title: Gradient field descriptor for sketch based retrieval and localization

abstract: We present an image retrieval system driven by free-hand sketched queries depicting shape. We introduce Gradient Field HoG (GF-HOG) as a depiction invariant image descriptor, encapsulating local spatial structure in the sketch and facilitating efficient codebook based retrieval. We show improved retrieval accuracy over 3 leading descriptors (Self Similarity, SIFT, HoG) across two datasets (Flickr160, ETHZ extended objects), and explain how GF-HOG can be combined with RANSAC to localize sketched objects within relevant images. We also demonstrate a prototype sketch driven photo montage application based on our system.


PHOTOGRAMMETRY FOR VIRTUAL EXPLORATION OF UNDERWATER ARCHEOLOGICAL SITES


title: Newborn Observation Track and Trigger (NOTT) Chart.

abstract: Use of early warning system scores and track-and-trigger charts is widespread in adult and paediatric hospitalized patients; but its use in neonatal group is not well recognized. The authors aimed to develop and implement neonatal observation track-and-trigger chart for use in babies on postnatal wards and to assess its efficacy in identifying neonates in need of urgent medical assessment and intervention. Core group developed Newborn Observation Track and Trigger (NOTT) chart for "At-Risk" and "At-High-Risk" Infants. A decision tree was devised based on 'low', 'medium' and 'high' scores. Sensitivity and specificity of NOTT chart's 'medium' and 'high' score was 96% and 90%, respectively. Positive and negative predictive value was 43% and 100%, respectively. NOTT chart is a valuable tool for assessment of neonates on postnatal wards. A prospective study, using large number of participants, would be beneficial to further corroborate this useful tool.


title: The spectral sensitivities of the middle- and long-wavelength-sensitive cones derived from measurements in observers of known genotype

abstract: The spectral sensitivities of middle- (M-) and long- (L-) wavelength-sensitive cones have been measured in dichromats of known genotype: M-cone sensitivities in nine protanopes, and L-cone sensitivities in 20 deuteranopes. We have used these dichromat cone spectral sensitivities, along with new luminous efficiency determinations, and existing spectral sensitivity and color matching data from normal trichromats, to derive estimates of the human M- and L-cone spectral sensitivities for 2 and 10° dia. central targets, and an estimate of the photopic luminosity function [V(l)] for 2° dia. targets, which we refer to as V 2l). These new estimates are consistent with dichromatic and trichromatic spectral sensitivities and color matches. © 2000 Elsevier Science Ltd. All rights reserved.


title: Gradual typing embedded securely in JavaScript

abstract: JavaScript's flexible semantics makes writing correct code hard and writing secure code extremely difficult. To address the former problem, various forms of gradual typing have been proposed, such as Closure and TypeScript. However, supporting all common programming idioms is not easy; for example, TypeScript deliberately gives up type soundness for programming convenience. In this paper, we propose a gradual type system and implementation techniques that provide important safety and security guarantees. We present TS# , a gradual type system and source-to-source compiler for JavaScript. In contrast to prior gradual type systems, TS# features full runtime reflection over three kinds of types: (1) simple types for higher-order functions, recursive datatypes and dictionary-based extensible records; (2) the type any, for dynamically type-safe TS# expressions; and (3) the type un, for untrusted, potentially malicious JavaScript contexts in which TS# is embedded. After type-checking, the compiler instruments the program with various checks to ensure the type safety of TS# despite its interactions with arbitrary JavaScript contexts, which are free to use eval, stack walks, prototype customizations, and other offensive features. The proof of our main theorem employs a form of type-preserving compilation, wherein we prove all the runtime invariants of the translation of TS# to JavaScript by showing that translated programs are well-typed in JS# , a previously proposed dependently typed language for proving functional correctness of JavaScript programs. We describe a prototype compiler, a secure runtime, and sample applications for TS#. Our examples illustrate how web security patterns that developers currently program in JavaScript (with much difficulty and still with dubious results) can instead be programmed naturally in TS#, retaining a flavor of idiomatic JavaScript, while providing strong safety guarantees by virtue of typing.


title: Software Testing Research: Achievements, Challenges, Dreams

abstract: Software engineering comprehends several disciplines devoted to prevent and remedy malfunctions and to warrant adequate behaviour. Testing, the subject of this paper, is a widespread validation approach in industry, but it is still largely ad hoc, expensive, and unpredictably effective. Indeed, software testing is a broad term encompassing a variety of activities along the development cycle and beyond, aimed at different goals. Hence, software testing research faces a collection of challenges. A consistent roadmap of the most relevant challenges to be addressed is here proposed. In it, the starting point is constituted by some important past achievements, while the destination consists of four identified goals to which research ultimately tends, but which remain as unreachable as dreams. The routes from the achievements to the dreams are paved by the outstanding research challenges, which are discussed in the paper along with interesting ongoing work.


title: A fast local descriptor for dense matching

abstract: We introduce a novel local image descriptor designed for dense wide-baseline matching purposes. We feed our descriptors to a graph-cuts based dense depth map estimation algorithm and this yields better wide-baseline performance than the commonly used correlation windows for which the size is hard to tune. As a result, unlike competing techniques that require many high-resolution images to produce good reconstructions, our descriptor can compute them from pairs of low-quality images such as the ones captured by video streams. Our descriptor is inspired from earlier ones such as SIFT and GLOH but can be computed much faster for our purposes. Unlike SURF which can also be computed efficiently at every pixel, it does not introduce artifacts that degrade the matching performance. Our approach was tested with ground truth laser scanned depth maps as well as on a wide variety of image pairs of different resolutions and we show that good reconstructions are achieved even with only two low quality images.


title: Channel as seen by an Argo float: Study from Arabian Sea

abstract: The salinity-temperature profile data from one of the Argos float (ID: 2901374) in the Arabian Sea has been analyzed from June 2010 to March 2012 in this study. A major part of this study is to investigate the variable characteristics of underwater acoustic communication channel for long periods in the area where this float has gathered the data. We have many measured salinity, temperature, and depth profiles along the trajectory of the float. From this measured data, sound speed profiles at different locations, months, and seasons can be calculated using sound speed equations. The latitude span starts from 19.7° and goes to 24.3° whereas longitude span starts from 58.7° to goes to 64.8° approximately. A ray theory based modeling tool has been used to predict the parameters like multipath extent and delay spread which are useful for channel characterization of the acoustic wireless communication. In this way, use of actual and recent ocean data can be used in performance prediction of acoustic communication system.


title: Sequential Bayesian optimisation for spatial-temporal monitoring

abstract: Bayesian Optimisation has received considerable attention in recent years as a general methodology to find the maximum of costly-to-evaluate objective functions. Most existing BO work focuses on where to gather a set of samples without giving special consideration to the sampling sequence, or the costs or constraints associated with that sequence. However, in real-world sequential decision problems such as robotics, the order in which samples are gathered is paramount, especially when the robot needs to optimise a temporally non-stationary objective function. Additionally, the state of the environment and sensing platform determine the type and cost of samples that can be gathered. To address these issues, we formulate Sequential Bayesian Optimisation (SBO) with side-state information within a Partially Observed Markov Decision Process (POMDP) framework that can accommodate discrete and continuous observation spaces. We build on previous work using Monte-Carlo Tree Search (MCTS) and Upper Confidence bound for Trees (UCT) for POMDPs and extend it to work with continuous state and observation spaces. Through a series of experiments on monitoring a spatial-temporal process with a mobile robot, we show that our UCT-based SBO POMDP optimisation outperforms myopic and non-myopic alternatives.


title: Image forgery detection by semi-automatic wavelet soft-Thresholding with error level analysis

abstract: Abstract In this paper a method for detection of image forgery in lossy compressed digital images known as error level analysis (ELA) is presented and it’s noisy components are filtered with automatic wavelet soft-thresholding. With ELA, a lossy compressed image is recompressed at a known error rate and the absolute differences between these images, known as error levels, are computed. This method might be weakened if the image noise generated by the compression scheme is too intense, creating the necessity of noise filtering. Wavelet thresholding is a proven denoising technique which is capable of removing an image’s noise avoiding altering other components, like high frequencies regions, by thresholding the wavelet transform coefficients, thus not causing blurring. Despite its effectiveness, the choice of the threshold is a known issue. However there are some approaches to select it automatically. In this paper, a lowpass filter is implemented through wavelet thresholding, attenuating error level noises. An efficient method to automatically determine the threshold level is used, showing good results in threshold selection for the presented problems. This automatic threshold level can be fine tuned by a parameter k . Standard test images have been doctored to simulate image tampering, error levels for these images are computed and wavelet thresholding is performed to attenuate noise. Results are presented, confirming the method’s efficiency at noise filtering while preserving necessary error levels. The main contribution of this paper is the investigation of Daubechies wavelets with semi-automatic soft-thresholding in order to highlight forgeries in images. These results can be further extended by expert systems to classify and identify forgeries.


