title: Facial beauty--establishing a universal standard.

abstract: There is a universal standard for facial beauty regardless of race, age, sex and other variables. Beautiful faces have ideal facial proportion. Ideal proportion is directly related to divine proportion, and that proportion is 1 to 1.618. All living organisms, including humans, are genetically encoded to develop to this proportion because there are extreme esthetic and physiologic benefits. The vast majority of us are not perfectly proportioned because of environmental factors. Establishment of a universal standard for facial beauty will significantly simplify the diagnosis and treatment of facial disharmonies and abnormalities. More important, treating to this standard will maximize facial esthetics, TMJ health, psychologic and physiologic health, fertility, and quality of life.


title: Arterial Stiffness and Risk of Coronary Heart Disease and Stroke: The Rotterdam Study

abstract: Background— Arterial stiffness has been associated with the risk of cardiovascular disease in selected groups of patients. We evaluated whether arterial stiffness is a predictor of coronary heart disease and stroke in a population-based study among apparently healthy subjects. Methods and Results— The present study included 2835 subjects participating in the third examination phase of the Rotterdam Study. Arterial stiffness was measured as aortic pulse wave velocity and carotid distensibility. Cox proportional hazards regression analysis was performed to compute hazard ratios. During follow-up, 101 subjects developed coronary heart disease (mean follow-up period, 4.1 years), and 63 subjects developed a stroke (mean follow-up period, 3.2 years). The risk of cardiovascular disease increased with increasing aortic pulse wave velocity index. Hazard ratios and corresponding 95% CIs of coronary heart disease for subjects in the second and third tertiles of the aortic pulse wave velocity index compared with subj...


title: Treatment of chronic pain by designer cells controlled by spearmint aromatherapy

abstract: Current treatment options for chronic pain are often associated with dose-limiting toxicities, or lead to drug tolerance or addiction. Here, we describe a pain management strategy, based on cell-engineering principles and inspired by synthetic biology, consisting of microencapsulated human designer cells that produce huwentoxin-IV (a safe and potent analgesic peptide that selectively inhibits the pain-triggering voltage-gated sodium channel NaV1.7) in response to volatile spearmint aroma and in a dose-dependent manner. Spearmint sensitivity was achieved by ectopic expression of the R-carvone-responsive olfactory receptor OR1A1 rewired via an artificial G-protein deflector to induce the expression of a secretion-engineered and stabilized huwentoxin-IV variant. In a model of chronic inflammatory and neuropathic pain, mice bearing the designer cells showed reduced pain-associated behaviour on oral intake or inhalation-based intake of spearmint essential oil, and absence of cardiovascular, immunogenic and behavioural side effects. Our proof-of-principle findings indicate that therapies based on engineered cells can achieve robust, tunable and on-demand analgesia for the long-term management of chronic pain.


Asymptomatic proteinuria in a child with recurrent urinary tract infections—answers


The Gene Ontology (GO) database and informatics


title: Two port reconfigurable CRLH leaky wave antenna with improved impedance matching and beam tuning

abstract: In this paper we propose a reconfigurable composite right left handed (CRLH) leaky wave antenna as a candidate solution for compact highly reconfigurable MIMO systems. The antenna design consists of a periodioc structure composed of cascaded CRLH microstrip unit cells with tunable electrical characteristics. Two separate ports are located on the same antenna structure such that a single physical antenna can be used as a two element array for reduced antenna space occupation on the communication device. The two element array allows for steering two beams symmetrically with respect to the broadside direction over a wide angular range. Ideally an infinite number of radiation patterns can be excited at each port while keeping good impedance matching and high isolation between the two ports. The gain achievable through this antenna varies between 0.7 and 6.2 dBi depending on the array configuration.


Categorization Using Improved k-Nearest neighbour Algorithm


title: Cryogenic 0.5–13 GHz low noise amplifier with 3 K mid-band noise temperature

abstract: A 0.5–13 GHz cryogenic MMIC low-noise amplifier (LNA) was designed and fabricated using a 130 nm InP HEMT process. A packaged LNA has been measured at both 300 K and 15 K. At 300 K the measured minimum noise temperature was 48 K at 7 GHz. At 15 K the measured minimum noise temperature was 3 K at 7 GHz and below 7 K within the entire 0.5–13 GHz band. The gain was between 34 dB and 40 dB at 300 K and between 38 dB and 44 dB at 4 K.


title: Class-D/DE Dual-Mode-Operation Resonant Converter for Improved-Efficiency Domestic Induction Heating System

abstract: Induction heating (IH) technology is nowadays widely present in domestic appliances because of its cleanness, high efficiency, and faster heating process. All of these advantages are due to its heating process, where the pot is directly heated by the induced currents generated with a varying magnetic field. As a result, the glass where the pot is supported is not directly heated and, consequently, efficiency and heating times are improved. IH systems are based on dc-link inverters to generate the required alternating current to feed the inductor. Usually, resonant converters are used to achieve higher efficiencies and power densities. In such systems, the maximum output power and efficiency are achieved at the resonant frequency, and the switching frequency is increased to reduce the output power. As a consequence, in these converters, the efficiency is also reduced in the low-medium output power range. This paper proposes the use of the half-bridge inverter in two operating modes to achieve higher efficiency in a wide output power range. The power converter topology can be reconfigured by changing the resonant capacitors through electromechanical relays. As a consequence, the entire efficiency of the cooking process is improved with a cost-effective procedure.


title: Task Modeling with Reusable Problem-Solving Methods

abstract: Problem-solving methods for knowledge-based systems establish the behavior of such systems by defining the roles in which domain knowledge is used and the ordering of inferences. Developers can compose problem-solving methods that accomplish complex application tasks from primitive, reusable methods. The key steps in this development approach are task analysis, method selection (from a library), and method configuration. Protege-ii is a knowledge-engineering environment that allows developers to select and configure problem-solving methods. In addition, Protege-ii generates domain-specific knowledge-acquisition tools that domain specialists can use to create knowledge bases on which the methods may operate. ::: ::: The board-game method is a problem-solving method that defines control knowledge for a class of tasks that developers can model in a highly specific way. The method adopts a conceptual model of problem solving in which the solution space is construed as a “game board” on which the problem solver moves “playing pieces” according to prespecified rules. This familiar conceptual model simplifies the developer's cognitive demands when configuring the board-game method to support new application tasks. We compare configuration of the board-game method to that of a chronological-backtracking problem-solving method for the same application tasks (for example, towers of Hanoi and the Sisyphus room-assignment problem). We also examine how method designers can specialize problem-solving methods by making ontological commitments to certain classes of tasks. We exemplify this technique by specializing the chronological-backtracking method to the board-game method.


title: Formal Analysis of Enhanced Authorization in the TPM 2.0

abstract: The Trusted Platform Module (TPM) is a system component that provides a hardware-based approach to establish trust in a platform by providing protected storage, robust platform integrity measurement, secure platform attestation and other secure functionalities. The access to TPM commands and TPM-resident key objects are protected via an authorization mechanism. Enhanced Authorization (EA) is a new mechanism introduced by the TPM 2.0 to provide a rich authorization model for specifying flexible access control policies for TPM-resident objects. In our paper, we conduct a formal verification of the EA mechanism. Firstly, we propose a model of the TPM 2.0 EA mechanism in a variant of the applied pi calculus. Secondly, we identify and formalize the security properties of the EA mechanism (Prop.1 and 2) in its design. We also give out a misuse problem that is easily to be neglected (Lemma 7). Thirdly, using the SAPIC tool and the tamarin prover, we have verified both the two security properties. Meanwhile, we have found 3 misuse cases and one of them leads to an attack on the application in [12].


title: Point cloud matching based on 3D self-similarity

abstract: Point cloud is one of the primitive representations of 3D data nowadays. Despite that much work has been done in 2D image matching, matching 3D points achieved from different perspective or at different time remains to be a challenging problem. This paper proposes a 3D local descriptor based on 3D self-similarities. We not only extend the concept of 2D self-similarity [1] to the 3D space, but also establish the similarity measurement based on the combination of geometric and photometric information. The matching process is fully automatic i.e. needs no manually selected land marks. The results on the LiDAR and model data sets show that our method has robust performance on 3D data under various transformations and noises.


title: A combined topical/non-topical approach to identifying web sites for children

abstract: Today children interact more and more frequently with information services. Especially in on-line scenarios there is a great amount of content that is not suitable for their age group. Due to the growing importance and ubiquity of the Internet in today's world, denying children any unsupervised Web access is often not possible. This work presents an automatic way of distinguishing web pages for children from those for adults in order to improve child-appropriate web search engine performance. A range of 80 different features based on findings from cognitive sciences and children's psychology are discussed and evaluated. We conducted a large scale user study on the suitability of web sites and give detailed information about the insights gained. Finally a comparison to traditional web classification methods as well as human annotator performance reveals that our automatic classifier can reach a performance close to that of human agreement.


