title: Analysis of the Factors Affecting Men’s Attitudes Toward Cosmetic Surgery: Body Image, Media Exposure, Social Network Use, Masculine Gender Role Stress and Religious Attitudes

abstract: BACKGROUND ::: Cosmetic surgery is no longer just for females. More men are opting for cosmetic procedures, with marked increases seen in both minimally invasive and surgical options over the last decade. Compared to females, relatively little work has specifically focused on factors predicting males' attitudes toward cosmetic surgery. Therefore, we evaluated a number of variables that may predict some facet of men's attitudes toward cosmetic surgery according to evidence reported in the literature METHODS: A total of 151 male patients who applied for a surgical or minimally invasive cosmetic surgery procedure (patient group) and 151 healthy male volunteers who do not desire any type of cosmetic procedure (control group) were asked to fill out questionnaires about measures of body image, media exposure (television and magazine), social network site use, masculine gender role stress and religious attitudes. ::: ::: ::: RESULTS ::: Our findings showed that lower ratings of body image satisfaction, increased time spent watching television, more frequent social network site use and higher degrees of masculine gender role stress were all significant predictors of attitudes toward cosmetic surgery among males. ::: ::: ::: CONCLUSION ::: The current study confirmed the importance of body image dissatisfaction as a predictor of the choice to undergo cosmetic procedure. More importantly, a new predictor of cosmetic procedure attitudes was identified, namely masculine gender role stress. Finally, we demonstrated the effects television exposure and social network site use in promoting acceptance of surgical and nonsurgical routes to appearance enhancement. ::: ::: ::: LEVEL OF EVIDENCE V ::: 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: Dual-Band Long-Range Passive RFID Tag Antenna Using an AMC Ground Plane

abstract: A dual-band passive radio frequency identification (RFID) tag antenna applicable for a recessed cavity in metallic objects such as heavy equipment, vehicles, aircraft, and containers with long read range is proposed by using an artificial magnetic conductor (AMC) ground plane. The proposed tag antenna consists of a bowtie antenna and a recessed cavity with the AMC ground plane installed on the bottom side of the cavity. The AMC ground plane is utilized to provide dual-band operation at European (869.5 869.7 MHz) and Korean (910 914 MHz) passive UHF RFID bands by replacing the bottom side of the metallic cavity of a PEC-like behavior and, therefore, changing the reflection phase of the ground plane. It is worthwhile to mention that the European and the Korean UHF RFID bands are allocated very closely, and the frequency separation ratio between the two bands is just about 0.045, which is very small. It is demonstrated by experiment that the maximum reading distance of the proposed tag antenna with optimized dimensions can be improved more than 3.1 times at the two RFID bands compared to a commercial RFID tag.


title: UAV Formation Control : Theory and Application

abstract: Unmanned airborne vehicles (UAVs) are finding use in military operations and starting to find use in civilian operations. UAVs often fly in formation, meaning that the distances between individual pairs of UAVs stay fixed, and the formation of UAVs in a sense moves as a rigid entity. In order to maintain the shape of a formation, it is enough to maintain the distance between a certain number of the agent pairs; this will result in the distance between all pairs being constant. We describe how to characterize the choice of agent pairs to secure this shape-preserving property for a planar formation, and we describe decentralized control laws which will stably restore the shape of a formation when the distances between nominated agent pairs become unequal to their prescribed values. A mixture of graph theory, nonlinear systems theory and linear algebra is relevant. We also consider a particular practical problem of flying a group of three UAVs in an equilateral triangle, with the centre of mass following a nominated trajectory reflecting constraints on turning radius, and with a requirement that the speeds of the UAVs are constant, and nearly (but not necessarily exactly) equal.


title: Applications of xerophytophysiology in plant production: Sub-irrigation improves tomato fruit yield and quality

abstract: Regulated deficit irrigations (RDI) are widely adopted to plant production to harden the plants and to improve the product quality. In the present study, sub-irrigation (SI) was tested as one of the RDI practices to potted tomato plants and related mechanisms were examined. As a consequence of osmotic adjustment, the osmotic potential in symplasm at fully turgid status was lower and consequently the leaf turgor potential was higher in leaves of the SI tomato plants. Leaf symplastic water fraction was larger or apoplastic water fraction was smaller in leaves of SI tomato plants. Both osmotic potential and relative water content at the point of incipient plasmolysis were lower in leaves of SI tomato plants. The high leaf turgor and high symplastic water fraction might account for high photosynthetic activities and high fruit yield. In conclusion, as one of the RDI practices based on xerophytophysiology, sub-irrigation can improve tomato plant physiology and consequently increase tomato fruit yield and quality as well as the resistance to disease and adverse environment.


title: Personalized Recommender System for Event Attendees An Experience Report of the European Data Forum 2015 Adaptive Conference Companion

abstract: This paper presents a smart event mobile application prototype featuring beacon-based indoor geolocation, personalized recommendations and automated user interface adaptation. Focus is given here to the recommendation function, which exploits professional social network mining and semantic concept identification. We report its use in real conditions during a conference, discuss the usefulness of recommendations and more precisely the impact of the recommendation features. We can show that through appropriate semantic enrichment the conversion rate increased by factor two and the system was able to introduce previously unseen/unknown items to the users thus enhancing serendipity.


title: Multilingual Subjectivity Analysis Using Machine Translation

abstract: Although research in other languages is increasing, much of the work in subjectivity analysis has been applied to English data, mainly due to the large body of electronic resources and tools that are available for this language. In this paper, we propose and evaluate methods that can be employed to transfer a repository of subjectivity resources across languages. Specifically, we attempt to leverage on the resources available for English and, by employing machine translation, generate resources for subjectivity analysis in other languages. Through comparative evaluations on two different languages (Romanian and Spanish), we show that automatic translation is a viable alternative for the construction of resources and tools for subjectivity analysis in a new target language.


title: A Survey of Data Semantization in Internet of Things

abstract: With the development of Internet of Things (IoT), more and more sensors, actuators and mobile devices have been deployed into our daily lives. The result is that tremendous data are produced and it is urgent to dig out hidden information behind these volumous data. However, IoT data generated by multi-modal sensors or devices show great differences in formats, domains and types, which poses challenges for machines to process and understand. Therefore, adding semantics to Internet of Things becomes an overwhelming tendency. This paper provides a systematic review of data semantization in IoT, including its backgrounds, processing flows, prevalent techniques, applications, existing challenges and open issues. It surveys development status of adding semantics to IoT data, mainly referring to sensor data and points out current issues and challenges that are worth further study.


title: Load value prediction via path-based address prediction: avoiding mispredictions due to conflicting stores

abstract: Current flagship processors excel at extracting instruction-level-parallelism (ILP) by forming large instruction windows. Even then, extracting ILP is inherently limited by true data dependencies. Value prediction was proposed to address this limitation. Many challenges face value prediction, in this work we focus on two of them. Challenge #1: store instructions change the values in memory, rendering the values in the value predictor stale, and resulting in value mispredictions and a retraining penalty. Challenge #2: value mispredictions trigger costly pipeline flushes. To minimize the number of pipeline flushes, value predictors employ stringent, yet necessary, high confidence requirements to guarantee high prediction accuracy. Such requirements can negatively impact training time and coverage. In this work, we propose Decoupled Load Value Prediction (DLVP), a technique that targets the value prediction challenges for load instructions. DLVP mitigates the stale state caused by stores by replacing value prediction with memory address prediction. Then, it opportunistically probes the data cache to retrieve the value(s) corresponding to the predicted address(es) early enough so value prediction can take place. Since the values captured in the data cache mirror the current program data (except for in-flight stores), this addresses the first challenge. Regarding the second challenge, DLVP reduces pipeline flushes by using a new context-based address prediction scheme that leverages load-path history to deliver high address prediction accuracy (over 99%) with relaxed confidence requirements. We call this address prediction scheme Path-based Address Prediction (PAP). With a modest 8KB prediction table, DLVP improves performance by up to 71%, and 4.8% on average, without increasing the core energy consumption. CCS CONCEPTS • Computer systems organization → Superscalar architectures; Pipeline computing; Reduced instruction set computing;


Knowledge-enabled Recommender Systems: Models, Challenges, Solutions


title: Remote sensing of vital sign of human body with radio frequency

abstract: Medical technology has improved remarkably in the field of health monitoring over the past few decades, becoming more sophisticated and less invasive as the years progress. Now, non-contact measurement of respiration and heartbeat with microwave Doppler radar phase modulation, offers an attractive alternative to commonly prescribed chest strap monitors. To monitor the vital signs an unmodulated radio frequency signal is transmitted toward the human body, where it is phase-modulated by the periodic physiological movement and reflected back to the receiver. The radar receiver captures the reflected signal and demodulates it to extract the vital sign signal components. The reflected signal from the body depends on the radar cross section (RCS) of body. In this paper the RCS of human body is measured in the X-band (8–12 GHz). There are several advantages to a noncontact measurement method: physically, it neither confines nor inhibits the subject and does not cause discomfort or skin irritation as electrodes and straps do. By measuring heartbeats, this technology can also be used to detect a person’s presence, search-and-rescue for earthquake or fire victims and border patrol. It has also attracted the interest of organizations requiring high security, such as see-through- wall radar and airport security monitoring.


