title: Geometry-Aware Deep Network for Single-Image Novel View Synthesis

abstract: This paper tackles the problem of novel view synthesis from a single image. In particular, we target real-world scenes with rich geometric structure, a challenging task due to the large appearance variations of such scenes and the lack of simple 3D models to represent them. Modern, learning-based approaches mostly focus on appearance to synthesize novel views and thus tend to generate predictions that are inconsistent with the underlying scene structure. By contrast, in this paper, we propose to exploit the 3D geometry of the scene to synthesize a novel view. Specifically, we approximate a real-world scene by a fixed number of planes, and learn to predict a set of homographies and their corresponding region masks to transform the input image into a novel view. To this end, we develop a new region-aware geometric transform network that performs these multiple tasks in a common framework. Our results on the outdoor KITTI and the indoor ScanNet datasets demonstrate the effectiveness of our network in generating high quality synthetic views that respect the scene geometry, thus outperforming the state-of-the-art methods.


title: Health consequences of obesity in youth: childhood predictors of adult disease.

abstract: Obesity now affects one in five children in the United States. Discrimination against overweight children begins early in childhood and becomes progressively institutionalized. Because obese children tend to be taller than their nonoverweight peers, they are apt to be viewed as more mature. The inappropriate expectations that result may have an adverse effect on their socialization. Many of the cardiovascular consequences that characterize adult-onset obesity are preceded by abnormalities that begin in childhood. Hyperlipidemia, hypertension, and abnormal glucose tolerance occur with increased frequency in obese children and adolescents. The relationship of cardiovascular risk factors to visceral fat independent of total body fat remains unclear. Sleep apnea, pseudotumor cerebri, and Blount's disease represent major sources of morbidity for which rapid and sustained weight reduction is essential. Although several periods of increased risk appear in childhood, it is not clear whether obesity with onset early in childhood carries a greater risk of adult morbidity and mortality. Obesity is now the most prevalent nutritional disease of children and adolescents in the United States. Although obesity-associated morbidities occur more frequently in adults, significant consequences of obesity as well as the antecedents of adult disease occur in obese children and adolescents. In this review, I consider the adverse effects of obesity in children and adolescents and attempt to outline areas for future research. I refer to obesity as a body mass index greater than the 95th percentile for children of the same age and gender.


title: Natural variation in learning rate and memory dynamics in parasitoid wasps: opportunities for converging ecology and neuroscience

abstract: Although the neural and genetic pathways underlying learning and memory formation seem strikingly similar among species of distant animal phyla, several more subtle inter- and intraspecific differences become evident from studies on model organisms. The true significance of such variation can only be understood when integrating this with information on the ecological relevance. Here, we argue that parasitoid wasps provide an excellent opportunity for multi-disciplinary studies that integrate ultimate and proximate approaches. These insects display interspecific variation in learning rate and memory dynamics that reflects natural variation in a daunting foraging task that largely determines their fitness: finding the inconspicuous hosts to which they will assign their offspring to develop. We review bioassays used for oviposition learning, the ecological factors that are considered to underlie the observed differences in learning rate and memory dynamics, and the opportunities for convergence of ecology and neuroscience that are offered by using parasitoid wasps as model species. We advocate that variation in learning and memory traits has evolved to suit an insect's lifestyle within its ecological niche.


title: Past Forward: Nostalgia as a Motivational Force

abstract: Nostalgia has endured a negative reputation, being branded an unhealthy preoccupation with one's past. This reputation is unwarranted. Nostalgia has remarkable implications for one's future. It strengthens approach orientation, raises optimism, evokes inspiration, boosts creativity, and kindles prosociality. Far from reflecting escapism from the present, nostalgia potentiates an attainable future.


title: Credit Risk Scoring Models

abstract: Credit scoring models play a fundamental role in the risk management practice at most banks. They are used to quantify credit risk at counterparty or transaction level in the different phases of the credit cycle (e.g. application, behavioural, collection models). The credit score empowers users to make quick decisions or even to automate decisions and this is extremely desirable when banks are dealing with large volumes of clients and relatively small margin of profits at individual transaction level (i.e. consumer lending, but increasingly also small business lending). In this article, we analyze the history and new developments related to credit scoring models. We find that with the new Basel Capital Accord, credit scoring models have been remotivated and given unprecedented significance. Banks, in particular, and most financial institutions worldwide, have either recently developed or modified existing internal credit risk models to conform with the new rules and best practices recently updated in the market. Moreover, we analyze the key steps of the credit scoring model’s lifecycle (i.e. assessment, implementation, validation) highlighting the main requirement imposed by Basel II. We conclude that banks that are going to implement the most advanced approach to calculate their capital requirements under Basel II will need to increase their attention and consideration of credit scoring models in the next future.


Portable Document Format ( PDF ) Security Analysis and Malware Threats


title: Clustering data streams using grid-based synopsis

abstract: Continually advancing technology has made it feasible to capture data online for onward transmission as a steady flow of newly generated data points, termed as data stream. Continuity and unboundedness of data streams make storage of data and multiple scans of data an impractical proposition for the purpose of knowledge discovery. Need to learn structures from data in streaming environment has been a driving force for making clustering a popular technique for knowledge discovery from data streams. Continuous nature of streaming data makes it infeasible to look for point membership among the clusters discovered so far, necessitating employment of a synopsis structure to consolidate incoming data points. This synopsis is exploited for building clustering scheme to meet subsequent user demands. The proposed Exclusive and Complete Clustering (ExCC) algorithm captures non-overlapping clusters in data streams with mixed attributes, such that each point either belongs to some cluster or is an outlier/noise. The algorithm is robust, adaptive to changes in data distribution and detects succinct outliers on-the-fly. It deploys a fixed granularity grid structure as synopsis and performs clustering by coalescing dense regions in grid. Speed-based pruning is applied to synopsis prior to clustering to ensure currency of discovered clusters. Extensive experimentation demonstrates that the algorithm is robust, identifies succinct outliers on-the-fly and is adaptive to change in the data distribution. ExCC algorithm is further evaluated for performance and compared with other contemporary algorithms.


Notes from Toyota-Land: An American Engineer in Japan


title: Real-Time Exemplar-Based Face Sketch Synthesis

abstract: This paper proposes a simple yet effective face sketch synthesis method. Similar to existing exemplar-based methods, a training dataset containing photo-sketch pairs is required, and a K-NN photo patch search is performed between a test photo and every training exemplar for sketch patch selection. Instead of using the Markov Random Field to optimize global sketch patch selection, this paper formulates face sketch synthesis as an image denoising problem which can be solved efficiently using the proposed method. Real-time performance can be obtained on a state-of-the-art GPU. Meanwhile quantitative evaluations on face sketch recognition and user study demonstrate the effectiveness of the proposed method. In addition, the proposed method can be directly extended to the temporal domain for consistent video sketch synthesis, which is of great importance in digital entertainment.


title: Assessing the quality of reports of randomized clinical trials: Is blinding necessary?

abstract: It has been suggested that the quality of clinical trials should be assessed by blinded raters to limit the risk of introducing bias into meta-analyses and systematic reviews, and into the peer-review process. There is very little evidence in the literature to substantiate this. This study describes the development of an instrument to assess the quality of reports of randomized clinical trials (RCTs) in pain research and its use to determine the effect of rater blinding on the assessments of quality. A multidisciplinary panel of six judges produced an initial version of the instrument. Fourteen raters from three different backgrounds assessed the quality of 36 research reports in pain research, selected from three different samples. Seven were allocated randomly to perform the assessments under blind conditions. The final version of the instrument included three items. These items were scored consistently by all the raters regardless of background and could discriminate between reports from the different samples. Blind assessments produced significantly lower and more consistent scores than open assessments. The implications of this finding for systematic reviews, meta-analytic research and the peer-review process are discussed.


MODELING AND FORECASTING CALL CENTER ARRIVALS : A LITERATURE SURVEY by Rouba


title: Endovascular treatment combined with stratified surgery is effective in the management of venous thoracic outlet syndrome complications: a long term ultrasound follow-up study in patients with thrombotic events due to venous thoracic outlet syndrome

abstract: Thoracic outlet syndrome (TOS) is caused by compression of peripheral nerves and vascular structures along their course through the upper thoracic aperture to the axilla. The aim of our study was to analyze long-term outcomes of different treatments stratified by symptom severity. We performed a retrospective analysis of a cohort of 73 consecutive patients treated at our institution presenting with TOS-associated venous thrombotic events. Treatment strategies and immediate outcome analysis were completed by long-term follow-up with duplex ultrasound controls 6–12 months after the initial clinical event. Conservative therapy was started in mildly symptomatic patients (n = 32), of which 12 required endovascular procedures because of treatment failure. Endovascular treatment was attempted in all highly symptomatic patients and in those with conservative treatment failure (n = 53), of which 12 required acute surgical intervention. Elective surgical treatment was indicated in 30 other patients because of persistent symptoms. Surgery was associated with a significantly lower rate of the ultrasound-detected signs of persisting vascular compression. However, the rate of persisting clinical symptoms was comparable to those treated only by endovascular or conservative therapy. Our data demonstrate that initial endovascular treatment proposed as first line therapy to highly symptomatic subjects and in those with conservative treatment failure improves the symptoms in 77% of patients avoiding the need of acute surgery. Acute and elective surgical decompression leads to lower rates of vascular compression signs without significant amelioration of persisting clinical symptoms as compared to endovascular or conservative therapy.