title: The learning and use of traversability affordance using range images on a mobile robot

abstract: We are interested in how the concept of affordances can affect our view to autonomous robot control, and how the results obtained from autonomous robotics can be reflected back upon the discussion and studies on the concept of affordances. In this paper, we studied how a mobile robot, equipped with a 3D laser scanner, can learn to perceive the traversability affordance and use it to wander in a room tilled with spheres, cylinders and boxes. The results showed that after learning, the robot can wander around avoiding contact with non-traversable objects (i.e. boxes, upright cylinders, or lying cylinders in certain orientation), but moving over traversable objects (such as spheres, and lying cylinders in a rollable orientation with respect to the robot) rolling them out of its way. We have shown that for each action approximately 1% of the perceptual features were relevant to determine whether it is afforded or not and that these relevant features are positioned in certain regions of the range image. The experiments are conducted both using a physics-based simulator and on a real robot.


title: Improved method of classification algorithms for crime prediction

abstract: The growing availability of information technologies has enabled law enforcement agencies to collect detailed data about various crimes. Classification is the procedure of finding a model (or function) that depicts and distinguishes data classes or notions, with the end goal of having the ability to utilize the model to predict the crime labels. In this research classification is applied to crime dataset to predict the “crime category” for diverse states of the United States of America (USA). The crime data set utilized within this research is real in nature, it was gathered from socio-economic data from 1990 US census. Law enforcement data from 1990 US LEMAS survey, and from the 1995 FBI UCR. This paper compares two different classification algorithms namely - Naive Bayesian and Back Propagation (BP) for predicting “Crime Category” for distinctive states in USA. The result from the analysis demonstrated that Naive Bayesian calculation out performed BP calculation and attained the accuracy of 90.2207% for group 1 and 94.0822% for group 2. This clearly indicates that Naive Bayesian calculation is supportive for prediction in diverse states in USA.


title: Where does AlphaGo go: from church-turing thesis to AlphaGo thesis and beyond

abstract: An investigation on the impact and significance of the AlphaGo vs. Lee Sedol Go match is conducted, and concludes with a conjecture of the AlphaGo Thesis and its extension in accordance with the Church-Turing Thesis in the history of computing. It is postulated that the architecture and method utilized by the AlphaGo program provide an engineering solution for tackling issues in complexity and intelligence. Specifically, the AlphaGo Thesis implies that any effective procedure for hard decision problems such as NP-hard can be implemented with AlphaGo-like approach. Deep rule-based networks are proposed in attempt to establish an understandable structure for deep neural networks in deep learning. The success of AlphaGo and corresponding thesis ensure the technical soundness of the parallel intelligence approach for intelligent control and management of complex systems and knowledge automation.


title: A framework for learning predictive structures from multiple tasks and unlabeled data

abstract: One of the most important issues in machine learning is whether one can improve the performance of a supervised learning algorithm by including unlabeled data. Methods that use both labeled and unlabeled data are generally referred to as semi-supervised learning. Although a number of such methods are proposed, at the current stage, we still don't have a complete understanding of their effectiveness. This paper investigates a closely related problem, which leads to a novel approach to semi-supervised learning. Specifically we consider learning predictive structures on hypothesis spaces (that is, what kind of classifiers have good predictive power) from multiple learning tasks. We present a general framework in which the structural learning problem can be formulated and analyzed theoretically, and relate it to learning with unlabeled data. Under this framework, algorithms for structural learning will be proposed, and computational issues will be investigated. Experiments will be given to demonstrate the effectiveness of the proposed algorithms in the semi-supervised learning setting.


title: The politics of comments: predicting political orientation of news stories with commenters' sentiment patterns

abstract: Political views frequently conflict in the coverage of contentious political issues, potentially causing serious social problems. We present a novel social annotation analysis approach for identification of news articles' political orientation. The approach focuses on the behavior of individual commenters. It uncovers commenters' sentiment patterns towards political news articles, and predicts the political orientation from the sentiments expressed in the comments. It takes advantage of commenters' participation as well as their knowledge and intelligence condensed in the sentiment of comments, thereby greatly reduces the high complexity of political view identification. We conduct extensive study on commenters' behaviors, and discover predictive commenters showing a high degree of regularity in their sentiment patterns. We develop and evaluate sentiment pattern-based methods for political view identification.


title: A battery modular multilevel management system (BM3) for electric vehicles and stationary energy storage systems

abstract: The dependency of energy systems on battery storage systems is constantly increasing, but there are still several unsolved problems. Current battery systems are inflexible, only cells with the same electrical parameters can be combined, and cell defects cause a high reduction of the overall battery lifetime or even a system black out. In addition, the maximum usable capacity and the maximum charging current are limited by the weakest cell in the system. Current Battery Management Systems (BMS) can increase the usable battery capacity to some extend and are able to enlarge the maximum usable charging current. With the Battery Modular Multilevel Management System (BM3) presented in this paper, a very flexible, fault tolerant, and cost-efficient battery system can be implemented. With the system it is possible to establish either serial or parallel connections between neighboring cells or to bypass a cell. Thus the cells can be operated according to their needs and their state of charge (SOC). Separate balancing means for balancing the cells SOC, however, become obsolete.


title: Improving Transferability of Adversarial Examples With Input Diversity

abstract: Though CNNs have achieved the state-of-the-art performance on various vision tasks, they are vulnerable to adversarial examples --- crafted by adding human-imperceptible perturbations to clean images. However, most of the existing adversarial attacks only achieve relatively low success rates under the challenging black-box setting, where the attackers have no knowledge of the model structure and parameters. To this end, we propose to improve the transferability of adversarial examples by creating diverse input patterns. Instead of only using the original images to generate adversarial examples, our method applies random transformations to the input images at each iteration. Extensive experiments on ImageNet show that the proposed attack method can generate adversarial examples that transfer much better to different networks than existing baselines. By evaluating our method against top defense solutions and official baselines from NIPS 2017 adversarial competition, the enhanced attack reaches an average success rate of 73.0%, which outperforms the top-1 attack submission in the NIPS competition by a large margin of 6.6%. We hope that our proposed attack strategy can serve as a strong benchmark baseline for evaluating the robustness of networks to adversaries and the effectiveness of different defense methods in the future. Code is available at https://github.com/cihangxie/DI-2-FGSM.


title: Active learning for clinical text classification: is it better than random sampling?

abstract: Objective This study explores active learning algorithms as a way to reduce the requirements for large training sets in medical text classification tasks. ::: ::: Design Three existing active learning algorithms (distance-based (DIST), diversity-based (DIV), and a combination of both (CMB)) were used to classify text from five datasets. The performance of these algorithms was compared to that of passive learning on the five datasets. We then conducted a novel investigation of the interaction between dataset characteristics and the performance results. ::: ::: Measurements Classification accuracy and area under receiver operating characteristics (ROC) curves for each algorithm at different sample sizes were generated. The performance of active learning algorithms was compared with that of passive learning using a weighted mean of paired differences. To determine why the performance varies on different datasets, we measured the diversity and uncertainty of each dataset using relative entropy and correlated the results with the performance differences. ::: ::: Results The DIST and CMB algorithms performed better than passive learning. With a statistical significance level set at 0.05, DIST outperformed passive learning in all five datasets, while CMB was found to be better than passive learning in four datasets. We found strong correlations between the dataset diversity and the DIV performance, as well as the dataset uncertainty and the performance of the DIST algorithm. ::: ::: Conclusion For medical text classification, appropriate active learning algorithms can yield performance comparable to that of passive learning with considerably smaller training sets. In particular, our results suggest that DIV performs better on data with higher diversity and DIST on data with lower uncertainty.


title: NOMA: Principles and recent results

abstract: Although non-orthogonal multiple access (NOMA) is recently considered for cellular systems, its key ideas such as successive interference cancellation (SIC) and superposition coding have been well studied in information theory. In this paper, we overview principles of NOMA based on information theory and present some recent results. Under a single-cell environment, we mainly focus on fundamental issues, e.g., power allocation and beamforming for downlink NOMA and coordinated and uncoordinated transmissions for uplink NOMA.


title: Combining Heterogeneous Data Sources for Civil Unrest Forecasting

abstract: Detecting and forecasting civil unrest events (protests, strikes, etc.) is of key interest to social scientists and policy makers because these events can lead to significant societal and cultural changes. We analyze protest dynamics in six countries of Latin America on a daily level, from November 2012 through August 2014, using multiple data sources that capture social, political and economic contexts within which civil unrest occurs. We use logistic regression models with Lasso to select a sparse feature set from our diverse datasets, in order to predict the probability of occurrence of civil unrest events in these countries. The models contain predictors extracted from social media sites (Twitter and blogs) and news sources, in addition to volume of requests to Tor, a widely-used anonymity network. Two political event databases and country-specific exchange rates are also used. Our forecasting models are evaluated using a Gold Standard Report (GSR), which is compiled by an independent group of social scientists and experts on Latin America. The experimental results, measured by F1-scores, are in the range 0.68 to 0.95, and demonstrate the efficacy of using a multi-source approach for predicting civil unrest. Case studies illustrate the insights into unrest events that are obtained with our methods.


