title: Saliency estimation using a non-parametric low-level vision model

abstract: Many successful models for predicting attention in a scene involve three main steps: convolution with a set of filters, a center-surround mechanism and spatial pooling to construct a saliency map. However, integrating spatial information and justifying the choice of various parameter values remain open problems. In this paper we show that an efficient model of color appearance in human vision, which contains a principled selection of parameters as well as an innate spatial pooling mechanism, can be generalized to obtain a saliency model that outperforms state-of-the-art models. Scale integration is achieved by an inverse wavelet transform over the set of scale-weighted center-surround responses. The scale-weighting function (termed ECSF) has been optimized to better replicate psychophysical data on color appearance, and the appropriate sizes of the center-surround inhibition windows have been determined by training a Gaussian Mixture Model on eye-fixation data, thus avoiding ad-hoc parameter selection. Additionally, we conclude that the extension of a color appearance model to saliency estimation adds to the evidence for a common low-level visual front-end for different visual tasks.


title: RFID‐Enabled Visibility and Retail Inventory Record Inaccuracy: Experiments in the Field

abstract: Accurate inventory records are key to effective store execution, affecting forecasting, ordering, and replenishment. Prior empirical research, however, shows that retailer inventory records are inherently inaccurate. Radio Frequency Identification (RFID) enables visibility into the movement of inventories in the supply chain. Using two different field experiments, the current research investigates the effectiveness of this visibility in reducing retail store inventory record inaccuracy (IRI). Study 1 used an interrupted time-series design and involved daily physical counts of all products in one category in 13 stores (8 treatments and 5 controls) of a major global retailer over 23 weeks. Results indicate a significant decrease in IRI of approximately 26% due to RFID-enabled visibility. Using an untreated control group design with pre-test and post-test, Study 2 expands the number of categories to five and the number of stores to 62 (31 treatment and 31 control stores). Results show that the effectiveness of RFID in reducing IRI varies by category (ranging from no statistically significant improvement to 81%). Results also suggest that RFID ameliorates the effects of known determinants of IRI and provide the key insight that the technology is most effective for product categories characterized by these determinants.


title: An Empirical Study of the Evolution of PHP Web Application Security

abstract: Web applications are increasingly subject to mass attacks, with vulnerabilities found easily in both open source and commercial applications as evinced by the fact that approximately half of reported vulnerabilities are found in web applications. In this paper, we perform an empirical investigation of the evolution of vulnerabilities in fourteen of the most widely used open source PHP web applications, finding that vulnerabilities densities declined from 28.12 to 19.96 vulnerabilities per thousand lines of code from 2006 to 2010. We also investigate whether complexity metrics or a security resources indicator (SRI) metric can be used to identify vulnerable web application showing that average cyclomatic complexity is an effective predictor of vulnerability for several applications, especially for those with low SRI scores.


title: Unity power factor control for three phase three level rectifiers without current sensors

abstract: Three level rectifiers with reduced number of switches (such as the Vienna rectifier) have been receiving wide interest in the past years to improve the input power quality of rectifier systems. In this paper, a new carrier-based pulse width modulation (PWM) control algorithm is proposed for such converters to eliminate the low frequency harmonics in the line current while achieving unity power factor at the rectifier input terminals. The operating constraints of the Vienna rectifier with the carrier-based modulation strategy are examined carefully and the proposed control algorithm ensures that appropriate voltage/current directional constraints are met. A promising cost reduction opportunity can be seen with elimination of input current sensing to operate the Vienna rectifier. The control algorithm is verified via Saber simulation and experimental results.


title: Osteopathic manual therapy versus conventional conservative therapy in the treatment of temporomandibular disorders: A randomized controlled trial

abstract: Summary Objective Temporomandibular disorders (TMD) is a term reflecting chronic, painful, craniofacial conditions usually of unclear etiology with impaired jaw function. The effect of osteopathic manual therapy (OMT) in patients with TMD is largely unknown, and its use in such patients is controversial. Nevertheless, empiric evidence suggests that OMT might be effective in alleviating symptoms. A randomized controlled clinical trial of efficacy was performed to test this hypothesis. Methods We performed a randomized, controlled trial that involved adult patients who had TMD. Patients were randomly divided into two groups: an OMT group (25 patients, 12 males and 13 females, age 40.6±11.03) and a conventional conservative therapy (CCT) group (25 patients, 10 males and 15 females, age 38.4±15.33). At the first visit (T0), at the end of treatment (after six months, T1) and two months after the end of treatment (T2), all patients were subjected to clinical evaluation. Assessments were performed by subjective pain intensity (visual analogue pain scale, VAS), clinical evaluation (Temporomandibular index) and measurements of the range of maximal mouth opening and lateral movement of the head around its axis. Results Patients in both groups improved during the six months. The OMT group required significantly less medication (non-steroidal medication and muscle relaxants) ( P Conclusions The two therapeutic modalities had similar clinical results in patients with TMD, even if the use of medication was greater in CCT group. Our findings suggest that OMT is a valid option for the treatment of TMD.


title: Visualisation of Linked Data - Reprise

abstract: Linked Data promises to serve as a disruptor of traditional approaches to data management and use, promoting the push from the traditional Web of documents to a Web of data. The ability for data consumers to adopt a follow your nose approach, traversing links defined within a dataset or across independently-curated datasets, is an essential feature of this new Web of Data, enabling richer knowledge retrieval thanks to synthesis across multiple sources of, and views on, interrelated datasets. But for the Web of Data to be successful, we must design novel ways of interacting with the corresponding very large amounts of complex, interlinked, multi-dimensional data throughout its management cycle. The design of user interfaces for Linked Data, and more specifically interfaces that represent the data visually, play a central role in this respect. Contributions to this special issue on Linked Data visualisation investigate different approaches to harnessing visualisation as a tool for exploratory discovery and basic-to-advanced analysis. The papers in this volume illustrate the design and construction of intuitive means for end-users to obtain new insight and gather more knowledge, as they follow links defined across datasets over the Web of Data.


title: Learning 5000 Relational Extractors

abstract: Many researchers are trying to use information extraction (IE) to create large-scale knowledge bases from natural language text on the Web. However, the primary approach (supervised learning of relation-specific extractors) requires manually-labeled training data for each relation and doesn't scale to the thousands of relations encoded in Web text. ::: ::: This paper presents LUCHS, a self-supervised, relation-specific IE system which learns 5025 relations --- more than an order of magnitude greater than any previous approach --- with an average F1 score of 61%. Crucial to LUCHS's performance is an automated system for dynamic lexicon learning, which allows it to learn accurately from heuristically-generated training data, which is often noisy and sparse.


title: Combining Top-N Recommenders with Metasearch Algorithms

abstract: Given the diversity of recommendation algorithms, choosing one technique is becoming increasingly difficult. In this paper, we explore methods for combining multiple recommendation approaches. We studied rank aggregation methods that have been proposed for the metasearch task (i.e., fusing the outputs of different search engines) but have never been applied to merge top-N recommender systems. These methods require no training data nor parameter tuning. We analysed two families of methods: voting-based and score-based approaches. These rank aggregation techniques yield significant improvements over state-of-the-art top-N recommenders. In particular, score-based methods yielded good results; however, some voting techniques were also competitive without using score information, which may be unavailable in some recommendation scenarios. The studied methods not only improve the state of the art of recommendation algorithms but they are also simple and efficient.


title: Surface probes for building materials moisture measurement by reflectometric method

abstract: The problem of building materials moisture, resulting from their porous structure, has a meaningful value for environmental engineering for the sake of its numerous, negative consequences. After breaking the limited states, the negative aspects both for building users’ health, mechanical, physical and chemical properties of barriers can be observed. Therefore, the essential matter is to provide the possibility of barriers moisture state evaluation. Among the techniques used for building materials moisture measurement, the TDR method application should be underlined for the sake of its simplicity in use and high precision, especially with the use of surface probes. The construction of probes presented in this paper enables non-invasive and accurate moisture measurements, both in laboratory and in situ conditions. The formulas obtained from conducted investigations make possible the evaluation of selected building materials moisture states.


title: Enhancing Genetic Algorithms using Multi Mutations

abstract: Mutation is one of the most important stages of the genetic algorithm because of its impact on the exploration of global optima, and to overcome premature convergence. There are many types of mutation, and the problem lies in selection of the appropriate type, where the decision becomes more difficult and needs more trial and error. This paper investigates the use of more than one mutation operator to enhance the performance of genetic algorithms. Novel mutation operators are proposed, in addition to two selection strategies for the mutation operators, one of which is based on selecting the best mutation operator and the other randomly selects any operator. Several experiments on some Travelling Salesman Problems (TSP) were conducted to evaluate the proposed methods, and these were compared to the well-known exchange mutation and rearrangement mutation. The results show the importance of some of the proposed methods, in addition to the significant enhancement of the genetic algorithm's performance, particularly when using more than one mutation operator.


