title: Linear classifier design under heteroscedasticity in Linear Discriminant Analysis

abstract: We derive a linear classifier for heteroscedastic linear discriminant analysis.The proposed scheme efficiently minimises the Bayes error for binary classification.A local neighbourhood search is also proposed for non-normal distributions.The proposed schemes are experimentally validated on twelve datasets. Under normality and homoscedasticity assumptions, Linear Discriminant Analysis (LDA) is known to be optimal in terms of minimising the Bayes error for binary classification. In the heteroscedastic case, LDA is not guaranteed to minimise this error. Assuming heteroscedasticity, we derive a linear classifier, the Gaussian Linear Discriminant (GLD), that directly minimises the Bayes error for binary classification. In addition, we also propose a local neighbourhood search (LNS) algorithm to obtain a more robust classifier if the data is known to have a non-normal distribution. We evaluate the proposed classifiers on two artificial and ten real-world datasets that cut across a wide range of application areas including handwriting recognition, medical diagnosis and remote sensing, and then compare our algorithm against existing LDA approaches and other linear classifiers. The GLD is shown to outperform the original LDA procedure in terms of the classification accuracy under heteroscedasticity. While it compares favourably with other existing heteroscedastic LDA approaches, the GLD requires as much as 60 times lower training time on some datasets. Our comparison with the support vector machine (SVM) also shows that, the GLD, together with the LNS, requires as much as 150 times lower training time to achieve an equivalent classification accuracy on some of the datasets. Thus, our algorithms can provide a cheap and reliable option for classification in a lot of expert systems.


title: The Sternberg memory search task as an index of pilot workload

abstract: Abstract The rationale for the use of the Sternberg memory search task as a diagnostic measure of pilot workload is described, and seven investigations that have employed this task in flight simulators or aircraft environments are summarized. The details of two further flight simulator experiments in which workload is measured by an auditory Sternberg task are reported. These results indicate the diagnostic value of the task in discriminating between the perceptual/central processing and response demands of a holding pattern and an approach pattern, respectively. Perceptual and response load were greater in an approach phase, relative to a holding phase. Neither phase imposed a substantial central processing load. Based upon the findings of the studies described, the paper concludes with a set of recommendations for employing the Sternberg task in aviation environments. These recommendations emphasize the importance of information display and response procedures, the choice of particular memory sets, the ...


title: Experimental investigation and propulsion control for a bio-inspired robotic undulatory fin

abstract: Undulatory fin propulsion, inspired by the locomotion of aquatic species such as electric eels and cuttlefish, holds considerable potential for endowing underwater vehicles with enhanced propulsion and maneuvering abilities, to address the needs of a growing number of applications. However, there are still gaps in our understanding of the effect of the fin undulations' characteristics on the generated thrust, particularly within the context of developing propulsion control strategies for such robotic systems. Towards this end, we present the design and experimental evaluation of a robotic fin prototype, comprised of eight individually-actuated fin rays. An artificial central pattern generator (CPG) is used to produce the rays' undulatory motion pattern. Experiments are performed inside a water tank, with the robotic fin suspended from a carriage, whose motion is constrained via a linear guide. The results from a series of detailed parametric investigations reveal several important findings regarding the effect of the undulatory wave kinematics on the propulsion speed and efficiency. Based on these findings, two alternative strategies for propulsion control of the robotic fin are proposed. In the first one, the speed is varied through changes in the undulation amplitude, while the second one involves simultaneous adjustment of the undulation frequency and number of waves. These two strategies are evaluated via experiments demonstrating open-loop velocity control, as well as closed-loop position control of the prototype.


title: (DE)$^2$CO: Deep Depth Colorization

abstract: The ability to classify objects is fundamental for robots. Besides knowledge about their visual appearance, captured by the RGB channel, robots heavily need also depth information to make sense of the world. While the use of deep networks on RGB robot images has benefited from the plethora of results obtained on databases like ImageNet, using convnets on depth images requires mapping them into three dimensional channels. This transfer learning procedure makes them processable by pre-trained deep architectures. Current mappings are based on heuristic assumptions over preprocessing steps and on what depth properties should be most preserved, resulting often in cumbersome data visualizations, and in sub-optimal performance in terms of generality and recognition results. Here we take an alternative route and we attempt instead to learn an optimal colorization mapping for any given pre-trained architecture, using as training data a reference RGB-D database. We propose a deep network architecture, exploiting the residual paradigm, that learns how to map depth data to three channel images. A qualitative analysis of the images obtained with this approach clearly indicates that learning the optimal mapping preserves the richness of depth information better than current hand-crafted approaches. Experiments on the Washington, JHUIT-50 and BigBIRD public benchmark databases, using CaffeNet, VGG16, GoogleNet, and ResNet50 clearly showcase the power of our approach, with gains in performance of up to 16% compared to state of the art competitors on the depth channel only, leading to top performances when dealing with RGB-D data


title: Entity Extraction, Linking, Classification, and Tagging for Social Media: A Wikipedia-Based Approach

abstract: Many applications that process social data, such as tweets, must extract entities from tweets (e.g., "Obama" and "Hawaii" in "Obama went to Hawaii"), link them to entities in a knowledge base (e.g., Wikipedia), classify tweets into a set of predefined topics, and assign descriptive tags to tweets. Few solutions exist today to solve these problems for social data, and they are limited in important ways. Further, even though several industrial systems such as OpenCalais have been deployed to solve these problems for text data, little if any has been published about them, and it is unclear if any of the systems has been tailored for social media. ::: ::: In this paper we describe in depth an end-to-end industrial system that solves these problems for social data. The system has been developed and used heavily in the past three years, first at Kosmix, a startup, and later at WalmartLabs. We show how our system uses a Wikipedia-based global "real-time" knowledge base that is well suited for social data, how we interleave the tasks in a synergistic fashion, how we generate and use contexts and social signals to improve task accuracy, and how we scale the system to the entire Twitter firehose. We describe experiments that show that our system outperforms current approaches. Finally we describe applications of the system at Kosmix and WalmartLabs, and lessons learned.


title: A Note on Linear Time Algorithms for Maximum Error Histograms

abstract: Histograms and Wavelet synopses provide useful tools in query optimization and approximate query answering. Traditional histogram construction algorithms, e.g., V-Optimal, use error measures which are the sums of a suitable function, e.g., square, of the error at each point. Although the best-known algorithms for solving these problems run in quadratic time, a sequence of results have given us a linear time approximation scheme for these algorithms. In recent years, there have been many emerging applications where we are interested in measuring the maximum (absolute or relative) error at a point. We show that this problem is fundamentally different from the other traditional {\rm{non}}{\hbox{-}}\ell_\infty error measures and provide an optimal algorithm that runs in linear time for a small number of buckets. We also present results which work for arbitrary weighted maximum error measures.


title: Impact of supply chain integration and selective disintegration on supply chain efficiency and organization performance

abstract: An efficient business system integration (BSI) is the key determinant factor for the organization to stay competitive. The goal of this study is to develop and execute a research project to study BSI in the oil & gas industry and how an efficient BSI in supply chain (SCI) can be used to gain advantage. A mixed (qualitative & quantitative) survey method was employed and structural equation modeling (SEM) is applied to test the theoretical framework and hypotheses. Findings reveal that, total integration do not offer optimum performance and selective disintegration (DIS) is necessary for supply chain efficiency (SCE) and organization performance (PER). This study helps to better understand the degree of integration required for optimum supply chain performance and came up with new construct DIS. Specifically, this study investigated the effect of SCI and DIS on SCE and PER. The compelled sample size precludes evaluating and testing of additional broad models of the relationship amongst the constructs. The main managerial lesson is that, in contrast to what has been written in many books and other popular publications, selective disintegration is necessary for optimum supply chain efficiency.


Simple Embedding-Based Word Sense Disambiguation


title: The Investigation of PIN diode switch on reconfigurable antenna

abstract: The investigation and simulation of PIN diode as a switch on reconfigurable antenna are described in this paper. The simulation of the PIN diode model is constructed on CST Simulation tools with the integration of single patch antenna. The radial stub biasing circuit is used for fabrication with the antenna. Three different locations of PIN diode with radial stubs biasing circuit are investigated and analyzed. The result shows the separation of biasing circuit and antenna give a better performance in term of return loss and current distribution.


title: A Reference Model for Internet of Things Middleware

abstract: Internet of Things (IoT) is a term used to describe an environment where billions of objects, constrained in terms of resources (“things”), are connected to the Internet, and interacting autonomously. With so many objects connected in IoT solutions, the environment in which they are placed becomes smarter. A software, called middleware, plays a key role since it is responsible for most of the intelligence in IoT, integrating data from devices, allowing them to communicate, and make decisions based on collected data. Then, considering requirements of IoT platforms, a reference architecture model for IoT middleware is analyzed, detailing the best operation approaches of each proposed module, as well as proposes basic security features for this type of software. This paper elaborates on a systematic review of the related literature, exploring the differences between the current Internet and IoT-based systems, presenting a deep discussion of the challenges and future perspectives on IoT middleware. Finally, it highlights the difficulties for achieving and enforcing a universal standard. Thus, it is concluded that middleware plays a crucial role in IoT solutions and the proposed architectural approach can be used as a reference model for IoT middleware.