An overview of neuroevolution techniques


title: Rotational Stiffness at Ridges of Timber Folded-Plate Structures

abstract: Folded-plate structures provide an efficient design using thin laminated veneer lumber panels. Inspired by Japanese furniture joinery, the multiple tab-and-slot joint was developed for the multi-assembly of timber panels with non-parallel edges without adhesive or metal joints. Because the global analysis of our origami structures reveals that the rotational stiffness at ridges affects the global behaviour, we propose an experimental and numerical study of this linear interlocking connection. Its geometry is governed by three angles that orient the contact faces. Nine combinations of these angles were tested and the rotational slip was measured with two different bending set-ups: closing or opening the fold formed by two panels. The non-linear behaviour was conjointly reproduced numerically using the finite element method and continuum damage mechanics.


title: Deep Metric and Hash-Code Learning for Content-Based Retrieval of Remote Sensing Images

abstract: The growing volume of Remote Sensing (RS) image archives demands for feature learning techniques and hashing functions which can: (1) accurately represent the semantics in the RS images; and (2) have quasi real-time performance during retrieval. This paper aims to address both challenges at the same time, by learning a semantic-based metric space for content based RS image retrieval while simultaneously producing binary hash codes for an efficient archive search. This double goal is achieved by training a deep network using a combination of different loss functions which, on the one hand, aim at clustering semantically similar samples (i.e., images), and, on the other hand, encourage the network to produce final activation values (i.e., descriptors) that can be easily binarized. Moreover, since RS annotated training images are too few to train a deep network from scratch, we propose to split the image representation problem in two different phases. In the first we use a general-purpose, pre-trained network to produce an intermediate representation, and in the second we train our hashing network using a relatively small set of training images. Experiments on two aerial benchmark archives show that the proposed method outperforms previous state-of-the-art hashing approaches by up to 5.4% using the same number of hash bits per image.


title: EyeSpyVR: Interactive Eye Sensing Using Off-the-Shelf, Smartphone-Based VR Headsets

abstract: Low cost virtual reality (VR) headsets powered by smartphones are becoming ubiquitous. Their unique position on the user's face opens interesting opportunities for interactive sensing. In this paper, we describe EyeSpyVR, a software-only eye sensing approach for smartphone-based VR, which uses a phone's front facing camera as a sensor and its display as a passive illuminator. Our proof-of-concept system, using a commodity Apple iPhone, enables four sensing modalities: detecting when the VR head set is worn, detecting blinks, recognizing the wearer's identity, and coarse gaze tracking - features typically found in high-end or specialty VR headsets. We demonstrate the utility and accuracy of EyeSpyVR in a series of studies with 70 participants, finding a worn detection of 100%, blink detection rate of 95.3%, family user identification accuracy of 81.4%, and mean gaze tracking error of 10.8° when calibrated to the wearer (12.9° without calibration). These sensing abilities can be used by developers to enable new interactive features and more immersive VR experiences on existing, off-the-shelf hardware.


title: Extrapolation in NLP

abstract: We argue that extrapolation to examples outside the training space will often be easier for models that capture global structures, rather than just maximise their local fit to the training data. We show that this is true for two popular models: the Decomposable Attention Model and word2vec.


title: 3D Skull Recognition Using 3D Matching Technique

abstract: Biometrics has become a "hot" area. Governments are funding research programs focused on biometrics. In this paper the problem of person recognition and verification based on a different biometric application has been addressed. The system is based on the 3DSkull recognition using 3D matching technique, in fact this paper present several bio-metric approaches in order of assign the weak point in term of used the biometric from the authorize person and insure the person who access the data is the real person. The feature of the simulate system shows the capability of using 3D matching system as an efficient way to identify the person through his or her skull by match it with database, this technique grantee fast processing with optimizing the false positive and negative as well .


title: Transient response and dynamic power dissipation comparison of various Dickson charge pump configurations based on charge transfer switches

abstract: Dickson charge pump is a switches-capacitor network providing a voltage gain and having open-circuit resistance dependent on the topology frequency and the size of storage capacitors. In this paper, the current status of research and development in the field of Dickson charge pump is extensively reviewed. Dickson charge pump is inductor-less DC to DC converter which uses a capacitor for its energy storage. The aim of this paper is to compare various techniques of Dickson charge pump currently explored in industry and academia. The parameters especially efficiency and voltage are compared. We have also discussed and evaluated the optimised criteria of capacitor sizing according to clock signals provided such that the charge pump performance could be properly optimised. We have simulated the transient response of DCP and provided the comprehensive study.


title: Fast Neural Cell Detection Using Light-Weight SSD Neural Network

abstract: Identifying the lineage path of neural cells is critical for understanding the development of brain. Accurate neural cell detection is a crucial step to obtain reliable delineation of cell lineage. To solve this task, in this paper we present an efficient neural cell detection method based on SSD (single shot multibox detector) neural network model. Our method adapts the original SSD architecture and removes the unnecessary blocks, leading to a light-weight model. Moreover, we formulate the cell detection as a binary regression problem, which makes our model much simpler. Experimental results demonstrate that, with only a small training set, our method is able to accurately capture the neural cells under severe shape deformation in a fast way.


title: Remembering World War II: The rhetoric and politics of national commemoration at the turn of the 21st century

abstract: This essay critically examines the reemergence of World War II in public culture. By analyzing four popular memory texts (the World, War II Memorial, Saving Private Ryan, The Greatest Generation, and the Women in Military Service for America Memorial) as well as the discourse circulating about them, it argues that these reconstructions of the past function rhetorically as civics lessons for a generation beset by fractious disagreements about the viability of U.S. culture and identity.


title: A review of the literature on employee turnover

abstract: “Employee turnover” as a term is widely used in business circles. Although several studies have been conducted on this topic, most of the researchers focus on the causes of employee turnover but little has been done on the examining the sources of employee turnover, effects and advising various strategies which can be used by managers in various organisations to ensure that there is employee continuity in their organisations to enhance organizational competitiveness. This paper examines the sources of employee turnover, effects and forwards some strategies on how to minimize employee turnover in organisations. ::: ::: ::: ::: Key words: Employee, turnover, sources, effects, strategies.


title: Centrosomal localization of RhoGDIβ and its relevance to mitotic processes in cancer cells

abstract: Rho GDP-dissociation inhibitors (RhoGDIs) are regulators of Rho family GTPases. RhoGDIβ has been implicated in cancer progression, but its precise role remains unclear. We determined the subcellular localization of RhoGDIβ and examined the effects of its overexpression and RNAi knockdown in cancer cells. Immunofluorescence staining showed that RhoGDIβ localized to centrosomes in human cancer cells. In HeLa cells, exogenous GFP-tagged RhoGDIβ localized to centrosomes and its overexpression caused prolonged mitosis and aberrant cytokinesis in which the cell shape was distorted. RNAi knockdown of RhoGDIβ led to increased incidence of monopolar spindle mitosis resulting in polyploid cells. These results suggest that RhoGDIβ has mitotic functions, including regulation of cytokinesis and bipolar spindle formation. The dysregulated expression of RhoGDIβ may contribute to cancer progression by disrupting these processes.


title: VConv-DAE: Deep Volumetric Shape Learning Without Object Labels

abstract: With the advent of affordable depth sensors, 3D capture becomes more and more ubiquitous and already has made its way into commercial products. Yet, capturing the geometry or complete shapes of everyday objects using scanning devices (e.g. Kinect) still comes with several challenges that result in noise or even incomplete shapes.


title: Toward a multilevel secure relational data model

abstract: Although there are several efforts underway to build multilevel secure relational database management systems, there is no clear consensus regarding what a multilevel secure relational data model exactly is. In part this lack of consensus on fundamental issues reflects the subtleties involved in extending the classical (single-level) relational model to a multilevel environment. Our aim in this paper is to discuss the most fundamental aspects of the multilevel secure relational model. Specifically, we consider two requirements: entit y integrity and update semantics. Our overall goal is to preserve as much as possible the simplicity and flexibility of the relational model without sacrificing security in the process.


title: Development of a Mobile Phone Addiction Craving Scale and Its Validation in a Spanish Adult Population

abstract: In some people, problematic cell phone use can lead to situations in which they lose control, similar to those observed in other cases of addiction. Although different scales have been developed to assess its severity, we lack an instrument that is able to determine the desire or craving associated with it. Thus, with the objective of evaluating craving for cell phone use, in this study, we develop and present the Mobile Phone Addiction Craving Scale (MPACS). It consists of eight Likert-style items, with 10 response options, referring to possible situations in which the interviewee is asked to evaluate the degree of restlessness that he or she feels if the cell phone is unavailable at the moment. It can be self-administered or integrated in an interview when abuse or problems are suspected. With the existence of a single dimension, reflected in the exploratory factor analysis (EFA), the scale presents adequate reliability and internal consistency (α=0.919). Simultaneously, we are able to show significantly increased correlations (r=0.785, p=0.000) with the Mobile Phone Problem Use Scale (MPPUS) and state anxiety (r=0.330, p=0.000). We are also able to find associations with impulsivity, measured using the UPPS (urgency, premeditation, perseverance, sensation seeking) scale, particularly in the dimensions of negative urgency (r=0.303, p=0.000) and positive urgency (r=0.290, p=0.000), which confirms its construct validity. The analysis of these results conveys important discriminant validity among the MPPUS user categories that are obtained using the criteria by Chow, Leung, and Yu (1). The MPACS demonstrates higher levels of craving in persons up to 35 years of age, reversing with age. In contrast, we do not find significant differences among the sexes. Finally, a receiver operating characteristic (ROC) analysis allows us to establish the scores from which we are able to determine the different levels of craving, from the absence of craving to that referred to as addiction. Based on these results, we can conclude that this scale is a reliable tool that complements ongoing studies on problematic cell phone use.


