factors, in practice as well as in theory? To address these questions, this paper integrates two models, one from the organizational literature (Porter's value chain), and one from the evaluation literature (evaluation logic models), into a generic, flexible and extensible evaluation model that supports the goal-oriented evaluation and management of digital libraries in specific sociotechnical contexts. A case study is provided.


Statistical Decision Theory and Bayesian Analysis, 2nd Edition


title: A New Buck–Boost Converter With Low-Voltage Stress and Reduced Conducting Components

abstract: This paper presents a new buck–boost converter. Unlike the single-switch buck–boost converter, the proposed converter has low-voltage stresses on semiconductors. Moreover, although both the conventional two-switch buck–boost (TSBB) and the proposed converters have the same number of passive and active components, and the proposed converter can reduce the conduction loss as a result of having fewer conducting components. Therefore, the proposed converter obtained a higher efficiency than the TSBB converter. A 48-V output voltage and 150-W output power prototype was fabricated to verify the effectiveness of the proposed converter.


title: Local voxelizer: A shape descriptor for surface registration

abstract: Surface registration brings multiple scans into a common coordinate system by aligning their overlapping components. This can be achieved by finding a few pairs of matched points on different scans using local shape descriptors and employing the matches to compute transformations to produce the alignment. By defining a unique local reference frame (LRF) and attaching an LRF to shape descriptors, the transformation can be computed using only one match based on aligning the LRFs. This paper proposes a local voxelizer descriptor, and the key ideas are to define a unique LRF using the support around a basis point, to perform voxelization for the local shape within a cubical volume aligned with the LRF, and to concatenate local features extracted from each voxel to construct the descriptor. An automatic rigid registration approach is given based on the local voxelizer and an expanding strategy that merges descriptor representations of aligned scans. Experiments show that our registration approach allows the acquisition of 3D models of various objects, and that the local voxelizer is robust to mesh noise and varying mesh resolution, in comparison to two state-of-the-art shape descriptors.


title: Scene text recognition using residual convolutional recurrent neural network

abstract: Text is a significant tool for human communication, and text recognition in scene images becomes more and more important. In this paper, we propose a residual convolutional recurrent neural network for solving the task of scene text recognition. The general convolutional recurrent neural network (CRNN) is realized by combining convolutional neural network (CNN) with recurrent neural network (RNN). The CNN part extracts features and the RNN part encodes and decodes feature sequences. In order to improve the accuracy rate of scene text recognition based on CRNN, we explore different deeper CNN architectures to get feature descriptors and analyze the corresponding text recognition results. Specifically, VGG and ResNet are introduced to train these different deep models and obtain the encoding information of images. The experimental results on public datasets demonstrate the effectiveness of our method.


title: Humans use directed and random exploration to solve the explore–exploit dilemma

abstract: All adaptive organisms face the fundamental tradeoff between pursuing a known reward (exploitation) and sampling lesser-known options in search of something better (exploration). Theory suggests at least two strategies for solving this dilemma: a directed strategy in which choices are explicitly biased toward information seeking, and a random strategy in which decision noise leads to exploration by chance. In this work we investigated the extent to which humans use these two strategies. In our “Horizon task,” participants made explore–exploit decisions in two contexts that differed in the number of choices that they would make in the future (the time horizon). Participants were allowed to make either a single choice in each game (horizon 1), or 6 sequential choices (horizon 6), giving them more opportunity to explore. By modeling the behavior in these two conditions, we were able to measure exploration-related changes in decision making and quantify the contributions of the two strategies to behavior. We found that participants were more information seeking and had higher decision noise with the longer horizon, suggesting that humans use both strategies to solve the exploration–exploitation dilemma. We thus conclude that both information seeking and choice variability can be controlled and put to use in the service of exploration.


title: Management of foreskin problems.

abstract: One hundred consecutive cases of boys with foreskin problems referred to local paediatric surgeons in the Edinburgh area were studied. Fifty five underwent circumcision and the remainder were managed more conservatively with or without the use of local or general anaesthetic. Although sometimes avoiding general anaesthetic, the more conservative methods involved more frequent visits to the clinic, a larger number of complications, and a longer follow up period. Despite the vogue for conservatism, circumcision still has an important part to play in the management of troublesome foreskins in children.