title: Bias in Online Freelance Marketplaces: Evidence from TaskRabbit and Fiverr

abstract: Online freelancing marketplaces have grown quickly in recent years. In theory, these sites offer workers the ability to earn money without the obligations and potential social biases associated with traditional employment frameworks. In this paper, we study whether two prominent online freelance marketplaces - TaskRabbit and Fiverr - are impacted by racial and gender bias. From these two platforms, we collect 13,500 worker profiles and gather information about workers' gender, race, customer reviews, ratings, and positions in search rankings. In both marketplaces, we find evidence of bias: we find that gender and race are significantly correlated with worker evaluations, which could harm the employment opportunities afforded to the workers. We hope that our study fuels more research on the presence and implications of discrimination in online environments.


title: Teaching and Developing Social and Emotional Skills with Technology

abstract: Supporting social interactions is a long-term focus for Human Computer Interaction (HCI) and Computer Supported Cooperative Work (CSCW). However, understanding how social and emotional skills are learned, and how this process can be supported by technology, is an important but underresearched area in HCI so far. To address this gap, we review existing approaches to social and emotions skills learning (SEL) in other fields, with a specific focus on SEL in education, in which a large number of evidence-based programs is widely deployed. In doing so, the primary aim of this article is to provide a foundation and set an agenda for future research on the design of technology that would support, and help teach, social and emotional skills. We identify the key challenges to successful learning shared by SEL programs in education—such as embedding skills learned in class also into everyday situations, promoting reflection, and providing additional opportunities for practice—and outline how these could be addressed by digital technology. Overall, our key argument is that much existing HCI work could be used in support of social and emotional skills learning in education, and possibly other domains, but that the topic has not been explored so far. We also highlight how the focus on supporting SEL would bring novel opportunities and challenges for HCI, as well as provide a basis for a strong HCI research agenda in this space.


title: Deep Convolutional Neural Networks for Human Embryonic Cell Counting

abstract: We address the problem of counting cells in time-lapse microscopy images of developing human embryos. Cell counting is considered as an important step in analyzing biological phenomenon such as embryo viability. Traditional approaches to counting cells rely on hand crafted features and cannot fully take advantage of the growth in data set sizes. In this paper, we propose a framework to automatically count the number of cells in developing human embryos. The framework employs a deep convolutional neural network model trained to count cells from raw microscopy images. We demonstrate the effectiveness of our approach on a data set of 265 human embryos. The results show that the proposed framework provides robust estimates of the number of cells in a developing embryo up to the 5-cell stage (i.e., 48 h post fertilization).


title: Control allocation for yaw stabilization in automotive vehicles using multiparametric nonlinear programming

abstract: We investigate the use of a nonlinear control allocation scheme for automotive vehicles. Such a scheme is useful in e.g. yaw or roll stabilization of the vehicle. The control allocation allows a modularization of the control task, such that a higher level control system specifies a desired moment to work on the vehicle, while the control allocation distributes this moment among the individual wheels by commanding appropriate wheel slips. The control allocation problem is defined as a nonlinear optimization problem, to which an explicit piecewise linear approximate solution function is computed off-line. Such a solution function can computationally efficiently be implemented in real time with at most a few hundred arithmetic operations per sample. Yaw stabilization of the vehicle yaw dynamics is used as an example of use of the control allocation. Simulations show that the controller stabilizes the vehicle in an extreme manoeuvre where the vehicle yaw dynamics otherwise becomes unstable.


title: People tracking and re-identification by face recognition for RGB-D camera networks

abstract: This paper describes a face recognition-based people tracking and re-identification system for RGB-D camera networks. The system tracks people and learns their faces online to keep track of their identities even if they move out from the camera's field of view once. For robust people re-identification, the system exploits the combination of a deep neural network- based face representation and a Bayesian inference-based face classification method. The system also provides a predefined people identification capability: it associates the online learned faces with predefined people face images and names to know the people's whereabouts, thus, allowing a rich human-system interaction. Through experiments, we validate the re-identification and the predefined people identification capabilities of the system and show an example of the integration of the system with a mobile robot. The overall system is built as a Robot Operating System (ROS) module. As a result, it simplifies the integration with the many existing robotic systems and algorithms which use such middleware. The code of this work has been released as open-source in order to provide a baseline for the future publications in this field.


title: Parallel implementation of the TestU01 statistical test suite

abstract: As the need of high quality random number generators is constantly increasing especially for cryptographic algorithms, the development of high throughput randomness generators has to be combined with the development of high performance statistical test suites. Unfortunately the implementations of the most popular batteries of test suites are not focused on efficiency and high performance, do not benefit of the processing power offered by today's multi-core processors and tend to become bottlenecks in the processing of large volumes of data generated by various random number generators. Hence there is a stringent need for providing highly efficient statistical tests and our research efforts and results on improving and parallelizing the TestU01 test suite intend to fill this need. Experimental results show that the parallel version of TestU01 takes full advantage of the system's available processing power, reducing the execution time up to 4 times on the tested multicore systems.


title: Biologically Inspired Design: A New Program for Computational Sustainability

abstract: Biological analogies could help address the increasingly critical and urgent problem of sustainable design.


title: D-FICCA: A density-based fuzzy imperialist competitive clustering algorithm for intrusion detection in wireless sensor networks

abstract: Owing to the scattered nature of Denial-of-Service attacks, it is tremendously challenging to detect such malicious behavior using traditional intrusion detection systems in Wireless Sensor Networks (WSNs). In the current paper, a hybrid clustering method is introduced, namely a density-based fuzzy imperialist competitive clustering algorithm (D-FICCA). Hereby, the imperialist competitive algorithm (ICA) is modified with a density-based algorithm and fuzzy logic for optimum clustering in WSNs. A density-based clustering algorithm helps improve the imperialist competitive algorithm for the formation of arbitrary cluster shapes as well as handling noise. The fuzzy logic controller (FLC) assimilates to imperialistic competition by adjusting the fuzzy rules to avoid possible errors of the worst imperialist action selection strategy. The proposed method aims to enhance the accuracy of malicious detection. D-FICCA is evaluated on a publicly available dataset consisting of real measurements collected from sensors deployed at the Intel Berkeley Research Lab. Its performance is compared against existing empirical methods, such as K-MICA, K-mean, and DBSCAN. The results demonstrate that the proposed framework achieves higher detection accuracy 87% and clustering quality 0.99 compared to existing approaches.


title: Numerical study of read scheme in one-selector one-resistor crossbar array

abstract: A comprehensive numerical circuit analysis of read schemes of a one selector–one resistance change memory (1S1R) crossbar array is carried out. Three schemes—the ground, V/2, and V/3 schemes—are compared with each other in terms of sensing margin and power consumption. Without the aid of a complex analytical approach or SPICE-based simulation, a simple numerical iteration method is developed to simulate entire current flows and node voltages within a crossbar array. Understanding such phenomena is essential in successfully evaluating the electrical specifications of selectors for suppressing intrinsic drawbacks of crossbar arrays, such as sneaky current paths and series line resistance problems. This method provides a quantitative tool for the accurate analysis of crossbar arrays and provides guidelines for developing an optimal read scheme, array configuration, and selector device specifications.


title: Mesh shrinkage and pain in laparoscopic ventral hernia repair: a randomized clinical trial comparing suture versus tack mesh fixation

abstract: BACKGROUND ::: Mesh fixation during laparoscopic ventral hernia repair can be performed using transfascial sutures or metal tacks. The aim of the present study is to compare mesh shrinkage and pain between two different techniques of mesh fixation in a prospective randomized trial. ::: ::: ::: METHODS ::: A randomized trial was performed. Patients with ventral hernia of maximal diameter 8 cm were assigned to mesh fixation using either transfascial nonabsorbable sutures or metal tacks for fixation of a parietene composite mesh. The borders of the mesh were marked using clips, and radiological images in prone position were used for assessment of mesh size and location. The primary endpoint was mesh shrinkage; secondary endpoints included postoperative pain, mesh dislocation, and surgical morbidity. ::: ::: ::: RESULTS ::: Demographic parameters were similar in both groups. A total of 40 patients were randomized, and 36 patients were available for follow-up. There was one hernia recurrence in each group. Pain was significantly higher following suture fixation after 6 weeks, but no difference was found after 6 months. Mesh shrinkage after 6 months was significantly higher using tacks for mesh fixation. ::: ::: ::: CONCLUSIONS ::: Transfascial sutures are associated with more pain within the first 6 postoperative weeks and less mesh shrinkage after 6 months compared with mesh fixation using metal tacks.


title: Genital Image, Sexual Anxiety, and Erectile Dysfunction Among Young Male Military Personnel

abstract: Abstract Introduction More than a third of young military personnel report experiencing some level of erectile dysfunction (ED). Preoccupation with body image, particularly genitals, is a distraction that can influence sexual anxiety (SA) and sexual functioning problems (SFPs), particularly ED. Aims This study assessed the relationships between male genital self-image (MGSI), SA, and ED in a sample of male military personnel age 40 or younger. Methods Data were from a larger study on SFPs in military populations. This sample consisted of 367 male military personnel age 40 or younger. Hierarchical regression analyses and process modeling using mediation analysis were performed to examine the effects of MGSI on ED with SA as an intermediate variable. We predicted that SA would mediate the relationship between MGSI and ED. Main Outcome Measures ED severity was assessed with the International Index of Erectile Function. MGSI was assessed using the MGSI Scale. SA was assessed with the SA subscale of the Sexual Needs Scale. Results As hypothesized, greater satisfaction with MGSI was predictive of significantly lower SA ( F [8, 352] = 4.07, P = 0.001) and lower ED ( F [8, 352] = 13.20, P = 0.001). Lower levels of SA were predictive of lower levels of ED ( F [8, 354] = 21.35, P b = −0.07, standard error = 0.03, confidence interval = [−0.14,−0.02], P Conclusions This study underscores the complex etiologic basis of SFPs, particularly ED, and highlights the importance of considering psychologic contributors to ED, such as SA and MGSI. Strategies aimed at reducing SA may be useful in improving ED in young military populations and are worth considering as complements to strategies that improve SFPs. Wilcox SL, Redmond S, and Davis TL. Genital image, sexual anxiety, and erectile dysfunction among young male military personnel. J Sex Med 2015;12:1389–1397.


