title: Apparent Age Estimation from Face Images Combining General and Children-Specialized Deep Learning Models

abstract: This work describes our solution in the second edition of the ChaLearn LAP competition on Apparent Age Estimation. Starting from a pretrained version of the VGG-16 convolutional neural network for face recognition, we train it on the huge IMDB-Wiki dataset for biological age estimation and then fine-tune it for apparent age estimation using the relatively small competition dataset. We show that the precise age estimation of children is the cornerstone of the competition. Therefore, we integrate a separate "children" VGG-16 network for apparent age estimation of children between 0 and 12 years old in our final solution. The "children" network is fine-tuned from the "general" one. We employ different age encoding strategies for training "general" and "children" networks: the soft one (label distribution encoding) for the "general" network and the strict one (0/1 classification encoding) for the "children" network. Finally, we highlight the importance of the state-of-the-art face detection and face alignment for the final apparent age estimation. Our resulting solution wins the 1st place in the competition significantly outperforming the runner-up.


title: A generalized assignment heuristic for vehicle routing

abstract: Abstract : We consider a common variant of the vehicle routing problem in which a vehicle fleet delivers products stored at a central depot to satisfy customer orders. Each vehicle has a fixed capacity, and each order uses a fixed portion of vehicle capacity. The routing decision involves determining which of the demands will be satisfied by each vehicle and what route each vehicle will follow in servicing its assigned demand in order to minimize total delivery cost. We present a heuristic for this problem in which an assignment of customers to vehicles is obtained by solving a generalized assignment problem with an objective function that approximates delivery cost. This heuristic has many attractive features. It has outperformed the best existing heuristics on a sample of standard test problems. It will always find a feasible solution if one exists, something no other existing heuristic can guarantee. It can be easily adapted to accommodate many additional problem complexities. By parametrically varying the number of vehicles in the fleet, our method can be used to optimally solve the problem of finding the minimum size fleet that can feasibly service the specified demand.


title: Co-citation, bibliographic coupling and a characterization of lattice citation networks

abstract: In this article we study directed, acyclic graphs. We introduce the head and tail order relations and study some of their properties. Recalling the notions of generalized bibliographic coupling and generalized co-citation, and introducing a new property, called the l - property, we come to a characterization of lattices. As document citation networks are concrete realizations of directed acyclic graphs all our results are directly applicable to citation analysis.


title: Adaptive Trajectory Tracking Control of Skid-Steered Mobile Robots

abstract: Skid-steered mobile robots have been widely used for terrain exploration and navigation. In this paper, we present an adaptive trajectory control design for a skid-steered wheeled mobile robot. Kinematic and dynamic modeling of the robot is first presented. A pseudo-static friction model is used to capture the interaction between the wheels and the ground. An adaptive control algorithm is designed to simultaneously estimate the wheel/ground contact friction information and control the mobile robot to follow a desired trajectory. A Lyapunov-based convergence analysis of the controller and the estimation of the friction model parameter are presented. Simulation and preliminary experimental results based on a four-wheel robot prototype are demonstrated for the effectiveness and efficiency of the proposed modeling and control scheme


title: Bridgeless Boost Rectifier With Low Conduction Losses and Reduced Diode Reverse-Recovery Problems

abstract: A bridgeless boost rectifier with low conduction losses and reduced diode reverse-recovery problems is proposed for power-factor correction. The proposed boost rectifier can reduce the conduction losses and alleviate the diode reverse-recovery problems by using a coupled inductor and two additional diodes. Zero-current turn-off of the output diodes is achieved, and the reverse-recovery currents of the additional diodes are slowed down to reduce the diode reverse-recovery losses. All inductive components are realized on a single magnetic core by utilizing the leakage inductance of the coupled inductor. Furthermore, for the use of this topology in the practical design, the linear peak current mode control is employed for the proposed boost rectifier. A detailed analysis and a control strategy are presented. Experimental results for a 300-W prototype are also discussed to show the performance of the proposed boost rectifier


title: Learning to suggest questions in social media

abstract: Social media systems with Q&A functionalities have accumulated large archives of questions and answers. Two representative types are online forums and community-based Q&A services. To enable users to explore the large number of questions and answers in social media systems effectively, it is essential to suggest interesting items to an active user. In this article, we address the problem of question suggestion, which targets at suggesting questions that are semantically related to a queried question. Existing bag-of-words approaches suffer from the shortcoming that they could not bridge the lexical chasm between semantically related questions. Therefore, we present a new framework, and propose the topic-enhanced translation-based language model (TopicTRLM), which fuses both the lexical and latent semantic knowledge. This fusing enables TopicTRLM to find semantically related questions to a given question even when there is little word overlap. Moreover, to incorporate the answer information into the model to make the model more complete, we also propose the topic-enhanced translation-based language model with answer ensemble. Extensive experiments have been conducted with real-world datasets. Experimental results indicate our approach is very effective and outperforms other popular methods in several metrics.


title: Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing

abstract: Employing mobile cloud computing (MCC) to enable mobile users to acquire benefits of cloud computing by an environmental friendly method is an efficient strategy for meeting current industrial demands. However, the restrictions of wireless bandwidth and device capacity have brought various obstacles, such as extra energy waste and latency delay, when deploying MCC. Addressing this issue, we propose a dynamic energy-aware cloudlet-based mobile cloud computing model (DECM) focusing on solving the additional energy consumptions during the wireless communications by leveraging dynamic cloudlets (DCL)-based model. In this paper, we examine our model by a simulation of practical scenario and provide solid results for the evaluations. The main contributions of this paper are twofold. First, this paper is the first exploration in solving energy waste problems within the dynamic networking environment. Second, the proposed model provides future research with a guideline and theoretical supports. HighlightsThe first attempt for extending the functionality of cloudlets.Achieve energy-aware performances in the dynamic networking environment.Provide future research in the field with the theoretical support and exploring directions.The model may be migrated and applied in multiple industries.


title: Challenges in cloud based ingest and encoding for high quality streaming media

abstract: The Netflix ingest and encoding pipeline is a cloud-based platform that generates video encodes for the Netflix streaming service. Due to the large throughput of the system, automated video quality assessment of the source videos and the generated encodes is essential in ensuring the quality of experience of viewers. This paper discusses the motivations for integrating video quality assessment in the production pipeline, outlines currently deployed solutions and presents the technical challenges in improving the system.


title: GTM: The Generative Topographic Mapping

abstract: Latent variable models represent the probability density of data in a space of several dimensions in terms of a smaller number of latent, or hidden, variables. A familiar example is factor analysis which is based on a linear transformations between the latent space and the data space. In this paper we introduce a form of non-linear latent variable model called the Generative Topographic Mapping, for which the parameters of the model can be determined using the EM algorithm. GTM provides a principled alternative to the widely used Self-Organizing Map (SOM) of Kohonen (1982), and overcomes most of the significant limitations of the SOM. We demonstrate the performance of the GTM algorithm on a toy problem and on simulated data from flow diagnostics for a multi-phase oil pipeline.


title: Incorporating Laboratory Values Into a Machine Learning Model Improves In-Hospital Mortality Predictions After Rapid Response Team Call

abstract: Objectives ::: Machine learning models have been used to predict mortality among patients requiring rapid response team activation. The goal of our study was to assess the impact of adding laboratory values into the model. ::: ::: ::: Design ::: A gradient boosted decision tree model was derived and internally validated to predict a primary outcome of in-hospital mortality. The base model was then augmented with laboratory values. ::: ::: ::: Setting ::: Two tertiary care hospitals within The Ottawa Hospital network. ::: ::: ::: Patients ::: Inpatients over the age of 18 years who experienced a rapid response team activation between January 1, 2015, and May 31, 2016. ::: ::: ::: Interventions ::: None. ::: ::: ::: Measurements and Main Results ::: A total of 2,061 rapid response team activations occurred during the study period. The in-hospital mortality rate was 29.4%. Patients who died were older (median age, 72 vs 68 yr; p < 0.001), had a longer length of stay (length of stay) prior to rapid response team activation (4 vs 2 d; p < 0.001), and more often had respiratory distress (31% vs 22%; p < 0.001). Our base model without laboratory values performed with an area under the receiver operating curve of 0.71 (95% CI, 0.71-0.72). When the base model was augmented with laboratory values, the area under the receiver operating curve improved to 0.77 (95% CI, 0.77-0.78). Important mortality predictors in the base model were age, estimated ratio of Pao2 to Fio2 (calculated using oxygen saturation and estimated Fio2), length of stay prior to rapid response team activation, and systolic blood pressure. ::: ::: ::: Conclusions ::: Machine learning models can identify rapid response team patients at a high risk of mortality and potentially supplement clinical decision making. Incorporating laboratory values into model development significantly improved predictive performance in this study.


