title: A novel multi-lane detection and tracking system

abstract: In this paper a novel spline-based multi-lane detection and tracking system is proposed. Reliable lane detection and tracking is an important component of lane departure warning systems, lane keeping support systems or lane change assistance systems. The major novelty of the proposed approach is the usage of the so-called Catmull-Rom spline in combination with the extended Kalman filter tracking. The new spline-based model enables an accurate and flexible modeling of the lane markings. At the same time the application of the extended Kalman filter contributes significantly to the system robustness and stability. There is no assumption about the parallelism or the shapes of the lane markings in our method. The number of lane markings is also not restrained, instead each lane marking is separately modeled and tracked. The system runs on a standard PC in real time (i.e. 30 fps) with WVGA image resolution (752 ×480). The test vehicle has been driven on the roads with challenging scenarios, like worn out lane markings, construction sites, narrow corners, exits and entries of the highways, etc., and good performance has been demonstrated. The quantitative evaluation has been performed using manually annotated video sequences.


title: Analysis of Random Step Frequency Radar and Comparison With Experiments

abstract: Linear stepped frequency radar is used in wide-band radar applications, such as airborne synthetic aperture radar (SAR), turntable inverse SAR, and ground penetration radar. The frequency is stepped linearly with a constant frequency change, and range cells are formed by fast Fourier transform processing. The covered bandwidth defines the range resolution, and the length of the frequency step restricts the nonambiguous range interval. A random choice of the transmitted frequencies suppresses the range ambiguity, improves covert detection, and reduces the signal interference between adjacent sensors. As a result of the random modulation, however, a noise component is added to the range/Doppler sidelobes. In this paper, relationships of random step frequency radar are compared with frequency-modulated continuous wave noise radar and the statistical characteristics of the ambiguity function and the sidelobe noise floor are analyzed. Algorithms are investigated, which reduce the sidelobes and the noise-floor contribution from strong dominating reflectors in the scene. Theoretical predictions are compared with Monte Carlo simulations and experimental data


Learning Structured Predictors from Bandit Feedback for Interactive NLP


title: Unsupervised Multiresolution Segmentation for Images with Low Depth of Field

abstract: Unsupervised segmentation of images with low depth of field (DOF) is highly useful in various applications. This paper describes a novel multiresolution image segmentation algorithm for low DOF images. The algorithm is designed to separate a sharply focused object-of-interest from other foreground or background objects. The algorithm is fully automatic in that all parameters are image independent. A multi-scale approach based on high frequency wavelet coefficients and their statistics is used to perform context-dependent classification of individual blocks of the image. Unlike other edge-based approaches, our algorithm does not rely on the process of connecting object boundaries. The algorithm has achieved high accuracy when tested on more than 100 low DOF images, many with inhomogeneous foreground or background distractions. Compared with he state of the art algorithms, this new algorithm provides better accuracy at higher speed.


title: Legalizing and Regulating Marijuana in Canada: Review of Potential Economic, Social, and Health Impacts

abstract: Notwithstanding a century of prohibition, marijuana is the most widely used illicit substance in Canada. Due to the growing public acceptance of recreational marijuana use and ineffectiveness of the existing control system in Canada, the issue surrounding legalizing this illicit drug has received considerable public and political attentions in recent years. Consequently, the newly elected Liberal Government has formally announced that Canada will introduce legislation in the spring of 2017 to start legalizing and regulating marijuana. This editorial aims to provide a brief overview on potential economic, social, and public health impacts of legal marijuana in Canada. The legalization could increase tax revenue through the taxation levied on marijuana products and could also allow the Government to save citizens’ tax dollars currently being spent on prohibition enforcement. Moreover, legalization could also remove the criminal element from marijuana market and reduce the size of Canada’s black market and its consequences for the society. Nevertheless, it may also lead to some public health problems, including increasing in the uptake of the drug, accidents and injuries. The legalization should be accompanied with comprehensive strategies to keep the drug out of the hands of minors while increasing awareness and knowledge on harmful effects of the drug. In order to get better insights on how to develop an appropriate framework to legalize marijuana, Canada should closely watch the development in the neighboring country, the United States, where some of its states viz, Colorado, Oregon, Washington, and Alaska have already legalized recreational use of marijuana.


title: Development of a SiC JFET-Based Six-Pack Power Module for a Fully Integrated Inverter

abstract: In this paper, a fully integrated silicon carbide (SiC)-based six-pack power module is designed and developed. With 1200-V, 100-A module rating, each switching element is composed of four paralleled SiC junction gate field-effect transistors (JFETs) with two antiparallel SiC Schottky barrier diodes. The stability of the module assembly processes is confirmed with 1000 cycles of -40°C to +200°C thermal shock tests with 1.3°C/s temperature change. The static characteristics of the module are evaluated and the results show 55 mΩ on-state resistance of the phase leg at 200°C junction temperature. For switching performances, the experiments demonstrate that while utilizing a 650-V voltage and 60-A current, the module switching loss decreases as the junction temperature increases up to 150°C. The test setup over a large temperature range is also described. Meanwhile, the shoot-through influenced by the SiC JFET internal capacitance as well as package parasitic inductances are discussed. Additionally, a liquid cooled three-phase inverter with 22.9 cm × 22.4 cm × 7.1 cm volume and 3.53-kg weight, based on this power module, is designed and developed for electric vehicle and hybrid electric vehicle applications. A conversion efficiency of 98.5% is achieved at 10 kHz switching frequency at 5 kW output power. The inverter is evaluated with coolant temperature up to 95°C successfully.


title: Towards Understanding the Invertibility of Convolutional Neural Networks

abstract: Several recent works have empirically observed that Convolutional Neural Nets (CNNs) are (approximately) invertible. To understand this approximate invertibility phenomenon and how to leverage it more effectively, we focus on a theoretical explanation and develop a mathematical model of sparse signal recovery that is consistent with CNNs with random weights. We give an exact connection to a particular model of model-based compressive sensing (and its recovery algorithms) and random-weight CNNs. We show empirically that several learned networks are consistent with our mathematical analysis and then demonstrate that with such a simple theoretical framework, we can obtain reasonable re- construction results on real images. We also discuss gaps between our model assumptions and the CNN trained for classification in practical scenarios.


Living Room Wars: Remediation, Boardgames, and the Early History of Video Wargaming


title: Traffic road line detection based on the vanishing point and contour information

abstract: This paper describes an algorithm to detect the road lane boundary for a driver who changes lane as ‘access road’, ‘passing lane’, ‘approach ramp’, etc. The proposed algorithm starts with binarization and labeling for the image. The binary image is generated by adaptive threshold. The binary image is labeled recursively to blobs. From each blob, the algorithm analyses principal axes calculated by Eigen value and vector. Those are based on the covariance matrix of pixel distribution for the blobs. Then it shows intersection points by the combination of each long principal axis. Among intersection points, the highest density cluster is selected to a dominant vanishing point as the parallel road lines. In experiments of road line detection from the urban road image, the proposed method shows the effectiveness of lane boundary detection in the rate of more than 94.3%.


title: A 25mA 0.13µm CMOS LDO regulator with power-supply rejection better than −56dB up to 10MHz using a feedforward ripple-cancellation technique

abstract: Low drop-out (LDO) linear regulators have become a key building block in portable communication systems for power management ICs. The LDO usually comes after a switching DC-DC converter to reduce the output ripples and provide a regulated voltage source for noise-sensitive blocks. For a higher level of integration, there is a need to increase the operating frequency of the switching converters [1]. This necessitates a subsequent LDO regulator with high ripple rejection at frequencies up to several MHz. These LDO regulators should also provide a low drop-out voltage to cope with the low supply voltage of the state-of-the-art CMOS technologies. In addition, due to the feedback nature of the system, the LDO should be stable for a wide range of supply currents while consuming a very low quiescent current.


title: Failure Analysis and Prediction for Big-Data Systems

abstract: Motivated by the high complexity of today’s datacenters, a large body of studies tries to understand workloads and resource utilization in datacenters. However, there is little work on exploring unsuccessful job and task executions. In this article, we study and predict three types of unsuccessful executions in traces of a Google datacenter, namely $\sf {fail}$ , $\sf {kill}$ , and $\sf {eviction}$ . We first quantitatively show their strongly negative impact on machine time and the resulting task slowdown. We analyze patterns of unsuccessful jobs and tasks, particularly focusing on their interdependencies, and we uncover their root causes by inspecting key workload and system attributes. Furthermore, we develop three on-line prediction models that can classify jobs and events into four classes upon arrival time, using independent or nested Neural Networks. We explore different combinations of feature sets and techniques to reduce the computational overhead. Our evaluation results show that the proposed models can accurately classify 94.4 percent of jobs and 76.8 percent of events into four classes.


title: Evolution of the mental health care system in Poland

abstract: Objective: The aim of this paper is to trace evolution of mental health system in Poland. Method: Available Polish literature and fundamental policy documents including mental health legislation are reviewed and major milestones in this evolution identified and then discussed against the background of political and social developments. Results: The mental health system evolved since the beginning of the 1970s from large hospitals towards community-based care. It was found that the changes were rather slow due to financial shortages and lack of clear demand from users. Recent transitions offer opportunities to increase impact of users and their families. On the other hand, however, introduction of health insurance system reduces prospects for well co-ordinated mental health policy and may lead to inequalities in access to services. Conclusion: It can be concluded that evolution of mental health services has been influenced by prevailing ideologies as much as by technical and professional considerations.