title: A 250KS/s, 0.8V ultra low power successive approximation register ADC using a Dynamic rail-to-rail comparator

abstract: A low voltage low power successive approximation register (SAR) analog-to-digital converter (ADC) based on a novel rail-to-rail comparator is proposed in this paper. The power consumption of the comparator is significantly reduced through dynamic operation while the speed is augmented by using an efficient regenerative latch. No biasing circuits are needed and there are no floating nodes in the comparator throughout the conversion process. The digital-to-analog converter (DAC) is formed from a binary array of MIM capacitors. The 250KS/s ADC implemented in a 0.18µm process consumes only 1.35µW of power at a supply voltage of 0.8V.


title: Plan-And-Write: Towards Better Automatic Storytelling

abstract: Automatic storytelling is challenging since it requires generating long, coherent natural language to describes a sensible sequence of events. Despite considerable efforts on automatic story generation in the past, prior work either is restricted in plot planning, or can only generate stories in a narrow domain. In this paper, we explore open-domain story generation that writes stories given a title (topic) as input. We propose a plan-and-write hierarchical generation framework that first plans a storyline, and then generates a story based on the storyline. We compare two planning strategies. The dynamic schema interweaves story planning and its surface realization in text, while the static schema plans out the entire storyline before generating stories. Experiments show that with explicit storyline planning, the generated stories are more diverse, coherent, and on topic than those generated without creating a full plan, according to both automatic and human evaluations.


title: Methodologies for Cross-Domain Data Fusion: An Overview

abstract: Traditional data mining usually deals with data from a single domain. In the big data era, we face a diversity of datasets from different sources in different domains. These datasets consist of multiple modalities, each of which has a different representation, distribution, scale, and density. How to unlock the power of knowledge from multiple disparate (but potentially connected) datasets is paramount in big data research, essentially distinguishing big data from traditional data mining tasks. This calls for advanced techniques that can fuse knowledge from various datasets organically in a machine learning and data mining task. This paper summarizes the data fusion methodologies, classifying them into three categories: stage-based, feature level-based, and semantic meaning-based data fusion methods. The last category of data fusion methods is further divided into four groups: multi-view learning-based, similarity-based, probabilistic dependency-based, and transfer learning-based methods. These methods focus on knowledge fusion rather than schema mapping and data merging, significantly distinguishing between cross-domain data fusion and traditional data fusion studied in the database community. This paper does not only introduce high-level principles of each category of methods, but also give examples in which these techniques are used to handle real big data problems. In addition, this paper positions existing works in a framework, exploring the relationship and difference between different data fusion methods. This paper will help a wide range of communities find a solution for data fusion in big data projects.


title: Deep Learning of Semisupervised Process Data With Hierarchical Extreme Learning Machine and Soft Sensor Application

abstract: Data-driven soft sensors have been widely utilized in industrial processes to estimate the critical quality variables which are intractable to directly measure online through physical devices. Due to the low sampling rate of quality variables, most of the soft sensors are developed on small number of labeled samples and the large number of unlabeled process data is discarded. The loss of information greatly limits the improvement of quality prediction accuracy. One of the main issues of data-driven soft sensor is to furthest exploit the information contained in all available process data. This paper proposes a semisupervised deep learning model for soft sensor development based on the hierarchical extreme learning machine (HELM). First, the deep network structure of autoencoders is implemented for unsupervised feature extraction with all the process samples. Then, extreme learning machine is utilized for regression through appending the quality variable. Meanwhile, the manifold regularization method is introduced for semisupervised model training. The new method can not only deeply extract the information that the data contains, but learn more from the extra unlabeled samples as well. The proposed semisupervised HELM method is applied in a high–low transformer to estimate the carbon monoxide content, which shows a significant improvement of the prediction accuracy, compared to traditional methods.


title: Semi-supervised ranking on very large graphs with rich metadata

abstract: Graph ranking plays an important role in many applications, such as page ranking on web graphs and entity ranking on social networks. In applications, besides graph structure, rich information on nodes and edges and explicit or implicit human supervision are often available. In contrast, conventional algorithms (e.g., PageRank and HITS) compute ranking scores by only resorting to graph structure information. A natural question arises here, that is, how to effectively and efficiently leverage all the information to more accurately calculate graph ranking scores than the conventional algorithms, assuming that the graph is also very large. Previous work only partially tackled the problem, and the proposed solutions are also not satisfying. This paper addresses the problem and proposes a general framework as well as an efficient algorithm for graph ranking. Specifically, we define a semi-supervised learning framework for ranking of nodes on a very large graph and derive within our proposed framework an efficient algorithm called Semi-Supervised PageRank. In the algorithm, the objective function is defined based upon a Markov random walk on the graph. The transition probability and the reset probability of the Markov model are defined as parametric models based on features on nodes and edges. By minimizing the objective function, subject to a number of constraints derived from supervision information, we simultaneously learn the optimal parameters of the model and the optimal ranking scores of the nodes. Finally, we show that it is possible to make the algorithm efficient to handle a billion-node graph by taking advantage of the sparsity of the graph and implement it in the MapReduce logic. Experiments on real data from a commercial search engine show that the proposed algorithm can outperform previous algorithms on several tasks.


title: A Closer Look on Artist Filters for Musical Genre Classification

abstract: Musical genre classification is the automatic classification of audio signals into user defined labels describing pieces of music. A problem inherent to genre classification experiments in music information retrieval research is the use of songs from the same artist in both training and test sets. We show that this does not only lead to overoptimistic accuracy results but also selectively favours particular classification approaches. The advantage of using models of songs rather than models of genres vanishes when applying an artist filter. The same holds true for the use of spectral features versus fluctuation patterns for preprocessing of the audio files.


title: The Direct Sampling method to perform multiple-point geostatistical simulations

abstract: [1] Multiple-point geostatistics is a general statistical framework to model spatial fields displaying a wide range of complex structures. In particular, it allows controlling connectivity patterns that have a critical importance for groundwater flow and transport problems. This approach involves considering data events (spatial arrangements of values) derived from a training image (TI). All data events found in the TI are usually stored in a database, which is used to retrieve conditional probabilities for the simulation. Instead, we propose to sample directly the training image for a given data event, making the database unnecessary. Our method is statistically equivalent to previous implementations, but in addition it allows extending the application of multiple-point geostatistics to continuous variables and to multivariate problems. The method can be used for the simulation of geological heterogeneity, accounting or not for indirect observations such as geophysics. We show its applicability in the presence of complex features, nonlinear relationships between variables, and with various cases of nonstationarity. Computationally, it is fast, easy to parallelize, parsimonious in memory needs, and straightforward to implement.


title: Enabling Technologies for Smart City Services and Applications

abstract: Smart mobile devices are fast becoming the epicentre of people's lives. Most smart phones are currently embedded with powerful and programmable sensors such as GPS, gyroscope, microphone, camera, accelerometer etc. These sensor-enabled smart-phones would form an important element of the future networked-infrastructure. A new wave of services is bound to erupt from such connected infrastructure and smart devices that will influence all aspects of our social ecosystem. In the context of Smart Cities, this position paper and the associated invited talk presents the Mobile Technology perspective of the Smart-city architecture by presenting a conceptualized framework and highlights the open and emerging research challenges in this landscape.


title: Onion ORAM: A constant bandwidth blowup oblivious RAM

abstract: We present Onion ORAM, an Oblivious RAM (ORAM) with constant worst-case bandwidth blowup that leverages poly-logarithmic server computation to circumvent the logarithmic lower bound on ORAM bandwidth blowup. Our construction does not require fully homomorphic encryption, but employs an additively homomorphic encryption scheme such as the Damgard-Jurik cryptosystem, or alternatively a BGV-style somewhat homomorphic encryption scheme without bootstrapping. At the core of our construction is an ORAM scheme that has “shallow circuit depth” over the entire history of ORAM accesses. We also propose novel techniques to achieve security against a malicious server, without resorting to expensive and non-standard techniques such as SNARKs. To the best of our knowledge, Onion ORAM is the first concrete instantiation of a constant bandwidth blowup ORAM under standard assumptions (even for the semi-honest setting).