title: Megagauss technology and pulsed power applications

abstract: This is the final report of a 3-year LDRD project at LANL. Because of recent changes in Russia, there are opportunities to acquire and evaluate technologies for ultrahigh-magnetic-field flux compressors and ultrahigh-energy, ultrahigh-current pulsed-power generators that could provide inexpensive access to various extreme matter conditions and high-energy-density physics regimes. Systems developed by the All-Russian Scientific Research Institute of Experimental Physics (VNIIEF) at Arzamas-16 (Sarova) have the potential of creating new thrusts in several areas of high-magnetic-field and high-energy-density R&D, including high-field and high-temperature superconductivity, Faraday effect, cyclotron resonance, isentropic compression, magneto-optical properties, plasma physics, astrophysics, energy research, etc. Through a formal collaboration supported and encouraged by high-ranking DOE officials and senior laboratory management, we have gained access to unique Russian technology, which substantially exceeds US capabilities in several areas, at a small fraction of the cost which would be incurred in an intensive and lengthy US development program.


title: LCL-filter design for grid-connected PCS using total harmonic distortion and ripple attenuation factor

abstract: This paper describes a design method of LCL filter for grid-connected three-phase PWM inverter. First, by analyzing the ripple component of phase voltages and currents according to the PWM pattern of grid-connected three-phase inverter, the RMS value of the current ripple can be calculated. Then based on the analysis, the current THD in the inverter-side can be defined. After that by analyzing the dependency between the current THD of the system and the current ripple attenuation, the parameter of LCL filter can be designed. Finally, the described LCL filter design method is verified by showing a good agreement between the target current and the actual one through the simulation and experiment.


title: Photorealistic facial expression synthesis by the conditional difference adversarial autoencoder

abstract: Photorealistic facial expression synthesis from single face image can be widely applied to face recognition, data augmentation for emotion recognition or entertainment. This problem is challenging, in part due to a paucity of labeled facial expression data, making it difficult for algorithms to disambiguate changes due to identity and changes due to expression. In this paper, we propose the conditional difference adversarial autoencoder, CDAAE, for facial expression synthesis. The CDAAE takes a facial image of a previously unseen person and generates an image of that human face with a target emotion or facial action unit label. The CDAAE adds a feedforward path to an autoencoder structure connecting low level features at the encoder to features at the corresponding level at the decoder. It handles the problem of disambiguating changes due to identity and changes due to facial expression by learning to generate the difference between low-level features of images of the same person but with different facial expressions. The CDAAE structure can be used to generate novel expressions by combining and interpolating between facial expressions/action units within the training set. Our experimental results demonstrate that the CDAAE can preserve identity information when generating facial expression for unseen subjects more faithfully than previous approaches. This is especially advantageous when training with small databases.


title: TopicViz: interactive topic exploration in document collections

abstract: Existing methods for searching and exploring large document collections focus on surface-level matches to user queries, ignoring higher-level semantic structure. In this paper we show how topic modeling - a technique for identifying latent themes across a large collection of documents - can support semantic exploration. We present TopicViz: an interactive environment which combines traditional search and citation-graph exploration with a force-directed layout that links documents to the latent themes discovered by the topic model. We describe usage scenarios in which TopicViz supports rapid sensemaking on large document collections.


title: Faster Set Intersection with SIMD instructions by Reducing Branch Mispredictions

abstract: Set intersection is one of the most important operations for many applications such as Web search engines or database management systems. This paper describes our new algorithm to efficiently find set intersections with sorted arrays on modern processors with SIMD instructions and high branch misprediction penalties. Our algorithm efficiently exploits SIMD instructions and can drastically reduce branch mispredictions. Our algorithm extends a merge-based algorithm by reading multiple elements, instead of just one element, from each of two input arrays and compares all of the pairs of elements from the two arrays to find the elements with the same values. The key insight for our improvement is that we can reduce the number of costly hard-to-predict conditional branches by advancing a pointer by more than one element at a time. Although this algorithm increases the total number of comparisons, we can execute these comparisons more efficiently using the SIMD instructions and gain the benefits of the reduced branch misprediction overhead. Our algorithm is suitable to replace existing standard library functions, such as std::set_intersection in C++, thus accelerating many applications, because the algorithm is simple and requires no preprocessing to generate additional data structures. We implemented our algorithm on Xeon and POWER7+. The experimental results show our algorithm outperforms the std::set_intersection implementation delivered with gcc by up to 5.2x using SIMD instructions and by up to 2.1x even without using SIMD instructions for 32-bit and 64-bit integer datasets. Our SIMD algorithm also outperformed an existing algorithm that can leverage SIMD instructions.


