title: Applications of data mining in robotics

abstract: From a high-level viewpoint, robotics is all about data. In other words, each robot agent for performing its designated tasks should deal with data in some form - from collecting data from the environment to generating internal data. For this reason and due to broad range of possible applications, managing and dealing with such data is an important issue in the field. On the other side, data - in its different types - is known as an invaluable treasure of knowledge and insight within any information system. In this regard, extracting such hidden knowledge through data mining has been in the center of focus for more than two decades. In fact, data mining could be considered as a super-influential phenomenon on all scientific fields and robotics is not an exception. Over the recent years, many studies have been conducted on different aspects of leveraging data mining techniques in the robotic domain to improve performance of robots in various ways. However, despite the current disparate and unorganized studies in different directions, there are some potential areas that have not received enough attention. In this paper, we provide a high-level overview of the field and try to organize the current studies as a reference point and road map for future works. Moreover, challenges and future possible directions will be studied.


title: User Personality and Resistance to Mandatory Information Systems in Organizations: A Theoretical Model and Empirical Test of Dispositional Resistance to Change

abstract: This research is driven by the assumption made in several user resistance studies that employees are generally resistant to change. It investigates the extent to which employees’ resistance to IT-induced change is caused by individuals’ predisposition to resist change. We develop a model of user resistance that assumes the influence of dispositional resistance to change on perceptual resistance to change, perceived ease of use, and usefulness, which in turn influence user resistance behavior. Using an empirical study of 106 HR employees forced to use a new human resources information system, the analysis reveals that 17.0–22.1 percent of the variance in perceived ease of use, usefulness, and perceptual resistance to change can be explained by the dispositional inclination to change initiatives. The four dimensions of dispositional resistance to change – routine seeking, emotional reaction, short-term focus and cognitive rigidity – have an even stronger effect than other common individual variables, such as age, gender, or working experiences. We conclude that dispositional resistance to change is an example of an individual difference that is instrumental in explaining a large proportion of the variance in beliefs about and user resistance to mandatory IS in organizations, which has implications for theory, practice, and future research.


title: Attention pooling-based convolutional neural network for sentence modelling

abstract: Convolutional neural network has been proven to be a powerful semantic composition model for modelling sentences. A standard convolutional neural network usually consists of several convolutional and pooling layers at the bottom of a linear or non-linear classifier. In this paper, a new pooling scheme termed Attention Pooling is proposed to retain the most significant information at the pooling stage. An intermediate sentence representation generated by the bidirectional long short-term memory is used as a reference for local representations produced by the convolutional layer to obtain attention weights. The sentence representation is formed by combining local representations using obtained attention weights. The intermediate sentence representation is used as an input to the top classifier as well in the testing phase. The salient features of the proposed attention pooling-based convolutional neural network are: (1) The model can be trained end-to-end with limited hyper-parameters; (2) Comprehensive information is extracted by the new pooling scheme and the combination of the convolutional layer and the bidirectional long-short term memory; (3) The model can implicitly separate the sentences from different classes. Experimental results demonstrate that the new model outperforms the state-of-the-art approaches on seven benchmark datasets for text classification. The learning capability of the proposed method is greatly improved and the classification accuracy is even enhanced significantly by over 2% on some datasets. The robustness of the proposed model is evidenced by some statistical tests.


title: Morphological Error Detection in 3D Segmentations

abstract: Deep learning algorithms for connectomics rely upon localized classification, rather than overall morphology. This leads to a high incidence of erroneously merged objects. Humans, by contrast, can easily detect such errors by acquiring intuition for the correct morphology of objects. Biological neurons have complicated and variable shapes, which are challenging to learn, and merge errors take a multitude of different forms. We present an algorithm, MergeNet, that shows 3D ConvNets can, in fact, detect merge errors from high-level neuronal morphology. MergeNet follows unsupervised training and operates across datasets. We demonstrate the performance of MergeNet both on a variety of connectomics data and on a dataset created from merged MNIST images.


Data Mining Techniques for Credit Risk Assessment Task


title: Fault Detection in Wireless Sensor Networks Through SVM Classifier

abstract: Wireless sensor networks (WSNs) are prone to many failures such as hardware failures, software failures, and communication failures. The fault detection in WSNs is a challenging problem due to sensor resources limitation and the variety of deployment field. Furthermore, the detection has to be precise to avoid negative alerts, and rapid to limit loss. The use of machine learning seems to be one of the most convenient solutions for detecting failure in WSNs. In this paper, support vector machines (SVMs) classification method is used for this purpose. Based on statistical learning theory, SVM is used in our context to define a decision function. As a light process in term of required resources, this decision function can be easily executed at cluster heads to detect anomalous sensor. The effectiveness of SVM for fault detection in WSNs is shown through an experimental study, comparing it to latest for the same application.


title: Cascade force control of lower limb hydraulic exoskeleton for human performance augmentation

abstract: Recently the research on hydraulically actuated exoskeleton becomes an attractive topic for those application requirements of human performance augmentation. The control goal of this type of exoskeleton system is to minimize the human machine interaction force. And it becomes more challenging for hydraulically actuated lower limb exoskeleton where the multi-variable nonlinear dynamics is quite complicated and multiple walking phases are existing. Furthermore, since the exoskeleton is driven by the hydraulic actuators, the accurate output force tracking can not be easily realized due to the large compressibility of hydraulic oil. This paper focuses on the human machine interaction force control and the walking phase partition of the hydraulically actuated lower limb exoskeleton. Firstly, a cascade interaction force control strategy is proposed for a 3-DOF support leg which is the basic partitioned module of the lower limb exoskeleton. The spring model is built for the dynamics of human-machine interface, and a high level controller minimizing the integral of human-machine interaction force is designed to generate the desired joint trajectories of the exoskeleton which can be considered as the human motion intent. Subsequently, an independent joint based PID controller is developed in the low level to achieve the good tracking of the above generated human motion intent. Secondly, the exoskeleton system in different walking phases is partitioned into three serial chain manipulator modules. For each serial chain manipulator module, the proposed cascade interaction force controller is applied to minimize the human machine interaction force at the end effector. Finally, the walking experiments with 20Kg load on a practical hydraulically actuated lower limb exoskeleton are carried out to validate the effectiveness of the proposed approach.


