title: A Survey on Spectrum Occupancy Measurement for Cognitive Radio

abstract: In order to support the ever growing demand of wireless communication systems and wireless local area network based 802.11 devices, the regulators are developing techniques to overcome the permanent scarcity of radio resources. Spectrum sensing in cognitive radio (CR) is considered as a key technology which estimates the availability of the licensed spectrum band for additional usages. The underutilized licensed band can be exploited among the secondary users provided that they would not cause any interference to the primary user. So, evaluation of spectrum occupancy is the main approach towards the successful deployment of CR. This paper studies the various spectrum occupancy models used in diverse locations by research campaigns worldwide. The detail analyses of the empirical results in different scenarios of measurement have been compared. The purpose of this survey is to evaluate up to what percentage the whole spectrum band is occupied by different services.


title: Shift-Net: Image Inpainting via Deep Feature Rearrangement

abstract: Deep convolutional networks (CNNs) have exhibited their potential in image inpainting for producing plausible results.However, in most existing methods, e.g., context encoder, the missing parts are predicted by propagating the surrounding convolutional features through a fully connected layer, which intends to produce semantically plausible but blurry result. In this paper, we introduce a special shift-connection layer to the U-Net architecture, namely Shift-Net, for filling in missing regions of any shape with sharp structures and fine-detailed textures. To this end, the encoder feature of the known region is shifted to serve as an estimation of the missing parts. A guidance loss is introduced on decoder feature to minimize the distance between the decoder feature after fully connected layer and the ground truth encoder feature of the missing parts. With such constraint, the decoder feature in missing region can be used to guide the shift of encoder feature in known region. An end-to-end learning algorithm is further developed to train the Shift-Net. Experiments on the Paris StreetView and Places datasets demonstrate the efficiency and effectiveness of our Shift-Net in producing sharper, fine-detailed, and visually plausible results.


title: Uncertainty and Hyperbolic Discounting By PARTHA DASGUPTA AND ERIC MASKIN*

abstract: We propose an evolutionary explanation for the pattern of intertemporal preference reversals often ascribed to "hyperbolic discounting." We take the view that preferences—manifested, for example, in urges, cravings, and inclinations— are the outcome of evolutionary forces, and so will induce animals or humans to make survival-maximizing choices in "typical" decision problems. We show that if the typical problem involves payoffs whose realization times are uncertain, then optimal preferences give rise to relatively patient behavior when the time horizon is long but induce a switch to impatience when the horizon grows short. Such reversals do not entail dynamic inconsistency in typical decision problems; behavior there is optimal. However, if a decision-maker is confronted with a choice for which the realization-time uncertainty falls outside the evolutionary norm, her preferences may well prompt her to behave inconsistently. We argue that, if such a choice problem recurs, her evolutionarily endowed aability to learn will lead her to make self-commitments against these urges.


title: Semi-supervised Deep Reinforcement Learning in Support of IoT and Smart City Services

abstract: Smart services are an important element of the smart cities and the Internet of Things (IoT) ecosystems where the intelligence behind the services is obtained and improved through the sensory data. Providing a large amount of training data is not always feasible; therefore, we need to consider alternative ways that incorporate unlabeled data as well. In recent years, deep reinforcement learning (DRL) has gained great success in several application domains. It is an applicable method for IoT and smart city scenarios where auto-generated data can be partially labeled by users’ feedback for training purposes. In this paper, we propose a semisupervised DRL model that fits smart city applications as it consumes both labeled and unlabeled data to improve the performance and accuracy of the learning agent. The model utilizes variational autoencoders as the inference engine for generalizing optimal policies. To the best of our knowledge, the proposed model is the first investigation that extends DRL to the semisupervised paradigm. As a case study of smart city applications, we focus on smart buildings and apply the proposed model to the problem of indoor localization based on Bluetooth low energy signal strength. Indoor localization is the main component of smart city services since people spend significant time in indoor environments. Our model learns the best action policies that lead to a close estimation of the target locations with an improvement of 23% in terms of distance to the target and at least 67% more received rewards compared to the supervised DRL model.


title: Learning with Options that Terminate Off-Policy

abstract: A temporally abstract action, or an option, is specified by a policy and a termination condition: the policy guides option behavior, and the termination condition roughly determines its length. Generally, learning with longer options (like learning with multi-step returns) is known to be more efficient. However, if the option set for the task is not ideal, and cannot express the primitive optimal policy exactly, shorter options offer more flexibility and can yield a better solution. Thus, the termination condition puts learning efficiency at odds with solution quality. We propose to resolve this dilemma by decoupling the behavior and target terminations, just like it is done with policies in off-policy learning. To this end, we give a new algorithm, Q(\beta), that learns the solution with respect to any termination condition, regardless of how the options actually terminate. We derive Q(\beta) by casting learning with options into a common framework with well-studied multi-step off-policy learning. We validate our algorithm empirically, and show that it holds up to its motivating claims.


title: High-Level Prediction Signals in a Low-Level Area of the Macaque Face-Processing Hierarchy

abstract: Summary Theories like predictive coding propose that lower-order brain areas compare their inputs to predictions derived from higher-order representations and signal their deviation as a prediction error. Here, we investigate whether the macaque face-processing system, a three-level hierarchy in the ventral stream, employs such a coding strategy. We show that after statistical learning of specific face sequences, the lower-level face area ML computes the deviation of actual from predicted stimuli. But these signals do not reflect the tuning characteristic of ML. Rather, they exhibit identity specificity and view invariance, the tuning properties of higher-level face areas AL and AM. Thus, learning appears to endow lower-level areas with the capability to test predictions at a higher level of abstraction than what is afforded by the feedforward sweep. These results provide evidence for computational architectures like predictive coding and suggest a new quality of functional organization of information-processing hierarchies beyond pure feedforward schemes.


title: Single Image Spectral Reconstruction for Multimedia Applications

abstract: In this paper, we present a method which can perform spectral reconstruction and illuminant recovery from a single colour image making use of an unlabelled training set of hyperspectral images. Our method employs colour and appearance information to drive the reconstruction process subject to the material properties of the objects in the scene. The idea is to reconstruct the image spectral irradiance making use of a set of prototypes extracted from the training set. These spectra, together with a set of convolutional features are hence obtained using sparse coding so as to reconstruct the image irradiance. With the reconstructed spectra in hand, we proceed to compute the illuminant power spectrum using a quadratic optimisation approach. We provide a quantitative analysis for our method and compare to a number of alternatives. We also show sample results on illuminant substitution and transfer, film simulation and image recolouring using mood board colour schemes.


title: Deep Relaxation: partial differential equations for optimizing deep neural networks

abstract: In this paper we establish a connection between non-convex optimization methods for training deep neural networks and nonlinear partial differential equations (PDEs). Relaxation techniques arising in statistical physics which have already been used successfully in this context are reinterpreted as solutions of a viscous Hamilton-Jacobi PDE. Using a stochastic control interpretation allows we prove that the modified algorithm performs better in expectation that stochastic gradient descent. Well-known PDE regularity results allow us to analyze the geometry of the relaxed energy landscape, confirming empirical evidence. The PDE is derived from a stochastic homogenization problem, which arises in the implementation of the algorithm. The algorithms scale well in practice and can effectively tackle the high dimensionality of modern neural networks.


title: Autonomous person following for telepresence robots

abstract: We present a method for a mobile robot to follow a person autonomously where there is an interaction between the robot and human during following. The planner takes into account the predicted trajectory of the human and searches future trajectories of the robot for the path with the highest utility. Contrary to traditional motion planning, instead of determining goal points close to the person, we introduce a task dependent goal function which provides a map of desirable areas for the robot to be at, with respect to the person. The planning framework is flexible and allows encoding of different social situations with the help of the goal function. We implemented our approach on a telepresence robot and conducted a controlled user study to evaluate the experiences of the users on the remote end of the telepresence robot. The user study compares manual teleoperation to our autonomous method for following a person while having a conversation. By designing a behavior specific to a flat screen telepresence robot, we show that the person following behavior is perceived as safe and socially acceptable by remote users. All 10 participants preferred our autonomous following method over manual teleoperation.