title: Fast Joint Compression and Summarization via Graph Cuts

abstract: Extractive summarization typically uses sentences as summarization units. In contrast, joint compression and summarization can use smaller units such as words and phrases, resulting in summaries containing more information. The goal of compressive summarization is to find a subset of words that maximize the total score of concepts and cutting dependency arcs under the grammar constraints and summary length constraint. We propose an efficient decoding algorithm for fast compressive summarization using graph cuts. Our approachfirstrelaxesthelength constraint using Lagrangian relaxation. Then we propose to bound the relaxed objective function by the supermodular binary quadratic programming problem, which can be solved efficiently using graph max-flow/min-cut. Since finding the tightest lower bound suffers from local optimality, we use convex relaxation for initialization. Experimental results on TAC2008 dataset demonstrate our method achieves competitive ROUGE score and has good readability, while is much faster than the integer linear programming (ILP) method.


title: Cloud identity management security issues & solutions: a taxonomy

abstract: Cloud computing systems represent one of the most complex computing systems currently in existence. Current applications of Cloud involve extensive use of distributed systems with varying degree of connectivity and usage. With a recent focus on large-scale proliferation of Cloud computing, identity management in Cloud based systems is a critical issue for the sustainability of any Cloud-based service. This area has also received considerable attention from the research community as well as the IT industry. Numerous Cloud Identity Management Systems (IDMSs) have been proposed so far; however, most of those systems are neither widely accepted nor considered highly reliable due to their constraints in terms of scope, applicability and security. In order to achieve reliability and effectiveness in IDMs for Cloud, further extensive research needs to be carried out to critically examine Cloud based IDMSs and their level of security. In this work, we have holistically analyzed Cloud IDMSs to better understand the general as well as the security aspects of this domain. From the security perspective, we present a comprehensive list of attacks that occur frequently in Cloud based IDMSs. In order to alleviate those attacks, we present a well-organized taxonomy tree covering the most desired features essential for any Cloud-based IDMSs. Additionally, we have specified various mechanisms of realization (such as access control polices, encryption, self-service) against each of the features of Cloud IDMSs. We have further used the proposed taxonomy as an assessment criterion for the evaluation of Cloud based IDMSs. Our in-depth analysis of various Cloud based IDMSs reveals that most of the systems do not offer support to all the essential features of Cloud IDMS and the ones that do, have their own certain weaknesses. None of the discussed techniques heuristically covers all the security features; moreover, they lack compliance to international standards which, understandably, undermines their credibility. Presented work will help Cloud subscribers and providers in understanding the available solutions as well as the involved risks, allowing them to make more knowledgeable decisions while selecting potential Cloud IDMSs that best suits their functional and security requirements.


title: Generations of Mobile Wireless Technology: A Survey

abstract: Wireless communication is the transfer of information over a distance without the use of enhanced electrical conductors or "wires”. The distances involved may be short (a few meters as in television remote control) or long (thousands or millions of kilometers for radio communications). When the context is clear, the term is often shortened to "wireless". It encompasses various types of fixed, mobile, and portable two-way radios, cellular telephones, Personal Digital Assistants (PDAs), and wireless networking. In this paper we will throw light on the evolution and development of various generations of mobile wireless technology along with their significance and advantages of one over the other. In the past few decades, mobile wireless technologies have experience 4 or 5 generations of technology revolution and evolution, namely from 0G to 4G. Current research in mobile wireless technology concentrates on advance implementation of 4G technology and 5G technology. Currently 5G term is not officially used. In 5G researches are being made on development of World Wide Wireless Web (WWWW), Dynamic Adhoc Wireless Networks (DAWN) and Real Wireless World.