title: Hexapod robot: Test platform for bio-inspired controllers

abstract: This paper aims on design, construction and control of a hexapod robot, which is a six-legged walking robot. The result of this project is a legged robot equipped with sonars, force-sensitive resistors and encoders, which will be used as a test platform for hexapod robot bio-inspired controllers. A graphical user interface was developed to control and display data from sensors. There are also described basic characteristics of walking robots and several approaches of hexapod bio-inspired controllers.


title: Towards Natural Language Understanding of Partial Speech Recognition Results in Dialogue Systems

abstract: We investigate natural language understanding of partial speech recognition results to equip a dialogue system with incremental language processing capabilities for more realistic human-computer conversations. We show that relatively high accuracy can be achieved in understanding of spontaneous utterances before utterances are completed.


title: I find your behavior disturbing: Static and dynamic app behavioral analysis for detection of Android malware

abstract: Malicious Android applications are currently the biggest threat in the scope of mobile security. To cope with their exponential growth and with their deceptive and hideous behaviors, static analysis signature based approaches are not enough to timely detect and tackle brand new threats such as polymorphic and composition malware. This work presents BRIDEMAID, a novel framework for analysis of Android apps' behavior, which exploits both a static and dynamic approach to detect malicious apps directly on mobile devices. The static analysis is based on n-grams matching to statically recognize malicious app execution patterns. The dynamic analysis is instead based on multi-level monitoring of device, app and user behavior to detect and prevent at runtime malicious behaviors. The framework has been tested against 2794 malicious apps reporting a detection accuracy of 99,7% and a negligible false positive rate, tested on a set of 10k genuine apps.


title: Supplemental dietary protein for grazing dairy cows: reproduction, condition loss, plasma metabolites, and insulin.

abstract: An experiment was conducted over a 2-yr period to investigate the influence of grain crude protein (CP) and rumen undegradable protein (RUP) concentration on reproduction and energy status of dairy cows grazing annual ryegrass (Lolium multiflorum) and oats (Avena sativa). Holstein cows (n = 122) were blocked by calving group [partum (0 d postpartum) vs. postpartum (41 +/- 19 d postpartum at study initiation)] and assigned to grain supplements containing high CP [22.8% of dry matter (DM)], moderate CP (16.6%), or moderate CP (16.2%)] supplemented with RUP from blood meal and corn gluten meal. Postpartum condition loss was greater and first-service pregnancy rate was lower for partum-group cows receiving high CP grain supplements compared with control cows receiving moderate CP supplements. The RUP supplements reduced grain consumption, increased days to first estrus, and reduced first-service pregnancy rate of partum-group cows. The reproduction of postpartum group cows was unaffected by protein supplements. Plasma urea nitrogen was higher for cows fed high CP diets, but plasma ammonia nitrogen, glycated hemoglobin, nonesterified fatty acids, beta-hydoxybutyrate, glucose, and insulin concentrations were similar to cows fed moderate CP. Excess postpartum condition loss, coupled with inconsistent protein supplement effects on days to first service and first-service pregnancy rate, suggest that energy deprivation may have contributed to the low fertility experienced by grazing cows in this study.


title: Hand segmentation for hand-object interaction from depth map

abstract: Hand segmentation for hand-object interaction is a necessary preprocessing step in many applications such as augmented reality, medical application, and human-robot interaction. However, typical methods are based on color information which is not robust to objects with skin color, skin pigment difference, and light condition variations. Thus, we propose hand segmentation method for hand-object interaction using only a depth map. It is challenging because of the small depth difference between a hand and objects during an interaction. To overcome this challenge, we propose the two-stage random decision forest (RDF) method consisting of detecting hands and segmenting hands. To validate the proposed method, we demonstrate results on the publicly available dataset of hand segmentation for hand-object interaction. The proposed method achieves high accuracy in short processing time comparing to the other state-of-the-art methods.


title: Universal approximation using incremental constructive feedforward networks with . . .

abstract: According to conventional neural network theories, single-hidden-layer feedforward networks (SLFNs) with additive or radial basis function (RBF) hidden nodes are universal approximators when all the parameters of the networks are allowed adjustable. However, as observed in most neural network implementations, tuning all the parameters of the networks may cause learning complicated and inefficient, and it may be difficult to train networks with nondifferential activation functions such as threshold networks. Unlike conventional neural network theories, this paper proves in an incremental constructive method that in order to let SLFNs work as universal approximators, one may simply randomly choose hidden nodes and then only need to adjust the output weights linking the hidden layer and the output layer. In such SLFNs implementations, the activation functions for additive nodes can be any bounded nonconstant piecewise continuous functions g:R→R and the activation functions for RBF nodes can be any integrable piecewise continuous functions g:R→R and ∫Rg(x)dx≠0. The proposed incremental method is efficient not only for SFLNs with continuous (including nondifferentiable) activation functions but also for SLFNs with piecewise continuous (such as threshold) activation functions. Compared to other popular methods such a new network is fully automatic and users need not intervene the learning process by manually tuning control parameters.


title: Enhancing the resilience of nurses and midwives: pilot of a mindfulness-based program for increased health, sense of coherence and decreased depression, anxiety and stress.

abstract: Health workers in general, and midwives and nurses in particular, experience high levels of stress/distress due to the nature of their work and workplaces; and, their socialization into ways of working that minimizes the likelihood of self-care. Increasing interest in the development of resilient workers has meant an enormous growth in interest in the role of holistic practices such as mindfulness meditation. Kabat-Zinn's mindfulness-based stress reduction (MBSR) is one of the most commonly used by those seeking to practise, theorize or research mindfulness across multiple contexts. The primary aim of this study was to pilot the effectiveness of an adapted mindfulness-based stress reduction intervention on the psychological wellbeing of nurses and midwives. More specifically, we sought to test the acceptability and feasibility of a modified MBSR intervention to inform a future randomized controlled trial (RCT). The pilot study used a pre and post intervention design. Twenty midwives and 20 nurses participated in a one-day workshop, undertook to meditate daily for 8 weeks and completed pre and post intervention measures: general health questionnaire (GHQ-12); sense of coherence (SOC) - orientation to life and the depression, anxiety and stress scale (DASS). A subgroup took part in interviews or focus group discussions of their experiences of the program and their ongoing mindfulness practice. The quantitative findings included significant improvements on the GHQ-12, SOC and the stress subscale of the DASS. Qualitative findings support the acceptability of the intervention, and highlighted a number of issues related to feasibility of any future RCT. In conclusion, mindfulness practice holds promise for increasing individual and workplace resilience, however, meaningful research evidence from carefully constructed studies will be required to engage and motivate participation and organizational support.


title: Byzantine Fault Tolerance, from Theory to Reality

abstract: Since its introduction nearly 20 years ago, the Byzantine Generals Problem has been the subject of many papers having the scrutiny of the fault tolerance community. Numerous Byzantine tolerant algorithms and architectures have been proposed. However, this problem is not yet sufficiently understood by those who design, build, and maintain systems with high dependability requirements. Today, there are still many misconceptions relating to Byzantine failure, what makes a system vulnerable, and indeed the very nature and reality of Byzantine faults. This paper revisits the Byzantine problem from a practitioner's perspective. It has the intention to provide the reader with a working appreciation of the Byzantine failure from a practical as well as a theoretical perspective. A discussion of typical failure properties and the difficulties in preventing the associated failure propagation is presented. These are illustrated with real Byzantine failure observations. Finally, various architectural solutions to the Byzantine problem are presented.


title: Footwear Print Retrieval System for Real Crime Scene Marks

abstract: Footwear impression evidence has been gaining increasing importance in forensic investigation. The most challenging task for a forensic examiner is to work with highly degraded footwear marks and match them to the most similar footwear print available in the database. Retrieval process from a large database can be made significantly faster if the database footwear prints are clustered beforehand. In this paper we propose a footwear print retrieval system which uses the fundamental shapes in shoes like lines, circles and ellipses as features and retrieves the most similar print from a clustered database. Prints in the database are clustered based on outsole patterns. Each footwear print pattern is characterized by the combination of shape features and represented by an Attributed Relational Graph. Similarity between prints is computed using Footwear Print Distance. The proposed system is invariant to distortions like scale, rotation, translation and works well with the partial prints, color prints and crime scene marks.


title: NVIDIA Jetson Platform Characterization

abstract: This study characterizes the NVIDIA Jetson TK1 and TX1 Platforms, both built on a NVIDIA Tegra System on Chip and combining a quad-core ARM CPU and an NVIDIA GPU. Their heterogeneous nature, as well as their wide operating frequency range, make it hard for application developers to reason about performance and determine which optimizations are worth pursuing. This paper attempts to inform developers’ choices by characterizing the platforms’ performance using Roofline models obtained through an empirical measurement-based approach as well as through a case study of a heterogeneous application (matrix multiplication). Our results highlight a difference of more than an order of magnitude in compute performance between the CPU and GPU on both platforms. Given that the CPU and GPU share the same memory bus, their Roofline models’ balance points are also more than an order of magnitude apart. We also explore the impact of frequency scaling: build CPU and GPU Roofline profiles and characterize both platforms’ balance point variation, power consumption, and performance per watt as frequency is scaled.