Outlier Rejection for Absolute Pose Estimation with Known Orientation.


title: The effect of postings information on searching behaviour Bibliometrics and beyond : some thoughts on web-based citation analysis

abstract: The idea of a unified citation index to the literature of science was first outlined by Eugene Garfield [1] in 1955 in the journal Science. Science Citation Index has since established itself as the gold standard for scientific information retrieval. It has also become the database of choice for citation analysts and evaluative bibliometricians worldwide. As scientific publication moves to the web, and novel approaches to scholarly communication and peer review establish themselves, new methods of citation and link analysis will emerge to capture often liminal expressions of peer esteem, influence and approbation. The web thus affords bibliometricians rich opportunities to apply and adapt their techniques to new contexts and content: the age of ‘bibliometric spectroscopy’ [2] is dawning.


title: Distributed Cooperative Communication and Link Prediction in Cloud Robotics

abstract: While deploying large scale heterogeneous robots in a wide geographical area, communicating among robots and robots with a central entity pose a major challenge due to robotic motion, distance and environmental constraints. In a cloud robotics scenario, communication challenges result in computational challenges as the computation is being performed at the cloud. Therefore fog nodes are introduced which shorten the distance between the robots and cloud and reduce the communication challenges. Fog nodes also reduce the computation challenges with extra compute power. However in the above scenario, maintaining continuous communication between the cloud and the robots either directly or via fog nodes is difficult. Therefore we propose a Distributed Cooperative Multi-robots Communication (DCMC) model where Robot to Robot (R2R), Robot to Fog (R2F) and Fog to Cloud (F2C) communications are being realized. Once the DCMC framework is formed, each robot establishes communication paths to maintain a consistent communication with the cloud. Further, due to mobility and environmental condition, maintaining link with a particular robot or a fog node becomes difficult. This requires pre-knowledge of the link quality such that appropriate R2R or R2F communication can be made possible. In a scenario where Global Positioning System (GPS) and continuous scanning of channels are not advisable due to energy or security constraints, we need an accurate link prediction mechanism. In this paper we propose a Collaborative Robotic based Link Prediction (CRLP) mechanism which predicts reliable communication and quantify link quality evolution in R2R and R2F communications without GPS and continuous channel scanning. We have validated our proposed schemes using joint Gazebo/Robot Operating System (ROS), MATLAB and Network Simulator (NS3) based simulations. Our schemes are efficient in terms of energy saving and accurate link prediction.


title: Survey of Text Mining Techniques, Challenges and their Applications

abstract: everyday life communication interaction among people leading to mutual learning and sharing of valuable knowledge, such as chat, messaging, comments, and posts on board etc. Also, social networking websites, search engines sharing huge data texts in websites. The text is nothing but the combination of characters. Therefore, analyzing and extracting information patterns from such data sets are more complex. Several methods have been proposed for analyzing such texts and extracting information. In this paper, we present different text mining techniques to discover various textual patterns from the different sources. This topic is also deals with the areas such as information retrieval, machine learning, statistics, computational data sciences and advanced data mining. We also discuss future challenges of this area using different techniques, particularly rough set based text mining techniques, improvements and research directions in this paper. Keywordsmining, Text mining, Rough sets,Classification, Summarization, and Text categorization.


title: Improving the M&A success rate: identity may be the key

abstract: Purpose ::: ::: ::: ::: ::: The purpose of this paper is to build a model of mergers and acquisitions (M&A) performance drawing from the concept of organizational identity theory. The paper proposes that successful performance of an M&A is dependent upon two things, namely, the alignment of the organizational identities of the two merging firms and the method used to integrate them. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: This is a conceptual paper based on current research and uses multiple real-world examples of M&A to elaborate the proposed model and highlight scholarly and practical implications. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The paper explains that the similarity of the identities of the merging organizations has a significant impact on the performance of the combined entity. Furthermore, the integration method used by the merged firms influences the success of the merged entity. The use of an identity approach helps unravel new variables impacting M&A performance. ::: ::: ::: ::: ::: Research limitations/implications ::: ::: ::: ::: ::: A limitation of the paper is that it does not address how, over time, identity management can overcome the resistance of two merging entities. In addition to testing the proposed relationships, further research can explore these identity dynamics in unraveling an M&A performance. ::: ::: ::: ::: ::: Practical implications ::: ::: ::: ::: ::: This paper highlights the importance of evaluating identity as an element of an M&A due diligence. Practitioners should be aware of the dangers of signaling one identity integration strategy but using contradictory actions in implementation. ::: ::: ::: ::: ::: Social implications ::: ::: ::: ::: ::: Given the importance of identity in a variety of organizational outcomes, the paper is timely in integrating the organizational identity (OI) concepts in understanding M&A performance issues. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: Given the importance of identity in a variety of organizational outcomes, the paper is timely in integrating the OI concepts in understanding M&A performance issues.


title: Survey of range improvement of commercial RFID tags with Power Optimized Waveforms

abstract: The power sensitivity of passive Radio Frequency Identification (RFID) tags heavily affects the read reliability and range. Inventory tracking systems rely heavily on strong read reliability while animal tracking in large fields rely heavily on long read range. Power Optimized Waveforms (POWs) provide a solution to improving both read reliability and read range by increasing RFID tag RF to DC power conversion efficiency. This paper presents a survey of the increases and decreases to read range of common RFID tags from Alien and Impinj with Higgs, Higgs 2, Higgs 3, Monza 3, and Monza 4 RFICs. In addition, POWs are explained in detail with examples and methods of integration into a reader.


title: Seamless Integration of Initial Sketching and Subsequent Detail Editing in Flower Modeling

abstract: We present an interactive modeling system for flower composition that supports seamless transformation from an initial sketch to a detailed three-dimensional (3D) model. To begin, the user quickly sketches the overall appearance of the desired model as a collection of two-dimensional (2D) strokes on hierarchical billboards. Then the user iteratively replaces the coarse sketch with a detailed 3D model referring to the initial sketch as a guide. Since a flower model consists of many repetitive components, the system helps the user to reuse 3D components to facilitate the modeling process. The global view of the entire model is always shown in a separate window to visualize how local modifications affect the global appearance. Our system helps the user make appropriate design decisions to keep the model consistent with the initial design, which is difficult in traditional bottom-up plant modeling systems in which the global view only emerges after all of the details are specified. ::: ::: Categories and Subject Descriptors (according to ACM CCS): I.3.6 [Computer Graphics]: Methodology and Techniques – Interaction Techniques.


title: An Improved Multiobjective Optimization Evolutionary Algorithm Based on Decomposition for Complex Pareto Fronts

abstract: The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been shown to be very efficient in solving multiobjective optimization problems (MOPs). In practice, the Pareto-optimal front (POF) of many MOPs has complex characteristics. For example, the POF may have a long tail and sharp peak and disconnected regions, which significantly degrades the performance of MOEA/D. This paper proposes an improved MOEA/D for handling such kind of complex problems. In the proposed algorithm, a two-phase strategy (TP) is employed to divide the whole optimization procedure into two phases. Based on the crowdedness of solutions found in the first phase, the algorithm decides whether or not to delicate computational resources to handle unsolved subproblems in the second phase. Besides, a new niche scheme is introduced into the improved MOEA/D to guide the selection of mating parents to avoid producing duplicate solutions, which is very helpful for maintaining the population diversity when the POF of the MOP being optimized is discontinuous. The performance of the proposed algorithm is investigated on some existing benchmark and newly designed MOPs with complex POF shapes in comparison with several MOEA/D variants and other approaches. The experimental results show that the proposed algorithm produces promising performance on these complex problems.


title: Knowledge Management, Innovation, and Firm Performance

abstract: Purpose – To provide important empirical evidence to support the role of knowledge management within firms.Design/methodology/approach – Data were collected using a mail survey sent to CEOs representing firms with 50 or more employees from a cross‐section of industries. A total of 1,743 surveys were mailed out and 443 were returned and usable (27.8 percent response rate). The sample was checked for response and non‐response bias. Hypotheses were tested using structural equation modelling.Findings – This paper presents knowledge management as a coordinating mechanism. Empirical evidence supports the view that a firm with a knowledge management capability will use resources more efficiently and so will be more innovative and perform better.Research limitations/implications – The sample slightly over‐represented larger firms. Data were also collected in New Zealand. As with most studies, it is important to replicate this study in different contexts.Practical implications – Knowledge management is embraced in...


title: Modeling and analysis of a tricopter/flying-wing convertible UAV with tilt-rotors

abstract: The aim of this paper is to provide a mathematical model for a convertible unmanned aerial vehicle that combines the capabilities of a flying wing and tricopter with tilt rotors. This article presents the mathematical model for airplane and tricopter modes as well as the way they are related during the transition phase. Also is presented a control strategy to hover flying. Finally, it is presented simulation results of tricopter mathematical model under controls developed.


title: Design of the Neutrosophic Membership Valued Fuzzy-PID Controller and Rotation Angle Control of a Permanent Magnet Direct Current Motor

abstract: In this paper, we propose a method based on the fuzzy logic controller (FLC) method, created by using neutrosophic membership values. This method is named as proportional integral derivative-neutrosophic valued fuzzy logic controller (PID-NFLC). We conducted a performance simulation test to compare the PID-NFLC with classical FLC for control of the rotation angle of a permanent magnet direct current (PMDC) motor. Rotation angle error and change of this error were used as the input variables of the FLC and NFLC units. In the suggested neutrosophic valued FLC (NFLC), placement of the input variables was evaluated on the universal set by using neutrosophic membership function approach. Error and error change were evaluated in two different NFLC unit separately from each other. Then, crisp outputs of the NFLC units are applied to PID controller. So, PID-NFLC was obtained. The proposed method and conventional FLC were tested in the SIMULINK. According to the test results, our suggested method is own lesser overshoot ratio and more fast rising time characteristics than the classical FLC. In addition, evaluation of error change, separately from the error in the proposed method is providing flexibility in the PMDC motor position control process.