Graph Pattern Mining for Business Decision Support


Scaling Distributed Machine Learning with System and Algorithm Co-design


title: A Survey of Radial Methods for Information Visualization

abstract: Radial visualization, or the practice of displaying data in a circular or elliptical pattern, is an increasingly common technique in information visualization research. In spite of its prevalence, little work has been done to study this visualization paradigm as a methodology in its own right. We provide a historical review of radial visualization, tracing it to its roots in centuries-old statistical graphics. We then identify the types of problem domains to which modern radial visualization techniques have been applied. A taxonomy for radial visualization is proposed in the form of seven design patterns encompassing nearly all recent works in this area. From an analysis of these patterns, we distill a series of design considerations that system builders can use to create new visualizations that address aspects of the design space that have not yet been explored. It is hoped that our taxonomy will provide a framework for facilitating discourse among researchers and stimulate the development of additional theories and systems involving radial visualization as a distinct design metaphor.


OceanQuery : report editor query language


title: Zirconium-Metalloporphyrin PCN-222: Mesoporous Metal-Organic Frameworks with Ultrahigh Stability as Biomimetic Catalysts

abstract: In nature, metalloporphyrins are well known for performing many biological functions in aqueous media, such as light harvesting, oxygen transportation, and catalysis. Heme, the iron–porphyrin derivative, is the cofactor for many enzyme/ protein families, including peroxidases, cytochromes, hemoglobins, and myoglobins. Using synthetic systems to mimic natural enzymes with high catalytic activity and substrate selectivity has been a sought-after goal in the last decade. Direct application of a heme as an oxidation catalyst in aqueous solution is usually challenging due to the formation of catalytically inactive dimers and catalyst self-destruction in the oxidizing reaction media. One promising approach is to load heme on supports, such as zeolites, clays, nanoparticles, hydrogels, or carbon materials, a practice which inevitably dilutes the density of active sites. An alternative approach is to protect the heme center by modifying the porphyrin to produce dendrimers or molecular crystals, which is a synthetically demanding method. Herein, we propose a unique strategy employing heme-like active centers as structural motifs for the assembly of highly stable porous materials, which should possess well-defined mesochannels and ultrahigh stability in aqueous solution. Metal-organic frameworks (MOFs) are a new class of crystalline porous materials with fascinating structures and intriguing properties, such as permanent porosity, high surface area, and uniform open cavities. The availability of various building blocks consisting of metals and organic linkers makes it possible to construct MOFs with unique properties for diverse applications. However, these desirable features of MOFs have rarely been applied to an enzymatic mimic, especially for catalysis in an aqueous medium, despite the fact that the assembly of ligands bearing high-density active sites into 3D frameworks may provide an ideal system to both enhance the catalytic activity and protect the cofactors. One of the main reasons is the lack of water-stable MOFs containing redox-active metal centers. Furthermore, most MOFs are microporous (pore size< 2 nm). Although they are suitable for gas storage, the small pore size slows down diffusion and limits the access of large substrate molecules to the active sites inside a MOF. Therefore, MOFs with mesopores, accessible redox sites, and ultrahigh stability, especially in aqueous media, are indispensible for any successful biomimetic attempt. Herein we have employed Fe-TCPP (TCPP= tetrakis(4carboxyphenyl)porphyrin) as a heme-like ligand and chosen highly stable Zr6 clusters as nodes for the assembly of stable Zr-MOFs. With carefully selected starting materials, we have successfully constructed a 3D heme-like MOF, designated as PCN-222(Fe) (Figure 1; PCN= porous coordination net-


title: Searching for Meaning in RNNs using Deep Neural Inspection

abstract: Recent variants of Recurrent Neural Networks (RNNs)---in particular, Long Short-Term Memory (LSTM) networks---have established RNNs as a deep learning staple in modeling sequential data in a variety of machine learning tasks. However, RNNs are still often used as a black box with limited understanding of the hidden representation that they learn. Existing approaches such as visualization are limited by the manual effort to examine the visualizations and require considerable expertise, while neural attention models change, rather than interpret, the model. We propose a technique to search for neurons based on existing interpretable models, features, or programs.


title: Convergence and Divergence: Developing a Semiglobal Marketing Strategy

abstract: Abstract Many markets are converging, as communications and logistic networks become more integrated and firms from all parts of the world are expanding operations on a global scale. At the same time, other markets are becoming more diverse, and marketers are increasingly encountering economic and cultural heterogeneity. The authors examine the implications of these trends and the extent to which they necessitate rethinking and refocusing global marketing strategy. First, they examine the perspective of global marketing strategy as an evolutionary process. Next, they divide markets into five major spheres, examining the economic and cultural diversity of markets in each. Next, they discuss the resultant need to develop and implement different strategies for markets in each of these spheres. Some conclusions are drawn relating to the difficulties involved in developing a coherent strategy in international markets. The authors advocate developing a semiglobal marketing strategy, which involves following dif...


title: AMBRA1 links autophagy to cell proliferation and tumorigenesis by promoting c-Myc dephosphorylation and degradation

abstract: Inhibition of a main regulator of cell metabolism, the protein kinase mTOR, induces autophagy and inhibits cell proliferation. However, the molecular pathways involved in the cross-talk between these two mTOR-dependent cell processes are largely unknown. Here we show that the scaffold protein AMBRA1, a member of the autophagy signalling network and a downstream target of mTOR, regulates cell proliferation by facilitating the dephosphorylation and degradation of the proto-oncogene c-Myc. We found that AMBRA1 favours the interaction between c-Myc and its phosphatase PP2A and that, when mTOR is inhibited, it enhances PP2A activity on this specific target, thereby reducing the cell division rate. As expected, such a de-regulation of c-Myc correlates with increased tumorigenesis in AMBRA1-defective systems, thus supporting a role for AMBRA1 as a haploinsufficient tumour suppressor gene.


title: Experimental demonstration of a 10BASE-T Ethernet visible light communications system using white phosphor light-emitting diodes

abstract: In this study, a compact design of a highly efficient, and a high luminosity light-emitting diode (LED)-based visible light communications system is presented, which is capable of providing standard room illumination levels and also wide-coverage Ethernet 10BASE-T optical wireless downlink communications over a distance of 2.3 m using commercial white light phosphor LEDs. The measured signal-to-noise ratio of the designed Ethernet system is >45 dB, thus allowing error-free communications with both on-off keying non-return zero and differential Manchester-coded modulation schemes at 10 Mbps. The uplink has been provided via a wireless infra-red link. A comparative study of a point-to-point wired local area network (LAN) and the optical wireless link confirms no discernible differences between them. The design of the transmitter is also shown to be scalable, with the frequency response for driving 25 LEDs being almost the same as driving a single LED. LED driving units are designed to match with the Ethernet sockets (RJ45) that conform to the existing LAN infrastructures (building and portable devices).


title: Modeling and Analyzing the Coexistence of Wi-Fi and LTE in Unlicensed Spectrum

abstract: We leverage stochastic geometry to characterize key performance metrics for neighboring Wi-Fi and LTE networks in unlicensed spectrum. Our analysis focuses on a single unlicensed frequency band, where the locations for the Wi-Fi access points (APs) and LTE eNodeBs (eNBs) are modeled as two independent homogeneous Poisson point processes. Three LTE coexistence mechanisms are investigated: (1) LTE with continuous transmission and no protocol modifications; (2) LTE with discontinuous transmission; and (3) LTE with listen-before-talk (LBT) and random back-off (BO). For each scenario, we have derived the medium access probability (MAP), the signal-to-interference-plus-noise ratio (SINR) coverage probability, the density of successful transmissions (DST), and the rate coverage probability for both Wi-Fi and LTE. Compared to the baseline scenario where one Wi-Fi network coexists with an additional Wi-Fi network, our results show that Wi-Fi performance is severely degraded when LTE transmits continuously. However, LTE is able to improve the DST and rate coverage probability of Wi-Fi while maintaining acceptable data rate performance when it adopts one or more of the following coexistence features: a shorter transmission duty cycle, lower channel access priority, or more sensitive clear channel assessment (CCA) thresholds.


title: Genetic Programming II: Automatic Discovery of Reusable Programs.

abstract: Reading Genetic Programming IE Automatic Discovery ofReusable Programs (GPII) in its entirety is not a task for the weak-willed because the book without appendices is about 650 pages. An entire previous book by the same author [1] is devoted to describing Genetic Programming (GP), while this book is a sequel extolling an extension called Automatically Defined Functions (ADFs). The author, John R. Koza, argues that ADFs can be used in conjunction with GP to improve its efficacy on large problems. "An automatically defined function (ADF) is a function (i.e., subroutine, procedure, module) that is dynamically evolved during a run of genetic programming and which may be called by a calling program (e.g., a main program) that is simultaneously being evolved" (p. 1). Dr. Koza recommends adding the ADF technique to the "GP toolkit." The book presents evidence that it is possible to interpret GP with ADFs as performing either a top-down process of problem decomposition or a bottom-up process of representational change to exploit identified regularities. This is stated as Main Point 1. Main Point 2 states that ADFs work by exploiting inherent regularities, symmetries, patterns, modularities, and homogeneities within a problem, though perhaps in ways that are very different from the style of programmers. Main Points 3 to 7 are appropriately qualified statements to the effect that, with a variety of problems, ADFs pay off be-


title: DistBlockNet: A Distributed Blockchains-Based Secure SDN Architecture for IoT Networks

abstract: The rapid increase in the number and diversity of smart devices connected to the Internet has raised the issues of flexibility, efficiency, availability, security, and scalability within the current IoT network. These issues are caused by key mechanisms being distributed to the IoT network on a large scale, which is why a distributed secure SDN architecture for IoT using the blockchain technique (DistBlockNet) is proposed in this research. It follows the principles required for designing a secure, scalable, and efficient network architecture. The DistBlockNet model of IoT architecture combines the advantages of two emerging technologies: SDN and blockchains technology. In a verifiable manner, blockchains allow us to have a distributed peer-to-peer network where non-confident members can interact with each other without a trusted intermediary. A new scheme for updating a flow rule table using a blockchains technique is proposed to securely verify a version of the flow rule table, validate the flow rule table, and download the latest flow rules table for the IoT forwarding devices. In our proposed architecture, security must automatically adapt to the threat landscape, without administrator needs to review and apply thousands of recommendations and opinions manually. We have evaluated the performance of our proposed model architecture and compared it to the existing model with respect to various metrics. The results of our evaluation show that DistBlockNet is capable of detecting attacks in the IoT network in real time with low performance overheads and satisfying the design principles required for the future IoT network.