title: IGBT and Diode Behavior During Short-Circuit Type 3

abstract: A short circuit during inverter operation can result in the so-called short-circuit types 2 and 3. The short-circuit type 3 results in an interaction with several current commutations between the Insulate-Gate Bipolar Transistor (IGBT) and the antiparallel diode. The switched ON IGBT, conducting no current before the short circuit occurs, has no plasma inside and can block any voltage. It behaves like a voltage-controlled current source, which can stress the diode by commutating the complete short-circuit current into the diode. An avalanche arises inside the diode, which can lead to a destruction of the diode. The current through the diode determines dv/dt and makes the diode a dominating semiconductor during the short circuit. The current source behavior of the IGBT can be verified with semiconductor simulations and measurements on a high-power 3.3-kV IGBT. In the simulations, the IGBT is replaced by a current source, which gives similar results. For the measurements, the gate–emitter voltage is varied to change the IGBT current without influencing the collector–emitter voltage.


title: Financial Analysis of a Pico-Cellular Home Network Deployment

abstract: The increasing demand for higher data rates in cellular networks has resulted in a trend to smaller cell sizes (femto cells) and pico-cellular hot spot coverage. In this paper the financial impact of pico-cellular home base station deployment in a macro-cellular network is explored. For increasing data rates, it becomes evident that current macro-cellular network deployment becomes less economically viable. It is shown that in urban areas a combination of publicly accessible home base stations, randomly deployed by the end user, and macro-cells for area coverage, deployed by an operator, can result in significant reductions (up to 70% in the investigated scenario) of the total annual network costs compared to a pure macro-cellular network.


title: A Comprehensive Data Quality Methodology for Web and Structured Data

abstract: Measuring and improving data quality in an organization or in a group of interacting organizations is a complex task. Several methodologies have been developed in the past providing a basis for the definition of a complete data quality program applying assessment and improvement techniques in order to guarantee high data quality levels. Since the main limitation of existing approaches is their specialization on specific issues or contexts, this paper presents the Comprehensive Data Quality (CDQ) methodology that aims at integrating and enhancing the phases, techniques and tools proposed by previous approaches. CDQ methodology is conceived to be at the same time complete, flexible and simple to apply. Completeness is achieved by considering existing techniques and tools and integrating them in a framework that can work in both intra and inter organizational contexts, and can be applied to all types of data. The methodology is flexible since it supports the user in the selection of the most suitable techniques and tools within each phase and in any context. Finally, CDQ is simple since it is organized in phases and each phase is char-acterized by a specific goal and techniques to apply. The methodology is explained by means of a running example.


title: Supervised radar signal classification

abstract: This work investigates radar signal classification and source identification using three classification models: Neural Networks (NN), Support Vector Machines (SVM) and Random Forests (RF). The available large dataset consists of pulse train characteristics such as signal frequencies, type of modulation, pulse repetition intervals, scanning type, scan period, etc., represented as a mixture of continuous, discrete and categorical data. Typically, considerable part of the data samples contains missing values. In our previous work we used only part of the radar dataset, applying listwise deletion to clean the samples with missing values and processed relatively small subset of complete data. In this work we apply three different imputation techniques to deal with the missing data: multiple imputation (MI), K-Nearest Neighbour Imputation (KNNI) and Bagged Tree Imputation (BTI). We employ the imputation methods to all data samples with up to 60% missingness, this way increasing more than twice the size of the initially used data subset. Subsequently the three classifiers (NN, SVM, and RF) are employed and the results are analysed and critically compared based on their accuracy to assess the model with the best performance.


Field Operation of a Robotic SWATH Boat for Shallow-Water Bathymetric Characterization


title: Religion, Social Networks, and Life Satisfaction:

abstract: Although the positive association between religiosity and life satisfaction is well documented, much theoretical and empirical controversy surrounds the question of how religion actually shapes life satisfaction. Using a new panel dataset, this study offers strong evidence for social and participatory mechanisms shaping religion’s impact on life satisfaction. Our findings suggest that religious people are more satisfied with their lives because they regularly attend religious services and build social networks in their congregations. The effect of within-congregation friendship is contingent, however, on the presence of a strong religious identity. We find little evidence that other private or subjective aspects of religiosity affect life satisfaction independent of attendance and congregational friendship.


title: Quizz: Targeted crowdsourcing with a billion (potential) users

abstract: We describe Quizz, a gamified crowdsourcing system that simultaneously assesses the knowledge of users and acquires new knowledge from them. Quizz operates by asking users to complete short quizzes on specific topics; as a user answers the quiz questions, Quizz estimates the user's competence. To acquire new knowledge, Quizz also incorporates questions for which we do not have a known answer; the answers given by competent users provide useful signals for selecting the correct answers for these questions. Quizz actively tries to identify knowledgeable users on the Internet by running advertising campaigns, effectively leveraging the targeting capabilities of existing, publicly available, ad placement services. Quizz quantifies the contributions of the users using information theory and sends feedback to the advertisingsystem about each user. The feedback allows the ad targeting mechanism to further optimize ad placement. Our experiments, which involve over ten thousand users, confirm that we can crowdsource knowledge curation for niche and specialized topics, as the advertising network can automatically identify users with the desired expertise and interest in the given topic. We present controlled experiments that examine the effect of various incentive mechanisms, highlighting the need for having short-term rewards as goals, which incentivize the users to contribute. Finally, our cost-quality analysis indicates that the cost of our approach is below that of hiring workers through paid-crowdsourcing platforms, while offering the additional advantage of giving access to billions of potential users all over the planet, and being able to reach users with specialized expertise that is not typically available through existing labor marketplaces.


title: Power and Channel Allocation for Non-orthogonal Multiple Access in 5G Systems: Tractability and Computation

abstract: Network capacity calls for significant increase for 5G cellular systems. A promising multi-user access scheme, non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC), is currently under consideration. In NOMA, spectrum efficiency is improved by allowing more than one user to simultaneously access the same frequency-time resource and separating multi-user signals by SIC at the receiver. These render resource allocation and optimization in NOMA different from orthogonal multiple access in 4G. In this paper, we provide theoretical insights and algorithmic solutions to jointly optimize power and channel allocation in NOMA. For utility maximization, we mathematically formulate NOMA resource allocation problems. We characterize and analyze the problems' tractability under a range of constraints and utility functions. For tractable cases, we provide polynomial-time solutions for global optimality. For intractable cases, we prove the NP-hardness and propose an algorithmic framework combining Lagrangian duality and dynamic programming (LDDP) to deliver near-optimal solutions. To gauge the performance of the obtained solutions, we also provide optimality bounds on the global optimum. Numerical results demonstrate that the proposed algorithmic solution can significantly improve the system performance in both throughput and fairness over orthogonal multiple access as well as over a previous NOMA resource allocation scheme.


title: A Model and Framework for Visualization Exploration

abstract: Visualization exploration is the process of extracting insight from data via interaction with visual depictions of that data. Visualization exploration is more than presentation; the interaction with both the data and its depiction is as important as the data and depiction itself. Significant visualization research has focused on the generation of visualizations (the depiction); less effort has focused on the exploratory aspects of visualization (the process). However, without formal models of the process, visualization exploration sessions cannot be fully utilized to assist users and system designers. Toward this end, we introduce the P-Set model of visualization exploration for describing this process and a framework to encapsulate, share, and analyze visual explorations. In addition, systems utilizing the model and framework are more efficient as redundant exploration is avoided. Several examples drawn from visualization applications demonstrate these benefits. Taken together, the model and framework provide an effective means to exploit the information within the visual exploration process


title: Efficient, Transparent and Comprehensive Runtime Code Manipulation

abstract: This thesis addresses the challenges of building a software system for general-purpose runtime code manipulation. Modern applications, with dynamically-loaded modules and dynamically-generated code, are assembled at runtime. While it was once feasible at compile time to observe and manipulate every instruction—which is critical for program analysis, instrumentation, trace gathering, optimization, and similar tools—it can now only be done at runtime. Existing runtime tools are successful at inserting instrumentation calls, but no general framework has been developed for fine-grained and comprehensive code observation and modification without high overheads. ::: This thesis demonstrates the feasibility of building such a system in software. We present DynamoRIO, a fully-implemented runtime code manipulation system that supports code transformations on any part of a program, while it executes. DynamoRIO uses code caching technology to provide efficient, transparent, and comprehensive manipulation of an unmodified application running on a stock operating system and commodity hardware. DynamoRIO executes large, complex, modern applications with dynamically-loaded, generated, or even modified code. Despite the formidable obstacles inherent in the IA-32 architecture, DynamoRIO provides these capabilities efficiently, with zero to thirty percent time and memory overhead on both Windows and Linux. ::: DynamoRIO exports an interface for building custom runtime code manipulation tools of all types. It has been used by many researchers, with several hundred downloads of our public release, and is being commercialized in a product for protection against remote security exploits, one of numerous applications of runtime code manipulation. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)


title: A bag of words approach for 3d object categorization

abstract: In this paper we propose a novel framework for 3D object categorization. The object is modeled it in terms of its sub-parts as an histogram of 3D visual word occurrences. We introduce an effective method for hierarchical 3D object segmentation driven by the minima rule that combines spectral clustering --- for the selection of seed-regions --- with region growing based on fast marching. The front propagation is driven by local geometry features, namely the Shape Index. Finally, after the coding of each object according to the Bag-of-Words paradigm, a Support Vector Machine is learnt to classify different objects categories. Several examples on two different datasets are shown which evidence the effectiveness of the proposed framework.