Robust Representation Learning for Low Resource Languages


title: A dataset for activity recognition in an unmodified kitchen using smart-watch accelerometers

abstract: Activity recognition from smart devices and wearable sensors is an active area of research due to the widespread adoption of smart devices and the benefits it provide for supporting people in their daily lives. Many of the available datasets for fine-grained primitive activity recognition focus on locomotion or sports activities with less emphasis on real-world day-to-day behavior. This paper presents a new dataset for activity recognition in a realistic unmodified kitchen environment. Data was collected using only smart-watches from 10 lay participants while they prepared food in an unmodified rented kitchen. The paper also providing baseline performance measures for different classifiers on this dataset. Moreover, a deep feature learning system and more traditional statistical features based approaches are compared. This analysis shows that - for all evaluation criteria - data-driven feature learning allows the classifier to achieve best performance compared with hand-crafted features.


title: Feature extraction using PCA for VHR satellite image time series spatio-temporal classification

abstract: Image feature extraction is a challenging task as it directly affects analysis of Satellite Image Time Series (SITS) which tackles a huge amount of information (spatial and spectral resolution increase). Therefore, in this paper, Principle Component Analysis (PCA) is applied for feature extraction to improve a multitemporal classification approach for Very High Resolution (VHR) SITS. The improved multitemporal classification succeeds to discern between regions behaviors (stable, periodic etc.), which is very useful in land cover monitoring. Experimental tests have been conducted on both synthesized and real SITS. Performance comparison between PCA and Fisher Feature Selection (Fisher-FS) algorithms is established.


title: The USTC NELSLIP Systems for Trilingual Entity Detection and Linking Tasks at TAC KBP 2016

abstract: This paper describes the USTC_NELSLIP systems submitted to the Trilingual Entity Detection and Linking (EDL) track in 2016 TAC Knowledge Base Population (KBP) contests. We have built two systems for entity discovery and mention detection (MD): one uses the conditional RNNLM and the other one uses the attention-based encoder-decoder framework. The entity linking (EL) system consists of two modules: a rule based candidate generation and a neural networks probability ranking model. Moreover, some simple string matching rules are used for NIL clustering. At the end, our best system has achieved an F1 score of 0.624 in the end-to-end typed mention ceaf plus metric.


title: STREAM: A First Programming Process

abstract: Programming is recognized as one of seven grand challenges in computing education. Decades of research have shown that the major problems novices experience are composition-based---they may know what the individual programming language constructs are, but they do not know how to put them together. Despite this fact, textbooks, educational practice, and programming education research hardly address the issue of teaching the skills needed for systematic development of programs. We provide a conceptual framework for incremental program development, called Stepwise Improvement, which unifies best practice in modern software development such as test-driven development and refactoring with the prevailing perspective of programming methodology, stepwise refinement. The conceptual framework enables well-defined characterizations of incremental program development. We utilize the conceptual framework to derive a programming process, STREAM, designed specifically for novices. STREAM is a carefully down-scaled version of a full and rich agile software engineering process particularly suited for novices learning object-oriented programming. In using it we hope to achieve two things: to help novice programmers learn faster and better while at the same time laying the foundation for a more thorough treatment of more advanced aspects of software engineering. In this article, two examples demonstrate the application of STREAM. The STREAM process has been taught in the introductory programming courses at our universities for the past three years and the results are very encouraging. We report on a small, preliminary study evaluating the learning outcome of teaching STREAM. The study indicates a positive effect on the development of students’ process competences.


title: Vision-based automatic tool wear monitoring system

abstract: The vision-based of automated tool wear monitoring systems are very important and efficient for unmanned machining systems. This research is use the machine vision inspection technique to automatic tool wear monitoring measurement of different coated drills. The tool wear images are captured using a machine vision system incorporating with an effective extract vertex algorithm based on subpixel edge detector and Gaussian filter is presented. Finally, Statically Process Control (SPC) technique is applied to detect vertices. The results show that the proposed algorithm is an effective method for the different coated drilling factor is recognized to make the most significant contribution to the over all performance. The TiAlN-coated drilling has the least wear rate amongst these coated drilling cutters and has the longest tool life in this experiment.


title: Evolutionary‐based heuristic generators for checkers and give‐away checkers

abstract: : Two methods of genetic evolution of linear and non-linear heuristic evaluation functions for the game of checkers and give-away checkers are presented in the paper. The first method is based on the simplistic assumption that a relation ‘close’ to partial order can be defined over the set of evaluation functions. Hence an explicit fitness function is not necessary in this case and direct comparison between heuristics (a tournament) can be used instead. In the other approach a heuristic is developed step-by-step based on the set of training games. First, the end-game positions are considered and then the method gradually moves ‘backwards’ in the game tree up to the starting position and at each step the best fitted specimen from the previous step (previous game tree depth) is used as the heuristic evaluation function in the alpha-beta search for the current step. Experimental results confirm that both approaches lead to quite strong heuristics and give hope that a more sophisticated and more problem-oriented evolutionary process might ultimately provide heuristics of quality comparable to those of commercial programs.


title: On the detection of inconsistencies in RDF data sets and their correction

abstract: The rapid development of Linked Data leads to a proliferation of errors in pub- lished data, primarily related to inconsistencies between data instances and their related ontologies. This problem alters the reliability of Semantic Web applications when they involve the analysis or the exploitation of heterogeneous rdf data sets. We focus in this article on a way to correct inconsistencies caused by the domain and the range of a property. We present an algorithm to identify the source of these inconsistencies in the ontology, and to provides guidelines to correct or improve the ontology. The localization of the inconsistencies is based on a quantitative comparison between the classes of domains and ranges deﬁned in the ontology and the ones built by the exhaustive analysis of instances used as subject or object in the properties. We show the usefulness of this method on a case study involving dbpedia: we use our approach to diagnose and correct common inconsistencies. Another experiment conducted on a large data set generated by sp2bench validates the scalability of the proposed algorithm.


Mobile Scrum


title: Medical Image Segmentation Using Genetic Algorithms

abstract: Genetic algorithms (GAs) have been found to be effective in the domain of medical image segmentation, since the problem can often be mapped to one of search in a complex and multimodal landscape. The challenges in medical image segmentation arise due to poor image contrast and artifacts that result in missing or diffuse organ/tissue boundaries. The resulting search space is therefore often noisy with a multitude of local optima. Not only does the genetic algorithmic framework prove to be effective in coming out of local optima, it also brings considerable flexibility into the segmentation procedure. In this paper, an attempt has been made to review the major applications of GAs to the domain of medical image segmentation.


title: A novel three-phase utility interface minimizing line current harmonics of high-power telecommunications rectifier modules

abstract: Based on the combination of a three-phase diode bridge and a DC/DC boost converter, a new three-phase three-switch three-level pulsewidth modulated (PWM) rectifier system is developed. It can be characterized by sinusoidal mains current consumption, controlled output voltage, and low-blocking voltage stress on the power transistors. The application could be, e.g., for feeding the DC link of a telecommunications power supply module. The stationary operational behavior, the control of the mains currents, and the control of the output voltage are analyzed. Finally, the stresses on the system components are determined by digital simulation and compared to the stresses in a conventional six-switch two-level PWM rectifier system.


A Comprehensive Reuse Model For COTS Software Products


title: Bladder volume estimation from electrical impedance tomography

abstract: Ubiquitous knowledge of bladder volume is of great interest to patients whose perception of bladder volume is impaired. A promising approach to provide frequent bladder volume estimates to the patient are automatic and noninvasive measurements by electrical impedance tomography (EIT). Previous studies have shown a linear correlation of abdominal electrical impedance and bladder volume. In this article, we present two methods to extract a volume estimate from EIT measurements. One method is based on the global impedance from a reconstructed image, the second method is based on a singular value decomposition of the raw voltage measurement vector. A performance evaluation in presence of noise is performed.


title: Intelligent email: reply and attachment prediction

abstract: We present two prediction problems under the rubric of Intelligent Email that are designed to support enhanced email interfaces that relieve the stress of email overload. Reply prediction alerts users when an email requires a response and facilitates email response management. Attachment prediction alerts users when they are about to send an email missing an attachment or triggers a document recommendation system, which can catch missing attachment emails before they are sent. Both problems use the same underlying email classification system and task specific features. Each task is evaluated for both single-user and cross-user settings.


title: A specialized event-driven network simulator for security and anonymity applications of Wireless Sensor Networks

abstract: This paper presents a novel software architecture for a specialized network simulator that is targeted towards analysis and verification of anonymity algorithms for Wireless Sensor Networks (WSNs). Even though many different network simulators exist and are popular such as NS-2, none of these can be adapted easily for testing of advanced WSN anonymity algorithms. For example, in trying to implement anonymity, i.e., hiding the identity of the source node in a WSN system, we need to generate fake messages. A good anonymity application has to randomize the generation of fake messages and also improve the ratio of real to fake messages so that excessive power is not being wasted by a WSN node. In order to be able to analyze the effectiveness of different anonymity algorithms, we developed an event-driven network simulator and provide statistical and visualization features. The simulator can be easily configured to any WSN topology and a messaging and routing protocol. The software architecture of the simulator allows for easy pluggability of different algorithms making it a valuable tool in WSN security/anonymity deployment and research.