Urethral caruncle in a 9-year-old girl: a case report and review of the literature


title: Specificity of cognitive emotion regulation strategies: A transdiagnostic examination

abstract: Despite growing interest in the role of regulatory processes in clinical disorders, it is not clear whether certain cognitive emotion regulation strategies play a more central role in psychopathology than others. Similarly, little is known about whether these strategies have effects transdiagnostically. We examined the relationship between four cognitive emotion regulation strategies (rumination, thought suppression, reappraisal, and problem-solving) and symptoms of three psychopathologies (depression, anxiety, and eating disorders) in an undergraduate sample (N = 252). Maladaptive strategies (rumination, suppression), compared to adaptive strategies (reappraisal, problem-solving), were more strongly associated with psychopathology and loaded more highly on a latent factor of cognitive emotion regulation. In addition, this latent factor of cognitive emotion regulation was significantly associated with symptoms of all three disorders. Overall, these results suggest that the use of maladaptive strategies might play a more central role in psychopathology than the non-use of adaptive strategies and provide support of a transdiagnostic view of cognitive emotion regulation.


title: BatTracker: High Precision Infrastructure-free Mobile Device Tracking in Indoor Environments

abstract: Continuous tracking of the device location in 3D space is a popular form of user input, especially for virtual/augmented reality (VR/AR), video games and health rehabilitation. Conventional inertial based approaches are well known for inaccuracy caused by large error drifts. Computer vision approaches can produce accuracy tracking but have privacy concerns and are subject to lighting conditions and computation complexity. Recent work exploits accurate acoustic distance measurements for high precision tracking. However, they require additional hardware (e.g., multiple external speakers), which adds to the costs and installation efforts, thus limiting the convenience and usability. In this paper, we propose BatTracker, which incorporates inertial and acoustic data for robust, high precision and infrastructure-free tracking in indoor environments. BatTracker leverages echoes from nearby objects and uses distance measurements from them to correct error accumulation in inertial based device position prediction. It incorporates Doppler shifts and echo amplitudes to reliably identify the association between echoes and objects despite noisy signals from multi-path reflection and cluttered environment. A probabilistic algorithm creates, prunes and evolves multiple hypotheses based on measurement evidences to accommodate uncertainty in device position. Experiments in real environments show that BatTracker can track a mobile device's movements in 3D space at sub-cm level accuracy, comparable to the state-of-the-art infrastructure based approaches, while eliminating the needs of any additional hardware.


title: Sword: Scalable and Flexible Workload Generator for Distributed Data Processing Systems

abstract: Workload generation is commonly employed for performance characterization, testing and benchmarking of computer systems and networks. Workload generation typically aims at simulating or emulating traffic generated by different types of applications, protocols and activities, such as web browsing, email, chat, as well as stream multimedia traffic. We present a Scalable WORkloaD generator (SWORD) that we have developed for the testing and benchmarking of high-volume data processing systems. The tool is not only scalable but is also flexible and extensible allowing the generation of workload of a variety of types of applications and of contents.


title: Constructing Narrative Event Evolutionary Graph for Script Event Prediction

abstract: Script event prediction requires a model to predict the subsequent event given an existing event context. Previous models based on event pairs or event chains cannot make full use of dense event connections, which may limit their capability of event prediction. To remedy this, we propose constructing an event graph to better utilize the event network information for script event prediction. In particular, we first extract narrative event chains from large quantities of news corpus, and then construct a narrative event evolutionary graph (NEEG) based on the extracted chains. NEEG can be seen as a knowledge base that describes event evolutionary principles and patterns. To solve the inference problem on NEEG, we present a scaled graph neural network (SGNN) to model event interactions and learn better event representations. Instead of computing the representations on the whole graph, SGNN processes only the concerned nodes each time, which makes our model feasible to large-scale graphs. By comparing the similarity between input context event representations and candidate event representations, we can choose the most reasonable subsequent event. Experimental results on widely used New York Times corpus demonstrate that our model significantly outperforms state-of-the-art baseline methods, by using standard multiple choice narrative cloze evaluation.


title: Real-Time Multi-Digit Recognition System Using Deep Learning on an Embedded System

abstract: In the machine learning technology, making computers read letters, characters and digits have been a hot issue in many areas of research. Among them, the recognition of handwritten numbers has still a long way to go, unlike the recognition of printed digits or the recognition of handwritten English sentences. In this paper we will introduce to you a multiple handwritten digit recognition system using deep learning.


title: Reporting practices in confirmatory factor analysis: An overview and some recommendations.

abstract: Reporting practices in 194 confirmatory factor analysis studies (1,409 factor models) published in American Psychological Association journals from 1998 to 2006 were reviewed and compared with established reporting guidelines. Three research questions were addressed: (a) how do actual reporting practices compare with published guidelines? (b) how do researchers report model fit in light of divergent perspectives on the use of ancillary fit indices (e.g., L.-T. Hu & P. M. Bentler, 1999; H. W. Marsh, K.-T., Hau, & Z. Wen, 2004)? and (c) are fit measures that support hypothesized models reported more often than fit measures that are less favorable? Results indicate some positive findings with respect to reporting practices including proposing multiple models a priori and near universal reporting of the chi-square significance test. However, many deficiencies were found such as lack of information regarding missing data and assessment of normality. Additionally, the authors found increases in reported values of some incremental fit statistics and no statistically significant evidence that researchers selectively report measures of fit that support their preferred model. Recommendations for reporting are summarized and a checklist is provided to help editors, reviewers, and authors improve reporting practices.


title: High-Gain L-Probe Excited Substrate Integrated Cavity Antenna Array with LTCC-Based Gap Waveguide Feeding Network for W-Band Application

abstract: The W-band high-gain L-probe excited substrate integrated cavity (SIC) antenna and its arrays on a low-temperature cofired ceramic (LTCC) substrate are presented in this paper. To achieve a high gain, the SIC antenna is excited using L-probe feeding techniques. The SIC of the antenna servers as main radiator and the L-probe is involved in the radiation. A novel low-loss LTCC-based ridge gap waveguide (GWG) is also designed in this work. This GWG forms a compact hybrid two-layer low-loss feeding network with a substrate integrated waveguide (SIW) feeding network for an $8 \times 8$ antenna array. A high-gain antenna array is achieved by combining the high-gain antenna element and low-loss hybrid feeding network. In addition, a reference antenna array with a feeding network composed solely of SIW is designed for comparison. With the reconfiguration of the radiating element, feeding network, and transition, the proposed antenna array shows a wide measured impedance bandwidth of 14.9% from 87 to 101 GHz for $\vert \text{S}11\vert and a gain of up to 23.8 dBi. The measured aperture efficiency of the proposed array is 42.3%, which is 8% higher than that of the reference array.


title: A Fully Integrated Ka-Band Front End for 5G Transceiver

abstract: We present the design and measured results of a fully integrated Ka-Band front end on a 0.15-µm GaAs pHEMT process. The integrated front end includes a three stage power amplifier, three stage low noise amplifier, and single pole, double throw switch. The integration of the front end is a crucial step to commercialize mm-Wave technology for 5G mobile communication. In addition to the fully integrated front end module, these three components were fabricated separately for individual characterization and analysis, and those results are presented in this paper.


title: High resolution matting via interactive trimap segmentation

abstract: We present a new approach to the matting problem which splits the task into two steps: interactive trimap extraction followed by trimap-based alpha matting. By doing so we gain considerably in terms of speed and quality and are able to deal with high resolution images. This paper has three contributions: (i) a new trimap segmentation method using parametric max-flow; (ii) an alpha matting technique for high resolution images with a new gradient preserving prior on alpha; (iii) a database of 27 ground truth alpha mattes of still objects, which is considerably larger than previous databases and also of higher quality. The database is used to train our system and to validate that both our trimap extraction and our matting method improve on state-of-the-art techniques.


title: A New Face Database Simultaneously Acquired in Visible, Near-Infrared and Thermal Spectrums

abstract: In this paper, we present a new hand database called Tecnocampus Hand Image Database that includes right hand, palm and dorsal images. All the images have been acquired with three different sensors (visible, near-infrared and thermal). This database consists of 100 people acquired in five different acquisition sessions, two images per session and palm/dorsal sides. The total amount of pictures is 6.000, and it is mainly developed for hand image biometric recognition purposes. In addition, the database has been studied from the information theory point of view, and we found that this highest level of information is achieved in thermal spectrum. Furthermore, a low level of mutual information between different spectrums is also demonstrated. This opens an interesting research field in multi-sensor data fusion.


title: Real-time neuroevolution in the NERO video game

abstract: In most modern video games, character behavior is scripted; no matter how many times the player exploits a weakness, that weakness is never repaired. Yet, if game characters could learn through interacting with the player, behavior could improve as the game is played, keeping it interesting. This paper introduces the real-time Neuroevolution of Augmenting Topologies (rtNEAT) method for evolving increasingly complex artificial neural networks in real time, as a game is being played. The rtNEAT method allows agents to change and improve during the game. In fact, rtNEAT makes possible an entirely new genre of video games in which the player trains a team of agents through a series of customized exercises. To demonstrate this concept, the Neuroevolving Robotic Operatives (NERO) game was built based on rtNEAT. In NERO, the player trains a team of virtual robots for combat against other players' teams. This paper describes results from this novel application of machine learning, and demonstrates that rtNEAT makes possible video games like NERO where agents evolve and adapt in real time. In the future, rtNEAT may allow new kinds of educational and training applications through interactive and adapting games.


title: Evaluating Web Search with a Bejeweled Player Model

abstract: The design of a Web search evaluation metric is closely related with how the user's interaction process is modeled. Each behavioral model results in a different metric used to evaluate search performance. In these models and the user behavior assumptions behind them, when a user ends a search session is one of the prime concerns because it is highly related to both benefit and cost estimation. Existing metric design usually adopts some simplified criteria to decide the stopping time point: (1) upper limit for benefit (e.g. RR, AP); (2) upper limit for cost (e.g. Precision@N, DCG@N). However, in many practical search sessions (e.g. exploratory search), the stopping criterion is more complex than the simplified case. Analyzing benefit and cost of actual users' search sessions, we find that the stopping criteria vary with search tasks and are usually combination effects of both benefit and cost factors. Inspired by a popular computer game named Bejeweled, we propose a Bejeweled Player Model (BPM) to simulate users' search interaction processes and evaluate their search performances. In the BPM, a user stops when he/she either has found sufficient useful information or has no more patience to continue. Given this assumption, a new evaluation framework based on upper limits (either fixed or changeable as search proceeds) for both benefit and cost is proposed. We show how to derive a new metric from the framework and demonstrate that it can be adopted to revise traditional metrics like Discounted Cumulative Gain (DCG), Expected Reciprocal Rank (ERR) and Average Precision (AP). To show effectiveness of the proposed framework, we compare it with a number of existing metrics in terms of correlation between user satisfaction and the metrics based on a dataset that collects users' explicit satisfaction feedbacks and assessors' relevance judgements. Experiment results show that the framework is better correlated with user satisfaction feedbacks.