title: An efficient verification method for microprocessors based on the virtual machine

abstract: This paper presents an efficient verification method for microprocessors based on virtual machine. Under memory and I/O device models provided by the virtual machine, our simulation tool can not only simulate test programs but also operating systems. This simulation environment is close to the real environment of microprocessors, so it is sufficient for functional verification of microprocessors before tape out. At the same time, our simulation tool can automatically compare the simulation results using the virtual machine as reference model and find the error positions. This method takes full advantage of the virtual machine and greatly improves speed and efficiency of the verification procedure. This method has been successfully applied in the verification of an embedded microprocessor Amex86 designed in our laboratory for six months by five persons.


title: SAIDR: A New Dynamic Model for SMS-Based Worm Propagation in Mobile Networks

abstract: Recently, short message service (SMS) has become one of the most popular applications for mobile users. However, it provides convenience for worms to spread in mobile networks. Due to the differences between computers and smartphones, the current propagation models of computer worms cannot be employed in the mobile network directly, especially in the SMS scenario. In this paper, we propose a worm propagation model based on SMS, named susceptible-affected-infectious-suspended-recovered. To accurately predict the worm propagation via SMS, first, we add the affected state to represent the state of users who have received the messages but have not clicked the malicious links. Second, since an infected node does not always send malicious messages to others, a novel state, the suspended state, is introduced to describe this situation. Furthermore, related stabilities of the worm-free equilibrium and the endemic equilibrium are studied. The worm-free equilibrium is locally and globally asymptotically stable if the basic reproduction number ${R}_{0} , whereas the endemic equilibrium is locally asymptotically stable if $R_{0}>1$ . Finally, comprehensive experiments have been done to support our conclusions and confirm the rationality.


title: Enhancing disease resistances of Super Hybrid Rice with four antifungal genes

abstract: A plant expression vector harboring four antifungal genes was delivered into the embryogenic calli of '9311', an indica restorer line of Super Hybrid Rice, via modified biolistic particle bombardment. Southern blot analysis indicated that in the regenerated hygromycin-resistant plants, all the four antifungal genes, including RCH10, RAC22, beta-Glu and B-RIP, were integrated into the genome of '9311', co-transmitted altogether with the marker gene hpt in a Mendelian pattern. Some transgenic R1 and R2 progenies, with all transgenes displaying a normal expression level in the Northern blot analysis, showed high resistance to Magnaporthe grisea when tested in the typical blast nurseries located in Yanxi and Sanya respectively. Furthermore, transgenic F1 plants, resulting from a cross of R2 homozygous lines with high resistance to rice blast with the non-transgenic male sterile line Peiai 64S, showed not only high resistance to M. grisea but also enhanced resistance to rice false smut (a disease caused by Ustilaginoidea virens) and rice kernel smut (another disease caused by Tilletia barclayana).


title: Anomaly Explanation Using Metadata

abstract: Anomaly detection is the well-studied task of identifying when data is atypical in some way with respect to its source. In this work, by contrast, we are interested in finding possible descriptions of what may be causing anomalies. We propose a new task, attaching semantics drawn from metadata to a portion of the anomalous examples from some data source. Such a partial description of the anomalous data in terms of the meta-data is useful both because it may help to explain what causes the identified anomalies, and also because it may help to identify the truly unusual examples that defy such simple categorization. This is especially significant when the data set is too large for a human analyst to inspect the anomalies manually. The challenge is that anomalies are, by definition, relatively rare, and so we are seeking to learn a precise characterization of a rare event. We examine algorithms for this task in a webcam domain, generating human-understandable explanations for a pixellevel characterization of anomalies. We find that using a recently proposed algorithm that prioritizes precision over recall, it is possible to attach good descriptions to a moderate fraction of the anomalies in webcam data so long as the data set is fairly large.


Sarcasm Detection in Chinese Using a Crowdsourced Corpus


title: Digital material fabrication using mask‐image‐projection‐based stereolithography

abstract: Purpose – The purpose of this paper is to present a mask‐image‐projection‐based stereolithography (MIP‐SL) process that can combine two base materials with various concentrations and structures to produce a solid object with desired material characteristics. Stereolithography is an additive manufacturing process in which liquid photopolymer resin is cross‐linked and converted to solid. The fabrication of digital material requires frequent resin changes during the building process. The process presented in this paper attempts to address the related challenges in achieving such fabrication capability.Design/methodology/approach – A two‐channel system design is presented for the multi‐material MIP‐SL process. In such a design, a coated thick film and linear motions in two axes are used to reduce the separation force of a cured layer. The material cleaning approach to thoroughly remove resin residue on built surfaces is presented for the developed process. Based on a developed testbed, experimental studies we...


title: The New Neurometabolic Cascade of Concussion

abstract: Objective: To review the underlying pathophysiologic processes of concussive brain injury and relate these neurometabolic changes to clinical sports-related issues such as injury to the developing brain, overuse injury, and repeated concussion. Data Sources: Over 100 articles from both basic science and clinical medical literature selected for relevance to concussive brain injury, postinjury pathophysiology, and recovery of function. Data Synthesis: The primary elements of the pathophysiologic cascade following concussive brain injury include abrupt neuronal depolarization, release of excitatory neurotransmitters, ionic shifts, changes in glucose metabolism, altered cerebral blood flow, and impaired axonal function. These alterations can be correlated with periods of postconcussion vulnerability and with neurobehavioral abnormalities. While the time course of these changes is well understood in experimental animal models, it is only beginning to be characterized following human concussion. Conclusions/Recommendations: Following concussion, cerebral pathophysiology can be adversely affected for days in animals and weeks in humans. Significant changes in cerebral glucose metabolism can exist even in head-injured patients with normal Glasgow Coma Scores, underscoring the need for indepth clinical assessment in an effort to uncover neurocognitive correlates of altered cerebral physiology. Improved guidelines for clinical management of concussion may be formulated as the functional significance and duration of these postinjury neurometabolic derangements are better delineated.


title: Correction of a pathogenic gene mutation in human embryos

abstract: Genome editing has potential for the targeted correction of germline mutations. Here we describe the correction of the heterozygous MYBPC3 mutation in human preimplantation embryos with precise CRISPR-Cas9-based targeting accuracy and high homology-directed repair efficiency by activating an endogenous, germline-specific DNA repair response. Induced double-strand breaks (DSBs) at the mutant paternal allele were predominantly repaired using the homologous wild-type maternal gene instead of a synthetic DNA template. By modulating the cell cycle stage at which the DSB was induced, we were able to avoid mosaicism in cleaving embryos and achieve a high yield of homozygous embryos carrying the wild-type MYBPC3 gene without evidence of off-target mutations. The efficiency, accuracy and safety of the approach presented suggest that it has potential to be used for the correction of heritable mutations in human embryos by complementing preimplantation genetic diagnosis. However, much remains to be considered before clinical applications, including the reproducibility of the technique with other heterozygous mutations.


title: Factors affecting the implementation of information assurance for eGovernment in Indonesia

abstract: Electronic government (eGovernment) services are aimed to improve government services to the public by improving the quality and availability of services that can be accessed regardless of time and place. Consequently, the services must always be available at any time, and any threat to the information and systems should receive attention to ensure business continuity in the event of an incident. Accordingly, in the implementation of eGovernment, information assurance (IA) should be considered. In Indonesia, the implementation of eGovernment is still in the early stage according to the eGovernment roadmap of Indonesia. However, there is no study so far that has focused on IA for eGovernment in Indonesia. Therefore, research on a framework of IA is needed to support the implementation of eGovernment in Indonesia. The aim of this research is to develop an IA framework for eGovernment within the Indonesian context. The development of the framework is divided into four stages, which are identifying the factors from IA international standards for organisations, determining success factors from literature, identifying the challenges, and evaluating and harmonising all the factors categorised into three categories. This research proposes an IA framework, which is expected to assist eGovernment implementation in Indonesia.


title: Multi-level submap based SLAM using nested dissection

abstract: We propose a novel batch algorithm for SLAM problems that distributes the workload in a hierarchical way. We show that the original SLAM graph can be recursively partitioned into multiple-level submaps using the nested dissection algorithm, which leads to the cluster tree, a powerful graph representation. By employing the nested dissection algorithm, our algorithm greatly minimizes the dependencies between two subtrees, and the optimization of the original SLAM graph can be done using a bottom-up inference along the corresponding cluster tree. To speed up the computation, we also introduce a base node for each submap and use it to represent the rigid transformation of the submap in the global coordinate frame. As a result, the optimization moves the base nodes rather than the actual submap variables. We demonstrate that our algorithm is not only exact but also much faster than alternative approaches in both simulations and real-world experiments.


title: A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery

abstract: Appropriate Site Specific Weed Management (SSWM) is crucial to ensure the crop yields. Within SSWM of large-scale area, remote sensing is a key technology to provide accurate weed distribution information. Compared with satellite and piloted aircraft remote sensing, unmanned aerial vehicle (UAV) is capable of capturing high spatial resolution imagery, which will provide more detailed information for weed mapping. The objective of this paper is to generate an accurate weed cover map based on UAV imagery. The UAV RGB imagery was collected in 2017 October over the rice field located in South China. The Fully Convolutional Network (FCN) method was proposed for weed mapping of the collected imagery. Transfer learning was used to improve generalization capability, and skip architecture was applied to increase the prediction accuracy. After that, the performance of FCN architecture was compared with Patch_based CNN algorithm and Pixel_based CNN method. Experimental results showed that our FCN method outperformed others, both in terms of accuracy and efficiency. The overall accuracy of the FCN approach was up to 0.935 and the accuracy for weed recognition was 0.883, which means that this algorithm is capable of generating accurate weed cover maps for the evaluated UAV imagery.