A Low Power Consumption Single Stage Source Coupled CMOS Voltage Controlled Oscillator (VCO) Using 0.18 µm


title: Forecasting financial time series volatility using Particle Swarm Optimization trained Quantile Regression Neural Network

abstract: Abstract Accurate forecasting of volatility from financial time series is paramount in financial decision making. This paper presents a novel, Particle Swarm Optimization (PSO)-trained Quantile Regression Neural Network namely PSOQRNN, to forecast volatility from financial time series. We compared the effectiveness of PSOQRNN with that of the traditional volatility forecasting models, i.e., Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and three Artificial Neural Networks (ANNs) including Multi-Layer Perceptron (MLP), General Regression Neural Network (GRNN), Group Method of Data Handling (GMDH), Random Forest (RF) and two Quantile Regression (QR)-based hybrids including Quantile Regression Neural Network (QRNN) and Quantile Regression Random Forest (QRRF). The results indicate that the proposed PSOQRNN outperformed these models in terms of Mean Squared Error (MSE), on a majority of the eight financial time series including exchange rates of USD versus JPY, GBP, EUR and INR, Gold Price, Crude Oil Price, Standard and Poor 500 (S&P 500) Stock Index and NSE India Stock Index considered here. It was corroborated by the Diebold–Mariano test of statistical significance. It also performed well in terms of other important measures such as Directional Change Statistic (Dstat) and Theil's Inequality Coefficient. The superior performance of PSOQRNN can be attributed to the role played by PSO in obtaining the better solutions. Therefore, we conclude that the proposed PSOQRNN can be used as a viable alternative in forecasting volatility.


title: Learning Multimodal Word Representation via Dynamic Fusion Methods

abstract: Multimodal models have been proven to outperform text-based models on learning semantic word representations. Almost all previous multimodal models typically treat the representations from different modalities equally. However, it is obvious that information from different modalities contributes differently to the meaning of words. This motivates us to build a multimodal model that can dynamically fuse the semantic representations from different modalities according to different types of words. To that end, we propose three novel dynamic fusion methods to assign importance weights to each modality, in which weights are learned under the weak supervision of word association pairs. The extensive experiments have demonstrated that the proposed methods outperform strong unimodal baselines and state-of-the-art multimodal models.


title: Precise 3-D Object Position Tracking using FMCW Radar

abstract: This paper presents a FMCW radar system for high precision three-dimensional object position tracking. Theoretical analyses of the 3-D triangulation concept and the accuracy limits are given. Three 2.45 GHz radar modules measure the distance from different spatial locations to an active reflector unit installed on the object to be localised. By modulating the radar signal in the active reflector unit, disturbing echoes and clutter noise are suppressed. An advanced signal processing concept evaluates the difference of frequency and phase terms of two adjacent modulation components. Test measurements demonstrate a 3-D position measurement with submillimetre precision. This performance makes the new approach suitable for a wide range of applications in robotics, industrial automation and in the medical area.


title: The digital divide and t-government in the United States: using the technology acceptance model to understand usage

abstract: AbstractThis paper applies the technology acceptance model to explore the digital divide and transformational government (t-government) in the United States. Successful t-government is predicated o...


title: Comprehensive study of data analytics tools (RapidMiner, Weka, R tool, Knime)

abstract: In today's era, data has been increasing in volume, velocity and variety. Due to large and complex collection of datasets, it is difficult to process on traditional data processing application. So, this leads to emerging of new technology called data analytics. Data analytics is a science of exploring raw data and elicitation the useful information and hidden pattern. The main aim of data analytics is to use advance analytics techniques for huge and different datasets. Datasets may vary in sizes from terabytes to zettabytes and can be structured or unstructured. The paper gives the comprehensive and theoretical analysis of four open source data analytics tools which are RapidMiner, Weka, R Tool and KNIME. The study describes the technical specification, features, and specialization for each selected tool. By employing the study the choice and selection of tools can be made easy. The tools are evaluated on basis of various parameters like volume of data used, response time, ease of use, price tag, analysis algorithm and handling.


title: Approaches to modeling business processes: a critical analysis of BPMN, workflow patterns and YAWL

abstract: We investigate three approaches describing models of business processes: the OMG standard BPMN in its recent version 2.0, the workflow patterns of the Workflow Pattern Initiative and their reference implementation YAWL. We show how the three approaches fail to provide practitioners with a suitable means precisely and faithfully to capture business scenarios and to analyze, communicate and manage the resulting models. On the positive side, we distill from the discussion six criteria which can help to recognize practical and reliable tool-supported business process description and modeling systems.


title: Vitamin D and Depression: A Systematic Review and Meta-Analysis Comparing Studies with and without Biological Flaws

abstract: Efficacy of Vitamin D supplements in depression is controversial, awaiting further literature analysis. Biological flaws in primary studies is a possible reason meta-analyses of Vitamin D have failed to demonstrate efficacy. This systematic review and meta-analysis of Vitamin D and depression compared studies with and without biological flaws. The systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The literature search was undertaken through four databases for randomized controlled trials (RCTs). Studies were critically appraised for methodological quality and biological flaws, in relation to the hypothesis and study design. Meta-analyses were performed for studies according to the presence of biological flaws. The 15 RCTs identified provide a more comprehensive evidence-base than previous systematic reviews; methodological quality of studies was generally good and methodology was diverse. A meta-analysis of all studies without flaws demonstrated a statistically significant improvement in depression with Vitamin D supplements (+0.78 CI +0.24, +1.27). Studies with biological flaws were mainly inconclusive, with the meta-analysis demonstrating a statistically significant worsening in depression by taking Vitamin D supplements (-1.1 CI -0.7, -1.5). Vitamin D supplementation (≥800 I.U. daily) was somewhat favorable in the management of depression in studies that demonstrate a change in vitamin levels, and the effect size was comparable to that of anti-depressant medication.


title: The Pursuit of Unhappiness: The Elusive Psychology of Well-Being

abstract: PART I: FUNDAMENTALS OF PRUDENTIAL PSYCHOLOGY 1. Taking Socrates's Question Seriously 2. Happiness, Well-Being, and the Good Life: A Primer 3. What Do We Want from a Theory of Happiness? Or how to make a mongrel concept hunt PART II: THE NATURE OF HAPPINESS 4. Hedonistic Theories of Happiness 5. Life Satisfaction Theories of Happiness 6. Emotional State Theories of Happiness 7. Happiness as Psychic Affirmation PART III: THE NATURE OF WELL-BEING 8. Well-Being and Virtue 9. Happiness, the Self, and Human Flourishing PART IV: PURSUING HAPPINESS 10. Do We Know How Happy We Are? 11. The Pursuit of Unhappiness 12. Happiness in Context: Notes on the Good Society


DenResNet : Ensembling Dense Networks and Residual Networks


title: Towards robotic assistants in nursing homes: Challenges and results

abstract: Abstract This paper describes a mobile robotic assistant, developed to assist elderly individuals with mild cognitive and physical impairments, as well as support nurses in their daily activities. We present three software modules relevant to ensure successful human–robot interaction: an automated reminder system; a people tracking and detection system; and finally a high-level robot controller that performs planning under uncertainty by incorporating knowledge from low-level modules, and selecting appropriate courses of actions. During the course of experiments conducted in an assisted living facility, the robot successfully demonstrated that it could autonomously provide reminders and guidance for elderly residents.


title: Test methodology for rain influence on automotive surround sensors

abstract: Automotive safety systems aim to provide maximum protection to vehicle occupants and vulnerable road users. These safety features rely on data from surround sensors such as radar, lidar and camera, which provide detailed information about the environment of the vehicle. A minor error in the measurements of these sensors can lead to major injuries or death. Hence, the reliability and accuracy of these sensor systems is mandatory. The performance of surround sensors depends on their local environment, because of the attenuation of the ambient atmosphere. Environmental influences such as rain additionally affects the accuracy of sensor systems. Therefore, these sensors must be tested under various weather conditions. This paper presents a new test methodology for rain influence on automotive surround sensors. Therefore, a rain simulator was designed and validated. The proposed test methodology was applied to radar, lidar and camera sensors in an experimental setup.


title: Framework for an Autonomic Transport System in Smart Cities

abstract: AbstractToday the concept of smart cities is discussed in scientific society and politics. A core function of smart cities is transportation. This paper gives a short overview on the concepts for Intelligent Transport Systems ITS for smart cities and proposes a framework for the design of an autonomic transportation system that provides personalized mobility services to its users in a smart city setting. The transportation poses extreme environment for ICT systems due to fast moving vehicles and users, requiring real-time acquisition and high performance processing of large scale data, and rapidly changing communication networks topologies and node densities. The aim of this paper is to propose a framework that will act as a reference for the design of future transportation systems that are able to cope with the ever rising system complexities and users' demands. Therefore, a backbone system providing information at different levels was designed following the principles of a corporative ICT that were proposed in [1]. The framework fulfills the main requirement providing suitable information about the local decision engines in vehicles and infrastructure interacting in smart cities traffic systems.


title: Profiling Successful Team Behaviors in League of Legends

abstract: Despite the increasing popularity of electronic sports (eSports), there is still a scarcity of academic works exploring the playing behavior of teams. Understanding the features that help to discriminate between successful and unsuccessful teams would help teams improving their strategies, such as determine performance metrics to reach. In this paper, we identify and characterize team behavior patterns based on historical matches from the very popular eSpor League of Legends web API. By applying machine learning and statistical analysis, we clustered teams' performance and investigate for each cluster how and to what extent these features have an influence on teams' success and failure. Some clusters are more likely to have winning teams than others, the results of our study helped to discover the characteristics that are associated with this predisposition and allowed us to model performance metrics of successful and unsuccessful team profiles. At all, we found 7 profiles in which were categorized into four levels in terms of winning team proportion: very low, moderate, high and very high.


title: Usability evaluation considered harmful (some of the time)

abstract: Current practice in Human Computer Interaction as encouraged by educational institutes, academic review processes, and institutions with usability groups advocate usability evaluation as a critical part of every design process. This is for good reason: usability evaluation has a significant role to play when conditions warrant it. Yet evaluation can be ineffective and even harmful if naively done 'by rule' rather than 'by thought'. If done during early stage design, it can mute creative ideas that do not conform to current interface norms. If done to test radical innovations, the many interface issues that would likely arise from an immature technology can quash what could have been an inspired vision. If done to validate an academic prototype, it may incorrectly suggest a design's scientific worthiness rather than offer a meaningful critique of how it would be adopted and used in everyday practice. If done without regard to how cultures adopt technology over time, then today's reluctant reactions by users will forestall tomorrow's eager acceptance. The choice of evaluation methodology - if any - must arise from and be appropriate for the actual problem or research question under consideration.