title: Imputing missing values using Inverse Distance Weighted Interpolation for time series data

abstract: Data mining is the process of analyzing and retrieving meaningful information from a database. Temporal data mining deals with time stamped data. In the real world, temporal data obtained may contain noisy, inconsistent data and in most cases the data may be missing; hence data preprocessing is one of the important steps that has to be carried out in data mining. Missing values may generate biased results and affect the accuracy of classification. In order to overcome this it is necessary to impute the missing values based on other information in the dataset. The work focuses on imputing missing values using Inverse Distance Weighted Interpolation method which best suits for data sampled at uneven intervals of time. This method assigns values to unknown points from a weighted sum of values of known points. Machine learning techniques applied to the imputed dataset will give better accuracy than that of the incomplete dataset.


title: The spatio-temporal generalized additive model for criminal incidents

abstract: Law enforcement agencies need to model spatio-temporal patterns of criminal incidents. With well developed models, they can study the causality of crimes and predict future criminal incidents, and they can use the results to help prevent crimes. In this paper, we described our newly developed spatio-temporal generalized additive model (S-T GAM) to discover underlying factors related to crimes and predict future incidents. The model can fully utilize many different types of data, such as spatial, temporal, geographic, and demographic data, to make predictions. We efficiently estimated the parameters for S-T GAM using iteratively re-weighted least squares and maximum likelihood and the resulting estimates provided for model interpretability. In this paper we showed the evaluation of S-T GAM with the actual criminal incident data from Charlottesville, Virginia. The evaluation results showed that S-T GAM outperformed the previous spatial prediction models in predicting future criminal incidents.


title: Toward a Theory of Evolution Strategies: Self-Adaptation

abstract: This paper analyzes the self-adaptation (SA) algorithm widely used to adapt strategy parameters of the evolution strategy (ES) in order to obtain maximal ES performance. The investigations are concentrated on the adaptation of one general mutation strength σ (called σSA) in (1, λ) ESs. The hypersphere serves as the fitness model. ::: ::: Starting from an introduction to the basic concept of self-adaptation, a framework for the analysis of σSA is developed on two levels: a microscopic level, concerning the description of the stochastic changes from one generation to the next, and a macroscopic level, describing the evolutionary dynamics of the σSA over time (generations). The σSA requires the fixing of a new strategy parameter, known as the learning parameter. The influence of this parameter on ES performance is investigated and rules for its tuning are presented and discussed. The results of the theoretical analysis are compared with ES experiments; it will be shown that applying Schwefel's τ-scaling rule guarantees the linear convergence order of the ES.


title: Design and Implementation of Smart Home Control Systems Based on Wireless Sensor Networks and Power Line Communications

abstract: Wireless sensor networks (WSNs) and power line communications (PLCs) are used in this work to implement a smart home control network. The goals are to reduce the impact of wireless interference on a smart home control network and unnecessary energy consumption of a smart home. An isolated WSN with one coordinator, which is integrated into the PLC transceiver, is established in each room. The coordinator is responsible for transferring environmental parameters obtained by WSNs to the management station via PLCs. The control messages for home appliances are directly transferred using PLCs rather than WSNs. According to the experimental results, the impact of wireless interference on the proposed smart home control network is substantially mitigated. Additionally, a smart control algorithm for lighting systems and an analysis of the illumination of a fluorescent lamp were presented. The energy saving of lighting systems relative to those without smart control was evaluated. Numerical results indicate that the electricity consumption on a sunny or cloudy day can be reduced by at least 40% under the smart control. Moreover, a prototype for the proposed smart home control network with the smart control algorithm was implemented. Experimental tests demonstrate that the proposed system for smart home control networks is practically feasible and performs well.


title: A Systematic Literature Review on Fault Prediction Performance in Software Engineering

abstract: Background: The accurate prediction of where faults are likely to occur in code can help direct test effort, reduce costs, and improve the quality of software. Objective: We investigate how the context of models, the independent variables used, and the modeling techniques applied influence the performance of fault prediction models. Method: We used a systematic literature review to identify 208 fault prediction studies published from January 2000 to December 2010. We synthesize the quantitative and qualitative results of 36 studies which report sufficient contextual and methodological information according to the criteria we develop and apply. Results: The models that perform well tend to be based on simple modeling techniques such as Naive Bayes or Logistic Regression. Combinations of independent variables have been used by models that perform well. Feature selection has been applied to these combinations when models are performing particularly well. Conclusion: The methodology used to build models seems to be influential to predictive performance. Although there are a set of fault prediction studies in which confidence is possible, more studies are needed that use a reliable methodology and which report their context, methodology, and performance comprehensively.


title: Design Criteria for Electric Vehicle Fast Charge Infrastructure Based on Flemish Mobility Behavior

abstract: This paper studies the technical design criteria for fast charge infrastructure, covering the mobility needs. The infrastructure supplements the residential and public slow charging infrastructure. Two models are designed. The first determines the charging demand, based on current mobility behavior in Flanders. The second model simulates a charge infrastructure that meets the resulting fast charge demand. The energy management is performed by a rule-based control algorithm, that directs the power flows between the fast chargers, the energy storage system, the grid connection, and the photovoltaic installation. There is a clear trade-off between the size of the energy storage system and the power rating of the grid connection. Finally, the simulations indicate that 99.7% of the vehicles visiting the fast charge infrastructure can start charging within 10 minutes with a configuration limited to 5 charging spots, instead of 9 spots when drivers are not willing to wait.


title: Using Community Feedback to Improve Community Interventions: Results From the Deep South Network for Cancer Control Project.

abstract: The Deep South Network for Cancer Control (DSNCC), initiated in 2000, is a dual-state, community-based participatory research infrastructure composed of academic and community partners committed to reducing cancer disparities among underserved African Americans in 12 designated counties of the Alabama Black Belt and the Mississippi Delta, 2 historically underserved areas of the country. Local residents trained as Community Health Advisors as Research Partners implemented a 3-tier community action plan (CAP) focused on promoting cancer screening, physical activity, and nutrition. Breast, cervical and colorectal cancer screening, healthy eating habits, and physical activity levels increased among many, but not all, African American women in the 12-county DSNCC coverage area. Seeking to improve our reach to include participants who reported they had never heard of the DSNCC or participated in the CAP, we conducted in-depth conversations with community residents about reasons for selective nonparticipation and ways to improve participation in the DSNCC community health interventions. Three patterns and their associated themes described ways to improve the penetration of CAP strategies and tailor them to effectively reach underserved African Americans in the intervention counties. We conclude with lessons learned for future interventions.


A Novel Yagi-Uda Dipole Array Fed by A Microstrip-To-CPS Transition


title: Fingerprint minutiae extraction using deep learning

abstract: The high variability of fingerprint data (owing to, e.g., differences in quality, moisture conditions, and scanners) makes the task of minutiae extraction challenging, particularly when approached from a stance that relies on tunable algorithmic components, such as image enhancement. We pose minutiae extraction as a machine learning problem and propose a deep neural network — MENet, for Minutiae Extraction Network — to learn a data-driven representation of minutiae points. By using the existing capabilities of several minutiae extraction algorithms, we establish a voting scheme to construct training data, and so train MENet in an automated fashion on a large dataset for robustness and portability, thus eliminating the need for tedious manual data labelling. We present a post-processing procedure that determines precise minutiae locations from the output of MENet. We show that MENet performs favourably in comparisons against existing minutiae extractors.


title: Small-Signal Analysis of Naturally Commutated Current-Fed Dual Active Bridge Converter and Control Implementation Using Cypress PSoC

abstract: A detailed small-signal analysis and a closed-loop control design of a naturally commutated bidirectional current-fed dual active bridge (CFDAB) dc/dc converter are presented. The proposed converter is suitable for a fuel cell vehicle (FCV) between a traction inverter and batteries. State-space averaging technique is used to derive the small-signal model. Based on the derived small-signal model, a closed two-loop controller is designed and implemented on mixed signal (analog and digital) processor Cypress programmable system-on-chip (PSoC) 5. A complete design procedure is presented. Simulation results using PSIM 9.0.4 and experimental waveforms from a 250-W prototype with the designed controller are shown to verify the stability of the control system. Experimental results for step changes in load clearly show satisfactory transient performance of the converter and validate the closed two-loop design. It is suitable for energy storage, interfacing two dc buses of different voltages in a microgrid and a solar photovoltaic power system to boost a low-voltage panel up to the peak of the utility voltage.


