title: The Charcot Lecture | Beating MS: A story of B cells, with twists and turns

abstract: Autoimmune B cells play a major role in mediating tissue damage in multiple sclerosis (MS). In MS, B cells are believed to cross the blood-brain barrier and undergo stimulation, antigen-driven affinity maturation and clonal expansion within the supportive CNS environment. These highly restricted populations of clonally expanded B cells and plasma cells can be detected in MS lesions, in cerebrospinal fluid, and also in peripheral blood.In phase II trials in relapsing MS, monoclonal antibodies that target circulating CD20-positive B lymphocytes dramatically reduced disease activity. These beneficial effects occurred within weeks of treatment, indicating that a direct effect on B cells—and likely not on putative autoantibodies—was responsible. The discovery that depletion of B cells has an impact on MS biology enabled a paradigm shift in understanding how the inflammatory phase of MS develops, and will hopefully lead to development of increasingly selective therapies against culprit B cells and related humor...


title: Software visualization

abstract: The field of software visualization (SV) investigates approaches and techniques for static and dynamic graphical representations of algorithms, programs (code), and processed data. SV is concerned primarily with the analysis of programs and their development. The goal is to improve our understanding of inherently invisible and intangible software, particularly when dealing with large information spaces that characterize domains like software maintenance, reverse engineering, and collaborative development. The main challenge is to find effective mappings from different software aspects to graphical representations using visual metaphors. This paper provides an overview of the SV research, describes current research directions, and includes an extensive list of recommended readings.


title: A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation

abstract: Narrative story generation is a challenging problem because it demands the generated sentences with tight semantic connections, which has not been well studied by most existing generative models. To address this problem, we propose a skeleton-based model to promote the coherence of generated stories. Different from traditional models that generate a complete sentence at a stroke, the proposed model first generates the most critical phrases, called skeleton, and then expands the skeleton to a complete and fluent sentence. The skeleton is not manually defined, but learned by a reinforcement learning method. Compared to the state-of-the-art models, our skeleton-based model can generate significantly more coherent text according to human evaluation and automatic evaluation. The G-score is improved by 20.1% in the human evaluation. The code is available at https://github.com/lancopku/Skeleton-Based-Generation-Model


title: Filtering , Negotiating and Shifting in the Understanding of Information System Requirements

abstract: Traditional requirement engineering approaches pay little attention to how the requirements are interpreted and shared by different parties in an organization. Our study extends the previous research of social and organiza- tional requirement elicitation by suggesting that requirement shaping during a project can be described as a process where attitudes and expectations are filtered, shifted and negotiated repeatedly. We studied a large e-commerce platform development project by applying grounded theory and observed that preconceptions, attitudes and expectations about systems development among project participants filtered the understanding of software require-


Another opiate for the masses?


title: Classification of noun-noun compound semantics in Dutch and Afrikaans

abstract: This article presents initial results on a supervised machine learning approach to determine the semantics of noun compounds in Dutch and Afrikaans. After a discussion of previous research on the topic, we present our annotation methods used to provide a training set of compounds with the appropriate semantic class. The support vector machine method used for this classification experiment utilizes a distributional lexical semantics representation of the compound’s constituents to make its classification decision. The collection of words that occur in the near context of the constituent are considered an implicit representation of the semantics of this constituent. Fscores were reached of 47.8% for Dutch and 51.1% for Afrikaans. Keywords—compound semantics; Afrikaans; Dutch; machine learning; distributional methods


title: Generation of OAM Beams Using Phased Array in the Microwave Band

abstract: A system is proposed to generate vortex electromagnetic (EM) beams in the microwave band, which generates high-order vortex beams at the X-frequency band for the first time. First, the orbital angular momentum (OAM)-generating system is designed and the signal model based on the uniform circular array is presented. Subsequently, the mathematical model with array error contributions is established and, comprehensively, numerical simulations are conducted to analyze how amplitude and phase errors affect the radiation field and the EM vortex imaging. The experimental results validate that the proposed system can readily generate vortex beams of high quality, which are in agreement with the simulated results. The work paves the way to applications of OAM-carrying beams as well as a novel information-rich radar paradigm.


title: Improving classification accuracy of feedforward neural networks for spiking neuromorphic chips

abstract: Deep Neural Networks (DNN) achieve human level performance in many image analytics tasks but DNNs are mostly deployed to GPU platforms that consume a considerable amount of power. New hardware platforms using lower precision arithmetic achieve drastic reductions in power consumption. More recently, brain-inspired spiking neuromorphic chips have achieved even lower power consumption, on the order of milliwatts, while still offering real-time processing. ::: However, for deploying DNNs to energy efficient neuromorphic chips the incompatibility between continuous neurons and synaptic weights of traditional DNNs, discrete spiking neurons and synapses of neuromorphic chips need to be overcome. Previous work has achieved this by training a network to learn continuous probabilities, before it is deployed to a neuromorphic architecture, such as IBM TrueNorth Neurosynaptic System, by random sampling these probabilities. ::: The main contribution of this paper is a new learning algorithm that learns a TrueNorth configuration ready for deployment. We achieve this by training directly a binary hardware crossbar that accommodates the TrueNorth axon configuration constrains and we propose a different neuron model. ::: Results of our approach trained on electroencephalogram (EEG) data show a significant improvement with previous work (76% vs 86% accuracy) while maintaining state of the art performance on the MNIST handwritten data set.


title: Appliance consumption signature database and recognition test protocols

abstract: We report on the creation of a database of appliance consumption signatures and two test protocols to be used for appliance recognition tasks. By means of plug-based low-end sensors measuring the electrical consumption at low frequency, typically every 10 seconds, we made two acquisition sessions of one hour on about 100 home appliances divided into 10 categories: mobile phones (via chargers), coffee machines, computer stations (including monitor), fridges and freezers, Hi-Fi systems (CD players), lamp (CFL), laptops (via chargers), microwave oven, printers, and televisions (LCD or LED). We measured their consumption in terms of real power (W), reactive power (var), RMS current (A) and phase of voltage relative to current (φ). We now give free access to this ACS-Fl database. The proposed test protocols will help the scientific community to objectively compare new algorithms.


Performance Evaluation of Active Noise Control Algorithm Using Matlab


title: 3D photography on your desk

abstract: A simple and inexpensive approach for extracting the three-dimensional shape of objects is presented. It is based on 'weak structured lighting'; it differs from other conventional structured lighting approaches in that it requires very little hardware besides the camera: a desk-lamp, a pencil and a checker-board. The camera faces the object, which is illuminated by the desk-lamp. The user moves a pencil in front of the light source casting a moving shadow on the object. The 3D shape of the object is extracted from the spatial and temporal location of the observed shadow. Experimental results are presented on three different scenes demonstrating that the error in reconstructing the surface is less than 1%.


title: XMSS -- A practical forward secure signature scheme based on minimal security assumptions

abstract: We present the hash-based signature scheme XMSS. It is the first provably (forward) secure and practical signature scheme with minimal security requirements: a pseudorandom and a second preimage resistant (hash) function family. Its signature size is reduced to less than 25% compared to the best provably secure hash based signature scheme.


title: Octaband Internal Antenna for 4G Mobile Handset

abstract: A compact internal antenna that is composed of a coupled loop with two branch lines is presented. The proposed antenna operates at two wide frequency bands (698 ~ 960 MHz/1710 ~ 2690 MHz) to cover octabands LTE 700/GSM 850/GSM 900/DCS 1800/PCS 1900/WCDMA 2100/LTE 2300/LTE 2500 for the 4G mobile handset. The proposed antenna is designed and fabricated on a support (polyester; er=3.2) with a limited volume, i.e., 7×11×46 mm3. The design methods and the measured results of the proposed antenna are presented in detail.


title: An Exact Method for the Determination of Differential Leakage Factors in Electrical Machines With Non-Symmetrical Windings

abstract: An exact and simple method for the determination of differential leakage factors in polyphase ac electrical machines with non-symmetrical windings is presented in this paper. The method relies on the properties of Gorges polygons that are used to transform an infinite series expressing the differential leakage factor into a finite sum in order to significantly simplify the calculations. Some examples are shown and discussed in order to practically demonstrate the effectiveness of the proposed method.


title: Driver intent inference at urban intersections using the intelligent driver model

abstract: Predicting turn and stop maneuvers of potentially errant drivers is a basic requirement for advanced driver assistance systems for urban intersections. Previous work has shown that an early estimate of the driver's intent can be inferred by evaluating the vehicle's speed during the intersection approach. In the presence of a preceding vehicle, however, the velocity profile might be dictated by car-following behaviour rather than by the need to slow down before doing a left or right turn. To infer the driver's intent under such circumstances, a simple, real-time capable approach using an explicit model to represent both car-following and turning behaviour is proposed. Models for typical turning behavior are extracted from real world data. Preliminary results based on a Bayes net classification are presented.


title: When we like what we know – A parametric fMRI analysis of beauty and familiarity

abstract: This paper presents a neuroscientific study of aesthetic judgments on written texts. In an fMRI experiment participants read a number of proverbs without explicitly evaluating them. In a post-scan rating they rated each item for familiarity and beauty. These individual ratings were correlated with the functional data to investigate the neural correlates of implicit aesthetic judgments. We identified clusters in which BOLD activity was correlated with individual post-scan beauty ratings. This indicates that some spontaneous aesthetic evaluation takes place during reading, even if not required by the task. Positive correlations were found in the ventral striatum and in medial prefrontal cortex, likely reflecting the rewarding nature of sentences that are aesthetically pleasing. On the contrary, negative correlations were observed in the classic left frontotemporal reading network. Midline structures and bilateral temporo-parietal regions correlated positively with familiarity, suggesting a shift from the task-network towards the default network with increasing familiarity.