Chapter 2 Pervasive Computing and Healthcare


Prurigo nodularis and lichen simplex chronicus


title: Time Series Classification in Reservoir- and Model-Space

abstract: We evaluate two approaches for time series classification based on reservoir computing. In the first, classical approach, time series are represented by reservoir activations. In the second approach, on top of the reservoir activations, a predictive model in the form of a readout for one-step-ahead-prediction is trained for each time series. This learning step lifts the reservoir features to a more sophisticated model space. Classification is then based on the predictive model parameters describing each time series. We provide an in-depth analysis on time series classification in reservoir- and model-space. The approaches are evaluated on 43 univariate and 18 multivariate time series. The results show that representing multivariate time series in the model space leads to lower classification errors compared to using the reservoir activations directly as features. The classification accuracy on the univariate datasets can be improved by combining reservoir- and model-space.


title: Towards Highly Accurate and Stable Face Alignment for High-Resolution Videos

abstract: In recent years, heatmap regression based models have shown their effectiveness in face alignment and pose estimation. However, Conventional Heatmap Regression (CHR) is not accurate nor stable when dealing with high-resolution facial videos, since it finds the maximum activated location in heatmaps which are generated from rounding coordinates, and thus leads to quantization errors when scaling back to the original high-resolution space. In this paper, we propose a Fractional Heatmap Regression (FHR) for high-resolution video-based face alignment. The proposed FHR can accurately estimate the fractional part according to the 2D Gaussian function by sampling three points in heatmaps. To further stabilize the landmarks among continuous video frames while maintaining the precise at the same time, we propose a novel stabilization loss that contains two terms to address time delay and non-smooth issues, respectively. Experiments on 300W, 300VW and Talking Face datasets clearly demonstrate that the proposed method is more accurate and stable than the state-ofthe-art models.


The class imbalance problem in pattern classification and learning


title: Evaluation of grasps for 3D objects with physical interpretations using object wrench space

abstract: In this paper, we present a practically useful and intuitive grasp quality measure that takes into account the shapes of object geometries and the torque limits of finger actuators. The proposed grasp quality measure is defined by the distance between the convex hulls of the absolute grasp wrench space (a-GWS) and the object wrench space (OWS), where a-GWS and OWS are, respectively, created by the active wrenches from the robot fingers with limited torque bounds and the uniform distribution of unitary normal disturbances on the surface of a polyhedral object. The computational algorithm for the grasp quality measure also yields the information of which spots of the object are fatal under the disturbance, which makes the algorithm practically useful. We demonstrate the validity of the proposed measure through numerical examples.


title: Automatic License Plate Recognition: a Review

abstract: In recent years, many researches on Intelligent Transportation Systems (ITS) have been reported. Automatic License Plate Recognition (ALPR) is one form of ITS technology that not only recognizes and counts vehicles, but distinguishes each as unique by recognizing and recording the license plate’s characters. This paper discusses the main techniques of ALPR. Several open problems are proposed at the end of the paper for future research.


title: Comparison of PCA -, MDF -, and RD-LDA - based feature extraction approaches for hand-based personal recognition

abstract: In this paper, Principal Component Analysis (PCA), Most Discriminant Features (MDF), and Regularized-Direct Linear Discriminant Analysis (RD-LDA) - based feature extraction approaches are tested and compared in an experimental personal recognition system. The system is multimodal and bases on features extracted from nine regions of an image of the palmar surface of the hand. For testing purposes 10 gray-scale images of right hand of 184 people were acquired. The experiments have shown that the best results are obtained with the RD-LDA - based features extraction approach (100% correctness for 920 identification tests and EER = 0.01% for 64170 verification tests).


title: Automated problem identification: regression vs classification via evolutionary deep networks

abstract: Regression or classification? This is perhaps the most basic question faced when tackling a new supervised learning problem. We present an Evolutionary Deep Learning (EDL) algorithm that automatically solves this by identifying the question type with high accuracy, along with a proposed deep architecture. Typically, a significant amount of human insight and preparation is required prior to executing machine learning algorithms. For example, when creating deep neural networks, the number of parameters must be selected in advance and furthermore, a lot of these choices are made based upon pre-existing knowledge of the data such as the use of a categorical cross entropy loss function. Humans are able to study a dataset and decide whether it represents a classification or a regression problem, and consequently make decisions which will be applied to the execution of the neural network. We propose the Automated Problem Identification (API) algorithm, which uses an evolutionary algorithm interface to TensorFlow to manipulate a deep neural network to decide if a dataset represents a classification or a regression problem. We test API on 16 different classification, regression and sentiment analysis datasets with up to 10,000 features and up to 17,000 unique target values. API achieves an average accuracy of $96.3\%$ in identifying the problem type without hardcoding any insights about the general characteristics of regression or classification problems. For example, API successfully identifies classification problems even with 1000 target values. Furthermore, the algorithm recommends which loss function to use and also recommends a neural network architecture. Our work is therefore a step towards fully automated machine learning.


title: Prochlo: Strong Privacy for Analytics in the Crowd

abstract: The large-scale monitoring of computer users' software activities has become commonplace, e.g., for application telemetry, error reporting, or demographic profiling. This paper describes a principled systems architecture---Encode, Shuffle, Analyze (ESA)---for performing such monitoring with high utility while also protecting user privacy. The ESA design, and its Prochlo implementation, are informed by our practical experiences with an existing, large deployment of privacy-preserving software monitoring. With ESA, the privacy of monitored users' data is guaranteed by its processing in a three-step pipeline. First, the data is encoded to control scope, granularity, and randomness. Second, the encoded data is collected in batches subject to a randomized threshold, and blindly shuffled, to break linkability and to ensure that individual data items get "lost in the crowd" of the batch. Third, the anonymous, shuffled data is analyzed by a specific analysis engine that further prevents statistical inference attacks on analysis results. ESA extends existing best-practice methods for sensitive-data analytics, by using cryptography and statistical techniques to make explicit how data is elided and reduced in precision, how only common-enough, anonymous data is analyzed, and how this is done for only specific, permitted purposes. As a result, ESA remains compatible with the established workflows of traditional database analysis. Strong privacy guarantees, including differential privacy, can be established at each processing step to defend against malice or compromise at one or more of those steps. Prochlo develops new techniques to harden those steps, including the Stash Shuffle, a novel scalable and efficient oblivious-shuffling algorithm based on Intel's SGX, and new applications of cryptographic secret sharing and blinding. We describe ESA and Prochlo, as well as experiments that validate their ability to balance utility and privacy.


Joint modeling of longitudinal and time-to-event data: an overview


title: Connectionist theory refinement: Genetically searching the space of network topologies

abstract: An algorithm that learns from a set of examples should ideally be able to exploit the available resources of (a) abundant computing power and (b) domain-specific knowledge to improve its ability to generalize. Connectionist theory-refinement systems, which use background knowledge to select a neural network's topology and initial weights, have proven to be effective at exploiting domain-specific knowledge; however, most do not exploit available computing power. This weakness occurs because they lack the ability to refine the topology of the neural networks they produce, thereby limiting generalization, especially when given impoverished domain theories. We present the Regent algorithm which uses (a) domain-specific knowledge to help create an initial population of knowledge-based neural networks and (b) genetic operators of crossover and mutation (specifically designed for knowledge-based networks) to continually search for better network topologies. Experiments on three real-world domains indicate that our new algorithm is able to significantly increase generalization compared to a standard connectionist theory-refinement system, as well as our previous algorithm for growing knowledge-based networks.


title: MCScript: A Novel Dataset for Assessing Machine Comprehension Using Script Knowledge

abstract: We introduce a large dataset of narrative texts and questions about these texts, intended to be used in a machine comprehension task that requires reasoning using commonsense knowledge. Our dataset complements similar datasets in that we focus on stories about everyday activities, such as going to the movies or working in the garden, and that the questions require commonsense knowledge, or more specifically, script knowledge, to be answered. We show that our mode of data collection via crowdsourcing results in a substantial amount of such inference questions. The dataset forms the basis of a shared task on commonsense and script knowledge organized at SemEval 2018 and provides challenging test cases for the broader natural language understanding community.