title: Impacts of Informal Caregiving on Caregiver Employment, Health, and Family

abstract: As the aging population increases, the demand for informal caregiving is becoming an ever more important concern for researchers and policy-makers alike. To shed light on the implications of informal caregiving, this paper reviews current research on its impact on three areas of caregivers' lives: employment, health, and family. Because the literature is inherently interdisciplinary, the research designs, sampling procedures, and statistical methods used are heterogeneous. Nevertheless, we are still able to draw several conclusions: first, despite the prevalence of informal caregiving and its primary association with lower levels of employment, the affected labor force is seemingly small. Second, such caregiving tends to lower the quality of the caregiver's psychological health, which also has a negative impact on physical health outcomes. Third, the implications for family life remain under investigated. The research findings also differ strongly among subgroups, although they do suggest that female, spousal, and intense caregivers tend to be the most affected by caregiving.


title: Person Identification Using Full-Body Motion and Anthropometric Biometrics from Kinect Videos

abstract: For person identification, motion and anthropometric biometrics are known to be less sensitive to photometric differences and more robust to obstructions such as glasses, hair, and hats. Existing gait-based methods are based on the accurate identification and acquisition of the gait cycle. This typically requires the subject to repeatedly perform a single action using a costly motion-capture facility, or 2D videos in simple backgrounds where the person can be easily segmented and tracked. For person identification these manufactured requirements limit the use gait-based biometrics in real scenarios that may have a variety of actions with varying levels of complexity. We propose a new person identification method that uses motion and anthropometric biometrics acquired from an inexpensive Kinect RGBD sensor. Different from previous gait-based methods we use all the body joints found by the Kinect SDK to analyze the motion patterns and anthropometric features over the entire track sequence. We show the proposed method can identify people that perform different actions (e.g. walk and run) with varying levels of complexity. When compared to a state-of-the-art gait-based method that uses depth images produced by the Kinect sensor the proposed method demonstrated better person identity performance.


title: Put the Phone Down: Testing a Complement-Interfere Model of Computer-Mediated Communication in the Context of Face-to-Face Interactions

abstract: If there ever was a key to happiness, this key would open a door that leads straight to a rich social life. And in the era of smartphones, this key to social connection is in our pockets anytime an...


title: Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation

abstract: We introduce a new loss function for the weakly-supervised training of semantic image segmentation models based on three guiding principles: to seed with weak localization cues, to expand objects based on the information about which classes can occur in an image, and to constrain the segmentations to coincide with object boundaries. We show experimentally that training a deep convolutional neural network using the proposed loss function leads to substantially better segmentations than previous state-of-the-art methods on the challenging PASCAL VOC 2012 dataset. We furthermore give insight into the working mechanism of our method by a detailed experimental study that illustrates how the segmentation quality is affected by each term of the proposed loss function as well as their combinations.


title: Earthquake Damage Assessment of Buildings Using VHR Optical and SAR Imagery

abstract: Rapid damage assessment after natural disasters (e.g., earthquakes) and violent conflicts (e.g., war-related destruction) is crucial for initiating effective emergency response actions. Remote-sensing satellites equipped with very high spatial resolution (VHR) multispectral and synthetic aperture radar (SAR) imaging sensors can provide vital information due to their ability to map the affected areas with high geometric precision and in an uncensored manner. In this paper, we present a novel method that detects buildings destroyed in an earthquake using pre-event VHR optical and post-event detected VHR SAR imagery. The method operates at the level of individual buildings and assumes that they have a rectangular footprint and are isolated. First, the 3-D parameters of a building are estimated from the pre-event optical imagery. Second, the building information and the acquisition parameters of the VHR SAR scene are used to predict the expected signature of the building in the post-event SAR scene assuming that it is not affected by the event. Third, the similarity between the predicted image and the actual SAR image is analyzed. If the similarity is high, the building is likely to be still intact, whereas a low similarity indicates that the building is destroyed. A similarity threshold is used to classify the buildings. We demonstrate the feasibility and the effectiveness of the method for a subset of the town of Yingxiu, China, which was heavily damaged in the Sichuan earthquake of May 12, 2008. For the experiment, we use QuickBird and WorldView-1 optical imagery, and TerraSAR-X and COSMO-SkyMed SAR data.


Benchmarking distributed data warehouse solutions for storing genomic variant information


title: Deduction of fighting game countermeasures using Neuroevolution of Augmenting Topologies

abstract: Nowadays artificial intelligence (AI) has been applied to many areas, including digital game with various genres. One of them is the fighting game. Implementation of AI in the fighting game aims to make players who are not constrained by the limiting factors which humans possess, such as speed of reflexes and hand gestures. One platform that can be used to make fighting game AI is FightingICE. This platform is official platform used in Computational Intelligence and Games (CIG) conference. One approach that can be used in the process of building the AI in the fighting game is opponent modeling, a method that utilize machine learning to make model based on the behavior of the opponent. There is one AI that utilize k-Nearest Neighbor (kNN) algorithm called MizunoAI. This AI has outstanding performance, but it suffers from inability to adjust its parameter automatically. To solve that problem, in this paper the algorithm used for modelling is changed to Neuroevolution of Augmenting Topologies (NEAT). NEAT is an algorithm with Artificial Neural Network (ANN) as basic architecture and Genetic Algorithm (GA) as the architecture optimizer. In the implementation, NEAT will receive six inputs of distance in the x-axis and y-axis, both players' hitpoints, and both players' stamina. Output of NEAT is the probability of all movements that can be done by the opponent. The entire output will be processed by a simulator to determine the best countermeasure based on the predictions. For testing, the AI made will be faced against four AI winners of CIG 2013, namely MizunoAI, Kaiju, T, and SejongAI. The results showed that the AI is capable to do well in modeling process. Although the performance is still behind the MizunoAI, the learning process can be seen along the match.


title: Content-based image retrieval using color moment and Gabor texture feature

abstract: Aim to currently content-based image retrieval method having high computational complexity and low retrieval accuracy problem, this paper proposes a content-based image retrieval method based on color and texture features. As its color features, color moments of the Hue, Saturation and Value (HSV) component images in HSV color space are used. As its texture features, Gabor texture descriptors are adopted. Users assign the weights to each feature respectively and calculate the similarity with combined features of color and texture according to normalized Euclidean distance. Experiment results show that the proposed method has higher retrieval accuracy than conventional methods using color and texture features even though its feature vector dimension results in a lower rate than the conventional method.


title: Application of VSI-EBG Structure to High-Speed Differential Signals for Wideband Suppression of Common-Mode Noise

abstract: In this paper, we present wideband common-mode (CM) noise suppression using a vertical stepped impedance electromagnetic bandgap (VSI-EBG) structure for high-speed differential signals in multilayer printed circuit boards. This technique is an original design that enables us to apply the VSI-EBG structure to differential signals without sacrificing the differential characteristics. In addition, the analytical dispersion equations for the bandgap prediction of the CM propagation in the VSIEBG structure are extracted, and the closed-form expressions for the bandgap cutoff frequencies are derived. Based on the dispersion equations, the effects of the impedance ratio, the EBG patch length, and via inductances on the bandgap of the VSI-EBG structure for differential signals are thoroughly examined. The proposed dispersion equations are verified through agreement with the full-wave simulation results. It is experimentally demonstrated that the proposed VSI-EBG structure for differential signaling suppresses the CM noise in the wideband frequency range without degrading the differential characteristics.


title: Exploitation of Ship Scattering in Polarimetric SAR for an Improved Classification Under High Clutter Conditions

abstract: This paper evaluates the potentialities of polarimetric ship scattering for basing classification methods that provide reasonable performance within cluttered scenes. Both simulated and airborne polarimetric synthetic aperture radar (SAR) images have been used to validate the conclusions of a previous phenomenological study. Numerical simulations have been carried out with GRECOSAR, a polarimetric interferometric SAR simulation tool that is able to process highly complex targets with a fast and accurate radar-cross-section prediction module. A representative set of scenarios has been defined, which includes various realistic ship models, sea states, and imaging geometries. In all of them, a two-scale sea surface model precisely accounting for sea-ship interaction and sea clutter has been added. The analysis of different images has shown that, with an adequate spatial resolution, ships may be characterized by a particular spatial arrangement and polarization state distribution of dominant scattering centers. This feature has allowed one to propose a new classification algorithm, which shows a promising behavior after various preliminary tests. In this paper, the performance of this technique is further evaluated with realistic clutter. The results show that robust classification is possible even in highly cluttered scenes if quad-pol imagery is available. On the contrary, in low clutter conditions, the usage of less restrictive solutions, like circular dual-pol schemes, is feasible and may still get an acceptable performance.


title: Just an Update on PMING Distance for Web-based Semantic Similarity in Artificial Intelligence and Data Mining

abstract: One of the main problems that emerges in the classic approach to semantics is the difficulty in acquisition and maintenance of ontologies and semantic annotations. On the other hand, the Internet explosion and the massive diffusion of mobile smart devices lead to the creation of a worldwide system, which information is daily checked and fueled by the contribution of millions of users who interacts in a collaborative way. Search engines, continually exploring the Web, are a natural source of information on which to base a modern approach to semantic annotation. A promising idea is that it is possible to generalize the semantic similarity, under the assumption that semantically similar terms behave similarly, and define collaborative proximity measures based on the indexing information returned by search engines. The PMING Distance is a proximity measure used in data mining and information retrieval, which collaborative information express the degree of relationship between two terms, using only the number of documents returned as result for a query on a search engine. In this work, the PMINIG Distance is updated, providing a novel formal algebraic definition, which corrects previous works. The novel point of view underlines the features of the PMING to be a locally normalized linear combination of the Pointwise Mutual Information and Normalized Google Distance. The analyzed measure dynamically reflects the collaborative change made on the web resources.