CLONAL SELECTION THEORY & CLONALG THE CLONAL SELECTION CLASSIFICATION ALGORITHM (CSCA)


title: ShiftCNN: Generalized Low-Precision Architecture for Inference of Convolutional Neural Networks

abstract: In this paper we introduce ShiftCNN, a generalized low-precision architecture for inference of multiplierless convolutional neural networks (CNNs). ShiftCNN is based on a power-of-two weight representation and, as a result, performs only shift and addition operations. Furthermore, ShiftCNN substantially reduces computational cost of convolutional layers by precomputing convolution terms. Such an optimization can be applied to any CNN architecture with a relatively small codebook of weights and allows to decrease the number of product operations by at least two orders of magnitude. The proposed architecture targets custom inference accelerators and can be realized on FPGAs or ASICs. Extensive evaluation on ImageNet shows that the state-of-the-art CNNs can be converted without retraining into ShiftCNN with less than 1% drop in accuracy when the proposed quantization algorithm is employed. RTL simulations, targeting modern FPGAs, show that power consumption of convolutional layers is reduced by a factor of 4 compared to conventional 8-bit fixed-point architectures.


Learning bilingual word embeddings with (almost) no bilingual data


title: Autonomous 2D SLAM and 3D mapping of an environment using a single 2D LIDAR and ROS

abstract: This paper describes an algorithm that performs an autonomous 3D reconstruction of an environment with a single 2D Laser Imaging Detection and Ranging (LIDAR) sensor, as well as its implementation on a mobile platform using the Robot Operating System (ROS). A ROS node was used to redirect the flow of data that can go to either the 2D Simultaneous Localization And Mapping (SLAM) ROS node or to the 3D Octomap ROS node depending on the operation performed at that moment, with neither of the nodes going out of sync or crashing. The autonomous algorithm is implemented with the State Machines (SMACH) library and it uses ROS interfaces such as services and actions to create a 3D model of the robot's surroundings without prior information or human intervention.


title: Seraph: an efficient, low-cost system for concurrent graph processing

abstract: Graph processing systems have been widely used in enterprises like online social networks to process their daily jobs. With the fast growing of social applications, they have to efficiently handle massive concurrent jobs. However, due to the inherent design for single job, existing systems incur great inefficiency in memory use and fault tolerance. Motivated by this, in this paper we introduce Seraph, a graph processing system that enables efficient job-level parallelism. Seraph is designed based on a decoupled data model, which allows multiple concurrent jobs to share graph structure data in memory. Seraph adopts a copy-on-write semantic to isolate the graph mutation of concurrent jobs, and a lazy snapshot protocol to generate consistent graph snapshots for jobs submitted at different time. Moreover, Seraph adopts an incremental checkpoint/regeneration model which can tremendously reduce the overhead of checkpointing. We have implemented Seraph, and the evaluation results show that Seraph significantly outperforms popular systems (such as Giraph and Spark) in both memory usage and job completion time, when executing concurrent graph jobs.


title: Agent Based Computational Finance: Suggested Readings and Early Research

abstract: Computational models for financial markets with many interacting agents have recently appeared as a tool for examining learning and evolutionary issues in market dynamics. This paper surveys some of the early research in this area with emphasis on the many unsolved problems that researchers will need to confront. Several early papers are emphasized which focus on some of these problems, and references to many other papers are also given.


Multi-channel Delivery of Services-- The Road from eGovernment to mGovernment : Further Technological Challenges and Implications


title: Teens Engage More with Fewer Photos: Temporal and Comparative Analysis on Behaviors in Instagram

abstract: Research has suggested that teens are more active and engaged than adults on social media. Most of such observations, however, have been made through the analysis of limited ethnographic or cross-sectional data. Using a temporally extended, large-scale dataset and comparative analyses to remedy this shortcoming, we examined how and why the age difference in the behaviors of users in Instagram might have occurred through the lenses of social cognition, developmental psychology, and human-computer interaction. We proposed two hypotheses -- teens as digital natives and the need for social interactions -- as the theoretical framework for understanding the factors that help explain the behavioral differences. Our computational analysis identified the following novel findings: (1) teens post fewer photos than adults; (2) teens remove more photos based on the number of Likes the photos received; and (3) teens have less diverse photo content. Our analysis was also able to confirm prior ethnographic accounts that teens are more engaged in Liking and commenting, and express their emotions and social interests more than adults. We discussed theoretical and practical interpretations and implications as well as future research directions from the results. Our datasets are available at: https://goo.gl/LqTYNv


Assessment of the ways of Fars Province's residents being informed of holding up the Health Week and its effect on their knowledge about Type 2 Diabetes


title: Robust H2 control for two-wheeled inverted pendulum using LEGO Mindstorms

abstract: In this paper, we assemble a two-wheeled inverted pendulum robot using LEGO Mindstorms. Robust H 2 control system is synthesized for the plant via focusing some uncertainties which appear in the process of the modeling. The mathematical model of the plant depends rationally on several uncertain parameters. To reduce the dependency to linear, descriptor representation and linear fractional transformation (LFT) is adopted. The effectiveness of the robust H 2 control is verified by simulations and experimental results. Finally, the usefulness of LEGO Mindstorms as an experimental equipment for verification is substantiated with comparing simulations and experimental results.


title: Game-theoretic Analysis of Computation Offloading for Cloudlet-based Mobile Cloud Computing

abstract: Mobile cloud computing (MC2) is emerging as a promising computing paradigm which helps alleviate the conflict between resource-constrained mobile devices and resource-consuming mobile applications through computation offloading. In this paper, we analyze the computation offloading problem in cloudlet-based mobile cloud computing. Different from most of the previous works which are either from the perspective of a single user or under the setting of a single wireless access point (AP), we research the computation offloading strategy of multiple users via multiple wireless APs. With the widespread deployment of WLAN, offloading via multiple wireless APs will obtain extensive application. Taking energy consumption and delay (including computing and transmission delay) into account, we present a game-theoretic analysis of the computation offloading problem while mimicking the selfish nature of the individuals. In the case of homogeneous mobile users, conditions of Nash equilibrium are analyzed, and an algorithm that admits a Nash equilibrium is proposed. For heterogeneous users, we prove the existence of Nash equilibrium by introducing the definition of exact potential game and design a distributed computation offloading algorithm to help mobile users choose proper offloading strategies. Numerical extensive simulations have been conducted and results demonstrate that the proposed algorithm can achieve desired system performance.


title: Field of view for near-field aperture synthesis imaging

abstract: Aperture synthesis techniques are increasingly being employed to provide high angular resolution images in situations where the object of interest is in the near field of the interferometric array. Previous work has showed that an aperture synthesis array can be refocused on an object in the near field of an array, provided that the object is smaller than the effective Fresnel zone size corresponding to the array-object range. We show here that, under paraxial conditions, standard interferometric techniques can be used to image objects that are substantially larger than this limit. We also note that interferometric self-calibration and phase-closure image-reconstruction techniques can be used to achieve near-field refocusing without requiring accurate object range information. We use our results to show that the field of view for high-resolution aperture synthesis imaging of geosynchronous satellites from the ground can be considerably larger than the largest satellites in Earth orbit.


title: CopY is a copper-inducible repressor of the Enterococcus hirae copper ATPases.

abstract: The cop operon of Enterococcus hirae effects copper homeostasis in this organism. It encodes a repressor, CopY, an activator, CopZ, and two P-type copper ATPases, CopA and CopB. Expression of all four genes is regulated by the ambient copper. In this regulation, CopY apparently acts as a copper-inducible repressor. By DNase I footprinting, it was shown that purified CopY protected two discrete sites in the region encompassing nucleotides -71 to -11 relative to the translational start site and containing hyphenated inverted repeats. Transcription is initiated between these repeats at nucleotide -42, in a domain that remained accessible to DNase I in the DNA-repressor complex. Chemical cross-linking revealed that CopY exists as a dimer in solution. In DNA band-shift assays, it was apparent that the CopY-DNA interaction occurred in two discrete steps. Half-maximal binding of repressor to the two operator sites was observed at 2 x 10(-9) M and 5 x 10(-9) M CopY, respectively. Copper ions released CopY from the promoter/operator with an apparent half-binding constant for Cu(I) of 20 microM. The site-directed mutations A-61T and A-30T essentially abolished the binding of CopY to the respective binding sites, and the double mutation A-61T/A-30T inactivated both binding sites. Thus, CopY is a copper-inducible repressor of the cop operon of E. hirae, exhibiting highly specific DNA-protein interactions with two sites on the cop promoter/operator and playing a key role in copper homeostasis in E. hirae.


title: Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms

abstract: Variational inference using the reparameterization trick has enabled large-scale approximate Bayesian inference in complex probabilistic models, leveraging stochastic optimization to sidestep intractable expectations. The reparameterization trick is applicable when we can simulate a random variable by applying a differentiable deterministic function on an auxiliary random variable whose distribution is fixed. For many distributions of interest (such as the gamma or Dirichlet), simulation of random variables relies on acceptance-rejection sampling. The discontinuity introduced by the accept-reject step means that standard reparameterization tricks are not applicable. We propose a new method that lets us leverage reparameterization gradients even when variables are outputs of a acceptance-rejection sampling algorithm. Our approach enables reparameterization on a larger class of variational distributions. In several studies of real and synthetic data, we show that the variance of the estimator of the gradient is significantly lower than other state-of-the-art methods. This leads to faster convergence of stochastic gradient variational inference.