title: The red one!: On learning to refer to things based on their discriminative properties

abstract: As a first step towards agents learning to communicate about their visual environment, we propose a system that, given visual representations of a referent (cat) and a context (sofa), identifies their discriminative attributes, i.e., properties that distinguish them (has_tail). Moreover, despite the lack of direct supervision at the attribute level, the model learns to assign plausible attributes to objects (sofa-has_cushion). Finally, we present a preliminary experiment confirming the referential success of the predicted discriminative attributes.


title: Active Learning and Visual Analytics for Stance Classification with ALVA

abstract: The automatic detection and classification of stance (e.g., certainty or agreement) in text data using natural language processing and machine-learning methods creates an opportunity to gain insight into the speakers’ attitudes toward their own and other people’s utterances. However, identifying stance in text presents many challenges related to training data collection and classifier training. To facilitate the entire process of training a stance classifier, we propose a visual analytics approach, called ALVA, for text data annotation and visualization. ALVA’s interplay with the stance classifier follows an active learning strategy to select suitable candidate utterances for manual annotaion. Our approach supports annotation process management and provides the annotators with a clean user interface for labeling utterances with multiple stance categories. ALVA also contains a visualization method to help analysts of the annotation and training process gain a better understanding of the categories used by the annotators. The visualization uses a novel visual representation, called CatCombos, which groups individual annotation items by the combination of stance categories. Additionally, our system makes a visualization of a vector space model available that is itself based on utterances. ALVA is already being used by our domain experts in linguistics and computational linguistics to improve the understanding of stance phenomena and to build a stance classifier for applications such as social media monitoring.


title: Theory-guided Data Science: A New Paradigm for Scientific Discovery

abstract: Data science models, although successful in a number of commercial domains, have had limited applicability in scientific problems involving complex physical phenomena. Theory-guided data science (TGDS) is an emerging paradigm that aims to leverage the wealth of scientific knowledge for improving the effectiveness of data science models in enabling scientific discovery. The overarching vision of TGDS is to introduce scientific consistency as an essential component for learning generalizable models. Further, by producing scientifically interpretable models, TGDS aims to advance our scientific understanding by discovering novel domain insights. Indeed, the paradigm of TGDS has started to gain prominence in a number of scientific disciplines such as turbulence modeling, material discovery, quantum chemistry, bio-medical science, bio-marker discovery, climate science, and hydrology. In this paper, we formally conceptualize the paradigm of TGDS and present a taxonomy of research themes in TGDS. We describe several approaches for integrating domain knowledge in different research themes using illustrative examples from different disciplines. We also highlight some of the promising avenues of novel research for realizing the full potential of theory-guided data science.


title: A Transient-Enhanced Low-Quiescent Current Low-Dropout Regulator With Buffer Impedance Attenuation

abstract: This paper presents a low-dropout regulator (LDO) for portable applications with an impedance-attenuated buffer for driving the pass device. Dynamically-biased shunt feedback is proposed in the buffer to lower its output resistance such that the pole at the gate of the pass device is pushed to high frequencies without dissipating large quiescent current. By employing the current-buffer compensation, only a single pole is realized within the regulation loop unity-gain bandwidth and over 65° phase margin is achieved under the full range of the load current in the LDO. The LDO thus achieves stability without using any low-frequency zero. The maximum output-voltage variation can be minimized during load transients even if a small output capacitor is used. The LDO with the proposed impedance-attenuated buffer has been implemented in a 0.35-μm twin-well CMOS process. The proposed LDO dissipates 20-μA quiescent current at no-load condition and is able to deliver up to 200-mA load current. With a 1-μF output capacitor, the maximum transient output-voltage variation is within 3% of the output voltage with load step changes of 200 mA/100 ns.


title: Controversy Detection Using Reactions on Social Media

abstract: In this work we demonstrate a method to detect controversy on news issues. This is done by performing an analysis of people's reaction on social media to news articles reporting these issues. Detecting controversial news topics on web is a relevant problem today. It helps to identify the issues upon which people have divided opinion and is specially useful on topics such as a presidential election, government reforms, climate change etc. We use sentiment analysis and word matching to accomplish this task. We show the application of our method for detecting controversial topics during the US Presidential elections 2016.


title: Role of Doa1 in the Saccharomyces cerevisiae DNA damage response.

abstract: The cellular response to DNA damage requires not only direct repair of the damage but also changes in the DNA replication machinery, chromatin, and transcription that facilitate survival. Here, we describe Saccharomyces cerevisiae Doa1, which helps to control the damage response by channeling ubiquitin from the proteosomal degradation pathway into pathways that mediate altered DNA replication and chromatin modification. DOA1 interacts with genes involved in PCNA ubiquitination, including RAD6, RAD18, RAD5, UBC13, and MMS2, as well as genes involved in histone H2B ubiquitination or deubiquitination, including RAD6, BRE1, LGE1, CDC73, UBP8, UBP10, and HTB2. In the absence of DOA1, damage-induced ubiquitination of PCNA does not occur. In addition, the level of ubiquitinated H2B is decreased under normal conditions and completely absent in the presence of DNA damage. In the case of PCNA, the defect associated with the doa1Delta mutant is alleviated by overexpression of ubiquitin, but in the case of H2B, it is not. The data suggest that Doa1 is the major source of ubiquitin for the DNA damage response and that Doa1 also plays an additional essential and more specific role in the monoubiquitination of histone H2B.


title: Chromatic Content of Spectral Lights

abstract: Abstract Using three novel formats, we compare four estimates of the spectral sensitivity of the opponent stage channels: a Linear Model, Jameson and Hurvich's [ Journal of the Optical Society of America, 45 , 546–552 (1955)] hue cancellation sensitivities, Gordon and Abramov's [ Optical Society of America Technical Digest Series, 15 , 12–15 (1987)] hue scaling, and hue matching. The three formats are: the spectrum locus in the opponent equiluminant plane, null lines in the CIE XYZ chart and response functions for unique hues. All sensitivities show departures from the Linear Model and from each other. Relative to the model, common features of all estimates are that violets are compressed; long-wavelength reds are amplified; the redness component of violet lights is greatly attenuated; and saturation of violet stimuli is underestimated.


title: The Neural Autoregressive Distribution Estimator

abstract: We describe a new approach for modeling the distribution of high-dimensional vectors of discrete variables. This model is inspired by the restricted Boltzmann machine (RBM), which has been shown to be a powerful model of such distributions. However, an RBM typically does not provide a tractable distribution estimator, since evaluating the probability it assigns to some given observation requires the computation of the so-called partition function, which itself is intractable for RBMs of even moderate size. Our model circumvents this diculty by decomposing the joint distribution of observations into tractable conditional distributions and modeling each conditional using a non-linear function similar to a conditional of an RBM. Our model can also be interpreted as an autoencoder wired such that its output can be used to assign valid probabilities to observations. We show that this new model outperforms other multivariate binary distribution estimators on several datasets and performs similarly to a large (but intractable) RBM.


title: Hexagonal Waveguide Based Circularly Polarized Horn Antennas for Sub-mm-Wave/Terahertz Band

abstract: In this paper, we introduce a class of circularly polarized horns realizable at sub-mm-wave and terahertz frequencies. The proposed designs exploit differential dispersion in hexagonal waveguides (a birefringencelike effect), which allows a linear to circular polarization conversion. These can be fabricated for terahertz applications, as they do not require waveguide partitions or elaborate waveguide assemblies needed in existing designs. In this paper, we discuss the theory of hexagonal waveguides, develop analytical design procedures, and show measurement results for a fabricated prototype. The prototype is developed for WR-8 band (90–140 GHz) operation, showing a midband gain of 18 dBi and 3 dB axial-ratio bandwidth of 37%.


title: Manifold-based multi-objective policy search with sample reuse

abstract: Abstract Many real-world applications are characterized by multiple conflicting objectives. In such problems optimality is replaced by Pareto optimality and the goal is to find the Pareto frontier, a set of solutions representing different compromises among the objectives. Despite recent advances in multi-objective optimization, achieving an accurate representation of the Pareto frontier is still an important challenge. Building on recent advances in reinforcement learning and multi-objective policy search, we present two novel manifold-based algorithms to solve multi-objective Markov decision processes. These algorithms combine episodic exploration strategies and importance sampling to efficiently learn a manifold in the policy parameter space such that its image in the objective space accurately approximates the Pareto frontier. We show that episode-based approaches and importance sampling can lead to significantly better results in the context of multi-objective reinforcement learning. Evaluated on three multi-objective problems, our algorithms outperform state-of-the-art methods both in terms of quality of the learned Pareto frontier and sample efficiency.


title: Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input

abstract: We demonstrate that current state-of-the-art approaches to Automated Essay Scoring (AES) are not well-suited to capturing adversarially crafted input of grammatical but incoherent sequences of sentences. We develop a neural model of local coherence that can effectively learn connectedness features between sentences, and propose a framework for integrating and jointly training the local coherence model with a state-of-the-art AES model. We evaluate our approach against a number of baselines and experimentally demonstrate its effectiveness on both the AES task and the task of flagging adversarial input, further contributing to the development of an approach that strengthens the validity of neural essay scoring models.