A TUTORIAL ON PRINCIPAL COMPONENT ANALYSIS Derivation, Discussion and Singular Value Decomposition


A learning scheme for a fuzzy k-NN rule


title: Novel Pseudo-Drain (PD) RF power cell in 0.13 um CMOS technology

abstract: This paper proposes a cost-effective RF power cell manufactured in an advanced 0.13 um CMOS technology. Without adding additional masks, cost, and process, the power performance can be improved just by using the standard N-well and shallow-trench-isolation processes to form a higher resistive region. This ldquoPseudo-Drainrdquo structure increases the breakdown voltage to more than 4.3V and is higher than the value of 2.5V of the standard 0.13 um core-MOS transistor. This transistor exhibits a high fTtimesBVDS product of 352 for CMOS power FETs. Cutoff frequency and maximum oscillation frequency of 83 GHz and 124 GHz were achieved at a drain bias of 1.2V, respectively; while the maximum power gain, output power and power-added efficiency were 25.6 dB, 19 dBm, and 55%, respectively. Good RF linearity and noise figure were also obtained, as demonstrated by an OIP3 and NFmin of 28.32 dBm and 0.4 dB. The presented RF power transistor is cost effective and can be used for power amplifier integration in RF-CMOS SOC.


title: What is computing?: bridging the gap between teenagers' perceptions and graduate students' experiences

abstract: Studies show that teenagers perceive computing to be boring, antisocial, and irrelevant to their lives. We interviewed 13 teenagers from local Atlanta schools and observed over 40 teenagers in after-school technology programs to learn more about their perceptions of computing. We then interviewed 22 graduate students in the Human-Centered Computing and Human-Computer Interaction programs at Georgia Tech in order to learn about the factors that motivated them to pursue degrees in computing. We found that teenagers perceived computing to be boring, solitary, and lacking real-world context, yet graduate students described their research as exciting, social, and having a direct and meaningful impact on the world around them. Our results suggest that there is an opportunity to increase interest in computing among teenagers by bridging the gap between their perceptions of computing and the actual opportunities that are offered in computing disciplines. In this paper, we first describe our interview results. We then discuss our findings and propose a design-based curriculum to teach teenagers core computing principles. The goal of this curriculum is to prepare and motivate them for careers in today's expanding, Internet-based, global economy. We suggest that by portraying computing as an innovative, creative, and challenging field with authentic, real-world applications, we may be able to motivate teenagers to become more excited to pursue careers in computing.


title: An HDP-HMM for systems with state persistence

abstract: The hierarchical Dirichlet process hidden Markov model (HDP-HMM) is a flexible, nonparametric model which allows state spaces of unknown size to be learned from data. We demonstrate some limitations of the original HDP-HMM formulation (Teh et al., 2006), and propose a sticky extension which allows more robust learning of smoothly varying dynamics. Using DP mixtures, this formulation also allows learning of more complex, multimodal emission distributions. We further develop a sampling algorithm that employs a truncated approximation of the DP to jointly resample the full state sequence, greatly improving mixing rates. Via extensive experiments with synthetic data and the NIST speaker diarization database, we demonstrate the advantages of our sticky extension, and the utility of the HDP-HMM in real-world applications.


title: Towards trojan-free trusted ICs: problem analysis and detection scheme

abstract: There have been serious concerns recently about the security of microchips from hardware trojan horse insertion during manufacturing. This issue has been raised recently due to outsourcing of the chip manufacturing processes to reduce cost. This is an important consideration especially in critical applications such as avionics, communications, military, industrial and so on. A trojan is inserted into a main circuit at manufacturing and is mostly inactive unless it is triggered by a rare value or time event; then it produces a payload error in the circuit, potentially catastrophic. Because of its nature, a trojan may not be easily detected by functional or ATPG testing. The problem of trojan detection has been addressed only recently in very few works. Our work analyzes and formulates the trojan detection problem based on a frequency analysis under rare trigger values and provides procedures to generate input trigger vectors and trojan test vectors to detect trojan effects. We also provide experimental results.


title: Deep-FSMN for Large Vocabulary Continuous Speech Recognition

abstract: In this paper, we present an improved feedforward sequential memory networks (FSMN) architecture, namely Deep-FSMN (DFSMN), by introducing skip connections between memory blocks in adjacent layers. These skip connections enable the information flow across different layers and thus alleviate the gradient vanishing problem when building very deep structure. As a result, DFSMN significantly benefits from these skip connections and deep structure. We have compared the performance of DFSMN to BLSTM both with and without lower frame rate (LFR) on several large speech recognition tasks, including English and Mandarin. Experimental results shown that DFSMN can consistently outperform BLSTM with dramatic gain, especially trained with LFR using CD-Phone as modeling units. In the 20000 hours Fisher (FSH) task, the proposed DFSMN can achieve a word error rate of 9.4% by purely using the cross-entropy criterion and decoding with a 3-gram language model, which achieves a 1.5% absolute improvement compared to the BLSTM. In a 20000 hours Mandarin recognition task, the LFR trained DFSMN can achieve more than 20% relative improvement compared to the LFR trained BLSTM. Moreover, we can easily design the lookahead filter order of the memory blocks in DFSMN to control the latency for real-time applications.


title: Designing a Smart Museum: When Cultural Heritage Joins IoT

abstract: The adoption of the Internet of Things (IoT) paradigm constitutes the basic building block to progress towards unified ICT platforms for a variety of applications within the large framework of the smart cities. Unfortunately, designing a general architecture for IoT is still a very complex challenge, since in such system may be involved several devices, link layer technologies and services. Cultural Heritage represents a worldwide resource of inestimable value and it gains more and more importance when embedded into the digital ecosystem of a smart city. In this paper we focus specifically to design a IoT architecture that is able to support the designing of a smart museum, a static cultural space that becomes intelligent thanks to the definition of an innovative model of sensors and services. Furthermore, the paper will present and discuss a real case of study, placed in a temporary art exhibition of sculptures in the Maschio Angioino Castle, located in Naples, Italy.


title: Optimized Authentication Scheme for Web Application

abstract: In this paper, we propose an optimized mutual authentication scheme which can keep most password authentication benefits, meanwhile improve the security property by using encryption primitives. Our proposed scheme not only offers webmasters a reasonable secure client authentication, but also offers good user experience. Security analysis demonstrates that the proposed authentication scheme can achieve the security requirements, and also resist the diverse possible attacks.


title: Automatic optimization of data perturbation distributions for multi-style training in speech recognition

abstract: Speech recognition performance using deep neural network based acoustic models is known to degrade when the acoustic environment and the speaker population in the target utterances are significantly different from the conditions represented in the training data. To address these mismatched scenarios, multi-style training (MTR) has been used to perturb utterances in an existing uncorrupted and potentially mismatched training speech corpus to better match target domain utterances. This paper addresses the problem of determining the distribution of perturbation levels for a given set of perturbation types that best matches the target speech utterances. An approach is presented that, given a small set of utterances from a target domain, automatically identifies an empirical distribution of perturbation levels that can be applied to utterances in an existing training set. Distributions are estimated for perturbation types that include acoustic background environments, reverberant room configurations, and speaker related variation like frequency and temporal warping. The end goal is for the resulting perturbed training set to characterize the variability in the target domain and thereby optimize ASR performance. An experimental study is performed to evaluate the impact of this approach on ASR performance when the target utterances are taken from a simulated far-field acoustic environment.


title: Rotor Chopper Control for Induction Motor Drive: TRC Strategy

abstract: A solid-state induction motor speed regulating scheme using a rotor chopper-controlled external resistor is investigated. This control scheme provides continuous and contactless variation of rotor resistance and thereby eliminates the undesirable features of the conventional rotor resistance control method. A thorough analysis of the steady-state performance of the system is presented. Both dc and ac circuit models are derived to describe the performance characteristics when the chopper operates under the time-ratio control (TRC) strategy. Effects of machine parameters on performance characteristics are studied. Theoretical results from the model are verified by comparison with experimental results. The torque?speed characteristic of this speed control system is essentially linear for a particular time ratio.


title: Joint action recognition and pose estimation from video

abstract: Action recognition and pose estimation from video are closely related tasks for understanding human motion, most methods, however, learn separate models and combine them sequentially. In this paper, we propose a framework to integrate training and testing of the two tasks. A spatial-temporal And-Or graph model is introduced to represent action at three scales. Specifically the action is decomposed into poses which are further divided to mid-level ST-parts and then parts. The hierarchical structure of our model captures the geometric and appearance variations of pose at each frame and lateral connections between ST-parts at adjacent frames capture the action-specific motion information. The model parameters for three scales are learned discriminatively, and action labels and poses are efficiently inferred by dynamic programming. Experiments demonstrate that our approach achieves state-of-art accuracy in action recognition while also improving pose estimation.


title: The Graft Tool: An All-Hexahedral Transition Algorithm For Creating A Multi-Directional Swept Volume Mesh

abstract: Sweeping algorithms have become very mature and can create a semi-structured mesh on a large set of solids. However, these algorithms require that all linking surfaces be mappable or submappable. This restriction excludes solids with imprints or protrusions on the linking surfaces. The grafting algorithm allows these solids to be swept. It then locally modifies the position and connectivity of the nodes on the linking surfaces to align with the graft surfaces. Once a high-quality surface mesh is formed on the graft surface, it is swept along the branch creating a 2 3/4-D mesh.


title: 60-GHz Four-Element Phased-Array Transmit/Receive System-in-Package Using Phase Compensation Techniques in 65-nm Flip-Chip CMOS Process

abstract: AThe 60-GHz four-element phased-array transmit/receive (TX/RX) system-in-package antenna modules with phase-compensated techniques in 65-nm CMOS technology are presented. The design is based on the all-RF architecture with 4-bit RF switched LC phase shifters, phase compensated variable gain amplifier (VGA), 4:1 Wilkinson power combining/dividing network, variable-gain low-noise amplifier, power amplifier, 6-bit unary digital-to-analog converter, bias circuit, electrostatic discharge protection, and digital control interface (DCI). The 2 × 2 TX/RX phased arrays have been packaged with four antennas in low-temperature co-fired ceramic modules through flip-chip bonding and underfill process, and phased-array beam steering have been demonstrated. The entire beam-steering functions are digitally controllable, and individual registers are integrated at each front-end to enable beam steering through the DCI. The four-element TX array results in an output of 5 dBm per channel. The four-element RX array results in an average gain of 25 dB per channel. The four-element array consumes 400 mW in TX and 180 mW in RX and occupies an area of 3.74 mm2 in the TX integrated circuit (IC) and 4.18 mm2 in the RX IC. The beam-steering measurement results show acceptable agreement of the synthesized and measured array pattern.