title: Evaluation of Prototype Automotive Head-Up Display Interface: Testing Driver's Focusing Ability through a VR Simulation

abstract: Contemporary automotive, navigation and infotainment requirements have evolved the traditional dashboard into a complex device that can often distract the driver. Head-Up Displays (HUDs) have recently attracted the attention in the field of automotive research, promoting the reduction of driver's reaction time and to improve spatial awareness. The aptitude of the proposed HUD interface lies within the driver's focusing ability to the HUD interface and the actual traffic. This paper analyses the performance behaviour through user-tests using different focal levels for the projection of a full-windshield HUD interface. For this purpose, a VR driving simulator has been developed to test the different depths of field configurations of a HUD while driving in various weather and traffic conditions with and without the HUD. Our simulation results reveal the users' preferences regarding the focal point of the superimposed interface and present a comparative evaluation of the different focal levels and their impact on drivers' behaviour and performance.


title: Structural Equation Modeling: Strengths, Limitations, and Misconceptions

abstract: Because structural equation modeling (SEM) has become a very popular data-analytic technique, it is important for clinical scientists to have a balanced perception of its strengths and limitations. We review several strengths of SEM, with a particular focus on recent innovations (e.g., latent growth modeling, multilevel SEM models, and approaches for dealing with missing data and with violations of normality assumptions) that underscore how SEM has become a broad data-analytic framework with flexible and unique capabilities. We also consider several limitations of SEM and some misconceptions that it tends to elicit. Major themes emphasized are the problem of omitted variables, the importance of lower-order model components, potential limitations of models judged to be well fitting, the inaccuracy of some commonly used rules of thumb, and the importance of study design. Throughout, we offer recommendations for the conduct of SEM analyses and the reporting of results.


title: SoK: Research Perspectives and Challenges for Bitcoin and Cryptocurrencies

abstract: Bit coin has emerged as the most successful cryptographic currency in history. Within two years of its quiet launch in 2009, Bit coin grew to comprise billions of dollars of economic value despite only cursory analysis of the system's design. Since then a growing literature has identified hidden-but-important properties of the system, discovered attacks, proposed promising alternatives, and singled out difficult future challenges. Meanwhile a large and vibrant open-source community has proposed and deployed numerous modifications and extensions. We provide the first systematic exposition Bit coin and the many related crypto currencies or 'altcoins.' Drawing from a scattered body of knowledge, we identify three key components of Bit coin's design that can be decoupled. This enables a more insightful analysis of Bit coin's properties and future stability. We map the design space for numerous proposed modifications, providing comparative analyses for alternative consensus mechanisms, currency allocation mechanisms, computational puzzles, and key management tools. We survey anonymity issues in Bit coin and provide an evaluation framework for analyzing a variety of privacy-enhancing proposals. Finally we provide new insights on what we term disinter mediation protocols, which absolve the need for trusted intermediaries in an interesting set of applications. We identify three general disinter mediation strategies and provide a detailed comparison.


title: MM-wave phased array antenna for whole-metal-covered 5G mobile phone applications

abstract: A novel mm-wave phased array antenna for whole metal covered 5G mobile handset is proposed. The proposed array antenna consists of eight rotated slot antenna elements that are arranged on the upper frame of the metal-body. The proposed array demonstrates good S-parameter characteristics in frequency band ranging from 27.1 to 28.6 GHz and mutual-coupling between antenna elements is less than −19 dB. The proposed array antenna has the hemi-spherical beam coverage suitable for 5G mobile communication.


title: Albumentations: fast and flexible image augmentations

abstract: Data augmentation is a commonly used technique for increasing both the size and the diversity of labeled training sets by leveraging input transformations that preserve corresponding output labels. In computer vision, image augmentations have become a common implicit regularization technique to combat overfitting in deep learning models and are ubiquitously used to improve performance. While most deep learning frameworks implement basic image transformations, the list is typically limited to some variations of flipping, rotating, scaling, and cropping. Moreover, image processing speed varies in existing image augmentation libraries. We present Albumentations, a fast and flexible open source library for image augmentation with many various image transform operations available that is also an easy-to-use wrapper around other augmentation libraries. We discuss the design principles that drove the implementation of Albumentations and give an overview of the key features and distinct capabilities. Finally, we provide examples of image augmentations for different computer vision tasks and demonstrate that Albumentations is faster than other commonly used image augmentation tools on most image transform operations.


Walking through Architectural Spaces: The Impact of Interior Forms on Human Brain Dynamics


title: Sensor Fusion as an Enabling Technology for Safety-critical Driver Assistance Systems

abstract: Driver assistance systems are incorporating more and more advanced safety functions. As these functions have to react quickly and reliably in emergency situations with a false alarm rate close to zero a high integrity of the environmental perception is required. This elevated level of signal integrity can be achieved by data fusion, where the information of several, in general heterogeneous sensors is combined to obtain a better model of the environment in terms of accuracy, object integrity, object identity, etc. As an example, we demonstrate the power of sensor fusion by an automatic emergency brake (AEB) system whose environmental perception is based upon a video camera and a radar sensor. In particular we discuss the improvement of kinematic attributes such as object lateral distance as well as the object’s confidence or probability of existence.


title: Ultrawideband All-Metal Flared-Notch Array Radiator

abstract: Simulations and measurements are presented for an all-metal flared-notch array element in both single and dual-polarization configurations. The ultrawideband radiator exhibits an operational bandwidth of 12:1 for broadside scan and 8:1 bandwidth at a 45-degree scan in all planes, maintaining active VSWR<;2. The feed consists of a direct coax-to-slot-line transition that mounts directly into the base of the radiator. The all-metal flared-notches are machined from common metal stock and fed via SMA coaxial connectors. No soldering is required for any part of the design-including the feed-and assembly is simple and modular. The array parts are machined using a high-precision wire-EDM cutting technology, ensuring that measurements (in the 700 MHz-9 GHz range) are repeatable and give close agreement with theory, even through multiple assembly cycles of the modular construction system. This paper presents results for a 32-element linear array of horizontal elements and also an 8 × 8 planar array of dual-polarized elements, comparing measurements with full-wave simulations of the complete finite array structures.


title: The microbiota-gut-brain axis: neurobehavioral correlates, health and sociality

abstract: Recent data suggest that the human body is not such a neatly self-sufficient island after all. It is more like a super-complex ecosystem containing trillions of bacteria and other microorganisms that inhabit all our surfaces; skin, mouth, sexual organs, and specially intestines. It has recently become evident that such microbiota, specifically within the gut, can greatly influence many physiological parameters, including cognitive functions, such as learning, memory and decision making processes. Human microbiota is a diverse and dynamic ecosystem, which has evolved in a mutualistic relationship with its host. Ontogenetically, it is vertically inoculated from the mother during birth, established during the first year of life and during lifespan, horizontally transferred among relatives, mates or close community members. This micro-ecosystem serves the host by protecting it against pathogens, metabolizing complex lipids and polysaccharides that otherwise would be inaccessible nutrients, neutralizing drugs and carcinogens, modulating intestinal motility, and making visceral perception possible. It is now evident that the bidirectional signaling between the gastrointestinal tract and the brain, mainly through the vagus nerve, the so called “microbiota–gut–vagus–brain axis,” is vital for maintaining homeostasis and it may be also involved in the etiology of several metabolic and mental dysfunctions/disorders. Here we review evidence on the ability of the gut microbiota to communicate with the brain and thus modulate behavior, and also elaborate on the ethological and cultural strategies of human and non-human primates to select, transfer and eliminate microorganisms for selecting the commensal profile.


title: Wearable Flexible Sensors: A Review

abstract: This paper provides a review on some of the significant research work done on wearable flexible sensors (WFSs). Sensors fabricated with the flexible materials have been attached to a person along with the embedded system to monitor a parameter and transfer the significant data to the monitoring unit for the further analyses. The use of wearable sensors has played a quite important role to monitor the physiological parameters of a person to minimize any malfunctioning happening in the body. This paper categorizes the work according to the materials used for designing the system, the network protocols, and different types of activities that were being monitored. The challenges faced by the current sensing systems and future opportunities for the WFSs regarding its market values are also briefly explained in this paper.


title: Analysis of PLL clock jitter in high-speed serial links

abstract: We analyze the effects of transmitter and receiver phased-locked loop (PLL) phase noise, which translates to time-domain clock/data jitter, on the performance of high-speed transceivers. Analytical expressions are derived to incorporate both transmitter and receiver clock jitter into serial link operation. A method to calculate the worst-case noise margin degradation due to clock jitter is discussed in order to obviate impractical time-domain simulations. This analysis relies on the assumption that the channel is linear and time-invariant and, hence, can be characterized by an impulse response. A simple extension to equalized serial links is also presented. The analysis is verified through behavioral simulations using a realistic/measured channel model.


title: Chipless RFID Tag Using Hybrid Coding Technique

abstract: Increasing the coding capacity of chipless RFID tags is a key factor while considering the development of miniaturized tags. A novel hybrid coding technique by combining phase deviation and frequency position encoding is proposed here. A coding capacity of 22.9 bits is obtained simply with five resonators within a reduced dimension of 2 cm × 4 cm. The proposed tag is based on 5 `C' like metallic strip resonators having resonance frequency within the band of 2.5 GHz to 7.5 GHz. The tag is potentially low-cost since only one conductive layer is needed for the fabrication. Different tag configurations are designed and validated with measurement results in bi-static configuration. A good agreement between measurement and simulation validates the theoretical predictions.