title: RCS Diversity of Electromagnetic Wave Carrying Orbital Angular Momentum

abstract: An electromagnetic (EM) wave with orbital angular momentum (OAM) has a helical wave front, which is different from that of the plane wave. The phase gradient can be found perpendicular to the direction of propagation and proportional to the number of OAM modes. Herein, we study the backscattering property of the EM wave with different OAM modes, i.e., the radar cross section (RCS) of the target is measured and evaluated with different OAM waves. As indicated by the experimental results, different OAM waves have the same RCS fluctuation for the simple target, e.g., a small metal ball as the target. However, for complicated targets, e.g., two transverse-deployed small metal balls, different RCSs can be identified from the same incident angle. This valuable fact helps to obtain RCS diversity, e.g., equal gain or selective combining of different OAM wave scattering. The majority of the targets are complicated targets or expanded targets; the RCS diversity can be utilized to detect a weak target traditionally measured by the plane wave, which is very helpful for anti-stealth radar to detect the traditional stealth target by increasing the RCS with OAM waves.


title: A Translator from Sinhala to English and English to Sinhala (SEES)

abstract: This paper presents a rule based machine translation system which is capable of translating sentences from Sinhala to English and vice versa. This is the first Sinhala to English and English to Sinhala machine translation system which comes with features such as a Sinhalese font translator, which is capable of interpreting Sinhalese words written in English characters (Singlish) to Sinhala characters, and an English grammar and spell checker. An entered sentence to the system will be tokenized and translated according to a rule. When translating Sinhala sentences to English the user input should be in Singlish and when translating English sentences to Sinhala input should be in English. The main objective of this translator is to enable a smooth flow translation of words, sentences and paragraphs to locals as well as foreigners and thereby eliminate the language barrier. A considerable amount of rules, patterns and words of both languages were used to develop this system. With 87% accuracy this pilot machine translation system translated 500 grammatically well-structured Sinhala sentences to English and 150 grammatically well-structured English sentences to Sinhala. The system is capable of translating approximately 70 sentences in one minute.


title: qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data

abstract: Although quantitative PCR (qPCR) is becoming the method of choice for expression profiling of selected genes, accurate and straightforward processing of the raw measurements remains a major hurdle. Here we outline advanced and universally applicable models for relative quantification and inter-run calibration with proper error propagation along the entire calculation track. These models and algorithms are implemented in qBase, a free program for the management and automated analysis of qPCR data.


title: Blind PARAFAC Receivers for DS-CDMA Systems

abstract: This paper links the direct-sequence code-division multiple access (DS-CDMA) multiuser separation-equalization-detection problem to the parallel factor (PARAFAC) model, which is an analysis tool rooted in psychometrics and chemometrics. Exploiting this link, it derives a deterministic blind PARAFAC DS-CDMA receiver with performance close to non-blind minimum mean-squared error (MMSE). The proposed PARAFAC receiver capitalizes on code, spatial, and temporal diversity-combining, thereby supporting small sample sizes, more users than sensors, and/or less spreading than users. Interestingly, PARAFAC does not require knowledge of spreading codes, the specifics of multipath (interchip interference), DOA-calibration information, finite alphabet/constant modulus, or statistical independence/whiteness to recover the information-bearing signals. Instead, PARAFAC relies on a fundamental result regarding the uniqueness of low-rank three-way array decomposition due to Kruskal (1977, 1988) (and generalized herein to the complex-valued case) that guarantees identifiability of all relevant signals and propagation parameters. These and other issues are also demonstrated in pertinent simulation experiments.


title: An Efficient Implementation of the Bellman-Ford Algorithm for Kepler GPU Architectures

abstract: Finding the shortest paths from a single source to all other vertices is a common problem in graph analysis. The Bellman-Ford's algorithm is the solution that solves such a single-source shortest path (SSSP) problem and better applies to be parallelized for many-core architectures. Nevertheless, the high degree of parallelism is guaranteed at the cost of low work efficiency, which, compared to similar algorithms in literature (e.g., Dijkstra's) involves much more redundant work and a consequent waste of power consumption. This article presents a parallel implementation of the Bellman-Ford algorithm that exploits the architectural characteristics of recent GPU architectures (i.e., NVIDIA Kepler, Maxwell) to improve both performance and work efficiency. The article presents different optimizations to the implementation, which are oriented both to the algorithm and to the architecture. The experimental results show that the proposed implementation provides an average speedup of $5 \times$ higher than the existing most efficient parallel implementations for SSSP, that it works on graphs where those implementations cannot work or are inefficient (e.g., graphs with negative weight edges, sparse graphs), and that it sensibly reduces the redundant work caused by the parallelization process.


title: Developmental trajectories as autism phenotypes

abstract: Numerous studies of ASD have attempted to link behavioral phenotypes to genetic findings, but reliance on cross-sectional behavioral data in samples that span wide age ranges may have limited this endeavor because ASD behaviors are not static within individuals across development. This study uses quantitative methods to describe specific aspects of changes in ASD-related and more general behaviors in order to yield trajectories that could be used in place of single time-point data as behavioral phenotypes in neurobiological studies of both ASD and other overlapping conditions. Building on previous analyses [Anderson et al, 2014], we examined trajectories of parent-reported social-communication deficits, social adaptive functioning, and two types of repetitive behaviors, repetitive sensory motor (RSM) behaviors and insistence on sameness (IS) behaviors, in a relatively large sample of participants referred for possible autism at age 2 years and followed into young adulthood (n=85). A strength of this sample was the diverse range of outcomes, including young adults with intellectual disability and persistent ASD related difficulties, those with IQs in the borderline or average range who continued to experience functional impairment related to ASD, and a small group of young adults (n=8) with IQs in the average range who were judged to be functioning at typical age appropriate levels at age 19 years, despite a previous childhood diagnosis of ASD.


title: Full vector modal analysis of microstructured optical fiber propagation characteristics

abstract: Microstructured optical fibers (MOFs) are optical fibers having a periodic air-silica cross-section. The air holes extend along the axis of the fiber for its entire length. The core of the fiber is formed by a missing hole in the periodic structure. Remarkable properties of MOFs have recently been reported. This paper presents new work in the modeling of the propagation characteristics of MOFs using the Finite Element Method (FEM) and the Galerkin Method (GM). This efficient electromagnetic simulation package provides a vectorial description of the electromagnetic fields and of the associated effective index. This information includes accurate determination of the spectral extent of the modes, cutoff properties and mode-field distributions. We show that FEM is well adapted for describing the fields at abrupt transitions of the refractive index while GM has the advantage to accurately analyze MOFs of significant complexity using only modest computational resources. This presentation will focus on the specific techniques required to determine single mode operation, dispersion properties and effective area through careful choice of the geometrical parameters of the fibers. We demonstrate that with suitable geometrical parameters, the zero dispersion wavelength can be shifted. This tool can also provide design criteria for fabricating MOFs and a corresponding map of effective area. This approach is validated by comparison with experimental results and measurements on actual MOFs fabricated at IRCOM and at Alcatel Research and Innovation Center.


Multi-Agent Reinforcement Learning: A Survey


title: Radar cross section measurement with 77 GHz automotive FMCW radar

abstract: In this paper, radar cross section (RCS) measurement for human subjects and vehicles in 77 GHz automotive frequency modulated continuous wave (FMCW) radar system is presented. In this system, it is impossible to utilize conventional RCS definition due to high frequency band and the modulation technique used. Therefore, we introduce a new parameter called pseudo-RCS that can replace the conventional RCS. Then, we conduct actual experiment in the road to measure the RCS of the human subjects and the vehicles. In the measurement, data of four human subjects and four different kinds of vehicles are recorded with the 77 GHz FMCW radar. From the actual measured data, we find RCS distributions of the human subjects and the vehicles. For the human subjects, the RCS values are distributed following the Nakagami distribution. On the other hand, the log-normal distribution is well fit for the RCS values in the case of the vehicle.


title: InnoDB Database Forensics: Reconstructing Data Manipulation Queries from Redo Logs

abstract: InnoDB is a powerful open-source storage engine for MySQL that gained much popularity during the recent years. This paper proposes methods for forensic analysis of InnoDB databases by analyzing the redo logs, primarily used for crash recovery within the storage engine. This new method can be very useful in forensic investigations where the attacker got admin privileges, or was the admin himself. While such a powerful attacker could cover tracks by manipulating the log files intended for fraud detection, data cannot be changed easily in the redo logs. Based on a prototype implementation, we show methods for recovering Insert, Delete and Update statements issued against a database.


title: Evolutionary Fuzzy ARTMAP Neural Networks for Classification of Semiconductor Defects

abstract: Wafer defect detection using an intelligent system is an approach of quality improvement in semiconductor manufacturing that aims to enhance its process stability, increase production capacity, and improve yields. Occasionally, only few records that indicate defective units are available and they are classified as a minority group in a large database. Such a situation leads to an imbalanced data set problem, wherein it engenders a great challenge to deal with by applying machine-learning techniques for obtaining effective solution. In addition, the database may comprise overlapping samples of different classes. This paper introduces two models of evolutionary fuzzy ARTMAP (FAM) neural networks to deal with the imbalanced data set problems in a semiconductor manufacturing operations. In particular, both the FAM models and hybrid genetic algorithms are integrated in the proposed evolutionary artificial neural networks (EANNs) to classify an imbalanced data set. In addition, one of the proposed EANNs incorporates a facility to learn overlapping samples of different classes from the imbalanced data environment. The classification results of the proposed evolutionary FAM neural networks are presented, compared, and analyzed using several classification metrics. The outcomes positively indicate the effectiveness of the proposed networks in handling classification problems with imbalanced data sets.