title: Mate Preference Necessities in Long- and Short-Term Mating: People Prioritize in Themselves What Their Mates Prioritize in Them

abstract: People were given highly constrained low budgets of mate dollars to allocate across various characteristics pertaining to their ideal partners and to their ideal selves for longand short-term mating. First, results replicated findings from Li et al. (2002) and Li & Kenrick (2006). For ideal long-term mates, men prioritized physical attractiveness and women prioritized social status. For ideal short-term mates, both sexes prioritized physical attractiveness. Second, people’s design of their ideal selves mirrored what the opposite sex ideally desired in their mates. For a long-term mating context, men prioritized social status in themselves and women prioritized physical attractiveness in themselves. For ideal short-term selves, both sexes prioritized physical attractiveness. Findings were consistent with a domain-specific view of psychological mechanisms, in that processes for valuing potential mates and processes for valuing one’s own mate value may be specialized mechanisms.


title: Crafting a multi-task CNN for viewpoint estimation

abstract: Convolutional Neural Networks (CNNs) were recently shown to provide state-of-the-art results for object category viewpoint estimation. However different ways of formulating this problem have been proposed and the competing approaches have been explored with very different design choices. This paper presents a comparison of these approaches in a unified setting as well as a detailed analysis of the key factors that impact performance. Followingly, we present a new joint training method with the detection task and demonstrate its benefit. We also highlight the superiority of classification approaches over regression approaches, quantify the benefits of deeper architectures and extended training data, and demonstrate that synthetic data is beneficial even when using ImageNet training data. By combining all these elements, we demonstrate an improvement of approximately 5% mAVP over previous state-of-the-art results on the Pascal3D+ dataset. In particular for their most challenging 24 view classification task we improve the results from 31.1% to 36.1% mAVP.


title: Improved empirical methods in reinforcement-learning evaluation

abstract: OF THE DISSERTATION IMPROVED EMPIRICAL METHODS IN REINFORCEMENT-LEARNING EVALUATION by VUKOSI N. MARIVATE Dissertation Director: Michael L. Littman The central question addressed in this research is ”can we define evaluation methodologies that encourage reinforcement-learning (RL) algorithms to work effectively with real-life data?” First, we address the problem of overfitting. RL algorithms are often tweaked and tuned to specific environments when applied, calling into question whether learning algorithms that work for one environment will work for others. We propose a methodology to evaluate algorithms on distributions of environments, as opposed to a single environment. We also develop a formal framework for characterizing the ”capacity” of a space of parameterized RL algorithms and bound the generalization error of a set of algorithms on a distribution of RL environments given a sample of environments. Second, we develop a method for evaluating RL algorithms offline using a static collection of data. Our motivation is that real-life applications of


title: Direct Yaw-Moment Control of an In-Wheel-Motored Electric Vehicle Based on Body Slip Angle Fuzzy Observer

abstract: A stabilizing observer-based control algorithm for an in-wheel-motored vehicle is proposed, which generates direct yaw moment to compensate for the state deviations. The control scheme is based on a fuzzy rule-based body slip angle (beta) observer. In the design strategy of the fuzzy observer, the vehicle dynamics is represented by Takagi-Sugeno-like fuzzy models. Initially, local equivalent vehicle models are built using the linear approximations of vehicle dynamics for low and high lateral acceleration operating regimes, respectively. The optimal beta observer is then designed for each local model using Kalman filter theory. Finally, local observers are combined to form the overall control system by using fuzzy rules. These fuzzy rules represent the qualitative relationships among the variables associated with the nonlinear and uncertain nature of vehicle dynamics, such as tire force saturation and the influence of road adherence. An adaptation mechanism for the fuzzy membership functions has been incorporated to improve the accuracy and performance of the system. The effectiveness of this design approach has been demonstrated in simulations and in a real-time experimental setting.


title: Antibacterial drug discovery in the resistance era

abstract: Abstract The looming antibiotic-resistance crisis has penetrated the consciousness of clinicians, researchers, policymakers, politicians and the public at large. The evolution and widespread distribution of antibiotic-resistance elements in bacterial pathogens has made diseases that were once easily treatable deadly again. Unfortunately, accompanying the rise in global resistance is a failure in antibacterial drug discovery. Lessons from the history of antibiotic discovery and fresh understanding of antibiotic action and the cell biology of microorganisms have the potential to deliver twenty-first century medicines that are able to control infection in the resistance era.


title: Merging and Evolution: Improving Convolutional Neural Networks for Mobile Applications

abstract: Compact neural networks are inclined to exploit"sparsely-connected"convolutions such as depthwise convolution and group convolution for employment in mobile applications. Compared with standard"fully-connected"convolutions, these convolutions are more computationally economical. However,"sparsely-connected"convolutions block the inter-group information exchange, which induces severe performance degradation. To address this issue, we present two novel operations named merging and evolution to leverage the inter-group information. Our key idea is encoding the inter-group information with a narrow feature map, then combining the generated features with the original network for better representation. Taking advantage of the proposed operations, we then introduce the Merging-and-Evolution (ME) module, an architectural unit specifically designed for compact networks. Finally, we propose a family of compact neural networks called MENet based on ME modules. Extensive experiments on ILSVRC 2012 dataset and PASCAL VOC 2007 dataset demonstrate that MENet consistently outperforms other state-of-the-art compact networks under different computational budgets. For instance, under the computational budget of 140 MFLOPs, MENet surpasses ShuffleNet by 1% and MobileNet by 1.95% on ILSVRC 2012 top-1 accuracy, while by 2.3% and 4.1% on PASCAL VOC 2007 mAP, respectively.


title: Semi-Supervised Text Classification Using EM.

abstract: For several decades, statisticians have advocated using a combination of labeled and unlabeled data to train classifiers by estimating parameters of a generative model through iterative Expectation-Maximization (EM) techniques. This chapter explores the effectiveness of this approach when applied to the domain of text classification. Text documents are represented here with a bag-of-words model, which leads to a generative classification model based on a mixture of multinomials. This model is an extremely simplistic representation of the complexities of written text. This chapter explains and illustrates three key points about semi-supervised learning for text classification with generative models. First, despite the simplistic representation, some text domains have a high positive correlation between generative model probability and classification accuracy. In these domains, a straightforward application of EM with the naive Bayes text model works well. Second, some text domains do not have this correlation. Here we can adopt a more expressive and appropriate generative model that does have a positive correlation. In these domains, semi-supervised learning again improves classification accuracy. Finally, EM suffers from the problem of local maxima, especially in high dimension domains such as text classification. We demonstrate that deterministic annealing, a variant of EM, can help overcome the problem of local maxima and increase classification accuracy further when the generative model is appropriate.


title: Barcodes: The persistent topology of data

abstract: This article surveys recent work of Carlsson and collaborators on applications of computational algebraic topology to problems of feature detection and shape recognition in high-dimensional data. The primary mathematical tool considered is a homology theory for point-cloud data sets — persistent homology — and a novel representation of this algebraic characterization — barcodes. We sketch an application of these techniques to the classification of natural images. 1. The shape of data When a topologist is asked, “How do you visualize a four-dimensional object?” the appropriate response is a Socratic rejoinder: “How do you visualize a threedimensional object?” We do not see in three spatial dimensions directly, but rather via sequences of planar projections integrated in a manner that is sensed if not comprehended. We spend a significant portion of our first year of life learning how to infer three-dimensional spatial data from paired planar projections. Years of practice have tuned a remarkable ability to extract global structure from representations in a strictly lower dimension. The inference of global structure occurs on much finer scales as well, with regards to converting discrete data into continuous images. Dot-matrix printers, scrolling LED tickers, televisions, and computer displays all communicate images via arrays of discrete points which are integrated into coherent, global objects. This also is a skill we have practiced from childhood. No adult does a dot-to-dot puzzle with anything approaching anticipation. 1.1. Topological data analysis. Problems of data analysis share many features with these two fundamental integration tasks: (1) how does one infer high dimensional structure from low dimensional representations; and (2) how does one assemble discrete points into global structure. The principal themes of this survey of the work of Carlsson, de Silva, Edelsbrunner, Harer, Zomorodian, and others are the following: (1) It is beneficial to replace a set of data points with a family of simplicial complexes, indexed by a proximity parameter. This converts the data set into global topological objects. (2) It is beneficial to view these topological complexes through the lens of algebraic topology — specifically, via a novel theory of persistent homology adapted to parameterized families. (3) It is beneficial to encode the persistent homology of a data set in the form of a parameterized version of a Betti number: a barcode. The author gratefully acknowledges the support of DARPA # HR0011-07-1-0002. The work reviewed in this article is funded by the DARPA program TDA: Topological Data Analysis.