title: Commercial Biomass Syngas Fermentation

abstract: The use of gas fermentation for the production of low carbon biofuels such as ethanol or butanol from lignocellulosic biomass is an area currently undergoing intensive research and development, with the first commercial units expected to commence operation in the near future. In this process, biomass is first converted into carbon monoxide (CO) and hydrogen (H 2 )-rich synthesis gas (syngas) via gasification, and subsequently fermented to hydrocarbons by acetogenic bacteria. Several studies have been performed over the last few years to optimise both biomass gasification and syngas fermentation with significant progress being reported in both areas. While challenges associated with the scale-up and operation of this novel process remain, this strategy offers numerous advantages compared with established fermentation and purely thermochemical approaches to biofuel production in terms of feedstock flexibility and production cost. In recent times, metabolic engineering and synthetic biology techniques have been applied to gas fermenting organisms, paving the way for gases to be used as the feedstock for the commercial production of increasingly energy dense fuels and more valuable chemicals.


title: A hybrid particle swarm optimization for job shop scheduling problem

abstract: A hybrid particle swarm optimization (PSO) for the job shop problem (JSP) is proposed in this paper. In previous research, PSO particles search solutions in a continuous solution space. Since the solution space of the JSP is discrete, we modified the particle position representation, particle movement, and particle velocity to better suit PSO for the JSP. We modified the particle position based on preference list-based representation, particle movement based on swap operator, and particle velocity based on the tabu list concept in our algorithm. Giffler and Thompson's heuristic is used to decode a particle position into a schedule. Furthermore, we applied tabu search to improve the solution quality. The computational results show that the modified PSO performs better than the original design, and that the hybrid PSO is better than other traditional metaheuristics.


title: Rooftop Solar Panel Cleaning Robot Using Omni Wheels

abstract: A solar panel cleaning robot was developed by using omni-wheels so that the robot can rotate and move freely and avoid turning the robot during operation. This research designed and developed drive system of the robot for climbing the inclined and slippery solar panels. The robot was equipped with sensors and encoders to accurately control its movement such as moved across gaps between panels and stops at the ends of panels. The results show that the robot was able to move on the inclined solar panels up to 10°inclined angle. The moving speed varied from 0.16-0.34 m/s when moving upward and downward inclined panels due to slipping. By comparing to the normal robot after 12 meters of cleaning distance, the omni wheel robot had position error of 0.3% (similar to normal robot) but saved about 64% of cleaning time by using rotating movement instead of turning.


title: Ordering-sensitive and Semantic-aware Topic Modeling

abstract: Topic modeling of textual corpora is an important and challenging problem. In most previous work, the "bag-of-words" assumption is usually made which ignores the ordering of words. This assumption simplifies the computation, but it unrealistically loses the ordering information and the semantic of words in the context. In this paper, we present a Gaussian Mixture Neural Topic Model (GMNTM) which incorporates both the ordering of words and the semantic meaning of sentences into topic modeling. Specifically, we represent each topic as a cluster of multi-dimensional vectors and embed the corpus into a collection of vectors generated by the Gaussian mixture model. Each word is affected not only by its topic, but also by the embedding vector of its surrounding words and the context. The Gaussian mixture components and the topic of documents, sentences and words can be learnt jointly. Extensive experiments show that our model can learn better topics and more accurate word distributions for each topic. Quantitatively, comparing to state-of-the-art topic modeling approaches, GMNTM obtains significantly better performance in terms of perplexity, retrieval accuracy and classification accuracy.


title: Noninvasive Brain-Computer Interface: Decoding Arm Movement Kinematics and Motor Control

abstract: Over the last few decades, the advances in disciplines such as neuroscience and engineering have introduced the brain–computer interface (BCI) as a promising tool for neurorehabilitation and neurophysiology research. BCI research primarily aims at development of assistive and rehabilitation strategies for motor-impaired users and, hence, sensorimotor-rhythm (SMR)-based BCIs are widely explored. This type of BCI performs detection and interpretation of the neural activity underlying various user intentions, specifically related to movement, and translates these brain functionalities to the external environment without relying on normal neuromuscular pathways. The interfaced device thus achieves motor control directly from the neural activity. Motor-control BCIs investigate various aspects of human motor skills and have successfully identified neural patterns related to motor execution, imagination, and intention from bilateral limbs. In this article, we provide a summary of recent advances in motor control BCI, specifically in movement kinematics decoding and motor control of localized areas of the limb. We further discuss the research challenges and future scope of work in this area of research.


title: Impact of Low Rotational Inertia on Power System Stability and Operation

abstract: Abstract Large-scale deployment of Renewable Energy Sources (RES) has led to significant generation shares of variable RES in power systems worldwide. RES units, notably inverter-connected wind turbines and photovoltaics (PV) that as such do not provide rotational inertia, are effectively displacing conventional generators and their rotating machinery. The traditional assumption that grid inertia is sufficiently high with only small variations over time is thus not valid for power systems with high RES shares. This has implications for frequency dynamics and power system stability and operation. Frequency dynamics are faster in power systems with low rotational inertia, making frequency control and power system operation more challenging. This paper investigates the impact of low rotational inertia on power system stability and operation, contributes new analysis insights and offers impact mitigation options.


title: Plasmacytoid dendritic cells: sensing nucleic acids in viral infection and autoimmune diseases

abstract: Plasmacytoid dendritic cells (pDCs) are important mediators of antiviral immunity through their ability to produce large amounts of type I interferons (IFNs) on viral infection. This function of pDCs is linked to their expression of Toll-like receptor 7 (TLR7) and TLR9, which sense viral nucleic acids within the early endosomes. Exclusion of self nucleic acids from TLR-containing early endosomes normally prevents pDC responses to them. However, in some autoimmune diseases, self nucleic acids can be modified by host factors and gain entrance to pDC endosomes, where they activate TLR signalling. Several pDC receptors negatively regulate type I IFN responses by pDCs during viral infection and for normal homeostasis.


title: Color gamut transform pairs

abstract: Digital control of color television monitors—in particular, via frame buffers—has added precise control of a large subset of human colorspace to the capabilities of computer graphics. This subset is the gamut of colors spanned by the red, green, and blue (RGB) electron guns exciting their respective phosphors. It is called the RGB monitor gamut . Full-blown color theory is a quite complex subject involving physics, psychology, and physiology, but restriction to the RGB monitor gamut simplifies matters substantially. It is linear, for example, and admits to familiar spatial representations. This paper presents a set of alternative models of the RGB monitor gamut based on the perceptual variables hue (H), saturation (S), and value (V) or brightness (L). Algorithms for transforming between these models are derived. Particular emphasis is placed on an RGB to HSV non-trigonometric pair of transforms which have been used successfully for about four years in frame buffer painting programs. These are fast, accurate, and adequate in many applications. Computationally more difficult transform pairs are sometimes necessary, however. Guidelines for choosing among the models are provided. Psychophysical corrections are described within the context of the definitions established by the NTSC (National Television Standards Committee).


title: Optimal Design and Fabrication of “CEDRA” Rescue Robot Using Genetic Algorithm

abstract: This article presents an overview of the mechanical design features, fabrication and control of a Rescue Robot (CEDRA) for operation in unstructured environments. As a preliminary step, the essential characteristics of a robot in damaged and unstable situations have been established. According to these features and kinematical equations of the robot, design parameters are optimized by means of Genetic Algorithm. Optimum parameters are then utilized in construction. Upon fabrication, this unit has been tested in clean laboratory environment, as well as, ill-conditioned arenas similar to earthquake zones. The obtained results have been satisfactory in all aspects, and improvements are currently underway to enhance capabilities of the rescue robot unit for various applications.Copyright © 2004 by ASME


title: Offline signature recognition using neural networks approach

abstract: The fact that the signature is widely used as a means of personal verification emphasizes the need for an automatic verification system because of the unfortunate side-effect of being easily abused by those who would feign the identification or intent of an individual. A great deal of work has been done in the area of off-line signature verification over the past few decades. Verification can be performed either Offline or Online based on the application. Online systems use dynamic information of a signature captured at the time the signature is made. Offline systems work on the scanned image of a signature. In this paper, we present a method for Offline Verification of signatures using a set of simple shape based geometric features. The features that are used are Area, Center of gravity, Eccentricity, Kurtosis and Skewness. Before extracting the features, preprocessing of a scanned image is necessary to isolate the signature part and to remove any spurious noise present. The system is initially trained using a database of signatures obtained from those individuals whose signatures have to be authenticated by the system. The details of preprocessing as well as the features depicted above are described throughout the discussion. Then artificial neural network (ANN) was used to verify and classify the signatures: exact or forged, and a classification ratio of about 93% was obtained under a threshold of 90%.The implementation details and simulation results are discussed in the thesis.


title: An integrative implementation framework for electronic customer relationship management: revisiting the general principles of usability and resistance

abstract: Electronic customer relationship management (eCRM) has become the latest paradigm in the world of customer relationship management. Recent business surveys suggest that up to 50% of such implementations do not yield measurable returns on investment. A secondary analysis of 13 case studies suggests that many of these limited success implementations can be attributed to usability and resistance factors. The objective of this paper is to review the general usability and resistance principles in order build an integrative framework for analyzing eCRM case studies. The conclusions suggest that if organizations want to get the most from their eCRM implementations they need to revisit the general principles of usability and resistance and apply them.