title: Integrating and Evaluating Neural Word Embeddings in Information Retrieval

abstract: Recent advances in neural language models have contributed new methods for learning distributed vector representations of words (also called word embeddings). Two such methods are the continuous bag-of-words model and the skipgram model. These methods have been shown to produce embeddings that capture higher order relationships between words that are highly effective in natural language processing tasks involving the use of word similarity and word analogy. Despite these promising results, there has been little analysis of the use of these word embeddings for retrieval. Motivated by these observations, in this paper, we set out to determine how these word embeddings can be used within a retrieval model and what the benefit might be. To this aim, we use neural word embeddings within the well known translation language model for information retrieval. This language model captures implicit semantic relations between the words in queries and those in relevant documents, thus producing more accurate estimations of document relevance. The word embeddings used to estimate neural language models produce translations that differ from previous translation language model approaches; differences that deliver improvements in retrieval effectiveness. The models are robust to choices made in building word embeddings and, even more so, our results show that embeddings do not even need to be produced from the same corpus being used for retrieval.


title: Dynamic elicitation of user preferences in a chat-based group recommender system

abstract: Group recommender systems (GRSs) support users in a group when facing decision making tasks. Many GRSs simply perform a one shot aggregation of the individual preferences and then compute recommendations. In this paper, we propose a different approach; it allows users to iteratively express and revise their preferences during the decision making process and supports such a conversational process with a chat-based interface. The implemented system observes users' interactions with the discussed options and offers appropriate support as well as recommendations to facilitate group members to come up with an agreement and a final decision. We have evaluated the effectiveness of our approach by conducting a controlled live user study. The empirical results show that the mobile application that implements the proposed approach achieves usability as well as high recommendation quality and choice satisfaction.


title: Global software development and collaboration: barriers and solutions

abstract: While organisations recognise the advantages offered by global software development, many socio-technical barriers affect successful collaboration in this inter-cultural environment. In this paper, we present a review of the global software development literature where we highlight collaboration problems experienced by a cross-section of organisations in twenty-six studies. We also look at the literature to answer how organisations are overcoming these barriers in practice. We build on our previous study on global software development where we define collaboration as four practices related to agreeing, allocating, and planning goals, objectives, and tasks among distributed teams. We found that the key barriers to collaboration are geographic, temporal, cultural, and linguistic distance; the primary solutions to overcoming these barriers include site visits, synchronous communication technology, and knowledge sharing infrastructure to capture implicit knowledge and make it explicit.


title: The Stability of Kernel Principal Components Analysis and its Relation to the Process Eigenspectrum

abstract: In this paper we analyze the relationships between the eigenvalues of the m x m Gram matrix K for a kernel k (·, ·) corresponding to a sample x1,...,xm drawn from a density p(x) and the eigenvalues of the corresponding continuous eigenproblem. We bound the differences between the two spectra and provide a performance bound on kernel PCA.


title: An Improvement to Feature Selection of Random Forests on Spark

abstract: The Random Forests algorithm belongs to the class of ensemble learning methods, which are common used in classification problem. In this paper, we studied the problem of adopting the Random Forests algorithm to learn raw data from real usage scenario. An improvement, which is stable, strict, high efficient, data-driven, problem independent and has no impact on algorithm performance, is proposed to investigate 2 actual issues of feature selection of the Random Forests algorithm. The first one is to eliminate noisy features, which are irrelevant to the classification. And the second one is to eliminate redundant features, which are highly relevant with other features, but useless. We implemented our improvement approach on Spark. Experiments are performed to evaluate our improvement and the results show that our approach has an ideal performance.


Low Resource Keyword Spotting


title: Spatially Perturbed Collision Sounds Attenuate Perceived Causality in 3D Launching Events

abstract: When a moving object collides with an object at rest, people immediately perceive a causal event: i.e., the first object has launched the second object forwards. However, when the second object's motion is delayed, or is accompanied by a collision sound, causal impressions attenuate and strengthen. Despite a rich literature on causal perception, researchers have exclusively utilized 2D visual displays to examine the launching effect. It remains unclear whether people are equally sensitive to the spatiotemporal properties of observed collisions in the real world. The present study first examined whether previous findings in causal perception with audiovisual inputs can be extended to immersive 3D virtual environments. We then investigated whether perceived causality is influenced by variations in the spatial position of an auditory collision indicator. We found that people are able to localize sound positions based on auditory inputs in VR environments, and spatial discrepancy between the estimated position of the collision sound and the visually observed impact location attenuates perceived causality.


title: A hardware-in-the-loop simulation for LOS rate estimation of strapdown seeker based on EKF

abstract: In order to capture the target more quickly, some seekers of air-to-ground missile were intentionally installed with an angle between the optical axis and the body axis in vertical plane, which is defined as the pitching angle of optical axis. Aiming at solving the problem that the strapdown seeker cannot measure the line-of-sight (LOS) rate directly, a LOS rate estimate method based on Extended Kalman Filter (EKF) has been presented and a hardware-in-the-loop simulation experiment using a real strapdown laser seeker with a pitching angle of optical axis was made to evaluate the scheme in this paper. It shows that the LOS angle and rate obtained from a real strapdown laser seeker can basically match with that derived from a mathematical model of a gimbaled laser seeker. With an accurate Circular Error Probable (CEP), it provides feasibility and effectiveness for engineering applications of a real strapdown laser seeker with a pitching angle of optical axis utilizing the EKF algorithm to estimate the LOS rate.


title: Web page classification: Features and algorithms

abstract: Classification of Web page content is essential to many tasks in Web information retrieval such as maintaining Web directories and focused crawling. The uncontrolled nature of Web content presents additional challenges to Web page classification as compared to traditional text classification, but the interconnected nature of hypertext also provides features that can assist the process. As we review work in Web page classification, we note the importance of these Web-specific features and algorithms, describe state-of-the-art practices, and track the underlying assumptions behind the use of information from neighboring pages.


title: Diagnosing the decline in pharmaceutical R&D efficiency

abstract: The past 60 years have seen huge advances in many of the scientific, technological and managerial factors that should tend to raise the efficiency of commercial drug research and development (RD). Yet the number of new drugs approved per billion US dollars spent on RD has halved roughly every 9 years since 1950, falling around 80-fold in inflation-adjusted terms. There have been many proposed solutions to the problem of declining RD efficiency. However, their apparent lack of impact so far and the contrast between improving inputs and declining output in terms of the number of new drugs make it sensible to ask whether the underlying problems have been correctly diagnosed. Here, we discuss four factors that we consider to be primary causes, which we call the 'better than the Beatles' problem; the 'cautious regulator' problem; the 'throw money at it' tendency; and the 'basic research-brute force' bias. Our aim is to provoke a more systematic analysis of the causes of the decline in RD efficiency.


title: The amygdala and reward

abstract: The amygdala — an almond-shaped group of nuclei at the heart of the telencephalon — has been associated with a range of cognitive functions, including emotion, learning, memory, attention and perception. Most current views of amygdala function emphasize its role in negative emotions, such as fear, and in linking negative emotions with other aspects of cognition, such as learning and memory. However, recent evidence supports a role for the amygdala in processing positive emotions as well as negative ones, including learning about the beneficial biological value of stimuli. Indeed, the amygdala's role in stimulus–reward learning might be just as important as its role in processing negative affect and fear conditioning.


title: Quantitative Analysis of the Full Bitcoin Transaction Graph

abstract: The Bitcoin scheme is a rare example of a large scale global payment system in which all the transactions are publicly accessible (but in an anonymous way). We downloaded the full history of this scheme, and analyzed many statistical properties of its associated transaction graph. In this paper we answer for the first time a variety of interesting questions about the typical behavior of users, how they acquire and how they spend their bitcoins, the balance of bitcoins they keep in their accounts, and how they move bitcoins between their various accounts in order to better protect their privacy. In addition, we isolated all the large transactions in the system, and discovered that almost all of them are closely related to a single large transaction that took place in November 2010, even though the associated users apparently tried to hide this fact with many strange looking long chains and fork-merge structures in the transaction graph.


title: Maturing Software Engineering Knowledge through Classifications: A Case Study on Unit Testing Techniques

abstract: Classification makes a significant contribution to advancing knowledge in both science and engineering. It is a way of investigating the relationships between the objects to be classified and identifies gaps in knowledge. Classification in engineering also has a practical application; it supports object selection. They can help mature software engineering knowledge, as classifications constitute an organized structure of knowledge items. Till date, there have been few attempts at classifying in software engineering. In this research, we examine how useful classifications in software engineering are for advancing knowledge by trying to classify testing techniques. The paper presents a preliminary classification of a set of unit testing techniques. To obtain this classification, we enacted a generic process for developing useful software engineering classifications. The proposed classification has been proven useful for maturing knowledge about testing techniques, and therefore, SE, as it helps to: 1) provide a systematic description of the techniques, 2) understand testing techniques by studying the relationships among techniques (measured in terms of differences and similarities), 3) identify potentially useful techniques that do not yet exist by analyzing gaps in the classification, and 4) support practitioners in testing technique selection by matching technique characteristics to project characteristics.


title: Ontology-Based Textual Emotion Detection

abstract: Emotion Detection from text is a very important area of natural language processing. This paper shows a new method for emotion detection from text which depends on ontology. This method is depending on ontology extraction from the input sentence by using a triplet extraction algorithm by the OpenNLP parser, then make an ontology matching with the ontology base that we created by similarity and word sense disambiguation. This ontology base consists of ontologies and the emotion label related to each one. We choose the emotion label of the sentence with the highest score of matching. If the extracted ontology doesn’t match any ontology from the ontology base we use the keyword-based approach. This method doesn’t depend only on keywords like previous approaches; it depends on the meaning of sentence words and the syntax and semantic analysis of the context.