title: Event-based feature tracking with probabilistic data association

abstract: Asynchronous event-based sensors present new challenges in basic robot vision problems like feature tracking. The few existing approaches rely on grouping events into models and computing optical flow after assigning future events to those models. Such a hard commitment in data association attenuates the optical flow quality and causes shorter flow tracks. In this paper, we introduce a novel soft data association modeled with probabilities. The association probabilities are computed in an intertwined EM scheme with the optical flow computation that maximizes the expectation (marginalization) over all associations. In addition, to enable longer tracks we compute the affine deformation with respect to the initial point and use the resulting residual as a measure of persistence. The computed optical flow enables a varying temporal integration different for every feature and sized inversely proportional to the length of the flow. We show results in egomotion and very fast vehicle sequences and we show the superiority over standard frame-based cameras.


title: Addressing the cold-start problem in location recommendation using geo-social correlations

abstract: Location-based social networks (LBSNs) have attracted an increasing number of users in recent years, resulting in large amounts of geographical and social data. Such LBSN data provide an unprecedented opportunity to study the human movement from their socio-spatial behavior, in order to improve location-based applications like location recommendation. As users can check-in at new places, traditional work on location prediction that relies on mining a user's historical moving trajectories fails as it is not designed for the cold-start problem of recommending new check-ins. While previous work on LBSNs attempting to utilize a user's social connections for location recommendation observed limited help from social network information. In this work, we propose to address the cold-start location recommendation problem by capturing the correlations between social networks and geographical distance on LBSNs with a geo-social correlation model. The experimental results on a real-world LBSN dataset demonstrate that our approach properly models the geo-social correlations of a user's cold-start check-ins and significantly improves the location recommendation performance.


title: Approximation Algorithms for Computing Maximin Share Allocations

abstract: We study the problem of computing maximin share allocations, a recently introduced fairness notion. Given a set of n agents and a set of goods, the maximin share of an agent is the best she can guarantee to herself, if she is allowed to partition the goods in any way she prefers, into n bundles, and then receive her least desirable bundle. The objective then is to find a partition, where each agent is guaranteed her maximin share. Such allocations do not always exist, hence we resort to approximation algorithms. Our main result is a 2/3-approximation that runs in polynomial time for any number of agents and goods. This improves upon the algorithm of Procaccia and Wang (2014), which is also a 2/3-approximation but runs in polynomial time only for a constant number of agents. To achieve this, we redesign certain parts of the algorithm in Procaccia and Wang (2014), exploiting the construction of carefully selected matchings in a bipartite graph representation of the problem. Furthermore, motivated by the apparent difficulty in establishing lower bounds, we undertake a probabilistic analysis. We prove that in randomly generated instances, maximin share allocations exist with high probability. This can be seen as a justification of previously reported experimental evidence. Finally, we provide further positive results for two special cases arising from previous works. The first is the intriguing case of three agents, where we provide an improved 7/8-approximation. The second case is when all item values belong to {0, 1, 2}, where we obtain an exact algorithm.


title: Auto-encoder based dimensionality reduction

abstract: Auto-encoder-a tricky three-layered neural network, known as auto-association before, constructs the "building block" of deep learning, which has been demonstrated to achieve good performance in various domains. In this paper, we try to investigate the dimensionality reduction ability of auto-encoder, and see if it has some kind of good property that might accumulate when being stacked and thus contribute to the success of deep learning.Based on the above idea, this paper starts from auto-encoder and focuses on its ability to reduce the dimensionality, trying to understand the difference between auto-encoder and state-of-the-art dimensionality reduction methods. Experiments are conducted both on the synthesized data for an intuitive understanding of the method, mainly on two and three-dimensional spaces for better visualization, and on some real datasets, including MNIST and Olivetti face datasets. The results show that auto-encoder can indeed learn something different from other methods. Besides, we preliminarily investigate the influence of the number of hidden layer nodes on the performance of auto-encoder and its possible relation with the intrinsic dimensionality of input data.


title: Fast Distributed Outlier Detection in Mixed-Attribute Data Sets

abstract: Efficiently detecting outliers or anomalies is an important problem in many areas of science, medicine and information technology. Applications range from data cleaning to clinical diagnosis, from detecting anomalous defects in materials to fraud and intrusion detection. Over the past decade, researchers in data mining and statistics have addressed the problem of outlier detection using both parametric and non-parametric approaches in a centralized setting. However, there are still several challenges that must be addressed. First, most approaches to date have focused on detecting outliers in a continuous attribute space. However, almost all real-world data sets contain a mixture of categorical and continuous attributes. Categorical attributes are typically ignored or incorrectly modeled by existing approaches, resulting in a significant loss of information. Second, there have not been any general-purpose distributed outlier detection algorithms. Most distributed detection algorithms are designed with a specific domain (e.g. sensor networks) in mind. Third, the data sets being analyzed may be streaming or otherwise dynamic in nature. Such data sets are prone to concept drift, and models of the data must be dynamic as well. To address these challenges, we present a tunable algorithm for distributed outlier detection in dynamic mixed-attribute data sets.


title: Multiple ramp schemes

abstract: A (t,k,n,S) ramp scheme is a protocol to distribute a secret s chosen in S among a set P of n participants in such a way that: (1) sets of participants of cardinality greater than or equal to k can reconstruct the secret s; (2) sets of participants of cardinality less than or equal to t have no information on s, whereas (3) sets of participants of cardinality greater than t and less than k might have "some" information on s. In this correspondence we analyze multiple ramp schemes, which are protocols to share many secrets among a set P of participants, using different ramp schemes. In particular, we prove a tight lower bound on the size of the shares held by each participant and on the dealer's randomness in multiple ramp schemes.


title: Female online shoppers

abstract: Purpose – As the number of female customers shopping in online clothing stores increases, understanding how female customers’ e-loyalty is formed and fostered in this type of online store becomes very pivotal. Yet, despite its importance, little research has been done on this issue. The purpose of this paper is to focus on female online clothing shoppers and aim to examine the mediators of e-loyalty in the context of online clothing stores: e-satisfaction and e-trust. Design/methodology/approach – A total of 482 subjects who are female and have online clothing store shopping experience in Taiwan were obtained and structural equation modeling was performed to analyze the measurement and structural models. Findings – The results show that both perceived online privacy and security are positively associated with e-trust, whereas web design is not. Furthermore, perceived delivery time and web site design are positively associated with e-satisfaction. Both e-trust and e-satisfaction in turn positively influenc...


title: Variance Stabilization for Noisy+Estimate Combination in Iterative Poisson Denoising

abstract: We denoise Poisson images with an iterative algorithm that progressively improves the effectiveness of variance-stabilizing transformations (VST) for Gaussian denoising filters. At each iteration, a combination of the Poisson observations with the denoised estimate from the previous iteration is treated as scaled Poisson data and filtered through a VST scheme. Due to the slight mismatch between a true scaled Poisson distribution and this combination, a special exact unbiased inverse is designed. We present an implementation of this approach based on the BM3D Gaussian denoising filter. With a computational cost at worst twice that of the noniterative scheme, the proposed algorithm provides significantly better quality, particularly at low signal-to-noise ratio, outperforming much costlier state-of-the-art alternatives.


NileTMRG at SemEval-2017 Task 8: Determining Rumour and Veracity Support for Rumours on Twitter.


title: Understanding Blog author's emotions with hierarchical Bayesian models

abstract: The research of inferring emotions in natural language is confronted with two major challenges: the lack of basic knowledge in emotion expressions and the co-occurrence of separate emotions through all language unites. In this paper, we propose a Bayesian inference method to explore the basic knowledge with respect to emotion expression in different semantic dimensions, and to infer the co-occurrence of multiple emotion labels through words to the document. Specifically, we incorporate emotions and semantic dimensions as the latent factors in determining the distribution of observed words in a corpus of Blog articles. For each Blog article, we further generalize emotions from words to the document by incorporating a document specific hierarchy in the emotion distributions. The basic knowledge and co-occurred emotion labels in words and documents are obtained through a Gibbs sampling inference. Our experiment is performed on the well-developed Chinese emotion corpus, i.e. Ren-CECps, which indicates both higher accuracy and better robustness in our word and document emotion predictions compared with those generated by the state-of-the-art emotion prediction algorithms, and demonstrates a distribution of emotions in different word semantic dimensions.


title: Challenging the Other: Exploring the Role of Opponent Gender in Digital Game Competition for Female Players

abstract: Abstract The present study investigated the effect of opponent gender on the game experience of female players. Concretely, it looked into skill perception and player emotions of women in same gender and cross-gender game competition. We set up a 2×2×2 (male vs. female opponent×low vs. high competitive women×lost vs. won game) experimental design in which women were instructed to play against a proclaimed male and female competitor. Unknowingly, however, participants played against an AI, which was configured to produce a winning and a losing condition for each opponent by manipulating difficulty. Results indicated that opponent gender only had an effect on perceived stress, which was higher with male opponents. Moreover, players evaluated their own gaming skills as lower and the skills of presumed male opponents as higher when they thought they were playing against men. Importantly, our results also showed that the above described pattern for self-perceived skills and perceived opponent skills was modula...


title: Adaptive Random Testing: the ART of Test Case Diversity

abstract: Random testing is not only a useful testing technique in itself, but also plays a core role in many other testing methods. Hence, any significant improvement to random testing has an impact throughout the software testing community. Recently, Adaptive Random Testing (ART) was proposed as an effective alternative to random testing. This paper presents a synthesis of the most important research results related to ART. In the course of our research and through further reflection, we have realised how the techniques and concepts of ART can be applied in a much broader context, which we present here. We believe such ideas can be applied in a variety of areas of software testing, and even beyond software testing. Amongst these ideas, we particularly note the fundamental role of diversity in test case selection strategies. We hope this paper serves to provoke further discussions and investigations of these ideas.