title: A new centralized localization algorithm for wireless sensor network

abstract: In most sensor network applications, the information gathered by sensors will be meaningless without the location of the sensor nodes. Node localization has been a topic of active research in recent years. Accurate self-localization capability is highly desirable in wireless sensor network (WSN). This paper proposes a new centralized localization algorithm for WSN. Given the distances between a sensor and its a few 1-hop neighbors, the proposed algorithm can estimate the sensorpsilas position with genetic algorithm(GA). The sensor doesnpsilat have to neighbor to anchors. The localization accuracy is very high. Four example problems are used to evaluate the performance of the proposed algorithm. Simulation results show that our algorithm can achieve higher accurate position estimation than semi-definite programming with gradient search localization (SDPL) [11] and simulated annealing based localization (SAL)[13].


title: CapacitiveMarker: novel interaction method using visual marker integrated with conductive pattern

abstract: The visual markers have spatial limitations to require certain distances between a camera and markers. Meanwhile, as capacitive multi-touch displays on mobile devices have become popular, many researchers proposed interaction techniques using conductive patterns and a capacitive display. In this study, we propose a novel visual marker, "CapacitiveMarker", which can be recognized both by a camera and capacitive display. The CapacitiveMarker consists of two layered markers: a visual marker printed on a seal and a conductive pattern on a plastic film. We also propose a new interaction method using CapacitiveMarker through applications.


title: Cross-national and Cross-industrial Comparison of Two Strategy Approaches for Global Industrial Evolution

abstract: Abstract This research focuses on analyzing the two prime science and technology (S&T) strategy approaches for industrial evolution based on the concept of S&T gap, namely, the optimist and pragmatist approaches. Particularly, the cases of global IC, pharmaceutical, and computer industries, are used to make cross-national and cross-industrial comparison of these two approaches. The optimist approach is developed based on the product life cycle theory which envisions technology transcending everyday limitations. With this perspective, market demand is the most critical factor in selecting the S&T strategy approaches. The pragmatist approach is formed based on the new trade theory which recognizes the power of science and technology but seeks to fit it into structures that already exist, and government must manage resources pouring into science and technology. Case studies of global IC, pharmaceutical, and computer industries during the 2nd half of the 20th century are used as research targets to reflect policy impacts on the technological evolution. The results of this study reveal that, strategy approaches have to be adapted and turned to the specific stage, technology level, and market segment that have been selected for intervention. This result of comparison also offers the criteria of strategy selection for developing different industry based on distinct national base.


title: A 27Gb/s Forwarded-Clock I/O Receiver Using an Injection-Locked LC-DCO in 45nm CMOS

abstract: This paper describes a method for both filtering and deskewing a link clock using a differential injection-locked LC-DCO and demonstrates a forwarded-clock data receiver using this technique operating at 27 Gb/s.


title: Emotion regulation through execution, observation, and imagery of emotional movements

abstract: According to Damasio’s somatic marker hypothesis, emotions are generated by conveying the current state of the body to the brain through interoceptive and proprioceptive afferent input. The resulting brain activation patterns represent unconscious emotions and correlate with subjective feelings. This proposition implies a corollary that the deliberate control of motor behavior could regulate feelings. We tested this possibility, hypothesizing that engaging in movements associated with a certain emotion would enhance that emotion and/or the corresponding valence. Furthermore, because motor imagery and observation are thought to activate the same mirror-neuron network engaged during motor execution, they might also activate the same emotional processing circuits, leading to similar emotional effects. Therefore, we measured the effects of motor execution, motor imagery and observation of whole-body dynamic expressions of emotions (happiness, sadness, fear) on affective state. All three tasks enhanced the corresponding affective state, indicating their potential to regulate emotions.


title: Data aggregation in wireless sensor networks with minimum delay and minimum use of energy: A comparative study

abstract: Electronic Workshops in Computing (eWiC), 2015. First published in the Electronic Workshops in Computing series at http://dx.doi.org/10.14236/ewic/bcsme2014.2


title: The influence of diabetes, hypertension, and stroke on ethnic differences in physical and cognitive functioning in an ethnically diverse older population.

abstract: Abstract Prevention of decline in cognitive and physical functioning in the elderly has become an important focus in geriatric medicine. Hispanics are among the fastest-growing group of elderly in the United States, yet few data are available on functional impairments in this group. We examined the association between ethnicity (non-Hispanic whites [NHW], English-speaking Hispanics [EH], and Spanish-speaking Hispanics [SH]) and cognitive status, self-assessed functional status, and physical performance in a community-dwelling sample of 589 people aged ⩾ 60 years. The purpose of this study was to examine the association between ethnicity and these measures of functional status and to evaluate the influence of comorbid stroke, diabetes, and hypertension on this association. We found that EH and SH had significantly lower scores on the MiniMental State Exam than NHW, but that this difference was almost entirely due to educational level. When SH whose educational attainment was grade 8 or higher were compared to NHWs, there were no differences in cognitive functioning. Those with diabetes and stroke had poorer cognitive functioning. Among those with stroke, EH and SH women had more self-assessed functional limitations (IADLs and ADLs) than NHW. Male SH and EH with diabetes also had more self-assessed functional limitations than NHW. SH with two or three of these conditions had more IADL limitations. Our results suggest that elderly communitydwelling Hispanics experience greater levels of disability and that this is due, in part, to lower socioeconomic status and higher prevalence of disabling conditions.


title: The Temporal Singularity: time-accelerated simulated civilizations and their implications

abstract: Provided significant future progress in artificial intelligence and computing, it may ultimately be possible to create multiple Artificial General Intelligences (AGIs), and possibly entire societies living within simulated environments. In that case, it should be possible to improve the problem solving capabilities of the system by increasing the speed of the simulation. If a minimal simulation with sufficient capabilities is created, it might manage to increase its own speed by accelerating progress in science and technology, in a way similar to the Technological Singularity. This may ultimately lead to large simulated civilizations unfolding at extreme temporal speedups, achieving what from the outside would look like a Temporal Singularity. Here we discuss the feasibility of the minimal simulation and the potential advantages, dangers, and connection to the Fermi paradox of the Temporal Singularity. The medium-term importance of the topic derives from the amount of computational power required to start the process, which could be available within the next decades, making the Temporal Singularity theoretically possible before the end of the century.


title: Integrating Reviews into Personalized Ranking for Cold Start Recommendation

abstract: Item recommendation task predicts a personalized ranking over a set of items for each individual user. One paradigm is the rating-based methods that concentrate on explicit feedbacks and hence face the difficulties in collecting them. Meanwhile, the ranking-based methods are presented with rated items and then rank the rated above the unrated. This paradigm takes advantage of widely available implicit feedback. It, however, usually ignores a kind of important information: item reviews. Item reviews not only justify the preferences of users, but also help alleviate the cold-start problem that fails the collaborative filtering. In this paper, we propose two novel and simple models to integrate item reviews into Bayesian personalized ranking. In each model, we make use of text features extracted from item reviews using word embeddings. On top of text features we uncover the review dimensions that explain the variation in users' feedback and these review factors represent a prior preference of users. Experiments on six real-world data sets show the benefits of leveraging item reviews on ranking prediction. We also conduct analyses to understand the proposed models.


title: Understanding Semantic Web and Ontologies: Theory and Applications

abstract: Semantic Web is actually an extension of the current one in that it represents information more meaningfully for humans and computers alike. It enables the description of contents and services in machine-readable form, and enables annotating, discovering, publishing, advertising and composing services to be automated. It was developed based on Ontology, which is considered as the backbone of the Semantic Web. In other words, the current Web is transformed from being machine-readable to machine-understandable. In fact, Ontology is a key technique with which to annotate semantics and provide a common, comprehensible foundation for resources on the Semantic Web. Moreover, Ontology can provide a common vocabulary, a grammar for publishing data, and can supply a semantic description of data which can be used to preserve the Ontologies and keep them ready for inference. This paper provides basic concepts of web services and the Semantic Web, defines the structure and the main applications of ontology, and provides many relevant terms are explained in order to provide a basic understanding of ontologies.


title: Cytogenetic studies on the interspecific hybrid Capsicum baccatum L.× C. frutescens L. and its progeny

abstract: SummaryMorphological and cytogenetical studies were carried out on the F1 and F2 hybrids and backcross derivatives of the interspecific cross Capsicum baccatum L. x C. frutescens L. The F1 and F2 hybrids displayed irregular meiosis with a maximum association of eight chromosomes in the former and one quadrivalent in the latter with the appropriate number of bivalents and univalents. It is inferred that C. baccatum differs from C. frutescens (yellow) by at least tow or three interchanges and from the white cultivar by a single interchange. Structural repatterning of chromosomes, erratic meiotic behaviour, genes for pollen sterility, segregational imbalances following intergenomic recombination are believed to be major factors causing sterility in the hybrids. The two species are sympatric and natural hybrids have not been realised.


title: DCSim: A data centre simulation tool for evaluating dynamic virtualized resource management

abstract: Computing today is shifting from hosting services in servers owned by individual organizations to data centres providing resources to a number of organizations on a shared infrastructure. Managing such a data centre presents a unique set of goals and challenges. Through the use of virtualization, multiple users can run isolated virtual machines (VMs) on a single physical host, allowing for a higher server utilization. By consolidating VMs onto fewer physical hosts, infrastructure costs can be reduced in terms of the number of servers required, power consumption, and maintenance. To meet constantly changing workload levels, running VMs may need to be migrated (moved) to another physical host. Algorithms to perform dynamic VM reallocation, as well as dynamic resource provisioning on a single host, are open research problems. Experimenting with such algorithms on the data centre scale is impractical. Thus, there is a need for simulation tools to allow rapid development and evaluation of data centre management techniques. We present DCSim, an extensible simulation framework for simulating a data centre hosting an Infrastructure as a Service cloud. We evaluate the scalability of DCSim, and demonstrate its usefulness in evaluating VM management techniques.