title: Federal Regulation of Personnel and Human Resource Management

abstract: Introduction: the regulatory model. Part One: Equal employment opportunity. Basic principles of EEO. Sex discrimination. Nationality. Other EEO classifications. Evidence and proof in EEO: the prima facie case. Evidence and proof in EEO: job relatedness. Affirmative action. Part Two: Safety and health. Workers' compensation laws. The occupational safety and health act. The impact of OSHA. Part Three: Pay and benefits. Regulation of employee pension and benefit plans: origins. The employee retirement income security act: provisions and impact. Post-ERISA benefits legislation. Compensation law. Part Four: Other employee rights. Privacy. Job security.


title: Tubularity Flow Field—A Technique for Automatic Neuron Segmentation

abstract: A segmentation framework is proposed to trace neurons from confocal microscopy images. With an increasing demand for high throughput neuronal image analysis, we propose an automated scheme to perform segmentation in a variational framework. Our segmentation technique, called tubularity flow field (TuFF) performs directional regional growing guided by the direction of tubularity of the neurites. We further address the problem of sporadic signal variation in confocal microscopy by designing a local attraction force field, which is able to bridge the gaps between local neurite fragments, even in the case of complete signal loss. Segmentation is performed in an integrated fashion by incorporating the directional region growing and the attraction force-based motion in a single framework using level sets. This segmentation is accomplished without manual seed point selection; it is automated. The performance of TuFF is demonstrated over a set of 2D and 3D confocal microscopy images where we report an improvement of >75% in terms of mean absolute error over three extensively used neuron segmentation algorithms. Two novel features of the variational solution, the evolution force and the attraction force, hold promise as contributions that can be employed in a number of image analysis applications.


title: Unsupervised method for sentiment analysis in online texts

abstract: Method to predict sentiment in informal texts using unsupervised dependency parsing.Algorithm based on sentiment propagation using linguistic content without training.Method to create lexicon using polarity expansion algorithm for specific domains.Our method compares favorably well with other unsupervised and supervised methods. In recent years, the explosive growth of online media, such as blogs and social networking sites, has enabled individuals and organizations to write about their personal experiences and express opinions. Classifying these documents using a polarity metric is an arduous task. We propose a novel approach to predicting sentiment in online textual messages such as tweets and reviews, based on an unsupervised dependency parsing-based text classification method that leverages a variety of natural language processing techniques and sentiment features primarily derived from sentiment lexicons. These lexicons were created by means of a semiautomatic polarity expansion algorithm in order to improve accuracy in specific application domains. The results obtained for the Cornell Movie Review, Obama-McCain Debate and SemEval-2015 datasets confirm the competitive performance and the robustness of the system.


title: Sensing characteristics based on Fano resonance in rectangular ring waveguide

abstract: Abstract Sensing characteristics based on Fano resonance in a rectangular ring resonator with a stub are investigated numerically. Simulation results show that a sharp and asymmetric Fano-line shape emerges in the proposed structure. Through tuning the width and length of the stub, it is found that the width and length play an important role in optimizing the sensing characteristic. Using the sharp and asymmetric Fano-line shape a highly sensitive plasmonic nanosensor with the sensitivity of 1000 nm/RIU and a tunable figure of merit (FOM) can be attained. The maximum FOM can reach up to 992,800 when the stub length d =120 nm, width l =130 nm and the refractive index difference Δ n =0.05, which is larger than that in previous reports. In addition, the results show that a larger FOM can be obtained by tuning the stub width than tuning its length. The proposed model and results provide guidance for fundamental research of the plasmonic nanosensor applications and designs.


title: Semantics-Driven Recognition of Collocations Using Word Embeddings

abstract: L2 learners often produce “ungrammatical” word combinations such as, e.g., *give a suggestion or *make a walk. This is because of the “collocationality” of one of their items (the base) that limits the acceptance of collocates to express a specific meaning (‘perform’ above). We propose an algorithm that delivers, for a given base and the intended meaning of a collocate, the actual collocate lexeme(s) (make / take above). The algorithm exploits the linear mapping between bases and collocates from examples and generates a collocation transformation matrix which is then applied to novel unseen cases. The evaluation shows a promising line of research in collocation discovery.


title: Rogue Access Point Detection: Taxonomy, Challenges, and Future Directions

abstract: Wireless Local Area Networks (WLANs) are increasingly integrated into our daily lives. Access Points (APs) are an integral part of the WLAN infrastructure, as they are responsible for coordinating wireless users and connecting them to the wired side of the network and, eventually, to the Internet. APs are deployed everywhere, from airports and shopping malls to coffee shops and hospitals, to provide Internet connectivity. One of the most serious security problems encountered by WLAN users is the existence of Rogue Access Points (RAPs). This article classifies existing solutions, identifies vulnerabilities, and suggests future directions for research into these RAPs. The ultimate objective is to classify existing detection techniques and find new RAP types that have not been classified by the research community. The literature typically categorizes Evil-twin, Unauthorized, Compromised, and Improperly Configured RAPs. Two other types have largely been abandoned by researchers, but can be classified as Denial of Service RAP attacks. These are deauthentication/disassociation attacks targeting wireless users, and the forging of the first message in a four-way handshake.


title: Grab 'n Run: Secure and Practical Dynamic Code Loading for Android Applications

abstract: Android introduced the dynamic code loading (DCL) mechanism to allow for code reuse, to achieve extensibility, to enable updating functionalities, or to boost application start-up performance. In spite of its wide adoption by developers, previous research has shown that the secure implementation of DCL-based functionality is challenging, often leading to remote code injection vulnerabilities. Unfortunately, previous attempts to address this problem by both the academic and Android developers communities are affected by either practicality or completeness issues, and, in some cases, are affected by severe vulnerabilities. In this paper, we propose, design, implement, and test Grab 'n Run, a novel code verification protocol and a series of supporting libraries, APIs, and tools, that address the problem by abstracting away from the developer many of the challenging implementation details. Grab 'n Run is designed to be practical: Among its tools, it provides a drop-in library, which requires no modifications to the Android framework or the underlying Dalvik/ART runtime, is very similar to the native API, and most code can be automatically rewritten to use it. Grab 'n Run also contains an application-rewriting tool, which allows to easily port legacy or third-party applications to use the secure APIs developed in this work. We evaluate the Grab 'n Run library with a user study, obtaining very encouraging results in vulnerability reduction, ease of use, and speed of development. We also show that the performance overhead introduced by our library is negligible. For the benefit of the security of the Android ecosystem, we released Grab 'n Run as open source.


title: Audio bandwidth extension based on ensemble echo state networks with temporal evolution

abstract: The bandwidth limitation of wideband audio systems degrades the subjective quality and naturalness of audio signals. In this paper, a new method for blind bandwidth extension of wideband audio signals is proposed based on ensemble echo state network with temporal evolution. The high-frequency components in the band of $7 \sim 14\ \text{kHz}$ are artificially restored only from the information in the wideband audio. For each region in the wideband feature space, a specific echo state network with recurrent structure is explored to dynamically model the local mapping relationship between wideband audio features and high-frequency spectral envelope. The transition process among regions is modeled by a hidden Markov model, and a network ensemble technique based on temporal evolution is used to fuse multiple echo state networks such that the high-frequency spectral envelope is estimated. Combining the high-frequency fine spectrum extended by spectral translation, the proposed method can effectively extend the wideband audio to super wideband. In addition, the proposed extension method is applied to the ITU-T G.729.1 wideband audio codec and is further evaluated in comparison with the ITU-T G.729.1 Annex E super-wideband audio codec and the hidden Markov model-based reference bandwidth extension method. Objective quality evaluation results indicate that the proposed method is preferred over the hidden Markov model-based reference bandwidth extension method in terms of log spectral distortion, cosh measure, and differential log spectral distortion. Further, the proposed method improves the auditory quality of the wideband audio and also gains a good performance in the subjective listening tests.


title: Analysis of Direct-On-Line Synchronous Reluctance Machine Start-Up Using a Magnetic Field Decomposition

abstract: Direct-on-line synchronous reluctance machines combine the characteristics of induction machines and synchronous reluctance machines. Saturation of core materials, the eddy currents, and the asymmetry of the rotor core and cage make it difficult to predict which kind of loads a machine can synchronize. In this paper, the start-up of a direct-on-line synchronous reluctance machine is analyzed with a magnetic field decomposition that makes it possible to quantify and isolate forces between any two distinct parts of an electric machine using a transient time-stepping finite element field solution. The results show explicitly which portion of the torque is produced by the rotor core and which by the rotor cage. Compared with conventional average torque analyses (also known as pseudo-constant-speed or quasi-steady-state analyses) used to distinguish between the torque on the rotor core and cage, the proposed method makes no assumptions on the state of the machine. This results in a more detailed view of the starting transient.