title: Interactions between the microbiota, immune and nervous systems in health and disease

abstract: The diverse collection of microorganisms that inhabit the gastrointestinal tract, collectively called the gut microbiota, profoundly influences many aspects of host physiology, including nutrient metabolism, resistance to infection and immune system development. Studies investigating the gut-brain axis demonstrate a critical role for the gut microbiota in orchestrating brain development and behavior, and the immune system is emerging as an important regulator of these interactions. Intestinal microbes modulate the maturation and function of tissue-resident immune cells in the CNS. Microbes also influence the activation of peripheral immune cells, which regulate responses to neuroinflammation, brain injury, autoimmunity and neurogenesis. Accordingly, both the gut microbiota and immune system are implicated in the etiopathogenesis or manifestation of neurodevelopmental, psychiatric and neurodegenerative diseases, such as autism spectrum disorder, depression and Alzheimer's disease. In this review, we discuss the role of CNS-resident and peripheral immune pathways in microbiota-gut-brain communication during health and neurological disease.


title: A Situationally Aware Voice-commandable Robotic Forklift Working Alongside People in Unstructured Outdoor Environments

abstract: One long-standing challenge in robotics is the realization of mobile autonomous robots able to operate safely in human workplaces, and be accepted by the human occupants. We describe the development of a multiton robotic forklift intended to operate alongside people and vehicles, handling palletized materials within existing, active outdoor storage facilities. The system has four novel characteristics. The first is a multimodal interface that allows users to efficiently convey task-level commands to the robot using a combination of pen-based gestures and natural language speech. These tasks include the manipulation, transport, and placement of palletized cargo within dynamic, human-occupied warehouses. The second is the robot's ability to learn the visual identity of an object from a single user-provided example and use the learned model to reliably and persistently detect objects despite significant spatial and temporal excursions. The third is a reliance on local sensing that allows the robot to handle variable palletized cargo and navigate within dynamic, minimally prepared environments without a global positioning system. The fourth concerns the robot's operation in close proximity to people, including its human supervisor, pedestrians who may cross or block its path, moving vehicles, and forklift operators who may climb inside the robot and operate it manually. This is made possible by interaction mechanisms that facilitate safe, effective operation around people. This paper provides a comprehensive description of the system's architecture and implementation, indicating how real-world operational requirements motivated key design choices. We offer qualitative and quantitative analyses of the robot operating in real settings and discuss the lessons learned from our effort.


title: Financial Reporting and Auditing Agent with Net Knowledge (FRAANK) and eXtensible Business Reporting Language (XBRL) 1

abstract: This paper describes the development and applications of FRAANK—Financial Reporting and Auditing Agent with Net Knowledge. The prototype of FRAANK presented here provides automated access to, and understanding and integration of, rapidly changing financial information available from various sources on the Internet. In particular, FRAANK implements intelligent parsing to extract accounting numbers from natural‐text financial statements available from the SEC EDGAR repository. FRAANK develops an “understanding” of the accounting numbers by means of matching the line‐item labels to synonyms of tags in an XBRL taxonomy. As a result, FRAANK converts the consolidated balance sheet, income statement, and statement of cash flows into XBRL‐tagged format. Based on FRAANK, we propose an empirical approach toward the evaluation and improvement of XBRL taxonomies and for identifying and justifying needs for specialized taxonomies by assessing a taxonomy fit to the historical data, i.e., the quarterly and annual EDGAR ...


title: Generation of a new IDS test dataset: Time to retire the KDD collection

abstract: Intrusion detection systems are generally tested using datasets compiled at the end of last century, justified by the need for publicly available test data and the lack of any other alternative datasets. Prominent amongst this legacy group is the KDD project. Whilst a seminal contribution at the time of compilation, these datasets no longer represent relevant architecture or contemporary attack protocols, and are beset by data corruptions and inconsistencies. Hence, testing of new IDS approaches against these datasets does not provide an effective performance metric, and contributes to erroneous efficacy claims. This paper introduces a new publicly available dataset which is representative of modern attack structure and methodology. The new dataset is contrasted with the legacy datasets, and the performance difference of commonly used intrusion detection algorithms is highlighted.


title: Adolescent Problematic Social Networking and School Experiences: The Mediating Effects of Sleep Disruptions and Sleep Quality

abstract: Abstract An important developmental task for adolescents is to become increasingly responsible for their own health behaviors. Establishing healthy sleep routines and controlling media use before bedtime are important for adequate, quality sleep so adolescents are alert during the day and perform well at school. Despite the prevalence of adolescent social media use and the large percentage of computers and cell phones in adolescents' bedrooms, no studies to date have investigated the link between problematic adolescent investment in social networking, their sleep practices, and associated experiences at school. A sample of 1,886 students in Australia aged between 12 and 18 years of age completed self-report data on problematic social networking use, sleep disturbances, sleep quality, and school satisfaction. Structural equation modeling (SEM) substantiated the serial mediation hypothesis: for adolescents, problematic social networking use significantly increased sleep disturbances, which adversely affecte...


title: RapidProM: Mine Your Processes and Not Just Your Data

abstract: The number of events recorded for operational processes is growing every year. This applies to all domains: from health care and e-government to production and maintenance. Event data are a valuable source of information for organizations that need to meet requirements related to compliance, efficiency, and customer service. Process mining helps to turn these data into real value: by discovering the real processes, by automatically identifying bottlenecks, by analyzing deviations and sources of non-compliance, by revealing the actual behavior of people, etc. Process mining is very different from conventional data mining and machine learning techniques. ProM is a powerful open-source process mining tool supporting hundreds of analysis techniques. However, ProM does not support analysis based on scientific workflows. RapidProM, an extension of RapidMiner based on ProM, combines the best of both worlds. Complex process mining workflows can be modeled and executed easily and subsequently reused for other data sets. Moreover, using RapidProM, one can benefit from combinations of process mining with other types of analysis available through the RapidMiner marketplace.


title: Multi-task assignment for crowdsensing in mobile social networks

abstract: Mobile crowdsensing is a new paradigm in which a crowd of mobile users exploit their carried smart devices to conduct complex computation and sensing tasks in mobile social networks (MSNs). In this paper, we focus on the task assignment problem in mobile crowdsensing. Unlike traditional task scheduling problems, the task assignment in mobile crowdsensing must follow the mobility model of users in MSNs. To solve this problem, we propose an oFfline Task Assignment (FTA) algorithm and an oNline Task Assignment (NTA) algorithm. Both FTA and NTA adopt a greedy task assignment strategy. Moreover, we prove that the FTA algorithm is an optimal offline task assignment algorithm, and give a competitive ratio of the NTA algorithm. In addition, we demonstrate the significant performance of our algorithms through extensive simulations, based on four real MSN traces and a synthetic MSN trace.


title: Doctor-patient communication: a review of the literature

abstract: Communication can be seen as the main ingredient in medical care. In reviewing doctor-patient communication, the following topics are addressed: (1) different purposes of medical communication; (2) analysis of doctor-patient communication; (3) specific communicative behaviors; (4) the influence of communicative behaviors on patient outcomes; and (5) concluding remarks. Three different purposes of communication are identified, namely: (a) creating a good inter-personal relationship; (b) exchanging information; and (c) making treatment-related decisions. Communication during medical encounters can be analyzed by using different interaction analysis systems (IAS). These systems differ with regard to their clinical relevance, observational strategy, reliability/validity and channels of communicative behavior. Several communicative behaviors that occur in consultations are discussed: instrumental (cure oriented) vs affective (care oriented) behavior, verbal vs non-verbal behavior, privacy behavior, high vs low controlling behavior, and medical vs everyday language vocabularies. Consequences of specific physician behaviors on certain patient outcomes, namely: satisfaction, compliance/adherence to treatment, recall and understanding of information, and health status/psychiatric morbidity are described. Finally, a framework relating background, process and outcome variables is presented.


title: Image and video based painterly animation

abstract: We present techniques for transforming images and videos into painterly animations depicting different artistic styles. Our techniques rely on image and video analysis to compute appearance and motion properties. We also determine and apply motion information from different (user-specified) sources to static and moving images. These properties that encode spatio-temporal variations are then used to render (or paint) effects of selected styles to generate images and videos with a painted look. Painterly animations are generated using a mesh of brush stroke objects with dynamic spatio-temporal properties. Styles govern the behavior of these brush strokes as well as their rendering to a virtual canvas. We present methods for modifying the properties of these brush strokes according to the input images, videos, or motions. Brush stroke color, length, orientation, opacity, and motion are determined and the brush strokes are regenerated to fill the canvas as the video changes. All brush stroke properties are temporally constrained to guarantee temporally coherent non-photorealistic animations.