title: Blockchain abbreviation: Implemented by message passing and shared memory (Extended abstract)

abstract: Blockchain's ever increasing size has become a major problem. Bitcoin [7], for example, has grown to 115120 MB as of May 2017, which is roughly 115 GB. This uncontrollable growth of the Blockchain is bound to become an issue in the future, as hard disks may become too small to store the entire Blockchain history and traversing the transactions databases may become increasingly slow. Already, there are lightweight clients in various Blockchain platforms (Bitcoin included), who do not store the entire chain locally but rely on a third party to send them the blocks they need. There are many issues with these clients, mainly security problems, since they go back to trusting a central authority rather than gaining trust from several distributed peers. These clients' knowledge of the Blockchain is solely based on some third party that should be trusted, while the conceptual base for Blockchain is trust distributing. In this paper we present two Blockchain abbreviation schemes. The first one is based on the Ethereum [8] project and proposes replacing the full Blockchain with a new Genesis block, which summarizes everyone's account balances at a certain point in time. One possible benefit is to use less communication while still storing the prefix of the old Blockchain (or signature of the Blockchain that can validate a version archived by other participants) in a local archive. Here we trade loss of transaction history for efficiency. Our second contribution is a UNIX based architecture using the file system, for implementing Blockchain. We demonstrate a Blockchain abbreviation technique for this architecture too.


Null arguments and homonymy flight in the development of Creole pronoun systems


title: Non-intrusive electrical load monitoring in commercial buildings based on steady-state and transient load-detection algorithms

abstract: Abstract Increased interest in energy scorekeeping, load forecasting and improved control of electricity-consuming equipment has focused attention on the instrumentation required to obtain the desired data. Work performed by the authors and other researchers has shown that individual loads can be detected and separated from rapid sampling of power at a single point serving a number of pieces of equipment, for example the electrical service entrance for an entire house or all of the central space-conditioning equipment in a commercial building. This technique has worked well in tests in houses but faces more difficult challenges in commercial buildings. We present our results for this centralized or non-intrusive load monitoring technique, applied to the space-conditioning equipment in an office/laboratory building in which equipment start-up and shut-down was centrally observed and analyzed on the basis of changes in steady-state power. We further describe our enhanced technique for distinguishing loads by matching start-up transients to known patterns, and present laboratory tests of fully automated detection hardware and software.


title: Effect of Selfish Node Behavior on Efficient Topology Design

abstract: The problem of topology control is to assign per-node transmission power such that the resulting topology is energy efficient and satisfies certain global properties such as connectivity. The conventional approach to achieve these objectives is based on the fundamental assumption that nodes are socially responsible. We examine the following question: if nodes behave in a selfish manner, how does it impact the overall connectivity and energy consumption in the resulting topologies? We pose the above problem as a noncooperative game and use game-theoretic analysis to address it. We study Nash equilibrium properties of the topology control game and evaluate the efficiency of the induced topology when nodes employ a greedy best response algorithm. We show that even when the nodes have complete information about the network, the steady-state topologies are suboptimal. We propose a modified algorithm based on a better response dynamic and show that this algorithm is guaranteed to converge to energy-efficient and connected topologies. Moreover, the node transmit power levels are more evenly distributed, and the network performance is comparable to that obtained from centralized algorithms.


title: Implementing Reliability: The Interaction of Requirements, Tactics and Architecture Patterns

abstract: An important way that the reliability of a software system is enhanced is through the implementation of specific run-time measures called runtime tactics. Because reliability is a system-wide property, tactic implementations affect the software structure and behavior at the system, or architectural level. For a given architecture, different tactics may be a better or worse fit for the architecture, depending on the requirements and how the architecture patterns used must change to accommodate the tactic: different tactics may be a better or worse fit for the architecture. We found three important factors that influence the implementation of reliability tactics. One is the nature of the tactic, which indicates whether the tactic influences all components of the architecture or just a subset of them. The second is the interaction between architecture patterns and tactics: specific tactics and patterns are inherently compatible or incompatible. The third is the reliability requirements which influence which tactics to use and where they should be implemented. Together, these factors affect how, where, and the difficulty of implementing reliability tactics. This information can be used by architects and developers to help make decisions about which patterns and tactics to use, and can also assist these users in learning what modifications and additions to the patterns are needed.


Correct by Construction Networks Using Stepwise Refinement


LSDSCC: a Large Scale Domain-Specific Conversational Corpus for Response Generation with Diversity Oriented Evaluation Metrics


title: GESF: A Universal Discriminative Mapping Mechanism for Graph Representation Learning

abstract: Graph embedding is a central problem in social network analysis and many other applications, aiming to learn the vector representation for each node. While most existing approaches need to specify the neighborhood and the dependence form to the neighborhood, which may significantly degrades the flexibility of representation, we propose a novel graph node embedding method (namely GESF) via the set function technique. Our method can 1) learn an arbitrary form of representation function from neighborhood, 2) automatically decide the significance of neighbors at different distances, and 3) be applied to heterogeneous graph embedding, which may contain multiple types of nodes. Theoretical guarantee for the representation capability of our method has been proved for general homogeneous and heterogeneous graphs and evaluation results on benchmark data sets show that the proposed GESF outperforms the state-of-the-art approaches on producing node vectors for classification tasks.


title: A Deep-Learning Approach for the Detection of Overexposure in Automotive Camera Images

abstract: To bring autonomous driving onto public roads, autonomous vehicles must be able to make safe driving decisions. In order to achieve this, they need to be self-aware, meaning they have to be aware of their current capabilities at all times. One step towards self-awareness is the assessment of the quality of the available sensor data and the estimation of its impact on the processing chain. Knowing when sensor data is compromised will contribute to safer driving decisions of the vehicle. In this contribution, we present a novel, deep-learning approach for overexposure detection in camera images as one step towards sensor data quality monitoring.


title: Intersecting Faces: Non-negative Matrix Factorization With New Guarantees

abstract: Non-negative matrix factorization (NMF) is a natural model of admixture and is widely used in science and engineering. A plethora of algorithms have been developed to tackle NMF, but due to the non-convex nature of the problem, there is little guarantee on how well these methods work. Recently a surge of research have focused on a very restricted class of NMFs, called separable NMF, where provably correct algorithms have been developed. In this paper, we propose the notion of subset-separable NMF, which substantially generalizes the property of separability. We show that subset-separability is a natural necessary condition for the factorization to be unique or to have minimum volume. We developed the Face-Intersect algorithm which provably and efficiently solves subset-separable NMF under natural conditions, and we prove that our algorithm is robust to small noise. We explored the performance of Face-Intersect on simulations and discuss settings where it empirically outperformed the state-of-art methods. Our work is a step towards finding provably correct algorithms that solve large classes of NMF problems.


title: Simultaneous image classification and annotation

abstract: Image classification and annotation are important problems in computer vision, but rarely considered together. Intuitively, annotations provide evidence for the class label, and the class label provides evidence for annotations. For example, an image of class highway is more likely annotated with words “road,” “car,” and “traffic” than words “fish,” “boat,” and “scuba.” In this paper, we develop a new probabilistic model for jointly modeling the image, its class label, and its annotations. Our model treats the class label as a global description of the image, and treats annotation terms as local descriptions of parts of the image. Its underlying probabilistic assumptions naturally integrate these two sources of information. We derive an approximate inference and estimation algorithms based on variational methods, as well as efficient approximations for classifying and annotating new images. We examine the performance of our model on two real-world image data sets, illustrating that a single model provides competitive annotation performance, and superior classification performance.


Do the Rey-osterrieth and Modified Taylor Complex Figures Show Similar Levels of Perceptual Clustering and Fragmentation ?


OctoMap: A probabilistic, flexible, and compact 3D map representation for robotic systems


title: Reducing Click and Skip Errors in Search Result Ranking

abstract: Search engines provide result summaries to help users quickly identify whether or not it is worthwhile to click on a result and read in detail. However, users may visit non-relevant results and/or skip relevant ones. These actions are usually harmful to the user experience, but few considered this problem in search result ranking. This paper optimizes relevance of results and user click and skip activities at the same time. Comparing two equally relevant results, our approach learns to rank the one that users are more likely to click on at a higher position. Similarly, it demotes non-relevant web pages with high click probabilities. Experimental results show this approach reduces about 10%-20% of the click and skip errors with a trade off of 2.1% decline in nDCG@10.


title: Paxos replicated state machines as the basis of a high-performance data store

abstract: Conventional wisdom holds that Paxos is too expensive to use for high-volume, high-throughput, data-intensive applications. Consequently, fault-tolerant storage systems typically rely on special hardware, semantics weaker than sequential consistency, a limited update interface (such as append-only), primary-backup replication schemes that serialize all reads through the primary, clock synchronization for correctness, or some combination thereof. We demonstrate that a Paxos-based replicated state machine implementing a storage service can achieve performance close to the limits of the underlying hardware while tolerating arbitrary machine restarts, some permanent machine or disk failures and a limited set of Byzantine faults. We also compare it with two versions of primary-backup. The replicated state machine can serve as the data store for a file system or storage array. We present a novel algorithm for ensuring read consistency without logging, along with a sketch of a proof of its correctness.