title: Quantum algorithms for computing short discrete logarithms and factoring RSA integers

abstract: In this paper we generalize the quantum algorithm for computing short discrete logarithms previously introduced by Eker{\aa} so as to allow for various tradeoffs between the number of times that the algorithm need be executed on the one hand, and the complexity of the algorithm and the requirements it imposes on the quantum computer on the other hand. Furthermore, we describe applications of algorithms for computing short discrete logarithms. In particular, we show how other important problems such as those of factoring RSA integers and of finding the order of groups under side information may be recast as short discrete logarithm problems. This immediately gives rise to an algorithm for factoring RSA integers that is less complex than Shor's general factoring algorithm in the sense that it imposes smaller requirements on the quantum computer. In both our algorithm and Shor's algorithm, the main hurdle is to compute a modular exponentiation in superposition. When factoring an n bit integer, the exponent is of length 2n bits in Shor's algorithm, compared to slightly more than n/2 bits in our algorithm.


title: Domain Adaptation with Regularized Optimal Transport

abstract: We present a new and original method to solve the domain adaptation problem using optimal transport. By searching for the best transportation plan between the probability distribution functions of a source and a target domain, a non-linear and invertible transformation of the learning samples can be estimated. Any standard machine learning method can then be applied on the transformed set, which makes our method very generic. We propose a new optimal transport algorithm that incorporates label information in the optimization: this is achieved by combining an efficient matrix scaling technique together with a majoration of a non-convex regularization term. By using the proposed optimal transport with label regularization, we obtain significant increase in performance compared to the original transport solution. The proposed algorithm is computationally efficient and effective, as illustrated by its evaluation on a toy example and a challenging real life vision dataset, against which it achieves competitive results with respect to state-of-the-art methods.


title: Vision-Based Estimation for Guidance, Navigation, and Control of an Aerial Vehicle

abstract: While a Global Positioning System (GPS) is the most widely used sensor modality for aircraft navigation, researchers have been motivated to investigate other navigational sensor modalities because of the desire to operate in GPS denied environments. Due to advances in computer vision and control theory, monocular camera systems have received growing interest as an alternative/collaborative sensor to GPS systems. Cameras can act as navigational sensors by detecting and tracking feature points in an image. Current methods have a limited ability to relate feature points as they enter and leave the camera field of view (FOV). A vision-based position and orientation estimation method for aircraft navigation and control is described. This estimation method accounts for a limited camera FOV by releasing tracked features that are about to leave the FOV and tracking new features. At each time instant that new features are selected for tracking, the previous pose estimate is updated. The vision-based estimation scheme can provide input directly to the vehicle guidance system and autopilot. Simulations are performed wherein the vision-based pose estimation is integrated with a nonlinear flight model of an aircraft. Experimental verification of the pose estimation is performed using the modelled aircraft.


title: Investigating interference between LoRa and IEEE 802.15.4g networks

abstract: The rapid growth of new radio technologies for Smart City/Building/Home applications means that models of cross-technology interference are needed to inform the development of higher layer protocols and applications. We systematically investigate interference interactions between LoRa and IEEE 802.15.4g networks. Our results show that LoRa can obtain high packet reception rates, even in presence of strong IEEE 802.15.4g interference. IEEE 802.15.4g is also shown to have some resilience to LoRa interference. Both effects are highly dependent on the LoRa radio's spreading factor and bandwidth configuration, as well as on the channelization. The results are shown to arise from the interaction between the two radios' modulation schemes. The data have implications for the design and analysis of protocols for both radio technologies.


Introspective Evaluation of Perception Performance for Parameter Tuning without Ground Truth


title: Representing and scheduling procedural generation using operator graphs

abstract: In this paper, we present the concept of operator graph scheduling for high performance procedural generation on the graphics processing unit (GPU). The operator graph forms an intermediate representation that describes all possible operations and objects that can arise during a specific procedural generation. While previous methods have focused on parallelizing a specific procedural approach, the operator graph is applicable to all procedural generation methods that can be described by a graph, such as L-systems, shape grammars, or stack based generation methods. Using the operator graph, we show that all partitions of the graph correspond to possible ways of scheduling a procedural generation on the GPU, including the scheduling strategies of previous work. As the space of possible partitions is very large, we describe three search heuristics, aiding an optimizer in finding the fastest valid schedule for any given operator graph. The best partitions found by our optimizer increase performance of 8 to 30x over the previous state of the art in GPU shape grammar and L-system generation.


title: Accurate Dense Stereo Matching Based on Image Segmentation Using an Adaptive Multi-Cost Approach

abstract: This paper presents a segmentation-based stereo matching algorithm using an adaptive multi-cost approach, which is exploited for obtaining accuracy disparity maps. The main contribution is to integrate the appealing properties of multi-cost approach into the segmentation-based framework. Firstly, the reference image is segmented by using the mean-shift algorithm. Secondly, the initial disparity of each segment is estimated by an adaptive multi-cost method, which consists of a novel multi-cost function and an adaptive support window cost aggregation strategy. The multi-cost function increases the robustness of the initial raw matching costs calculation and the adaptive window reduces the matching ambiguity effectively. Thirdly, an iterative outlier suppression and disparity plane parameters fitting algorithm is designed to estimate the disparity plane parameters. Lastly, an energy function is formulated in segment domain, and the optimal plane label is approximated by belief propagation. The experimental results with the Middlebury stereo datasets, along with synthesized and real-world stereo images, demonstrate the effectiveness of the proposed approach.


title: Effectiveness of Iphone’s Touch ID: KSA Case Study

abstract: A new trend of incorporating Touch ID sensors in mobile devices is appearing. Last year, Apple released a new model of its famous iPhone (5s). One of the most anticipated and hailed features of the new device was its Touch ID. Apple advertised that the new technology will increase the security of its device, and it will also be used in different applications as a proof of identity. To make the issue more controversial, Apple announced a new financial service (Apple Pay) that allows iPhone 6 users to use their iPhone as a replacement to credit cards. The minute the new technology was introduced; many questions appeared that needed immediate answers. Users were concerned about how it will work? Is it easy to use? Is it really safe? And whether it will be effective in protecting their private data or not? In this paper we provide a comprehensive study of this feature. We discuss the advantages and disadvantages of using it. Then we analyze and share the results of a survey that we conducted to measure the effectiveness of such feature in the Kingdom of Saudi Arabia (KSA). In this study, we only focus on users from KSA, because if the device fails to protect mobile's data, severe consequences might happen. Due to cultural believes in KSA, releasing mobile contents to unauthorized people could lead to crimes. Survey analysis revealed somewhat controversial results, while 76% of all participants believe that this technology will improve the device security, only 33% use it to lock/unlock their devices, and even a smaller percentage use it to make purchases. Keywords—IPhone 5s; IPhone 6; IPhone 6 plus; Fingerprint;


title: Detection, segmentation and classification of 3D urban objects using mathematical morphology and supervised learning

abstract: We propose an automatic and robust approach to detect, segment and classify urban objects from 3D point clouds. Processing is carried out using elevation images and the result is reprojected onto the 3D point cloud. First, the ground is segmented and objects are detected as discontinuities on the ground. Then, connected objects are segmented using a watershed approach. Finally, objects are classified using SVM with geometrical and contextual features. Our methodology is evaluated on databases from Ohio (USA) and Paris (France). In the former, our method detects 98% of the objects, 78% of them are correctly segmented and 82% of the well-segmented objects are correctly classified. In the latter, our method leads to an improvement of about 15% on the classification step with respect to previous works. Quantitative results prove that our method not only provides a good performance but is also faster than other works reported in the literature.


title: A Single-Stage Solid-State Transformer for PWM AC Drive With Source-Based Commutation of Leakage Energy

abstract: A solid-state transformer is a three-phase ac/ac converter with a high-frequency transformer. Due to advanced features like high power density, on demand var support and frequency regulation, solid-state transformer is an enabling technology for the modern power distribution system. It can also find application in high-power-density motor drives. The single-stage solid-state transformer considered in this paper is capable of bidirectional power flow and open loop power factor correction. This topology uses a minimum amount of copper and has relatively few semiconductor switches. One major problem in this converter is the commutation of leakage energy which results in power loss, reduction in switching frequency, loss of output voltage, and additional common-mode voltage switching. This paper presents a source-based commutation strategy along with a novel modulation technique resulting in elimination of additional snubber circuits, minimization of the frequency of leakage inductance commutation, recovery of the leakage energy, and soft switching of the output converter. The topology and its proposed control have been analyzed. Simulation and experimental results confirm the operation.


title: Idiosyncratic Effect of Corporate Solvency Management Strategies on Corporate Performance Valuation - A Study of Chemical Industry

abstract: The study identifies and evaluates the association among corporate solvency management strategies and the corporate performance valuation in Chemical industry of Pakistan. The study uses purposive sampling or judgmental sampling for selecting 30 sample companies from the sector; covering 10 years financial statements data ranging from year 2002 to 2011. Balanced panel data is taken for the purpose of study. Levin, Lin & Chu test is used to check the stationarity of data whereas White Test is used to check the heteroskedasticity of data. Panel Least square technique with fixed effects is used to generalize the relationship between studied variables. The study observed that the performance of the chemical sector in terms of market to book value is affected by internal firm and industry specific factors related to solvency management strategic decisions. Findings of the study provide with the overview of historic performance and the potential performance of the selected sector to help policy makers including finance, economics and industry experts for


title: T: a multithreaded massively parallel architecture

abstract: What should the architecture of each node in a general purpose, massively parallel architecture (MPA) be? We frame the question in concrete terms by describing two fundamental problems that must be solved well in any general purpose MPA. From this, we systematically develop the required logical organization of an MPA node, and present some details of * T (pronounced Start , a concrete architecture designed to these requirements. * T is a direct descendant of dynamic dataflow architectures, and unifies them with von Neumann architectures. We discuss a hand-compiled example and some compilation issues.