title: Conformance checking in UML artifact-centric business process models

abstract: Business artifacts have appeared as a new paradigm to capture the information required for the complete execution and reasoning of a business process. Likewise, conformance checking is gaining popularity as a crucial technique that enables evaluating whether recorded executions of a process match its corresponding model. In this paper, conformance checking techniques are incorporated into a general framework to specify business artifacts. By relying on the expressive power of an artifact-centric specification, BAUML, which combines UML state and activity diagrams (among others), the problem of conformance checking can be mapped into the Petri net formalism and its results be explained in terms of the original artifact-centric specification. In contrast to most existing approaches, ours incorporates data constraints into the Petri nets, thus achieving conformance results which are more precise. We have also implemented a plug-in, within the ProM framework, which is able to translate a BAUML into a Petri net to perform conformance checking. This shows the feasibility of our approach.


title: Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation

abstract: Generating novel graph structures that optimize given objectives while obeying some given underlying rules is fundamental for chemistry, biology and social science research. This is especially important in the task of molecular graph generation, whose goal is to discover novel molecules with desired properties such as drug-likeness and synthetic accessibility, while obeying physical laws such as chemical valency. However, designing models to find molecules that optimize desired properties while incorporating highly complex and non-differentiable rules remains to be a challenging task. Here we propose Graph Convolutional Policy Network (GCPN), a general graph convolutional network based model for goal-directed graph generation through reinforcement learning. The model is trained to optimize domain-specific rewards and adversarial loss through policy gradient, and acts in an environment that incorporates domain-specific rules. Experimental results show that GCPN can achieve 61% improvement on chemical property optimization over state-of-the-art baselines while resembling known molecules, and achieve 184% improvement on the constrained property optimization task.


title: Bayesian Curve Fitting Using MCMC With Applications to Signal Segmentation

abstract: We propose some Bayesian methods to address the problem of fitting a signal modeled by a sequence of piecewise constant linear (in the parameters) regression models, for example, autoregressive or Volterra models. A joint prior distribution is set up over the number of the changepoints/knots, their positions, and over the orders of the linear regression models within each segment if these are unknown. Hierarchical priors are developed and, as the resulting posterior probability distributions and Bayesian estimators do not admit closed-form analytical expressions, reversible jump Markov chain Monte Carlo (MCMC) methods are derived to estimate these quantities. Results are obtained for standard denoising and segmentation of speech data problems that have already been examined in the literature. These results demonstrate the performance of our methods.


title: Adjacent Vehicle Collision Warning System using Image Sensor and Inertial Measurement Unit

abstract: Advanced driver assistance systems are the newest addition to vehicular technology. Such systems use a wide array of sensors to provide a superior driving experience. Vehicle safety and driver alert are important parts of these system. This paper proposes a driver alert system to prevent and mitigate adjacent vehicle collisions by proving warning information of on-road vehicles and possible collisions. A dynamic Bayesian network (DBN) is utilized to fuse multiple sensors to provide driver awareness. It detects oncoming adjacent vehicles and gathers ego vehicle motion characteristics using an on-board camera and inertial measurement unit (IMU). A histogram of oriented gradient feature based classifier is used to detect any adjacent vehicles. Vehicles front-rear end and side faces were considered in training the classifier. Ego vehicles heading, speed and acceleration are captured from the IMU and feed into the DBN. The network parameters were learned from data via expectation maximization(EM) algorithm. The DBN is designed to provide two type of warning to the driver, a cautionary warning and a brake alert for possible collision with other vehicles. Experiments were completed on multiple public databases, demonstrating successful warnings and brake alerts in most situations.


title: Reinforcement learning for non-prehensile manipulation: Transfer from simulation to physical system

abstract: Reinforcement learning has emerged as a promising methodology for training robot controllers. However, most results have been limited to simulation due to the need for a large number of samples and the lack of automated-yet-safe data collection methods. Model-based reinforcement learning methods provide an avenue to circumvent these challenges, but the traditional concern has been the mismatch between the simulator and the real world. Here, we show that control policies learned in simulation can successfully transfer to a physical system, composed of three Phantom robots pushing an object to various desired target positions. We use a modified form of the natural policy gradient algorithm for learning, applied to a carefully identified simulation model. The resulting policies, trained entirely in simulation, work well on the physical system without additional training. In addition, we show that training with an ensemble of models makes the learned policies more robust to modeling errors, thus compensating for difficulties in system identification.


title: Detection Of Skin Cancer Using Image Processing Techniques

abstract: Smartphones are playing major role in e-health in such a way that m-health is playing a significant role in healthcare industry. Image processing techniques are instrumental in healthcare industry to detect abnormalities in human body. Skin cancer (Melanoma) is one of the most deadly cancers, but when diagnosed early, it can be cured. Reports tell that more than million deaths occur due to Skin cancer itself. This paper speaks about how skin cancer can be detected in early stages using smartphone application by analyzing properties of the cancer, Asymmetry, Border, Color variation, Diameter and Expansion(ABCDE).These properties are analyzed using different image processing techniques like Grey scale conversion, Segmentation, contour tracing and histogram analysis. KeywordsSkin cancer, Melanoma, ABCDE, segmentation, contour tracing, edge detection.


title: Food reward: Brain substrates of wanting and liking

abstract: Abstract What are the neural substrates of food reward? Are reward and pleasure identical? Can taste pleasure be assessed in animals? Is reward necessarily conscious? These questions have re-emerged in recent years, and there is now sufficient evidence to prompt re-examination of many preconceptions concerning reward and its relation to brain systems. This paper reviews evidence from many sources regarding both the psychological structure of food reward and the neural systems that mediate it. Special attention is paid to recent evidence from “taste reactivity” studies of affective reactions to food. I argue that this evidence suggests the following surprising possibilities regarding the functional components and brain substrates of food reward. (1) Reward contains distinguishable psychological or functional components—“liking” (pleasure/palatability) and “wanting” (appetite/incentive motivation). These can be manipulated and measured separately. (2) Liking and wanting have separable neural substrates. Mediation of liking related to food reward involves neurotransmitter systems such as opioid and GABA/benzodiazepine systems, and anatomical structures such as ventral pallidum and brainstem primary gustatory relays. Mediation of wanting related to food reward involves mesotelencephalic dopamine systems, and divisions of nucleus accumbens and amygdala. Both liking and wanting arise from vastly distributed neural systems, but the two systems are separable. (3) Neural processing of food reward is not confined to the limbic forebrain. Aspects of food reward begin to be processed in the brainstem. A neural manipulation can enhance reward or produce aversion but no single lesion or transection is likely abolish all properties of food reward. (4) Both wanting and liking can exist without subjective awareness. Conscious experience can distort or blur the underlying reward processes that gave rise to it. Subjective reports may contain false assessments of underlying processes, or even fail at all to register important reward processes. The core processes of liking and wanting that constitute reward are distinct from the subjective report or conscious awareness of those processes.


title: Triple frequency circular patch antenna

abstract: An enhanced but simple triple band circular ring patch antenna with a new slotting technique is presented, which is most suitable for X-band, Ku-band and K-band applications. This compact micro strip antenna is obtained by inserting small rectangular strip in a circular ring patch antenna. The antenna has been designed and simulated on an FR4 substrate with dielectric constant of 4.4 and thickness of 2mm. The design is analysed by Finite Element Method based HFSS Simulator Software (version 14.0), The simulated return losses obtained are −35.80dB, −42.39dB, and −44.98dB at 8.96 GHz, 14.44 GHz, 18.97 GHz respectively. Therefore, this antenna can be applicable for X-band, Ku-band and K-band applications respectively.


title: Micro-Shuttle Lifting of the Neck: A Percutaneous Loop Suspension Method Using a Novel Double-Ended Needle

abstract: BACKGROUND ::: Most younger patients expect to be able to achieve significant improvements and lift to their neck, yet they don't want to undergo extensive surgery. They are now able to do that and restore their youthful appearance thanks to new concepts the techniques through volume redistribution. ::: ::: ::: OBJECTIVES ::: The authors' goal was to achieve results that are comparable to a necklift and durable through minimally invasive surgery, utilizing punctures instead of incisions. ::: ::: ::: METHODS ::: The concept of micro-shuttle lifting creates a percutaneous hammock to achieve the lifting of all different planes of the neck at once. This is accomplished by putting nonabsorbable sutures on nonundermined platsyma through the use of a double-ended (micro-shuttle) needle and anchoring it to fixed thread loops around the ears. Mitigation of gravitational force is accomplished through the loop suspensions, to obtain effective skin redraping over the suture-created internal splint. ::: ::: ::: RESULTS ::: This combined technique for the neck was applied in 221 selected patients between December 2005 and May 2014, with follow-up ranging from 8 months to 7 years. The mean age of the patients was 42.5 years. Outcomes were satisfactory in all but 12 cases, of which 7 found the result inadequate. The operation time for the neck was less than 40 minutes under local anesthesia or local anesthesia with sedation, and the recovery time was 5-7 days. ::: ::: ::: CONCLUSIONS ::: The sustainability of this percutaneous procedure does not rely on the suspensions, but rather on the skin redraping in the new position in a similar manner to orthopedic fracture treatment. In selected patients, this safe and simple percutaneous necklifting method can be quickly and easily performed under local anesthesia with long-term durability, low morbidity, and a high patient satisfaction rate. LEVEL OF EVIDENCE 4: Therapeutic.