title: FRASH: A framework to test algorithms of similarity hashing

abstract: Automated input identification is a very challenging, but also important task. Within computer forensics this reduces the amount of data an investigator has to look at by hand. Besides identifying exact duplicates, which is mostly solved using cryptographic hash functions, it is necessary to cope with similar inputs (e.g., different versions of a file), embedded objects (e.g., a JPG within a Word document), and fragments (e.g., network packets), too. Over the recent years a couple of different similarity hashing algorithms were published. However, due to the absence of a definition and a test framework, it is hardly possible to evaluate and compare these approaches to establish them in the community. The paper at hand aims at providing an assessment methodology and a sample implementation called FRASH: a framework to test algorithms of similarity hashing. First, we describe common use cases of a similarity hashing algorithm to motivate our two test classes efficiency and sensitivity & robustness. Next, our open and freely available framework is briefly described. Finally, we apply FRASH to the well-known similarity hashing approaches ssdeep and sdhash to show their strengths and weaknesses.


title: SNP Set Association Analysis for Genome-Wide Association Studies

abstract: Genome-wide association study (GWAS) is a promising approach for identifying common genetic variants of the diseases on the basis of millions of single nucleotide polymorphisms (SNPs). In order to avoid low power caused by overmuch correction for multiple comparisons in single locus association study, some methods have been proposed by grouping SNPs together into a SNP set based on genomic features, then testing the joint effect of the SNP set. We compare the performances of principal component analysis (PCA), supervised principal component analysis (SPCA), kernel principal component analysis (KPCA), and sliced inverse regression (SIR). Simulated SNP sets are generated under scenarios of 0, 1 and ≥2 causal SNPs model. Our simulation results show that all of these methods can control the type I error at the nominal significance level. SPCA is always more powerful than the other methods at different settings of linkage disequilibrium structures and minor allele frequency of the simulated datasets. We also apply these four methods to a real GWAS of non-small cell lung cancer (NSCLC) in Han Chinese population


title: Estimating Absolute-Phase Maps Using ESPIRiT and Virtual Conjugate Coils

abstract: Purpose: To develop an ESPIRiT-based method to estimate coil sensitivities with image phase as a building block for efficient and robust image reconstruction with phase constraints. Theory and Methods: ESPIRiT is a new framework for calibration of the coil sensitivities and reconstruction in parallel Magnetic Resonance Imaging (MRI). Applying ESPIRiT to a combined set of physical and virtual conjugate coils (VCC-ESPIRiT) implicitly exploits conjugate symmetry in k-space similar to VCC-GRAPPA. Based on this method, a new post-processing step is proposed for the explicit computation of coil sensitivities that include the absolute phase of the image. The accuracy of the computed maps is directly validated using a test based on projection onto fully sampled coil images and also indirectly in phase-constrained parallel-imaging reconstructions. Results: The proposed method can estimate accurate sensitivities which include low-resolution image phase. In case of high-frequency phase variations VCC-ESPIRiT yields an additional set of maps that indicates the existence of a high-frequency phase component. Taking this additional set of maps into account can improve the robustness of phase-constrained parallel imaging. Conclusion: The extended VCC-ESPIRiT is a useful tool for phase-constrained imaging.


title: Multispectral Deep Neural Networks for Pedestrian Detection

abstract: Multispectral pedestrian detection is essential for around-the-clock applications, e.g., surveillance and autonomous driving. We deeply analyze Faster R-CNN for multispectral pedestrian detection task and then model it into a convolutional network (ConvNet) fusion problem. Further, we discover that ConvNet-based pedestrian detectors trained by color or thermal images separately provide complementary information in discriminating human instances. Thus there is a large potential to improve pedestrian detection by using color and thermal images in DNNs simultaneously. We carefully design four ConvNet fusion architectures that integrate two-branch ConvNets on different DNNs stages, all of which yield better performance compared with the baseline detector. Our experimental results on KAIST pedestrian benchmark show that the Halfway Fusion model that performs fusion on the middle-level convolutional features outperforms the baseline method by 11% and yields a missing rate 3.5% lower than the other proposed architectures.


title: Indoor scene classification using combined 3D and gist features

abstract: Scene categorization is an important mechanism for providing high-level context which can guide methods for a more detailed analysis of scenes. State-of-the-art techniques like Torralba's Gist features show a good performance on categorizing outdoor scenes but have problems in categorizing indoor scenes. In contrast to object based approaches, we propose a 3D feature vector capturing general properties of the spatial layout of indoor scenes like shape and size of extracted planar patches and their orientation to each other. This idea is supported by psychological experiments which give evidence for the special role of 3D geometry in categorizing indoor scenes. In order to study the influence of the 3D geometry we introduce in this paper a novel 3D indoor database and a method for defining 3D features on planar surfaces extracted in 3D data. Additionally, we propose a voting technique to fuse 3D features and 2D Gist features and show in our experiments a significant contribution of the 3D features to the indoor scene categorization task.


title: Self-Organization in Small Cell Networks: A Reinforcement Learning Approach

abstract: In this paper, a decentralized and self-organizing mechanism for small cell networks (such as micro-, femto- and picocells) is proposed. In particular, an application to the case in which small cell networks aim to mitigate the interference caused to the macrocell network, while maximizing their own spectral efficiencies, is presented. The proposed mechanism is based on new notions of reinforcement learning (RL) through which small cells jointly estimate their time-average performance and optimize their probability distributions with which they judiciously choose their transmit configurations. Here, a minimum signal to interference plus noise ratio (SINR) is guaranteed at the macrocell user equipment (UE), while the small cells maximize their individual performances. The proposed RL procedure is fully distributed as every small cell base station requires only an observation of its instantaneous performance which can be obtained from its UE. Furthermore, it is shown that the proposed mechanism always converges to an epsilon Nash equilibrium when all small cells share the same interest. In addition, this mechanism is shown to possess better convergence properties and incur less overhead than existing techniques such as best response dynamics, fictitious play or classical RL. Finally, numerical results are given to validate the theoretical findings, highlighting the inherent tradeoffs facing small cells, namely exploration/exploitation, myopic/foresighted behavior and complete/incomplete information.


title: IMAGE DENOISING TECHNIQUES: LITERATURE REVIEW

abstract: In various fields and applications use of images are becoming increasingly popular like in field of medical, education etc. But the problem is that noise will be inevitably introduced in the image during image acquisition process. Another problem that arises after denoising process is the destruction of the image edge structures and introduction of artifacts. For this there are several techniques proposed by other authors for image denoising as well as for edge preservation. In this paper, we aim to provide a review of some of those techniques that can be used in image processing (denoising). This paper outlines the brief description of noise, types of noise, image denoising and then the review of different techniques and their approaches to remove that noise. The aim of this review paper is to provide some brief and useful knowledge of denoising techniques for applications using images to provide an ease of selecting the optimal technique according to their needs. Keywords—Image Denoising; Salt-and-pepper noise; Gaussian noise; Gaussian distribution; MSE; PSNR; Thresholding


title: A computational challenge problem in materials discovery: synthetic problem generator and real-world datasets

abstract: Newly-discovered materials have been central to recent technological advances. They have contributed significantly to breakthroughs in electronics, renewable energy and green buildings, and overall, have promoted the advancement of global human welfare. Yet, only a fraction of all possible materials have been explored. Accelerating the pace of discovery of materials would foster technological innovations, and would potentially address pressing issues in sustainability, such as energy production or consumption. ::: ::: The bottleneck of this discovery cycle lies, however, in the analysis of the materials data. As materials scientists have recently devised techniques to efficiently create thousands of materials and experimentalists have developed new methods and tools to characterize these materials, the limiting factor has become the data analysis itself. Hence, the goal of this paper is to stimulate the development of new computational techniques for the analysis of materials data, by bringing together the complimentary expertise of materials scientists and computer scientists. ::: ::: In collaboration with two major research laboratories in materials science, we provide the first publicly available dataset for the phase map identification problem. In addition, we provide a parameterized synthetic data generator to assess the quality of proposed approaches, as well as tools for data visualization and solution evaluation.


title: Providing Privacy-Aware Incentives in Mobile Sensing Systems

abstract: Mobile sensing relies on data contributed by users through their mobile device (e.g., smart phone) to obtain useful information about people and their surroundings. However, users may not want to contribute due to lack of incentives and concerns on possible privacy leakage. To effectively promote user participation, both incentive and privacy issues should be addressed. Although incentive and privacy have been addressed separately in mobile sensing, it is still an open problem to address them simultaneously. In this paper, we propose two credit-based privacy-aware incentive schemes for mobile sensing systems, where the focus is on privacy protection instead of on the design of incentive mechanisms. Our schemes enable mobile users to earn credits by contributing data without leaking which data they have contributed, and ensure that malicious users cannot abuse the system to earn unlimited credits. Specifically, the first scheme considers scenarios where an online trusted third party (TTP) is available, and relies on the TTP to protect user privacy and prevent abuse attacks. The second scheme considers scenarios where no online TTP is available. It applies blind signature, partially blind signature, and a novel extended Merkle tree technique to protect user privacy and prevent abuse attacks. Security analysis and cost evaluations show that our schemes are secure and efficient.