title: Examining the Needs and Experiences of Domestic Violence Survivors in Transitional Housing

abstract: Transitional Housing (TH) programs were developed for domestic violence (DV) survivors in order to provide the time, financial assistance, and supports needed for survivors to achieve long-term safety and housing stability. Previous research indicates TH may be effective for homeless families, but there is a paucity of evidence related to DV survivors’ need for or use of TH. TH is an expensive housing intervention that is space limited and requires survivors to relocate at program end. It is therefore imperative to understand who is best suited for, interested in, and helped by DVTH. Thirty current survivors in a DVTH program were interviewed in order to elucidate the benefits and drawbacks of DVTH. The interviews were semi-structured, and both quantitative and qualitative data were collected. Survivors in severe danger from their abusive partners and ex-partners, those with greater psychosocial needs, and immigrant survivors identified aspects unique to DVTH (e.g., high-level security, intensive services) as being critical to their safety and well-being. A small number of survivors would have chosen a less intensive and structured housing option, such as Rapid Re-housing (RR), that would have allowed them to remain in their housing after assistance ended, had such an option been available. DVTH appears to be an important option for some DV survivors, but more housing options are needed across communities to meet survivors’ myriad needs.


title: The mythos of model interpretability

abstract: In machine learning, the concept of interpretability is both important and slippery.


title: Supervised clustering of genes

abstract: BackgroundWe focus on microarray data where experiments monitor gene expression in different tissues and where each experiment is equipped with an additional response variable such as a cancer type. Although the number of measured genes is in the thousands, it is assumed that only a few marker components of gene subsets determine the type of a tissue. Here we present a new method for finding such groups of genes by directly incorporating the response variables into the grouping process, yielding a supervised clustering algorithm for genes.ResultsAn empirical study on eight publicly available microarray datasets shows that our algorithm identifies gene clusters with excellent predictive potential, often superior to classification with state-of-the-art methods based on single genes. Permutation tests and bootstrapping provide evidence that the output is reasonably stable and more than a noise artifact.ConclusionsIn contrast to other methods such as hierarchical clustering, our algorithm identifies several gene clusters whose expression levels clearly distinguish the different tissue types. The identification of such gene clusters is potentially useful for medical diagnostics and may at the same time reveal insights into functional genomics.


title: Video forgery detection using HOG features and compression properties

abstract: In this paper, we propose a novel video forgery detection technique to detect the spatial and temporal copy paste tampering. It is a challenge to detect the spatial and temporal copy-paste tampering in videos as the forged patch may drastically vary in terms of size, compression rate and compression type (I, B or P) or other changes such as scaling and filtering. In our proposed algorithm, the copy-paste forgery detection is based on Histogram of Oriented Gradients (HOG) feature matching and video compression properties. The benefit of using HOG features is that they are robust against various signal processing manipulations. The experimental results show that the forgery detection performance is very effective. We also compare our results against a popular copy-paste forgery detection algorithm. In addition, we analyze the experimental results for different forged patch sizes under varying degree of modifications such as compression, scaling and filtering.


title: A Scheduler for Multipath TCP

abstract: We first explain problems with the default scheduler used by the Linux kernel MPTCP implementation. Then we propose the design of a new scheduler. Preliminary empirical results show that our proposed scheduler improves the throughput in MPTCP by alleviating the problems caused by the default scheduler.


title: Scalable Techniques for Mining Causal Structures

abstract: Mining for association rules in market basket data has proved a fruitful area of research. Measures such as conditional probability (confidence) and correlation have been used to infer rules of the form “the existence of item A implies the existence of item B.” However, such rules indicate only a statistical relationship between A and B. They do not specify the nature of the relationship: whether the presence of A causes the presence of B, or the converse, or some other attribute or phenomenon causes both to appear together. In applications, knowing such causal relationships is extremely useful for enhancing understanding and effecting change. While distinguishing causality from correlation is a truly difficult problem, recent work in statistics and Bayesian learning provide some avenues of attack. In these fields, the goal has generally been to learn complete causal models, which are essentially impossible to learn in large-scale data mining applications with a large number of variables. ::: ::: In this paper, we consider the problem of determining casual relationships, instead of mere associations, when mining market basket data. We identify some problems with the direct application of Bayesian learning ideas to mining large databases, concerning both the scalability of algorithms and the appropriateness of the statistical techniques, and introduce some initial ideas for dealing with these problems. We present experimental results from applying our algorithms on several large, real-world data sets. The results indicate that the approach proposed here is both computationally feasible and successful in identifying interesting causal structures. An interesting outcome is that it is perhaps easier to infer the lack of causality than to infer causality, information that is useful in preventing erroneous decision making.


title: Understanding and evaluating the behavior of technical users. A study of developer interaction at StackOverflow

abstract: This paper is focused on analyzing the details of one of the most widely used Q&A technical discussion forums on the Internet: StackOverflow. StackOverflow is a huge source of information for both academics and industry practitioners and its analysis can provide useful insights. This is evident by a growing body of work dedicated to the study of this platform. There are several papers that have taken the `platform' as an analyzing point to gain a few useful insights into the practices adopted by software developers. However, different from current literature, the objective of this paper is to explore the habit of users, more specifically, we want to find out how do technical users behave online? In doing so, we attempt to present a new picture that focuses more on the human aspect of this online community. We neither focus on the technical aspects of the platform nor do we focus on the technical capabilities of developers, rather, the focus of this paper is to explore the humanistic point of view of technical users. To do that, we mine StackOverflow's repositories to find a few patterns that raise several questions. The observations presented in this paper highlight real world implications of having open call based software engineering platforms. Further, we conduct a survey of StackOverflow users to find an answer to one of the highly talked about topics on Meta (a discussion forum for StackOverflow). The subject of the survey concerns changing participation habit of users. As the paper progresses forward, we discuss factors that raise the issue of professionalism and ethics at this online venue. The findings show several malpractices happening at StackOverflow. We present a detailed analysis and discuss these issues in this paper.


title: AttriInfer: Inferring User Attributes in Online Social Networks Using Markov Random Fields

abstract: In the attribute inference problem, we aim to infer users' private attributes (e.g., locations, sexual orientation, and interests) using their public data in online social networks. State-of-the-art methods leverage a user's both public friends and public behaviors (e.g., page likes on Facebook, apps that the user reviewed on Google Play) to infer the user's private attributes. However, these methods suffer from two key limitations: 1) suppose we aim to infer a certain attribute for a target user using a training dataset, they only leverage the labeled users who have the attribute, while ignoring the label information of users who do not have the attribute; 2) they are inefficient because they infer attributes for target users one by one. As a result, they have limited accuracies and applicability in real-world social networks. ::: ::: In this work, we propose AttriInfer, a new method to infer user attributes in online social networks. AttriInfer can leverage both friends and behaviors, as well as the label information of training users who have an attribute and who do not have the attribute. Specifically, we model a social network as a pairwise Markov Random Field (pMRF). Given a training dataset, which consists of some users who have a certain attribute and some users who do not have a certain attribute, we compute the posterior probability that a target user has the attribute and use the posterior probability to infer attributes. In the basic version of AttriInfer, we use Loopy Belief Propagation (LBP) to compute the posterior probability. However, LBP is not scalable to very large-scale real-world social networks and not guaranteed to converge. Therefore, we further optimize LBP to be scalable and guaranteed to converge. We evaluated our method and compare it with state-of-the-art methods using a real-world Google+ dataset with 5.7M users. Our results demonstrate that our method substantially outperforms state-of-the-art methods in terms of both accuracy and efficiency.


title: Object-Extent Pooling for Weakly Supervised Single-Shot Localization

abstract: In the face of scarcity in detailed training annotations, the ability to perform object localization tasks in real-time with weak-supervision is very valuable. However, the computational cost of generating and evaluating region proposals is heavy. We adapt the concept of Class Activation Maps (CAM) into the very first weakly-supervised 'single-shot' detector that does not require the use of region proposals. To facilitate this, we propose a novel global pooling technique called Spatial Pyramid Averaged Max (SPAM) pooling for training this CAM-based network for object extent localisation with only weak image-level supervision. We show this global pooling layer possesses a near ideal flow of gradients for extent localization, that offers a good trade-off between the extremes of max and average pooling. Our approach only requires a single network pass and uses a fast-backprojection technique, completely omitting any region proposal steps. To the best of our knowledge, this is the first approach to do so. Due to this, we are able to perform inference in real-time at 35fps, which is an order of magnitude faster than all previous weakly supervised object localization frameworks.


title: Control of a Quadrotor with Reinforcement Learning

abstract: In this letter, we present a method to control a quadrotor with a neural network trained using reinforcement learning techniques. With reinforcement learning, a common network can be trained to directly map state to actuator command making any predefined control structure obsolete for training. Moreover, we present a new learning algorithm that differs from the existing ones in certain aspects. Our algorithm is conservative but stable for complicated tasks. We found that it is more applicable to controlling a quadrotor than existing algorithms. We demonstrate the performance of the trained policy both in simulation and with a real quadrotor. Experiments show that our policy network can react to step response relatively accurately. With the same policy, we also demonstrate that we can stabilize the quadrotor in the air even under very harsh initialization (manually throwing it upside-down in the air with an initial velocity of 5 m/s). Computation time of evaluating the policy is only 7 μs per time step, which is two orders of magnitude less than common trajectory optimization algorithms with an approximated model.