title: Simplified design of an X/Ku-band Vivaldi array for rocket exhaust plume diagnostics

abstract: This paper describes the design and fabrication of an 8-element wideband Vivaldi antenna array with an integrated feed network. This array was created for use in an X/Ku-band measurement system for characterizing dynamic rocket plasma, extending previous measurements of this phenomenon. The array is instrumented to help distinguish plume-induced mechanical changes from electrical ones.


title: Generation and Measurement of Ultrashort Light Pulses

abstract: An experimental arrangement for the generation of ultrashort light pulses by mode locking of a Nd:glass laser with a saturable dye and their half‐width measurement is described. The pulses, being too short for determining their duration by conventional means, were studied by measuring their autocorrelation function with a method based on optical second‐harmonic generation. Measurements were made for two positions of the dye cell within the resonator; first, at some small distance from one of the mirrors and second, with one mirror forming one of the walls of the dye cell. For the former dye cell position the output considered of two or more pulses with a half‐width of approximately 30 psec, separated by about 120 psec. With the latter arrangement however, short single pulses were generated having a duration of 8–12.5 psec. In addition this arrangement greatly improved the reproducibility of the pulses and the stability of the output from flash to flash.


title: Emotion classification in microblog texts using class sequential rules

abstract: This paper studies the problem of emotion classification in microblog texts. Given a microblog text which consists of several sentences, we classify its emotion as anger, disgust, fear, happiness, like, sadness or surprise if available. Existing methods can be categorized as lexicon based methods or machine learning based methods. However, due to some intrinsic characteristics of the microblog texts, previous studies using these methods always get unsatisfactory results. This paper introduces a novel approach based on class sequential rules for emotion classification of microblog texts. The approach first obtains two potential emotion labels for each sentence in a microblog text by using an emotion lexicon and a machine learning approach respectively, and regards each microblog text as a data sequence. It then mines class sequential rules from the dataset and finally derives new features from the mined rules for emotion classification of microblog texts. Experimental results on a Chinese benchmark dataset show the superior performance of the proposed approach.


title: Early Mortality Syndrome Outbreaks: A Microbial Management Issue in Shrimp Farming?

abstract: and impoverished microbial community (and a consequent lack of competition) favors fast-growing bacteria (such as many pathogenicVibriospp.) in recolonizing the environment [4]. Considering that EMS/ AHPND most probably is caused by a Vibrio, this practice is thus more likely to stimulate proliferation of the EMS/ AHPND-causing agent in the pond than counteract it. In fact, important lessons can be learned from the outbreaks of luminescent vibriosis in the early 1990s. This disease was caused byVibrio harveyiand closely related species—all belonging to the Harveyi clade of vibrios (and therefore closely related to the causative agent of EMS/AHPND) [5]. Luminescent vibriosis occurred during the first 10‐45 days after stocking of shrimp postlarvae in the growout ponds. The outbreak of the disease was found to be preceded by a substantial increase in the number of opportunistic vibrios in the pond water [6], and this increase followed pond disinfection and was associated with a perturbed microbial community in combination with the presence of nutrients [6,7].


title: Fundamentals of low-noise analog circuit design

abstract: This paper presents a tutorial treatment of the fundamentals of noise in solid-state analog electronic circuits. It is written for upper division students and practicing engineers who wish to gain a basic knowledge of the theory of electronic noise and techniques for low-noise circuit design. The paper presents an overview of noise fundamentals, a description of noise models for passive devices and active solid-state devices, methods of calculating the noise performance of amplifiers, and techniques for minimizing noise in circuit design. The theory and methods are applicable to both discrete and integrated circuits. >


Development of Isolated Word Speech Recognition System


title: Comparison of the communication time of a high gain versus a low gain monopole-like low profile antenna on a 3-unit CubeSat

abstract: This paper investigates the effect of the antenna pattern on the communication time between a ground station and a low earth orbit satellite with passive attitude control, i.e. the antenna is nadir pointing. Two low profile antennas that fit on a 3-unit CubeSat are considered, namely a high gain patch and a low gain monopole-like patch antenna. The communication system is for high speed S-band communication.


title: Mobility Models, Traces and Impact of Mobility on Opportunistic Routing Algorithms: A Survey

abstract: Mobile Opportunistic Network (MON) is characterized by intermittent connectivity where communication largely depends on the mobility pattern of the participating nodes. In MON, a node can take the custody of a packet for a long time and carry it until a new forwarding path has been established, unlike mobile adhoc network (MANET), where a node must drop the packet otherwise. Therefore, routing in MON depends on the repeated make-and-break of communication links, which again depends on the mobility of the nodes as they encounter and drift away from each other. MONs can simply be formed by humans carrying hand-held devices (like Personal Digital Assistant [PDAs] or cell phones) or on-board devices installed in vehicles. Therefore, with mobility playing a major role in the performance of MON, researchers have repeatedly tried to understand the nature of mobility with respect to humans, vehicles, and wild animals. To study the nature of mobility, researchers have collected mobility traces, proposed mobility models, and analyzed the performance of MON with respect to various mobility parameters. This article provides a detailed survey of different mobility models which have been proposed to date and how mobility largely determines the performance of opportunistic routing. We divide the article into four major sections. First, we provide a detailed survey of all the synthetic mobility models which have been developed to date. Second, we study the various mobility traces which have been collected and analyzed. Third, we study how mobility parameters affect the performance of MON. Finally, we highlight on some of the research areas and open challenges which yet remain unsolved.


title: RoadTracer: Automatic Extraction of Road Networks from Aerial Images

abstract: Mapping road networks is currently both expensive and labor-intensive. High-resolution aerial imagery provides a promising avenue to automatically infer a road network. Prior work uses convolutional neural networks (CNNs) to detect which pixels belong to a road (segmentation), and then uses complex post-processing heuristics to infer graph connectivity. We show that these segmentation methods have high error rates because noisy CNN outputs are difficult to correct. We propose RoadTracer, a new method to automatically construct accurate road network maps from aerial images. RoadTracer uses an iterative search process guided by a CNN-based decision function to derive the road network graph directly from the output of the CNN. We compare our approach with a segmentation method on fifteen cities, and find that at a 5% error rate, RoadTracer correctly captures 45% more junctions across these cities.


title: Image restoration by sparse 3D transform-domain collaborative filtering

abstract: We propose an image restoration technique exploiting regularized inversion and the recent block-matching and 3D ::: filtering (BM3D) denoising filter. The BM3D employs a non-local modeling of images by collecting similar image ::: patches in 3D arrays. The so-called collaborative filtering applied on such a 3D array is realized by transformdomain ::: shrinkage. In this work, we propose an extension of the BM3D filter for colored noise, which we use in ::: a two-step deblurring algorithm to improve the regularization after inversion in discrete Fourier domain. The ::: first step of the algorithm is a regularized inversion using BM3D with collaborative hard-thresholding and the ::: seconds step is a regularized Wiener inversion using BM3D with collaborative Wiener filtering. The experimental ::: results show that the proposed technique is competitive with and in most cases outperforms the current best ::: image restoration methods in terms of improvement in signal-to-noise ratio.


title: An enterprise application integration methodology for e-government

abstract: Purpose – Achieving goals of better integrated and responsive government services requires moving away from stand alone applications toward more comprehensive, integrated architectures. As a result there is a mounting pressure to integrate disparate systems to support information exchange and cross‐agency business processes. There are substantial barriers that governments must overcome to achieve these goals and to profit from enterprise application integration (EAI).Design/methodology/approach – In the research presented here we develop and test a methodology aimed at overcoming the barriers blocking adoption of EAI. This methodology is based on a discrete‐event simulation of public sector structure, business processes and applications in combination with an EAI perspective.Findings – The testing suggests that our methodology helps to provide insight into the myriad of existing applications, and the implications of EAI. Moreover, it helps to identify novel options, gain stakeholder commitment, let them a...


title: A Multi-transformational Model for Background Subtraction with Moving Cameras

abstract: We introduce a new approach to perform background subtraction in moving camera scenarios. Unlike previous treatments of the problem, we do not restrict the camera motion or the scene geometry. The proposed approach relies on Bayesian selection of the transformation that best describes the geometric relation between consecutive frames. Based on the selected transformation, we propagate a set of learned background and foreground appearance models using a single or a series of homography transforms. The propagated models are subjected to MAP-MRF optimization framework that combines motion, appearance, spatial, and temporal cues; the optimization process provides the final background/foreground labels. Extensive experimental evaluation with challenging videos shows that the proposed method outperforms the baseline and state-of-the-art methods in most cases.


title: Online Allocation of Virtual Machines in a Distributed Cloud

abstract: One of the primary functions of a cloud service provider is to allocate cloud resources to users upon request. Requests arrive in real-time and resource placement decisions must be made as and when a request arrives, without any prior knowledge of future arrivals. In addition, when a cloud service provider operates a geographically diversified cloud that consists of a large number of small data centers, the resource allocation problem becomes even more complex. This is due to the fact that resource request can have additional constraints on data center location, service delay guarantee, and so on, which is especially true for the emerging network function virtualization application. In this paper, we propose a generalized resource placement methodology that can work across different cloud architectures, resource request constraints, with real-time request arrivals and departures. The proposed algorithms are online in the sense that allocations are made without any knowledge of resource requests that arrive in the future, and the current resource allocations are made in such a manner as to permit the acceptance of as many future arrivals as possible. We derive worst case competitive ratio for the algorithms. We show through experiments and case studies the superior performance of the algorithms in practice.