title: Improved Pre-Ejection Period Estimation From Ballistocardiogram and Electrocardiogram Signals by Fusing Multiple Timing Interval Features

abstract: Sympathetic nervous system (SNS) activity plays a significant role in cardiovascular control. Preejection period (PEP) is a noninvasive biomarker that reflects SNS activity. In this paper, unobtrusive estimation of PEP of the heart using ballistocardiogram (BCG) and electrocardiogram (ECG) signals is investigated. Although previous work has shown that the time intervals from ECG R-peak to BCG I and J peaks are correlated with PEP, relying on a single BCG beat can be prone to errors. An approach is proposed based on multiple regression and use of initial training data sets with a reference standard, impedance cardiography (ICG). For evaluation, healthy subjects were asked to stand on a force plate to record BCG and ECG signals. Regression coefficients were obtained using leave-one-out cross-validation and the true PEP values were obtained using ECG and ICG. Regression coefficients were averaged over two different recordings from the same subjects. The estimation performance was evaluated based on the data, via leave-one-out cross-validation. Multiple regression is shown to reduce the mean absolute error and the root mean square error, and has a reduced confidence interval compared with the models based on only a single feature. This paper shows that the fusion of multiple timing intervals can be useful for improved PEP estimation.


title: The Many Worlds Interpretation

abstract: We explore the Many-Worlds Interpretation of quantum mechanics, beginning with a discussion of the original proposal by Everett, and extending to a discussion of modern manyworlds ideas, including discussion of some current criticisms of the many-worlds interpretation.


title: Developing a Usability Evaluation Method for e-Learning Applications: Beyond Functional Usability

abstract: In this article, the development of a questionnaire-based usability evaluation method for e-learning applications is described. The method extends the current practice by focusing not only on cognitive but also affective considerations that may influence e-learning usability. The method was developed according to an established methodology in HCI research and relied upon a conceptual framework that combines Web and instructional design parameters and associates them with the most prominent affective learning dimension, which is intrinsic motivation to learn. The latter is proposed as a new usability measure that is considered more appropriate to evaluate e-learning designs. Two large empirical studies were conducted in order to evaluate usability of e-learning courses offered in corporate environments. The results provide valuable evidence for reliability and validity of the method, thus providing evidence that usability practitioners can use it with confidence when evaluating the design of e-learning app...


title: An Efficient Density-based Clustering Algorithm for Higher-Dimensional Data

abstract: DBSCAN is a typically used clustering algorithm due to its clustering ability for arbitrarily-shaped clusters and its robustness to outliers. Generally, the complexity of DBSCAN is O(n^2) in the worst case, and it practically becomes more severe in higher dimension. Grid-based DBSCAN is one of the recent improved algorithms aiming at facilitating efficiency. However, the performance of grid-based DBSCAN still suffers from two problems: neighbour explosion and redundancies in merging, which make the algorithms infeasible in high-dimensional space. In this paper, we propose a novel algorithm named GDPAM attempting to extend Grid-based DBSCAN to higher data dimension. In GDPAM, a bitmap indexing is utilized to manage non-empty grids so that the neighbour grid queries can be performed efficiently. Furthermore, we adopt an efficient union-find algorithm to maintain the clustering information in order to reduce redundancies in the merging. The experimental results on both real-world and synthetic datasets demonstrate that the proposed algorithm outperforms the state-of-the-art exact/approximate DBSCAN and suggests a good scalability.


title: The heritability of IQ

abstract: IQ heritability, the portion of a population's IQ variability attributable to the effects of genes1, has been investigated for nearly a century, yet it remains controversial. Covariance between relatives may be due not only to genes, but also to shared environments, and most previous models have assumed different degrees of similarity induced by environments specific to twins, to non-twin siblings (henceforth siblings), and to parents and offspring. We now evaluate an alternative model that replaces these three environments by two maternal womb environments, one for twins and another for siblings, along with a common home environment. Meta-analysis of 212 previous studies shows that our ‘maternal-effects’ model fits the data better than the ‘family-environments’ model. Maternal effects, often assumed to be negligible, account for 20% of covariance between twins and 5% between siblings, and the effects of genes are correspondingly reduced, with two measures of heritability being less than 50%. The shared maternal environment may explain the striking correlation between the IQs of twins, especially those of adult twins that were reared apart. IQ heritability increases during early childhood, but whether it stabilizes thereafter remains unclear. A recent study of octogenarians2, for instance, suggests that IQ heritability either remains constant through adolescence and adulthood3, or continues to increase with age2. Although the latter hypothesis has recently been endorsed4, it gathers only modest statistical support in our analysis when compared to the maternal-effects hypothesis. Our analysis suggests that it will be important to understand the basis for these maternal effects if ways in which IQ might be increased are to be identified.


title: Inferring Brain Networks through Graphical Models with Hidden Variables

abstract: Inferring the interactions between different brain areas is an important step towards understanding brain activity. Most often, signals can only be measured from some specific brain areas (e.g., cortex in the case of scalp electroencephalograms). However, those signals may be affected by brain areas from which no measurements are available (e.g., deeper areas such as hippocampus). In this paper, the latter are described as hidden variables in a graphical model; such model quantifies the statistical structure in the neural recordings, conditioned on hidden variables, which are inferred in an automated fashion from the data. ::: ::: As an illustration, electroencephalograms (EEG) of Alzheimer's disease patients are considered. It is shown that the number of hidden variables in AD EEG is not significantly different from healthy EEG. However, there are fewer interactions between the brain areas, conditioned on those hidden variables. Explanations for these observations are suggested.


title: A Capacitive Fringing Field Sensor Design for Moisture Measurement Based on Printed Circuit Board Technology

abstract: Interdigitated electrode capacitive fringing field sensors have been utilized in numerous applications. Although various technologies are used to realize these types of sensors, printed circuit board technology is particularly advantageous for realizing this type of sensor through fabricating the interdigitated electrode structures in the patterned Cu foil. Additionally, the solder mask coating can insulate the electrodes to prevent shorting in the presence of water. Using this approach, prototype sensors were designed, simulated, fabricated, and successfully evaluated. Applications include water detection and quantity measurement and soil moisture content measurement.


title: Learning moods and emotions from color combinations

abstract: In this paper, we tackle the problem of associating combinations of colors to abstract categories (e.g. capricious, classic, cool, delicate, etc.). It is evident that such concepts would be difficult to distinguish using single colors, therefore we consider combinations of colors or color palettes. We leverage two novel databases for color palettes and we learn categorization models using low and high level descriptors. Preliminary results show that Fisher representation based on GMMs is the most rewarding strategy in terms of classification performance over a baseline model. We also suggest a process for cleaning weakly annotated data, whilst preserving the visual coherence of categories. Finally, we demonstrate how learning abstract categories on color palettes can be used in the application of color transfer, personalization and image re-ranking.


title: Bladder exstrophy-epispadias complex

abstract: The bladder exstrophy-epispadias complex (BEEC) represents an anterior midline defect with variable expression comprising a spectrum of anomalies involving the abdominal wall, pelvis, urinary tract, genitalia, and occasionally the spine and anus. The vast majority of BEEC cases are classified as non-syndromic and the etiology of this malformation is still unknown. This review presents the current state of knowledge on this multifactorial disorder, including historical retrospect, phenotypic and anatomical characterization, epidemiology, proposed developmental mechanisms, existing animal models, and implicated genetic and environmental components. These published lines of evidence argue strongly that BEEC occurs as a result of strong genetic predisposition that is yet to be deciphered.


title: Sensorless control of ultrahigh-speed PM brushless motor using PLL and third harmonic back EMF

abstract: Application-specific integrated circuit (ASIC) ML4425 is often used for sensorless control of permanent-magnet (PM) brushless direct current (BLDC) motor drives. It integrates the terminal voltage of the unenergized winding that contains the back electromotive force (EMF) information and uses a phase-locked loop (PLL) to determine the proper commutation sequence for the BLDC motor. However, even without pulsewidth modulation, the terminal voltage is distorted by voltage pulses due to the freewheel diode conduction. The pulses, which appear very wide in an ultrahigh-speed (120 kr/min) drive, are also integrated by the ASIC. Consequently, the motor commutation is significantly retarded, and the drive performance is deteriorated. In this paper, it is proposed that the ASIC should integrate the third harmonic back EMF instead of the terminal voltage, such that the commutation retarding is largely reduced and the motor performance is improved. Basic principle and implementation of the new ASIC-based sensorless controller will be presented, and experimental results will be given to verify the control strategy. On the other hand, phase delay in the motor currents arises due to the influence of winding inductance, reducing the drive performance. Therefore, a novel circuit with discrete components is proposed. It also uses the integration of third harmonic back EMF and the PLL technique and provides controllable advanced commutation to the BLDC motor.


title: An Extensible SAT-solver

abstract: In this article, we present a small, complete, and efficient SAT-solver in the style of conflict-driven learning, as exemplified by Chaff. We aim to give sufficient details about implementation to enable the reader to construct his or her own solver in a very short time.This will allow users of SAT-solvers to make domain specific extensions or adaptions of current state-of-the-art SAT-techniques, to meet the needs of a particular application area. The presented solver is designed with this in mind, and includes among other things a mechanism for adding arbitrary boolean constraints. It also supports solving a series of related SAT-problems efficiently by an incremental SAT-interface.