title: Minimized transient and steady-state cross regulation in 55nm CMOS single-inductor dual-output (SIDO) step-down DC-DC converter

abstract: A single-inductor dual-output (SIDO) step-down converter with continuous conduction mode (CCM) control is proposed for high efficiency system-on-a-chip (Soc) integration. The cross regulation in steady-state output voltage ripple, which is rarely mentioned, is effectively reduced as well as in transient response. The low-voltage energy distribution controller (LV-EDC) circuit guarantees good voltage regulation and low output voltage ripple simultaneously. In addition, within the allowable output voltage ripple, the automatic energy bypass (AEB) mechanism can reduce the number of energy delivery path to decrease the switching loss. Two outputs are operated with the load-dependent energy path for improving the power conversion efficiency. The chip fabricated in 55 nm CMOS achieves 91 % peak efficiency, low output voltage ripple, and excellent load transient response.


title: A touring machine: Prototyping 3D mobile augmented reality systems for exploring the urban environment

abstract: We describe a prototype system that combines together the overlaid 3D graphics of augmented reality with the untethered freedom of mobile computing. The goal is to explore how these two technologies might together make possible wearable computer systems that can support users in their everyday interactions with the world. We introduce an application that presents information about our university's campus, using a head-tracked, see-through, head-worn, 3D display, and an untracked, opaque, handheld, 2D display with stylus and trackpad. We provide an illustrated explanation of how our prototype is used, and describe our rationale behind designing its software infrastructure and selecting the hardware on which it runs.


title: A Cloud Computing Solution for Patient's Data Collection in Health Care Institutions

abstract: Existing processes for patients' vital data collection require a great deal of labor work to collect, input and analyze the information. These processes are usually slow and error-prone, introducing a latency that prevents real-time data accessibility. This scenario restrains the clinical diagnostics and monitoring capabilities. We propose a solution to automate this process by using “sensors” attached to existing medical equipments that are inter-connected to exchange service. The proposal is based on the concepts of utility computing and wireless sensor networks. The information becomes available in the “cloud” from where it can be processed by expert systems and/or distributed to medical staff. The proof-of-concept design applies commodity computing integrated to legacy medical devices, ensuring cost-effectiveness and simple integration.


title: Strategic human resource management: Where do we go from here

abstract: The authors identify the key challenges facing strategic human resource management (SHRM) going forward and discuss several new directions in both the scholarship and practice of SHRM. They focus on a clearer articulation of the “black box” between HR and firm performance, emphasizing the integration of strategy implementation as the central mediating variable in this relationship. There are direct implications for the nature of fit and contingencies in SHRM. They also highlight the significance of a differentiated HR architecture not just across firms but also within firms.


title: Two distinct alpha-L-fucosidases from Bifidobacterium bifidum are essential for the utilization of fucosylated milk oligosaccharides and glycoconjugates.

abstract: Bifidobacteria are predominant bacteria present in the intestines of breast-fed infants and offer important health benefits for the host. Human milk oligosaccharides are one of the most important growth factors for bifidobacteria and are frequently fucosylated at their non-reducing termini. Previously, we identified 1,2-alpha-l-fucosidase (AfcA) belonging to the novel glycoside hydrolase (GH) family 95, from Bifidobacterium bifidum JCM1254 (Katayama T, Sakuma A, Kimura T, Makimura Y, Hiratake J, Sakata K, Yamanoi T, Kumagai H, Yamamoto K. 2004. Molecular cloning and characterization of Bifidobacterium bifidum 1,2-alpha-l-fucosidase (AfcA), a novel inverting glycosidase (glycoside hydrolase family 95). J Bacteriol. 186:4885-4893). Here, we identified a gene encoding a novel 1,3-1,4-alpha-l-fucosidase from the same strain and termed it afcB. The afcB gene encodes a 1493-amino acid polypeptide containing an N-terminal signal sequence, a GH29 alpha-l-fucosidase domain, a carbohydrate binding module (CBM) 32 domain, a found-in-various-architectures (FIVAR) domain and a C-terminal transmembrane region, in this order. The recombinant enzyme was expressed in Escherichia coli and was characterized. The enzyme specifically released alpha1,3- and alpha1,4-linked fucosyl residues from 3-fucosyllactose, various Lewis blood group substances (a, b, x, and y types), and lacto-N-fucopentaose II and III. However, the enzyme did not act on glycoconjugates containing alpha1,2-fucosyl residue or on synthetic alpha-fucoside (p-nitrophenyl-alpha-l-fucoside). The afcA and afcB genes were introduced into the B. longum 105-A strain, which has no intrinsic alpha-l-fucosidase. The transformant carrying afcA could utilize 2'-fucosyllactose as the sole carbon source, whereas that carrying afcB was able to utilize 3-fucosyllactose and lacto-N-fucopentaose II. We suggest that AfcA and AfcB play essential roles in degrading alpha1,2- and alpha1,3/4-fucosylated milk oligosaccharides, respectively, and also glycoconjugates, in the gastrointestinal tracts.


title: ARS: Cross-layer adaptive request scheduling to mitigate TCP incast in data center networks

abstract: In data center networks, many network-intensive applications typically suffer TCP incast throughput collapse when bursty concurrent TCP flows share a single bottleneck link. To address the TCP incast problem, we first reveal theoretically and empirically that controlling the number of concurrent flows is much more effective in reducing the incast probability than controlling the congestion window. We further propose a novel cross-layer design called Adaptive Request Schedule (ARS), which dynamically adjusts the number of concurrent TCP flows by batching application requests according to the congestion state acquired from transport layer. ARS is deployed only at the aggregator-side, while making no modification on hundreds or thousands of workers. Broad applicability is another advantage of ARS. We integrated ARS transparently (i.e., without modification) with DCTCP and TCP NewReno on NS2 simulation and a physical testbed, respectively. The experimental results show that ARS significantly reduces the incast probability across different TCP protocols and that the network goodput can be increased consistently by on average 6x under severe congestion.


title: Event-based summarization using a centrality-as-relevance model

abstract: Event detection is a fundamental information extraction task, which has been explored largely in the context of question answering, topic detection and tracking, knowledge base population, news recommendation, and automatic summarization. In this article, we explore an event detection framework to improve a key phrase-guided centrality-based summarization model. Event detection is based on the fuzzy fingerprint method, which is able to detect all types of events in the ACE 2005 Multilingual Corpus. Our base summarization approach is a two-stage method that starts by extracting a collection of key phrases that will be used to help the centrality-as-relevance retrieval model. We explored three different ways to integrate event information, achieving state-of-the-art results in text and speech corpora: (1) filtering of nonevents, (2) event fingerprints as features, and (3) combination of filtering of nonevents and event fingerprints as features.


title: A high-precision time-to-digital converter for pulsed time-of-flight laser radar applications

abstract: A time-to-digital converter (TDC) has been designed, and six units have been constructed and tested. It consists of integrated digital time-interval measurement electronics with a /spl plusmn/10-ns resolution that can be improved to /spl plusmn/10 ps by an analog interpolation method. Identical construction of the interpolation electronics leads to stable performance of the TDC. The drifts of all six TDC units remain within /spl plusmn/10 ps over a temperature range from -10/spl deg/C to +50/spl deg/C. The stability can be improved further by a real-time calibration procedure developed here. The single-shot precision of the TDC is better than 15 ps (standard deviation), but precision can be improved to below 0.1 ps by averaging about 10 000 measurements at the maximum measurement speed of 100 kHz. The time range of the TDC is currently 2.55 /spl mu/s, but this can be increased by adding an external digital counter. The TDC suffers from a periodic nonlinearity over the measured time range from 0 to 1.3 /spl mu/s, the amplitude and period of the nonlinearity being /spl plusmn/20 ps and 40 ns, respectively. The reason lies in the integrated digital part of the electronics. The linearity of interpolation in the 10-ns time range improves, however, as more results are averaged.


title: Voltage Multiplier Cells Applied to Non-Isolated DC–DC Converters

abstract: This paper introduces the use of the voltage multiplier technique applied to the classical non-isolated dc-dc converters in order to obtain high step-up static gain, reduction of the maximum switch voltage, zero current switching turn-on. The diodes reverse recovery current problem is minimized and the voltage multiplier also operates as a regenerative clamping circuit, reducing the problems with layout and the EMI generation. These characteristics allows the operation with high static again and high efficiency, making possible to design a compact circuit for applications where the isolation is not required. The operation principle, the design procedure and practical results obtained from the implemented prototypes are presented for the single-phase and multiphase dc-dc converters. A boost converter was tested with the single-phase technique, for an application requiring an output power of 100 W, operating with 12 V input voltage and 100 V output voltage, obtaining efficiency equal to 93%. The multiphase technique was tested with a boost interleaved converter operating with an output power equal to 400 W, 24 V input voltage and 400 V output voltage, obtaining efficiency equal to 95%.


title: ZnO@Ag2S core–shell nanowire arrays for environmentally friendly solid-state quantum dot-sensitized solar cells with panchromatic light capture and enhanced electron collection

abstract: A solid-state environmentally friendly Ag2S quantum dot-sensitized solar cell (QDSSC) is demonstrated. The photovoltaic device is fabricated by applying ZnO@Ag2S core–shell nanowire arrays (NWAs) as light absorbers and electron conductors, and poly-3-hexylthiophene (P3HT) as a solid-state hole conductor. Ag2S quantum dots (QDs) were directly grown on the ZnO nanowires by the successive ionic layer adsorption and reaction (SILAR) method to obtain the core–shell nanostructure. The number of SILAR cycles for QD formation and the length of the core–shell NWs significantly affect the photocurrent. The device with a core–shell NWAs photoanode shows a power conversion efficiency increase by 32% compared with the device based on a typical nanoparticle-based photoanode with Ag2S QDs. The enhanced performance is attributed to enhanced collection of the photogenerated electrons utilizing the ZnO nanowire as an efficient pathway for transporting the photogenerated electrons from the QD to the contact.