title: Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization

abstract: Performance of machine learning algorithms depends critically on identifying a good set of hyperparameters. While recent approaches use Bayesian optimization to adaptively select configurations, we focus on speeding up random search through adaptive resource allocation and early-stopping. We formulate hyperparameter optimization as a pure-exploration non-stochastic infinite-armed bandit problem where a predefined resource like iterations, data samples, or features is allocated to randomly sampled configurations. We introduce a novel algorithm, Hyperband, for this framework and analyze its theoretical properties, providing several desirable guarantees. Furthermore, we compare Hyperband with popular Bayesian optimization methods on a suite of hyperparameter optimization problems. We observe that Hyperband can provide over an order-of-magnitude speedup over our competitor set on a variety of deep-learning and kernel-based learning problems.


title: Real-time scene text localization and recognition

abstract: An end-to-end real-time scene text localization and recognition method is presented. The real-time performance is achieved by posing the character detection problem as an efficient sequential selection from the set of Extremal Regions (ERs). The ER detector is robust to blur, illumination, color and texture variation and handles low-contrast text. In the first classification stage, the probability of each ER being a character is estimated using novel features calculated with O(1) complexity per region tested. Only ERs with locally maximal probability are selected for the second stage, where the classification is improved using more computationally expensive features. A highly efficient exhaustive search with feedback loops is then applied to group ERs into words and to select the most probable character segmentation. Finally, text is recognized in an OCR stage trained using synthetic fonts. The method was evaluated on two public datasets. On the ICDAR 2011 dataset, the method achieves state-of-the-art text localization results amongst published methods and it is the first one to report results for end-to-end text recognition. On the more challenging Street View Text dataset, the method achieves state-of-the-art recall. The robustness of the proposed method against noise and low contrast of characters is demonstrated by “false positives” caused by detected watermark text in the dataset.


title: Are there social limits to adaptation to climate change

abstract: While there is a recognised need to adapt to changing climatic conditions, there is an emerging discourse of limits to such adaptation. Limits are traditionally analysed as a set of immutable thresholds in biological, economic or technological parameters. This paper contends that limits to adaptation are endogenous to society and hence contingent on ethics, knowledge, attitudes to risk and culture. We review insights from history, sociology and psychology of risk, economics and political science to develop four propositions concerning limits to adaptation. First, any limits to adaptation depend on the ultimate goals of adaptation underpinned by diverse values. Second, adaptation need not be limited by uncertainty around future foresight of risk. Third, social and individual factors limit adaptation action. Fourth, systematic undervaluation of loss of places and culture disguises real, experienced but subjective limits to adaptation. We conclude that these issues of values and ethics, risk, knowledge and culture construct societal limits to adaptation, but that these limits are mutable.


title: Direct and Indirect Discrimination Prevention Methods

abstract: Along with privacy, discrimination is a very important issue when considering the legal and ethical aspects of data mining. It is more than obvious that most people do not want to be discriminated because of their gender, religion, nationality, age and so on, especially when those attributes are used for making decisions about them like giving them a job, loan, insurance, etc. Discovering such potential biases and eliminating them from the training data without harming their decision-making utility is therefore highly desirable. For this reason, anti-discrimination techniques including discrimination discovery and prevention have been introduced in data mining. Discrimination prevention consists of inducing patterns that do not lead to discriminatory decisions even if the original training datasets are inherently biased. In this chapter, by focusing on the discrimination prevention, we present a taxonomy for classifying and examining discrimination prevention methods. Then, we introduce a group of pre-processing discrimination prevention methods and specify the different features of each approach and how these approaches deal with direct or indirect discrimination. A presentation of metrics used to evaluate the performance of those approaches is also given. Finally, we conclude our study by enumerating interesting future directions in this research body.


EDISON: Feature Extraction for NLP, Simplified.


title: A Fast PC Algorithm for High Dimensional Causal Discovery with Multi-Core PCs

abstract: Discovering causal relationships from observational data is a crucial problem and it has applications in many research areas. The PC algorithm is the state-of-the-art constraint based method for causal discovery. However, runtime of the PC algorithm, in the worst-case, is exponential to the number of nodes (variables), and thus it is inefficient when being applied to high dimensional data, e.g., gene expression datasets. On another note, the advancement of computer hardware in the last decade has resulted in the widespread availability of multi-core personal computers. There is a significant motivation for designing a parallelized PC algorithm that is suitable for personal computers and does not require end users’ parallel computing knowledge beyond their competency in using the PC algorithm. In this paper, we develop parallel-PC, a fast and memory efficient PC algorithm using the parallel computing technique. We apply our method to a range of synthetic and real-world high dimensional datasets. Experimental results on a dataset from the DREAM 5 challenge show that the original PC algorithm could not produce any results after running more than 24 hours; meanwhile, our parallel-PC algorithm managed to finish within around 12 hours with a 4-core CPU computer, and less than six hours with a 8-core CPU computer. Furthermore, we integrate parallel-PC into a causal inference method for inferring miRNA-mRNA regulatory relationships. The experimental results show that parallel-PC helps improve both the efficiency and accuracy of the causal inference algorithm.


title: Panoramic video stitching from commodity HDTV cameras

abstract: Digital camera and smartphone technologies have made high-quality images and video pervasive and abundant. Combining or stitching collections of images from a variety of viewpoints into an extended panoramic image is a common and popular function for such devices. Extending this functionality to video however, poses many new challenges due to the demand for both spatial and temporal continuity. Multi-view video stitching (also called panoramic video stitching) is an emerging, common research area in computer vision, image/video processing and computer graphics and has wide applications in virtual reality, virtual tourism, surveillance, and human computer interaction. In this paper, we study and solve the major technical and practical problems in the complete process of stitching a high-resolution multi-view video into a high-resolution panoramic video. The challenges addressed include video stabilization, efficient high-definition multi-view video alignment and stitching, color correction, and blurred frame detection and repair. The proposed approaches have been successfully applied in a high-quality virtual reality system--the Virtual Exercise Environment (VEE) system.


title: Context based object categorization: A critical survey

abstract: The goal of object categorization is to locate and identify instances of an object category within an image. Recognizing an object in an image is difficult when images include occlusion, poor quality, noise or background clutter, and this task becomes even more challenging when many objects are present in the same scene. Several models for object categorization use appearance and context information from objects to improve recognition accuracy. Appearance information, based on visual cues, can successfully identify object classes up to a certain extent. Context information, based on the interaction among objects in the scene or global scene statistics, can help successfully disambiguate appearance inputs in recognition tasks. In this work we address the problem of incorporating different types of contextual information for robust object categorization in computer vision. We review different ways of using contextual information in the field of object categorization, considering the most common levels of extraction of context and the different levels of contextual interactions. We also examine common machine learning models that integrate context information into object recognition frameworks and discuss scalability, optimizations and possible future approaches.