Design of Optimal PID Controller Using NSGA-II Algorithm and Level Diagram


title: Spiking neural networks — Algorithms, hardware implementations and applications

abstract: Spiking Neural Networks (SNNs) are the third generation of artificial neural networks that closely mimic the time encoding and information processing aspects of the human brain. It has been postulated that these networks are more efficient for realizing cognitive computing systems compared to second generation networks that are widely used in machine learning algorithms today. In this paper, we review the learning algorithms, hardware demonstrations and potential applications of SNN based learning systems.


title: Cryptographic Mersenne Twister and Fubuki Stream/Block Cipher

abstract: We propose two stream ciphers based on a non-secure pseudorandom number generator (called the mother generator). The mother generator is here chosen to be the Mersenne Twister (MT), a widely used 32-bit integer generator having 19937 bits of internal state and period 219937 − 1. One proposal is CryptMT, which computes the accumulative product of the output of MT, and use the most significant 8 bits as a secure random numbers. Its period is proved to be 219937 − 1, and it is 1.5-2.0 times faster than the most optimized AES in counter-mode. The other proposal, named Fubuki, is designed to be usable also as a block cipher. It prepares nine different kinds of encryption functions (bijections from blocks to blocks), each of which takes a parameter. Fubuki encrypts a sequence of blocks (= a plain message) by applying these encryption functions iteratedly to each of the blocks. Both the combination of the functions and their parameters are pseudorandomly chosen by using its mother generator MT. The key and the initial value are passed to the initialization scheme of MT.


title: Emotional Regulation and Emotional Development

abstract: Current neofunctionalist views of emotion underscore the biologically adaptive and psychologically constructive contributions of emotion to organized behavior, but little is known of the development of the emotional regulatory processes by which this is fostered. Emotional regulation refers to the extrinsic and intrinsic processes responsible for monitoring, evaluating, and modifying emotional reactions. This review provides a developmental outline of emotional regulation and its relation to emotional development throughout the life-span. The biological foundations of emotional self-regulation and individual differences in regulatory tendencies are summarized. Extrinsic influences on the early regulation of a child's emotion and their long-term significance are then discussed, including a parent's direct intervention strategies, selective reinforcement and modeling processes, affective induction, and the caregiver's ecological control of opportunity for heightened emotion and its management. Intrinsic contributors to the growth of emotional self-regulatory capacities include the emergence of language and cognitive skills, the child's growing emotional and self-understanding (and cognized strategies of emotional self-control), and the emergence of a “theory of personal emotion” in adolescence.


title: Protecting the Stack with Metadata Policies and Tagged Hardware

abstract: The program call stack is a major source of exploitable security vulnerabilities in low-level, unsafe languages like C. In conventional runtime implementations, the underlying stack data is exposed and unprotected, allowing programming errors to turn into security violations. In this work, we design novel metadata-tag based, stack-protection security policies for a general-purpose tagged architecture. Our policies specifically exploit the natural locality of dynamic program call graphs to achieve cacheability of the metadata rules that they require. Our simple Return Address Protection policy has a performance overhead of 1.2% but just protects return addresses. The two richer policies we present, Static Authorities and Depth Isolation, provide object-level protection for all stack objects. When enforcing memory safety, our Static Authorities policy has a performance overhead of 5.7% and our Depth Isolation policy has a performance overhead of 4.5%. When enforcing data-flow integrity (DFI), in which we only detect a violation when a corrupted value is read, our Static Authorities policy has a performance overhead of 3.6% and our Depth Isolation policy has a performance overhead of 2.4%. To characterize our policies, we provide a stack threat taxonomy and show which threats are prevented by both prior work protection mechanisms and our policies.


title: Whole-Body Sensory Concept for Compliant Mobile Robots

abstract: Most of the conventional approaches to mobile robot navigation avoid any kind of contact with the environment or with humans. As nowadays distance sensors typically have a limited - and often only two-dimensional - field of view, collisions with the environment or contacts with humans cannot be fully avoided in practical mobile robot applications. On the other hand, direct physical contact can be used for intuitive communication between a robot and humans. In this paper, we present a whole-body sensory concept based on a 6-DoF force-torque sensor to perceive physical interaction between the robot and humans. To distinguish between external contact and disturbance forces that result from the motion of the mobile platform or oscillations, we present a novel model-free filtering approach based on a neural network. In extensive experiments carried out with our robot Canny we demonstrate the effectiveness and advantages of the neural network approach, which clearly outperforms a classical model-based one.


title: An unsupervised discriminative extreme learning machine and its applications to data clustering

abstract: Extreme Learning Machine (ELM), which was initially proposed for training single-layer feed-forward networks (SLFNs), provides us a unified efficient and effective framework for regression and multiclass classification. Though various ELM variants were proposed in recent years, most of them focused on the supervised learning scenario while little effort was made to extend it into unsupervised learning paradigm. Therefore, it is of great significance to put ELM into learning tasks with only unlabeled data. One popular approach for mining knowledge from unlabeled data is based on the manifold assumption, which exploits the geometrical structure of data by assuming that nearby points will also be close to each other in transformation space. However, considering the manifold information only is insufficient for discriminative tasks. In this paper, we propose an improved unsupervised discriminative ELM (UDELM) model, whose main advantage is to combine the local manifold learning with global discriminative learning together. UDELM can be efficiently optimized by solving a generalized eigen-value decomposition problem. Extensive comparisons over several state-of-the-art models on clustering image and emotional EEG data demonstrate the efficacy of UDELM.


Early churn prediction with personalized targeting in mobile social games


Xhosa-English Machine Translation : Working with a Low-Resource Language


title: Trace metal effects on ectomycorrhizal growth, diversity, and colonization of host seedlings

abstract: Copper and zinc are micronutrients essential for fungal and plant growth; however, these metals are toxic at elevated concentrations. Increased metal concentrations may significantly reduce ectomycorrhizal growth and colonization of tree seedlings. Axenic screening of ectomycorrhizal isolates is an effective way to examine basic fungal tolerance to a given metal or metals. Axenic tolerance was determined by colony growth on amended agar plates as well as biomass in liquid culture. Suillus granulatus and Pisolithus tinctorius were more tolerant to copper than Paxillus involutus, however none of the species showed growth past 100 ppm Cu. Suillus granulatus was most tolerant to zinc, followed by Paxillus involutus and finally Pisolithus tinctorius. Sectoring was observed on several plates containing colonies of Suillus granulatus strain VT 1990. Of the ten isolates tested (8 sectors, VT 1990 “parent strain”, and VT 2176), one sector showed a higher tolerance for copper than VT 1990, VT 2176, and the other sectors. One isolate from nature, VT 2176, and three sectors (A, B, and D) did not grow well on agar amended with high levels of zinc. Inter-simple sequence repeat (ISSR) fingerprinting indicated that some genetic variation existed with in the sectors, but that the variation did not correspond to distinct clusters representing different levels of metal tolerance.


title: Data Preparation for Mining World Wide Web Browsing Patterns

abstract: The World Wide Web (WWW) continues to grow at an astounding rate in both the sheer volume of traffic and the size and complexity of Web sites. The complexity of tasks such as Web site design, Web server design, and of simply navigating through a Web site have increased along with this growth. An important input to these design tasks is the analysis of how a Web site is being used. Usage analysis includes straightforward statistics, such as page access frequency, as well as more sophisticated forms of analysis, such as finding the common traversal paths through a Web site. Web Usage Mining is the application of data mining techniques to usage logs of large Web data repositories in order to produce results that can be used in the design tasks mentioned above. However, there are several preprocessing tasks that must be performed prior to applying data mining algorithms to the data collected from server logs. This paper presents several data preparation techniques in order to identify unique users and user sessions. Also, a method to divide user sessions into semantically meaningful transactions is defined and successfully tested against two other methods. Transactions identified by the proposed methods are used to discover association rules from real world data using the WEBMINER system [15].


title: Interleaved boost-flyback converter with boundary conduction mode for power factor correction

abstract: This paper presents a new interleaved pulse-width modulation (PWM) boost-flyback converter to achieve power factor correction (PFC) and regulate DC bus voltage. The adopted boost-flyback converter has a high voltage conversion ratio to overcome the limit of conventional boost or buck-boost converter with narrow turn-off period. The proposed converter has wide turn-off period compared with a conventional boost converter. Thus, the higher output voltage can be achieved in the proposed converter. The interleaved PWM can further reduce the input and output ripple currents such that the sizes of inductor and capacitor are reduced. Since boundary conduction mode (BCM) is adopted to achieve power factor correction, power switches are turned on at zero current switching (ZCS) and switching losses are reduced. The circuit configuration, principle operation, system analysis, and design consideration of the proposed converter are presented in detail. Finally, experiments conducted on a laboratory prototype rated at 500W were presented to verify the effectiveness of the converter.


title: PicAlert!: a system for privacy-aware image classification and retrieval

abstract: Photo publishing in Social Networks and other Web2.0 applications has become very popular due to the pervasive availability of cheap digital cameras, powerful batch upload tools and a huge amount of storage space. A portion of uploaded images are of a highly sensitive nature, disclosing many details of the users' private life. We have developed a web service which can detect private images within a user's photo stream and provide support in making privacy decisions in the sharing context. In addition, we present a privacy-oriented image search application which automatically identifies potentially sensitive images in the result set and separates them from the remaining pictures.