title: Learning to Extract International Relations from Political Context

abstract: We describe a new probabilistic model for extracting events between major political actors from news corpora. Our unsupervised model brings together familiar components in natural language processing (like parsers and topic models) with contextual political information— temporal and dyad dependence—to infer latent event classes. We quantitatively evaluate the model’s performance on political science benchmarks: recovering expert-assigned event class valences, and detecting real-world conflict. We also conduct a small case study based on our model’s inferences. A supplementary appendix, and replication software/data are available online, at: http://brenocon.com/irevents


title: Industrial-Size Scheduling with ASP+CP

abstract: Answer Set Programming (ASP) combines a powerful, theoretically principled knowledge representation formalism and powerful solvers. To improve efficiency of computation on certain classes of problems, researchers have recently developed hybrid languages and solvers, combining ASP with language constructs and solving techniques from Constraint Programming (CP). The resulting ASP+CP solvers exhibit remarkable performance on "toy" problems. To the best of our knowledge, however, no hybrid ASP+CP language and solver have been used in practical, industrial-size applications. In this paper, we report on the first such successful application, consisting of the use of the hybrid ASP+CP system ezcsp to solve sophisticated industrial-size scheduling problems.


title: Improving one-class collaborative filtering by incorporating rich user information

abstract: One-Class Collaborative Filtering (OCCF) is an emerging setup in collaborative filtering in which only positive examples or implicit feedback can be observed. Compared with the traditional collaborative filtering setting where the data has ratings, OCCF is more realistic in many scenarios when no ratings are available. In this paper, we propose to improve OCCF accuracy by exploiting the rich user information that is often naturally available in community-based interactive information systems, including a user's search query history, purchasing and browsing activities. We propose two ways to incorporate such user information into the OCCF models: one is to linearly combine scores from different sources and the other is to embed user information into collaborative filtering. Experimental results on a large-scale retail data set from a major e-commerce company show that the proposed methods are effective and can improve the performance of the One-Class Collaborative Filtering over baseline methods through leveraging rich user information.


Security Issues and Future Challenges of Cloud Service Authentication


title: DV Based Positioning in Ad Hoc Networks

abstract: Many ad hoc network protocols and applications assume the knowledge of geographic location of nodes. The absolute position of each networked node is an assumed fact by most sensor networks which can then present the sensed information on a geographical map. Finding position without the aid of GPS in each node of an ad hoc network is important in cases where GPS is either not accessible, or not practical to use due to power, form factor or line of sight conditions. Position would also enable routing in sufficiently isotropic large networks, without the use of large routing tables. We are proposing APS --- a localized, distributed, hop by hop positioning algorithm, that works as an extension of both distance vector routing and GPS positioning in order to provide approximate position for all nodes in a network where only a limited fraction of nodes have self positioning capability.


Experiential Attributes and Consumer Judgments


title: 10 In-Process Metrics for Software Testing

abstract: In-process tracking and measurements play a critical role in software development, particularly for software testing. Although there are many discussions and publications on this subject and numerous proposed metrics, few in-process metrics are presented with sufficient experiences of industry implementation to demonstrate their usefulness. This paper describes several in-process metrics whose usefulness has been proven with ample implementation experiences at the IBM Rochester AS/400® software development laboratory. For each metric, we discuss its purpose, data, interpretation, and use and present a graphic example with real-life data. We contend that most of these metrics, with appropriate tailoring as needed, are applicable to most software projects and should be an integral part of software testing.


title: fMRI resting state networks define distinct modes of long-distance interactions in the human brain

abstract: Abstract Functional magnetic resonance imaging (fMRI) studies of the human brain have suggested that low-frequency fluctuations in resting fMRI data collected using blood oxygen level dependent (BOLD) contrast correspond to functionally relevant resting state networks (RSNs). Whether the fluctuations of resting fMRI signal in RSNs are a direct consequence of neocortical neuronal activity or are low-frequency artifacts due to other physiological processes (e.g., autonomically driven fluctuations in cerebral blood flow) is uncertain. In order to investigate further these fluctuations, we have characterized their spatial and temporal properties using probabilistic independent component analysis (PICA), a robust approach to RSN identification. Here, we provide evidence that: i. RSNs are not caused by signal artifacts due to low sampling rate (aliasing); ii. they are localized primarily to the cerebral cortex; iii. similar RSNs also can be identified in perfusion fMRI data; and iv. at least 5 distinct RSN patterns are reproducible across different subjects. The RSNs appear to reflect “default” interactions related to functional networks related to those recruited by specific types of cognitive processes. RSNs are a major source of non-modeled signal in BOLD fMRI data, so a full understanding of their dynamics will improve the interpretation of functional brain imaging studies more generally. Because RSNs reflect interactions in cognitively relevant functional networks, they offer a new approach to the characterization of state changes with pathology and the effects of drugs.


title: Improving classification accuracy of project dispute resolution using hybrid artificial intelligence and support vector machine models

abstract: Highlights? SVMs have a potential improvement space by integrating hybrid intelligence (HI). ? This work compares the performance of various classification models using HI. ? Disputed construction projects were collected for a real case study. ? This work demonstrates the capability of HI to improve dispute resolution prediction. Support vector machines (SVMs) have been applied successfully to construction knowledge domains. However, SVMs, as a baseline model, still have a potential improvement space by integrating hybrid intelligence. This work compares the performance of various classification models using the combination of fuzzy logic, a fast and messy genetic algorithm, and SVMs. A set of public-private partnership projects was collected as a real case study in construction management. The data were split into mutually independent folds for cross validation. Experimental results demonstrate that the proposed hybrid artificial intelligence system has the best and most reliable classification accuracy at 77.04%, a 24.76% improvement compared with that of SVMs in predicting project dispute resolution (PDR) outcomes (i.e., mediation, arbitration, litigation, negotiation, and administrative appeals) when the dispute category and phase in which a dispute occurs are known during project execution. This work demonstrates the improvement capability of hybrid intelligence in classifying PDR predictions related to public infrastructure projects.


title: Real-Time Neural Style Transfer for Videos

abstract: Recent research endeavors have shown the potential of using feed-forward convolutional neural networks to accomplish fast style transfer for images. In this work, we take one step further to explore the possibility of exploiting a feed-forward network to perform style transfer for videos and simultaneously maintain temporal consistency among stylized video frames. Our feed-forward network is trained by enforcing the outputs of consecutive frames to be both well stylized and temporally consistent. More specifically, a hybrid loss is proposed to capitalize on the content information of input frames, the style information of a given style image, and the temporal information of consecutive frames. To calculate the temporal loss during the training stage, a novel two-frame synergic training mechanism is proposed. Compared with directly applying an existing image style transfer method to videos, our proposed method employs the trained network to yield temporally consistent stylized videos which are much more visually pleasant. In contrast to the prior video style transfer method which relies on time-consuming optimization on the fly, our method runs in real time while generating competitive visual results.


title: Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning

abstract: Most successful information extraction systems operate with access to a large collection of documents. In this work, we explore the task of acquiring and incorporating external evidence to improve extraction accuracy in domains where the amount of training data is scarce. This process entails issuing search queries, extraction from new sources and reconciliation of extracted values, which are repeated until sufficient evidence is collected. We approach the problem using a reinforcement learning framework where our model learns to select optimal actions based on contextual information. We employ a deep Q-network, trained to optimize a reward function that reflects extraction accuracy while penalizing extra effort. Our experiments on two databases -- of shooting incidents, and food adulteration cases -- demonstrate that our system significantly outperforms traditional extractors and a competitive meta-classifier baseline.


title: Competing in the dark: an efficient algorithm for bandit linear optimization

abstract: We introduce an efficient algorithm for the problem of online linear optimization in the bandit setting which achieves the optimal O∗( √ T ) regret. The setting is a natural generalization of the nonstochastic multi-armed bandit problem, and the existence of an efficient optimal algorithm has been posed as an open problem in a number of recent papers. We show how the difficulties encountered by previous approaches are overcome by the use of a self-concordant potential function. Our approach presents a novel connection between online learning and interior point methods.


title: An integrative circuit–host modelling framework for predicting synthetic gene network behaviours

abstract: One fundamental challenge in synthetic biology is the lack of quantitative tools that accurately describe and predict the behaviours of engineered gene circuits. This challenge arises from multiple factors, among which the complex interdependence of circuits and their host is a leading cause. Here we present a gene circuit modelling framework that explicitly integrates circuit behaviours with host physiology through bidirectional circuit–host coupling. The framework consists of a coarse-grained but mechanistic description of host physiology that involves dynamic resource partitioning, multilayered circuit–host coupling including both generic and system-specific interactions, and a detailed kinetic module of exogenous circuits. We showed that, following training, the framework was able to capture and predict a large set of experimental data concerning the host and its foreign gene overexpression. To demonstrate its utility, we applied the framework to examine a growth-modulating feedback circuit whose dynamics is qualitatively altered by circuit–host interactions. Using an extended version of the framework, we further systematically revealed the behaviours of a toggle switch across scales from single-cell dynamics to population structure and to spatial ecology. This work advances our quantitative understanding of gene circuit behaviours and also benefits the rational design of synthetic gene networks. An integrative modelling framework is developed to predict the behaviours of synthetic circuits and theirimpacts on host physiology.