title: A Proactive Cloud Management Architecture for Private Clouds

abstract: Operation management for a private cloud infrastructure faces many challenges including efficient resource allocation, load-balancing, and quick response to real-time workload changes. Traditional manual IT operation management is inadequate for this highly dynamic and complex environment. This work presents a distributed service architecture which is designed to provide an automated, shared, off-site operation management service for private clouds. The service architecture incorporates important concepts such as: Metric Templates for minimising the network overhead for transmission of cloud metrics, a Cloud Snapshot that provides a global view of the current status of the cloud, supporting optimal decision making, and a Calendar-based Data Storage Model to reduce the storage required for cloud metric data and increase analysis performance. A proactive response to cloud events is generated based on statistical analysis of historical metrics and predicted usage. The architecture, functional components and operation management strategies are described. A prototype implementation of the proposed architecture was deployed as a service on the IBM SmartCloud. The effectiveness and usability of the proposed proactive operation management solution has been comprehensively evaluated using a simulated private cloud with dynamic workloads.


title: Integrating a Lexicon based approach and K nearest neighbour for Malay sentiment analysis

abstract: Sentiment analysis or opinion mining refers to the automatic extraction of sentiments from a natural language text. Although many studies focusing on sentiment analysis have been conducted, there remains a limited amount of studies that focus on sentiment analysis in the Malay language. In this article, a new approach for automatic sentiment analysis of Malay movie reviews is proposed, implemented and evaluated. In contrast to most studies that focus on supervised or unsupervised machine learning approaches, this research aims to propose a new model for Malay sentiment analysis based on a combination of both approaches. We used sentiment lexicons in the new model to generate a new set of features to train a k-Nearest Neighbour (k-NN) classifier. We further illustrated that our hybrid method outperforms the state of-the-art unigram baseline.


title: "This is a Throwaway Account": Temporary Technical Identities and Perceptions of Anonymity in a Massive Online Community

abstract: This paper explores temporary identities on social media platforms and individuals' uses of these identities with respect to their perceptions of anonymity. Given the research on multiple profile maintenance, little research has examined the role that some social media platforms play in affording users with temporary identities. Further, most of the research on anonymity stops short of the concept of varying perceptions of anonymity. This paper builds on these research areas by describing the phenomenon of temporary "throwaway accounts" and their uses on reddit.com, a popular social news site. In addition to ethnographic trace analysis to examine the contexts in which throwaway accounts are adopted, this paper presents a predictive model that suggests that perceptions of anonymity significantly shape the potential uses of throwaway accounts and that women are much more likely to adopt temporary identities than men.


title: The Future Renewable Electric Energy Delivery and Management (FREEDM) System: The Energy Internet

abstract: This paper presents an architecture for a future electric power distribution system that is suitable for plug-and-play of distributed renewable energy and distributed energy storage devices. Motivated by the success of the (information) Internet, the architecture described in this paper was proposed by the NSF FREEDM Systems Center, Raleigh, NC, as a roadmap for a future automated and flexible electric power distribution system. In the envisioned “Energy Internet,” a system that enables flexible energy sharing is proposed for consumers in a residential distribution system. The key technologies required to achieve such a vision are presented in this paper as a result of the research partnership of the FREEDM Systems Center.


title: Exploiting Burstiness in Reviews for Review Spammer Detection. ICWSM

abstract: Online product reviews have become an important source of user opinions. Due to profit or fame, imposters have been writing deceptive or fake reviews to promote and/or to demote some target products or services. Such imposters are called review spammers. In the past few years, several approaches have been proposed to deal with the problem. In this work, we take a different approach, which exploits the burstiness nature of reviews to identify review spammers. Bursts of reviews can be either due to sudden popularity of products or spam attacks. Reviewers and reviews appearing in a burst are often related in the sense that spammers tend to work with other spammers and genuine reviewers tend to appear together with other genuine reviewers. This paves the way for us to build a network of reviewers appearing in different bursts. We then model reviewers and their cooccurrence in bursts as a Markov Random Field (MRF), and employ the Loopy Belief Propagation (LBP) method to infer whether a reviewer is a spammer or not in the graph. We also propose several features and employ feature induced message passing in the LBP framework for network inference. We further propose a novel evaluation method to evaluate the detected spammers automatically using supervised classification of their reviews. Additionally, we employ domain experts to perform a human evaluation of the identified spammers and non-spammers. Both the classification result and human evaluation result show that the proposed method outperforms strong baselines, which demonstrate the effectiveness of the method.


title: A hybrid method for imputation of missing values using optimized fuzzy c-means with support vector regression and a genetic algorithm

abstract: Missing values in datasets should be extracted from the datasets or should be estimated before they are used for classification, association rules or clustering in the preprocessing stage of data mining. In this study, we utilize a fuzzy c-means clustering hybrid approach that combines support vector regression and a genetic algorithm. In this method, the fuzzy clustering parameters, cluster size and weighting factor are optimized and missing values are estimated. The proposed novel hybrid method yields sufficient and sensible imputation performance results. The results are compared with those of fuzzy c-means genetic algorithm imputation, support vector regression genetic algorithm imputation and zero imputation.


title: From Spin to Swindle: Identifying Falsification in Financial Text

abstract: Despite legislative attempts to curtail financial statement fraud, it continues unabated. This study makes a renewed attempt to aid in detecting this misconduct using linguistic analysis with data mining on narrative sections of annual reports/10-K form. Different from the features used in similar research, this paper extracts three distinct sets of features from a newly constructed corpus of narratives (408 annual reports/10-K, 6.5 million words) from fraud and non-fraud firms. Separately each of these three sets of features is put through a suite of classification algorithms, to determine classifier performance in this binary fraud/non-fraud discrimination task. From the results produced, there is a clear indication that the language deployed by management engaged in wilful falsification of firm performance is discernibly different from truth-tellers. For the first time, this new interdisciplinary research extracts features for readability at a much deeper level, attempts to draw out collocations using n-grams and measures tone using appropriate financial dictionaries. This linguistic analysis with machine learning-driven data mining approach to fraud detection could be used by auditors in assessing financial reporting of firms and early detection of possible misdemeanours.


title: Classification of Acceleration Data for Biometric Gait Recognition on Mobile Devices.

abstract: Ubiquitous mobile devices like smartphones and tablets are often not secured against unauthorized access as the users tend to not use passwords because of convenience reasons. Therefore, this study proposes an alternative user authentication method for mobile devices based on gait biometrics. The gait characteristics are captured using the built-in accelerometer of a smartphone. Various features are extracted from the measured accelerations and utilized to train a support vector machine (SVM). Among the extracted features are the Meland Bark-frequency cepstral coefficients (MFCC, BFCC) which are commonly used in speech and speaker recognition and have not been used for gait recognition previously. The proposed approach showed competitive recognition performance, yielding 5.9% FMR at 6.3% FNMR in a mixedday scenario.


title: High Accuracy Thermal Compression Bonding Technology for Large-Sized Substrate

abstract: For improvement of the productivity and the cost reduction, FOWLP (fan-out wafer level package) or FOPLP (fan-out panel level package) process using large-sized substrate has been actively developed in recent years. Above all, we will report about the novel TCB (thermal compression bonding) equipment which is even applicable to a 3D-IC stacking process and a fine pitch FOWLP or FOPLP of RDL-first process using large-sized substrate. We made the prototype which was applicable to CoW of 12 inch wafer and evaluated it. The prototype includes a high rigid fixed stage which is designed to the same size as the bonding head, and a substrate handling mechanism which lifts a substrate from the stage and transfers it to other prescribed position every one bonding cycle. By using this equipment, it is possible to thermally press a chip on even large-sized substrate without causing deterioration in bonding accuracy resulting from stage distortion. Also, it does not need to manage UTOS (Underfill time on stage), when the equipment is used for "2 step bonding process" which can improve the throughput of TCB. In this presentation, we will report about the investigation result of the heat influence on adjacent chip and about the temperature difference in the 3D-IC stacking process by using the prototype. In addition, we will present a novel multi-layer collecting bonding process which is applicable to a large-sized substrate effectively, and report about the result of the evaluation of solder connection of the TSV TEG chip.


title: Automatic single cell segmentation on highly multiplexed tissue images

abstract: The combination of mass cytometry and immunohistochemistry (IHC) enables new histopathological imaging methods in which dozens of proteins and protein modifications can be visualized simultaneously in a single tissue section. The power of multiplexing combined with spatial information and quantification was recently illustrated on breast cancer tissue and was described as next-generation IHC. Robust, accurate, and high-throughput cell segmentation is crucial for the analysis of this new generation of IHC data. To this end, we propose a watershed-based cell segmentation, which uses a nuclear marker and multiple membrane markers, the latter automatically selected based on their correlation. In comparison with the state-of-the-art segmentation pipelines, which are only using a single marker for object detection, we could show that the use of multiple markers can significantly increase the segmentation power, and thus, multiplexed information should be used and not ignored during the segmentation. Furthermore, we provide a novel, user-friendly open-source toolbox for the automatic segmentation of multiplexed histopathological images. © 2015 International Society for Advancement of Cytometry


title: Automated epilepsy diagnosis using interictal scalp EEG

abstract: Over 50 million people worldwide suffer from epilepsy. Traditional diagnosis of epilepsy relies on tedious visual screening by highly trained clinicians from lengthy EEG recording that contains the presence of seizure (ictal) activities. Nowadays, there are many automatic systems that can recognize seizure-related EEG signals to help the diagnosis. However, it is very costly and inconvenient to obtain long-term EEG data with seizure activities, especially in areas short of medical resources. We demonstrate in this paper that we can use the interictal scalp EEG data, which is much easier to collect than the ictal data, to automatically diagnose whether a person is epileptic. In our automated EEG recognition system, we extract three classes of features from the EEG data and build Probabilistic Neural Networks (PNNs) fed with these features. We optimize the feature extraction parameters and combine these PNNs through a voting mechanism. As a result, our system achieves an impressive 94.07% accuracy.