title: More than a neuroanatomical representation in The Creation of Adam by Michelangelo Buonarroti, a representation of the Golden Ratio

abstract: Michelangelo Buonarroti (1475-1564) was a master anatomist as well as an artistic genius. He dissected numerous cadavers and developed a profound understanding of human anatomy. Among his best-known artworks are the frescoes painted on the ceiling of the Sistine Chapel (1508-1512), in Rome. Currently, there is some debate over whether the frescoes merely represent the teachings of the Catholic Church at the time or if there are other meanings hidden in the images. In addition, there is speculation regarding the image of the brain embedded in the fresco known as "The Creation of Adam," which contains anatomic features of the midsagittal and lateral surfaces of the brain. Within this context, we report our use of Image Pro Plus Software 6.0 to demonstrate mathematical evidence that Michelangelo painted "The Creation of Adam" using the Divine Proportion/Golden Ratio (GR) (1.6). The GR is classically associated with greater structural efficiency and is found in biological structures and works of art by renowned artists. Thus, according to the evidence shown in this article, we can suppose that the beauty and harmony recognized in all Michelangelo's works may not be based solely on his knowledge of human anatomical proportions, but that the artist also probably knew anatomical structures that conform to the GR display greater structural efficiency. It is hoped that this report will at least stimulate further scientific and scholarly contributions to this fascinating topic, as the study of these works of art is essential for the knowledge of the history of Anatomy.


title: Penis size: Survey of female perceptions of sexual satisfaction

abstract: BackgroundDoes the size of the male penis, in terms of length or width, make a difference in female sexual satisfaction?MethodTo study the effect of penis width vs. length on female sexual satisfaction, 50 sexually active female undergraduate students were asked which felt better, i. e., was penis width or length more important for their sexual satisfaction.ResultsNone reported they did not know, or that width and length were equally satisfying. A large majority, 45 of 50, reported width was more important (p < .001).ConclusionImplications are discussed, including the fact that the data seem to contradict Masters and Johnson about penis size having no physiological effect on female sexual satisfaction.


title: A New Fuzzy Clustering Validity Index With a Median Factor for Centroid-Based Clustering

abstract: Determining the number of clusters, which is usually approved by domain experts or evaluated by clustering validity indexes, is an important issue in clustering analysis. This study discusses the effectiveness of clustering validity indexes for centroid-based partitional clustering algorithms. Most general-purpose clustering validity indexes take the minimum/maximum distance between a pair of data objects, a pair of cluster centroids, or an object and a centroid as an important evaluation factor; however, they may present unstable results, especially when two centroids are allocated closely. To alleviate this problem, a new clustering validity index, which is termed the Wu-and-Li index (WLI), is proposed in this paper. Our proposed WLI partially allows, to some extent, the existence of closely allocated centroids in the clustering results by considering not only the minimum but the median distances between a pair of centroids as well; therefore possessing better stability. The performances of WLI and some existing clustering validity indexes are evaluated and compared by running the fuzzy c-means algorithm for clustering various types of datasets, including artificial datasets, UCI datasets, and images. Experimental results have shown that WLI has the more accurate and satisfactory performance than other indexes.


title: Digital Transformation

abstract: This instalment of the Software Technology department discusses how the digital transformation is affecting software technology and the software industry.


title: Design of an electric spring for power quality improvement in PV-based DC grid

abstract: Electric Spring (ES) concept has been emerged based on the mechanical spring concept. ES can be employed in AC or DC grid for demand side management, and improving the power quality and energy management. Renewable Energy Sources (RESs) are intermittent in nature, so the ES is introduced in the DC grid for stabilizing the voltage fluctuations incurred by the RESs power changes such as PV systems. The DC ES is inserted in the DC grid in series with the non-critical loads (e.g. thermostatically controlled loads as air conditioning systems, electric water heaters and refrigerators or charging of charging of plug in electric vehicles) forming a smart load. In this paper, a comparison between the series and shunt DC ES is held considering four different operation modes. An ES based on four-quadrant DC-DC converter is analyzed and designed for providing a bi-directional power flow. To explore the effect of the series ES on the non-critical load, simulation results for both open and closed loop operations, using Matlab/Simulink, are shown to validate the presented approach. Moreover, experimental results are presented for a prototyped ES.


title: Loss-aware Binarization of Deep Networks

abstract: Deep neural network models, though very powerful and highly successful, are computationally expensive in terms of space and time. Recently, there have been a number of attempts on binarizing the network weights and activations. This greatly reduces the network size, and replaces the underlying multiplications to additions or even XNOR bit operations. However, existing binarization schemes are based on simple matrix approximation and ignore the effect of binarization on the loss. In this paper, we propose a proximal Newton algorithm with diagonal Hessian approximation that directly minimizes the loss w.r.t. the binarized weights. The underlying proximal step has an efficient closed-form solution, and the second-order information can be efficiently obtained from the second moments already computed by the Adam optimizer. Experiments on both feedforward and recurrent networks show that the proposed loss-aware binarization algorithm outperforms existing binarization schemes, and is also more robust for wide and deep networks.


title: Multi-pose Face Recognition for Person Retrieval in Camera Networks

abstract: In this paper, we study the use of facial appearancefeatures for the re-identification of persons using distributedcamera networks in a realistic surveillance scenario.In contrast to features commonly used for person reidentification,such as whole body appearance, facial featuresoffer the advantage of remaining stable over muchlarger intervals of time. The challenge in using faces forsuch applications, apart from low captured face resolutions,is that their appearance across camera sightings is largelyinfluenced by lighting and viewing pose. Here, a numberof techniques to address these problems are presented andevaluated on a database of surveillance-type recordings. Asystem for online capture and interactive retrieval is presentedthat allows to search for sightings of particular personsin the video database. Evaluation results are presentedon surveillance data recorded with four cameras over severaldays. A mean average precision of 0.60 was achievedfor inter-camera retrieval using just a single track as queryset, and up to 0.86 after relevance feedback by an operator.


title: Enhancement of Facial Scars With Dermabrasion

abstract: This article describes the physiology of wound healing, discusses considerations and techniques for dermabrasion, and presents case studies and figures for a series of patients who underwent dermabrasion after surgeries for facial trauma.


title: Optimal Distributed Generation Allocation in Distribution Systems for Loss Minimization

abstract: An efficient analytical (EA) method is proposed for optimally installing multiple distributed generation (DG) technologies to minimize power loss in distribution systems. Different DG types are considered, and their power factors are optimally calculated. The proposed EA method is also applied to the problem of allocating an optimal mix of different DG types with various generation capabilities. Furthermore, the EA method is integrated with the optimal power flow (OPF) algorithm to develop a new method, EA-OPF which effectively addresses overall system constraints. The proposed methods are tested using 33-bus and 69-bus distribution test systems. The calculated results are validated using the simulation results of the exact optimal solution obtained by an exhaustive OPF algorithm for both distribution test systems. The results show that the performances of the proposed methods are superior to existing methods in terms of computational speed and accuracy.


title: Anthropometric measurements in the elderly: age and gender differences

abstract: In clinical practice and epidemiological surveys, anthropometric measurements represent an important component of nutritional assessment in the elderly. The anthropometric standards derived from adult populations may not be appropriate for the elderly because of body composition changes occurring during ageing. Specific anthropometric reference data for the elderly are necessary. In the present study we investigated anthropometric characteristics and their relationship to gender and age in a cross-sectional sample of 3,356 subjects, randomly selected from an elderly Italian population. In both sexes, weight and height significantly decreased with age while knee height did not. The BMI was significantly higher in women than in men (27.6 SD 5.7 v. 26.4 SD 3.7; P<0.001) and it was lower in the oldest than in the youngest subjects (P<0.05) of both genders. The 75th year of age was a turning point for BMI as for other anthropometric measurements. According to BMI values, the prevalence of malnutrition was lower than 5 % in both genders, whereas obesity was shown to have a higher prevalence in women than in men (28% v. 16%; P<0.001). Waist circumference and waist: hip ratio values were higher for the youngest men than for the oldest men (P<0.05), whereas in women the waist: hip ratio values were higher in the oldest women, suggesting that visceral redistribution in old age predominantly affects females. In conclusion, in the elderly the oldest subjects showed a thinner body frame than the youngest of both genders, and there was a more marked fat redistribution in women.


title: Beyond Classification: Structured Regression for Robust Cell Detection Using Convolutional Neural Network

abstract: Robust cell detection serves as a critical prerequisite for many biomedical image analysis applications. In this paper, we present a novel convolutional neural network (CNN) based structured regression model, which is shown to be able to handle touching cells, inhomogeneous background noises, and large variations in sizes and shapes. The proposed method only requires a few training images with weak annotations (just one click near the center of the object). Given an input image patch, instead of providing a single class label like many traditional methods, our algorithm will generate the structured outputs (referred to as proximity patches). These proximity patches, which exhibit higher values for pixels near cell centers, will then be gathered from all testing image patches and fused to obtain the final proximity map, where the maximum positions indicate the cell centroids. The algorithm is tested using three data sets representing different image stains and modalities. The comparative experiments demonstrate the superior performance of this novel method over existing state-of-the-art.