title: Compact magnetic loop probe for microwave EM field-mapping and its applications in dielectric constant measurement

abstract: A novel compact CPW-type magnetic loop probe (2.3times2.3 mm) is proposed. Unlike the conventional one[1], the new probe move the impedance-transition to the CPW line part, so that the new probe possesses the property of symmetry (to suppress the influence of induced magnetic field created by the probe) as well as inherits/enhances the ability to suppress the loop self resonance(up to 10 GHz). Based on this probe, a 2.5D automatic vector near-field measurement system is developed by using the automatic network analyzer (ANA) as the signal detector. In this preliminary study, the magnetic field distribution of a section of short-circuited microstrip line and a square-loop type resonator are scanned by the novel probe. For reference, the electric field measurement results and theoretical ones by 3D simulator are all given. The good comparison between the measured results with the reference data shows the good performance of this probe. In addition, an extended study on extracting the effective dielectric constant of the substrate by our probe is also included with the experimental validation.


title: Microglia in neurodegenerative disease

abstract: Microglia rapidly adopt an activated phenotype in response to brain injury and disease, which suggests that they could act as diagnostic markers of neurodegenerative disease onset or progression. In this article, Perry et al. review the relationship between activated microglia and neurodegenerative diseases and discuss the interactions between microglial phenotype, systemic inflammation and neurodegenerative disease progression.


title: Interpolation and extrapolation in conceptual spaces: a case study in the music domain

abstract: In most knowledge representation settings, atomic properties correspond to natural language labels. Although these labels are usually taken to be primitive, automating some forms of commonsense inference requires background knowledge on the cognitive meaning of these labels. We consider two such forms of commonsense reasoning, which we refer to as interpolative and extrapolative reasoning. In both cases, rule-based knowledge is augmented with knowledge about the geometric representation of labels in a conceptual space. Specifically, to support interpolative reasoning, we need to know which labels are conceptually between which other labels, considering that intermediary conditions tend to lead to intermediary conclusions. Extrapolative reasoning is based on information about the direction of change that is needed when replacing one label by another, taking the view that parallel changes in the conditions of rules tend to lead to parallel changes in the conclusions. In this paper, we propose a practical method to acquire such knowledge about the conceptual spaces representation of labels. We illustrate the method in the domain of music genres, starting from meta-data that was obtained from the music recommendation website last.fm.


title: Smart City Components Architicture

abstract: The research is essentially to modularize the structure of utilities and develop a system for following up the activities electronically on the city scale. The GIS operational platform will be the base for managing the infrastructure development components with the systems interoperability for the available city infrastructure related systems. The concentration will be on the available utility networks in order to develop a comprehensive, common, standardized geospatial data models. The construction operations for the utility networks such as electricity, water, Gas, district cooling, irrigation, sewerage and communication networks; are need to be fully monitored on daily basis, in order to utilize the involved huge resources and man power. These resources are allocated only to convey the operational status for the construction and execution sections that used to do the required maintenance. The need for a system that serving the decision makers for following up these activities with a proper geographical representation will definitely reduce the operational cost for the long term.


title: A nonlinear dynamic artificial neural network model of memory

abstract: Abstract Nonlinearity and dynamics in psychology are found in various domains such as neuroscience, cognitive science, human development, etc. However, the models that have been proposed are mostly of a computational nature and ignore dynamics. In those models that do include dynamic properties, only fixed points are used to store and retrieve information, leaving many principles of nonlinear dynamic systems (NDS) aside; for instance, chaos is often perceived as a nuisance. This paper considers a nonlinear dynamic artificial neural network (NDANN) that implements NDS principles while also complying with general neuroscience constraints. After a theoretical presentation, simulation results will show that the model can exhibit multi-valued, fixed-point, region-constrained attractors and aperiodic (including chaotic) behaviors. Because the capabilities of NDANN include the modeling of spatiotemporal chaotic activities, it may be an efficient tool to help bridge the gap between biological memory neural models and behavioral memory models.


title: METHODOLOGY Effect sizes and p values: What should be reported and what should be replicated?

abstract: Despite publication of many well-argued critiques of null hypothesis testing (NHT). behavioral science researchers continue to rely heavily on this set of practices. Although we agree with most critics' catalogs of NHT's flaws, this article also takes the unusual stance of identifying virtues that may explain why NHT continues to he so extensively used. These virtues include providing results in the form of a dichotomous (yes/no) hypothesis evaluation and providing an index (p value) Mini has a justifiable mapping onto confidence in repeatability of a null hypothesis rejection. The most-criticized flaws of NHT can be avoided when the importance of a hypothesis, rather than the p value of its test, is used to determine that a finding is worthy of report, and when p=.05 is treated as insufficient basis for confidence in the replicability of an isolated non-null finding. Together with many recent critics of NHT, we also urge reporting of important hypothesis tests in enough descriptive detail to permit secondary uses such as meta-analysis.


title: Friends, Fans, and Followers: Do Ads Work on Social Networks?

abstract: ABSTRACT Social-networking sites (SNS) such as Facebook and Twitter are growing in both popularity and number of users. For advertisers and the sites themselves, it is crucial that users accept advertising as a component of the SNS. Anecdotal evidence indicates that social-networking advertising (SNA) can be effective when users accept it, but the perception of excessive commercialization may lead to user abandonment. Empirical support for these propositions, however, is lacking. Based on media uses and gratification theory, the authors propose and empirically test a model of content-related, structural, and socialization factors that affect users9 attitudes toward SNA.


title: A Survey on Data-Flow Testing

abstract: Data-flow testing (DFT) is a family of testing strategies designed to verify the interactions between each program variable’s definition and its uses. Such a test objective of interest is referred to as a def-use pair. DFT selects test data with respect to various test adequacy criteria (i.e., data-flow coverage criteria) to exercise each pair. The original conception of DFT was introduced by Herman in 1976. Since then, a number of studies have been conducted, both theoretically and empirically, to analyze DFT’s complexity and effectiveness. In the past four decades, DFT has been continuously concerned, and various approaches from different aspects are proposed to pursue automatic and efficient data-flow testing. This survey presents a detailed overview of data-flow testing, including challenges and approaches in enforcing and automating it: (1) it introduces the data-flow analysis techniques that are used to identify def-use pairs; (2) it classifies and discusses techniques for data-flow-based test data generation, such as search-based testing, random testing, collateral-coverage-based testing, symbolic-execution-based testing, and model-checking-based testing; (3) it discusses techniques for tracking data-flow coverage; (4) it presents several DFT applications, including software fault localization, web security testing, and specification consistency checking; and (5) it summarizes recent advances and discusses future research directions toward more practical data-flow testing.