title: Additive manufacturing of microwave components: Different approaches and methodologies

abstract: In this paper, two different approaches for the additive manufacturing of microwave component are discussed. The first approach consists is the popular and cheap fused deposition modeling (FDM). In this paper it is shown that, by using different infill factor, FDM is suitable for the manufacturing of devices with controlled dielectric constant. The second approach is the Stereolithography (SLA). With this approach, better results can be obtained in terms of resolution. Furthermore a very easy way to copper plate the surface of microwave devices is shown and it effectiveness is demonstrated through the manufacturing and measurement of a two-pole filter with mushroom-shaped resonators.


title: A Layered Approach to People Detection in 3D Range Data

abstract: People tracking is a key technology for autonomous systems, intelligent cars and social robots operating in populated environments. What makes the task difficult is that the appearance of humans in range data can change drastically as a function of body pose, distance to the sensor, self-occlusion and occlusion by other objects. In this paper we propose a novel approach to pedestrian detection in 3D range data based on supervised learning techniques to create a bank of classifiers for different height levels of the human body. In particular, our approach applies AdaBoost to train a strong classifier from geometrical and statistical features of groups of neighboring points at the same height. In a second step, the AdaBoost classifiers mutually enforce their evidence across different heights by voting into a continuous space. Pedestrians are finally found efficiently by mean-shift search for local maxima in the voting space. Experimental results carried out with 3D laser range data illustrate the robustness and efficiency of our approach even in cluttered urban environments. The learned people detector reaches a classification rate up to 96% from a single 3D scan.


title: What effect does distributed version control have on OSS project organization?

abstract: Many Open Source Software (OSS) projects are moving form Centralized Version Control (CVC) to Distributed Version Control (DVC). The effect of this shift on project organization and developer collaboration is not well understood. In this paper, we use a theoretical argument to evaluate the appropriateness of using DVC in the context of two very common organization forms in OSS: a dictatorship and a peer group. We find that DVC facilitates large hierarchical communities as well as smaller groups of developers, while CVC allows for consensus-building by a peer group. We also find that the flexibility of DVC systems allows for diverse styles of developer collaboration. With CVC, changes flow up and down (and publicly) via a central repository. In contrast, DVC facilitates collaboration in which work output can flow sideways (and privately) between collaborators, with no repository being inherently more important or central. These sideways flows are a relatively new concept. Developers on the Linux project, who tend to be experienced DVC users, cluster around "sandboxes:" repositories where developers can work together on a particular topic, isolating their changes from other developers. In this work, we focus on two large, mature OSS projects to illustrate these findings. However, we suggest that social media sites like GitHub may engender other original styles of collaboration that deserve further study.


title: A tutorial on the free-energy framework for modelling perception and learning.

abstract: This paper provides an easy to follow tutorial on the free-energy framework for modelling perception developed by Friston, which extends the predictive coding model of Rao and Ballard. These models assume that the sensory cortex infers the most likely values of attributes or features of sensory stimuli from the noisy inputs encoding the stimuli. Remarkably, these models describe how this inference could be implemented in a network of very simple computational elements, suggesting that this inference could be performed by biological networks of neurons. Furthermore, learning about the parameters describing the features and their uncertainty is implemented in these models by simple rules of synaptic plasticity based on Hebbian learning. This tutorial introduces the free-energy framework using very simple examples, and provides step-by-step derivations of the model. It also discusses in more detail how the model could be implemented in biological neural circuits. In particular, it presents an extended version of the model in which the neurons only sum their inputs, and synaptic plasticity only depends on activity of pre-synaptic and post-synaptic neurons.


title: Visual attention as a multilevel selection process

abstract: Natural visual scenes are cluttered and contain many different objects that cannot all be processed simultaneously. Therefore, attentional mechanisms are needed to select relevant and to filter out irrelevant information. Evidence from functional brain imaging reveals that attention operates at various processing levels within the visual system and beyond. First, the lateral geniculate nucleus appears to be the first stage in the processing of visual information that is modulated by attention, consistent with the idea that it may play an important role as an early gatekeeper in controlling neural gain. Second, areas at intermediate cortical-processing levels, such as V4 and TEO, appear to be important sites at which attention filters out unwanted information by means of receptive field mechanisms. Third, the attention mechanisms that operate in the visual system appear to be controlled by a distributed network of higher order areas in the frontal and parietal cortex, which generate top-down signals that are transmitted via feedback connections to the visual system. And fourth, the pulvinar of the thalamus may operate by integrating and coordinating attentional functions in concert with the fronto-parietal network, although much needs to be learned about its functional properties. The overall view that emerges from the studies reviewed in this article is that neural mechanisms of selective attention operate at multiple stages in the visual system and beyond and are determined by the visual processing capabilities of each stage. In this respect, attention can be considered in terms of a multilevel selection process.


title: Real time head pose tracking from multiple cameras with a generic model

abstract: We present a robust approach to real-time 3D head pose tracking using multiple cameras with unknown camera placements. Many important applications do not want prior multi-camera calibration. We exploit a generic face model to overcome the difficulties due to the lack of prior knowledge of camera placement and the severe head appearance difference across cameras. We propose a fast drift-free solution based on feature point tracking using reference frames of high confidence over the temporal and spatial domains. Our algorithm tracks feature points from Harris feature detector, but not necessarily points of face landmarks. The relative camera placement is refined progressively at the same time as the user's head pose is resolved. Compared to single camera tracking, the use of multiple cameras increases the reliability of tracking by covering a wider range of poses as well as providing more accurate head pose estimation. We have tested the algorithm on many subjects in a variety of environments. A live demonstration will be shown at the conference.


title: VTEAM: A General Model for Voltage-Controlled Memristors

abstract: Memristors are novel electrical devices used for a variety of applications, including memory, logic circuits, and neuromorphic systems. Memristive technologies are attractive due to their nonvolatility, scalability, and compatibility with CMOS. Numerous physical experiments have shown the existence of a threshold voltage in some physical memristors. Additionally, as shown in this brief, some applications require voltage-controlled memristors to operate properly. In this brief, a Voltage ThrEshold Adaptive Memristor (VTEAM) model is proposed to describe the behavior of voltage-controlled memristors. The VTEAM model extends the previously proposed ThrEshold Adaptive Memristor (TEAM) model, which describes current-controlled memristors. The VTEAM model has similar advantages as the TEAM model, i.e., it is simple, general, and flexible, and can characterize different voltage-controlled memristors. The VTEAM model is accurate (below 1.5% in terms of the relative root-mean-square error) and computationally efficient as compared with existing memristor models and experimental results describing different memristive technologies.


title: Modeling and simulation of DDoS attack using Omnet++

abstract: Distributed Denial of Service (DDoS) attack continues to evolve over the past two decades. Its easy accomplishment, hard tracing and disastrous effects make it a popular choice among the attacker's community. The goal of DDoS attack is to overwhelm the target's resources to such an extent that it becomes incapable to serve the legitimate client. Till date, researchers have proposed many solutions to detect and mitigate DDoS attack, but a very little attention is given to impact analysis of attack. The purpose of this paper is to study the impact of DDoS attack with respect to two parameters: attack intensity and buffer size. In this paper, we model and analyze different DDoS attack scenario using different attack rate and different buffer size of network components. The results in this paper indicate that performance of WebServer degrades with increase in attack intensity and low buffer size.


title: A joint model for action localization and classification in untrimmed video with visual attention

abstract: In this paper, we introduce a joint model that learns to directly localize the temporal bounds of actions in untrimmed videos as well as precisely classify what actions occur. Most existing approaches tend to scan the whole video to generate action instances, which are really inefficient. Instead, inspired by human perception, our model is formulated based on a recurrent neural network to observe different locations within a video over time. And, it is capable of producing temporal localizations by only observing a fixed number of fragments, and the amount of computation it performs is independent of input video size. The decision policy for determining where to look next is learned by REINFORCE which is powerful in non-differentiable settings. In addition, different from relevant ways, our model runs localization and classification serially, and possesses a strategy for extracting appropriate features to classify. We evaluate our model on ActivityNet dataset, and it greatly outperforms the baseline. Moreover, compared with a recent approach, we show that our serial design can bring about 9% increase in detection performance.


title: Simulating virtual environments within virtual environments as the basis for a psychophysics of presence

abstract: A new definition of immersion with respect to virtual environment (VE) systems has been proposed in earlier work, based on the concept of simulation. One system (A) is said to be more immersive than another (B) if A can be used to simulate an application as if it were running on B. Here we show how this concept can be used as the basis for a psychophysics of presence in VEs, the sensation of being in the place depicted by the virtual environment displays (Place Illusion, PI), and also the illusion that events occurring in the virtual environment are real (Plausibility Illusion, Psi). The new methodology involves matching experiments akin to those in color science. Twenty participants first experienced PI or Psi in the initial highest level immersive system, and then in 5 different trials chose transitions from lower to higher order systems and declared a match whenever they felt the same level of PI or Psi as they had in the initial system. In each transition they could change the type of illumination model used, or the field-of-view, or the display type (powerwall or HMD) or the extent of self-representation by an avatar. The results showed that the 10 participants instructed to choose transitions to attain a level of PI corresponding to that in the initial system tended to first choose a wide field-of-view and head-mounted display, and then ensure that they had a virtual body that moved as they did. The other 10 in the Psi group concentrated far more on achieving a higher level of illumination realism, although having a virtual body representation was important for both groups. This methodology is offered as a way forward in the evaluation of the responses of people to immersive virtual environments, a unified theory and methodology for psychophysical measurement.