title: Application partitioning algorithms in mobile cloud computing: Taxonomy, review and future directions

abstract: Mobile cloud computing (MCC) enables the development of computational intensive mobile applications by leveraging the application processing services of computational clouds. Contemporary distributed application processing frameworks use runtime partitioning of elastic applications in which additional computing resources are occurred in runtime application profiling and partitioning. A number of recent studies have highlighted the different aspects of MCC. Current studies, however, have overlooked into the mechanism of application partitioning for MCC. We consider application partitioning to be an independent aspect of dynamic computational offloading and therefore we review the current status of application partitioning algorithms (APAs) to identify the issues and challenges. To the best of our knowledge, this paper is the first to propose a thematic taxonomy for APAs in MCC. The APAs are reviewed comprehensively to qualitatively analyze the implications and critical aspects. Furthermore, the APAs are analyzed based on partitioning granularity, partitioning objective, partitioning model, programming language support, presence of a profiler, allocation decision, analysis technique, and annotation. This paper also highlights the issues and challenges in partitioning of elastic application to assist in selecting appropriate research domains and exploring lightweight techniques of distributed application processing in MCC.


title: Large-scale image retrieval with compressed Fisher vectors

abstract: The problem of large-scale image search has been traditionally addressed with the bag-of-visual-words (BOV). In this article, we propose to use as an alternative the Fisher kernel framework. We first show why the Fisher representation is well-suited to the retrieval problem: it describes an image by what makes it different from other images. One drawback of the Fisher vector is that it is high-dimensional and, as opposed to the BOV, it is dense. The resulting memory and computational costs do not make Fisher vectors directly amenable to large-scale retrieval. Therefore, we compress Fisher vectors to reduce their memory footprint and speed-up the retrieval. We compare three binarization approaches: a simple approach devised for this representation and two standard compression techniques. We show on two publicly available datasets that compressed Fisher vectors perform very well using as little as a few hundreds of bits per image, and significantly better than a very recent compressed BOV approach.


Language Identification in Mixed Script Social Media Text


title: Engineering Autonomous Driving Software

abstract: A larger number of people with heterogeneous knowledge and skills running a project together needs an adaptable, target, and skill-specific engineering process. This especially holds for a project to develop a highly innovative, autonomously driving vehicle to participate in the 2007 DARPA Urban Challenge. In this contribution, we present essential elements of a software and systems engineering process to develop a so-called artificial intelligence capable of driving autonomously in complex urban situations. The process itself includes agile concepts, like a test first approach, continuous integration of all software modules, and a reliable release and configuration management assisted by software tools in integrated development environments. However, one of the most important elements for an efficient and stringent development is the ability to efficiently test the behavior of the developed system in a flexible and modular system simulation for urban situations both interactively and unattendedly. We call this the simulate first approach.


title: DialogueRNN: An Attentive RNN for Emotion Detection in Conversations

abstract: Emotion detection in conversations is a necessary step for a number of applications, including opinion mining over chat history, social media threads, debates, argumentation mining, understanding consumer feedback in live conversations, and so on. Currently systems do not treat the parties in the conversation individually by adapting to the speaker of each utterance. In this paper, we describe a new method based on recurrent neural networks that keeps track of the individual party states throughout the conversation and uses this information for emotion classification. Our model outperforms the state-of-the-art by a significant margin on two different datasets.


title: Neural SLAM: Learning to Explore with External Memory

abstract: We present an approach for agents to learn representations of a global map from sensor data, to aid their exploration in new environments. To achieve this, we embed procedures mimicking that of traditional Simultaneous Localization and Mapping (SLAM) into the soft attention based addressing of external memory architectures, in which the external memory acts as an internal representation of the environment. This structure encourages the evolution of SLAM-like behaviors inside a completely differentiable deep neural network. We show that this approach can help reinforcement learning agents to successfully explore new environments where long-term memory is essential. We validate our approach in both challenging grid-world environments and preliminary Gazebo experiments. A video of our experiments can be found at: this https URL.


title: Why adductor magnus muscle is large: The function based on muscle morphology in cadavers

abstract: The aim of this study was to examine anatomical properties of the adductor magnus through a detailed classification, and to hypothesize its function and size to gather enough information about morphology. Ten cadaveric specimens of the adductor magnus were used. The muscle was separated into four portios (AM1-AM4) based on the courses of the corresponding perforating arteries, and its volume, muscle length, muscle fiber length and physiological cross-sectional area were assessed. The architectural characteristics of these four portions of the adductor magnus were then classified with the aid of principal component analysis. The results led us into demarcating the most proximal part of the adductor magnus (AM1) from the remaining parts (AM2, AM3, and AM4). Classification of the adductor magnus in terms of architectural characteristics differed from the more traditional anatomical distinction. The AM2, AM3, and AM4, having longer muscle fiber lengths than the AM1, appear to be designed as displacers for moving the thigh through a large range of motion. The AM1 appears instead to be oriented principally toward stabilizing the hip joint. The large mass of the adductor magnus should thus be regarded as a complex of functionally differentiable muscle portions.


title: Virtual reality and information technology for archaeological site promotion

abstract: Polyacrylate elastomers are prepared by polymerizing a mixture of an acrylate monomer and a diolefin such as isoprene to yield a polymer containing carbon-to-carbon unsaturation and this polymer is then treated with bromine to yield a bromine containing acrylate polymer which is curable with zinc oxide, zinc oxide and sulphur or sulphur and alkali metal salts of fatty acids.


title: Mobile Payment: Towards a Customer-Centric Model

abstract: Mobile payment normally occurs as a wireless transaction of monetary value and includes the initiation, authorization and the realization of the payment. Such transactions are facilitated by purpose-built mobile payment systems that are part of the service infrastructure supporting the functioning of mobile business applications. A number of stakeholder groups may be involved in concluding a mobile payment transaction, among them customers, mobile operators, financial institutions, merchants, and intermediaries. In this paper, mobile payment systems are characterised from the point of view of the stakeholder groups. Building on existing work, a supply and demand model for the investigation of mPayment services is presented, and applied to a case study.


title: Improving Word Representations with Document Labels

abstract: Word representations, aiming to build vectors for each word, have been successfully used in a variety of applications. Most word representations are learned from large amounts of documents ignoring other information. This is rather suboptimal because the side information of the documents, such as document labels, is not used in learning word representations. In this paper, we focus on how to exploit these side information to improve word representations. We propose to incorporate document labels into the learning process of word representations in two frameworks: neural network and matrix factorization. The experimental results on word analogy and word similarity task show that our models can better capture the semantic and syntactic information than the original models. Our models also improve the performance of word representations on text classification task.


title: Clinical and Microbiological Profile of Microsporidial Keratoconjunctivitis in Southern India

abstract: PURPOSE ::: To report the clinical and microbiological profile of microsporidial keratoconjunctivitis in immunocompetent individuals in southern India. ::: ::: ::: DESIGN ::: Retrospective noncomparative case series. ::: ::: ::: PARTICIPANTS ::: Nineteen cases of microsporidial keratoconjunctivitis diagnosed between January 2002 and December 2004 were studied. ::: ::: ::: METHODS ::: All medical and laboratory records of patients with infectious keratitis during the study period were reviewed. In all cases, after relevant history recording and slit-lamp examination, corneal/conjunctival scrapings were collected. Multiple scrapings were taken from the lesions and examined by fluorescence or light microscopy after staining with potassium hydroxide and calcofluor white (KOH + CFW), Gram's stain, Kinyoun's, and Giemsa stain. In some cases, the serum was tested for human immunodeficiency virus antibody by enzyme-linked immunosorbent assay. ::: ::: ::: MAIN OUTCOME MEASURES ::: Clinical course, demographic features, predisposing factors, microbiological profile, treatment, and final outcome. ::: ::: ::: RESULTS ::: Of 4822 cases of suspected microbial keratitis seen during the period, 19 (0.4%) were identified as microbiologically proven microsporidial keratitis. All patients were apparently healthy; 8 had a history of ocular trauma, and 3 mentioned bathing in unclean river water. All but 2 had unilateral involvement. The mean age of the patients was 38.4+/-13.7 years (range, 23-73). The duration of symptoms ranged from 1 day to 2 years, with 11 of 19 (58%) presenting within a week of onset of symptoms. Slit-lamp examination revealed multifocal, coarse, punctate, raised epithelial lesions in all patients. A mild to moderate nonpurulent conjunctivitis was present in all cases, with papillary and/or follicular reaction in 14. Corneal/conjunctival scrapings demonstrated microsporidial spores by KOH + CFW in 16 of 17 cases tested, whereas the diagnosis was made by Giemsa stain in 2 cases and by Gram's stain in one. Kinyoun's (1% acid fast) stain was confirmatory in all cases. In all patients, treatment was started after debridement and included oral and/or topical antimicrobial agents. At last follow-up, 15 of 19 achieved a visual acuity of 20/20. ::: ::: ::: CONCLUSIONS ::: Microsporidial keratoconjunctivitis should be considered in the differential diagnosis of atypical punctate epithelial keratitis associated with conjunctivitis and can be diagnosed by routine microbiological methods. The disease can occur in healthy individuals, and the outcome of treatment is often satisfactory.


title: Analysis and design of a slotted waveguide antenna array using hollow substrate integrated waveguide

abstract: In this paper, a 6 × 6 planar slot array based on a hollow substrate integrated waveguide (HSIW) is presented. To eliminate the tilting of the main beam, the slot array is fed from the centre at the back of the HSIW, which results in a blockage area. To reduce the impact on sidelobe levels, a slot extrusion technique is introduced. A simplified multiway power divider is demonstrated to feed the array elements and the optimisation procedure is described. To verify the antenna design, a 6 × 6 planar array is fabricated and measured in a low temperature co-fired ceramic (LTCC) technology. The HSIW has lower loss, comparable to standard WR28, and a high gain of 17.1 dBi at 35.5 GHz has been achieved in the HSIW slot array.