title: Wireless Home Automation System Using Zigbee

abstract: Home Automation industry is growing rapidly; this is fuelled by provide supporting systems for the elderly and the disabled, especially those who live alone. Coupled with this, the world population is confirmed to be getting older. Home automation systems must comply with the household standards and convenience of usage. This paper details the overall design of a wireless home automation system (WHAS) which has been built and implemented. The automation centers on recognition of voice commands and uses low-power RF ZigBee wireless communication modules which are relatively cheap. The home automation system is intended to control all lights and electrical appliances in a home or office using voice commands. The system has been tested and verified. The verification tests included voice recognition response test, indoor ZigBee communication test. The tests involved a mix of 10 male and female subjects with different Indian languages. 7 different voice commands were sent by each person. Thus the test involved sending a total of 70 commands and 80.05% of these commands were recognized correctly.


title: A Novel Interest Flooding Attacks Detection and Countermeasure Scheme in NDN

abstract: As one of the promising candidates for the next generation network, Named Data Networking (NDN) has more advantages than the TCP/IP network in areas such as mobility, content distribution and security. Although NDN is designed to defense the majority Distributed Denial of Service (DDoS) attack in the current Internet, it anticipates some new varietal DDoS attacks. A representative DDoS form is called Interest Flooding Attacks (IFA), which can be launched easily by overflowing the PIT and can do immeasurable damage to the NDN. The existing IFA detection and countermeasure methods are mainly based on the PIT abnormal state statistics. However, these methods may cause misjudgment and damage the legitimate users, especially in the case of low-rate DDoS attacks or network congestion. In this paper, we propose an IFA detection scheme based on cumulative entropy by monitoring the content request abnormal distribution and then provide the malicious prefix identification method by relative entropy theory. An Interest traceback countermeasure is also used to restrain the attacker after detection. Therefore, the proposed scheme can reduce the IFA misjudgment and protect the legitimate user, and at the same time, can avoid overreaction to normal traffic fluctuation. Simulation results reveal that our methods can effectively mitigate the IFA in NDN.


Toward the sustainability of health interventions implemented in sub-Saharan Africa: a systematic review and conceptual framework


title: Graph construction and b-matching for semi-supervised learning

abstract: Graph based semi-supervised learning (SSL) methods play an increasingly important role in practical machine learning systems. A crucial step in graph based SSL methods is the conversion of data into a weighted graph. However, most of the SSL literature focuses on developing label inference algorithms without extensively studying the graph building method and its effect on performance. This article provides an empirical study of leading semi-supervised methods under a wide range of graph construction algorithms. These SSL inference algorithms include the Local and Global Consistency (LGC) method, the Gaussian Random Field (GRF) method, the Graph Transduction via Alternating Minimization (GTAM) method as well as other techniques. Several approaches for graph construction, sparsification and weighting are explored including the popular k-nearest neighbors method (kNN) and the b-matching method. As opposed to the greedily constructed kNN graph, the b-matched graph ensures each node in the graph has the same number of edges and produces a balanced or regular graph. Experimental results on both artificial data and real benchmark datasets indicate that b-matching produces more robust graphs and therefore provides significantly better prediction accuracy without any significant change in computation time.


title: A general theory for inertial navigator error modeling

abstract: This paper presents a general theory for the development of error equations for an inertial navigator. A large space of possibilities is presented, based on how one defines the error parameters. For attitude, rotation vector parameterizations of the errors are introduced that are based on the difference between true can computed versions of each coordinate frame. Since these definitions are valid for large error angles, one can obtain general nonlinear equations for the psi- and phi-angle differential equations. These definitions are applicable to any mechanization and to either strapdown or platform navigators. This allows the development of general nonlinear formulas for the error dynamics, as well as new linear formulas using a different parameterization of the errors. There are two commonly used choices for the errors in position and velocity [1]; the more commonly used choice is to form the error as that of the true variable in the true frame less the computed variable in the computed frame. The alternate approach is one in which the error is the true variable less the computed variable, with both errors coordinatized in the computed frame. This latter approach gives new error equations not previously considered in the literature. Transformations between the new error parameterizations and the traditional ones are presented.


title: Aesthetic package design: A behavioral, neural, and psychological investigation

abstract: In four experiments, this research sheds light on aesthetic experiences by rigorously investigating behavioral, neural, and psychological properties of package design. We find that aesthetic packages significantly increase the reaction time of consumers' choice responses; that they are chosen over products with well-known brands in standardized packages, despite higher prices; and that they result in increased activation in the nucleus accumbens and the ventromedial prefrontal cortex, according to functional magnetic resonance imaging (fMRI). The results suggest that reward value plays an important role in aesthetic product experiences. Further, a closer look at psychometric and neuroimaging data finds that a paper-and-pencil measure of affective product involvement correlates with aesthetic product experiences in the brain. Implications for future aesthetics research, package designers, and product managers are discussed.


FINITE ELEMENT ANALYSIS OF FERROFLUID COOLING OF HEAT-GENERATING DEVICES


title: Behavioral Cloning for Lateral Motion Control of Autonomous Vehicles Using Deep Learning

abstract: Current trend of the automotive industry combined with research by the major tech companies has proved that self-driving vehicles are the future. With successful demonstration of neural network based autonomous driving, NVIDIA has introduced a new paradigm for autonomous driving software. The biggest challenge for self-driving cars is autonomous lateral control. An end-to-end model seems very promising in providing a complete software stack for autonomous driving. Although this system is not ready to be provided as a feature in the market today, it is one of the many steps in the right direction to make self-driving cars a reality. The work described in this paper focusses on how an end-to-end model is implemented. The subtleties of training a successful end-to-end model are highlighted with the aim of providing an insight on deep learning and software required for neural network training. Detailed analyses of data acquisition and training systems are provided and installation procedures for all required tools and software discussed. TORCS is used for developing and testing the end-to-end model. Approximately ten hours of driving data was collected from two different tracks. Using four hours of data from a track, we trained a deep neural network to steer a car inside simulation. Even with such a small training set, the end-to-end model developed demonstrated capabilities to maintain lanes and complete laps in different tracks. For a multilane track, like the one used for training, the model demonstrated an autonomy of 96.62%. For single lane unknown tracks, the model steered the vehicle successfully for 89.02% of the time. The results indicate that end-to-end learning and behavioral cloning can be used to drive autonomously in new and unknown scenarios.


title: Autonomous Functional Locomotion in a Tendon-Driven Limb via Limited Experience

abstract: Robots will become ubiquitously useful only when they can use few attempts to teach themselves to perform different tasks, even with complex bodies and in dynamical environments. Vertebrates, in fact, successfully use trial-and-error to learn multiple tasks in spite of their intricate tendon-driven anatomies. Roboticists find such tendon-driven systems particularly hard to control because they are simultaneously nonlinear, under-determined (many tendon tensions combine to produce few net joint torques), and over-determined (few joint rotations define how many tendons need to be reeled-in/payed-out). We demonstrate---for the first time in simulation and in hardware---how a model-free approach allows few-shot autonomous learning to produce effective locomotion in a 3-tendon/2-joint tendon-driven leg. Initially, an artificial neural network fed by sparsely sampled data collected using motor babbling creates an inverse map from limb kinematics to motor activations, which is analogous to juvenile vertebrates playing during development. Thereafter, iterative reward-driven exploration of candidate motor activations simultaneously refines the inverse map and finds a functional locomotor limit-cycle autonomously. This biologically-inspired algorithm, which we call G2P (General to Particular), enables versatile adaptation of robots to changes in the target task, mechanics of their bodies, and environment. Moreover, this work empowers future studies of few-shot autonomous learning in biological systems, which is the foundation of their enviable functional versatility.


title: The effects of physical activity on attention deficit hyperactivity disorder symptoms: the evidence.

abstract: UNLABELLED ::: Evidence supports the beneficial effects of physical activity (PA) on cognitive performance and suggests that effects might be particularly large for children. However, limited research has explored PA as a means of managing behavioral symptoms and improving cognitive performance of children with attention deficit hyperactivity disorder (ADHD). The etiology of ADHD and the putative mechanisms for the effects of PA on cognitive performance suggest that PA might be especially important for this population. ::: ::: ::: OBJECTIVE ::: The purpose of this paper is to review the literature regarding the potential of PA for ADHD symptom management, particularly in regard to behavioral and cognitive symptoms. ::: ::: ::: METHODS ::: Literature was reviewed for published and unpublished research specifically examining the effects of PA on cognitive and/or behavioral symptoms of ADHD. Additionally, potential mechanisms were addressed. ::: ::: ::: RESULTS ::: Albeit limited, current research generally supports the potential for acute and chronic PA to mitigate ADHD symptoms. ::: ::: ::: CONCLUSION ::: Given the generally supportive extant literature and the challenges that face children with ADHD, future research exploring the potential of PA with this population is advocated.


title: The implementation of a Deep Recurrent Neural Network Language Model on a Xilinx FPGA

abstract: Recently, FPGA has been increasingly applied to problems such as speech recognition, machine learning, and cloud computation such as the Bing search engine used by Microsoft. This is due to FPGAs great parallel computation capacity as well as low power consumption compared to general purpose processors. However, these applications mainly focus on large scale FPGA clusters which have an extreme processing power for executing massive matrix or convolution operations but are unsuitable for portable or mobile applications. This paper describes research on single-FPGA platform to explore the applications of FPGAs in these fields. In this project, we design a Deep Recurrent Neural Network (DRNN) Language Model (LM) and implement a hardware accelerator with AXI Stream interface on a PYNQ board which is equipped with a XILINX ZYNQ SOC XC7Z020 1CLG400C. The PYNQ has not only abundant programmable logic resources but also a flexible embedded operation system, which makes it suitable to be applied in the natural language processing field. We design the DRNN language model with Python and Theano, train the model on a CPU platform, and deploy the model on a PYNQ board to validate the model with Jupyter notebook. Meanwhile, we design the hardware accelerator with Overlay, which is a kind of hardware library on PYNQ, and verify the acceleration effect on the PYNQ board. Finally, we have found that the DRNN language model can be deployed on the embedded system smoothly and the Overlay accelerator with AXI Stream interface performs at 20 GOPS processing throughput, which constitutes a 70.5X and 2.75X speed up compared to the work in Ref.30 and Ref.31 respectively.