title: Design Challenges and Misconceptions in Neural Sequence Labeling

abstract: We investigate the design challenges of constructing effective and efficient neural sequence labeling systems, by reproducing twelve neural sequence labeling models, which include most of the state-of-the-art structures, and conduct a systematic model comparison on three benchmarks (i.e. NER, Chunking, and POS tagging). Misconceptions and inconsistent conclusions in existing literature are examined and clarified under statistical experiments. In the comparison and analysis process, we reach several practical conclusions which can be useful to practitioners.


title: The visual code navigator: an interactive toolset for source code investigation

abstract: We present the Visual Code Navigator, a set of three interrelated visual tools that we developed for exploring large source code software projects from three different perspectives, or views: the syntactic view shows the syntactic constructs in the source code. The symbol view shows the objects a file makes available after compilation, such as function signatures, variables, and namespaces. The evolution view looks at different versions in a project lifetime of a number of selected source files. The views share one code model, which combines hierarchical syntax based and line based information from multiple source files versions. We render this code model using a visual model that extends the pixel-filling, space partitioning properties of shaded cushion treemaps with novel techniques. We discuss how our views allow users to interactively answer complex questions on various code elements by simple mouse clicks. We validate the efficiency and effectiveness of our toolset by an informal user study on the source code of VTK, a large, industry-size C++ code base


title: Upright positional MRI of the lumbar spine.

abstract: Supine magnetic resonance imaging (MRI) is routinely used in the assessment of low back pain and radiculopathy. However, imaging findings often correlate poorly with clinical findings. This is partly related to the positional dependence of spinal stenosis, which reflects dynamic changes in soft-tissue structures (ligaments, disc, dural sac, epidural fat, and nerve roots). Upright MRI in the flexed, extended, rotated, standing, and bending positions, allows patients to reproduce the positions that bring about their symptoms and may uncover MRI findings that were not visible with routine supine imaging. Assessment of the degree of spinal stability in the degenerate and postoperative lumbar spine is also possible. The aim of this review was to present the current literature concerning both the normal and symptomatic spine as imaged using upright MRI and to illustrate the above findings using clinical examples.


title: A Comparison of deep learning methods for environmental sound

abstract: Environmental sound detection is a challenging application of machine learning because of the noisy nature of the signal, and the small amount of (labeled) data that is typically available. This work thus presents a comparison of several state-of-the-art Deep Learning models on the IEEE challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) 2016 challenge task and data, classifying sounds into one of fifteen common indoor and outdoor acoustic scenes, such as bus, cafe, car, city center, forest path, library, train, etc. In total, 13 hours of stereo audio recordings are available, making this one of the largest datasets available. We perform experiments on six sets of features, including standard Mel-frequency cepstral coefficients (MFCC), Binaural MFCC, log Mel-spectrum and two different large- scale temporal pooling features extracted using OpenSMILE. On these features, we apply five models: Gaussian Mixture Model (GMM), Deep Neural Network (DNN), Recurrent Neural Network (RNN), Convolutional Deep Neural Net- work (CNN) and i-vector. Using the late-fusion approach, we improve the performance of the baseline 72.5% by 15.6% in 4-fold Cross Validation (CV) avg. accuracy and 11% in test accuracy, which matches the best result of the DCASE 2016 challenge. With large feature sets, deep neural network models out- perform traditional methods and achieve the best performance among all the studied methods. Consistent with other work, the best performing single model is the non-temporal DNN model, which we take as evidence that sounds in the DCASE challenge do not exhibit strong temporal dynamics.


title: Effects of Social Bots in the Iran-Debate on Twitter

abstract: 2018 started with massive protests in Iran, bringing back the impressions of the so called "Arab Spring" and it's revolutionary impact for the Maghreb states, Syria and Egypt. Many reports and scientific examinations considered online social networks (OSN's) such as Twitter or Facebook to play a critical role in the opinion making of people behind those protests. Beside that, there is also evidence for directed manipulation of opinion with the help of social bots and fake accounts. So, it is obvious to ask, if there is an attempt to manipulate the opinion-making process related to the Iranian protest in OSN by employing social bots, and how such manipulations will affect the discourse as a whole. Based on a sample of ca. 900,000 Tweets relating to the topic "Iran" we show, that there are Twitter profiles, that have to be considered as social bot accounts. By using text mining methods, we show that these social bots are responsible for negative sentiment in the debate. Thereby, we would like to illustrate a detectable effect of social bots on political discussions on Twitter.


Should football teams be taxed ? Determining crime externalities from football matches .


title: An effective and fast iris recognition system based on a combined multiscale feature extraction technique

abstract: The randomness of iris pattern makes it one of the most reliable biometric traits. On the other hand, the complex iris image structure and the various sources of intra-class variations result in the difficulty of iris representation. Although, a number of iris recognition methods have been proposed, it has been found that several accurate iris recognition algorithms use multiscale techniques, which provide a well-suited representation for iris recognition. In this paper and after a thorough analysis and summarization, a multiscale edge detection approach has been employed as a pre-processing step to efficiently localize the iris followed by a new feature extraction technique which is based on a combination of some multiscale feature extraction techniques. This combination uses special Gabor filters and wavelet maxima components. Finally, a promising feature vector representation using moment invariants is proposed. This has resulted in a compact and efficient feature vector. In addition, a fast matching scheme based on exclusive OR operation to compute bits similarity is proposed where the result experimentation was carryout out using CASIA database. The experimental results have shown that the proposed system yields attractive performances and could be used for personal identification in an efficient and effective manner and comparable to the best iris recognition algorithm found in the current literature.


title: Combined Spoken Language Translation

abstract: EU-BRIDGE 1 is a European research project which is aimed at developing innovative speech translation technology. One of the collaborative efforts within EU-BRIDGE is to produce joint submissions of up to four different partners to the evaluation campaign at the 2014 International Workshop on Spoken Language Translation (IWSLT). We submitted combined translations to the German!English spoken language translation (SLT) track as well as to the German!English, English!German and English!French machine translation (MT) tracks. In this paper, we present the techniques which were applied by the different individual translation systems of RWTH Aachen University, the University of Edinburgh, Karlsruhe Institute of Technology, and Fondazione Bruno Kessler. We then show the combination approach developed at RWTH Aachen University which combined the individual systems. The consensus translations yield empirical gains of up to 2.3 points in BLEU and 1.2 points in TER compared to the best individual system.


title: On Two Desiderata for Creativity Support Tools

abstract: This paper discusses two important desiderata for developing creativity support tools, namely ideation and empowerment. We then use them to guide us in designing a new individual creativity support tool codenamed Creative-Pad. Creative-Pad is designed to assist individual advertising creative to develop creative ideas for advertisements. For ideation, Creative-Pad searches and filters information automatically from the internet to present to the user with related words and exemplar sentences. For empowerment, CreativePad is designed in such a way that the user is neither distracted nor burdened to do any other tasks unrelated to conjuring up a creative idea for a new advertisement. Creative-Pad is fully implemented and some preliminary results of its use by advertising creatives are reported.


title: Benefits of Augmented Reality in Educational Environments - A Systematic Literature Review

abstract: By augmenting the real world with virtual information, Augmented Reality (AR) provides new possibilities for education. Although AR is frequent- ly applied in educational environments, the value of AR applications in these environments is not yet investigated in its entirety. Additionally, educators face different directions of AR applications, which may differ regarding their poten- tial benefits. To help overcome these challenges, we conduct a systematic litera- ture review to synthesize a set of 25 publications. We identify 14 different ben- efits and cluster these into six different groups. We use the Five Directions of AR in educational environments by Yuen et al. (1) to further detail possible benefits for different directions of AR applications. Our findings indicate that specific directions of AR applications are more likely to lead to certain benefits such as increased motivation. Future research is needed to investigate the cau- sality between benefits and directions of AR in more detail.


title: Trends in mobile payments research: A literature review

abstract: Mobile payments (m-payments) are increasingly being adopted by organisations as a new way of doing business in the 21 st century. During the last few years, the use of m-payments as a new payment channel has resulted in an increase in the volume of literature dedicated to the topic. For this reason, this paper presents the findings of a review of literature aimed at identifying the key research themes and methodologies researched. In order to uncover these trends the authors reviewed the top twenty cited papers since 1999 and the twenty most recently published papers on m-payments since August 2014.


title: An index based task assignment policy for achieving optimal power-delay tradeoff in edge cloud systems

abstract: Edge cloud is a promising architecture in order to address the latency problem in mobile cloud computing. However, as compared with remote clouds, edge clouds have limited computational resources, and higher operating costs. In this paper, we design policies which carry out the assignment of tasks that are generated at the mobile subscribers with edge clouds in an online fashion. The proposed policies achieve an optimal power-delay trade-off in the system. Here, the delay experienced by a mobile computing task includes the time spent waiting for transmission to the edge cloud, and the execution time at the edge cloud servers. We perform a theoretical analysis after modeling the system as a continuous-time queueing system. The contribution of this paper is two-fold: Firstly, the algorithm to determine the optimal policy is obtained by proposing an equivalent discrete-time Markov decision process. Secondly, an easily implementable index policy is proposed by analyzing the dual of the original problem. Extensive simulations illustrate the effectiveness of the proposed policies.