title: Geometric 3D point cloud compression

abstract: Our main goal is to compress and decompress 3D data using geometric methods.The proposed method extracts planes and makes color segmentation.The result from segmentation is triangulated and triangles stored.Thus, we can reach great ratio compression with low color and point loss.It's designed to work with man-made scenarios,but can be applied to any general one. The use of 3D data in mobile robotics applications provides valuable information about the robot's environment but usually the huge amount of 3D information is unmanageable by the robot storage and computing capabilities. A data compression is necessary to store and manage this information but preserving as much information as possible. In this paper, we propose a 3D lossy compression system based on plane extraction which represent the points of each scene plane as a Delaunay triangulation and a set of points/area information. The compression system can be customized to achieve different data compression or accuracy ratios. It also supports a color segmentation stage to preserve original scene color information and provides a realistic scene reconstruction. The design of the method provides a fast scene reconstruction useful for further visualization or processing tasks.


title: Networking concepts comparison for avionics architecture

abstract: The purpose of this paper is to evaluate alternative networking concepts (standards and protocols), with a particular emphasis on comparing ARINC 664 network standard with legacy avionics networks. The conclusions of this comparison are reinforced with an example network solution for the avionics architecture using the Avionics Full- Duplex Switched Ethernet (AFDX) protocol. The networking of modules (hardware and software) and applications on an aircraft is crucial, and often new designs and upgrades rely on legacy network architectures. In the past, such systems in defense applications have been successfully integrated around the 1553B bus architecture, while commercial applications have satisfied FAA requirements with systems integrated around the ARINC 429 bus architecture. The new applications and capabilities being requested by stakeholders for Avionics systems of the future require increased bandwidth and latency requirements that suggest likely inadequacies in legacy bus architectures. There is continuing pressure by pilots for more information displayed on increasingly more intuitive graphical displays and interfaces; while the ground control and logistics teams want additional and more timely airplane status data - this data is consolidated from a multitude of sub-systems and sensors on board, including event logs and trend data. These new demands may require us to leverage new technologies to keep pace with stakeholder expectations today and in the future. A recent advancement in networking technology is ARINC 664, which defines a deterministic version of an Ethernet network. Boeing and Airbus have adopted Avionic Full- Duplex Switched Ethernet for their newer airplanes [1-3], and NASA is considering ARINC 664 for the new Crew Exploration Vehicle [4]. The B787 is slated to accommodate 100 applications in part due to the availability of larger network bandwidth [5]. NASA hopes to benefit from commercial-off-the-shelf (COTS) Ethernet components which include reduced overall costs, faster system development and less-costly maintenance for the system network. Both found ARINC 664 to be the best fit in ARINC 653 based systems. Integrating an Avionics Full-Duplex Switched Ethernet may benefit the defense industry avionics customer by lowering life cycle cost and accommodating increasing requirements. This paper addresses the speed, reliability, and flexibility of modern network protocols and explores new options for avionics architectures. This applies to either a complete redesign or a phased avionics upgrade in legacy airplanes. For a historical context, this paper also summarizes the value and utility of legacy networking protocols, together with their downsides. Networking standards and protocols are evaluated as a function of critical requirements pertaining to performance, certification, security, reliability, evolvability, cost, and flexibility to meet changing requirements. Methods such as Quality Function Deployment (QFD) and Analytic Hierarchy Process (AHP) are used to evaluate the three network architectures. The impacts on security and reliability are explored, and additional aspects are highlighted for future research.


title: Ultra wideband CPW bow-tie antenna

abstract: A novel UWB slot bow-tie antenna fed by CPW is proposed in this paper. This antenna has been demonstrated to provide a wideband with return loss less than −10 dB throughout the entire bands. The antenna has also omnidirectional radiation characteristics and reasonable gain values over the same frequency band.


title: Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation

abstract: Quantitative magnetic resonance analysis often requires accurate, robust, and reliable automatic extraction of anatomical structures. Recently, template-warping methods incorporating a label fusion strategy have demonstrated high accuracy in segmenting cerebral structures. In this study, we propose a novel patch-based method using expert manual segmentations as priors to achieve this task. Inspired by recent work in image denoising, the proposed nonlocal patch-based label fusion produces accurate and robust segmentation. Validation with two different datasets is presented. In our experiments, the hippocampi of 80 healthy subjects and the lateral ventricles of 80 patients with Alzheimer's disease were segmented. The influence on segmentation accuracy of different parameters such as patch size and number of training subjects was also studied. A comparison with an appearance-based method and a template-based method was also carried out. The highest median kappa index values obtained with the proposed method were 0.884 for hippocampus segmentation and 0.959 for lateral ventricle segmentation.


title: Towards Modeling False Memory With Computational Knowledge Bases.

abstract: One challenge to creating realistic cognitive models of memory is the inability to account for the vast common-sense knowledge of human participants. Large computational knowledge bases such as WordNet and DBpedia may offer a solution to this problem but may pose other challenges. This paper explores some of these difficulties through a semantic network spreading activation model of the Deese-Roediger-McDermott false memory task. In three experiments, we show that these knowledge bases only capture a subset of human associations, while irrelevant information introduces noise and makes efficient modeling difficult. We conclude that the contents of these knowledge bases must be augmented and, more important, that the algorithms must be refined and optimized, before large knowledge bases can be widely used for cognitive modeling.


title: Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels

abstract: Deep neural networks (DNNs) have achieved tremendous success in a variety of applications across many disciplines. Yet, their superior performance comes with the expensive cost of requiring correctly annotated large-scale datasets. Moreover, due to DNNs' rich capacity, errors in training labels can hamper performance. To combat this problem, mean absolute error (MAE) has recently been proposed as a noise-robust alternative to the commonly-used categorical cross entropy (CCE) loss. However, as we show in this paper, MAE can perform poorly with DNNs and challenging datasets. Here, we present a theoretically grounded set of noise-robust loss functions that can be seen as a generalization of MAE and CCE. Proposed loss functions can be readily applied with any existing DNN architecture and algorithm, while yielding good performance in a wide range of noisy label scenarios. We report results from experiments conducted with CIFAR-10, CIFAR-100 and FASHION-MNIST datasets and synthetically generated noisy labels.


title: Growth in inhibitory control among low-income, ethnic-minority preschoolers: A group-based modeling approach

abstract: Abstract The emergence of self-regulation skills such as inhibitory control in children is an important developmental process associated with adjustment across multiple domains. Individual differences in inhibitory control are associated with family socioeconomic status but have not been studied in relation to variations in risk found within a low-income (i.e., high risk) sample ( N = 407). Using a group-based modeling approach, change in inhibitory control was examined from 30 to 42 months of age in a sample of low-income Hispanic and African-American children. Patterns of change in inhibitory control were examined in relation to cumulative risk and child externalizing behavior using the Child Behavior Checklist measured at 42 months. A three-group solution was identified as optimally fitting the data, representing patterns of change in inhibitory control: a normative group, exhibiting increasing inhibitory control (48%); a low-stable group (40%); and a high-stable group (12%). Group membership did not differ by child ethnicity or gender. Higher cumulative risk was found among the children in the low-stable group than in the other groups. Mothers reported more externalizing problems at 42 months for children in the low-stable group than in the other groups, even controlling for the effect of cumulative risk. Intervention and policy implications are discussed.


title: FPGA-based Control System for Miniature Robots

abstract: Resource-constrained miniature robots require small but high-performance onboard processing unit and re-configurable electrical hardware to carry out different missions. The advances in Field Programmable Gate Array (FPGA) technology offer a system on programmable chip (SoPC) solution to this demand. This paper describes the technical achievement in developing FPGA-based control system which utilizes the hardware/software re-configurable feature of the advanced FPGA device to achieve the goal. We have implemented the hardware module inside the FPGA chip to generate PWM output and to count quadrate encoder pulses, which are the basic building blocks to drive DC motors. Software is developed to implement PID control algorithm using the PWM-Encoder module and on-chip processor. The functional correctness of the closed-loop control system is verified by the experimental tests. The performance analysis shows that the hardware module occupies less FPGA space and the power dissipation is very much comparable to other design alternatives of similar caliber. The preliminary results demonstrate that the PWM-Encoder module, in the form of user Intellectual Property (IP), can be duplicated and re-configured to control as many motors as needed. The success gives us a confidence boost to continue our effort to build user IP library for robotics applications which will benefit the robotics community by providing development tools and FPGA building blocks to satisfy the demand for an extremely flexible, high-performance, onboard computing unit.


title: Anatomy and physiology of the clitoris, vestibular bulbs, and labia minora with a review of the female orgasm and the prevention of female sexual dysfunction.