title: Internet banking adoption strategies for a developing country: the case of Thailand

abstract: Purpose – The objective of the paper is to identify the factors that encourage consumers to adopt internet banking services in Thailand and to use the study's findings to develop strategies for banks on how to maximize the rate of adoption.Design/methodology/approach – Quantitative research with a sample size of 600 achieved by sending questionnaires to 15 people in each of 40 large companies in Bangkok. The study is based on the Decomposed Planned Behaviour.Findings – The attitudinal factors that appear to encourage the adoption of internet banking in Thailand most are “Features of the web site” and “Perceived usefulness”, while the most significant impediment to adoption is a perceived behavioural control, namely “External environment”. The significant moderating factors are gender, educational level, income, internet experience and internet banking experience, but not age.Research limitations/implications – In this study, encouragement factors are those that are able to be controlled by banks, while im...


title: A generalized accurate modelling method for automotive bulk current injection (BCI) test setups up to 1 GHz

abstract: Development of accurate system models of immunity test setups might be extremely time consuming or even impossible. Here a new generalized approach to develop accurate component-based models of different system-level EMC test setups is proposed on the example of a BCI test setup. An equivalent circuit modelling of the components in LF range is combined with measurement-based macromodelling in HF range. The developed models show high accuracy up to 1 GHz. The issues of floating PCB configurations and incorporation of low frequency behaviour could be solved. Both frequency and time-domain simulations are possible. Arbitrary system configurations can be assembled quickly using the proposed component models. Any kind of system simulation like parametric variation and worst-case analysis can be performed with high accuracy.


A Dataset for Grasping and Manipulation using ROS


title: Can facial cosmetics affect the matching accuracy of face recognition systems?

abstract: The matching performance of automated face recognition has significantly improved over the past decade. At the same time several challenges remain that significantly affect the deployment of such systems in security applications. In this work, we study the impact of a commonly used face altering technique that has received limited attention in the biometric literature, viz., non-permanent facial makeup. Towards understanding its impact, we first assemble two databases containing face images of subjects, before and after applying makeup. We present experimental results on both databases that reveal the effect of makeup on automated face recognition and suggest that this simple alteration can indeed compromise the accuracy of a bio-metric system. While these are early results, our findings clearly indicate the need for a better understanding of this face altering scheme and the importance of designing algorithms that can successfully overcome the obstacle imposed by the application of facial makeup.


title: Identification of transcriptional regulatory elements for Ntng1 and Ntng2 genes in mice

abstract: BackgroundHigher brain function is supported by the precise temporal and spatial regulation of thousands of genes. The mechanisms that underlie transcriptional regulation in the brain, however, remain unclear. The Ntng1 and Ntng2 genes, encoding axonal membrane adhesion proteins netrin-G1 and netrin-G2, respectively, are paralogs that have evolved in vertebrates and are expressed in distinct neuronal subsets in a complementary manner. The characteristic expression patterns of these genes provide a part of the foundation of the cortical layer structure in mammals.ResultsWe used gene-targeting techniques, bacterial artificial chromosome (BAC)-aided transgenesis techniques, and in vivo enhancer assays to examine transcriptional mechanisms in vivo to gain insight into how the characteristic expression patterns of these genes are acquired. Analysis of the gene expression patterns in the presence or absence of netrin-G1 and netrin-G2 functional proteins allowed us to exclude the possibility that a feedback or feedforward mechanism mediates their characteristic expression patterns. Findings from the BAC deletion series revealed that widely distributed combinations of cis-regulatory elements determine the differential gene expression patterns of these genes and that major cis-regulatory elements are located in the 85–45 kb upstream region of Ntng2 and in the 75–60 kb upstream region and intronic region of Ntng1. In vivo enhancer assays using 2-kb evolutionarily conserved regions detected enhancer activity in the distal upstream regions of both genes.ConclusionsThe complementary expression patterns of Ntng1 and Ntng2 are determined by transcriptional cis-regulatory elements widely scattered in these loci. The cis-regulatory elements characterized in this study will facilitate the development of novel genetic tools for functionally dissecting neural circuits to better understand vertebrate brain function.