Traffic Light Control System Based on FPGA


title: Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification

abstract: Automatic sentiment classification has been extensively studied and applied in recent years. However, sentiment is expressed differently in different domains, and annotating corpora for every possible domain of interest is impractical. We investigate domain adaptation for sentiment classifiers, focusing on online reviews for different types of products. First, we extend to sentiment classification the recently-proposed structural correspondence learning (SCL) algorithm, reducing the relative error due to adaptation between domains by an average of 30% over the original SCL algorithm and 46% over a supervised baseline. Second, we identify a measure of domain similarity that correlates well with the potential for adaptation of a classifier from one domain to another. This measure could for instance be used to select a small set of domains to annotate whose trained classifiers would transfer well to many other domains.


title: High-performance porous carbon catalysts doped by iron and nitrogen for degradation of bisphenol F via peroxymonosulfate activation

abstract: Abstract Fabrication of high-performance, cost-effective and environmentally friendly carbocatalysts for environmental remediation is still a challenge. In this study, a series of iron and nitrogen co-doped porous carbon catalysts (Fe-N/C) were prepared through pyrolysis of Fe-doped zeolitic imidazolate framework-8. The catalytic performance of the Fe-N/C was evaluated for the degradation of bisphenol F via peroxymonosulfate (PMS) activation. Fe-N/C at appropriate Fe doping (0.5-5.0%) possessed hierarchically porous architecture with abundant micro- and meso-pores, rich defects, enhanced N doping and conductivity. Compared with N/C, Fe-N/C retained original polyhedral morphology and the particle size could be tuned by controlling Fe doping amount. An optimized catalyst, 1.0%Fe-N/C was obtained, which exhibited superior catalytic activity for the degradation of bisphenol F. The rate constant was 34.0 and 6.1 times of that for N/C and benchmark catalyst Co3O4, respectively. More importantly, the 1.0%Fe-N/C-PMS system was not affected by pH and common water components, and had high selectivity of organic contaminants. The mechanism of PMS activation by 1.0%Fe-N/C was examined with chemical, electrochemical and physical analyses (chemical probes, solvent exchange, ESR spectra and radical trapping). The results indicated that singlet oxygen was proved as the primary reactive species responsible for the degradation. Furthermore, Fe-Nx, pyridinic/graphitic N and structural defects were possible catalytically active sites. This study provides a new insight for development of high-performance carbocatalysts toward environmental remediation.


title: An Empirical Comparison of Four Initialization Methods for the K-Means Algorithm

abstract: In this paper, we aim to compare empirically four initialization methods for the K-Means algorithm: random, Forgy, MacQueen and Kaufman. Although this algorithm is known for its robustness, it is widely reported in the literature that its performance depends upon two key points: initial clustering and instance order. We conduct a series of experiments to draw up (in terms of mean, maximum, minimum and standard deviation) the probability distribution of the square-error values of the final clusters returned by the K-Means algorithm independently on any initial clustering and on any instance order when each of the four initialization methods is used. The results of our experiments illustrate that the random and the Kaufman initialization methods outperform the rest of the compared methods as they make the K-Means more effective and more independent on initial clustering and on instance order. In addition, we compare the convergence speed of the K-Means algorithm when using each of the four initialization methods. Our results suggest that the Kaufman initialization method induces to the K-Means algorithm a more desirable behaviour with respect to the convergence speed than the random initialization method.


title: A compact single band 802.11 n front-end module for MIMO applications using multi-layer organic technology

abstract: This paper presents a novel module technology for 802.11 b/g/n applications. As an example, the authors demonstrate the first 6 mm times 8 mm times 1 mm module which includes four antenna ports, three receive sections, and two transmit sections. The need for such modules has become imminent due to the arrival of multiple input multiple output (MIMO) based high data rate devices. The packaging technology used is multi-layer organic (MLO) technology comprising high performance liquid crystalline polymers (LCP). The transmit (TX) section includes embedded filters and baluns, silicon germanium (SiGe) based power amplifiers with control circuitry, harmonic filters, power detectors and single pole double throw switches. Similarly, the receive (RX) section includes embedded cellular rejection filters, low noise SiGe based amplifiers, and switches. Key features of the module consists of less than 3% EVM, < 160 mA of current and less than 50 dbm/MHz harmonics at 19 dbm output power. The transmit filter baluns before the power amplifiers provide for greater than 25 db rejection of cellular frequencies below 2 GHz and transceiver spurs above 3 GHz. The RX sections provides greater than 12 db gain with 2.5 db noise figure, and superior rejection of cellular frequencies. Finally, the isolation chain to chain (RX to RX and TX to TX) is greater than 25 db which is imperative for MIMO performance.


title: Design of a sampling switch for a 0.4-V SAR ADC using a multi-stage charge pump

abstract: This paper presents the design of a sampling switch to be used in the input interface to an ultra low-power 8-bit, 1-kS/s SAR ADC in 65 nm CMOS working at a supply voltage of 0.4 V. Important design trade-offs for the sampling switch in this low-voltage and low-power scenario are elaborated upon. The design of a multi-stage charge pump which generates the requisite boosted control voltage is described. A combination of the multi-stage charge pump and a leakage-reduced transmission-gate (TG) switch meets the speed requirement while mitigating leakage without employing additional voltages. Performance of the sampling switch has been characterized over process and temperature (PT) corners. In post-layout simulation, the sampling switch provides a linearity corresponding to 9.42 bits to 13.5 bits over PT corners with a worst-case power consumption of 216 pW while occupying an area of 25.4 μm × 24.7 μm.


title: A Switchable 3-D-Coverage-Phased Array Antenna Package for 5G Mobile Terminals

abstract: This letter proposes a new design of millimeter-wave (mm-Wave) array antenna package with beam steering characteristic for the fifth-generation (5G) mobile applications. In order to achieve a broad three-dimensional scanning coverage of the space with high-gain beams, three identical subarrays of patch antennas have been compactly arranged along the edge region of the mobile phone printed circuit board (PCB) to form the antenna package. By switching the feeding to one of the subarrays, the desired direction of coverage can be achieved. The proposed design has >10-dB gain in the upper spherical space, good directivity, and efficiency, which is suitable for 5G mobile communications. In addition, the impact of the user's hand on the antenna performance has been investigated.


title: Botnet detection using graph-based feature clustering

abstract: Detecting botnets in a network is crucial because bots impact numerous areas such as cyber security, finance, health care, law enforcement, and more. Botnets are becoming more sophisticated and dangerous day-by-day, and most of the existing rule based and flow based detection methods may not be capable of detecting bot activities in an efficient and effective manner. Hence, designing a robust and fast botnet detection method is of high significance. In this study, we propose a novel botnet detection methodology based on topological features of nodes within a graph: in degree, out degree, in degree weight, out degree weight, clustering coefficient, node betweenness, and eigenvector centrality. A self-organizing map clustering method is applied to establish clusters of nodes in the network based on these features. Our method is capable of isolating bots in clusters of small sizes while containing the majority of normal nodes in the same big cluster. Thus, bots can be detected by searching a limited number of nodes. A filtering procedure is also developed to further enhance the algorithm efficiency by removing inactive nodes from consideration. The methodology is verified using the CTU-13 datasets, and benchmarked against a classification-based detection method. The results show that our proposed method can efficiently detect the bots despite their varying behaviors.


title: A Wireless Design of Low-Cost Irrigation System Using ZigBee Technology

abstract: At present, labor-saving and water-saving technology is a key issue in irrigation. A wireless solution for intelligent field irrigation system dedicated to Jew's-ear planting in Lishui, Zhejiang, China, based on ZigBee technology was proposed in this paper. Instead of conventional wired connection, the wireless design made the system easy installation and maintenance. The hardware architecture and software algorithm of wireless sensor/actuator node and portable controller, acting as the end device and coordinator in ZigBee wireless sensor network respectively, were elaborated in detail. The performance of the whole system was evaluated in the end. The long-time smooth and proper running of the system in the field proved its high reliability and practicability. As an explorative application of wireless sensor network in irrigation management, this paper offered a methodology to establish large-scale remote intelligent irrigation system.


title: Sketch-based Face Editing in Video Using Identity Deformation Transfer

abstract: Abstract We address the problem of using hand-drawn sketches to create exaggerated deformations to faces in videos, such as enlarging the shape or modifying the position of eyes or mouth. This task is formulated as a 3D face model reconstruction and deformation problem. We first recover the facial identity and expressions from the video by fitting a face morphable model for each frame. At the same time, user’s editing intention is recognized from input sketches as a set of facial modifications. Then a novel identity deformation algorithm is proposed to transfer these facial deformations from 2D space to the 3D facial identity directly while preserving the facial expressions. After an optional stage for further refining the 3D face model, these changes are propagated to the whole video with the modified identity. Both the user study and experimental results demonstrate that our sketching framework can help users effectively edit facial identities in videos, while high consistency and fidelity are ensured at the same time.