title: Conceptual graphs for Semantic Web applications

abstract: In this paper, we aim at showing the advantages of Conceptual Graph formalism for the Semantic Web through several real-world applications in the framework of Corporate Semantic Webs. We describe the RDF(S)-dedicated semantic search engine, CORESE, based on a correspondence between RDF(S) and Conceptual Graphs, and we illustrate the interest of Conceptual Graphs through the analysis of several real-world applications based on CORESE.


title: Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection

abstract: The fundamental role of hypernymy in NLP has motivated the development of many methods for the automatic identification of this relation, most of which rely on word distribution. We investigate an extensive number of such unsupervised measures, using several distributional semantic models that differ by context type and feature weighting. We analyze the performance of the different methods based on their linguistic motivation. Comparison to the state-of-the-art supervised methods shows that while supervised methods generally outperform the unsupervised ones, the former are sensitive to the distribution of training instances, hurting their reliability. Being based on general linguistic hypotheses and independent from training data, unsupervised measures are more robust, and therefore are still useful artillery for hypernymy detection.


title: A WIDEBAND SLOTTED WAVEGUIDE ANTENNA ARRAY FOR SAR SYSTEMS

abstract: Possibilities for the extension of the operational frequency band of slotted waveguide antennas are studied. It is shown that by using both conventional longitudinal slots and subarraying techniques it is possible to reach the relative bandwidth of about 15%. This result is illustrated by the development of a novel slotted waveguide antenna for high-resolution SAR applications. The antenna operates in the X-band and forms the beam of 4 - £ 6 - with the gain of about 30dB.


1 Capital-Gender Complementarity


title: DISCRN: A Distributed Storytelling Framework for Intelligence Analysis.

abstract: Abstract Storytelling connects entities (people, organizations) using their observed relationships to establish meaningful storylines. This can be extended to spatiotemporal storytelling that incorporates locations, time, and graph computations to enhance coherence and meaning. But when performed sequentially these computations become a bottleneck because the massive number of entities make space and time complexity untenable. This article presents DISCRN, or distributed spatiotemporal ConceptSearch-based storytelling, a distributed framework for performing spatiotemporal storytelling. The framework extracts entities from microblogs and event data, and links these entities using a novel ConceptSearch to derive storylines in a distributed fashion utilizing key-value pair paradigm. Performing these operations at scale allows deeper and broader analysis of storylines. The novel parallelization techniques speed up the generation and filtering of storylines on massive datasets. Experiments with microblog posts...


title: Security Analysis and Improvements for IEEE 802.11i

abstract: This paper analyzes the IEEE 802.11i wireless networking standard with respect to data confidentiality, integrity, mutual authentication, and availability. Under our threat model, 802.11i appears to provide effective data confidentiality and integrity when CCMP is used. Furthermore, 802.11i may provide satisfactory mutual authentication and key management, although there are some potential implementation oversights that may cause severe problems. Since the 802.11i design does not emphasize availability, several DoS attacks are possible. We review the known DoS attacks on unprotected management frames and EAP frames, and discuss ways of mitigating them in 802.11i. The practicality of a DoS attack against Michael MIC Failure countermeasure is discussed and improvements are proposed. Two new DoS attacks and possible repairs are identified: RSN IE Poisoning and 4-Way Handshake Blocking. Finally some tradeoffs in failure-recovery strategies are discussed and an improved variant of 802.11i is proposed to address all the discussed vulnerabilities.


title: Dynamic model and motion planning for a pendulum-actuated spherical rolling robot

abstract: This paper deals with the dynamics and motion planning for a spherical rolling robot with a pendulum actuated by two motors. First a dynamic model for the rolling robot is established. In general, not all feasible kinematic trajectories of the rolling carrier are dynamically realizable. A notable exception is when the contact trajectories on the sphere and on the plane are geodesic lines. Based on this consideration, a motion planning strategy for complete reconfiguration of the rolling robot is proposed. The strategy consists of two trivial movements and a non-trivial maneuver that is based on tracing multiple spherical triangles. To compute the sizes and the number of triangles, a reachability diagram is constructed. To define the control torques realizing the rest-to-rest motion along the geodesic lines, two possible approaches are suggested. A computational algorithm, implementing the motion planning strategy, is developed and verified under simulation.


title: A survey on power control issues in wireless sensor networks

abstract: A vehicle suspension assembly for tandem drive axles has a suspension system for each axle. Each suspension system incorporates two lower torque beams on each side of the vehicle and a single central upper torque rod. A central hanger is mounted between the two axles. The lower torque beams are pivotally connected between the hanger and an axle seat. A straddle bracket connects the axle to the axle seat in a hinged fashion. The upper central torque rod is connected between the upper side of the axle housing and the chassis. The lower torque beams act as a parallelogram to stabilize the axle seat. They also act together with a portion of the axle seat as a composite lower torque rod cooperating with the upper torque rod to stabilize the axle.


title: Various Approaches for Driver and Driving Behavior Monitoring: A Review

abstract: In recent years, driver drowsiness and distraction have been important factors in a large number of accidents because they reduce driver perception level and decision making capability, which negatively affect the ability to control the vehicle. One way to reduce these kinds of accidents would be through monitoring driver and driving behavior and alerting the driver when they are drowsy or in a distracted state. In addition, if it were possible to predict unsafe driving behavior in advance, this would also contribute to safe driving. In this paper, we will discuss various monitoring methods for driver and driving behavior as well as for predicting unsafe driving behaviors. In respect to measurement methods of driver drowsiness, we discussed visual and non-visual features of driver behavior, as well as driving performance behaviors related to vehicle-based features. Visual feature measurements such as eye related measurements, yawning detection, facial expression are discussed in detail. As for non-visual features, we explore various physiological signals and possible drowsiness detection methods that use these signals. As for vehicle-based features, we describe steering wheel movement and the standard deviation of lateral position. To detect driver distraction, we describe head pose and gaze direction methods. To predict unsafe driving behavior, we explain predicting methods based on facial expressions and car dynamics. Finally, we discuss several issues to be tackled for active driver safety systems. They are 1) hybrid measures for drowsiness detection, 2) driving context awareness for safe driving, 3) the necessity for public data sets of simulated and real driving conditions.


title: Two new von Kries based chromatic adaptation transforms found by numerical optimization

abstract: In this article, two new von Kries based chromatic adaptation transforms are proposed. The numerical optimization procedure adopted for deriving them simultaneously exploits the whole sets of data available and the existing chromatic adaptation transforms. Experimental results report several statistics to prove the effectiveness of our proposals with respect to the state-of-the-art. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2010


title: Health sensing by wearable sensors and mobile phones: A survey

abstract: With the global trend of population aging in industrialized countries, efficient information and communication technologies (ICT) for aiding elders or patients' healthcare have attracted great research attentions. Among these technologies, health state sensing by wearable sensors and mobile phones is an important foundation. It monitors the real-time body states; stores, or sends the result to remote family members or doctors. In this way, it can either help people to pay more attention to the overlooked phenomenon, such as the clue of dangerous disease, or help people to issue panic alert when emergency happens. There are many critical issues in health sensing. First, the sensors must be non-intrusive to people's comfort and safety, while providing good accuracy. At the same time, because of being worn by people, numerous noises posed by body motions must be efficiently processed for reducing false alarming. At last, different health or decease signals generally require different sensing technologies and instrument. To tease out the technology advantages that address these challenges and diversities, this paper presented a survey on the state of the art of health sensing technologies using body sensor networks and mobile phones. It classify related works by their application goals, including i) fall detection, ii) gait analyzing, iii) activity qualification, iv) heart state sensing, and v) sleep sensing. It also conducts summary and comparison of related sensing systems and algorithms, to reveal the development lines in each subarea.


title: Time for a view on screen time

abstract: In Britain today, children by the age of 10 years have regular access to an average of five different screens at home. In addition to the main family television, for example, many very young children have their own bedroom TV along with portable handheld computer game consoles (eg, Nintendo, Playstation, Xbox), smartphone with games, internet and video, a family computer and a laptop and/or a tablet computer (eg, iPad). Children routinely engage in two or more forms of screen viewing at the same time, such as TV and laptop.1 Viewing is starting earlier in life. Nearly one in three American infants has a TV in their bedroom, and almost half of all infants watch TV or DVDs for nearly 2 h/day.2 ::: ::: Across the industrialised world, watching screen media is the main pastime of children. Over the course of childhood, children spend more time watching TV than they spend in school.3 When including computer games, internet and DVDs, by the age of seven years, a child born today will have spent one full year of 24 h days watching screen media. By the age of 18 years, the average European child will have spent 3 years of 24 h days watching screen media; at this rate, by the age of 80 years, they will have spent 17.6 years4 glued to media screens. ::: ::: Yet, irrespective of the content or educational value of what is being viewed, the sheer amount of average daily screen time (ST) during discretionary hours after school is increasingly being considered an independent risk factor for disease, and is recognised as such by other governments and medical bodies but not, however, in Britain or in most of the EU. To date, views of the British and European medical establishments on increasingly high levels of child ST remain conspicuous …


title: 28 GHz Compact Omnidirectional Circularly Polarized Antenna for Device-to-Device Communications in the Future 5G Systems

abstract: An omnidirectional circularly polarized (OCP) antenna operating at 28 GHz is reported and has been found to be a promising candidate for device-to-device (D2D) communications in the next generation (5G) wireless systems. The OCP radiation is realized by systematically integrating electric and magnetic dipole elements into a compact disc-shaped configuration (9.23 mm $^{3} =0.008~\lambda _{0}^{3}$ at 28 GHz) in such a manner that they are oriented in parallel and radiate with the proper phase difference. The entire antenna structure was printed on a single piece of dielectric substrate using standard PCB manufacturing technologies and, hence, is amenable to mass production. A prototype OCP antenna was fabricated on Rogers 5880 substrate and was tested. The measured results are in good agreement with their simulated values and confirm the reported design concepts. Good OCP radiation patterns were produced with a measured peak realized RHCP gain of 2.2 dBic. The measured OCP overlapped impedance and axial ratio bandwidth was 2.2 GHz, from 26.5 to 28.7 GHz, an 8 % fractional bandwidth, which completely covers the 27.5 to 28.35 GHz band proposed for 5G cellular systems.