title: New 3D printed microwave metamaterial absorbers with conductive printing materials

abstract: In this paper a FSS is presented, which is fully fabricated by means of a 3D printer. Commercial printing filaments, usually applied for 3D printing of metal like structures were investigated and used. A flat periodic FSS absorber structure was developed and optimized for 10 GHz, fabricated with the 3D printer, measured afterwards and compared with the simulations. Further simulations were made with the new materials at higher frequencies and the results confirm that 3D printing technology works well and could be used to the best advantage for absorbers and other applications in the frequency range below 100 GHz.


title: CPG-based gait transition of a quadruped robot

abstract: Quadruped animals have high agility and grace skills in the motion over natural environment. An important feature of such animals is their abilities in the selection of appropriate gait in the response to the terrain irregularities and also the smooth transition between them in order to increase the stability or conserve the energy. In this paper, a Cartesian CPG-based controller for the gait generation and the transition between them will be developed. The proposed approach uses nonlinear coupled oscillators for generating rhythmic signals. These patterns can be modulated by a programmed excitation signal and proportionally changing the frequency, amplitude and the coupling parameters among oscillators. According to these variations, the step length, duration of swing and stance phases of all legs, relative phase between the legs and the sequence of leg lifting can be adjusted smoothly especially by the duration of gait transition. Then, trajectories of swing legs along the z- and x-axeis will be computed through the data obtained form the CPG controller. The reference ZMP path will be desgined on the basis of the footprints of legs. Then, the COG trajectories will be calculated with a preview servo controller. Finally, some simulations are conducted to verify the effectiveness of the proposed approach both in the gait generation and smooth gait transition for a quadruped robot.


A Parallel Genetic Algorithm For Solving The School Timetabling Problem


title: HapTouch and the 2+1 State Model: Potentials of Haptic Feedback on Touch Based In-Vehicle Information Systems

abstract: Haptic feedback on touch-sensitive displays provides significant benefits in terms of reducing error rates, increasing interaction speed and minimizing visual distraction. This particularly holds true for multitasking situations such as the interaction with mobile devices or touch-based in-vehicle systems. In this paper, we explore how the interaction with tactile touchscreens can be modeled and enriched using a 2+1 state transition model. The model expands an approach presented by Buxton. We present HapTouch -- a force-sensitive touchscreen device with haptic feedback that allows the user to explore and manipulate interactive elements using the sense of touch. We describe the results of a preliminary quantitative study to investigate the effects of tactile feedback on the driver's visual attention, driving performance and operating error rate. In particular, we focus on how active tactile feedback allows the accurate interaction with small on-screen elements during driving. Our results show significantly reduced error rates and input time when haptic feedback is given.


title: Magnetic resonance imaging of male and female genitals during coitus and female sexual arousal

abstract: Abstract Objective: To find out whether taking images of the male and female genitals during coitus is feasible and to find out whether former and current ideas about the anatomy during sexual intercourse and during female sexual arousal are based on assumptions or on facts. Design: Observational study. Setting: University hospital in the Netherlands. Methods: Magnetic resonance imaging was used to study the female sexual response and the male and female genitals during coitus. Thirteen experiments were performed with eight couples and three single women. Results: The images obtained showed that during intercourse in the “missionary position” the penis has the shape of a boomerang and 1/3 of its length consists of the root of the penis. During female sexual arousal without intercourse the uterus was raised and the anterior vaginal wall lengthened. The size of the uterus did not increase during sexual arousal. Conclusion: Taking magnetic resonance images of the male and female genitals during coitus is feasible and contributes to understanding of anatomy.


title: Socio-cognitive gamification: general framework for educational games

abstract: Gamification of learning material has received much interest from researchers in the past years. This paper aims to further improve such learning experience by applying socio-cognitive gamification to educational games. Dynamic difficulty adjustment (DDA) is a well-known tool in optimizing gaming experience. It is a process to control the parameters in a video game automatically based on user experience in real-time. This method can be extended by using a biofeedback-approach, where certain aspects of the player’s ability is estimated based on physiological measurement (e.g. eye tracking, ECG, EEG). Here, we outline the design of a biofeedback-based framework that supports dynamic difficulty adjustment in educational games. It has a universal architecture, so the concept can be employed to engage users in non-game contexts as well. The framework accepts input from the games, from the physiological sensors and from the so-called supervisor unit. This special unit empowers a new social aspect by enabling another user to observe or intervene during the interaction. To explain the game-user interaction itself in educational games we propose a hybrid model.