A 1.5-V, 10-bit, 14.3-MS/s CMOS Pipeline Analog-to-Digital Converter


title: A quasi-experimental study of a reminiscence program focused on autobiographical memory in institutionalized older adults with cognitive impairment

abstract: Abstract Working with past memories through reminiscence interventions has been practiced for several decades with successful outcomes on mental health in older adults. Few studies however have focused on autobiographical memory recall in older individuals with cognitive impairment. This study aims to analyze the impact of an individual reminiscence program in a group of older persons with cognitive decline living in nursing homes on the dimensions of cognition, autobiographical memory, mood, behavior and anxiety. A two-group pre-test and post-test design with single blinded assessment was conducted. Forty-one participants were randomized to an experimental group (n = 20) and a control group (n = 21). The first group attended five weekly individual reminiscence sessions. Changes in the outcome measures were examined for cognition (Montreal Cognitive Assessment; Autobiographical Memory Test), behavior (Alzheimer Disease Assessment Subscale Non-Cog) and emotional status (Cornell Scale for Depression in Dementia; Geriatric Depression Scale, and Geriatric Anxiety Inventory). Participants attending reminiscence sessions exhibited better outcomes compared to the control group in cognition, anxiety and depression (p


title: Reinforcement learning based spectrum-aware routing in multi-hop cognitive radio networks

abstract: Routing in multi-hop cognitive radio networks (CRN) should be spectrum-aware. In this paper, two adaptive reinforcement learning based spectrum-aware routing protocols are introduced. Q-Learning and Dual Reinforcement Learning are applied respectively for them. The cognitive nodes store a table of Q values that estimate the numbers of available channels on the routes and update them while routing. So they can adaptively learn good routes which have more available channels from just local information. Compared to the previous spectrumaware routing protocols in multi-hop cognitive radio networks, they are simpler and easier to implement, more cost-effective, and can avoid drawbacks in on-demand protocols but still keep adaptive and dynamic routing. Both of our protocols perform better than the spectrum-aware shortest path protocol when network load is not too low. In the meantime, spectrum-aware DRQ-routing learns the optimal routing policy 1.5 times as fast as the spectrum-aware Q-routing at low and medium network load. It also learns a routing policy which is more than seven times as good as that of spectrum-aware Q-routing at high network load.


title: Distribution of the Exchange Rate Implicit in Option Prices 1 : Application to TASE

abstract: This study presents a method of estimating the implied distribution of the future NIS/$ exchange rate implicit in the NIS/$ options traded on the TASE (Tel Aviv Stock Exchange), using options with the same maturity but different strike prices. The distribution of the exchange rate as perceived by the markets is reflected by the option prices with different strike prices, because of the high sensitivity of the prices to expected developments in the financial markets. The distribution estimated here is a double-log-normal distribution of the exchange rate that combines two separate log-normal distributions of the exchange rate. The estimation used here has an advantage over the usual log-normal distribution in that it identifies market expectations regarding the future course of the exchange rate that incorporate the possibility that this will not be continuous, thus enabling more extensive information to be derived from the markets. In addition to expected changes in the exchange rate and the risk level, the model also enables us calculate the possibility of a jump in the exchange rate, and of leptokurtosis and skewness in the distribution.


title: Computer-aided system for defect inspection in the PCB manufacturing process

abstract: This paper presents a visual inspection system aimed at the automatic detection and classification of bare-PCB manufacturing errors. The interest of this CAE system lies in a twofold approach. On the one hand, we propose a modification of the subtraction method based on reference images that allows higher performance in the process of defect detection. On the other hand, this method is combined with a particle classification algorithm based on two measures of light intensity. As a result of this strategy, a machine vision application has been implemented to assist people in etching, inspection and verification tasks of PCBs.


title: Reducing ammonia emission from poultry manure composting via struvite formation

abstract: Composting can provide a viable alternative for poultry manure management in the Fraser Valley of British Columbia, Canada. However, it has a high potential for ammonia emission because of the high nitrogen content of poultry litter. The objective of this study was to reduce ammonia emission by precipitating ammonia into struvite before it can volatilize from the composting matrix. Nitrogen transformation occurs during composting, but struvite formation can help to conserve nitrogen, and does not allow a large amount of nitrogen to be released into the environment as ammonia or nitrate, because ammonium-nitrogen becomes bound in struvite. It would also be possible to remove phosphorus from the manure. A laboratory-scale experiment was conducted to examine the feasibility of struvite formation during poultry manure composting. Magnesium and phosphate salts were supplemented to create favorable conditions to form struvite. Results indicated that ammonia emission was reduced by 40% to 84%, while the nitrogen retention in compost was enhanced. The struvite formed in compost was confirmed by means of X-ray diffraction; and scanning electron microscopy with energy dispersive X-ray spectroscopy. Copyright © 2007 Society of Chemical Industry


title: Pose-Invariant Face Recognition Using Markov Random Fields

abstract: One of the key challenges for current face recognition techniques is how to handle pose variations between the probe and gallery face images. In this paper, we present a method for reconstructing the virtual frontal view from a given nonfrontal face image using Markov random fields (MRFs) and an efficient variant of the belief propagation algorithm. In the proposed approach, the input face image is divided into a grid of overlapping patches, and a globally optimal set of local warps is estimated to synthesize the patches at the frontal view. A set of possible warps for each patch is obtained by aligning it with images from a training database of frontal faces. The alignments are performed efficiently in the Fourier domain using an extension of the Lucas-Kanade algorithm that can handle illumination variations. The problem of finding the optimal warps is then formulated as a discrete labeling problem using an MRF. The reconstructed frontal face image can then be used with any face recognition technique. The two main advantages of our method are that it does not require manually selected facial landmarks or head pose estimation. In order to improve the performance of our pose normalization method in face recognition, we also present an algorithm for classifying whether a given face image is at a frontal or nonfrontal pose. Experimental results on different datasets are presented to demonstrate the effectiveness of the proposed approach.


title: Developing PDA for Low-Bitrate Low-Delay Video Delivery

abstract: Pervasive wireless multimedia applications often require Personal Digital Assistants (PDAs) for processing and playback. The capability of PDAs, however, are generally much lower than desktop PCs. When these devices are used to play back video delivered over a network from a desktop server, their buffers can easily overflow, seriously degrading the video quality. In this paper, we report our implementation of some special stream processing techniques to deal with the capability mismatch between a PC and PDAs for low-delay live video streaming. These techniques are, the Selective Packet Drop (SPD) algorithm, the Game API (GAPI) optimization and the speed adaptation algorithm. All of them can be easily implemented. We show that our system provides much better video quality than systems without our techniques.


A GPS & GSM Based Vehicle Tracking and Employee


title: A High-Performance Continuously Tunable MEMS Bandpass Filter at 1 GHz

abstract: This paper reports a continuously tunable lumped bandpass filter implemented in a third-order coupled resonator configuration. The filter is fabricated on a Borosilicate glass substrate using a surface micromachining technology that offers hightunable passive components. Continuous electrostatic tuning is achieved using three tunable capacitor banks, each consisting of one continuously tunable capacitor and three switched capacitors with pull-in voltage of less than 40 V. The center frequency of the filter is tuned from 1 GHz down to 600 MHz while maintaining a 3-dB bandwidth of 13%-14% and insertion loss of less than 4 dB. The maximum group delay is less than 10 ns across the entire tuning range. The temperature stability of the center frequency from -50°C to 50°C is better than 2%. The measured tuning speed of the filter is better than 80 s, and the is better than 20 dBm, which are in good agreement with simulations. The filter occupies a small size of less than 1.5 cm × 1.1 cm. The implemented filter shows the highest performance amongst the fully integrated microelectromechanical systems filters operating at sub-gigahertz range.


title: The Geometry of Musical Rhythm

abstract: Musical rhythm is considered from the point of view of geometry. The interaction between the two fields yields new insights into rhythm and music theory, as well as new problems for research in mathematics and computer science. Recent results are reviewed, and new open problems are proposed.


title: How information management capability influences firm performance

abstract: How do information technology capabilities contribute to firm performance? This study develops a conceptual model linking IT-enabled information management capability with three important organizational capabilities (customer management capability, process management capability, and performance management capability). We argue that these three capabilities mediate the relationship between information management capability and firm performance. We use a rare archival data set from a conglomerate business group that had adopted a model of performance excellence for organizational transformation based on the Baldrige criteria. This data set contains actual scores from high quality assessments of firms and intraorganizational units of the conglomerate, and hence provides unobtrusive measures of the key constructs to validate our conceptual model. ::: ::: We find that information management capability plays an important role in developing other firm capabilities for customer management, process management, and performance management. In turn, these capabilities favorably influence customer, financial, human resources, and organizational effectiveness measures of firm performance. Among key managerial implications, senior leaders must focus on creating necessary conditions for developing IT infrastructure and information management capability because they play a foundational role in building other capabilities for improved firm performance. The Baldrige model also needs some changes to more explicitly acknowledge the role and importance of information management capability so that senior leaders know where to begin in their journey toward business excellence.


title: ERMS: An Elastic Replication Management System for HDFS

abstract: The Hadoop Distributed File System (HDFS) is a distributed storage system that stores large-scale data sets reliably and streams those data sets to applications at high bandwidth. HDFS provides high performance, reliability and availability by replicating data, typically three copies of every data. The data in HDFS changes in popularity over time. To get better performance and higher disk utilization, the replication policy of HDFS should be elastic and adapt to data popularity. In this paper, we describe ERMS, an elastic replication management system for HDFS. ERMS provides an active/standby storage model for HDFS. It utilizes a complex event processing engine to distinguish real-time data types, and then dynamically increases extra replicas for hot data, cleans up these extra replicas when the data cool down, and uses erasure codes for cold data. ERMS also introduces a replica placement strategy for the extra replicas of hot data and erasure coding parities. The experiments show that ERMS effectively improves the reliability and performance of HDFS and reduce storage overhead.