title: Character recognition using neural networks

abstract: It has been 50 years since the idea popped up that calculating systems can be made on the replica of the biological neural networks. Still, the development of this science branch made the improvement of these systems possible only in the last 25–30 years [6]. Nowadays, neural computing is a very extensive, separate science. Its solid theory basis made it possible to use them to solve many kind of problems in artificial computing, thus improving the experience of the science. Neural networks are commonly used to solve sample-recognition problems. One of these is character recognition. The solution of this problem is one of the easier implementations of neural networks. With the help of Matlab's Neural Network Toolbox, we tried to recognize printed and handwritten characters by projecting them on different sized grids (5×7, 7×11, 9×13). The results showed that the precision of the character recognition depends on the resolution of the character projection. Also, we realized, that not every writing style can be recognized using the same network with the same precision. This shows that the variety of human handwriting habits can't be fully covered with one neural network.


title: Efficient Clustering-Based Outlier Detection Algorithm for Dynamic Data Stream

abstract: Anomaly detection is currently an important and active research problem in many fields and involved in numerous applications. Most of the existing methods are based on distance measure. But in case of data stream these methods are not very efficient as computational point of view. Most of the exiting work on outlier detection in data stream declare a point as an outlier/inlier as soon as it arrive due to limited memory resources as compared to the huge data stream, to declare an outlier as it arrive often can lead us to a wrong decision, because of dynamic nature of the incoming data. In this paper we introduced a clustering based approach, which divide the stream in chunks and cluster each chunk using k-mean in fixed number of clusters. Instead of keeping only the summary information, which often used in case of clustering data stream, we keep the candidate outliers and mean value of every cluster for the next fixed number of steam chunks, to make sure that the detected candidate outliers are the real outliers. By employing the mean value of the clusters of previous chunk with mean values of the current chunk of stream, we decide better outlierness for data stream objects. Several experiments on different dataset confirm that our technique can find better outliers with low computational cost than the other exiting distance based approaches of outlier detection in data stream.


title: Positive Affect Moderates the Relationship Between Salivary Testosterone and a Health Behavior Composite in University Females

abstract: Testosterone is released in both men and women and plays an important role in social functioning and motivation. Greater testosterone in women has been associated with negative physical health outcomes, while lower testosterone has been associated with psychological disorders. The following cross-sectional study examined the contribution of salivary testosterone, positive and negative affect, and demographic variables in predicting a composite health behavior score (cigarette use, hours of sleep, fruit/vegetable intake, following an exercise routine). The sample (mean age 21.17, SD = 6.13) consisted of 87 female university students asked to complete a demographic and lifestyle behavior questionnaire, the Positive and Negative Affect Schedule, and provide a saliva sample. Participants self-identified as Latina (37.9%), European American (32.2%), Asian American (5.7%), African American (4.6%), or Mixed/other (19.5%). Hierarchical regression analyses were used to examine whether positive and negative affect served as a moderator between salivary testosterone and a health behavior composite. Results indicated that positive affect moderated the relationship between salivary testosterone and the composite health behavior score (t = − 2.42, p = .018, Adj. R2 = .21, F (5, 81) = 5.07, p < .001) such that the healthiest behaviors were observed in participants with high positive affect and low salivary testosterone. Findings remained after adjusting for oral contraceptive use, income level, relationship status, and ethnicity. These results provide a preliminary foundation for future research examining the interplay of neuroendocrine function, psychological factors (i.e., positive affect), and behavior. Further empirical studies can focus on expanding this research in larger, representative samples.


Twitter at the Grammys: A Social Media Corpus for Entity Linking and Disambiguation


title: Insights from Machine-Learned Diet Success Prediction

abstract: To support people trying to lose weight and stay healthy, more and more fitness apps have sprung up including the ability to track both calories intake and expenditure. Users of such apps are part of a wider ``quantified self'' movement and many opt-in to publicly share their logged data. In this paper, we use public food diaries of more than 4,000 long-term active MyFitnessPal users to study the characteristics of a (un-)successful diet. Concretely, we train a machine learning model to predict repeatedly being over or under self-set daily calories goals and then look at which features contribute to the model's prediction. Our findings include both expected results, such as the token ``mcdonalds'' or the category ``dessert'' being indicative for being over the calories goal, but also less obvious ones such as the difference between pork and poultry concerning dieting success, or the use of the ``quick added calories'' functionality being indicative of over-shooting calorie-wise. This study also hints at the feasibility of using such data for more in-depth data mining, e.g., looking at the interaction between consumed foods such as mixing protein- and carbohydrate-rich foods. To the best of our knowledge, this is the first systematic study of public food diaries.


title: Delayed and Inconsistent Information and the Evolution of Trust

abstract: Trust is essential in virtually all economic interactions. In this paper, we build on previous work analyzing the “trust game” from the perspective of evolutionary dynamics. There are two players: an “investor” and a “trustee.” The investor begins with one monetary unit and can choose to transfer it to the trustee. The transfer, if made, is multiplied by a factor b>1 representing the gains that arise from cooperation. The trustee can then return a fraction of his choosing to the investor. Previous work has shown that adding information to this game can lead to trusting and trustworthy behavior. But in those models, information spreads instantaneously and investors never face conflicting information. Here, we relax both of these assumptions. We introduce delays in information propagation so that an investor may still be acting on old information after a trustee has changed his behavior. And we give investors “memories” and thereby allow for the possibility that they might face conflicting information about trustees. In both cases, we find that the trust and trustworthiness induced by information is robust to delays and conflicts. Even if it takes time for information to spread, and even if investors sometimes deem information to be unreliable, the benefits of trust are realized with just moderate levels of information about trustees. We conclude that information (or “reputation”) is a robust explanation for the trust and trustworthiness observed among humans.


title: The Economics of Multi-Hop Ride Sharing : Creating new mobility networks through IS

abstract: Ride sharing allows to share costs of traveling by car, e.g., for fuel or highway tolls. Furthermore, it reduces congestion and emissions by making better use of vehicle capacities. Ride sharing is hence beneficial for drivers, riders, as well as society. While the concept has existed for decades, ubiquity of digital and mobile technology and user habituation to peer-to-peer services and electronic markets have resulted in particular growth in recent years. This paper explores the novel idea of multi-hop ride sharing and illustrates how information systems can leverage its potential. Based on empirical ride sharing data, we provide a quantitative analysis of the structure and the economics of electronic ride sharing markets. We explore the potential and competitiveness of multi-hop ride sharing and analyze its implications for platform operators. We find that multi-hop ride sharing proves competitive against other modes of transportation and has the potential to greatly increase ride availability and city connectedness, especially under high reliability requirements. To fully realize this potential, platform operators should implement multi-hop search, assume active control of pricing and booking processes, improve coordination of transfers, enhance data services, and try to expand their market share.


title: Hidradenitis suppurativa.

abstract: BACKGROUND ::: Hidradenitis suppurativa is a chronic, recurrent, suppurative cutaneous disease. Despite its incidence, optimal medical or surgical treatment remains unclear. This review describes the disease, ranging from pathogenesis to treatment and prognosis. ::: ::: ::: METHODS ::: Articles were sourced from PubMed and Medline, using the MeSH terms 'hidradenitis suppurativa' and 'acne inversa'. Selection of articles was based on peer review, journal, relevance and English language. ::: ::: ::: RESULTS AND CONCLUSION ::: On the basis of histological findings, the disease is now considered inflammatory and originating from the hair follicle; therefore, the term 'acne inversa' is favoured by some experts. The exact aetiology remains obscure but smoking seems to be a major triggering factor. Treatment should be individualized according to the site and extent of the disease. Absolute cessation of smoking is essential in the treatment of hidradenitis. Management with antibiotics or other medications may relieve early symptoms, but radical surgery may be necessary for control and to prevent recurrence.


Chapter 2 Measuring the Thermal Conductivity of Thin Films : 3 Omega and Related Electrothermal


title: Designing Fuzzy Rule Based Expert System for Cyber Security

abstract: The state of cyber security has begun to attract more attention and interest outside the community of computer security experts. Cyber security is not a single problem, but rather a group of highly different problems involving different sets of threats. Fuzzy Rule based system for cyber security is a system consists of a rule depository and a mechanism for accessing and running the rules. The depository is usually constructed with a collection of related rule sets. The aim of this study is to develop a fuzzy rule based technical indicator for cyber security with the use of expert system. Rule based systems employ fuzzy rule to automate complex processes. Common cyber threats assumed for cyber experts are used as linguistic variables in this paper.


title: Camera handoff and placement for automated tracking systems with multiple omnidirectional cameras

abstract: In a multi-camera surveillance system, both camera handoff and placement play an important role in generating an automated and persistent object tracking, typical of most surveillance requirements. Camera handoff should comprise three fundamental components, time to trigger handoff process, the execution of consistent labeling, and the selection of the next optimal camera. In this paper, we design an observation measure to quantitatively formulate the effectiveness of object tracking so that we can trigger camera handoff timely and select the next camera appropriately before the tracked object falls out of the field of view (FOV) of the currently observing camera. In the meantime, we present a novel solution to the consistent labeling problem in omnidirectional cameras. A spatial mapping procedure is proposed to consider both the noise inherent to the tracking algorithms used by the system and the lens distortion introduced by omnidirectional cameras. This does not only avoid the tedious process, but also increases the accuracy, to obtain the correspondence between omnidirectional cameras without human interventions. We also propose to use the Wilcoxon Signed-Rank Test to improve the accuracy of trajectory association between pairs of objects. In addition, since we need a certain amount of time to successfully carry out the camera handoff procedure, we introduce an additional constraint to optimally reserve sufficient cameras' overlapped FOVs for the camera placement. Experiments show that our proposed observation measure can quantitatively formulate the effectiveness of tracking, so that camera handoff can smoothly transfer objects of interest. Meanwhile, our proposed consistent labeling approach can perform as accurately as the geometry-based approach without tedious calibration processes and outperform Calderara's homography-based approach. Our proposed camera placement method exhibits a significant increase in the camera handoff success rate at the cost of slightly decreased coverage, as compared to Erdem and Sclaroff's method without considering the requirement on overlapped FOVs.