title: Detecting fake websites: the contribution of statistical learning theory

abstract: Fake websites have become increasingly pervasive, generating billions of dollars in fraudulent revenue at the expense of unsuspecting Internet users. The design and appearance of these websites makes it difficult for users to manually identify them as fake. Automated detection systems have emerged as a mechanism for combating fake websites, however most are fairly simplistic in terms of their fraud cues and detection methods employed. Consequently, existing systems are susceptible to the myriad of obfuscation tactics used by fraudsters, resulting in highly ineffective fake website detection performance. In light of these deficiencies, we propose the development of a new class of fake website detection systems that are based on statistical learning theory (SLT). Using a design science approach, a prototype system was developed to demonstrate the potential utility of this class of systems. We conducted a series of experiments, comparing the proposed system against several existing fake website detection systems on a test bed encompassing 900 websites. The results indicate that systems grounded in SLT can more accurately detect various categories of fake websites by utilizing richer sets of fraud cues in combination with problem-specific knowledge. Given the hefty cost exacted by fake websites, the results have important implications for e-commerce and online security.


title: Bidirectional DC-DC Converter Using Variable Inductor Concept for Electric Vehicle Applications

abstract: This paper presents the study of bidirectional DC- DC converter with a Variable Inductor (VI) for Electric Vehicles (EVs) applications. By replacing the conventional saturating power inductors with VI, the saturation level and the amount of current ripple can be controlled resulting in the size reduction of the magnetic core and enhancing its current handling capability. In applications where there are dynamic load variations such as EVs, the power inductor is forced to operate in saturation region during transient variations of loads, and this leads to more losses. In this paper a case study is performed to address this issue using a current controlled device which is called the variable inductor. The study is based on experimental tests and the experimental results are included to illustrate the feasibility of the VI for EV applications.


title: Blockchain Technology as s Support Infrastructure in e-Government

abstract: The blockchain technology, including Bitcoin and other crypto currencies, has been adopted in many application areas during recent years. However, the main attention has been on the currency and not so much on the underlying blockchain technology, including peer-to-peer networking, security and consensus mechanisms. This paper argues that we need to look beyond the currency applications and investigate the potential use of the blockchain technology in governmental tasks such as digital ID management and secure document handling. The paper discusses the use of blockchain technology as a platform for various applications in e-Government and furthermore as an emerging support infrastructure by showing that blockchain technology demonstrates a potential for authenticating many types of persistent documents.


title: Stimulating Online Reviews by Combining Financial Incentives and Social Norms

abstract: In hopes of motivating consumers to provide larger volumes of useful reviews, many retailers offer financial incentives. Here, we explore an alternative approach, social norms. We inform individuals about the volume of reviews authored by peers. We test the effectiveness of using financial incentives, social norms, and a combination of both strategies in motivating consumers. In two randomized experiments, one in the field conducted in partnership with a large online clothing retailer based in China and a second on Amazon Mechanical Turk, we compare the effectiveness of each strategy in stimulating online reviews in larger numbers and of greater length. We find that financial incentives are more effective at inducing larger volumes of reviews, but the reviews that result are not particularly lengthy, whereas social norms have a greater effect on the length of reviews. Importantly, we show that the combination of financial incentives and social norms yields the greatest overall benefit by motivating review...


Survey of Current Network Intrusion Detection Techniques


title: New transformerless, scalable Modular Multilevel Converters for HVDC-transmission

abstract: In this paper a novel concept of high voltage direct current converter (HVDC) is presented employing the modular multilevel converter (M2C). Converters using IGBT-power devices, are getting increased importance in order to meet the global needs for reliable and environment friendly power supply and distribution. The new topology is compared to HVDC-transmission based on two level (2level) converters with direct series connected IGBT- devices or known multilevel converters. Main design aspects, concerning the rating of the power devices, the power losses and achievable efficiencies are investigated. For this purpose, a new computation method is presented and applied. This enables clear insight into the influence of various design parameters in a generalized form. Additionally, the characteristics and the differences of the topologies, with respect to passive filters, the necessary pulse frequencies and the arm currents are examined. As an example of the results, a comparison of the semiconductor losses and the efficiencies of the different topologies are given.