title: Eleventh International meeting on Trypanosoma evansi : report of the working group

abstract: Three objectives have been achieved by the Working Group since its creation in 1983: more detailed information (and consequently better awareness) of zones infected by T. evansi; refinement of diagnostic techniques and the development of test kits suitable for field use; development of a new synthetic trypanocide. Exchange of strains between specialist laboratories should be encouraged in order to compare isolates from Africa, Asia and South America by using current techniques of biotechnology, and to open the way to better knowledge of the pathogenicity of T. evansi and to the discovery of effective prophylactic measures. Research reported to the meeting was concerned with the taxonomy and genetics of T. evansi, the cloning and sequencing of nuclear and kinetoplastic DNA (kDNA), chromosomal polymorphism in relation to antigenic variation, the detection of lymphocytic interleukin 2 and its receptors in infected ponies, the use of monoclonal group antibodies to detect T. evansi, and the importance of natural receptivity of the host. A text concerning the diagnosis of surra (T. evansi infection) was drafted and forwarded to the Office International des Epizooties (OIE) for incorporation in the OIE Manual of recommended diagnostic techniques and requirements for biological products. A concise dossier was presented on the pharmacology and pharmacodynamics of the new trypanocide, melarsomine (proprietary name Cymelarsan). The recommended active dosage was 0.25 mg/kg body weight, given as a single intramuscular or subcutaneous injection. Laboratory tests were also reported with ronidazole, demonstrating its trypanocidal activity in rats.


title: Model-based safety assessment: Review of the discipline and its challenges

abstract: Since its emergence in 1990s, Model-Based Safety Assessment (MBSA) has enjoyed significant interest from both academia and industry. The last decade has seen not only the development of a number of methods, techniques and tools, but also the gradual adoption of MBSA techniques by industry and its acceptance by regulators. However, the field of MBSA encompasses a large number of fundamentally dissimilar techniques. This paper presents a simple classification schema for MBSA techniques based on two criteria — provenance of the model and engineering semantics of component dependencies captured by the model. The classification organizes the existing techniques into a number of coherent families. Applicability, limitations and challenges of most prominent families of MBSA techniques are presented, and some of the common challenges faced by MBSA discipline are discussed.


title: A survey on server-side approaches to securing web applications

abstract: Web applications are one of the most prevalent platforms for information and service delivery over the Internet today. As they are increasingly used for critical services, web applications have become a popular and valuable target for security attacks. Although a large body of techniques have been developed to fortify web applications and mitigate attacks launched against them, there has been little effort devoted to drawing connections among these techniques and building the big picture of web application security research. This article surveys the area of securing web applications from the server side, with the aim of systematizing the existing techniques into a big picture that promotes future research. We first present the unique aspects of the web application development that cause inherent challenges in building secure web applications. We then discuss three commonly seen security vulnerabilities within web applications: input validation vulnerabilities, session management vulnerabilities, and application logic vulnerabilities, along with attacks that exploit these vulnerabilities. We organize the existing techniques along two dimensions: (1) the security vulnerabilities and attacks that they address and (2) the design objective and the phases of a web application during which they can be carried out. These phases are secure construction of new web applications, security analysis/testing of legacy web applications, and runtime protection of legacy web applications. Finally, we summarize the lessons learned and discuss future research opportunities in this area.


title: One-Class Classification with Extreme Learning Machine

abstract: One-class classification problem has been investigated thoroughly for past decades. Among one of the most effective neural network approaches for one-class classification, autoencoder has been successfully applied for many applications. However, this classifier relies on traditional learning algorithms such as backpropagation to train the network, which is quite time-consuming. To tackle the slow learning speed in autoencoder neural network, we propose a simple and efficient one-class classifier based on extreme learning machine (ELM). The essence of ELM is that the hidden layer need not be tuned and the output weights can be analytically determined, which leads to much faster learning speed. The experimental evaluation conducted on several real-world benchmarks shows that the ELM based one-class classifier can learn hundreds of times faster than autoencoder and it is competitive over a variety of one-class classification methods.


title: Simulation of phasor measurement unit (PMU) in MATLAB

abstract: The advent of phasor measurement unit in power system is revolutionizing the conventional grid towards smart grid. Phasor measurement units are extremely expensive devices which takes into account many aspects of the power system that are not disclosed by the manufacturer while estimating phasors of currents and voltage. This paper aims to build a laboratory prototype of PMU which can estimate the phasor updating process of a commercial PMU at the benefit of improved measurement accuracy, reduced manufacturing cost and increased timely information.


title: Hypsodonty and tooth facet development in relation to diet and habitat in herbivorous ungulates: implications for understanding tooth wear

abstract: 1. The evolution of high-crowned teeth or hypsodonty in herbivorous mammals is widely interpreted as a species-specific adaptation to increasingly wear-inducing diets and environments at evolutionary time scales, with internal abrasives (such as phytoliths in grasses) and/or external abrasives (such as dust or grit) as putative causative factors. The mesowear score (MS) instead describes tooth wear experienced by individual animals during their lifetime. ::: 2. Under the assumption that the abrasiveness that causes the MS in individuals is the same abrasiveness to which species adapted by evolving hypsodonty, one would expect a close correlation between the MS and the hypsodonty index (HI). Alternatively, if these two measures reflect different aspects of wear, one would expect differences in the way that proxies of diet or environment/climate correlate with each parameter. ::: 3. In order to test these hypotheses, we collated a dataset on the HI, MS, percentage of grass in the natural diet (%grass), habitat (open, intermediate, closed) and annual precipitation (PREC) in extant mammalian herbivores. The availability of a quantitative MS constrained the dataset to 75 species. Data were analysed with and without phylogenetic generalized least squares (PGLS). ::: 4. Correlations with PREC were stronger for HI than for MS, whereas correlations with %grass were similar for HI and MS. Habitat had a significant influence on the relationship with %grass for HI but not for MS. Habitat also had a significant influence on the relationship between HI and MS. MS improved the predictive power of HI for %grass, but not for PREC. ::: 5. These results suggest that while the MS indicates predominantly the wear effect of the diet (internal abrasives), HI represents an adaptation to a wear effect that comprises both diet and environment (external abrasives). The additional environmental wear effect must reduce tooth height without causing macroscopic changes in tooth facet development as described by the MS. ::: 6. The most parsimonious explanation for the apparent discrepancy between HI and MS is that external abrasives of very fine particle size play a major role in naturally occurring tooth wear. The experimental testing of this hypothesis will enhance our understanding of the processes involved in tooth wear.


title: Visualizing motional correlations in molecular dynamics using geometric deformations

abstract: In macromolecules, an allosteric effect is said to occur when a change at one site of a molecule affects a distant site. Understanding these allosteric effects can be important for understanding how the functions of complex molecules such as proteins are regulated. One potential application of this knowledge is the development of small molecules that alter the function of proteins involved in diseases. Studying motional correlation can help researchers to discover how a change at a source site affects the target site and thus how allosteric ligands that could serve as drugs are able to exert their therapeutic effects. By improving our ability to analyze these correlated relationships, it may be possible to develop new medications to combat deadly diseases such as Hepatitis C. We present four visual techniques which represent motional correlation on rendered three-dimensional molecular models, providing new ways to view clusters of correlated residues and paths of allosteric interactions. These techniques give us a new way of investigating the presence of motional correlations in complex molecules. We compare each of these techniques to determine which are the most useful for representing motional correlations.


title: Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples

abstract: We identify obfuscated gradients as a phenomenon that leads to a false sense of security in defenses against adversarial examples. While defenses that cause obfuscated gradients appear to defeat optimization-based attacks, we find defenses relying on this effect can be circumvented. ::: For each of the three types of obfuscated gradients we discover, we describe indicators of defenses exhibiting this effect and develop attack techniques to overcome it. In a case study, examining all defenses accepted to ICLR 2018, we find obfuscated gradients are a common occurrence, with 7 of 8 defenses relying on obfuscated gradients. Using our new attack techniques, we successfully circumvent all 7 of them.


title: A comparative study of CNN, BoVW and LBP for classification of histopathological images

abstract: Despite the progress made in the field of medical imaging, it remains a large area of open research, especially due to the variety of imaging modalities and disease-specific characteristics. This paper is a comparative study describing the potential of using local binary patterns (LBP), deep features and the bag-of-visual words (BoVW) scheme for the classification of histopathological images. We introduce a new dataset, \emph{KIMIA Path960}, that contains 960 histopathology images belonging to 20 different classes (different tissue types). We make this dataset publicly available. The small size of the dataset and its inter- and intra-class variability makes it ideal for initial investigations when comparing image descriptors for search and classification in complex medical imaging cases like histopathology. We investigate deep features, LBP histograms and BoVW to classify the images via leave-one-out validation. The accuracy of image classification obtained using LBP was 90.62\% while the highest accuracy using deep features reached 94.72\%. The dictionary approach (BoVW) achieved 96.50\%. Deep solutions may be able to deliver higher accuracies but they need extensive training with a large number of (balanced) image datasets.


title: Development of a smart power meter for AMI based on ZigBee communication

abstract: Many governments deploy ubiquitous IT project, which aims to combine the latest wireless network and wide-band technologies etc. to accomplish a ubiquitous wireless communication network. The ubiquitous wireless communication network can be utilized for the Advanced Metering Infrastructure (AMI). Therefore, this paper tries to use the new wireless communication technologies to design and implement a ZigBee-based smart power meter. An outage recording system is also designed and embedded into the smart meter. The microcontroller of Microchip dsPIC30F series is used to develop the proposed smart power meter. A ZigBee system is then deigned and integrated into the proposed power meter, and used to transmit the detailed power consumption data and outage event data to rear-end processing system. The proposed smart power meter cannot only be used for power consumption data collection but also for outage event data recording. The proposed system has great potential to be used to build the area-based AMI. Experimental results demonstrate the validity of the proposed system. Besides, the application of ZigBee communication in power area may, expectedly, lead to make a definite contribution to ubiquitous IT project.