title: Epistemological Problems of Artificial Intelligence

abstract: Publisher Summary Artificial intelligence (AI) problem has been divided into two parts—an epistemological part and a heuristic part. This chapter further explains this division, explains some of the epistemological problems, and presents some new results and approaches. The epistemological part of AI studies what kinds of facts about the world are available to an observer with given opportunities to observe, how these facts can be represented in the memory of a computer, and what rules permit legitimate conclusions to be drawn from these facts. It leaves aside the heuristic problems of how to search spaces of possibilities and how to match patterns. Considering epistemological problems separately has the following advantages. (1) The same problems of what information is available to an observer and what conclusions can be drawn from information arise in connection with a variety of problem solving tasks. (2) A single solution of the epistemological problems can support a wide variety of heuristic approaches to a problem. (3) AI is a very difficult scientific problem, so there are great advantages in finding parts of the problem that can be separated out and separately attacked.


title: Blind PSF estimation and methods of deconvolution optimization

abstract: We have shown that the left side null space of the autoregression (AR) matrix operator is the lexicographical presentation of the point spread function (PSF) on condition the AR parameters are common for original and blurred images. The method of inverse PSF evaluation with regularization functional as the function of surface area is offered. The inverse PSF was used for primary image estimation. Two methods of original image estimate optimization were designed basing on maximum entropy generalization of sought and blurred images conditional probability density and regularization. The first method uses balanced variations of convolution and deconvolution transforms to obtaining iterative schema of image optimization. The variations balance was defined by dynamic regularization basing on condition of iteration process convergence. The regularization has dynamic character because depends on current and previous image estimate variations. The second method implements the regularization of deconvolution optimization in curved space with metric defined on image estimate surface. It is basing on target functional invariance to fluctuations of optimal argument value. The given iterative schemas have faster convergence in comparison with known ones, so they can be used for reconstruction of high resolution images series in real time.


title: Hybrid Histograms of Oriented Gradients-compressive sensing framework feature extraction for face recognition

abstract: Face recognition is still a challenge in many applications especially in surveillance and security systems. In almost all applications of face recognition, large data sets are used, creating challenges with real time processing and efficiency. In this paper, we propose a hybrid feature extraction method to enhance speed and recognition efficiency. We propose to use features obtained using Histograms of Oriented Gradients (HOG) within a compressive sensing (CS) framework. A HOG feature descriptor algorithm will allow us to extract the face features vector with the advantage of being invariant to changes in appearance or to illumination variations while compressive sensing is used to reduce the density of the resulting face features. This reduction will improve the performance and the computational cost of the system. The classification process is executed using K-Nearest Neighbors algorithm and Probabilistic Neural Network classifier. Our initial experimental results show that the proposed framework is capable of identifying and recognizing faces with different expressions, poses, illumination, and backgrounds in real time.


title: An Adaptive Architecture for Physical Agents

abstract: In this paper we describe ICARUS, an adaptive architecture for intelligent physical agents. We contrast the framework's assumptions with those of earlier architectures, taking examples from an in-city driving task to illustrate our points. Key differences include: primacy of perception and action over problem solving, separate memories for categories and skills, a hierarchical organization on both memories, strong correspondence between long-term and short-term structures, and cumulative learning of skill hierarchies. We support claims for ICARUS' generality by reporting our experience with driving and three other domains. In closing, we discuss limitations of the current architecture and propose extensions that would remedy them.


title: Personalized recommendation in social tagging systems using hierarchical clustering

abstract: Collaborative tagging applications allow Internet users to annotate resources with personalized tags. The complex network created by many annotations, often called a folksonomy, permits users the freedom to explore tags, resources or even other user's profiles unbound from a rigid predefined conceptual hierarchy. However, the freedom afforded users comes at a cost: an uncontrolled vocabulary can result in tag redundancy and ambiguity hindering navigation. Data mining techniques, such as clustering, provide a means to remedy these problems by identifying trends and reducing noise. Tag clusters can also be used as the basis for effective personalized recommendation assisting users in navigation. We present a personalization algorithm for recommendation in folksonomies which relies on hierarchical tag clusters. Our basic recommendation framework is independent of the clustering method, but we use a context-dependent variant of hierarchical agglomerative clustering which takes into account the user's current navigation context in cluster selection. We present extensive experimental results on two real world dataset. While the personalization algorithm is successful in both cases, our results suggest that folksonomies encompassing only one topic domain, rather than many topics, present an easier target for recommendation, perhaps because they are more focused and often less sparse. Furthermore, context dependent cluster selection, an integral step in our personalization algorithm, demonstrates more utility for recommendation in multi-topic folksonomies than in single-topic folksonomies. This observation suggests that topic selection is an important strategy for recommendation in multi-topic folksonomies.


title: Minimax Statistical Learning and Domain Adaptation with Wasserstein Distances

abstract: As opposed to standard empirical risk minimization (ERM), distributionally robust optimization aims to minimize the worst-case risk over a larger ambiguity set containing the original empirical distribution of the training data. In this work, we describe a minimax framework for statistical learning with ambiguity sets given by balls in Wasserstein space. In particular, we prove a generalization bound that involves the covering number properties of the original ERM problem. As an illustrative example, we provide generalization guarantees for domain adaptation problems where the Wasserstein distance between the source and target domain distributions can be reliably estimated from unlabeled samples.


title: Neurosecurity: security and privacy for neural devices.

abstract: An increasing number of neural implantable devices will become available in the near future due to advances in neural engineering. This discipline holds the potential to improve many patients' lives dramatically by offering improved-and in some cases entirely new-forms of rehabilitation for conditions ranging from missing limbs to degenerative cognitive diseases. The use of standard engineering practices, medical trials, and neuroethical evaluations during the design process can create systems that are safe and that follow ethical guidelines; unfortunately, none of these disciplines currently ensure that neural devices are robust against adversarial entities trying to exploit these devices to alter, block, or eavesdrop on neural signals. The authors define "neurosecurity"-a version of computer science security principles and methods applied to neural engineering-and discuss why neurosecurity should be a critical consideration in the design of future neural devices.


title: Directional multiscale modeling of images using the contourlet transform

abstract: The contourlet transform is a new two-dimensional extension of the wavelet transform using multiscale and directional filter banks. The contourlet expansion is composed of basis images oriented at various directions in multiple scales, with flexible aspect ratios. Given this rich set of basis images, the contourlet transform effectively captures smooth contours that are the dominant feature in natural images. We begin with a detailed study on the statistics of the contourlet coefficients of natural images: using histograms to estimate the marginal and joint distributions and mutual information to measure the dependencies between coefficients. This study reveals the highly non-Gaussian marginal statistics and strong interlocation, interscale, and interdirection dependencies of contourlet coefficients. We also find that conditioned on the magnitudes of their generalized neighborhood coefficients, contourlet coefficients can be approximately modeled as Gaussian random variables. Based on these findings, we model contourlet coefficients using a hidden Markov tree (HMT) model with Gaussian mixtures that can capture all interscale, interdirection, and interlocation dependencies. We present experimental results using this model in image denoising and texture retrieval applications. In denoising, the contourlet HMT outperforms other wavelet methods in terms of visual quality, especially around edges. In texture retrieval, it shows improvements in performance for various oriented textures.


title: DBPal: A Learned NL-Interface for Databases

abstract: In this demo, we present DBPal, a novel data exploration tool with a natural language interface. DBPal leverages recent advances in deep models to make query understanding more robust in the following ways: First, DBPal uses novel machine translation models to translate natural language statements to SQL, making the translation process more robust to paraphrasing and linguistic variations. Second, to support the users in phrasing questions without knowing the database schema and the query features, DBPal provides a learned auto-completion model that suggests to users partial query extensions during query formulation and thus helps to write complex queries.


Agreement with Satoshi - On the Formalization of Nakamoto Consensus.


title: www.elsevier.com/locate/fss Interval efficiency assessment using data envelopment analysis �

abstract: This paper studies how to conduct efficiency assessment using data envelopment analysis (DEA) in interval and/or fuzzy input-output environments. A new pair of interval DEA models is constructed on the basis of interval arithmetic, which differs from the existing DEA models handling interval data in that the former is a linear CCR model without the need of extra variable alternations and uses a fixed and unified production frontier (i.e. the same constraint set) to measure the efficiencies of decision-making units (DMUs) with interval input and output data, while the latter is usually a nonlinear optimization problem with the need of extra variable alternations or scale transformations and utilizes variable production frontiers (i.e. different constraint sets) to measure interval efficiencies. Ordinal preference information and fuzzy data are converted into interval data through the estimation of permissible intervals and @a-level sets, respectively, and are incorporated into the interval DEA models. The proposed interval DEA models are developed for measuring the lower and upper bounds of the best relative efficiency of each DMU with interval input and output data, which are different from the interval formed by the worst and the best relative efficiencies of each DMU. A minimax regret-based approach (MRA) is introduced to compare and rank the efficiency intervals of DMUs. Two numerical examples are provided to show the applications of the proposed interval DEA models and the preference ranking approach.


title: Semi-Autonomous Avatars: A New Direction for Expressive User Embodiment

abstract: Computer animated characters are rapidly becoming a regular part of our lives. They are starting to take the place of actors in films and television and are now an integral part of most computer games. Perhaps most interestingly in on-line games and chat rooms they are representing the user visually in the form of avatars, becoming our on-line identities, our embodiments in a virtual world. Currently online environments such as “Second Life” are being taken up by people who would not traditionally have considered playing games before, largely due to a greater emphasis on social interaction. These environments require avatars that are more expressive and that can make on-line social interactions seem more like face-to-face conversations. ::: Computer animated characters come in many different forms. Film characters require a substantial amount of off-line animator effort to achieve high levels of quality; these techniques are not suitable for real time applications and are not the focus of this chapter. Non-player characters (typically the bad guys) in games use limited artificial intelligence to react autonomously to events in real time. However avatars are completely controlled by their users, reacting to events solely through user commands. This chapter will discuss the distinction between fully autonomous characters and completely controlled avatars and how the current differentiation may no longer be useful, given that avatar technology may need to include more autonomy to live up to the demands of mass appeal. We will firstly discuss the two categories and present reasons to combine them. We will then describe previous work in this area and finally present our own framework for semi-autonomous avatars.