title: The New Frontier in RAN Heterogeneity: Multi-Tier Drone-Cells

abstract: In cellular networks, the locations of the radio access network (RAN) elements are determined mainly based on the long-term traffic behaviour. However, when the random and hard-to-predict spatio-temporal distribution of the traffic (load,demand) does not fully match the fixed locations of the RAN elements (supply), some performance degradation becomes inevitable. The concept of multi-tier cells (heterogeneous networks, HetNets) has been introduced in 4G networks to alleviate this mismatch. However, as the traffic distribution deviates more and more from the long-term average, even the HetNet architecture will have difficulty in coping up with the erratic supply-demand mismatch, unless the RAN is grossly over-engineered (which is a financially non-viable solution). In this article, we study the opportunistic utilization of low-altitude unmanned aerial platforms equipped with base stations (BSs), i.e., drone-BSs, in 5G networks. In particular, we envisage a multi-tier drone-cell network complementing the terrestrial HetNets. The variety of equipment, and non-rigid placement options allow utilizing multitier drone-cell networks to serve diversified demands. Hence, drone-cells bring the supply to where the demand is, which sets new frontiers for the heterogeneity in 5G networks. We investigate the advancements promised by drone-cells, and discuss the challenges associated with their operation and management. We propose a drone-cell management framework (DMF) benefiting from the synergy among software defined networking (SDN), network functions virtualization (NFV), and cloud-computing. We demonstrate DMF mechanisms via a case study, and numerically show that it can reduce the cost of utilizing drone-cells in multitenancy cellular networks.


title: Neural Machine Translation with Source-Side Latent Graph Parsing

abstract: This paper presents a novel neural machine translation model which jointly learns translation and source-side latent graph representations of sentences. Unlike existing pipelined approaches using syntactic parsers, our end-to-end model learns a latent graph parser as part of the encoder of an attention-based neural machine translation model, and thus the parser is optimized according to the translation objective. In experiments, we first show that our model compares favorably with state-of-the-art sequential and pipelined syntax-based NMT models. We also show that the performance of our model can be further improved by pre-training it with a small amount of treebank annotations. Our final ensemble model significantly outperforms the previous best models on the standard English-to-Japanese translation dataset.


title: MCTS Based on Simple Regret

abstract: UCT, a state-of-the art algorithm for Monte Carlo tree search (MCTS) in games and Markov decision processes, is based on UCB, a sampling policy for the Multi-armed Bandit problem (MAB) that minimizes the cumulative regret. However, search differs from MAB in that in MCTS it is usually only the final "arm pull" (the actual move selection) that collects a reward, rather than all "arm pulls". Therefore, it makes more sense to minimize the simple regret, as opposed to the cumulative regret. We begin by introducing policies for multiarmed bandits with lower finite-time and asymptotic simple regret than UCB, using it to develop a two-stage scheme (SR+CR) for MCTS which outperforms UCT empirically. ::: ::: Optimizing the sampling process is itself a metareasoning problem, a solution of which can use value of information (VOI) techniques. Although the theory of VOI for search exists, applying it to MCTS is non-trivial, as typical myopic assumptions fail. Lacking a complete working VOI theory for MCTS, we nevertheless propose a sampling scheme that is "aware" of VOI, achieving an algorithm that in empirical evaluation outperforms both UCT and the other proposed algorithms.


title: Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning

abstract: Imbalanced-learn is an open-source python toolbox aiming at providing a wide range of methods to cope with the problem of imbalanced dataset frequently encountered in machine learning and pattern recognition. The implemented state-of-the-art methods can be categorized into 4 groups: (i) under-sampling, (ii) over-sampling, (iii) combination of over- and under-sampling, and (iv) ensemble learning methods. The proposed toolbox only depends on numpy, scipy, and scikit-learn and is distributed under MIT license. Furthermore, it is fully compatible with scikit-learn and is part of the scikit-learn-contrib supported project. Documentation, unit tests as well as integration tests are provided to ease usage and contribution. The toolbox is publicly available in GitHub: this https URL


title: Network dynamics and field evolution: the growth of interorganizational collaboration in the life sciences

abstract: A recursive analysis of network and institutional evolution is offered to account for the decentralized structure of the commercial field of the life sciences. Four alternative logics of attachment—accumulative advantage, homophily, follow‐the‐trend, and multiconnectivity—are tested to explain the structure and dynamics of interorganizational collaboration in biotechnology. Using multiple novel methods, the authors demonstrate how different rules for affiliation shape network evolution. Commercialization strategies pursued by early corporate entrants are supplanted by universities, research institutes, venture capital, and small firms. As organizations increase their collaborative activities and diversify their ties to others, cohesive subnetworks form, characterized by multiple, independent pathways. These structural components, in turn, condition the choices and opportunities available to members of a field, thereby reinforcing an attachment logic based on differential connections to diverse partners.


title: Damage Detection in Pavement Structures Using Self-powered Sensors

abstract: This paper presents a new approach for the continuous health monitoring of asphalt concrete pavements using self-powered wireless sensors. Numerical and experimental studies were carried out to evaluate the damage detection performance of the proposed self-sustained sensing system. A three-dimensional finite element analysis was performed to obtain the pavement responses under moving tire loading. Damage was introduced as bottom-up fatigue cracks at the bottom of the asphalt layer. Thereafter, features extracted from the simulated dynamic strain data for a number of sensing nodes were used to detect damage. Laboratory tests were carried out on an asphalt concrete specimen in three point bending mode to verify the sensor response. The results indicate that the proposed method is effective in detecting different damage states including crack propagation.


title: 29P. Exploring the Impact of Knowledge Management Capabilities on Organizational Effectiveness

abstract: Research on knowledge management success often focuses on aggregate concepts of knowledge management capabilities when assessing their impact on organizational effectiveness. As such, little is known about the role of the individual resources that make up an organization's knowledge management capability and their impact on organizational effectiveness. To better understand these relationships, this study investigates a component model of knowledge management capabilities. Data collected from 189 managers and structural equation modeling are used to assess the research model. The results show that individual resources are differentially related to organizational effectiveness, with only some resources (e.g. organizational structure) having significant relationships vis-a-vis organizational effectiveness and others exhibiting null effects. Implications for practice and future research are discussed.


title: An internet census taken by an illegal botnet: a qualitative assessment of published measurements

abstract: On March 17, 2013, an Internet census data set and an accompanying report were released by an anonymous author or group of authors. It created an immediate media buzz, mainly because of the unorthodox and unethical data collection methodology (i.e., exploiting default passwords to form the Carna botnet), but also because of the alleged unprecedented large scale of this census (even though legitimate census studies of similar and even larger sizes have been performed in the past). Given the unknown source of this released data set, little is known about it. For example, can it be ruled out that the data is faked? Or if it is indeed real, what is the quality of the released data? The purpose of this paper is to shed light on these and related questions and put the contributions of this anonymous Internet census study into perspective. Indeed, our findings suggest that the released data set is real and not faked, but that the measurements suffer from a number of methodological flaws and also lack adequate meta-data information. As a result, we have not been able to verify several claims that the anonymous author(s) made in the published report. In the process, we use this study as an educational example for illustrating how to deal with a large data set of unknown quality, hint at pitfalls in Internet-scale measurement studies, and discuss ethical considerations concerning third-party use of this released data set for publications.


title: Personality Development: Continuity and Change Over the Life Course

abstract: The development of personality across the human life course may be observed from three different standpoints: the person as actor (behaving), agent (striving), and author (narrating). Evident even in infancy, broad differences in social action patterns foreshadow the long-term developmental elaboration of early temperament into adult dispositional traits. Research on personal strivings and other motivational constructs provides a second perspective on personality, one that becomes psychologically salient in childhood with the consolidation of an agentic self and the articulation of more-or-less stable goals. Layered over traits and goals, internalized life stories begin to emerge in adolescence and young adulthood, as the person authors a narrative identity to make meaning out of life. The review traces the development of traits, goals, and life stories from infancy through late adulthood and ends by considering their interplay at five developmental milestones: age 2, the transition to adolescence, emerging adulthood, midlife, and old age.