title: DeepDeSRT: Deep Learning for Detection and Structure Recognition of Tables in Document Images

abstract: This paper presents a novel end-to-end system for table understanding in document images called DeepDeSRT. In particular, the contribution of DeepDeSRT is two-fold. First, it presents a deep learning-based solution for table detection in document images. Secondly, it proposes a novel deep learning-based approach for table structure recognition, i.e. identifying rows, columns, and cell positions in the detected tables. In contrast to existing rule-based methods, which rely on heuristics or additional PDF metadata (like, for example, print instructions, character bounding boxes, or line segments), the presented system is data-driven and does not need any heuristics or metadata to detect as well as to recognize tabular structures in document images. Furthermore, in contrast to most existing table detection and structure recognition methods, which are applicable only to PDFs, DeepDeSRT processes document images, which makes it equally suitable for born-digital PDFs (as they can automatically be converted into images) as well as even harder problems, e.g. scanned documents. To gauge the performance of DeepDeSRT, the system is evaluated on the publicly available ICDAR 2013 table competition dataset containing 67 documents with 238 pages overall. Evaluation results reveal that DeepDeSRT outperforms state-of-the-art methods for table detection and structure recognition and achieves F1-measures of 96.77% and 91.44% for table detection and structure recognition, respectively. Additionally, DeepDeSRT is evaluated on a closed dataset from a real use case of a major European aviation company comprising documents which are highly unlike those in ICDAR 2013. Tested on a randomly selected sample from this dataset, DeepDeSRT achieves high detection accuracy for tables which demonstrates the sound generalization capabilities of our system.


title: R-SDN: A recusive approach for scaling SDN

abstract: Software-Defined Networking provides a clean separation between control plane and data plane. In addition, control plane can configure and manage data plane in a centralized way. However, a centralized control plane introduces a problem in scaling SDN network. With the idea of network virtualization, we propose a recursive way to construct a new architecture of SDN so that the problem of scaling can be solved. A recursive algorithm is put forward to implement this new idea. As a kind of recursive model, Fibonacci series can be applied to R-SDN. Then, we take routing as typical scenario of experiment to test performance on this new architecture. Finally, we analyse experiment results based on principle and acquire some conclusions.


title: Octree-based decimation of marching cubes surfaces

abstract: The marching cubes (MC) algorithm is a method for generating isosurfaces. It also generates an excessively large number of triangles to represent an isosurface; this increases the rendering time. This paper presents a decimation method to reduce the number of triangles generated. Decimation is carried out before creating a large number of triangles. Four major steps comprise the algorithm: surface tracking, merging, crack patching and triangulation. Surface tracking is an enhanced implementation of the MC algorithm. Starting from a seed point, the surface tracker visits only those cells likely to compose part of the desired isosurface. The cells making up the extracted surface are stored in an octree that is further processed. A bottom-up approach is taken in merging the cells containing a relatively flat approximating surface. The finer surface details are maintained. Cells are merged as long as the error due to such an operation is within a user-specified error parameter, or a cell acquires more than one connected surface component in it. A crack patching method is described that forces edges of smaller cells to lie along those of the larger neighboring cells. The overall saving in the number of triangles depends both on the specified error value and the nature of the data. Use of the hierarchical octree data structure also presents the potential of incremental representation of surfaces. We can generate a highly smoothed surface representation which can be progressively refined as the user-specified error value is decreased.


title: Darpa Communicator Evaluation: Progress From 2000 To 2001

abstract: ABSTRACTThis paper describes the evaluation methodology and resultsof the DARPA Communicator spoken dialog system evaluationexperiments in 2000 and 2001. Nine spoken dialog systems in thetravel planning domain participated in the experiments resulting ina total corpus of 1904 dialogs. We describe and compare the ex-perimental design of the 2000 and 2001 DARPA evaluations. Wedescribe how we established a performance baseline in 2001 forcomplex tasks. We present our overall approach to data collection,the metrics collected, and the application of PARADISE to thesedata sets. We compare the results we achieved in 2000 for a num-ber of core metrics with those for 2001. These results demonstratelarge performance improvements from 2000 to 2001 and show thatthe Communicator program goal of conversational interaction forcomplex tasks has been achieved.1. INTRODUCTIONThe objective of the DARPA Communicator project is to supportrapid development of multi-modal speech-enabled dialog systemswith advanced conversational capabilities. Figure 1 illustrates theCommunicator challenge problem; asystem must support complexconversational interaction to complete this task within 10 minutes.You are in Denver, Friday night at 8pm on the road to the air-port after a great meeting. As a result of the meeting, youneed to attend a group meeting in San Diego on Point Lomaon Monday at 8:30, a meeting Tuesday morning at Miramar at7:30, then one from 3-5 pm in Monterey; you need reservations(car, hotel, air).You pull over to the side of the road and whip out your Com-municator. Through spoken dialog (augmented with a displayand pointing), you make the appropriate reservations, discovera conﬂict, and send an e-mail message (dictated) to inform thegroup of the changed schedule. Do this in 10 minutes.Fig. 1. Darpa Communicator Challenge ProblemDuring the course of the Communicator program, we havebeen involved in developing methods for measuring progress to-wards the program goals and assessing advances in the componenttechnologies required to achieve such goals. In previous work, wereport on an exploratory data collection experiment with nine par-ticipating Communicator systems in the travel planning domain


title: A layer-wise deep stacking model for social image popularity prediction

abstract: In this paper, we present a Layer-wise Deep Stacking (LDS) model to predict the popularity of Flickr-like social posts. LDS stacks multiple regression models in multiple layers, which enables the different models to complement and reinforce each other. To avoid overfitting, a dropout module is introduced to randomly activate the data being fed into the regression models in each layer. In particular, a detector is devised to determine the depth of LDS automatically by monitoring the performance of the features achieved by the LDS layers. Extensive experiments conducted on a public dataset consisting of 432K Flickr image posts manifest the effectiveness and significance of the LDS model and its components. LDS achieves competitive performance on multiple metrics: Spearman’s Rho: 83.50%, MAE: 1.038, and MSE: 2.011, outperforming state-of-the-art approaches for social image popularity prediction.


title: A Two Stream Siamese Convolutional Neural Network for Person Re-identification

abstract: Person re-identification is an important task in video surveillance systems. It can be formally defined as establishing the correspondence between images of a person taken from different cameras at different times. In this paper, we present a two stream convolutional neural network where each stream is a Siamese network. This architecture can learn spatial and temporal information separately. We also propose a weighted two stream training objective function which combines the Siamese cost of the spatial and temporal streams with the objective of predicting a person's identity. We evaluate our proposed method on the publicly available PRID2011 and iLIDS-VID datasets and demonstrate the efficacy of our proposed method. On average, the top rank matching accuracy is 4% higher than the accuracy achieved by the cross-view quadratic discriminant analysis used in combination with the hierarchical Gaussian descriptor (GOG+XQDA), and 5% higher than the recurrent neural network method.


title: A study of the existing problems of estimating the information transfer rate in online brain–computer interfaces

abstract: Objective. Today, the brain–computer interface (BCI) community lacks a standard method to evaluate an online BCI’s performance. Even the most commonly used metric, the information transfer rate (ITR), is often reported differently, even incorrectly, in many papers, which is not conducive to BCI research. This paper aims to point out many of the existing problems and give some suggestions and methods to overcome these problems. Approach. First, the preconditions inherent in ITR calculation based on Wolpaw’s definition are summarized and several incorrect ITR calculations, which go against the preconditions, are indicated. Then, the issues affecting ITR estimation during the test of online BCI systems are discussed in detail. Finally, a task-oriented online BCI test platform was proposed, which may help BCI evaluations in real-world applications. Main results. The guidelines for ITR calculation in online BCIs testing are proposed. The platform executed in the Beijing BCI Competition 2010 shows that it can be used as a common way to compare the online performances (including the ITR) of existing BCI paradigms. Significance: The proposed guidelines and task-oriented test platform may reduce the uncertainty and artifacts of online BCI performance evaluation; they provide a relatively objective way to compare different BCI’s performances in real-world BCI applications, which is a forward step toward developing standards for BCI performance evaluation. (Some figures may appear in colour only in the online journal)


title: Wearable Haptic Systems for the Fingertip and the Hand: Taxonomy, Review, and Perspectives

abstract: In the last decade, we have witnessed a drastic change in the form factor of audio and vision technologies, from heavy and grounded machines to lightweight devices that naturally fit our bodies. However, only recently, haptic systems have started to be designed with wearability in mind. The wearability of haptic systems enables novel forms of communication, cooperation, and integration between humans and machines. Wearable haptic interfaces are capable of communicating with the human wearers during their interaction with the environment they share, in a natural and yet private way. This paper presents a taxonomy and review of wearable haptic systems for the fingertip and the hand, focusing on those systems directly addressing wearability challenges. The paper also discusses the main technological and design challenges for the development of wearable haptic interfaces, and it reports on the future perspectives of the field. Finally, the paper includes two tables summarizing the characteristics and features of the most representative wearable haptic systems for the fingertip and the hand.