title: Class-AB rail-to-rail CMOS analog buffer

abstract: In this paper, a low-power rail-to-rail CMOS analog buffer is presented. The circuit is based on an input stage made up of two complementary class AB differential pairs, while a simple additional circuit allows rail-to-rail operation at the output terminal. The proposed circuit combines low static power consumption and high drive capability, hence is very suitable for applications with large capacitive loads. The buffer has been designed in a 0.35-/spl mu/m CMOS technology to operate with a /spl plusmn/1.5 V dual supply. Simulated results are provided in order to demonstrate the proper operation of the proposed circuit. A rail-to-rail signal swing is achieved and a THD lower than -44 dB is obtained for a 2.4-V/sub pp/ 100-kHz input sinewave signal, whereas the input capacitance is lower than 32 fF.


title: Edge Mesh: A New Paradigm to Enable Distributed Intelligence in Internet of Things

abstract: In recent years, there has been a paradigm shift in Internet of Things (IoT) from centralized cloud computing to edge computing (or fog computing). Developments in ICT have resulted in the significant increment of communication and computation capabilities of embedded devices and this will continue to increase in coming years. However, existing paradigms do not utilize low-level devices for any decision-making process. In fact, gateway devices are also utilized mostly for communication interoperability and some low-level processing. In this paper, we have proposed a new computing paradigm, named Edge Mesh, which distributes the decision-making tasks among edge devices within the network instead of sending all the data to a centralized server. All the computation tasks and data are shared using a mesh network of edge devices and routers. Edge Mesh provides many benefits, including distributed processing, low latency, fault tolerance, better scalability, better security, and privacy. These benefits are useful for critical applications, which require higher reliability, real-time processing, mobility support, and context awareness. We first give an overview of existing computing paradigms to establish the motivation behind Edge Mesh. Then, we describe in detail about the Edge Mesh computing paradigm, including the proposed software framework, research challenges, and benefits of Edge Mesh. We have also described the task management framework and done a preliminary study on task allocation problem in Edge Mesh. Different application scenarios, including smart home, intelligent transportation system, and healthcare, are presented to illustrate the significance of Edge Mesh computing paradigm.


title: Game Theoretic Mechanisms for Resource Management in Massive Wireless IoT Systems

abstract: As a result of rapid advancement in communication technologies, the Internet of Things (i.e., ubiquitous connectivity among a very large number of persons and physical objects) is now becoming a reality. Nonetheless, a variety of challenges remain to be addressed, one of them being the efficient resource management in IoT. On one hand, central resource allocation is infeasible for large numbers of entities, due to excessive computational cost as well as immoderate overhead required for information acquisition. On the other hand, the devices connecting to IoT are expected to act smart, making decisions and performing tasks without human intervention. These characteristics render distributed resource management an essential feature of future IoT. Traditionally, game theory is applied to effectively analyze the interactive decision making of agents with conflicting interests. Nevertheless, conventional game models are not adequate to model large-scale systems, since they suffer from many shortcomings including analytical complexity, slow convergence, and excessive overhead due to information acquisition/exchange. In this article, we explore some non-conventional game theoretic models that fit the inherent characteristics of future large-scale IoT systems. Specifically, we discuss evolutionary games, mean field games, minority games, mean field bandit games, and mean field auctions. We provide the basics of each of these game models and discuss the potential IoT-related resource management problems that can be solved by using these models. We also discuss challenges, pitfalls, and future research directions.


title: TunePad: Computational Thinking Through Sound Composition

abstract: Computational thinking skills will be important for the next generation of students. However, there is a disparity in the populations joining the field. Integrating computational thinking into artistic fields has shown to increase participation in computer science. In this paper, we present our initial design prototype for TunePad, a sound composition tablet application controlled by a block-based programming environment. TunePad is designed to introduce learners to computational thinking and to prepare them for text-based coding environments. From our preliminary testing, with children ages 7-14, we observed that our design actively engages learners and communicates how the programming blocks control the sounds being played. This testing is a prelude to more formal studies as we continue to improve the design and interface of TunePad. With this work, we intend to engage students in computational thinking who may not have otherwise been exposed, giving the opportunity to more people to enter the computer science field.


Albanian Part-of-Speech Tagging: Gold Standard and Evaluation.


title: CLRPL: Context-Aware and Load Balancing RPL for Iot Networks Under Heavy and Highly Dynamic Load

abstract: The IPv6 routing protocol for low power and lossy networks (RPL) was accepted as the standard routing protocol for Internet of Things (IoT) by Internet engineering task force on March 2012. Since then, it has been used in different IoT applications. Though RPL considerably deals with IoT network requirements, there are still some open problems to solve, since it was not initially designed for IoT applications. In this paper, we address the problem of packet loss and power depletion in an RPL-based network under heavy and highly dynamic load. We address this problem in three steps: First, we present a context-aware objective function (CAOF), which computes the rank, considering the context of the node. CAOF also avoids thundering herd phenomenon by gradual shifting from a high rank value toward the real rank value. Second, we present a new routing metric, known as context-aware routing metric (CARF) which considers the status of remaining power and queue utilization of parent chain toward the root in a recursive manner while lessening the effect of upstream parents as it gets farther down the path. Through comprehensive evaluations, we show that this metric leads to a better decision about the proper parent in a network with high traffic dynamicity, rather than deciding merely based on the parent rank. Third, we present a new parent selection mechanism, which selects the best parent based on CARF and some other metrics while avoiding routing loops by a simple yet effective countermeasure. Evaluation results reveal improvements in network lifetime while decreasing packet loss in comparison with standard specification of RPL.


title: Another view of nonholonomic mechanical control systems

abstract: In this paper we consider nonholonomic control systems on Riemannian manifolds. Such systems evolve on sub-bundles of tangent bundles, defined by the nonholonomic constraints. This paper promotes the view of such systems as the restriction to the nonholonomic sub-bundle of "Newton-law" type problems on the entire tangent bundle, defined by, in general, non-Riemannian connections. These connections should be related to specific geometric properties of the nonholonomic system. We define a particular class of connections based on special symmetry properties, and demonstrate the validity of the construction through three examples.


title: Vaccine protection against Zika virus from Brazil

abstract: Zika virus (ZIKV) is a flavivirus that is responsible for the current epidemic in Brazil and the Americas. ZIKV has been causally associated with fetal microcephaly, intrauterine growth restriction, and other birth defects in both humans and mice. The rapid development of a safe and effective ZIKV vaccine is a global health priority, but very little is currently known about ZIKV immunology and mechanisms of immune protection. Here we show that a single immunization with a plasmid DNA vaccine or a purified inactivated virus vaccine provides complete protection in susceptible mice against challenge with a strain of ZIKV involved in the outbreak in northeast Brazil. This ZIKV strain has recently been shown to cross the placenta and to induce fetal microcephaly and other congenital malformations in mice. We produced DNA vaccines expressing ZIKV pre-membrane and envelope (prM-Env), as well as a series of deletion mutants. The prM-Env DNA vaccine, but not the deletion mutants, afforded complete protection against ZIKV, as measured by absence of detectable viraemia following challenge, and protective efficacy correlated with Env-specific antibody titers. Adoptive transfer of purified IgG from vaccinated mice conferred passive protection, and depletion of CD4 and CD8 T lymphocytes in vaccinated mice did not abrogate this protection. These data demonstrate that protection against ZIKV challenge can be achieved by single-shot subunit and inactivated virus vaccines in mice and that Env-specific antibody titers represent key immunologic correlates of protection. Our findings suggest that the development of a ZIKV vaccine for humans is likely to be achievable.


title: A literature review of empirical research on learning analytics in medical education

abstract: The number of publications in the field of medical education is still markedly low, despite recognition of the value of the discipline in the medical education literature, and exponential growth of publications in other fields. This necessitates raising awareness of the research methods and potential benefits of learning analytics (LA). The aim of this paper was to offer a methodological systemic review of empirical LA research in the field of medical education and a general overview of the common methods used in the field in general. Search was done in Medline database using the term "LA." Inclusion criteria included empirical original research articles investigating LA using qualitative, quantitative, or mixed methodologies. Articles were also required to be written in English, published in a scholarly peer-reviewed journal and have a dedicated section for methods and results. A Medline search resulted in only six articles fulfilling the inclusion criteria for this review. Most of the studies collected data about learners from learning management systems or online learning resources. Analysis used mostly quantitative methods including descriptive statistics, correlation tests, and regression models in two studies. Patterns of online behavior and usage of the digital resources as well as predicting achievement was the outcome most studies investigated. Research about LA in the field of medical education is still in infancy, with more questions than answers. The early studies are encouraging and showed that patterns of online learning can be easily revealed as well as predicting students' performance.