Use of the Danish Adoption Register for the study of obesity and thinness.


title: Broadband Circularly Polarized Beam-Steering Antenna Array

abstract: A wideband circular polarized (CP) switched-beam patch antenna array is presented in this communication. The switched-beam system consists of four two-section branch-line couplers, two wideband 45 phase shifters and 1 × 4 proximity-coupled L-probe feeding CP microstrip patch antenna array. With such an arrangement, the proposed array can obtain a wide axial ratio (AR) bandwidth than that of conventional switched-beam CP antenna array. Measured results showed this array has an impedance bandwidth over a frequency range of 4.04-5.78 GHz (~36%) for |S11| <; -10 dB and AR bandwidth from 4.3 to 5.75 GHz (~29%) for AR <; 3dB when the main beam of array is scanned to different four directions.


title: Fringe-Effect Capacitive Proximity Sensors for Tamper Proof Enclosures

abstract: Capacitive sensors can be constructed for tamper resistant enclosures to prevent unauthorized intrusions. The use printed circuit can provide serpentine geometric patterns of conductors. Any penetration of this envelope interferes with the conductive serpentine, producing a detectable warning of intrusion. Capacitive sensors discussed in this paper are suitable for implementing in the area of physical security cryptographic modules. These sensors conform to Levels 3 and 4 of the Federal Information Processing Standard (FIPS) 140-2


title: Impact of controlled language on translation quality and post-editing in a statistical machine translation environment

abstract: This paper investigates the relationships among controlled language (CL), machine translation (MT) quality, and post-editing (PE). Previous research has shown that the use of CL improves the quality of MT. By extension, we assume that the use of CL will lead to greater productivity or reduced PE effort. The paper examines whether this three-way relationship among CL, MT quality, and PE holds. Beginning with a set of CL rules, we determine what types of CL rules have the greatest cross-linguistic impact on MT quality. We create two sets of English data, one which violates the CL rules and the other which conforms to them. We translate both sets of sentences into four typologically different languages (Dutch, Chinese, Arabic, and French) using MSR-MT, a statistical machine translation system developed at Microsoft. We measure the degree of impact of CL rules on MT quality based on the difference in human evaluation as well as BLEU scores between the two sets of MT output. Finally, we examine whether the use of CL improves productivity in terms of reduced PE effort, using character-based edit-distance.


title: Document Representation and Query Expansion Models for Blog Recommendation

abstract: We explore several different document representation models and two query expansion models for the task of recommending blogs to a user in response to a query. Blog relevance ranking differs from traditional document ranking in ad-hoc information retrieval in several ways: (1) the unit of output (the blog) is composed of a collection of documents (the blog posts) rather than a single document, (2) the query represents an ongoing – and typically multifaceted – interest in the topic rather than a passing ad-hoc information need and (3) due to the propensity of spam, splogs, and tangential comments, the blogosphere is particularly challenging to use as a source for high-quality query expansion terms. We address these differences at the document representation level, by comparing retrieval models that view either the blog or its constituent posts as the atomic units of retrieval, and at the query expansion level, by making novel use of the links and anchor text in Wikipedia to expand a user’s initial query. We develop two complementary models of blog retrieval that perform at comparable levels of precision and recall. We also show consistent and significant improvement across all models using our Wikipedia expansion strategy.


title: Automatic Categorization of Traditional Clothing Using Convolutional Neural Network

abstract: The research proposes an approach that can automatically classify real world pictures of some traditional clothing worn in Bangladesh into the predefined classes using Convolutional Neural Networks (CNN). The research is driven by considering the growing market of online shops in mind. For classification purpose, we have collected clothing images from several online stores and labeled them accordingly. Our CNN model is based on the Google Inception model. For comparison purposes we have tried several architectures of CNN and some variations to see how our model perform against them. We have tested our own model with three different optimizers – SGD, Adam and RmsProp. Among these optimizers, RmsProp performed the best. The final result shows our model could classify the images of the training and testing set with 92.05% and 89.22% accuracy respectively.


title: A unifying framework for robot control with redundant DOFs

abstract: Recently, Udwadia (Proc. R. Soc. Lond. A 2003:1783---1800, 2003) suggested to derive tracking controllers for mechanical systems with redundant degrees-of-freedom (DOFs) using a generalization of Gauss' principle of least constraint. This method allows reformulating control problems as a special class of optimal controllers. In this paper, we take this line of reasoning one step further and demonstrate that several well-known and also novel nonlinear robot control laws can be derived from this generic methodology. We show experimental verifications on a Sarcos Master Arm robot for some of the derived controllers. The suggested approach offers a promising unification and simplification of nonlinear control law design for robots obeying rigid body dynamics equations, both with or without external constraints, with over-actuation or underactuation, as well as open-chain and closed-chain kinematics.


title: Customizing ISO 9126 quality model for evaluation of B2B applications

abstract: A software quality model acts as a framework for the evaluation of attributes of an application that contribute to the software quality. In this paper, a quality model is presented for evaluation of B2B applications. First, the most well-known quality models are studied, and reasons for using ISO 9126 quality model as the basis are discussed. This model, then, is customized in accordance with special characteristics of B2B applications. The customization is done by extracting the quality factors from web applications and B2B e-commerce applications, weighting these factors from the viewpoints of both developers and end users, and adding them to the model. Finally, as a case study, ISACO portal is evaluated by the proposed model.


title: Optimal minimum distance-based precoder for MIMO spatial multiplexing systems

abstract: We describe a new precoder based on optimization of the minimum Euclidean distance d/sub min/ between signal points at the receiver side and for use in multiple-input multiple-output (MIMO) spatial multiplexing systems. Assuming that channel state information (CSI) can be made available at the transmitter, the three steps ( noise whitening, channel diagonalization and dimension reduction), which are currently used in investigations on MIMO systems, are performed. Thanks to this representation, an optimal d/sub min/ precoder is derived in the case of two different transmitted data streams. For quadrature phase-shift keying (QPSK) modulation, a numerical approach shows that the precoder design depends on the channel characteristics. Comparisons with maximum signal-to-noise ratio (SNR) strategy and other precoders based on criteria, such as water-filling (WF), minimum mean square error (MMSE), and maximization of the minimum singular value of the global channel matrix, are performed to illustrate the significant bit-error-rate (BER) improvement of the proposed precoder.


title: The role of culture in health communication.

abstract: This paper examines the role of culture as a factor in enhancing the effectiveness of health communication. We describe culture and how it may be applied in audience segmentation and introduce a model of health communication planning--McGuire's communication/persuasion model--as a framework for considering the ways in which culture may influence health communication effectiveness. For three components of the model (source, message, and channel factors), the paper reviews how each affects communication and persuasion, and how each may be affected by culture. We conclude with recommendations for future research on culture and health communication.


title: Semi-supervised Outlier Detection using Generative And Adversary Framework

abstract: In a conventional binary/multi-class classification task, the decision boundary is supported by data from two or more classes. However, in one-class classification task, only data from one class are available. To build an robust outlier detector using only data from a positive class, we propose a corrupted GAN(CorGAN), a deep convolutional Generative Adversary Network requiring no convergence during training. In the adversarial process of training CorGAN, the Generator is supposed to generate outlier samples for negative class, and the Discriminator as an one-class classifier is trained to distinguish data from training datasets (i.e. positive class) and generated data from the Generator (i.e. negative class). To improve the performance of the Discriminator (one-class classifier), we also propose a lot of techniques to improve the performance of the model. The proposed model outperforms the traditional method PCA + PSVM and the solution based on Autoencoder.


title: Input selection for fast feature engineering

abstract: The application of machine learning to large datasets has become a vital component of many important and sophisticated software systems built today. Such trained systems are often based on supervised learning tasks that require features, signals extracted from the data that distill complicated raw data objects into a small number of salient values. A trained system's success depends substantially on the quality of its features. Unfortunately, feature engineering—the process of writing code that takes raw data objects as input and outputs feature vectors suitable for a machine learning algorithm—is a tedious, time-consuming experience. Because “big data” inputs are so diverse, feature engineering is often a trial-and-error process requiring many small, iterative code changes. Because the inputs are so large, each code change can involve a time-consuming data processing task (over each page in a Web crawl, for example). We introduce Zombie, a data-centric system that accelerates feature engineering through intelligent input selection, optimizing the “inner loop” of the feature engineering process. Our system yields feature evaluation speedups of up to 8× in some cases and reduces engineer wait times from 8 to 5 hours in others.


title: Positive academic emotions moderate the relationship between self‐regulation and academic achievement

abstract: Research has shown how academic emotions are related to achievement and to cognitive/motivational variables that promote achievement. Mediated models have been proposed to account for the relationships among academic emotions, cognitive/motivational variables, and achievement, and research has supported such mediated models, particularly with negative emotions. Aims. The study tested the hypotheses: (1) self-regulation and the positive academic emotions of enjoyment and pride are positive predictors of achievement; and (2) enjoyment and pride both moderate the relationship between self-regulation and achievement. Sample. Participants were 1,345 students enrolled in various trigonometry classes in one university. Methods. Participants answered the Academic Emotions Questionnaire-Math (Pekrun, Goetz, & Frenzel, 2005) and a self-regulation scale (Pintrich, Smith, Garcia, & McKeachie, 1991) halfway through their trigonometry class. The students' final grades in the course were regressed to self-regulation, positive emotions, and the interaction terms to test the moderation effects. Results and Conclusions. Enjoyment and pride were both positive predictors of grades; more importantly, both moderated the relationship between self-regulation and grades. For students who report higher levels of both positive emotions, self-regulation was positively associated with grades. However, for those who report lower levels of pride, self-regulation was not related to grades; and, for those who reported lower levels of enjoyment, self-regulation was negatively related to grades. The results are discussed in terms of how positive emotions indicate positive appraisals of task/outcome value, and thus enhance the positive links between cognitive/motivational variables and learning.