Mitigating Information Security Risks by Increasing User Security Awareness: A Case Study of an Information Security Awareness System


Spectrum Auctions


title: The Future of Computer Trading in Financial Markets driver review – DR 4The Global Financial Markets: an Ultra-Large-Scale Systems perspective Contents

abstract: We argue here that, in recent years, the world's financial markets have become a globally interconnected complex adaptive ultra-large-scale socio-technical system-of-systems, and that this has important consequences for how the financial markets should be engineered and managed in future. Major failures in the financial markets can now occur at super-human speeds, as was witnessed in the "Flash Crash" of May 6th 2010. Events such as the Flash Crash may become more commonplace in future, unless lessons are learned from other fields where complex adaptive socio-technical systems-of-systems have to be engineered for high-integrity, safety-critical applications. In this document we review the literature on failures in risky technology and high-integrity approaches to safety-critical SoS engineering. We conclude with an argument that, in the specific case of the global financial markets, there is an urgent need to develop major national strategic modeling and predictive simulation capabilities, comparable to national-scale meteorological monitoring and modeling capabilities. The intent here is not to predict the price-movements of particular financial instruments or asset classes, but rather to provide test-rigs for principled evaluation of systemic risk, estimating probability density functions over spaces of possible outcomes, and thereby identifying potentially high-consequence failure modes in the simulations, before they occur in real life, by which time it is typically too late.


title: Empathy in Traditional and Cyber Bullying/Victimization Involvement From Early to Middle Adolescence: A Cross Sectional Study

abstract: The purpose of this study was to investigate the relationship of affective and cognitive empathy with traditional and cyber bullying/victimization experiences among Greek participants who attended the fifth and sixth grade of elementary school (n=126), junior high school (n=140), and senior high school (n=157). Overall, results indicated negative correlations of empathy (especially affective) with traditional and cyber bullying/victimization. Negative correlations were particularly observed among elementary school participants, while affective empathy was found to be negatively related to bullying involvement, especially among girls. Overall, empathy (especially affective) may not be sufficiently developed among elementary school students, thus precluding them from understanding and caring about others' emotional state. The negative correlation of empathy and cyber-bullying among junior high school participants might be related to the characteristics of computer mediated communication. Implications of the findings are discussed.


title: RISE: Randomized Input Sampling for Explanation of Black-box Models

abstract: Deep neural networks are being used increasingly to automate data analysis and decision making, yet their decision-making process is largely unclear and is difficult to explain to the end users. In this paper, we address the problem of Explainable AI for deep neural networks that take images as input and output a class probability. We propose an approach called RISE that generates an importance map indicating how salient each pixel is for the model's prediction. In contrast to white-box approaches that estimate pixel importance using gradients or other internal network state, RISE works on black-box models. It estimates importance empirically by probing the model with randomly masked versions of the input image and obtaining the corresponding outputs. We compare our approach to state-of-the-art importance extraction methods using both an automatic deletion/insertion metric and a pointing metric based on human-annotated object segments. Extensive experiments on several benchmark datasets show that our approach matches or exceeds the performance of other methods, including white-box approaches. ::: Project page: this http URL


title: REVIEW AND EVALUATION OF WELL-KNOWN METHODS FOR MOVING OBJECT DETECTION AND TRACKING IN VIDEOS

abstract: Moving object detection and tracking (D&T) are important initial steps in object recognition, context analysis and indexing processes for visual surveillance systems. It is a big challenge for researchers to make a decision on which D&T algorithm is more suitable for which situation and/or environment and to determine how accurately object D&T (real-time or non-real-time) is made. There is a variety of object D&T algorithms (i.e. methods) and publications on their performance comparison and evaluation via performance metrics. This paper provides a systematic review of these algorithms and performance measures and assesses their effectiveness via metrics.


title: Head tracking and hand segmentation during hand over face occlusion in sign language

abstract: This paper presents a method to accurately segment the hand over the face. The similarity of colours and the important variability of the hand shape make it challenging. We propose a method based on the combination of two features: pixel colour and edges orientation. First, a specific skin model is used to find, before occlusion, the face position and the face template. Then, during occlusion the face template is registered using local gradient orientations to track the face position. Colour information is extracted from changes on pixel colours and edges are classified as belonging to the hand or to the face by mapping edges orientation to the face template. Finally by merging both features and by using an hysteresis threshold, which considers connectivity, a robust hand segmentation is reached. Experiments were performed using the Dicta-Sign corpus and showed the versatility of the proposed approach.


title: DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging

abstract: Brain imaging efforts are being increasingly devoted to decode the functioning of the human brain. Among neuroimaging techniques, resting-state fMRI (R-fMRI) is currently expanding exponentially. Beyond the general neuroimaging analysis packages (e.g., SPM, AFNI and FSL), REST and DPARSF were developed to meet the increasing need of user-friendly toolboxes for R-fMRI data processing. To address recently identified methodological challenges of R-fMRI, we introduce the newly developed toolbox, DPABI, which was evolved from REST and DPARSF. DPABI incorporates recent research advances on head motion control and measurement standardization, thus allowing users to evaluate results using stringent control strategies. DPABI also emphasizes test-retest reliability and quality control of data processing. Furthermore, DPABI provides a user-friendly pipeline analysis toolkit for rat/monkey R-fMRI data analysis to reflect the rapid advances in animal imaging. In addition, DPABI includes preprocessing modules for task-based fMRI, voxel-based morphometry analysis, statistical analysis and results viewing. DPABI is designed to make data analysis require fewer manual operations, be less time-consuming, have a lower skill requirement, a smaller risk of inadvertent mistakes, and be more comparable across studies. We anticipate this open-source toolbox will assist novices and expert users alike and continue to support advancing R-fMRI methodology and its application to clinical translational studies.


title: A 0.5 V Sub-Microwatt CMOS Image Sensor With Pulse-Width Modulation Read-Out

abstract: Energy minimization is a critical goal in size-constrained wireless sensors. Sensing elements are traditionally power hungry and require special attention in low energy systems. In this work, we study ultra-low power image sensors. In particular, we explore the use of aggressive voltage scaling in CMOS image sensors for applications ranging from retinal prostheses to battlefield monitoring and surveillance. We begin with a discussion of the challenges faced by a traditional 3T active pixel sensor as the supply voltage scales to 0.5 V and below. We then discuss an image sensor with pulse-width modulation read-out that is optimized for 0.5 V operation. A 0.13 μm test-chip with a 128 × 128 pixel array is shown to be functional with V dd as low as 0.45 V with energy consumption of 140 nJ/frame at V dd = 0.5 V (8.5 frames per second) and power consumption of only 700 nW at V dd = 0.5 V (0.5 frames per second). A focus is also placed on quantifying the noise implications of low voltage operation on the test-chip, which has a measured signal-to-noise ratio of 23.4 dB in saturation at V dd = 0.5 V.


title: HSQL: A Highly Scalable Cloud Database for Multi-user Query Processing

abstract: With the ever increasing size of data sets, traditional parallel relational database solution can be prohibitively expensive and may suffer limited scalability. To perform large-scale data processing in a cost-effective manner, several NoSQL data processing systems have been proposed. In this paper, we devise a new, distributed B-tree column indexing scheme for HBase, which can support indexing for non-row-key columns, as well as parallel B-tree search in large data table. Our experiment results demonstrate both performance and scalability advantage of our indexing scheme on point queries, range queries, and aggregation operations, compared with HBase.


title: Social Relationships and Mortality Risk: A Meta-analytic Review

abstract: Background ::: The quality and quantity of individuals' social relationships has been linked not only to mental health but also to both morbidity and mortality. ::: Objectives ::: This meta-analytic review was conducted to determine the extent to which social relationships influence risk for mortality, which aspects of social relationships are most highly predictive, and which factors may moderate the risk. ::: Data Extraction ::: Data were extracted on several participant characteristics, including cause of mortality, initial health status, and pre-existing health conditions, as well as on study characteristics, including length of follow-up and type of assessment of social relationships. ::: Results ::: Across 148 studies (308,849 participants), the random effects weighted average effect size was OR = 1.50 (95% CI 1.42 to 1.59), indicating a 50% increased likelihood of survival for participants with stronger social relationships. This finding remained consistent across age, sex, initial health status, cause of death, and follow-up period. Significant differences were found across the type of social measurement evaluated (p<0.001); the association was strongest for complex measures of social integration (OR = 1.91; 95% CI 1.63 to 2.23) and lowest for binary indicators of residential status (living alone versus with others) (OR = 1.19; 95% CI 0.99 to 1.44). ::: Conclusions ::: The influence of social relationships on risk for mortality is comparable with well-established risk factors for mortality. ::: ::: Please see later in the article for the Editors' Summary


title: Gene ontology analysis for RNA-seq: accounting for selection bias

abstract: We present GOseq, an application for performing Gene Ontology (GO) analysis on RNA-seq data. GO analysis is widely used to reduce complexity and highlight biological processes in genome-wide expression studies, but standard methods give biased results on RNA-seq data due to over-detection of differential expression for long and highly expressed transcripts. Application of GOseq to a prostate cancer data set shows that GOseq dramatically changes the results, highlighting categories more consistent with the known biology.


title: 5G: An Overview on Challenges and Key Solutions

abstract: The next generation of mobile communications, 5G, is currently being developed and expected to be commercially available by 2020. Unlike the previous generations, 5G will bring massive changes by enabling ultra-reliable communication, ultra-dense deployment, higher data rates, lower latencies and higher spectrum efficiency and energy efficiency. Nonetheless, the today’s hardware structure and technologies used cannot support the requirements proposed for 5G. Therefore, new solutions and key technologies need to be investigated. This article provides an overview on the existing challenges in order to reach the requirements proposed for the next generation, and the key technologies that act as solutions to these challenges.