title: Looking Beyond Appearances: Synthetic Training Data for Deep CNNs in Re-identificatio

abstract: Abstract Re-identification is generally carried out by encoding the appearance of a subject in terms of outfit, suggesting scenarios where people do not change their attire. In this paper we overcome this restriction, by proposing a framework based on a deep convolutional neural network, SOMAnet, that additionally models other discriminative aspects, namely, structural attributes of the human figure (e.g. height, obesity, gender). Our method is unique in many respects. First, SOMAnet is based on the Inception architecture, departing from the usual siamese framework. This spares expensive data preparation (pairing images across cameras) and allows the understanding of what the network learned. Second, and most notably, the training data consists of a synthetic 100K instance dataset, SOMAset, created by photorealistic human body generation software. SOMAset will be released with a open source license to enable further developments in re-identification. Synthetic data represents a cost-effective way of acquiring semi-realistic imagery (full realism is usually not required in re-identification since surveillance cameras capture low-resolution silhouettes), while at the same time providing complete control of the samples in terms of ground truth. Thus it is relatively easy to customize the data w.r.t. the surveillance scenario at-hand, e.g. ethnicity. SOMAnet, trained on SOMAset and fine-tuned on recent re-identification benchmarks, matches subjects even with different apparel.


title: Supply Chain Risk Management: Literature Review and Future Research

abstract: Supply chain risk management has increasingly becoming a more popular research area recently. Various papers, with different focus and approaches, have been published since a few years ago. This paper aims to survey supply chain risk management (SCRM) literature. Paper published in relevant journals from 2000 to 2007 are analysed and classified into five categories: conceptual, descriptive, empirical, exploratory cross-sectional, and exploratory longitudinal. We also looked at the papers in terms of the types of risks, the unit of analysis, the industry sectors, and the risk management process or strategies addressed. The literature review will provide the basis for outlining future research opportunities in this field.


title: Low loss, 3.7GHz wideband BAW filters, using high power single crystal AlN-on-SiC resonators

abstract: Bulk acoustic wave (BAW) filters operating at center frequency of 3.7GHz, comprising of BAW resonators utilizing single crystal aluminum nitride (AlN) piezoelectric films epitaxially grown on silicon carbide (SiC) substrates, are reported. Metal-organic chemical vapor deposition (MOCVD) growth was used to obtain single crystal AlN films on 150-mm diameter c-plane semi-insulating SiC substrates with (0004) X-ray diffraction (XRD) rocking curve full-width half-maximum (FWHM) of 0.025°. The fabricated filters (1.25×0.9 sq.mm) had a center frequency of 3.71GHz and a 3dB bandwidth of 100MHz, an insertion loss of 2.0dB and narrow band rejection of 40dB and out-of-band rejection in excess of 37dB to 8GHz. Individual resonators on the same wafer show an electro-mechanical coupling as high as 7.63% and maximum quality-factors up to 1572. Insertion loss of 5ohm resonators configured as individual 2-port devices changed by 0.15dB after high power survival test at 10W. This is the first demonstration of single crystal AlN-on-SiC based BAW resonator and filter technology at 3.7GHz and illustrates the potential of a single crystal AlN-on-SiC based BAW technology platform enabling compact, high power and high performance filter solutions for high frequency mobile, Wi-Fi and infrastructure applications.


title: SFV: Reinforcement Learning of Physical Skills from Videos

abstract: Data-driven character animation based on motion capture can produce highly naturalistic behaviors and, when combined with physics simulation, can provide for natural procedural responses to physical perturbations, environmental changes, and morphological discrepancies. Motion capture remains the most popular source of motion data, but collecting mocap data typically requires heavily instrumented environments and actors. In this paper, we propose a method that enables physically simulated characters to learn skills from videos (SFV). Our approach, based on deep pose estimation and deep reinforcement learning, allows data-driven animation to leverage the abundance of publicly available video clips from the web, such as those from YouTube. This has the potential to enable fast and easy design of character controllers simply by querying for video recordings of the desired behavior. The resulting controllers are robust to perturbations, can be adapted to new settings, can perform basic object interactions, and can be retargeted to new morphologies via reinforcement learning. We further demonstrate that our method can predict potential human motions from still images, by forward simulation of learned controllers initialized from the observed pose. Our framework is able to learn a broad range of dynamic skills, including locomotion, acrobatics, and martial arts. (Video1)


title: Comparison of Ground Reaction Force in Treadmill Walking and in Overground Walking

abstract: of a thesis at the University of Miami. Thesis supervised by Professor Shihab S. Asfour. No. of pages in text. (112) Treadmills prove to be useful in gait analysis and in rehabilitation. Acquiring instrumented treadmills with built in force plate(s) to measure the Ground Reaction Force (GRF) may not be financially feasible for many general purpose biomechanics laboratories; additionally some instrumented treadmills only measure the vertical component of GRF. To provide a solution for such situations, this study examined a method by which the components of GRF are measured through placing a treadmill over floor-mounted force plates. The GRF measured during the treadmill walk was compared to GRF measured in overground walking to validate the method and then to develop a set of equations to be used in predicting the components of GRF when using none instrumented treadmills. A total of twelve male subjects participated in this study. The analysis of the data did not reveal statistical differences in the early-stance and midstance peaks of GRF (VGRFP1, VGRFP2, APGRFP1, APGRFP2, and MLGRFP1) between treadmill and overground walking (p < 0.05). Statistical differences between treadmill and overground walking were found during late-stance peaks of GRF (VGRFP3, and MLGRFP2) (p < 0.05). In late-stance, vertical GRF peaks and mediallateral GRF peaks were less in treadmill walking than in overground walking by 5–6% and by 1–2% respectively. Regression equations were developed to estimate associations between GRF in treadmill walking and measurements obtained in overground walking.


title: Compiling for niceness: mitigating contention for QoS in warehouse scale computers

abstract: As the class of datacenters recently coined as warehouse scale computers (WSCs) continues to leverage commodity multicore processors with increasing core counts, there is a growing need to consolidate various workloads on these machines to fully utilize their computation power. However, it is well known that when multiple applications are co-located on a multicore machine, contention for shared memory resources can cause severe cross-core performance interference. To ensure that the quality of service (QoS) of user-facing applications does not suffer from performance interference, WSC operators resort to disallowing co-location of latency-sensitive applications with other applications. This policy translates to low machine utilization and millions of dollars wasted in WSCs. This paper presents QoS-Compile, the first compilation approach that statically manipulates application contentiousness to enable the co-location of applications with varying QoS requirements, and as a result, can greatly improve machine utilization. Our technique first pinpoints an application's code regions that tend to cause contention and performance interference. QoS-Compile then transforms those regions to reduce their contentious nature. In essence, to co-locate applications of different QoS priorities, our compilation technique uses pessimizing transformations to throttle down the memory access rate of the contentious regions in low priority applications to reduce their interference to high priority applications. Our evaluation using synthetic benchmarks, SPEC benchmarks and large-scale Google applications show that QoS-Compile can greatly reduce contention, improve QoS of applications, and improve machine utilization. Our experiments show that our technique improves applications' QoS performance by 21% and machine utilization by 36% on average.


title: Modelling the NFV forwarding graph for an optimal network service deployment

abstract: The NFV is focused at virtualizing network functions such as gateways, path computing devices, resource allocation mechanisms, proxies, firewalls and transcoders, traditionally carried out by specialized hardware devices, and migrating these functions to software-based appliances. In this paper, an analytic model for the NFV forwarding graph is proposed with the aim to optimize the execution time of the network services deployed.


title: Reconfigurable smart water quality monitoring system in IoT environment

abstract: Since the effective and efficient system of water quality monitoring (WQM) are critical implementation for the issue of polluted water globally, with increasing in the development of Wireless Sensor Network (WSN) technology in the Internet of Things (IoT) environment, real time water quality monitoring is remotely monitored by means of real-time data acquisition, transmission and processing. This paper presents a reconfigurable smart sensor interface device for water quality monitoring system in an IoT environment. The smart WQM system consists of Field Programmable Gate Array (FPGA) design board, sensors, Zigbee based wireless communication module and personal computer (PC). The FPGA board is the core component of the proposed system and it is programmed in very high speed integrated circuit hardware description language (VHDL) and C programming language using Quartus II software and Qsys tool. The proposed WQM system collects the five parameters of water data such as water pH, water level, turbidity, carbon dioxide (CO2) on the surface of water and water temperature in parallel and in real time basis with high speed from multiple different sensor nodes.


title: Unwanted but consensual sexting among young adults: Relations with attachment and sexual motivations

abstract: A wide body of research has examined unwanted but consensual sex in a face-to-face context, focusing on intercourse, petting, kissing, and other sexual activity that people consent to even though they do not want to. Recent research has shown many people engage in sexual interactions via computer-mediated mediums; yet, to date, there are no studies that have investigated whether unwanted but consensual sexual activity exists in these contexts. In this study, we examined the extent to which 93 women and 62 men had consented to unwanted sexting within committed relationships and the attachment characteristics and motivations that are associated with this behavior. Approximately one half of the sample (52.3%) had engaged in unwanted but consensual sexting with a committed partner, and most did so for flirtation, foreplay, to fulfill a partner's needs, or for intimacy. Among men, neither of the attachment dimensions was related to unwanted but consensual sexting. However, among women, anxious attachment was significantly related to frequency of consenting to unwanted sexting, and consenting to avoid an argument was a mediator in the relationship between anxious attachment and consenting to unwanted sexting. These results are compared to previous work on unwanted but consensual sex, and future directions are discussed.