title: Capacity Constrained Routing Algorithms for Evacuation Planning: A Summary of Results

abstract: Evacuation planning is critical for numerous important applications, e.g. disaster emergency management and homeland defense preparation. Efficient tools are needed to produce evacuation plans that identify routes and schedules to evacuate affected populations to safety in the event of natural disasters or terrorist attacks. The existing linear programming approach uses time-expanded networks to compute the optimal evacuation plan and requires a user-provided upper bound on evacuation time. It suffers from high computational cost and may not scale up to large transportation networks in urban scenarios. In this paper we present a heuristic algorithm, namely Capacity Constrained Route Planner(CCRP), which produces sub-optimal solution for the evacuation planning problem. CCRP models capacity as a time series and uses a capacity constrained routing approach to incorporate route capacity constraints. It addresses the limitations of linear programming approach by using only the original evacuation network and it does not require prior knowledge of evacuation time. Performance evaluation on various network configurations shows that the CCRP algorithm produces high quality solutions, and significantly reduces the computational cost compared to linear programming approach that produces optimal solutions. CCRP is also scalable to the number of evacuees and the size of the network.


title: A rubric for assessing teachers' lesson activities with respect to TPACK for meaningful learning with ICT

abstract: Teachers' technological pedagogical content knowledge (TPACK) for meaningful learning with ICT describes their knowledge for designing ICT lesson activities with respect to five dimensions: active, constructive, authentic, intentional, and cooperative. The ICT lesson activities designed by teachers can be assessed to determine the strengths and weaknesses of their TPACK for meaningful learning with ICT in practice. This study describes the conception, validation, and implementation of a rubric for assessing ICT lesson activities with respect to the dimensions of meaningful learning with ICT. It was conducted with 55 Singaporean pre-service teachers trained to teach Chinese as a second language. The 217 lesson activities they designed during a compulsory ICT module were rated using the rubric. High ratings were obtained for the active dimension because the activities involved students using and manipulating ICT tools. However, the ratings for the other dimensions were lower because opportunities for students' personal meaning-making, exploration of real-world phenomena, collaboration through divergent knowledge construction, as well as self-diagnosis and management of learning gaps were not as well supported in these activities. The uses of this rubric for enhancing TPACK assessment, ICT course design and the development of TPACK activity types will be discussed in the paper.


title: User-centred versus system-centred evaluation of a personalization system

abstract: Some of the most popular measures to evaluate information filtering systems are usually independent of the users because they are based in relevance judgments obtained from experts. On the other hand, the user-centred evaluation allows showing the different impressions that the users have perceived about the system running. This work is focused on discussing the problem of user-centred versus system-centred evaluation of a Web content personalization system where the personalization is based on a user model that stores long term (section, categories and keywords) and short term interests (adapted from user provided feedback). The user-centred evaluation is based on questionnaires filled in by the users before and after using the system and the system-centred evaluation is based on the comparison between ranking of documents, obtained from the application of a multi-tier selection process, and binary relevance judgments collected previously from real users. The user-centred and system-centred evaluations performed with 106 users during 14 working days have provided valuable data concerning the behaviour of the users with respect to issues such as document relevance or the relative importance attributed to different ways of personalization. The results obtained shows general satisfaction on both the personalization processes (selection, adaptation and presentation) and the system as a whole.


title: A Simple Method for Measurement of Femoral Anteversion-Validation and Assessment of Reproducibility.

abstract: OBJECTIVES ::: To propose a simple method for measurement of femoral anteversion (AV) with a conventional fluoroscope, to evaluate the interobserver and intraobserver reliability of this method on the basis of human cadaver femurs, and to validate such method on the basis of computed tomography (CT). ::: ::: ::: METHODS ::: Fourteen human cadavers with 28 intact femurs were included in the study. Three blinded observers measured femoral AV of the specimen with a fluoroscope. The session was repeated 8 weeks later and CT of the femurs were performed. Mean AV values and the difference between single and mean AV values were calculated. Interobserver/intraobserver reliability of the proposed method was assessed. Correlation between AV values measured with fluoroscope and CT was calculated. ::: ::: ::: RESULTS ::: Mean AV was 18.1° when measured with fluoroscope and 19.2° when measured with CT. Mean differences between single AV values were 2.2° with fluoroscope and 2.5° with CT. The mean maximum observer variation per specimen was 8.8°. Interobserver reliability was excellent (intraclass correlation coefficient: 0.853) and intraobserver reliability was good (intraclass correlation coefficient: 0.682). A high correlation was found between AV values measured with fluoroscope and CT (rho = 0.739, P < 0.01). ::: ::: ::: CONCLUSION ::: The presented technique allows reliable and simple measurement of femoral AV with a conventional fluoroscope. The mean interobserver variation is comparable to what has been reported for CT. Maximum interobserver variation was <15° in all specimens. A clinical study will be necessary to prove the value of this technique for intraoperative adjustment of femoral AV according to the intact contralateral side.


title: The turn: Integration of information seeking and retrieval in context Springer.

abstract: T The Turn analyzes the research of information seeking and retrieval (ISR search and works tasks and situations in context; interaction between humans and machines; information acquisition, relevance and information use; research design and methodology based on a structured set of explicit variables - all set into the holistic cognitive approach. The present monograph invites the reader into a construction project - there is much research to do for a contextual understanding of IS&R. The Turn represents a wide-ranging perspective of IS&R by providing a novel unique research framework, covering both individual and social aspects of information behavior, including the generation, searching, retrieval and use of information. Regarding traditional laboratory information retrieval research, the monograph proposes the extension of research toward actors, search and work tasks, IR interaction and utility of information. Regarding traditional information seeking research, it proposes the extension toward information access technology and work task contexts. The Turn is the first synthesis of research in the broad area of IS&R ranging from systems oriented laboratory IR research to social science oriented information seeking studies.


title: Application of Big Data, Fast Data, and Data Lake Concepts to Information Security Issues

abstract: Today we witness the appearance of some additional to Big Data concepts: data lakes and fast data. Are they simply the new marketing labels for the old Big Data IT or really new ones? Thus the key goal of the paper is to identify the relationship between these three concepts, giving special attention to their application to information security (IS) issues. The reason lies in the fact that volumes of IS-related information is one thing, but the real problem for securing enterprises' IT infrastructure assets is the speed with which things related to IS happen.


abstract: Title. Caring for a relative with dementia: family caregiver burden ::: ::: Aim. This paper is a report of part of a study to investigate the burden experienced by families giving care to a relative with dementia, the consequences of care for the mental health of the primary caregiver and the strategies families use to cope with the care giving stressors. ::: ::: Background. The cost of caring for people with dementia is enormous, both monetary and psychological. Partners, relatives and friends who take care of patients experience emotional, physical and financial stress, and care giving demands are central to decisions on patient institutionalization. ::: ::: Method. A volunteer sample of 172 caregiver/care recipient dyads participated in the study in Cyprus in 2004–2005. All patients were suffering from probable Alzheimer's type dementia and were recruited from neurology clinics. Data were collected using the Memory and Behaviour Problem Checklist, Burden Interview, Center for Epidemiological Studies-Depression scale and Ways of Coping Questionnaire. ::: ::: Findings. The results showed that 68·02% of caregivers were highly burdened and 65% exhibited depressive symptoms. Burden was related to patient psychopathology and caregiver sex, income and level of education. There was no statistically significant difference in level of burden or depression when patients lived in the community or in institutions. High scores in the burden scale were associated with use of emotional-focused coping strategies, while less burdened relatives used more problem-solving approaches to care-giving demands. ::: ::: Conclusion. Caregivers, especially women, need individualized, specific training in how to understand and manage the behaviour of relatives with dementia and how to cope with their own feelings.


title: The development of Honda humanoid robot

abstract: In this paper, we present the mechanism, system configuration, basic control algorithm and integrated functions of the Honda humanoid robot. Like its human counterpart, this robot has the ability to move forward and backward, sideways to the right or the left, as well as diagonally. In addition, the robot can turn in any direction, walk up and down stairs continuously. Furthermore, due to its unique posture stability control, the robot is able to maintain its balance despite unexpected complications such as uneven ground surfaces. As a part of its integrated functions, this robot is able to move on a planned path autonomously and to perform simple operations via wireless teleoperation.


title: Deep Multimetric Learning for Shape-Based 3D Model Retrieval

abstract: Recently, feature-learning-based 3D shape retrieval methods have been receiving more and more attention in the 3D shape analysis community. In these methods, the hand-crafted metrics or the learned linear metrics are usually used to compute the distances between shape features. Since there are complex geometric structural variations with 3D shapes, the single hand-crafted metric or learned linear metric cannot characterize the manifold, where 3D shapes lie well. In this paper, by exploring the nonlinearity of the deep neural network and the complementarity among multiple shape features, we propose a novel deep multimetric network for 3D shape retrieval. The developed multimetric network minimizes a discriminative loss function that, for each type of shape feature, the outputs of the network from the same class are encouraged to be as similar as possible and the outputs from different classes are encouraged to be as dissimilar as possible. Meanwhile, the Hilbert-Schmidt independence criterion is employed to enforce the outputs of different types of shape features to be as complementary as possible. Furthermore, the weights of the learned multiple distance metrics can be adaptively determined in our developed deep metric network. The weighted distance metric is then used as the similarity for shape retrieval. We conduct experiments with the proposed method on the four benchmark shape datasets. Experimental results demonstrate that the proposed method can obtain better performance than the learned deep single metric and outperform the state-of-the-art 3D shape retrieval methods.