title: Applicability of Homomorphic Encryption and CryptDB in Social and Business Applications: Securing Data Stored on the Third Party Servers while Processing through Applications

abstract: Confidentiality in third party services like cloud computing has become a major concern. IT industry and government organizations are very serious about security factor in cloud computing, because its usage has reached all the way from a common man having a mobile phone to large scale business enterprises. In this paper, we present security threats in social and business applications accessing the data stored in cloud computing scenario. Also, we critically discuss homomorphic encryption and CryptDB schemes which are applicable to protect data from malicious third party service environments (cloud computing) and also from insiders for these applications. We also present empirical results of partial homomorpic encryption algorithms over one lakh 10-digit numbers, using Linux virtual machine on VirtualBox, VMPlayer and KVM. The result for four algorithms (namely Paillier, ElGamal, RSA and Benaloh) as performed on the above four different platforms are computed to show their respective overhead values as compared to plain data operations. In case of Paillier Algorithm the overhead is 17, 15, 22 and 12 times for addition operation and 278, 399,518 and 346 times for multiplication operation respectively. Similarly, in case of Elgamal algorithm 1.72, 1.6, 11.7 and 8.9 times for multiplication operation; in case of RSA algorithm 1.79, 1.5, 3.48 and 1.5 times for multiplication operation and in case of Benaloh algorithm is 5.6, 5.36, 5.48 and 3.5 times for addition operation respectively. These performances clearly indicate that these algorithms are quite feasible enough to be used in context of social and business applications by third party service providers


title: Direct Sparse Odometry with Rolling Shutter

abstract: Neglecting the effects of rolling-shutter cameras for visual odometry (VO) severely degrades accuracy and robustness. In this paper, we propose a novel direct monocular VO method that incorporates a rolling-shutter model. Our approach extends direct sparse odometry which performs direct bundle adjustment of a set of recent keyframe poses and the depths of a sparse set of image points. We estimate the velocity at each keyframe and impose a constant-velocity prior for the optimization. In this way, we obtain a near real-time, accurate direct VO method. Our approach achieves improved results on challenging rolling-shutter sequences over state-of-the-art global-shutter VO.


title: Deep Learning for Radio Resource Allocation in Multi-Cell Networks

abstract: The increased complexity and heterogeneity of emerging 5G and B5G wireless networks will require a paradigm shift from traditional resource allocation mechanisms. Deep learning (DL) is a powerful tool where a multi-layer neural network can be trained to model a resource management algorithm using network data.Therefore, resource allocation decisions can be obtained without intensive online computations which would be required otherwise for the solution of resource allocation problems. In this context, this article focuses on the application of DL to obtain solutions for the radio resource allocation problems in multi-cell networks. Starting with a brief overview of a DNN as a DL model, relevant DNN architectures and the data training procedure, we provide an overview of existing state-of-the-art applying DL in the context of radio resource allocation. A qualitative comparison is provided in terms of their objectives, inputs/outputs, learning and data training methods. Then, we present a supervised DL model to solve the sub-band and power allocation problem in a multi-cell network. Using the data generated by a genetic algorithm, we first train the model and then test the accuracy of the proposed model in predicting the resource allocation solutions. Simulation results show that the trained DL model is able to provide the desired optimal solution 86.3 percent of the time.


title: An 11-bit 250-nW 10-kS/s SAR ADC with doubled input range for biomedical applications

abstract: This paper presents a low-power, area-efficient 11-b single-ended successive-approximation-register (SAR) analog-to-digital converter (ADC) targeted for biomedical applications. The design features an energy-efficient switching technique with an error cancelling capacitor network. The input range is twice the reference voltage. The ADC’s loading of the previous stage is reduced by using a single-ended structure, and by eliminating the largest capacitor in the array. The common mode voltage of the input signal can be used as reference voltage. All building blocks were designed in subthreshold for power efficiency, with an asynchronous self-controlled SAR logic. The ADC was fabricated in 0.18 – $\mu \text{m}$ CMOS 2P4M process. The measured peak SNDR was 60.5 dB, the SFDR was 72 dB, the DNL +0.6/−0.37 LSB, and the INL +0.94/−0.89 LSB. The total power consumption was 250 nW from 0.75-V supply voltage.


title: Axial Flux Motors Using Compacted Insulated Iron Powder and Laminations - Design and Test Results

abstract: The paper describes the design and construction of an axial flux permanent magnet machine in which the teeth are manufactured from compacted insulated iron powder and the core back is formed from a strip-punched lamination formed, into a circle to grip the teeth. This new method of construction overcomes the problems associated with punching and winding axial flux machines formed using index-punched spirally wound laminations (the current state of the art). The construction has been shown to be mechanically stable and rather simple to manufacture. As well as production advantages it offers very high fill factor coils which can significantly enhance the performance of this type of machine


title: Self-disclosure in computer-mediated communication : The role of self-awareness and visual anonymity

abstract: Three studies examined the notion that computer-mediated communication (CMC) can be characterised by high levels of self-disclosure. In Study One, significantly higher levels of spontaneous self-disclosure were found in computer-mediated compared to face-to-face discussions. Study Two examined the role of visual anonymity in encouraging self-disclosure during CMC. Visually anonymous participants disclosed significantly more information about themselves than non-visually anonymous participants. In Study Three, private and public self-awareness were independently manipulated, using video-conferencing cameras and accountability cues, to create a 2 × 2 design public self-awareness (high and low)×private self-awareness (high and low). It was found that heightened private self-awareness, when combined with reduced public self-awareness, was associated with significantly higher levels of spontaneous self-disclosure during computer-mediated communication. Copyright © 2001 John Wiley & Sons, Ltd.


title: Automatic categorization of email into folders: benchmark experiments on Enron and SRI corpora

abstract: Office workers everywhere are drowning in email—not only spam, but also large quantities of legitimate email to be read and organized for browsing. Although there have been extensive investigations of automatic document categorization, email gives rise to a number of unique challenges, and there has been relatively little study of classifying email into folders. This paper presents an extensive benchmark study of email foldering using two large corpora of real-world email messages and foldering schemes: one from former Enron employees, another from participants in an SRI research project. We discuss the challenges that arise from differences between email foldering and traditional document classification. We show experimental results from an array of automated classification methods and evaluation methodologies, including a new evaluation method of foldering results based on the email timeline, and including enhancements to the exponential gradient method Winnow, providing top-tier accuracy with a fraction the training time of alternative methods. We also establish that classification accuracy in many cases is relatively low, confirming the challenges of email data, and pointing toward email foldering as an important area for further research.


title: A novel CNTFET-based ternary logic gate design

abstract: This paper presents a novel design of ternary logic inverters using carbon nanotube FETs (CNTFETs). Multiple-valued logic (MVL) circuits have attracted substantial interest due to the capability of increasing information content per unit area. In the past extensive design techniques for MVL circuits (especially ternary logic inverters) have been proposed for implementation in CMOS technology. In CNTFET device, the threshold voltage of the transistor can be controlled by controlling the chirality vector (i.e. the diameter); in this paper this feature is exploited to design ternary logic inverters. New designs are proposed and compared with existing CNTFET-based designs. Extensive simulation results using SPICE demonstrate that power delay product is improved by 300% comparing to the conventional ternary gate design.


title: Disparity Estimation in Stereo Sequences using Scene Flow

abstract: Fang.F.Liu@philips.comPhilips Germany Vasanth Philomin Vasanth.Philomin@philips.comPhilips Germany Abstract This paper presents a method for estimating disparity images from a stereo image sequence. While many existing stereo algorithms work well on a single pair of stereo images, it is not sufficient to simply apply them to temporal frames independently without considering the temporal consistency between adjacent frames. Our method integrates the state-of-the-art stereo algorithm with the scene flow concept in order to capture the temporal correspondences. It computes the dense disparity images and scene flow in a practical and unified process: the disparity is initialized by a hybrid stereo approach which employs the over-segmentation based stereo and pixelwise iterative stereo; then the scene flow, estimated via a variational approach, is used to predict the disparity image and to compute its confidence map for the next frame. The prediction is modeled as a prior probability distribution and is built into an energy function defined for stereo matching on the next frame. The disparity can be estimated by minimizing this energy function. Experimental results show that the algorithm is able to estimate the disparity images in an accurate and temporally consistent fashion.


title: A Faceted Crawler for the Twitter Service

abstract: Researchers, nowadays, have at their disposal valuable data from social networking applications, of which Twitter and Facebook are the most prominent examples. To retrieve this content, the Twitter service provides 2 distinct Application Programming Interfaces (APIs): a probe-based and a streaming one, each of which imposes different limitations on the data collection process. In this paper, we present a general architecture to facilitate faceted crawling of the service, which simplifies retrieval. We give implementation details of our system, while providing a simple way to express the crawling process, i.e., the crawl flow. We experimentally evaluate it on a variety of faceted crawls, depicting its efficacy for the online medium.


title: Analyzing Integrity Protection in the SELinux Example Policy

abstract: In this paper, we present an approach for analyzing the integrity protection in the SELinux example policy. The SELinux example policy is intended as an example from which administrators customize to create a policy for their site's security goals, but the complexity of the model and size of the policy make this quite complex. Our aim is to provide an access control model to express site security goals and resolve them against the SELinux policy. Ultimately, we aim to define a minimal trusted computing base (TCB) that satisfies Clark-Wilson integrity, by first testing for the more restrictive Biba integrity policy and resolving conflicts using Clark-Wilson semantics. Our policy analysis tool, Gokyo, implements the following approach: (1) it represents the SELinux example policy and our integrity goals; (2) it identifies conflicts between them; (3) it estimates the resolutions to these conflicts; and (4) provides information for deciding upon a resolution. Using Gokyo, we derive a proposal for a minimal TCB for SELinux includes 30 subject types, and we identify the work remaining to ensure that TCB is integrity-protected. Our analysis is performed on the SELinux example policy for Linux 2.4.19.