title: Endogenous Waves in Hippocampal Slices

abstract: Sharp waves (SPWs) are thought to play a major role in intrinsic hippocampal operations during states in which subcortical and cortical inputs to hippocampus are reduced. This study describes evidence that such activity occurs spontaneously in appropriately prepared rat hippocampal slices. Irregular waves, with an average frequency of approximately 4 Hz, were recorded from field CA3 in slices prepared from the temporal region of hippocampus. The waves persisted for hours and were not accompanied by aberrant discharges. Multi-electrode analyses established that they were locally generated within each of the subfields of CA3 and yet were coherent between subfields. The sharp waves were reversibly blocked by either cholinergic or serotonergic stimulation. Various lines of evidence indicate that they are propagated by the CA3 associational system.


title: Comparison of irrigation automation algorithms for drip-irrigated apple trees

abstract: We developed a decision support system integrated with a wireless sensor network.We developed and field-tested seven irrigation scheduling algorithms for drip-irrigated apples.We used the time-temperature threshold (TTT) method in apple trees for the first time.The thermal-based treatments competed well with neutron probe (NP).Thermal/weather-based irrigation scheduling reduced applied water by about 70% compared with conventional irrigation. Seven irrigation scheduling algorithms and an automatic control system along with a wireless network of soil, thermal and weather sensors were developed and assessed in Prosser, WA in the growing season of 2013. The system was comprised of six wireless sensor and valve actuating nodes installed across an apple orchard, a central base station made up of a transceiver connected to a laptop, and a graphical user interface (GUI). The irrigation algorithms/treatments included the time-temperature threshold (TTT), crop water stress index with dynamic threshold (CWSI), soil-based using granular matrix sensors (SOIL), weather-based using a temperature-only-based evapotranspiration (ET) model and soil water balance (WB), a combination of SOIL and WB (SL+WB), a conventional irrigation practice used in the region (CNTRL), and soil-based using a neutron probe (NP) as benchmark. Different treatments were compared based on the total irrigation water (It) applied during the season. They were also compared based on simplicity and expense for a grower to implement. Soil water content (źs) and stem water potential (źstem) were monitored in a number of treatment plots. The total applied water for CNTRL was significantly higher than all other treatments (p<0.001). The thermal-based TTT and CWSI treatments applied the same amount of water as NP and WB (p<0.001). CWSI and TTT substantially reduced water applied (70%) while maintaining źstem within the non-stressed range. In addition, źs in the treatment plots of TTT and CWSI did not exceed the maximum allowed deficit recommended for apple trees (MAD of 50%) showing a strong agreement with NP. The SOIL and SL+WB treatments resulted in tangible under-irrigation as leaf drop, decreased leaf turgidity, growth reduction and abnormally small fruits were seen. Among all the strategies, WB seemed to bear the characteristics of being economical, easy to implement and fairly accurate. Our preliminary results also support the use of wireless sensor network for automatic irrigation management of drip-irrigated apple trees.


title: Automated Vehicles: Take-Over Request and System Prompt Evaluation

abstract: As automated driving technology advances, the driver’s role continues to shift from active vehicle control to passive monitoring of the automated driving system and environment. This study comprised three experiments on controlled test tracks in mixed traffic that investigated driver interactions with prototype Level 2 and Level 3 partially automated driving systems. The study investigated which human-machine interface (HMI) characteristics are most effective at issuing a Take-Over Request (TOR) during the operation of a Level 2 automated driving system, how to prompt drivers to attend to the road when distracted during the operation of a Level 2 automated driving system (and whether these prompts are effective over time), and which HMI characteristics are most effective at issuing a TOR during the operation of a Level 3 automated driving system. In addition, participants’ trust in the automated driving system they experienced was gauged through multiple Likert-type surveys and an after-experience interview.


title: Fast classification of univariate and multivariate time series through shapelet discovery

abstract: Time-series classification is an important problem for the data mining community due to the wide range of application domains involving time-series data. A recent paradigm, called shapelets, represents patterns that are highly predictive for the target variable. Shapelets are discovered by measuring the prediction accuracy of a set of potential (shapelet) candidates. The candidates typically consist of all the segments of a dataset; therefore, the discovery of shapelets is computationally expensive. This paper proposes a novel method that avoids measuring the prediction accuracy of similar candidates in Euclidean distance space, through an online clustering/pruning technique. In addition, our algorithm incorporates a supervised shapelet selection that filters out only those candidates that improve classification accuracy. Empirical evidence on 45 univariate datasets from the UCR collection demonstrates that our method is 3---4 orders of magnitudes faster than the fastest existing shapelet discovery method, while providing better prediction accuracy. In addition, we extended our method to multivariate time-series data. Runtime results over four real-life multivariate datasets indicate that our method can classify MB-scale data in a matter of seconds and GB-scale data in a matter of minutes. The achievements do not compromise quality; on the contrary, our method is even superior to the multivariate baseline in terms of classification accuracy.


title: The capacity of wireless networks

abstract: When n identical randomly located nodes, each capable of transmitting at W bits per second and using a fixed range, form a wireless network, the throughput /spl lambda/(n) obtainable by each node for a randomly chosen destination is /spl Theta/(W//spl radic/(nlogn)) bits per second under a noninterference protocol. If the nodes are optimally placed in a disk of unit area, traffic patterns are optimally assigned, and each transmission's range is optimally chosen, the bit-distance product that can be transported by the network per second is /spl Theta/(W/spl radic/An) bit-meters per second. Thus even under optimal circumstances, the throughput is only /spl Theta/(W//spl radic/n) bits per second for each node for a destination nonvanishingly far away. Similar results also hold under an alternate physical model where a required signal-to-interference ratio is specified for successful receptions. Fundamentally, it is the need for every node all over the domain to share whatever portion of the channel it is utilizing with nodes in its local neighborhood that is the reason for the constriction in capacity. Splitting the channel into several subchannels does not change any of the results. Some implications may be worth considering by designers. Since the throughput furnished to each user diminishes to zero as the number of users is increased, perhaps networks connecting smaller numbers of users, or featuring connections mostly with nearby neighbors, may be more likely to be find acceptance.