N-best Rescoring for Parsing Based on Dependency-Based Word Embeddings


title: Work Experiences and Personality Development in Young Adulthood

abstract: This longitudinal study provides an analysis of the relationship between personality traits and work experiences with a special focus on the relationship between changes in personality and work experiences in young adulthood. Longitudinal analyses uncovered 3 findings. First, measures of personality taken at age 18 predicted both objective and subjective work experiences at age 26. Second, work experiences were related to changes in personality traits from age 18 to 26. Third, the predictive and change relations between personality traits and work experiences were corresponsive: Traits that “selected” people into specific work experiences were the same traits that changed in response to those same work experiences. The relevance of the findings to theories of personality development is discussed. During the period of “emerging adulthood” (i.e., ages 15 to 30; Arnett, 2000), most men and women become more socially dominant, warm, responsible, agreeable, and emotionally stable. These normative developmental changes in personality functioning have been observed in multiple birth cohorts, in different western nations, using both longitudinal and cross-sectional research designs (Helson & Kwan, 2000; Helson & Moane, 1987; McCrae et al., 1999; McGue, Bacon, & Lykken, 1993; Roberts, Caspi, & Moffitt, 2001; Roberts, Helson, & Klohnen, 2002; Robins, Fraley, Roberts, & Trzesniewski, 2001). However, these normative changes during the transition from adolescence to adulthood also conceal significant individual differences in change (Roberts, 1997; Roberts et al., 2001; Roberts & Chapman, 2000; Robins et al., 2001). That is, not all people change in the same direction or to the same extent over the same developmental period. The existence of individual differences in change suggests the hypothesis that certain life experiences differentially contribute to normative developmental changes. In this article, we test whether work experiences in young adulthood are related to changes in personality traits during this formative time of life. We examine this developmental process by following a sample of adolescents from age 18 to age 26 and by measuring their personalities and their work experiences over the same time period.


title: An Assistive Navigation System Based on Augmented Reality and Context Awareness for People With Mild Cognitive Impairments

abstract: This paper presents a system for supplying spatial orientation and support to cognitively impaired people in their daily activities. The system is a technological solution based on external aid at a practical level (substitution-based rehabilitation). In particular, we propose a model focused on points of interest or well-known places, in which user-friendly routes to a destination are generated based on the user context rather than the conventional street names and quantitative distances. Moreover, the system offers augmented reality views that include contextual information. This philosophy of navigation more closely matches the needs of the user than do conventional navigation systems; the proposal is especially useful for users who are not accustomed to using new technologies (e.g., elderly people), people experiencing disorientation and, more generally, individuals with a slight cognitive deficit. The system also includes an application that allows the relatives of the user to establish tasks that must be performed at a specific location and to monitor the activities of the user to detect potentially risky situations.


title: Modeling User Preferences in Recommender Systems: A Classification Framework for Explicit and Implicit User Feedback

abstract: Recommender systems are firmly established as a standard technology for assisting users with their choices; however, little attention has been paid to the application of the user model in recommender systems, particularly the variability and noise that are an intrinsic part of human behavior and activity. To enable recommender systems to suggest items that are useful to a particular user, it can be essential to understand the user and his or her interactions with the system. These interactions typically manifest themselves as explicit and implicit user feedback that provides the key indicators for modeling users’ preferences for items and essential information for personalizing recommendations. In this article, we propose a classification framework for the use of explicit and implicit user feedback in recommender systems based on a set of distinct properties that include Cognitive Effort, User Model, Scale of Measurement, and Domain Relevance. We develop a set of comparison criteria for explicit and implicit user feedback to emphasize the key properties. Using our framework, we provide a classification of recommender systems that have addressed questions about user feedback, and we review state-of-the-art techniques to improve such user feedback and thereby improve the performance of the recommender system. Finally, we formulate challenges for future research on improvement of user feedback.


title: Fuzzy Ontology-Based Sentiment Analysis of Transportation and City Feature Reviews for Safe Traveling

abstract: Abstract Traffic congestion is rapidly increasing in urban areas, particularly in mega cities. To date, there exist a few sensor network based systems to address this problem. However, these techniques are not suitable enough in terms of monitoring an entire transportation system and delivering emergency services when needed. These techniques require real-time data and intelligent ways to quickly determine traffic activity from useful information. In addition, these existing systems and websites on city transportation and travel rely on rating scores for different factors (e.g., safety, low crime rate, cleanliness, etc.). These rating scores are not efficient enough to deliver precise information, whereas reviews or tweets are significant, because they help travelers and transportation administrators to know about each aspect of the city. However, it is difficult for travelers to read, and for transportation systems to process, all reviews and tweets to obtain expressive sentiments regarding the needs of the city. The optimum solution for this kind of problem is analyzing the information available on social network platforms and performing sentiment analysis. On the other hand, crisp ontology-based frameworks cannot extract blurred information from tweets and reviews; therefore, they produce inadequate results. In this regard, this paper proposes fuzzy ontology-based sentiment analysis and semantic web rule language (SWRL) rule-based decision-making to monitor transportation activities (accidents, vehicles, street conditions, traffic volume, etc.) and to make a city-feature polarity map for travelers. This system retrieves reviews and tweets related to city features and transportation activities. The feature opinions are extracted from these retrieved data, and then fuzzy ontology is used to determine the transportation and city-feature polarity. A fuzzy ontology and an intelligent system prototype are developed by using Protege web ontology language (OWL) and Java, respectively. The experimental results show satisfactory improvement in tweet and review analysis and opinion mining.


title: Effectiveness of Variable-Gain Kalman Filter Based on Angle Error Calculated from Acceleration Signals in Lower Limb Angle Measurement with Inertial Sensors

abstract: The wearable sensor system developed by our group, which measured lower limb angles using Kalman-filtering-based method, was suggested to be useful in evaluation of gait function for rehabilitation support. However, it was expected to reduce variations of measurement errors. In this paper, a variable-Kalman-gain method based on angle error that was calculated from acceleration signals was proposed to improve measurement accuracy. The proposed method was tested comparing to fixed-gain Kalman filter and a variable-Kalman-gain method that was based on acceleration magnitude used in previous studies. First, in angle measurement in treadmill walking, the proposed method measured lower limb angles with the highest measurement accuracy and improved significantly foot inclination angle measurement, while it improved slightly shank and thigh inclination angles. The variable-gain method based on acceleration magnitude was not effective for our Kalman filter system. Then, in angle measurement of a rigid body model, it was shown that the proposed method had measurement accuracy similar to or higher than results seen in other studies that used markers of camera-based motion measurement system fixing on a rigid plate together with a sensor or on the sensor directly. The proposed method was found to be effective in angle measurement with inertial sensors.


title: Building camera arrays for light-field capture, display, and analysis

abstract: We describe a series of multi-view and mosaicking cameras we have designed over the past few years for video conferencing, entertainment, sports and special event coverage, light field capture (for display) and ranging applications. These are elements of a continuing exploration of what happens when cameras cost nothing, take no space, and incur negligible bandwidth - i.e., now.


title: Faster Algorithms for Rectangular Matrix Multiplication

abstract: Let {\alpha} be the maximal value such that the product of an n x n^{\alpha} matrix by an n^{\alpha} x n matrix can be computed with n^{2+o(1)} arithmetic operations. In this paper we show that \alpha>0.30298, which improves the previous record \alpha>0.29462 by Coppersmith (Journal of Complexity, 1997). More generally, we construct a new algorithm for multiplying an n x n^k matrix by an n^k x n matrix, for any value k\neq 1. The complexity of this algorithm is better than all known algorithms for rectangular matrix multiplication. In the case of square matrix multiplication (i.e., for k=1), we recover exactly the complexity of the algorithm by Coppersmith and Winograd (Journal of Symbolic Computation, 1990). ::: These new upper bounds can be used to improve the time complexity of several known algorithms that rely on rectangular matrix multiplication. For example, we directly obtain a O(n^{2.5302})-time algorithm for the all-pairs shortest paths problem over directed graphs with small integer weights, improving over the O(n^{2.575})-time algorithm by Zwick (JACM 2002), and also improve the time complexity of sparse square matrix multiplication.


title: Color image quantization for frame buffer display

abstract: Algorithms for adaptive, tapered quantization of color images are described. The research is motivated by the desire to display high-quality reproductions of color images with small frame buffers. It is demonstrated that many color images which would normally require a frame buffer having 15 bits per pixel can be quantized to 8 or fewer bits per pixel with little subjective degradation. In most cases, the resulting images look significantly better than those made with uniform quantization. The color image quantization task is broken into four phases: 1) Sampling the original image for color statistics 2) Choosing a colormap based on the color statistics 3) Mapping original colors to their nearest neighbors in the colormap 4) Quantizing and redrawing the original image (with optional dither). Several algorithms for each of phases 2-4 are described, and images created by each given.


title: *droid: Assessment and Evaluation of Android Application Analysis Tools

abstract: The security research community has invested significant effort in improving the security of Android applications over the past half decade. This effort has addressed a wide range of problems and resulted in the creation of many tools for application analysis. In this article, we perform the first systematization of Android security research that analyzes applications, characterizing the work published in more than 17 top venues since 2010. We categorize each paper by the types of problems they solve, highlight areas that have received the most attention, and note whether tools were ever publicly released for each effort. Of the released tools, we then evaluate a representative sample to determine how well application developers can apply the results of our community’s efforts to improve their products. We find not only that significant work remains to be done in terms of research coverage but also that the tools suffer from significant issues ranging from lack of maintenance to the inability to produce functional output for applications with known vulnerabilities. We close by offering suggestions on how the community can more successfully move forward.