title: Sources of differences in communication apprehension between Chinese in Taiwan and Americans

abstract: This study investigated whether cross‐cultural differences in communication apprehension (CA) can be explained with regard to cultural orientations, personality traits and component theory. To this end, a total of 618 undergraduates, studying in Taiwan (n = 298) and the United States (n = 320), participated in this study. Participants filled out the Personal Report of Communication Apprehension along with Self‐Construal Scale, the Revised NEO Personality Inventory, Fear of Negative Evaluation, and Self‐Perceived Communication Competence scale. The results indicated that Chinese in Taiwan scored significantly higher in communication apprehension than Americans. The influence of culture on CA was mediated by independence self‐construal, neuroticism, extroversion, fear of negative evaluation, and communication competence. The strongest mediating effect was found for self‐perception of communication competence. The implications of these findings were further discussed.


title: Normalizing Flows on Riemannian Manifolds

abstract: We consider the problem of density estimation on Riemannian manifolds. Density estimation on manifolds has many applications in fluid-mechanics, optics and plasma physics and it appears often when dealing with angular variables (such as used in protein folding, robot limbs, gene-expression) and in general directional statistics. In spite of the multitude of algorithms available for density estimation in the Euclidean spaces $\mathbf{R}^n$ that scale to large n (e.g. normalizing flows, kernel methods and variational approximations), most of these methods are not immediately suitable for density estimation in more general Riemannian manifolds. We revisit techniques related to homeomorphisms from differential geometry for projecting densities to sub-manifolds and use it to generalize the idea of normalizing flows to more general Riemannian manifolds. The resulting algorithm is scalable, simple to implement and suitable for use with automatic differentiation. We demonstrate concrete examples of this method on the n-sphere $\mathbf{S}^n$.


title: High power, high efficiency E-band GaN amplifier MMICs

abstract: An advanced high power, high frequency GaN semiconductor process has been used to design high performance E-band power amplifier MMICs demonstrating unprecedented output power, PAE and reliability. To the author's knowledge, these power amplifier designs demonstrate performance beyond previously published results for E-band power amplifier MMICs.


title: Improved keyword spotting based on keyword/garbage models

abstract: We propose two simple methods to improve the performance of a keyword spotting system. In our application, the users are allowed to change the keywords anytime if they want. Thus we focused on phone-based GMM-HMM models since they do not require keyword-specific training data. However, the GMM-HMM based models usually have very high false alarm rate, i.e., a keyword is not present but the system gives a positive decision. We found that we can utilize the uncertainty of the system when a non-keyword is presented. Two simple methods are proposed to incorporate the uncertainty into the confidence measure. Our experiments show that these two methods can substantially reduce the false alarm rate from 75.05% to 5.71%. Meanwhile, the false reject rate increases from 1.04% to 5.71%.


title: Microscopy refocusing and dark-field imaging by using a simple LED array

abstract: The condenser is one of the main components in most transmitted light compound microscopes. In this Letter, we show that such a condenser can be replaced by a programmable LED array to achieve greater imaging flexibility and functionality. Without mechanically scanning the sample or changing the microscope setup, the proposed approach can be used for dark-field imaging, bright-field imaging, microscopy sectioning, and digital refocusing. Images of a starfish embryo were acquired by using such an approach for demonstration.


title: Adversarial hierarchical-task network planning for complex real-time games

abstract: Real-time strategy (RTS) games are hard from an AI point of view because they have enormous state spaces, combinatorial branching factors, allow simultaneous and durative actions, and players have very little time to choose actions. For these reasons, standard game tree search methods such as alphabeta search or Monte Carlo Tree Search (MCTS) are not sufficient by themselves to handle these games. This paper presents an alternative approach called Adversarial Hierarchical Task Network (AHTN) planning that combines ideas from game tree search with HTN planning. We present the basic algorithm, relate it to existing adversarial hierarchical planning methods, and present new extensions for simultaneous and durative actions to handle RTS games. We also present empirical results for the µRTS game, comparing it to other state of the art search algorithms for RTS games.


title: Adopting An Extended UTAUT2 To Predict Consumer Adoption Of M-Technologies In Saudi Arabia

abstract: M-Internet and M-Government have received a considerable adoption rate among Saudi users. Drawing on the perspectives of the Unified Theory of Acceptance and Use of Technology (UTAUT2), trust, and perceived risk, this research examines the consumer adoption of M-Internet and MGovernment. This study will reflect upon a number of studies in different countries that tested UTAUT2 variables in addition to trust and perceived risk. The outcomes show that usage intention is influenced by enablers such as effort expectancy, performance expectancy and trust as well as inhibitors such as perceived risk. The findings of this study provide several important implications for M-Internet and MGovernment service providers and researchers in the Kingdom of Saudi Arabia such as rising concerns on trust and perceived risk in order to ease consumer adoption of M-Internet and M-Government.


title: Better Hypothesis Testing for Statistical Machine Translation: Controlling for Optimizer Instability

abstract: In statistical machine translation, a researcher seeks to determine whether some innovation (e.g., a new feature, model, or inference algorithm) improves translation quality in comparison to a baseline system. To answer this question, he runs an experiment to evaluate the behavior of the two systems on held-out data. In this paper, we consider how to make such experiments more statistically reliable. We provide a systematic analysis of the effects of optimizer instability---an extraneous variable that is seldom controlled for---on experimental outcomes, and make recommendations for reporting results more accurately.


title: A New Approach to Linear Filtering and Prediction Problems

abstract: The clssical filleting and prediclion problem is re-examined using the Bode-Shannon representation of random processes and the ?stat-tran-sition? method of analysis of dynamic systems. New result are: (1) The formulation and Methods of solution of the problm apply, without modification to stationary and nonstationary stalistics end to growing-memory and infinile -memory filters. (2) A nonlinear difference (or differential) equalion is dericed for the covariance matrix of the optimal estimalion error. From the solution of this equation the coefficients of the difference, (or differential) equation of the optimal linear filter are obtained without further caleulations. (3) Tke fillering problem is shoum to be the dual of the nois-free regulator problem. The new method developed here, is applied to do well-known problems, confirming and extending, earlier results. The discussion is largely, self-contatained, and proceeds from first principles; basic concepts of the theory of random processes are reviewed in the Appendix.


title: Integration of Aerial Thermal Imagery, LiDAR Data and Ground Surveys for Surface Temperature Mapping in Urban Environments

abstract: A single-band surface temperature retrieval method is proposed, aiming at achieving a better accuracy by exploiting the integration of aerial thermal images with LiDAR data and ground surveys. LiDAR data allow the generation of a high resolution digital surface model and a detailed modeling of the Sky-View Factor (SVF). Ground surveys of surface temperature and emissivity, instead, are used to estimate the atmospheric parameters involved in the model (through a bounded least square adjustment) and for a first assessment of the accuracy of the results. The RMS of the difference between the surface temperatures computed from the model and measured on the check sites ranges between 0.8 °C and 1.0 °C, depending on the algorithm used to calculate the SVF. Results are in general better than the ones obtained without considering SVF and prove the effectiveness of the integration of different data sources. The proposed approach has the advantage of avoiding the modeling of the atmosphere conditions, which is often difficult to achieve with the desired accuracy; on the other hand, it is highly dependent on the accuracy of the data measured on the ground.


Labial Fusion: A Cause of Recurrent Urinary Tract Infections


title: ATOM: Automated tracking, orchestration and monitoring of resource usage in infrastructure as a service systems

abstract: We present ATOM, an efficient and effective framework to enable automated tracking, monitoring, and orchestration of resource usage in an Infrastructure as a Service (IaaS) system. We design a novel tracking method to continuously track important performance metrics with low overhead, and develop a principal component analysis (PCA) based approach with quality guarantees to continuously monitor and automatically find anomalies based on the approximate tracking results. Lastly, when potential anomalies are identified, we use introspection tools to perform memory forensics on virtual machines (VMs) to identify malicious behavior inside a VM. We deploy ATOM in an IaaS system to monitor VM resource usage, and to detect anomalies. Various attacks are used as examples to demonstrate how ATOM is both effective and efficient to track and monitor resource usage, detect anomalies, and orchestrate system resource usage.


title: Direct and Indirect Aggression During Childhood and Adolescence: A Meta-Analytic Review of Gender Differences, Intercorrelations, and Relations to Maladjustment

abstract: This meta-analytic review of 148 studies on child and adolescent direct and indirect aggression examined the magnitude of gender differences, intercorrelations between forms, and associations with maladjustment. Results confirmed prior findings of gender differences (favoring boys) in direct aggression and trivial gender differences in indirect aggression. Results also indicated a substantial intercorrelation (= .76) between these forms. Despite this high intercorrelation, the 2 forms showed unique associations with maladjustment: Direct aggression is more strongly related to externalizing problems, poor peer relations, and low prosocial behavior, and indirect aggression is related to internalizing problems and higher prosocial behavior. Moderation of these effect sizes by method of assessment, age, gender, and several additional variables were systematically investigated.