title: Evolving Behaviour Trees for the Commercial Game DEFCON

abstract: Behaviour trees provide the possibility of improving on existing Artificial Intelligence techniques in games by being simple to implement, scalable, able to handle the complexity of games, and modular to improve reusability. This ultimately improves the development process for designing automated game players. We cover here the use of behaviour trees to design and develop an AI-controlled player for the commercial real-time strategy game DEFCON. In particular, we evolved behaviour trees to develop a competitive player which was able to outperform the game’s original AI-bot more than 50% of the time. We aim to highlight the potential for evolving behaviour trees as a practical approach to developing AI-bots in games.


title: Visual and Inertial Odometry for a Disaster Recovery Humanoid

abstract: Disaster recovery robots must operate in unstructured environments where wheeled or tracked motion may not be feasible or where it may be subject to extreme slip. Many industrial disaster scenarios also preclude reliance on gnss or other external signals as robots are deployed indoors or underground. Two of the candidates for precise positioning in these scenarios are visual odometry and inertial navigation. This paper presents some practical experience in the design and analysis of a combined visual and inertial odometry system for the Carnegie Mellon University Highly Intelligent Mobile Platform (CHIMP); a humanoid robot competing in the darpa Robotics Challenge.


title: Integrating ensemble-urban cellular automata model with an uncertainty map to improve the performance of a single model

abstract: Transition rules are the core of urban cellular automata CA models. Although the logistic cellular automata Logistic-CA is commonly used for rules extraction, it cannot always achieve satisfactory performance because of the spatial heterogeneity and the inherent complexity of urban expansion. This article presents an ensemble-urban cellular automata Ensemble-CA model to achieve better transition rules. First, an uncertainty map that assesses the performance of transition rules spatially was achieved. Then, two auxiliary models i.e. classification and regression tree, CART; and artificial neural network, ANN, both of which have been stabilized with a Bagging algorithm, were prepared for integration using a proposed self-adaptive -nearest neighbors -NN combination algorithm. Thereafter, those unconfident sites were replaced with the ensemble output. This model was applied to Guangzhou, China, for an urban growth simulation from 2003 to 2008. Static validation confirmed that this ensemble framework i.e. without substitution of uncertain sites can achieve better performance 0.87 in terms of receiver operating characteristic ROC statistics area under the curve, AUC, and outperformed the best single model ANN, 0.82 and other common strategies e.g. weighted average, 0.83. After the substitution of unconfident sites, the AUC of Logistic-CA was elevated from 0.78 to 0.81. Subsequently, two urban growth mechanisms i.e. pixel-and patch-based were implemented separately based on the integrated transition rules. Experimental results revealed that the accuracy obtained from simulation of the Ensemble-CA increased considerably. The obtained kappa outperformed the single model, with improvements of 1.74% and 2.76% for pixel-and patch-based approaches, respectively. Correspondingly, landscape similarity index LSI improvements of these two mechanisms were 4.24% and 1.82%.


title: One-third probability embedding: a new ±1 histogram compensating image least significant bit steganography scheme

abstract: A new method is introduced for the least significant bit (LSB) image steganography in spatial domain providing the capacity of one bit per pixel. Compared to the recently proposed image steganography techniques, the new method called one-third LSB embedding reduces the probability of change per pixel to one-third without sacrificing the embedding capacity. This improvement results in a better imperceptibility and also higher robustness against well-known LSB detectors. Bits of the message are carried using a function of three adjacent cover pixels. It is shown that no significant improvement is achieved by increasing the length of the pixel sequence employed. A closed-form expression for the probability of change per pixel in terms of the number of pixels used in the pixel groups has been derived. Another advantage of the proposed algorithm is to compensate, as much as possible, for any changes in the image histogram. It has been demonstrated that one-third probability embedding outperforms histogram compensating version of the LSB matching in terms of keeping the image histogram unchanged.


title: DeepID3: Face Recognition with Very Deep Neural Networks

abstract: The state-of-the-art of face recognition has been significantly advanced by the emergence of deep learning. Very deep neural networks recently achieved great success on general object recognition because of their superb learning capacity. This motivates us to investigate their effectiveness on face recognition. This paper proposes two very deep neural network architectures, referred to as DeepID3, for face recognition. These two architectures are rebuilt from stacked convolution and inception layers proposed in VGG net and GoogLeNet to make them suitable to face recognition. Joint face identification-verification supervisory signals are added to both intermediate and final feature extraction layers during training. An ensemble of the proposed two architectures achieves 99.53% LFW face verification accuracy and 96.0% LFW rank-1 face identification accuracy, respectively. A further discussion of LFW face verification result is given in the end.


title: Integrating Additional Chord Information Into HMM-Based Lyrics-to-Audio Alignment

abstract: Aligning lyrics to audio has a wide range of applications such as the automatic generation of karaoke scores, song-browsing by lyrics, and the generation of audio thumbnails. Existing methods are restricted to using only lyrics and match them to phoneme features extracted from the audio (usually mel-frequency cepstral coefficients). Our novel idea is to integrate the textual chord information provided in the paired chords-lyrics format known from song books and Internet sites into the inference procedure. We propose two novel methods that implement this idea: First, assuming that all chords of a song are known, we extend a hidden Markov model (HMM) framework by including chord changes in the Markov chain and an additional audio feature (chroma) in the emission vector; second, for the more realistic case in which some chord information is missing, we present a method that recovers the missing chord information by exploiting repetition in the song. We conducted experiments with five changing parameters and show that with accuracies of 87.5% and 76.7%, respectively, both methods perform better than the baseline with statistical significance. We introduce the new accompaniment interface Song Prompter, which uses the automatically aligned lyrics to guide musicians through a song. It demonstrates that the automatic alignment is accurate enough to be used in a musical performance.


title: On single image scale-up using sparse-representations,” Curves and Surfaces

abstract: This paper deals with the single image scale-up problem using sparse-representation modeling. The goal is to recover an original image from its blurred and down-scaled noisy version. Since this problem is highly ill-posed, a prior is needed in order to regularize it. The literature offers various ways to address this problem, ranging from simple linear space-invariant interpolation schemes (e.g., bicubic interpolation), to spatially-adaptive and non-linear filters of various sorts. We embark from a recently-proposed successful algorithm by Yang et. al. [1,2], and similarly assume a local Sparse-Land model on image patches, serving as regularization. Several important modifications to the above-mentioned solution are introduced, and are shown to lead to improved results. These modifications include a major simplification of the overall process both in terms of the computational complexity and the algorithm architecture, using a different training approach for the dictionary-pair, and introducing the ability to operate without a training-set by boot-strapping the scale-up task from the given low-resolution image. We demonstrate the results on true images, showing both visual and PSNR improvements.


title: Systematic review of qualitative studies exploring parental beliefs and attitudes toward childhood vaccination identifies common barriers to vaccination.

abstract: OBJECTIVE ::: To determine whether a systematic review of qualitative studies can lead to identification of consistent themes across studies, using barriers toward childhood vaccination as an example. ::: ::: ::: STUDY DESIGN AND SETTING ::: We performed a systematic literature search of studies identified in 10 electronic databases. Two independent reviewers selected the relevant abstracts and articles, then extracted information. Content analysis methodology was used to create a coding template for barriers and then to identify how many studies identified specific barriers. ::: ::: ::: RESULTS ::: Fifteen studies were included in this overview. Eight studies used semistructured interviews, five used focus groups, and two used both methodologies. Themes fell under four major headings: issues of harm, issues of distrust, access issues, and other issues. Barriers identified in more than half of the studies included concern about the risk of adverse effects, concern that vaccinations are painful, distrust of by those advocating vaccines (including belief in conspiracy), belief that vaccination should not occur when the child has a minor illness, unpleasant staff or poor communication, and lack of awareness of the vaccination schedule. ::: ::: ::: CONCLUSION ::: Systematically reviewing qualitative studies on barriers to childhood vaccination provided important information on barriers that are consistently identified by parents in several different studies.


title: Automatic Detection and Reconstruction of 2-D/3-D Building Shapes From Spaceborne TomoSAR Point Clouds

abstract: Modern spaceborne synthetic aperture radar (SAR) sensors, such as TerraSAR-X/TanDEM-X and COSMO-SkyMed, can deliver very high resolution (VHR) data beyond the inherent spatial scales of buildings. Processing these VHR data with advanced interferometric techniques, such as SAR tomography (TomoSAR), allows for the generation of four-dimensional point clouds, containing not only the 3-D positions of the scatterer location but also the estimates of seasonal/temporal deformation on the scale of centimeters or even millimeters, making them very attractive for generating dynamic city models from space. Motivated by these chances, the authors have earlier proposed approaches that demonstrated first attempts toward reconstruction of building facades from this class of data. The approaches work well when high density of facade points exists, and the full shape of the building could be reconstructed if data are available from multiple views, e.g., from both ascending and descending orbits. However, there are cases when no or only few facade points are available. This usually happens for lower height buildings and renders the detection of facade points/regions very challenging. Moreover, problems related to the visibility of facades mainly facing toward the azimuth direction (i.e., facades orthogonally oriented to the flight direction) can also cause difficulties in deriving the complete structure of individual buildings. These problems motivated us to reconstruct full 2-D/3-D shapes of buildings via exploitation of roof points. In this paper, we present a novel and complete data-driven framework for the automatic (parametric) reconstruction of 2-D/3-D building shapes (or footprints) using unstructured TomoSAR point clouds particularly generated from one viewing angle only. The proposed approach is illustrated and validated by examples using TomoSAR point clouds generated using TerraSAR-X high-resolution spotlight data stacks acquired from ascending orbit covering two different test areas, with one containing simple moderate-sized buildings in Las Vegas, USA and the other containing relatively complex building structures in Berlin, Germany.