Poster : Framework for Security Verification of Secure Processor Architectures


title: Beyond General Intelligence ( IQ ) and Emotional Intelligence ( EQ ) : The Role of Cultural Intelligence ( CQ ) on Cross-Border Leadership Effectiveness in a Globalized World

abstract: Emphasizing the importance of cross-border effectiveness in the contemporary globalized world, we propose that cultural intelligence—the leadership capability to manage effectively in culturally diverse settings—is a critical leadership competency for those with cross-border responsibilities. We tested this hypothesis with multisource data, including multiple intelligences, in a sample of 126 Swiss military officers with both domestic and cross-border leadership responsibilities. Results supported our predictions: (1) general intelligence predicted both domestic and cross-border leadership effectiveness; (2) emotional intelligence was a stronger predictor of domestic leadership effectiveness, and (3) cultural intelligence was a stronger predictor of cross-border leadership effectiveness. Overall, results show the value of cultural intelligence as a critical leadership competency in today's globalized world.


Simultaneous Localisation and Mapping ( SLAM ) : Part I The Essential Algorithms


title: Aesthetics-Guided Summarization from Multiple User Generated Videos

abstract: In recent years, with the rapid development of camera technology and portable devices, we have witnessed a flourish of user generated videos, which are gradually reshaping the traditional professional video oriented media market. The volume of user generated videos in repositories is increasing at a rapid rate. In today's video retrieval systems, a simple query will return many videos which seriously increase the viewing burden. To manage these video retrievals and provide viewers with an efficient way to browse, we introduce a system to automatically generate a summarization from multiple user generated videos and present their salience to viewers in an enjoyable manner. Among multiple consumer videos, we find their qualities to be highly diverse due to various factors such as a photographer's experience or environmental conditions at the time of capture. Such quality inspires us to include a video quality evaluation component into the video summarization since videos with poor qualities can seriously degrade the viewing experience. We first propose a probabilistic model to evaluate the aesthetic quality of each user generated video. This model compares the rich aesthetics information from several well-known photo databases with generic unlabeled consumer videos, under a human perception component indicating the correlation between a video and its constituting frames. Subjective studies were carried out with the results indicating that our method is reliable. Then a novel graph-based formulation is proposed for the multi-video summarization task. Desirable summarization criteria is incorporated as the graph attributes and the problem is solved through a dynamic programming framework. Comparisons with several state-of-the-art methods demonstrate that our algorithm performs better than other methods in generating a skimming video in preserving the essential scenes from the original multiple input videos, with smooth transitions among consecutive segments and appealing aesthetics overall.


title: A Wideband Transformer-Coupled CMOS Power Amplifier for $X$-Band Multifunction Chips

abstract: This paper presents a wideband transformer-coupled CMOS power amplifier (PA). On-chip transmission-line transformers are used as key components of matching networks at output, input, and interstage. The wideband on-chip transformer is harnessed without any additional inductive devices so that a wideband power characteristic can be achieved. The PA is fabricated using a 0.18-μm CMOS process. It provides a saturated output power of 21.5 dBm with the power-added efficiency (PAE) of 20.3%, and the output 1-dB gain-compressed power (P1 dB) is 20.2 dBm with the PAE of 14.8% at 9.5 GHz, respectively. The small-signal gain is 25.3 dB and the 3-dB bandwidth is 6.5 GHz (6.5-13 GHz). The die area is 1.34 mm × 0.47 mm. Among the reported X/Ku-band CMOS PAs, this amplifier achieves the highest figure of merit, and also shows suitable performances for phased-array systems.


title: Euclidean Distance Transform Soft Shadow Mapping

abstract: The real-time computation of high-quality soft shadows is a challenging task in computer graphics. The existing approximate solutions for soft shadow computation are prone to aliasing artifacts for small penumbra sizes and banding or light leaking artifacts for large penumbra sizes. In this paper, we aim to minimize these problems with the use of a normalized Euclidean distance transform for soft shadow mapping. We compute anti-aliased hard shadows in the camera view, and, on the basis of the percentage-closer soft shadows framework, we estimate the penumbra size of the shadow silhouette and compute a filtered normalized Euclidean distance transform to generate the soft shadows at the penumbra location. Our approach yields real-time performance and generates soft shadows with less artifacts than related work.


title: Focusing Attention: Towards Accurate Text Recognition in Natural Images

abstract: Scene text recognition has been a hot research topic in computer vision due to its various applications. The state of the art is the attention-based encoder-decoder framework that learns the mapping between input images and output sequences in a purely data-driven way. However, we observe that existing attention-based methods perform poorly on complicated and/or low-quality images. One major reason is that existing methods cannot get accurate alignments between feature areas and targets for such images. We call this phenomenon “attention drift”. To tackle this problem, in this paper we propose the FAN (the abbreviation of Focusing Attention Network) method that employs a focusing attention mechanism to automatically draw back the drifted attention. FAN consists of two major components: an attention network (AN) that is responsible for recognizing character targets as in the existing methods, and a focusing network (FN) that is responsible for adjusting attention by evaluating whether AN pays attention properly on the target areas in the images. Furthermore, different from the existing methods, we adopt a ResNet-based network to enrich deep representations of scene text images. Extensive experiments on various benchmarks, including the IIIT5k, SVT and ICDAR datasets, show that the FAN method substantially outperforms the existing methods.


Memristor-The Missing Circuit Element


title: Reliable Client Accounting for P2PInfrastructure Hybrids

abstract: Content distribution networks (CDNs) have started to adopt hybrid designs, which employ both dedicated edge servers and resources contributed by clients. Hybrid designs combine many of the advantages of infrastructure-based and peer-to-peer systems, but they also present new challenges. This paper identifies reliable client accounting as one such challenge. Operators of hybrid CDNs are accountable to their customers (i.e., content providers) for the CDN's performance. Therefore, they need to offer reliable quality of service and a detailed account of content served. Service quality and accurate accounting, however, depend in part on interactions among untrusted clients. Using the Akamai NetSession client network in a case study, we demonstrate that a small number of malicious clients used in a clever attack could cause significant accounting inaccuracies. ::: ::: We present a method for providing reliable accounting of client interactions in hybrid CDNs. The proposed method leverages the unique characteristics of hybrid systems to limit the loss of accounting accuracy and service quality caused by faulty or compromised clients. We also describe RCA, a system that applies this method to a commercial hybrid content-distribution network. Using trace-driven simulations, we show that RCA can detect and mitigate a variety of attacks, at the expense of a moderate increase in logging overhead.


title: On the Robustness of Semantic Segmentation Models to Adversarial Attacks

abstract: Deep Neural Networks (DNNs) have been demonstrated to perform exceptionally well on most recognition tasks such as image classification and segmentation. However, they have also been shown to be vulnerable to adversarial examples. This phenomenon has recently attracted a lot of attention but it has not been extensively studied on multiple, large-scale datasets and complex tasks such as semantic segmentation which often require more specialised networks with additional components such as CRFs, dilated convolutions, skip-connections and multiscale processing. In this paper, we present what to our knowledge is the first rigorous evaluation of adversarial attacks on modern semantic segmentation models, using two large-scale datasets. We analyse the effect of different network architectures, model capacity and multiscale processing, and show that many observations made on the task of classification do not always transfer to this more complex task. Furthermore, we show how mean-field inference in deep structured models and multiscale processing naturally implement recently proposed adversarial defenses. Our observations will aid future efforts in understanding and defending against adversarial examples. Moreover, in the shorter term, we show which segmentation models should currently be preferred in safety-critical applications due to their inherent robustness.


title: A Model Driven Development Framework for Serious Games

abstract: Serious games have become more important in the field of education. The tight connection of education to computer game playing by presenting learning content in a game-like environment creates a better usage experience thus leading to higher usage and potentially better learning outcomes. To create successful serious games, it is necessary to combine programming knowledge and didactical knowledge to properly include learning objectives. Most educators are not able to develop such games on their own due to lack of programming and game design skills. In my dissertation, I am focusing on a solution based on model driven development techniques that shall allow educators to create didactically sound serious games. My idea is it to provide a domain specific modeling language for serious games and a visual modeling tool, which allows the generation of games from visual models.


title: Network-based marketing: Identifying likely adopters via consumer networks

abstract: Network-based marketing refers to a collection of marketing techniques that take advantage of links between consumers to increase sales. We concentrate on the consumer networks formed using direct interactions (e.g., communications) between consumers. We survey the diverse literature on such marketing with an emphasis on the statistical methods used and the data to which these methods have been applied. We also provide a discussion of challenges and opportunities for this burgeoning research topic. Our survey highlights a gap in the literature. Because of inadequate data, prior studies have not been able to provide direct, statistical support for the hypothesis that network linkage can directly affect product/service adoption. Using a new data set that represents the adoption of a new telecommunications service, we show very strong support for the hypothesis. Specifically, we show three main results: (1) ``Network neighbors''--those consumers linked to a prior customer--adopt the service at a rate 3--5 times greater than baseline groups selected by the best practices of the firm's marketing team. In addition, analyzing the network allows the firm to acquire new customers who otherwise would have fallen through the cracks, because they would not have been identified based on traditional attributes. (2) Statistical models, built with a very large amount of geographic, demographic and prior purchase data, are significantly and substantially improved by including network information. (3) More detailed network information allows the ranking of the network neighbors so as to permit the selection of small sets of individuals with very high probabilities of adoption.


Spectral Networks and Deep Locally Connected Networks on Graphs