Performance Analysis of FDA Based Face Recognition Using Correlation , ANN and SVM


TO VISION , COLOUR MODELS AND IMAGE COMPRESSION


1 Automatic keyword extraction from individual documents


title: A history of chaos theory

abstract: Whether every effect can be precisely linked to a given cause or to a list of causes has been a matter of debate for centuries, particularly during the 17th century when astronomers became capable of predicting the trajectories of planets. Recent mathematical models applied to physics have included the idea that given phenomena cannot be predicted precisely although they can be predicted to some extent in line with the chaos theory Concepts such as deterministic models, sensitivity to initial conditions, strange attractors, and fractal dimensions are inherent to the development of this theory, A few situations involving normal or abnormal endogenous rhythms in biology have been analyzed following the principles of chaos theory This is particularly the case with cardiac arrhythmias, but less so with biological clocks and circadian rhythms.


title: Memory Interference as a Determinant of Language Comprehension

abstract: The parameters of the human memory system constrain the operation of language comprehension processes. In the memory literature, both decay and interference have been proposed as causes of forgetting; however, while there is a long history of research establishing the nature of interference effects in memory, the effects of decay are much more poorly supported. Nevertheless, research investigating the limitations of the human sentence processing mechanism typically focus on decay-based explanations, emphasizing the role of capacity, while the role of interference has received comparatively little attention. This paper reviews both accounts of difficulty in language comprehension by drawing direct connections to research in the memory domain. Capacity-based accounts are found to be untenable, diverging substantially from what is known about the operation of the human memory system. In contrast, recent research investigating comprehension difficulty using a retrieval-interference paradigm is shown to be wholly consistent with both behavioral and neuropsychological memory phenomena. The implications of adopting a retrievalinterference approach to investigating individual variation in language comprehension are discussed.


title: Big Data Processing in Cloud Computing Environments

abstract: With the rapid growth of emerging applications like social network analysis, semantic Web analysis and bioinformatics network analysis, a variety of data to be processed continues to witness a quick increase. Effective management and analysis of large-scale data poses an interesting but critical challenge. Recently, big data has attracted a lot of attention from academia, industry as well as government. This paper introduces several big data processing technics from system and application aspects. First, from the view of cloud data management and big data processing mechanisms, we present the key issues of big data processing, including cloud computing platform, cloud architecture, cloud database and data storage scheme. Following the Map Reduce parallel processing framework, we then introduce Map Reduce optimization strategies and applications reported in the literature. Finally, we discuss the open issues and challenges, and deeply explore the research directions in the future on big data processing in cloud computing environments.


title: A systematic comparison between visual cues for boundary detection

abstract: The detection of object boundaries is a critical first step for many visual processing tasks. Multiple cues (we consider luminance, color, motion and binocular disparity) available in the early visual system may signal object boundaries but little is known about their relative diagnosticity and how to optimally combine them for boundary detection. This study thus aims at understanding how early visual processes inform boundary detection in natural scenes. We collected color binocular video sequences of natural scenes to construct a video database. Each scene was annotated with two full sets of ground-truth contours (one set limited to object boundaries and another set which included all edges). We implemented an integrated computational model of early vision that spans all considered cues, and then assessed their diagnosticity by training machine learning classifiers on individual channels. Color and luminance were found to be most diagnostic while stereo and motion were least. Combining all cues yielded a significant improvement in accuracy beyond that of any cue in isolation. Furthermore, the accuracy of individual cues was found to be a poor predictor of their unique contribution for the combination. This result suggested a complex interaction between cues, which we further quantified using regularization techniques. Our systematic assessment of the accuracy of early vision models for boundary detection together with the resulting annotated video dataset should provide a useful benchmark towards the development of higher-level models of visual processing.


title: ConTagNet: Exploiting User Context for Image Tag Recommendation

abstract: In recent years, deep convolutional neural networks have shown great success in single-label image classification. However, images usually have multiple labels associated with them which may correspond to different objects or actions present in the image. In addition, a user assigns tags to a photo not merely based on the visual content but also the context in which the photo has been captured. Inspired by this, we propose a deep neural network which can predict multiple tags for an image based on the content as well as the context in which the image is captured. The proposed model can be trained end-to-end and solves a multi-label classification problem. We evaluate the model on a dataset of 1,965,232 images which is drawn from the YFCC100M dataset provided by the organizers of Yahoo-Flickr Grand Challenge. We observe a significant improvement in the prediction accuracy after integrating user-context and the proposed model performs very well in the Grand Challenge.


title: Real-Time Adaptive Image Compression

abstract: We present a machine learning-based approach to lossy image compression which outperforms all existing codecs, while running in real-time. ::: Our algorithm typically produces files 2.5 times smaller than JPEG and JPEG 2000, 2 times smaller than WebP, and 1.7 times smaller than BPG on datasets of generic images across all quality levels. At the same time, our codec is designed to be lightweight and deployable: for example, it can encode or decode the Kodak dataset in around 10ms per image on GPU. ::: Our architecture is an autoencoder featuring pyramidal analysis, an adaptive coding module, and regularization of the expected codelength. We also supplement our approach with adversarial training specialized towards use in a compression setting: this enables us to produce visually pleasing reconstructions for very low bitrates.


title: From Freebase to Wikidata: The Great Migration

abstract: Collaborative knowledge bases that make their data freely available in a machine-readable form are central for the data strategy of many projects and organizations. The two major collaborative knowledge bases are Wikimedia's Wikidata and Google's Freebase. Due to the success of Wikidata, Google decided in 2014 to offer the content of Freebase to the Wikidata community. In this paper, we report on the ongoing transfer efforts and data mapping challenges, and provide an analysis of the effort so far. We describe the Primary Sources Tool, which aims to facilitate this and future data migrations. Throughout the migration, we have gained deep insights into both Wikidata and Freebase, and share and discuss detailed statistics on both knowledge bases.


title: Deep Image Prior

abstract: Deep convolutional networks have become a popular tool for image generation and restoration. Generally, their excellent performance is imputed to their ability to learn realistic image priors from a large number of example images. In this paper, we show that, on the contrary, the structure of a generator network is sufficient to capture a great deal of low-level image statistics prior to any learning. In order to do so, we show that a randomly-initialized neural network can be used as a handcrafted prior with excellent results in standard inverse problems such as denoising, super-resolution, and inpainting. Furthermore, the same prior can be used to invert deep neural representations to diagnose them, and to restore images based on flash-no flash input pairs. ::: Apart from its diverse applications, our approach highlights the inductive bias captured by standard generator network architectures. It also bridges the gap between two very popular families of image restoration methods: learning-based methods using deep convolutional networks and learning-free methods based on handcrafted image priors such as self-similarity. Code and supplementary material are available at this https URL .


title: Genetically Improved PSO Algorithm for Efficient Data Clustering

abstract: Clustering is an important research topic in data mining that appears in a wide range of unsupervised classification applications. Partitional clustering algorithms such as the k-means algorithm are the most popular for clustering large datasets. The major problem with the k-means algorithm is that it is sensitive to the selection of the initial partitions and it may converge to local optima. In this paper, we present a hybrid two-phase GAI-PSO+k-means data clustering algorithm that performs fast data clustering and can avoid premature convergence to local optima. In the first phase we utilize the new genetically improved particle swarm optimization algorithm (GAI-PSO) which is a population-based heuristic search technique modeled on the hybrid of cultural and social rules derived from the analysis of the swarm intelligence (PSO) and the concepts of natural selection and evolution (GA). The GAI-PSO combines the standard velocity and position update rules of PSOs with the ideas of selection, mutation and crossover from GAs. The GAI-PSO algorithm searches the solution space to find the optimal initial cluster centroids for the next phase. The second phase is a local refining stage utilizing the k-means algorithm which can efficiently converge to the optimal solution. The proposed algorithm combines the ability of the globalized searching of the evolutionary algorithms and the fast convergence of the k-means algorithm and can avoid the drawback of both. The performance of the proposed algorithm is evaluated through several benchmark datasets. The experimental results show that the proposed algorithm is highly forceful and outperforms the previous approaches such as SA, ACO, PSO and k-means for the partitional clustering problem.


title: Use of alternative and complementary therapies in labor and delivery care: a cross-sectional study of midwives’ training in Catalan hospitals accredited as centers for normal birth

abstract: BACKGROUND ::: The use of complementary and alternative medicine (CAM) and complementary and alternative therapies (CAT) during pregnancy is increasing. Scientific evidence for CAM and CAT in the field of obstetrics mainly covers pain relief in labor. Midwives are responsible for labor and delivery care: hence, their knowledge of CAM and CAT is important. The aims of this study are to describe the professional profile of midwives who provide care for natural childbirth in Catalan hospitals accredited as centers for normal birth, to assess midwives' level of training in CAT and their use of these therapies, and to identify specific resources for CAT in labor wards. ::: ::: ::: METHODS ::: A descriptive, cross-sectional, quantitative method was used to assess the level of training and use of CAT by midwives working at 28 hospitals in Catalonia, Spain, accredited as public normal birth centers. ::: ::: ::: RESULTS ::: Just under a third of midwives (30.4%) trained in CAT after completion of basic training. They trained in an average of 5.97 therapies (SD 3.56). The number of CAT in which the midwives were trained correlated negatively with age (r = - 0.284; p < 0.001) and with their time working at the hospital in years (r = - 0.136; p = 0.036). Midwives trained in CAT considered that the following therapies were useful or very useful for pain relief during labor and delivery: relaxation techniques (64.3%), hydrotherapy (84.8%) and the application of compresses to the perineum (75.9%). The availability of resources for providing CAT during normal birth care varied widely from center to center. ::: ::: ::: CONCLUSIONS ::: Age may influence attitudes towards training. It is important to increase the number of midwives trained in CAM for pain relief during childbirth, in order to promote the use of CAT and ensure efficiency and safety. CAT resources at accredited hospitals providing normal childbirth care should also be standardized.