title: CoRaL--Policy Language and Reasoning Techniques for Spectrum Policies

abstract: We present the cognitive radio (policy) language (CoRaL), a new language for expressing policies that govern the behavior of cognitive radios that opportunistically share spectrum. A Policy Reasoner validates radio transmissions to ensure that they are compliant with the spectrum policies. The Policy Reasoner also discovers spectrum sharing opportunities by deriving what requirements must be fulfilled for transmissions to be valid, i.e., in compliance with policies. A novel mix of reasoning techniques is required to implement such a reasoner.


title: IoT: The era of LPWAN is starting now

abstract: This paper focusses on LPWAN segment of IoT, describing network constraints and comparing existing and upcoming solutions in unlicensed and licensed frequency bands.


title: Deep Kernel Learning

abstract: We introduce scalable deep kernels, which combine the structural properties of deep learning architectures with the non-parametric flexibility of kernel methods. Specifically, we transform the inputs of a spectral mixture base kernel with a deep architecture, using local kernel interpolation, inducing points, and structure exploiting (Kronecker and Toeplitz) algebra for a scalable kernel representation. These closed-form kernels can be used as drop-in replacements for standard kernels, with benefits in expressive power and scalability. We jointly learn the properties of these kernels through the marginal likelihood of a Gaussian process. Inference and learning cost $O(n)$ for $n$ training points, and predictions cost $O(1)$ per test point. On a large and diverse collection of applications, including a dataset with 2 million examples, we show improved performance over scalable Gaussian processes with flexible kernel learning models, and stand-alone deep architectures.


title: Ivy: Exploring Spatially Situated Visual Programming for Authoring and Understanding Intelligent Environments

abstract: The availability of embedded, digital systems has led to a multitude of interconnected sensors and actuators being distributed among smart objects and built environments. Programming and understanding the behaviors of such systems can be challenging given their inherent spatial nature. To explore how spatial and contextual information can facilitate the authoring of intelligent environments, we introduce Ivy, a spatially situated visual programming tool using immersive virtual reality. Ivy allows users to link smart objects, insert logic constructs, and visualize real-time data flows between real-world sensors and actuators. Initial feedback sessions show that participants of varying skill levels can successfully author and debug programs in example scenarios.


title: Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification

abstract: In the framework of computer-aided diagnosis of eye diseases, retinal vessel segmentation based on line operators is proposed. A line detector, previously used in mammography, is applied to the green channel of the retinal image. It is based on the evaluation of the average grey level along lines of fixed length passing through the target pixel at different orientations. Two segmentation methods are considered. The first uses the basic line detector whose response is thresholded to obtain unsupervised pixel classification. As a further development, we employ two orthogonal line detectors along with the grey level of the target pixel to construct a feature vector for supervised classification using a support vector machine. The effectiveness of both methods is demonstrated through receiver operating characteristic analysis on two publicly available databases of color fundus images.


Smile Esthetics – A Literature


title: Impact of an intelligent jammer on the emergency services' communication system

abstract: C2000 is a communication system based on the Terrestrial Trunked Radio (TETRA) protocol and is used in the Netherlands by emergency services. We investigated the ::: resilience of the voice component of C2000 against intelligent intentional electromagnetic interference (IEMI). Low power signals interfering with the system’s higher protocols have the advantage of staying covert. The analysis shows that if the access assignment channel is corrupted, the mobile stations cannot start conversations with the base station. TETRA’s modulation scheme is also investigated. /4 Differential Quaternary Phase Shift Keying (DQPSK) is interfered with a continuous wave and a Quaternary Phase Shift Keying (QPSK) signal. The results show that a continuous wave created the largest error vector magnitude (EVM), but creates a peak in the received spectrum. The power of the QPSK signal, however, is distributed over a bandwidth and is more difficult to detect than the continuous wave in the received spectrum. From this we conclude that the QPSK signal functions better as an intelligent interference signal compared to the continuous wave. In this thesis it is shown that it is possible to create a jammer that combines the vulnerability in the TETRA protocol with the QPSK signal to disrupt the communication system, while staying covert. This can cause dangerous situations during large calamities, but also during small felonies where adversaries can disrupt the communication locally.


Teechain: Scalable Blockchain Payments using Trusted Execution Environments


title: Operative strategy in postero-medial fracture-dislocation of the proximal tibia

abstract: Abstract Objective In 1981, Moore introduced a new classification for dislocation-type fractures caused by high-energy mechanisms. The most common medial Moore-type fractures are entire condyle fractures with the avulsion of the median eminence (tibial anterior cruciate ligament (ACL) insertion). They are usually associated with a posterolateral depression of the tibial plateau and an injury of the lateral menisco-tibial capsule. This uniform injury of the knee is increasingly observed in the recent years after skiing injuries due to the high-speed carving technique. This uprising technique uses shorter skis with more sidecut allowing much higher curve speeds and increases the forces on the knee joint. The aim of this study was to describe the injury pattern, our developed operative approach for reconstruction and outcome. Methods A total of 28 patients with 29 postero-medial fracture dislocation of the proximal tibia from 2001 until 2009 were analysed. Clinical and radiological follow-up was performed after 4 years on average (1 year in minimum). Evaluation criteria included the Lysholm score for everyday knee function and the Tegner score evaluating the activity level. All fractures were stabilised post primarily. The surgical main approach was medial. First, the exposure of the entire medial condyle fracture was performed following the fracture line to the articular border. The posterolateral impaction was addressed directly through the main fracture gap from anteromedial to posterolateral. Small plateau fragments were removed, larger fragments reduced and preliminarily fixed with separate K-wire(s). The postero-medial part of the condyle was then prepared for main reduction and application of a buttress T-plate in a postero-medial position, preserving the pes anserinus and medial collateral ligament. In addition, a parapatellar medial mini-arthrotomy through the same main medial approach was performed. Through this mini-arthrotomy, the avulsed anterior eminence with attached distal ACL is retained by a transosseous suture back to the tibia. Results We could evaluate 26 patients (93%); two patients were lost to follow-up due to foreign residence. Median age was 51 years (20–77 years). The fractures were treated post primarily at an average of 4 days; in 18 cases a two-staged procedure with initial knee-spanning external fixator was used. All fractures healed without secondary displacement or infection. As many as 25 patients showed none to moderate osteoarthritis after a median of 4 years. One patient showed a severe osteoarthritis after 8 years. All patients judge the clinical result as good to excellent. The Lysholm score reached 95 (75–100) of maximal 100 points and the Tegner activity score 5 (3–7) of maximal 10 points (competitive sports). The patients achieved a median flexion of 135° (100–145°). Conclusion In our view, it is crucial to recognise the different components of the injury in the typical postero-medial fracture dislocation of the proximal tibia. The described larger medial approach for this type of medial fracture dislocation allows repairing most of the injured aspects of the tibial head, namely the medial condyle with postero-medial buttressing, the distal insertion of the ACL and the posterolateral impaction of the plateau.