abstract: This review, with 21 figures and 1 video, aims to clarify some important aspects of the anatomy and physiology of the female erectile organs (triggers of orgasm), which are important for the prevention of female sexual dysfunction. The clitoris is the homologue of the male's glans and corpora cavernosa, and erection is reached in three phases: latent, turgid, and rigid. The vestibular bulbs cause "vaginal" orgasmic contractions, through the rhythmic contraction of the bulbocavernosus muscles. Because of the engorgement with blood during sexual arousal, the labia minora become turgid, doubling or tripling in thickness. The corpus spongiosum of the female urethra becomes congested during sexual arousal; therefore, male erection equals erection of the female erectile organs. The correct anatomical term to describe the erectile tissues responsible for female orgasm is the female penis. Vaginal orgasm and the G-spot do not exist. These claims are found in numerous articles that have been written by Addiego F, Whipple B, Jannini E, Buisson O, O'Connell H, Brody S, Ostrzenski A, and others, have no scientific basis. Orgasm is an intense sensation of pleasure achieved by stimulation of erogenous zones. Women do not have a refractory period after each orgasm and can, therefore, experience multiple orgasms. Clitoral sexual response and the female orgasm are not affected by aging. Sexologists should define having sex/love making when orgasm occurs for both partners with or without vaginal intercourse.


title: Flip-Rotate-Pooling Convolution and Split Dropout on Convolution Neural Networks for Image Classification

abstract: This paper presents a new version of Dropout called Split Dropout (sDropout) and rotational convolution techniques to improve CNNs' performance on image classification. The widely used standard Dropout has advantage of preventing deep neural networks from overfitting by randomly dropping units during training. Our sDropout randomly splits the data into two subsets and keeps both rather than discards one subset. We also introduce two rotational convolution techniques, i.e. rotate-pooling convolution (RPC) and flip-rotate-pooling convolution (FRPC) to boost CNNs' performance on the robustness for rotation transformation. These two techniques encode rotation invariance into the network without adding extra parameters. Experimental evaluations on ImageNet2012 classification task demonstrate that sDropout not only enhances the performance but also converges faster. Additionally, RPC and FRPC make CNNs more robust for rotation transformations. Overall, FRPC together with sDropout bring $1.18\%$ (model of Zeiler and Fergus~\cite{zeiler2013visualizing}, 10-view, top-1) accuracy increase in ImageNet 2012 classification task compared to the original network.


title: High Efficiency GaN HEMT Power Amplifier/Rectifier Module Design Using Time Reversal Duality

abstract: A general design theory for high efficiency microwave power amplifiers and rectifiers is presented using the time reversal duality concept. Effective techniques for achieving high efficiency microwave power amplifiers can also be implemented in the design of high efficiency rectifiers. As a design example, a harmonic reactive load type (class-R) GaN-HEMT power amplifier and its time reversed dual rectifier were developed at 5.4GHz. The fabricated amplifier delivered a maximum drain efficiency of 82%, whereas the rectifier presented 78% power efficiency. A DC-DC conversion efficiency of 47% was measured with the pulse-width modulation (PWM) technique for a wide dynamic power range of 90 mW to 860 mW.


title: Soft-NMS — Improving Object Detection with One Line of Code

abstract: Non-maximum suppression is an integral part of the object detection pipeline. First, it sorts all detection boxes on the basis of their scores. The detection box M with the maximum score is selected and all other detection boxes with a significant overlap (using a pre-defined threshold) with M are suppressed. This process is recursively applied on the remaining boxes. As per the design of the algorithm, if an object lies within the predefined overlap threshold, it leads to a miss. To this end, we propose Soft-NMS, an algorithm which decays the detection scores of all other objects as a continuous function of their overlap with M. Hence, no object is eliminated in this process. Soft-NMS obtains consistent improvements for the coco-style mAP metric on standard datasets like PASCAL VOC 2007 (1.7% for both R-FCN and Faster-RCNN) and MS-COCO (1.3% for R-FCN and 1.1% for Faster-RCNN) by just changing the NMS algorithm without any additional hyper-parameters. Using Deformable-RFCN, Soft-NMS improves state-of-the-art in object detection from 39.8% to 40.9% with a single model. Further, the computational complexity of Soft-NMS is the same as traditional NMS and hence it can be efficiently implemented. Since Soft-NMS does not require any extra training and is simple to implement, it can be easily integrated into any object detection pipeline. Code for Soft-NMS is publicly available on GitHub (this http URL).


title: Modeling and Control of CSTR using Model based Neural Network Predictive Control

abstract: This paper presents a predictive control strategy based on neural network model of the plant is applied to Continuous Stirred Tank Reactor (CSTR). This system is a highly nonlinear process; therefore, a nonlinear predictive method, e.g., neural network predictive control, can be a better match to govern the system dynamics. In the paper, the NN model and the way in which it can be used to predict the behavior of the CSTR process over a certain prediction horizon are described, and some comments about the optimization procedure are made. Predictive control algorithm is applied to control the concentration in a continuous stirred tank reactor (CSTR), whose parameters are optimally determined by solving quadratic performance index using the optimization algorithm. An efficient control of the product concentration in cstr can be achieved only through accurate model. Here an attempt is made to alleviate the modeling difficulties using Artificial Intelligent technique such as Neural Network. Simulation results demonstrate the feasibility and effectiveness of the NNMPC technique.


title: Appropriateness of plantar pressure measurement devices: A comparative technical assessment

abstract: Accurate plantar pressure measurements are mandatory in both clinical and research contexts. Differences in accuracy, precision and reliability of the available devices have prevented so far the onset of standardization processes or the definition of reliable reference datasets. In order to comparatively assess the appropriateness of the most used pressure measurement devices (PMD) on-the-market, in 2006 the Institute the author is working for approved a two-year scientific project aimed to design, validate and implement dedicated testing methods for both in-factory and on-the field assessment. A first testing phase was also performed which finished in December 2008. Five commercial PMDs using different technologies— resistive, elastomer-based capacitive, air-based capacitive—were assessed and compared with respect to absolute pressure measurements, hysteresis, creep and COP estimation. The static and dynamic pressure tests showed very high accuracy of capacitive, elastomer-based technology (RMSE < 0.5%), and quite a good performance of capacitive, air-based technology (RMSE < 5%). High accuracy was also found for the resistive technology by TEKSCAN (RMSE < 2.5%), even though a complex ad hoc calibration was necessary.


title: Learning to SMILE(S)

abstract: This paper shows how one can directly apply natural language processing (NLP) methods to classification problems in cheminformatics. Connection between these seemingly separate fields is shown by considering standard textual representation of compound, SMILES. The problem of activity prediction against a target protein is considered, which is a crucial part of computer aided drug design process. Conducted experiments show that this way one can not only outrank state of the art results of hand crafted representations but also gets direct structural insights into the way decisions are made.


title: Altered caudate connectivity is associated with executive dysfunction after traumatic brain injury

abstract: Traumatic brain injury often produces executive dysfunction. This characteristic cognitive impairment often causes long-term problems with behaviour and personality. Frontal lobe injuries are associated with executive dysfunction, but it is unclear how these injuries relate to corticostriatal interactions that are known to play an important role in behavioural control. We hypothesized that executive dysfunction after traumatic brain injury would be associated with abnormal corticostriatal interactions, a question that has not previously been investigated. We used structural and functional MRI measures of connectivity to investigate this. Corticostriatal functional connectivity in healthy individuals was initially defined using a data-driven approach. A constrained independent component analysis approach was applied in 100 healthy adult dataset from the Human Connectome Project. Diffusion tractography was also performed to generate white matter tracts. The output of this analysis was used to compare corticostriatal functional connectivity and structural integrity between groups of 42 patients with traumatic brain injury and 21 age-matched controls. Subdivisions of the caudate and putamen had distinct patterns of functional connectivity. Traumatic brain injury patients showed disruption to functional connectivity between the caudate and a distributed set of cortical regions, including the anterior cingulate cortex. Cognitive impairments in the patients were mainly seen in processing speed and executive function, as well as increased levels of apathy and fatigue. Abnormalities of caudate functional connectivity correlated with these cognitive impairments, with reductions in right caudate connectivity associated with increased executive dysfunction, information processing speed and memory impairment. Structural connectivity, measured using diffusion tensor imaging between the caudate and anterior cingulate cortex was impaired and this also correlated with measures of executive dysfunction. We show for the first time that altered subcortical connectivity is associated with large-scale network disruption in traumatic brain injury and that this disruption is related to the cognitive impairments seen in these patients.


title: Exploring Topic Models on Short Texts: A Case Study with Crisis Data

abstract: In recent years, social media platforms like Twitter and Facebook have become one of the crucial sources of information for a wide spectrum of users. As a result, these platforms have also become great resources to provide support for emergency management. During any crisis, it is necessary to sieve through a huge amount of social media texts within a short span of time to extract meaningful information from them. Extraction of topic keywords from these unstructured social media texts play a significant role in building any application for emergency management. Topic models have the ability to discover latent topics and latent feature representations of words in a document collection and can be used for this purpose. The main aim of this paper is twofold: to explore topic model implementations and look at its effectiveness on short messages (as short as 140 characters); and to perform an exploratory data analysis on short texts related to crises collected from Twitter, and look at different visualizations to understand the commonality and differences between topics and different crisis related data.


title: Coherent Matrix Completion

abstract: Matrix completion concerns the recovery of a low-rank matrix from a subset of its revealed entries, and nuclear norm minimization has emerged as an effective surrogate for this combinatorial problem. Here, we show that nuclear norm minimization can recover an arbitrary n×n matrix of rank r from O(nr log2(n)) revealed entries, provided that revealed entries are drawn proportionally to the local row and column coherences (closely related to leverage scores) of the underlying matrix. Our results are order-optimal up to logarithmic factors, and extend existing results for nuclear norm minimization which require strong incoherence conditions on the types of matrices that can be recovered, due to assumed uniformly distributed revealed entries. We further provide extensive numerical evidence that a proposed two-phase sampling algorithm can perform nearly as well as local-coherence sampling and without requiring a priori knowledge of the matrix coherence structure. Finally, we apply our results to quantify how weighted nuclear norm minimization can improve on unweighted minimization given an arbitrary set of sampled entries.