Introduction to Choice Theory


Picture Books : An Automated Story Generator


title: SoK: "Plug & Pray" Today – Understanding USB Insecurity in Versions 1 Through C

abstract: USB-based attacks have increased in complexity in recent years. Modern attacks now incorporate a wide range of attack vectors, from social engineering to signal injection. To address these challenges, the security community has responded with a growing set of fragmented defenses. In this work, we survey and categorize USB attacks and defenses, unifying observations from both peer-reviewed research and industry. Our systematization extracts offensive and defensive primitives that operate across layers of communication within the USB ecosystem. Based on our taxonomy, we discover that USB attacks often abuse the trust-by-default nature of the ecosystem, and transcend different layers within a software stack; none of the existing defenses provide a complete solution, and solutions expanding multiple layers are most effective. We then develop the first formal verification of the recently released USB Type-C Authentication specification, and uncover fundamental flaws in the specification's design. Based on the findings from our systematization, we observe that while the spec has successfully pinpointed an urgent need to solve the USB security problem, its flaws render these goals unattainable. We conclude by outlining future research directions to ensure a safer computing experience with USB.


title: Agile big data analytics: AnalyticsOps for data science

abstract: Big data analytic (BDA) systems leverage data distribution and parallel processing across a cluster of resources. This introduces a number of new challenges specifically for analytics. The analytics portion of the complete lifecycle has typically followed a waterfall process — completing one step before beginning the next. While efforts have been made to map different types of analytics to an agile methodology, the steps are often described as breaking activities into smaller tasks while the overall process is still consistent with step-by-step waterfall. BDA changes a number of the activities in the analytics lifecycle, as well as their ordering. The goal of agile analytics — to reach a point of optimality between generating value from data and the time spent getting there. This paper discusses the implications of an agile process for BDA in cleansing, transformation, and analytics.


title: Double resonance transition from rectangular waveguide to Substrate Integrated Waveguide

abstract: In this paper the authors present a simple right-angle transition between a rectangular waveguide (RWG) and Substrate Integrated Waveguide (SIW). The coupling between both waveguide types is realised by using two slots etched in the broad wall of the SIW. Different lengths of the slots create a double resonance behaviour that results in a broad bandwidth of the element. The standard flange of the rectangular waveguide can be easily flush-mounted to the substrate of the SIW, and therefore, the proposed component has a simple mechanical configuration. A sample transition is designed to work in the lower part of the Ka-band. The simulation results show that the proposed component has the return loss better than 15 dB in the bandwidth of 1.9 GHz. The insertion loss is better than 1 dB including the losses of a short section of SIW.


title: Classification of sensor errors for the statistical simulation of environmental perception in automated driving systems

abstract: A virtual world provides a completely controlled and safe testing environment for the development and testing of current and future automated driving systems. In order to provide conditions close to reality, the input data for the automated driving system generated by sensors for environmental perception have to match closely between virtual and real world. The data gathered by perception sensors like radar, lidar or camera sensors generally provide a lossy description of the environment. Therefore, a sensor error model has to be employed that captures the characteristics of the sensory perception process. We propose a general description of a statistical sensor model, constructed to achieve equivalent sensor output on a statistical level. To construct the model, we define model units that each deal with a specific aspect of the sensory perception process on the object level. We propose a classification scheme and hierarchy for the different error types and describe a methodology for using real world reference data as input for the statistical model.


title: Prevention of depression through nutritional strategies in high-risk persons: rationale and design of the MooDFOOD prevention trial

abstract: Background ::: Obesity and depression are two prevalent conditions that are costly to individuals and society. The bidirectional association of obesity with depression, in which unhealthy dietary patterns may play an important role, has been well established. Few experimental studies have been conducted to investigate whether supplementing specific nutrients or improving diet and food-related behaviors can prevent depression in overweight persons.


abstract: Simultaneous localization and mapping with infinite planes is attractive because of the reduced complexity with respect to both sparse point-based and dense volumetric methods. We show how to include infinite planes into a least-squares formulation for mapping, using a homogeneous plane parametrization with a corresponding minimal representation for the optimization. Because it is a minimal representation, it is suitable for use with Gauss-Newton, Powell's Dog Leg and incremental solvers such as iSAM. We also introduce a relative plane formulation that improves convergence. We evaluate our proposed approach on simulated data to show its advantages over alternative solutions. We also introduce a simple mapping system and present experimental results, showing real-time mapping of select indoor environments with a hand-held RGB-D sensor.


title: Self-Presentation and Gender on MySpace

abstract: Abstract Within the cultural context of MySpace, this study explores the ways emerging adults experience social networking. Through focus group methodology, the role of virtual peer interaction in the development of personal, social, and gender identities was investigated. Findings suggest that college students utilize MySpace for identity exploration, engaging in social comparison and expressing idealized aspects of the selves they wish to become. The public nature of self and relationship displays introduce feedback mechanisms by which emerging adults can legitimize images as associated with the self. Also, male–female differences in self-presentation parallel, and possibly intensify, gender norms offline. Our study suggests that social networking sites provide valuable opportunities for emerging adults to realize possible selves; however, increased pressure for female sexual objectification and intensified social comparison may also negatively impact identity development. A balanced view, presenting both opportunities and drawbacks, should be encouraged in policies regarding youth participation in social networking sites.


title: An autonomous all terrain robotic system for field demining missions

abstract: Mines deployed in post-war countries pose severe threats to civilians and hamper the reconstruction effort in war hit societies. In the scope of the EU FP7 TIRAMISU Project, a toolbox for humanitarian demining missions is being developed by the consortium members. In this article we present the FSR Husky, an affordable, lightweight and autonomous all terrain robotic system, developed to assist human demining operation teams. Intended to be easily deployable on the field, our robotic solution has the ultimate goal of keeping humans away from the threat, safeguarding their lives. A detailed description of the modular robotic system architecture is presented, and several real world experiments are carried out to validate the robot's functionalities and illustrate continuous work in progress on minefield coverage, mine detection, outdoor localization, navigation, and environment perception. Proposal of a new affordable, lightweight and autonomous all-terrain robot for demining operations.Complete description of the mechanical system and design of the modular robotic architecture.Validation of the robot's intelligent capabilities in outdoor experiments.Work in progress and lessons learned for minefield coverage, and mine detection.


title: Customer Lifetime Value Prediction Using Embeddings

abstract: We describe the Customer LifeTime Value (CLTV) prediction system deployed at ASOS.com, a global online fashion retailer. CLTV prediction is an important problem in e-commerce where an accurate estimate of future value allows retailers to effectively allocate marketing spend, identify and nurture high value customers and mitigate exposure to losses. The system at ASOS provides daily estimates of the future value of every customer and is one of the cornerstones of the personalised shopping experience. The state of the art in this domain uses large numbers of handcrafted features and ensemble regressors to forecast value, predict churn and evaluate customer loyalty. Recently, domains including language, vision and speech have shown dramatic advances by replacing handcrafted features with features that are learned automatically from data. We detail the system deployed at ASOS and show that learning feature representations is a promising extension to the state of the art in CLTV modelling. We propose a novel way to generate embeddings of customers, which addresses the issue of the ever changing product catalogue and obtain a significant improvement over an exhaustive set of handcrafted features.


title: Neural Network Meets DCN: Traffic-driven Topology Adaptation with Deep Learning

abstract: The emerging optical/wireless topology reconfiguration technologies have shown great potential in improving the performance of data center networks. However, it also poses a big challenge on how to find the best topology configurations to support the dynamic traffic demands. In this work, we present xWeaver, a traffic-driven deep learning solution to infer the high-performance network topology online. xWeaver supports a powerful network model that enables the topology optimization over different performance metrics and network architectures. With the design of properly-structured neural networks, it can automatically derive the critical traffic patterns from data traces and learn the underlying mapping between the traffic patterns and topology configurations specific to the target data center. After offline training, xWeaver generates the optimized (or near-optimal) topology configuration online, and can also smoothly update its model parameters for new traffic patterns. We build an optical-circuit-switch-based testbed to demonstrate the function and transmission efficiency of our proposed solution. We further perform extensive simulations to show the significant performance gain of xWeaver, in supporting higher network throughput and smaller flow completion time.