title: Stakeholder engagement, social auditing and corporate sustainability

abstract: Purpose – To identify the applicability of social auditing as an approach of engaging stakeholders in assessing and reporting on corporate sustainability and its performance.Design/methodology/approach – Drawing upon the framework of AA1000 and the social auditing studies, this paper links stakeholder engagement, social auditing and corporate sustainability with a view to applying dialogue‐based social auditing to address corporate sustainability.Findings – This paper identifies a “match” between corporate sustainability and social auditing, as both aim at improving the social, environmental and economic performance of an organisation, considering the well‐being of a wider range of stakeholders and requiring the engagement of stakeholders in the process. This paper suggests that social auditing through engaging stakeholders via dialogue could be applied to build trusts, identify commitment and promote co‐operation amongst stakeholders and corporations.Research limitations/implications – This research requ...


title: IoT based mobile healthcare system for human activity recognition

abstract: Developments in information and communication technologies have led to the wider usage of Internet of Things (IoT). In the modern health care applications, the usage of IoT technologies brings physicians and patients together for automated and intelligent daily activity monitoring for elderly people. Mobile devices and wearable body sensors are gradually implemented for the monitoring of personal health care and wellbeing. One of the main technologies of IoT improvements in healthcare monitoring system is the wearable sensor technology. Furthermore, integration of IoT in healthcare has led to initiate smart applications such as mobile healthcare (m-Healthcare) and intelligent healthcare monitoring systems. In this study an intelligent m-healthcare system based on IoT technology is presented to provide pervasive human activity recognition by using data mining techniques. In this paper, we present a user-dependent data mining approach for off-line human activity classification and a robust and precise human activity recognition model is developed based on IoT technology. The proposed model utilizes the dataset contains body motion and vital signs recordings for ten volunteers of diverse profile while performing 12 physical activities for human activity recognition purpose. Results show that the proposed system is superior in performance with 99.89 % accuracy and is highly effective, robust and reliable in delivering m-Healthcare services during different activities.


title: Extended property paths: writing more SPARQL queries in a succinct way

abstract: We introduce Extended Property Paths (EPPs), a significant enhancement of SPARQL property paths. EPPs allow to capture in a succinct way a larger class of navigational queries than property paths. We present the syntax and formal semantics of EPPs and introduce two different evaluation strategies. The first is based on an algorithm implemented in a custom query processor. The second strategy leverages a translation algorithm of EPPs into SPARQL queries that can be executed on existing SPARQL processors. We compare the two evaluation strategies on real data to highlight their pros and cons.


title: Storing complex data sharing policies with the Min Mask Sketch

abstract: More data is currently being collected and shared by software applications than ever before. In many cases, the user is asked if either all or none of their data can be shared. We hypothesize that in some cases, users would like to share data in more complex ways. In order to implement the sharing of data using more complicated privacy preferences, complex data sharing policies must be used. These complex sharing policies require more space to store than a simple "all or nothing" approach to data sharing. In this paper, we present a new probabilistic data structure, called the Min Mask Sketch, to efficiently store these complex data sharing policies. We describe an implementation for the Min Mask Sketch in PostgreSQL and analyze the practicality and feasibility of using a probabilistic data structure for storing complex data sharing policies.


title: On-chip Spiral Inductors With Patterned Ground Shields For Si-based RF IC's

abstract: This paper presents a patterned ground shield in- serted between an on-chip spiral inductor and silicon substrate. The patterned ground shield can be realized in standard silicon technologies without additional processing steps. The impacts of shield resistance and pattern on inductance, parasitic resistances and capacitances, and quality factor are studied extensively. Experimental results show that a polysilicon patterned ground shield achieves the most improvement. At 1-2 GHz, the addition of the shield increases the inductor quality factor up to 33% and reduces the substrate coupling between two adjacent inductors by as much as 25 dB. We also demonstrate that the quality factor of a 2-GHz tank can be nearly doubled with a shielded inductor. In this paper, we present a patterned ground shield, which is compatible with standard silicon technologies, to reduce the unwanted substrate effects. To provide some background, Section II presents a discussion on the fundamental definitions of an inductor and an tank . Next, a physical model for spiral inductors on silicon is described. The magnetic energy storage and loss mechanisms in an on-chip inductor are discussed. Based on this insight, it is shown that energy loss can be reduced by shielding the electric field of the inductor from the silicon substrate. Then, the drawbacks of a solid ground shield are analyzed. This leads to the design of a patterned ground shield. Design guidelines for parameters such as shield pattern and resistance are given. In Section III, experiment design, on-wafer testing technique, and parasitic extraction procedure are presented. Experimental results are then reported to study the effects of shield resistance and pattern on inductance, parasitic resistances and capacitances, and inductor . Next, the improvement in of a 2-GHz tank using a shielded inductor is illustrated. A study of the noise coupling between two adjacent inductors and the efficiency of the ground shield for isolation are also presented. Lastly, Section IV gives some conclusions.