title: Sources of Perceived Self-Efficacy as Predictors of Physical Activity in Older Adults

abstract: According to Bandura's self-efficacy theory, there are four sources of self-efficacy: past experience, vicarious experience, verbal persuasion, and perception of physical states. The aims of the study were twofold: To review previous research on the sources of self-efficacy and to examine the sources in predicting self-efficacy for exercise in older adults. A sample of 309 older adults was assessed at two time points for exercise, exercise-specific self-efficacy, and four sources of self-efficacy. Past experiences, vicarious experiences, and subjective health had significant direct effects on self-efficacy and indirect effects on exercise via self-efficacy. Persuasive arguments did not predict self-efficacy. This suggests that future research should target past experience and vicarious experience as sources of self-efficacy.


title: Systems Engineering for Industrial Cyber–Physical Systems Using Aspects

abstract: One of the biggest challenges in cyber–physical system (CPS) design is their intrinsic complexity, heterogeneity, and multidisciplinary nature. Emerging distributed CPSs integrate a wide range of heterogeneous aspects such as physical dynamics, control, machine learning, and error handling. Furthermore, system components are often distributed over multiple physical locations, hardware platforms, and communication networks. While model-based design (MBD) has tremendously improved the design process, CPS design remains a difficult task. Models are meant to improve understanding of a system, yet this quality is often lost when models become too complicated. In this paper, we show how to use aspect-oriented (AO) modeling techniques in MBD as a systematic way to segregate domains of expertise and cross-cutting concerns within the model. We demonstrate these concepts on actor-oriented models of an industrial robotic swarm application and illustrate the use of AO modeling techniques to manage the complexity. We also show how to use AO modeling for design-space exploration.


title: ASHACL: Alternative Shapes Constraint Language

abstract: ASHACL, a variant of the W3C Shapes Constraint Language, is designed to determine whether an RDF graph meets some conditions. These conditions are grouped into shapes, which validate whether particular RDF terms each meet the constraints of the shape. Shapes are themselves expressed as RDF triples in an RDF graph, called a shapes graph.


title: A taper optimization for pattern synthesis of microstrip series-fed patch array antennas

abstract: An EM based straight forward design and pattern synthesis technique for series fed microstrip patch array antennas is proposed. An optimization of each antenna element (λ/4-transmission line, λ/2-patch, λ/4-transmission line) of the array is performed separately. By introducing an equivalent circuit along with an EM parameter extraction method, each antenna element can be optimized for its resonance frequency and taper amplitude, so to shape the aperture distribution for the cascaded elements. It will be shown that the array design based on the multiplication of element factor and array factor fails in case of patch width tapering, due to the inconsistency of the element patterns. To overcome this problem a line width tapering is suggested which keeps the element patterns nearly constant while still providing a broad amplitude taper range. A symmetric 10 element antenna array with a Chebyshev tapering (-20dB side lobe level) operating at 5.8 GHz has been designed, compared for the two tapering methods and validated with measurement.


title: Color Learning and Illumination Invariance on Mobile Robots: A Survey

abstract: Recent developments in sensor technology have made it feasible to use mobile robots in several fields, but robots still lack the ability to accurately sense the environment. A major challenge to the widespread deployment of mobile robots is the ability to function autonomously, learning useful models of environmental features, recognizing environmental changes, and adapting the learned models in response to such changes. This article focuses on such learning and adaptation in the context of color segmentation on mobile robots in the presence of illumination changes. The main contribution of this article is a survey of vision algorithms that are potentially applicable to color-based mobile robot vision. We therefore look at algorithms for color segmentation, color learning and illumination invariance on mobile robot platforms, including approaches that tackle just the underlying vision problems. Furthermore, we investigate how the inter-dependencies between these modules and high-level action planning can be exploited to achieve autonomous learning and adaptation. The goal is to determine the suitability of the state-of-the-art vision algorithms for mobile robot domains, and to identify the challenges that still need to be addressed to enable mobile robots to learn and adapt models for color, so as to operate autonomously in natural conditions.


title: Social engineering in social networking sites: Affect-based model

abstract: While social engineering represents a real and ominous threat to many organizations, companies, governments, and individuals, social networking sites (SNSs), have been identified as among the most common means of social engineering attacks. Owing to factors that reduce the ability of users to detect social engineering tricks and increase the ability of attackers to launch them, SNSs seem to be perfect breeding ground for exploiting the vulnerabilities of people, and the weakest link in security. This work will contribute to the knowledge of social engineering by identifying different entities and subentities that affect social engineering based attacks in SNSs. Moreover, this paper includes an intensive and comprehensive overview of different aspects of social engineering threats in SNSs.


title: Trust Management Framework for Internet of Things

abstract: Internet of Things (IoT) can connect a large numberof things (or agents) through communication networks for varioustypes of applications. Like in many other applications, it isvery important for all the agents in IoT systems to collaboratewith each other following predefined protocols. In this paper, we proposed a general trust management framework aiming tohelp agents to evaluate their partners' trustworthiness. We run asimulation for a food nutrition analysis example. It shows that byusing trust, the analysis error can be reduced. Also, we illustratetwo possible types of attacks, and show how to use different trustfactors or environments together to alleviate the damage.


title: Pathophysiology of Degenerative Disc Disease

abstract: The intervertebral disc is characterized by a tension-resisting annulus fibrosus and a compression-resisting nucleus pulposus composed largely of proteoglycan. The most important function of the annulus and nucleus is to provide mechanical stability to the disc. Degenerative disc disease in the lumbar spine is a serious health problem. Although the three joint complex model of the degenerative process is widely accepted, the etiological basis of this degeneration is poorly understood. With the recent progress in molecular biology and modern biological techniques, there has been dramatic improvement in the understanding of aging and degenerative changes of the disc. Knowledge of the pathophysiology of the disc degeneration can help in the appropriate choice of treatment and to develop tissue engineering for biological restoration of degenerated discs.


title: Deep Canonical Correlation Analysis

abstract: We introduce Deep Canonical Correlation Analysis (DCCA), a method to learn complex nonlinear transformations of two views of data such that the resulting representations are highly linearly correlated. Parameters of both transformations are jointly learned to maximize the (regularized) total correlation. It can be viewed as a nonlinear extension of the linear method canonical correlation analysis (CCA). It is an alternative to the nonparametric method kernel canonical correlation analysis (KCCA) for learning correlated nonlinear transformations. Unlike KCCA, DCCA does not require an inner product, and has the advantages of a parametric method: training time scales well with data size and the training data need not be referenced when computing the representations of unseen instances. In experiments on two real-world datasets, we find that DCCA learns representations with significantly higher correlation than those learned by CCA and KCCA. We also introduce a novel non-saturating sigmoid function based on the cube root that may be useful more generally in feedforward neural networks.


title: Collaborative detection of cyberbullying behavior in Twitter data

abstract: As the size of Twitter© data is increasing, so are undesirable behaviors of its users. One of such undesirable behavior is cyberbullying, which may even lead to catastrophic consequences. Hence, it is critical to efficiently detect cyberbullying behavior by analyzing tweets, if possible in realtime. Prevalent approaches to identify cyberbullying are mainly stand-alone and thus, are time-consuming. This research improves detection task using the principles of collaborative computing. Different collaborative paradigms are suggested and discussed in this paper. Preliminary results indicate an improvement in time and accuracy of the detection mechanism over the stand-alone paradigm.


title: The Dual-State Theory of Prefrontal Cortex Dopamine Function with Relevance to Catechol-O-Methyltransferase Genotypes and Schizophrenia

abstract: There is now general consensus that at least some of the cognitive deficits in schizophrenia are related to dysfunctions in the prefrontal cortex (PFC) dopamine (DA) system. At the cellular and synaptic level, the effects of DA in PFC via D1- and D2-class receptors are highly complex, often apparently opposing, and hence difficult to understand with regard to their functional implications. Biophysically realistic computational models have provided valuable insights into how the effects of DA on PFC neurons and synaptic currents as measured in vitro link up to the neural network and cognitive levels. They suggest the existence of two discrete dynamical regimes, a D1-dominated state characterized by a high energy barrier among different network patterns that favors robust online maintenance of information and a D2-dominated state characterized by a low energy barrier that is beneficial for flexible and fast switching among representational states. These predictions are consistent with a variety of electrophysiological, neuroimaging, and behavioral results in humans and nonhuman species. Moreover, these biophysically based models predict that imbalanced D1:D2 receptor activation causing extremely low or extremely high energy barriers among activity states could lead to the emergence of cognitive, positive, and negative symptoms observed in schizophrenia. Thus, combined experimental and computational approaches hold the promise of allowing a detailed mechanistic understanding of how DA alters information processing in normal and pathological conditions, thereby potentially providing new routes for the development of pharmacological treatments for schizophrenia.