title: A Sentence Simplification System for Improving Relation Extraction

abstract: In this demo paper, we present a text simplification approach that is directed at improving the performance of state-of-the-art Open Relation Extraction (RE) systems. As syntactically complex sentences often pose a challenge for current Open RE approaches, we have developed a simplification framework that performs a pre-processing step by taking a single sentence as input and using a set of syntactic-based transformation rules to create a textual input that is easier to process for subsequently applied Open RE systems.


title: Face alignment by coarse-to-fine shape searching

abstract: We present a novel face alignment framework based on coarse-to-fine shape searching. Unlike the conventional cascaded regression approaches that start with an initial shape and refine the shape in a cascaded manner, our approach begins with a coarse search over a shape space that contains diverse shapes, and employs the coarse solution to constrain subsequent finer search of shapes. The unique stage-by-stage progressive and adaptive search i) prevents the final solution from being trapped in local optima due to poor initialisation, a common problem encountered by cascaded regression approaches; and ii) improves the robustness in coping with large pose variations. The framework demonstrates real-time performance and state-of-the-art results on various benchmarks including the challenging 300-W dataset.


title: Lessons Learned in Using Social Media for Disaster Relief- ASU Crisis Response Game

abstract: In disasters such as the earthquake in Haiti and the tsunami in Japan, people used social media to ask for help or report injuries. The popularity, efficiency, and ease of use of social media has led to its pervasive use during the disaster. This creates a pool of timely reports about the disaster, injuries, and help requests. This offers an alternative opportunity for first responders and disaster relief organizations to collect information about the disaster, victims, and their needs. It also presents a challenge for these organizations to aggregate and process the requests from different social media. Given the sheer volume of requests, it is necessary to filter reports and select those of high priority for decision making. Little is known about how the two phases should be smoothly integrated. In this paper we report the use of social media during a simulated crisis and crisis response process, the ASU Crisis Response Game. Its main objective is to creat a training capability to understand how to use social media in crisis. We report lessons learned from this exercise that may benefit first responders and NGOs who use social media to manage relief efforts during the disaster.


title: System-level throughput of NOMA using intra-beam superposition coding and SIC in MIMO downlink when channel estimation error exists

abstract: We investigate the influence of channel estimation error on the achievable system-level throughput performance of our previous non-orthogonal multiple access (NOMA) scheme in a multiple-input multiple-output (MIMO) downlink. The NOMA scheme employs intra-beam superposition coding of a multiuser signal at the transmitter and spatial filtering of inter-beam interference followed by an intra-beam successive interference canceller (SIC) at the user terminal receiver. The intra-beam SIC cancels the inter-user interference within a beam. This configuration achieves reduced overhead for the downlink reference signaling for channel estimation at the user terminal in the case of non-orthogonal user multiplexing and enables the SIC receiver to be applied to the MIMO downlink. The channel estimation error in the NOMA scheme causes residual interference in the SIC process, which decreases the achievable user throughput. Furthermore, the channel estimation error causes error In the transmission rate control for the respective users, which may result in decoding error at not only the destination user terminal but also at other user terminals for the SIC process. However, we show that by using a simple transmission rate back-off algorithm, the impact of the channel estimation error is effectively abated and the NOMA scheme achieves clear average and cell-edge user throughput gains relative to orthogonal multiple access (OMA) similar to the case with perfect channel estimation.


title: Higher-order Coreference Resolution with Coarse-to-fine Inference

abstract: We introduce a fully differentiable approximation to higher-order inference for coreference resolution. Our approach uses the antecedent distribution from a span-ranking architecture as an attention mechanism to iteratively refine span representations. This enables the model to softly consider multiple hops in the predicted clusters. To alleviate the computational cost of this iterative process, we introduce a coarse-to-fine approach that incorporates a less accurate but more efficient bilinear factor, enabling more aggressive pruning without hurting accuracy. Compared to the existing state-of-the-art span-ranking approach, our model significantly improves accuracy on the English OntoNotes benchmark, while being far more computationally efficient.


title: Cross-Domain Adversarial Auto-Encoder

abstract: In this paper, we propose the Cross-Domain Adversarial Auto-Encoder (CDAAE) to address the problem of cross-domain image inference, generation and transformation. We make the assumption that images from different domains share the same latent code space for content, while having separate latent code space for style. The proposed framework can map cross-domain data to a latent code vector consisting of a content part and a style part. The latent code vector is matched with a prior distribution so that we can generate meaningful samples from any part of the prior space. Consequently, given a sample of one domain, our framework can generate various samples of the other domain with the same content of the input. This makes the proposed framework different from the current work of cross-domain transformation. Besides, the proposed framework can be trained with both labeled and unlabeled data, which makes it also suitable for domain adaptation. Experimental results on data sets SVHN, MNIST and CASIA show the proposed framework achieved visually appealing performance for image generation task. Besides, we also demonstrate the proposed method achieved superior results for domain adaptation. Code of our experiments is available in https://github.com/luckycallor/CDAAE.


title: Investigating Backtranslation in Neural Machine Translation

abstract: A prerequisite for training corpus-based machine translation (MT) systems – either Statistical MT (SMT) or Neural MT (NMT) – is the availability of high-quality parallel data. This is arguably more important today than ever before, as NMT has been shown in many studies to outperform SMT, but mostly when large parallel corpora are available; in cases where data is limited, SMT can still outperform NMT. Recently researchers have shown that back-translating monolingual data can be used to create synthetic parallel corpora, which in turn can be used in combination with authentic parallel data to train a high-quality NMT system. Given that large collections of new parallel text become available only quite rarely, back-translation has become the norm when building state-of-the-art NMT systems, especially in resource-poor scenarios. However, we assert that there are many unknown factors regarding the actual effects of back-translated data on the translation capabilities of an NMT model. Accordingly, in this work we investigate how using back-translated data as a training corpus – both as a separate standalone dataset as well as combined with human-generated parallel data – affects the performance of an NMT model. We use incrementally larger amounts of back-translated data to train a range of NMT systems for German-to-English, and analyse the resulting translation performance.