Towards Implicit Content-Introducing for Generative Short-Text Conversation Systems


title: Discriminative Orderlet Mining for Real-Time Recognition of Human-Object Interaction

abstract: This paper presents a novel visual representation, called orderlets, for real-time human action recognition with depth sensors. An orderlet is a middle level feature that captures the ordinal pattern among a group of low level features. For skeletons, an orderlet captures specific spatial relationship among a group of joints. For a depth map, an orderlet characterizes a comparative relationship of the shape information among a group of subregions. The orderlet representation has two nice properties. First, it is insensitive to small noise since an orderlet only depends on the comparative relationship among individual features. Second, it is a frame-level representation thus suitable for real-time online action recognition. Experimental results demonstrate its superior performance on online action recognition and cross-environment action recognition.


title: Deep Convolution Neural Networks for Automatic Eyeglasses Removal

abstract: The facial image under eyeglasses occlusion can degrade face recognition performance. Inspired by the success of deep convolutional neural networks (DCNN) on super resolution, in this paper, a method based on deep convolutional neural network is developed for automatic eyeglasses removal from frontal facial images. To remove eyeglasses on facial images, the proposed approach applied deep convolution neural networks (end-to-end DCNN) to reconstruct the eyeglasses region. We adopt the deep convolutional neural networks (DCNN) approach is designed and trained to learn the mapping between pairs of face images with or without eyeglasses from a large face database in video surveillance. The extensive experiments show that the proposed algorithm can effectively remove eyeglasses, and also can keep the stability of face recognition under eyeglasses on occlusion.


title: A Negation Handling Technique for Sentiment Analysis

abstract: Traditional lexicon-based approaches for sentiment analysis are usually not able to model negation, as they do not provide proper techniques to identify the right negation window. In this work we address the problem of the automatic determination of the scope of negation and we present a negation handling algorithm based on dependency-based parse trees. The proposal is based on the use of grammatical relations among words to model a sentence, and hence to determine words that are affected by negation. The proposed algorithm has been coupled with a semantic disambiguation technique to identify the sentiment of a sentence. Experiments on different datasets have proven that our proposal improves the accuracy of the sentiment analysis. The proposed algorithm has been implemented as part of a Social Information Discovery system, which allows for an integrated near-real-time analysis of discussions from multiple social networks.


title: Adaptive data acquisition strategies for energy-efficient, smartphone-based, continuous processing of sensor streams

abstract: There is a growing interest in applications that utilize continuous sensing of individual activity or context, via sensors embedded or associated with personal mobile devices (e.g., smartphones). Reducing the energy overheads of sensor data acquisition and processing is essential to ensure the successful continuous operation of such applications, especially on battery-limited mobile devices. To achieve this goal, this paper presents a framework, called ACQUA, for ‘acquisition-cost’ aware continuous query processing. ACQUA replaces the current paradigm, where the data is typically streamed (pushed) from the sensors to the one or more smartphones, with a pull-based asynchronous model, where a smartphone retrieves appropriate blocks of relevant sensor data from individual sensors, as an integral part of the query evaluation process. We describe algorithms that dynamically optimize the sequence (for complex stream queries with conjunctive and disjunctive predicates) in which such sensor data streams are retrieved by the query evaluation component, based on a combination of (a) the communication cost & selectivity properties of individual sensor streams, and (b) the occurrence of the stream predicates in multiple concurrently executing queries. We also show how a transformation of a group of stream queries into a disjunctive normal form provides us with significantly greater degrees of freedom in choosing this sequence, in which individual sensor streams are retrieved and evaluated. While the algorithms can apply to a broad category of sensor-based applications, we specifically demonstrate their application to a scenario where multiple stream processing queries execute on a single smartphone, with the sensors transferring their data over an appropriate PAN technology, such as Bluetooth or IEEE 802.11. Extensive simulation experiments indicate that ACQUA’s intelligent batch-oriented data acquisition process can result in as much as 80 % reduction in the energy overhead of continuous query processing, without any loss in the fidelity of the processing logic.


title: A Foundation for Multi-dimensional Databases

abstract: We present a multi-dimensional database model, which we believe can serve as a conceptual model for On-Line Analytical Processing (OLAP)-based applications. Apart from providing the functionalities necessary for OLAP-based applications, the main feature of the model we propose is a clear separation between structural aspects and the contents. This separation of concerns allows us to define data manipulation languages in a reasonably simple, transparent way. In particular, we show that the data cube operator can be expressed easily. Concretely, we define an algebra and a calculus and show them to be equivalent. We conclude by comparing our approach to related work. The conceptual multi-dimensional database model developed here is orthogonal to its implementation, which is not a subject of the present paper.


title: The Labor Market Imperative for CTE: Changes and Challenges for the 21st Century.

abstract: The labor market in the United States is evolving in unexpected ways following the 2007 recession. Career and technical education (CTE) has historically linked young people to the workplace. In this article we examine the U.S. labor market and provide multiple perspectives on its growth and direction and implications for future directions for CTE. We examine the often conflicting data-based arguments and suggest that in a future increasingly dominated by robotics and artificial intelligence, new entrants to the workforce will require very strong social, employability, and work-readiness skills that reflect the behavioral, attitudinal, and character traits highly valued in the workplace and in society. These noncognitive, nontechnical skills can be developed in the CTE classroom, but require both new technical content in these classrooms and changes in how CTE teachers deliver their curriculum.


title: A framework for end-user instruction of a robot assistant for manufacturing

abstract: Small Manufacturing Entities (SMEs) have not incorporated robotic automation as readily as large companies due to rapidly changing product lines, complex and dexterous tasks, and the high cost of start-up. While recent low-cost robots such as the Universal Robots UR5 and Rethink Robotics Baxter are more economical and feature improved programming interfaces, based on our discussions with manufacturers further incorporation of robots into the manufacturing work flow is limited by the ability of these systems to generalize across tasks and handle environmental variation. Our goal is to create a system designed for small manufacturers that contains a set of capabilities useful for a wide range of tasks, is both powerful and easy to use, allows for perceptually grounded actions, and is able to accumulate, abstract, and reuse plans that have been taught. We present an extension to Behavior Trees that allows for representing the system capabilities of a robot as a set of generalizable operations that are exposed to an end-user for creating task plans. We implement this framework in CoSTAR, the Collaborative System for Task Automation and Recognition, and demonstrate its effectiveness with two case studies. We first perform a complex tool-based object manipulation task in a laboratory setting. We then show the deployment of our system in an SME where we automate a machine tending task that was not possible with current off the shelf robots.


title: Design and Implementation of Robot Arm Control Using LabVIEW and ARM Controller

abstract: The design, analysis and implementation of a robot arm system, which is expressed towards its performance with an analytical model by using LabVIEW and embedded system tools was presented in this article. Mathematical modeling of kinematics plays an essential role in design and implementation of a robot arm control. The projected work was focused to control the end effector of the robot arm to achieve any accessible point in an amorphous region using LabVIEW, ARM (Advanced RISC (Reduced Instruction Set Computing) Machine) microcontroller and Dexter ER2 robotic arm. LabVIEW uses analytical method to design the inverse kinematic model of the robot arm. The inverse kinematic model and robot arm control was implemented using LabVIEW and ARM microcontroller. LabVIEW uses the parallel communication to send the joint angles of the robot arm to the ARM. ARM microcontroller uses five PWM (Pulse Width Modulation) signals in order to control the robot arm, which was geared up with servo motors. Robot arm was controlled manually through the LabVIEW GUI (Graphical User Interface) controls. The present paper discussed about the mechanical configuration, analytical modeling, software and hardware of the above said work.


title: Sentiment Analysis: From Opinion Mining to Human-Agent Interaction

abstract: The opinion mining and human-agent interaction communities are currently addressing sentiment analysis from different perspectives that comprise, on the one hand, disparate sentiment-related phenomena and computational representations, and on the other hand, different detection and dialog management methods. In this paper we identify and discuss the growing opportunities for cross-disciplinary work that may increase individual advances. Sentiment/opinion detection methods used in human-agent interaction are indeed rare and, when they are employed, they are not different from the ones used in opinion mining and consequently not designed for socio-affective interactions (timing constraint of the interaction, sentiment analysis as an input and an output of interaction strategies). To support our claims, we present a comparative state of the art which analyzes the sentiment-related phenomena and the sentiment detection methods used in both communities and makes an overview of the goals of socio-affective human-agent strategies. We propose then different possibilities for mutual benefit, specifying several research tracks and discussing the open questions and prospects. To show the feasibility of the general guidelines proposed we also approach them from a specific perspective by applying them to the case of the Greta embodied conversational agents platform and discuss the way they can be used to make a more significative sentiment analysis for human-agent interactions in two different use cases: job interviews and dialogs with museum visitors.