title: Foolbox v0.8.0: A Python toolbox to benchmark the robustness of machine learning models

abstract: Even todays most advanced machine learning models are easily fooled by almost imperceptible perturbations of their inputs. Foolbox is a new Python package to generate such adversarial perturbations and to quantify and compare the robustness of machine learning models. It is build around the idea that the most comparable robustness measure is the minimum perturbation needed to craft an adversarial example. To this end, Foolbox provides reference implementations of most published adversarial attack methods alongside some new ones, all of which perform internal hyperparameter tuning to find the minimum adversarial perturbation. Additionally, Foolbox interfaces with most popular deep learning frameworks such as PyTorch, Keras, TensorFlow, Theano and MXNet and allows different adversarial criteria such as targeted misclassification and top-k misclassification as well as different distance measures. The code is licensed under the MIT license and is openly available at this https URL . The most up-to-date documentation can be found at this http URL .


title: Multiple Object Tracking: A Review

abstract: Multiple Object Tracking (MOT) is an important computer vision task which has gained increasing attention due to its academic and commercial potential. Although different kinds of approaches have been proposed to tackle this problem, it still has many issues unsolved. For example, factors such as continuous appearance changes and severe occlusions result in difficulties for the task. In order to help the readers understand and learn this topic, we contribute a comprehensive and systematic review. We review the recent advances in various aspects about this topic and propose some interesting directions for future research. ::: To our best knowledge, there has not been any review about this topic in the community. The main contribution of this review is threefold: 1) All key aspects in the multiple object tracking system, including what scenarios the researchers are working on, how their work can be categorized, what needs to be considered when developing a MOT system and how to evaluate a MOT system, are discussed in a clear structure. This review work could not only provide researchers, especially new comers to the topic of MOT, a general understanding of the state-of-the-arts, but also help them to comprehend the aspects of a MOT system and the inter-connected aspects. 2) Instead of listing and summarizing individual publications, we categorize the approaches in the key aspects involved in a MOT system. In each aspect, the methods are divided into different groups and each group is discussed in details for the principles, advances and drawbacks. 3) We provide some potential directions with insights for MOT, which are still open issues and need more research efforts. This would be helpful for researchers to identify further interesting problems.


title: Friction modeling in linear chemical-mechanical planarization

abstract: In this article, we develop an analytical model of the relationship between the wafer/pad friction and process configuration. We also provide experimental validation of this model for in situ process monitoring. CMP thus demonstrates that the knowledge and methodologies developed for friction modeling and control can be used to advance the understanding, monitoring, and control of semiconductor manufacturing processes. Meanwhile, relevant issues and challenges in real-time monitoring of CMP are presented as sources of future development.


title: A new TCP/AQM for stable operation in fast networks

abstract: This paper is aimed at designing a congestion control system that scales gracefully with network capacity, providing high utilization, low queueing delay, dynamic stability, and fairness among users. In earlier work we had developed fluid-level control laws that achieve the first three objectives for arbitrary networks and delays, but were forced to constrain the resource allocation policy. In this paper we extend the theory to include dynamics at TCP sources, preserving the earlier features at fast time-scales, but permitting sources to match their steady-state preferences, provided a bound on round-trip-times is known. We develop two packet-level implementations of this protocol, using (i) ECN marking, and (ii) queueing delay, as means of communicating the congestion measure from links to sources. We discuss parameter choices and demonstrate using ns-2 simulations the stability of the protocol and its equilibrium features in terms of utilization, queueing and fairness. We also demonstrate the scalability of these features to increases in capacity, delay, and load, in comparison with other deployed and proposed protocols.


title: Pedestrian classification in automotive radar systems

abstract: Urban traffic is dangerous for all car drivers and especially for pedestrians. There are about 5000 fatalities on German streets every year, which is absolutely too much. Human beings have strong limitations in the ability to measure precisely the distance and the speed difference between cars, which is the reason for several accidents. Therefore, the European Union has called all car manufactureres to intensify their research activities in protecting vulnerable road users. An automotive radar sensor measures the target range and radial velocity very accurately and with high resolution even in multiple target situations and there is tremendous progress in 24 GHz radar sensor development. The objective of this paper is to protect vulnerable road users by a target recognition scheme in any urban scenario. One main objective is the difference between lateral moving vehicles and pedestrians in terms of feature extraction and classification. Therefore, a pedestrian detection procedure is additionally integrated into the 24 GHz automotive radar sensor.


title: Learning to Perform Physics Experiments via Deep Reinforcement Learning

abstract: When encountering novel objects, humans are able to infer a wide range of physical properties such as mass, friction and deformability by interacting with them in a goal driven way. This process of active interaction is in the same spirit as a scientist performing experiments to discover hidden facts. Recent advances in artificial intelligence have yielded machines that can achieve superhuman performance in Go, Atari, natural language processing, and complex control problems; however, it is not clear that these systems can rival the scientific intuition of even a young child. In this work we introduce a basic set of tasks that require agents to estimate properties such as mass and cohesion of objects in an interactive simulated environment where they can manipulate the objects and observe the consequences. We found that state of art deep reinforcement learning methods can learn to perform the experiments necessary to discover such hidden properties. By systematically manipulating the problem difficulty and the cost incurred by the agent for performing experiments, we found that agents learn different strategies that balance the cost of gathering information against the cost of making mistakes in different situations.


title: HCI and Autonomous Vehicles: Contextual Experience Informs Design

abstract: The interaction between drivers and their cars will change significantly with the introduction of autonomous vehicles. The driver's role will shift towards a supervisory control of their autonomous vehicle. The eventual relief from the driving task enables a complete new area of research and practice in human-computer interaction and interaction design. In this one-day workshop, participants will explore the opportunities the design space of autonomous driving will bring to HCI researchers and designers. On the day before workshop participants are invited to visit (together with workshop organizers) Google Partnerplex and Stanford University. At Google participants will have the opportunity to explore Google's autonomous car simulator and might have the chance to experience one of the Google Cars (if available). At Stanford participants are invited to ride in a Wizard-of-Oz autonomous vehicle. Based on this first-hand experience we will discuss design approaches and prototype interaction systems during the next day's workshop. The outcome of this workshop will be a set of concepts, interaction sketches, and low-fidelity paper prototypes that address constraints and potentials of driving in an autonomous car.


title: Sanitization’s Slippery Slope: The Design and Study of a Text Revision Assistant

abstract: For privacy reasons, sensitive content may be revised before it is released. The revision often consists of redaction, that is, the "blacking out" of sensitive words and phrases. Redaction has the side effect of reducing the utility of the content, often so much that the content is no longer useful. Consequently, government agencies and others are increasingly exploring the revision of sensitive content as an alternative to redaction that preserves more content utility. We call this practice sanitization. In a sanitized document, names might be replaced with pseudonyms and sensitive attributes might be replaced with hypernyms. Sanitization adds to redaction the challenge of determining what words and phrases reduce the sensitivity of content. We have designed and developed a tool to assist users in sanitizing sensitive content. Our tool leverages the Web to automatically identify sensitive words and phrases and quickly evaluates revisions for sensitivity. The tool, however, does not identify all sensitive terms and mistakenly marks some innocuous terms as sensitive. This is unavoidable because of the difficulty of the underlying inference problem and is the main reason we have designed a sanitization assistant as opposed to a fully-automated tool. We have conducted a small study of our tool in which users sanitize biographies of celebrities to hide the celebrity's identity both both with and without our tool. The user study suggests that while the tool is very valuable in encouraging users to preserve content utility and can preserve privacy, this usefulness and apparent authoritativeness may lead to a "slippery slope" in which users neglect their own judgment in favor of the tool's.


title: Resource allocation games with changing resource capacities

abstract: In this paper we study a class of resource allocation games which are inspired by the El Farol Bar problem. We consider a system of competitive agents that have to choose between several resources characterized by their time dependent capacities. The agents using a particular resource are rewarded if their number does not exceed the resource capacity, and punished otherwise. Agents use a set of strategies to decide what resource to choose, and use a simple reinforcement learning scheme to update the accuracy of strategies. A strategy in our model is simply a lookup table that suggests to an agent what resource to choose based on the actions of its neighbors at the previous time step. In other words, the agents form a social network whose connectivity controls the average number of neighbors with whom each agent interacts. This statement of the adaptive resource allocation problem allows us to fully parameterize it by a small set of numbers. We study the behavior of the system via numeric simulations of 100 to 5000 agents using one to ten resources. Our results indicate that for a certain range of parameters the system as a whole adapts effectively to the changing capacity levels and results in very little under- or over-utilization of the resources.