title: Efficient deep neural network acceleration through FPGA-based batch processing

abstract: Deep neural networks are an extremely successful and widely used technique for various pattern recognition and machine learning tasks. Due to power and resource constraints, these computationally intensive networks are difficult to implement in embedded systems. Yet, the number of applications that can benefit from the mentioned possibilities is rapidly rising. In this paper, we propose a novel architecture for processing previously learned and arbitrary deep neural networks on FPGA-based SoCs that is able to overcome these limitations. A key contribution of our approach, which we refer to as batch processing, achieves a mitigation of required weight matrix transfers from external memory by reusing weights across multiple input samples. This technique combined with a sophisticated pipelining and the usage of high performance interfaces accelerates the data processing compared to existing approaches on the same FPGA device by one order of magnitude. Furthermore, we achieve a comparable data throughput as a fully featured x86-based system at only a fraction of its energy consumption.


title: Darkfield Adapter for Whole Slide Imaging: Adapting a Darkfield Internal Reflection Illumination System to Extend WSI Applications

abstract: We present a new method for whole slide darkfield imaging. Whole Slide Imaging (WSI), also sometimes called virtual slide or virtual microscopy technology, produces images that simultaneously provide high resolution and a wide field of observation that can encompass the entire section, extending far beyond any single field of view. For example, a brain slice can be imaged so that both overall morphology and individual neuronal detail can be seen. We extended the capabilities of traditional whole slide systems and developed a prototype system for darkfield internal reflection illumination (DIRI). Our darkfield system uses an ultra-thin light-emitting diode (LED) light source to illuminate slide specimens from the edge of the slide. We used a new type of side illumination, a variation on the internal reflection method, to illuminate the specimen and create a darkfield image. This system has four main advantages over traditional darkfield: (1) no oil condenser is required for high resolution imaging (2) there is less scatter from dust and dirt on the slide specimen (3) there is less halo, providing a more natural darkfield contrast image, and (4) the motorized system produces darkfield, brightfield and fluorescence images. The WSI method sometimes allows us to image using fewer stains. For instance, diaminobenzidine (DAB) and fluorescent staining are helpful tools for observing protein localization and volume in tissues. However, these methods usually require counter-staining in order to visualize tissue structure, limiting the accuracy of localization of labeled cells within the complex multiple regions of typical neurohistological preparations. Darkfield imaging works on the basis of light scattering from refractive index mismatches in the sample. It is a label-free method of producing contrast in a sample. We propose that adapting darkfield imaging to WSI is very useful, particularly when researchers require additional structural information without the use of further staining.


title: A Unified Abstraction for Messaging on the Semantic Web

abstract: Since its inception, the Internet has been a hotbed of several successful communications channels, starting off with e-mail, Internet Relay Chat and Usenet newsgroups and more recently adding Web annotation, instant messaging, and news feeds. However, these channels were developed fairly independently, and in many cases their respective functionalities have grown to overlap significantly. For instance, users of these systems have separate identifiers for e-mail, chat, and instant messaging, and clients for these systems all have their own implementations of threaded message views. We believe these problems stem from a lack of a common user interface and data model. In this paper we use basic concepts from the Semantic Web and RDF to unify and model these seemingly disparate messaging paradigms. We also demonstrate a generalized user interface for messaging that uses the data model we have developed. From this process we realize a number of synergies that result from the reduction of overlap and the finer-grained control users are given over message composition, transmission, storage and retrieval.