title: Single-microphone wind noise reduction by adaptive postfiltering

abstract: This paper presents a novel time-domain algorithm for detecting and attenuating the acoustic effect of wind noise in speech signals originating from mobile terminals. The detection part makes use of metrics that exploits the properties of the spectral envelop of wind noise as well as its non-periodic and non-harmonic nature. LPC analyses of various orders are carried out and the results used to distinguish between wind and speech frames and to estimate the magnitude and location of the wind noise ‘resonance’. The suppression part entails constructing a parameterized postfilter of an appropriate order having a ‘null’ where the wind noise ‘resonance’ is. Wind-only frames are used to estimate the wind noise energy, from which the emphasis parameters of the post-filter are adjusted to provide an appropriate attenuation. The proposed scheme may be combined with background-noise suppression algorithms, or with speech-formant-enhancing post-filters in the context of a speech codec.


title: The Rationality and Irrationality of Financing Green Start-Ups

abstract: Green start-ups contribute towards a transition to a more sustainable economy by developing sustainable and environmentally friendly innovation and bringing it to the market. Due to specific product/service characteristics, entrepreneurial motivation and company strategies that might differ from that of other start-ups, these companies might struggle even more than usual with access to finance in the early stages. This conceptual paper seeks to explain these challenges through the theoretical lenses of entrepreneurial finance and behavioural finance. While entrepreneurial finance theory contributes to a partial understanding of green start-up finance, behavioural finance is able to solve a remaining explanatory deficit produced by entrepreneurial finance theory. Although some behavioural finance theorists are suggesting that the current understanding of economic rationality underlying behavioural finance research is inadequate, most scholars have not yet challenged these assumptions, which constrict a comprehensive and realistic description of the reality of entrepreneurial finance in green start-ups. The aim of the paper is thus, first, to explore the specifics of entrepreneurial finance in green start-ups and, second, to demonstrate the need for a more up-to-date conception of rationality in behavioural finance theory in order to enable realistic empirical research in this field.


title: Power-Scaled Spectral Flux and Peak-Valley Group-Delay Methods for Robust Musical Onset Detection

abstract: A robust onset detection method has to deal with wide dynamic ranges and diverse transient behaviors prevalent in real-world music signals. This paper presents contributions to robust onset detection by proposing two novel onset detection methods. The first one, termed power-scaled spectral flux (PSSF), applies power scaling to the spectral flux to better balance the wide dynamic range in the spectrogram. The second method, called peak-valley groupdelay (PVGD), enhances the robustness to noise terms by detecting peak-valley pairs from the summed group-delay function to capture the attack-decay envelope. The proposed methods are evaluated on a piano dataset and a diverse dataset of 12 different Western and Turkish instruments. To tackle the problem from a fundamental signal processing perspective, in this study we do not consider advanced methods such as late fusion, multi-band processing, and neural networks. Experimental result shows that the proposed methods yield competitive accuracy for the two datasets, improving the F-score for the former dataset from 0.956 to 0.963, and the F-score for the latter dataset from 0.712 to 0.754, comparing to existing methods.


title: Physical Randomness Extractors: Generating Random Numbers with Minimal Assumptions

abstract: How to generate provably true randomness with minimal assumptions? This question is important not only for the efficiency and the security of information processing, but also for understanding how extremely unpredictable events are possible in Nature. All current solutions require special structures in the initial source of randomness, or a certain independence relation among two or more sources. Both types of assumptions are impossible to test and difficult to guarantee in practice. Here we show how this fundamental limit can be circumvented by extractors that base security on the validity of physical laws and extract randomness from untrusted quantum devices. In conjunction with the recent work of Miller and Shi (arXiv:1402:0489), our physical randomness extractor uses just a single and general weak source, produces an arbitrarily long and near-uniform output, with a close-to-optimal error, secure against all-powerful quantum adversaries, and tolerating a constant level of implementation imprecision. The source necessarily needs to be unpredictable to the devices, but otherwise can even be known to the adversary. Our central technical contribution, the Equivalence Lemma, provides a general principle for proving composition security of untrusted-device protocols. It implies that unbounded randomness expansion can be achieved simply by cross-feeding any two expansion protocols. In particular, such an unbounded expansion can be made robust, which is known for the first time. Another significant implication is, it enables the secure randomness generation and key distribution using public randomness, such as that broadcast by NIST's Randomness Beacon. Our protocol also provides a method for refuting local hidden variable theories under a weak assumption on the available randomness for choosing the measurement settings.


title: The Simulation of Smart Antennas in Network Simulator-2 Using MATLAB

abstract: This paper presents an extension for network simulator-2 (ns-2) that enables the simulation of adaptive antennas in realistic channel conditions using MATLAB. A hybrid discrete-event discrete-time approach is adopted to exploit the scalability properties of ns-2 for the network simulation, and the modeling capabilities of MATLAB for the physical layer implementation. The developed package takes into account the physical antenna system and the signal processing algorithm to model in detail the antenna radiation pattern generated by each network node. The wireless propagation model of ns-2, which typically accounts only for path-loss attenuation and shadowing, is modified to include the Power Azimuth Spectrum (PAS), thus improving the accuracy of the estimated Signal to Interference plus Noise Ratio (SINR) at each node in presence of multipath. The proposed numerical model, which can be interfaced with any wireless network supported by ns-2, is used to evaluate the throughput of a Wireless Mesh Network (WMN).


title: The Neuroscience of Human Decision-Making Through the Lens of Learning and Memory.

abstract: We are called upon to make decisions, large and small, many times a day. Whether in the voting booth, the stock exchange, or the cafeteria line, we identify potential options, estimate and compare their subjective values, and make a choice. Decision-making has only recently become a focus for cognitive neuroscience. The last two decades have seen rapid progress in our understanding of the brain basis of at least some aspects of this rather complex aspect of cognition. This work has provided fresh perspectives on poorly understood brain regions, such as orbitofrontal cortex and ventral striatum. It has led to interesting interdisciplinary exchanges with diverse fields, notably economics, but also ecology and political science, among others. The novel perspectives arising from these exchanges have begun to be related to better understood aspects of cognition. In particular, it is increasingly clear that decision-making is tightly interlinked with learning and memory. Key early insights in decision neuroscience came from what were essentially reinforcement learning tasks. Recent work has made similar links to aspects of declarative memory. Indeed, decision-making can be seen as the link between memory of the past and future actions. This chapter reviews selected topics in decision neuroscience, with a particular focus on the links to learning and memory, and a particular emphasis on regions within prefrontal cortex.


Diet-resistant obesity is characterized by a distinct plasma proteomic signature and impaired muscle fiber metabolism


title: Grey Matter Density Predicts the Improvement of Naming Abilities After tDCS Intervention in Agrammatic Variant of Primary Progressive Aphasia

abstract: Agrammatic variant primary progressive aphasia is a neurodegenerative disorder specifically characterized by language deficits. A recent study has demonstrated a beneficial effect of transcranial direct current stimulation (tDCS) in combination with language training on naming accuracy in these patients. The aim of the study was to evaluate whether the improvement of naming accuracy after tDCS during language training was related to regional grey matter (GM) density. Eighteen avPPA patients underwent a brain magnetic resonance imaging before receiving a treatment that consisted of tDCS over the left dorsolateral prefrontal cortex during individualized language training (10 daily therapy sessions, 5 days per week from Monday to Friday). Performances on neuropsychological tests and naming of objects (treated and untreated) and actions were assessed at baseline, post-treatment and 3 months after treatment. Correlations between individual changes after treatment on neuropsychological tests and on picture naming task and voxel-based GM volume at baseline were performed. We found that the improvement in the naming of treated objects was positively correlated with GM volume in the left fusiform, left middle temporal, and right inferior temporal gyri whereas action naming change was related to GM density in the left middle temporal gyrus. In conclusion baseline density of GM in these brain regions was associated with greater treatment response on naming performances, suggesting that intervention in early disease stages might be most successful. These findings have implication for designing future rehabilitation protocols in language variants of frontotemporal dementia.


title: Achieving trustworthiness in qualitative research: A pan-paradigmatic perspective

abstract: Abstract In this article, as two researchers from different traditions in qualitative research (consensual qualitative research and grounded theory), the authors present their shared views on the critical elements of trustworthiness in qualitative data. In addition to making specific recommendations about the integrity of data, the balance between participant meaning and researcher interpretation, and clear communication and application of the findings, they identify ways in which these issues are difficult to negotiate within and across different qualitative approaches. The authors present examples from various qualitative studies, emphasize the need for a shared language to reduce confusion between qualitative traditions and with researchers from a more strictly quantitative orientation, and recommend particular approaches to establishing trustworthiness in qualitative research.


title: Tumor metastasis: mechanistic insights and clinical challenges

abstract: Metastatic disease is the primary cause of death for most cancer patients. Complex and redundant pathways involving the tumor cell and the microenvironment mediate tumor invasion at the primary site, survival and arrest in the bloodstream, and progressive outgrowth at a distant site. Understanding these pathways and their dynamic interactions will help identify promising molecular targets for cancer therapy and key obstacles to their clinical development.


title: ROC Graphs: Notes and Practical Considerations for Researchers

abstract: Receiver Operating Characteristics (ROC) graphs are a useful technique for organizing classifiers and visualizing their performance. ROC graphs are commonly used in medical decision making, and in recent years have been increasingly adopted in the machine learning and data mining research communities. Although ROC graphs are apparently simple, there are some common misconceptions and pitfalls when using them in practice. This article serves both as a tutorial introduction to ROC graphs and as a practical guide for using them in research.