title: Efficacy and Safety of Exercise Training in Patients With Chronic Heart Failure

abstract: CONTEXT ::: Guidelines recommend that exercise training be considered for medically stable outpatients with heart failure. Previous studies have not had adequate statistical power to measure the effects of exercise training on clinical outcomes. ::: ::: ::: OBJECTIVE ::: To test the efficacy and safety of exercise training among patients with heart failure. ::: ::: ::: DESIGN, SETTING, AND PATIENTS ::: Multicenter, randomized controlled trial of 2331 medically stable outpatients with heart failure and reduced ejection fraction. Participants in Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training (HF-ACTION) were randomized from April 2003 through February 2007 at 82 centers within the United States, Canada, and France; median follow-up was 30 months. ::: ::: ::: INTERVENTIONS ::: Usual care plus aerobic exercise training, consisting of 36 supervised sessions followed by home-based training, or usual care alone. ::: ::: ::: MAIN OUTCOME MEASURES ::: Composite primary end point of all-cause mortality or hospitalization and prespecified secondary end points of all-cause mortality, cardiovascular mortality or cardiovascular hospitalization, and cardiovascular mortality or heart failure hospitalization. ::: ::: ::: RESULTS ::: The median age was 59 years, 28% were women, and 37% had New York Heart Association class III or IV symptoms. Heart failure etiology was ischemic in 51%, and median left ventricular ejection fraction was 25%. Exercise adherence decreased from a median of 95 minutes per week during months 4 through 6 of follow-up to 74 minutes per week during months 10 through 12. A total of 759 patients (65%) in the exercise training group died or were hospitalized compared with 796 patients (68%) in the usual care group (hazard ratio [HR], 0.93 [95% confidence interval {CI}, 0.84-1.02]; P = .13). There were nonsignificant reductions in the exercise training group for mortality (189 patients [16%] in the exercise training group vs 198 patients [17%] in the usual care group; HR, 0.96 [95% CI, 0.79-1.17]; P = .70), cardiovascular mortality or cardiovascular hospitalization (632 [55%] in the exercise training group vs 677 [58%] in the usual care group; HR, 0.92 [95% CI, 0.83-1.03]; P = .14), and cardiovascular mortality or heart failure hospitalization (344 [30%] in the exercise training group vs 393 [34%] in the usual care group; HR, 0.87 [95% CI, 0.75-1.00]; P = .06). In prespecified supplementary analyses adjusting for highly prognostic baseline characteristics, the HRs were 0.89 (95% CI, 0.81-0.99; P = .03) for all-cause mortality or hospitalization, 0.91 (95% CI, 0.82-1.01; P = .09) for cardiovascular mortality or cardiovascular hospitalization, and 0.85 (95% CI, 0.74-0.99; P = .03) for cardiovascular mortality or heart failure hospitalization. Other adverse events were similar between the groups. ::: ::: ::: CONCLUSIONS ::: In the protocol-specified primary analysis, exercise training resulted in nonsignificant reductions in the primary end point of all-cause mortality or hospitalization and in key secondary clinical end points. After adjustment for highly prognostic predictors of the primary end point, exercise training was associated with modest significant reductions for both all-cause mortality or hospitalization and cardiovascular mortality or heart failure hospitalization. ::: ::: ::: TRIAL REGISTRATION ::: clinicaltrials.gov Identifier: NCT00047437.


Why a radical overhaul of dentistry is needed


Concept for Robust Compliance Control of Robot Fingers


title: UWB Bandpass Filter Using Microstrip-to-CPW Transition With Broadband Balun

abstract: A novel ultra-wideband bandpass filter (BPF) is presented using a back-to-back microstrip-to-coplanar waveguide (CPW) transition employed as the broadband balun structure in this letter. The proposed BPF is based on the electromagnetic coupling between open-circuited microstrip line and short-circuited CPW. The equivalent circuit of half of the filter is used to calculate the input impedance. The broadband microstip-to-CPW transition is designed at the center frequency of 6.85 GHz. The simulated and measured results are shown in this letter.


title: Identifying At-Risk Employees: Modeling Psychosocial Precursors of Potential Insider Threats

abstract: In many insider crimes, managers and other coworkers observed that the offenders had exhibited signs of stress, disgruntlement, or other issues, but no alarms were raised. Barriers to using such psychosocial indicators include the inability to recognize the signs and the failure to record the behaviors so that they can be assessed. A psychosocial model was developed to assess an employee's behavior associated with an increased risk of insider abuse. The model is based on case studies and research literature on factors/correlates associated with precursor behavioral manifestations of individuals committing insider crimes. To test the model's agreement with human resources and management professionals, we conducted an experiment with positive results. If implemented in an operational setting, the model would be part of a set of management tools for employee assessment to identify employees who pose a greater insider threat.


title: Cloud computing adoption framework

abstract: This article presents a cloud computing adoption framework (CCAF) security suitable for business clouds. CCAF multilayered security is based on the development and integration of three major security technologies: firewall, identity management, and encryption based on the development of enterprise file sync and share technologies. This article presents the vision, related works, and views on security framework. Core technologies have been explained in detail, and experiments were designed to demonstrate the robustness of the CCAF multilayered security. In penetration testing, CCAF multilayered security could detect and block 99.95% viruses and trojans, and could achieve ? 85 % of blocking for 100 h of continuous attack. Detection and blocking took < 0.012 s /trojan or virus. A full CCAF multilayered security protection could block all SQL (structured query language) injection, providing real protection to data. CCAF multilayered security did not report any false alarm. All F -measures for CCAF test results were ? 99.75 % . The mechanism of blending of CCAF multilayered security with policy, real services, and business activities has been illustrated. Research contributions have been justified and CCAF multilayered security can be beneficial for volume, velocity, and veracity of big data services operated in the cloud. We demonstrate CCAF multi-layered security.We explain the mappings between CCAF multi-layered architecture and core technologiesWe performed penetration testing and SQL injection on CCAF multi-layered security.Results and analysis by CCAF are better than those produced by the other tools.CCAF multi-layered security blends with policy, services and business activities.


title: Recognition of Handwritten Music Symbols with Convolutional Neural Codes

abstract: There are large collections of music manuscripts preserved over the centuries. In order to analyze these documents it is necessary to transcribe them into a machine-readable format. This process can be done automatically using Optical Music Recognition (OMR) systems, which typically consider segmentation plus classification workflows. This work is focused on the latter stage, presenting a comprehensive study for classification of handwritten musical symbols using Convolutional Neural Networks (CNN). The power of these models lies in their ability to transform the input into a meaningful representation for the task at hand, and that is why we study the use of these models to extract features (Neural Codes) for other classifiers. For the evaluation we consider four datasets containing different configurations and notation styles, along with a number of network models, different image preprocessing techniques and several supervised learning classifiers. Our results show that a remarkable accuracy can be achieved using the proposed framework, which significantly outperforms the state of the art in all datasets considered.