title: Utility-maximizing event stream suppression

abstract: Complex Event Processing (CEP) has emerged as a technology for monitoring event streams in search of user specified event patterns. When a CEP system is deployed in sensitive environments the user may wish to mitigate leaks of private information while ensuring that useful nonsensitive patterns are still reported. In this paper we consider how to suppress events in a stream to reduce the disclosure of sensitive patterns while maximizing the detection of nonsensitive patterns. We first formally define the problem of utility-maximizing event suppression with privacy preferences, and analyze its computational hardness. We then design a suite of real-time solutions to solve this problem. Our first solution optimally solves the problem at the event-type level. The second solution, at the event-instance level, further optimizes the event-type level solution by exploiting runtime event distributions using advanced pattern match cardinality estimation techniques. Our user study and experimental evaluation over both real-world and synthetic event streams show that our algorithms are effective in maximizing utility yet still efficient enough to offer near real-time system responsiveness.


title: Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking

abstract: This paper proposes a new neural architecture for collaborative ranking with implicit feedback. Our model, LRML (Latent Relational Metric Learning) is a novel extension of metric learning approaches for recommendation. More specifically, instead of simple push pull mechanisms between user and item pairs, we propose to learn latent relations for each user item interaction. This helps to alleviate the potential geometric inflexibility of existing metric learning approaches. This not only enables better performance but also a greater extent of modeling capability, allowing our model to scale to larger number of interactions. In order to do so, we employ a augmented memory module and learn to attend over these memory blocks to construct latent relations. The attention module is controlled by the user-item interaction, making the learned relation vector specific to each user-item pair. Hence, this can be interpreted as learning an exclusive and optimal relational translation for each user-item interaction. The proposed architecture not only demonstrates the state-of-the-art performance across multiple recommendation benchmarks. LRML outperforms other metric learning models by 6%-7.5% in terms of Hits@10 and nDCG@10 on large datasets such as Netflix and MovieLens20M. Moreover, qualitative studies also demonstrate evidence that our proposed model is able to infer and encode explicit sentiment, temporal and attribute information despite being only trained on implicit feedback. As such, this ascertains the ability of LRML to uncover hidden relational structure within implicit datasets.


title: An architectural approach to building systems from COTS software components

abstract: As software systems become increasingly complex to build developers are turning more and more to integrating pre-built components from third party developers into their systems. This use of Commercial Off-The-Shelf (COTS) software components in system construction presents new challenges to system architects and designers. This paper is an experience report that describes issues raised when integrating COTS components, outlines strategies for integration, and presents some informal rules we have developed that ease the development and maintenance of such systems.


title: Transforming Wikipedia into Named Entity Training Data

abstract: Statistical named entity recognisers require costly hand-labelled training data and, as a result, most existing corpora are small. We exploit Wikipedia to create a massive corpus of named entity annotated text. We transform Wikipedia’s links into named entity annotations by classifying the target articles into common entity types (e.g. person, organisation and location). Comparing to MUC, CONLL and BBN corpora, Wikipedia generally performs better than other cross-corpus train/test pairs.


Induction motor versus permanent magnet synchronous motor in motion control applications : a comparative study


title: Overview of Computational Fluid Dynamics Simulation of Reactor-Scale Biomass Pyrolysis

abstract: Computational fluid dynamics (CFD) has been widely used in both scientific studies and industrial applications of reactor-scale biomass pyrolysis. In this Perspective, the state-of-the-art progress in CFD modeling of reactor-scale biomass pyrolysis was summarized and discussed. First, because of the importance of biomass pyrolysis reaction kinetics to the predictability of CFD, the commonly used pyrolysis reaction kinetics in CFD modeling of reactor-scale biomass pyrolysis were reviewed. The characteristics of each reaction kinetics were described. Then, the theoretical basis and practical applications of three main CFD modeling approaches, i.e., porous media model, multifluid model, and discrete particle model for simulating reactor-scale biomass pyrolysis were presented. The activities and progresses with respect to each CFD modeling approach for reactor-scale biomass pyrolysis were reviewed. Aspects such as experimental validation, modeling speed, and capability were discussed. Finally, the paper was c...


title: Comparative analysis of data mining methods for bankruptcy prediction

abstract: A great deal of research has been devoted to prediction of bankruptcy, to include application of data mining. Neural networks, support vector machines, and other algorithms often fit data well, but because of lack of comprehensibility, they are considered black box technologies. Conversely, decision trees are more comprehensible by human users. However, sometimes far too many rules result in another form of incomprehensibility. The number of rules obtained from decision tree algorithms can be controlled to some degree through setting different minimum support levels. This study applies a variety of data mining tools to bankruptcy data, with the purpose of comparing accuracy and number of rules. For this data, decision trees were found to be relatively more accurate compared to neural networks and support vector machines, but there were more rule nodes than desired. Adjustment of minimum support yielded more tractable rule sets.


title: You Are What You Eat: Exploring Rich Recipe Information for Cross-Region Food Analysis

abstract: Cuisine is a style of cooking and usually associated with a specific geographic region. Recipes from different cuisines shared on the web are an indicator of culinary cultures in different countries. Therefore, analysis of these recipes can lead to deep understanding of food from the cultural perspective. In this paper, we perform the first cross-region recipe analysis by jointly using the recipe ingredients, food images, and attributes such as the cuisine and course (e.g., main dish and dessert). For that solution, we propose a culinary culture analysis framework to discover the topics of ingredient bases and visualize them to enable various applications. We first propose a probabilistic topic model to discover cuisine-course specific topics. The manifold ranking method is then utilized to incorporate deep visual features to retrieve food images for topic visualization. At last, we applied the topic modeling and visualization method for three applications: 1) multimodal cuisine summarization with both recipe ingredients and images, 2) cuisine-course pattern analysis including topic-specific cuisine distribution and cuisine-specific course distribution of topics, and 3) cuisine recommendation for both cuisine-oriented and ingredient-oriented queries. Through these three applications, we can analyze the culinary cultures at both macro and micro levels. We conduct the experiment on a recipe database Yummly-66K with 66,615 recipes from 10 cuisines in Yummly. Qualitative and quantitative evaluation results have validated the effectiveness of topic modeling and visualization, and demonstrated the advantage of the framework in utilizing rich recipe information to analyze and interpret the culinary cultures from different regions.


title: Optimization of electrostatic separation processes using response surface modeling

abstract: The efficiency of electrostatic separation processes depends on a multitude of factors, including the characteristics of the granular mixtures to be sorted, the feed rate, the configuration of the electrode system, the applied high-voltage, the environmental conditions. In a previous paper the authors demonstrated the possibility of optimizing the operation of industrial electrostatic separators using rather simple computed-assisted experimental design techniques. The aim of the present work is to analyze the peculiarities of application of a more sophisticated group of response surface experimental design techniques that make use of quadratic functions for modeling the electrostatic separation process. The 11 electrostatic separation tests, corresponding to a central composite design, were carried out on samples of chopped electric wire wastes. The CARPCO laboratory roll-type electrostatic separator employed for this study enabled a rigorous control of two factors: the applied high-voltage level and the speed of the rotating roll electrode. The objective was to maximize the benefits from the recycling of both constituents of the binary copper-PVC granular mixture. The optimum operating conditions computed with the quadratic model derived from the experimental results were in good agreement with the data of pilot-plant tests. The response surface methodology can be easily applied to most of the industrial applications of electrostatic separation technologies.


title: Rayyan—a web and mobile app for systematic reviews

abstract: Background ::: Synthesis of multiple randomized controlled trials (RCTs) in a systematic review can summarize the effects of individual outcomes and provide numerical answers about the effectiveness of interventions. Filtering of searches is time consuming, and no single method fulfills the principal requirements of speed with accuracy. Automation of systematic reviews is driven by a necessity to expedite the availability of current best evidence for policy and clinical decision-making. ::: ::: We developed Rayyan (http://rayyan.qcri.org), a free web and mobile app, that helps expedite the initial screening of abstracts and titles using a process of semi-automation while incorporating a high level of usability. For the beta testing phase, we used two published Cochrane reviews in which included studies had been selected manually. Their searches, with 1030 records and 273 records, were uploaded to Rayyan. Different features of Rayyan were tested using these two reviews. We also conducted a survey of Rayyan’s users and collected feedback through a built-in feature.


title: Localizing and Quantifying Damage in Social Media Images

abstract: Traditional post-disaster assessment of damage heavily relies on expensive GIS data, especially remote sensing image data. In recent years, social media has become a rich source of disaster information that may be useful in assessing damage at a lower cost. Such information includes text (e.g., tweets) or images posted by eyewitnesses of a disaster. Most of the existing research explores the use of text in identifying situational awareness information useful for disaster response teams. The use of social media images to assess disaster damage is limited. In this paper, we propose a novel approach, based on convolutional neural networks and class activation maps, to locate damage in a disaster image and to quantify the degree of the damage. Our proposed approach enables the use of social network images for post-disaster damage assessment, and provides an inexpensive and feasible alternative to the more expensive GIS approach.


title: Theoretical Analysis of Image-to-Image Translation with Adversarial Learning

abstract: Recently, a unified model for image-to-image translation tasks within adversarial learning framework has aroused widespread research interests in computer vision practitioners. Their reported empirical success however lacks solid theoretical interpretations for its inherent mechanism. In this paper, we reformulate their model from a brand-new geometrical perspective and have eventually reached a full interpretation on some interesting but unclear empirical phenomenons from their experiments. Furthermore, by extending the definition of generalization for generative adversarial nets to a broader sense, we have derived a condition to control the generalization capability of their model. According to our derived condition, several practical suggestions have also been proposed on model design and dataset construction as a guidance for further empirical researches.