Autonomous recovery of a fixed-wing UAV using a net suspended by two multirotor UAVs


title: Geolocation by time difference of arrival using hyperbolic asymptotes

abstract: The paper proposes a new simplified algorithm to estimate the location of an emitter by utilizing time difference of arrival (TDOA) measurements. This is achieved by recasting the estimation problem in prolate spheroidal coordinates. Prolate spheroidal coordinates greatly simplify the TDOA equations, producing a set of linear equations in the far field limit. The set of linear equations corresponds to the hyperbolic asymptotes of the TDOA measurements. We also develop a systematic approach that associates the hyperbolic asymptotes with the emitter. In the near field, the far field solution can be used to "seed" the iterative maximum likelihood (ML) estimate, enabling convergence to the ML solution.


title: Toward a More Complete, Flexible, and Safer Speed Planning for Autonomous Driving via Convex Optimization

abstract: In this paper, we present a complete, flexible and safe convex-optimization-based method to solve speed planning problems over a fixed path for autonomous driving in both static and dynamic environments. Our contributions are five fold. First, we summarize the most common constraints raised in various autonomous driving scenarios as the requirements for speed planner developments and metrics to measure the capacity of existing speed planners roughly for autonomous driving. Second, we introduce a more general, flexible and complete speed planning mathematical model including all the summarized constraints compared to the state-of-the-art speed planners, which addresses limitations of existing methods and is able to provide smooth, safety-guaranteed, dynamic-feasible, and time-efficient speed profiles. Third, we emphasize comfort while guaranteeing fundamental motion safety without sacrificing the mobility of cars by treating the comfort box constraint as a semi-hard constraint in optimization via slack variables and penalty functions, which distinguishes our method from existing ones. Fourth, we demonstrate that our problem preserves convexity with the added constraints, thus global optimality of solutions is guaranteed. Fifth, we showcase how our formulation can be used in various autonomous driving scenarios by providing several challenging case studies in both static and dynamic environments. A range of numerical experiments and challenging realistic speed planning case studies have depicted that the proposed method outperforms existing speed planners for autonomous driving in terms of constraint type covered, optimality, safety, mobility and flexibility.


title: A Novel UWB Antenna With Dual Notched Bands for WiMAX and WLAN Applications

abstract: A novel planar ultrawideband (UWB) antenna with dual notched bands is proposed and investigated. The antenna consists of a square patch and a modified grounded plane. To realize dual notched bands characteristics, a T-shaped stub embedded in the square slot of the radiation patch and a pair of U-shaped parasitic strips beside the feed line is used. The advantage of this antenna is the high rejection level in the stopband. The measured results show that the proposed dual-notched-bands planar antenna shows a very wide bandwidth from 2.8 to 11.0 GHz defined by voltage standing wave ratio VSWR <; 2, with two notched bands of 3.3-4.0 GHz (WiMAX band) and 5.05-5.90 GHz (WLAN band), respectively. Both the experimental and simulated results of the proposed antenna are presented, indicating that the antenna is a good candidate for various UWB applications.


title: Magoroh Maruyama (1963): The Second Cybernetics: Deviation-Amplifying Mutual Causal Processes, American Scientist 5:2, pp. 164--179. THE SECOND CYBERNETICS Deviation-Amplifying Mutual Causal Processes

abstract: Since its inception, cybernetics was more or less identified as a science of self-regulating and equilibrating systems. Thermostats, physiological regulation of body temperature, automatic steering devices, economic and political processes were studied under a general mathematical model of deviation-counteracting feedback networks. By focusing on the deviation-counteracting aspect of the mutual causal relationships however, the cyberneticians paid less attention to the systems in which the mutual causal effects are deviation-amplifying. Such systems are ubiquitous: accumulation of capital in industry, evolution of living organisms, the rise of cultures of various types, interpersonal processes which produce menial illness, international conflicts, and the processes that are loosely termed as "vicious circles" and "compound interests"; in short, all processes of mutual causal relationships that amplify an insignificant or accidental initial kick, build up deviation and diverge from the initial condition. In contrast to the progress in the study of equilibrating systems, the deviation-amplifying systems have not been given as much investment of time and energy by the mathematical scientists on the one hand, and understanding and practical application on the part of geneticists, ecologists, politicians and psychotherapists on the other hand. The deviation-counteracting mutual causal systems and the deviation-amplifying mutual causal systems may appear to be opposite types of systems. But they have one essential feature in common: they are both mutual causal systems, i.e., the elements within a system influence each other either simultaneously or alternatingly. The difference between the two types of systems is that the deviation-counteracting system has mutual negative feedbacks between the elements in it while the deviationamplifying system has mutual positive feedbacks between the elements in it.


title: A Novel Vector Synthetic Dipole Antenna and Its Common Aperture Array

abstract: A novel vector synthetic dipole (VSD) antenna is first demonstrated in this communication. Its structure is very simple. The antenna consists of a reflector, four aluminum plates, and a feeding substrate. It can provide a wide bandwidth from 690 to 960 MHz when $\vert \text{S}_{11}\vert dB or VSWR $118 \times 118\times 78$ mm3, far less than the size of the similar antennas. Extremely high port isolation and low cross polarization are obtained due to the adoption of the vector synthesis technique. Then, an upper band element is put into the cavity formed by the lower band VSD element, realizing common aperture. Another upper band element is settled next to them with a certain distance, making the whole structure easy to be extended to an array. By using reasonable phase compensation, this hybrid antenna can obtain a good radiation pattern. To further verify the design, an array that consists of two lower band elements and four upper band elements is manufactured and tested. The proposed antenna has the merits of compact size, wide bandwidth, high isolation, and good radiation performance. It will find wide application in the current and future communication systems.


title: A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends

abstract: Deep learning has exploded in the public consciousness, primarily as predictive and analytical products suffuse our world, in the form of numerous human-centered smart-world systems, including targeted advertisements, natural language assistants and interpreters, and prototype self-driving vehicle systems. Yet to most, the underlying mechanisms that enable such human-centered smart products remain obscure. In contrast, researchers across disciplines have been incorporating deep learning into their research to solve problems that could not have been approached before. In this paper, we seek to provide a thorough investigation of deep learning in its applications and mechanisms. Specifically, as a categorical collection of state of the art in deep learning research, we hope to provide a broad reference for those seeking a primer on deep learning and its various implementations, platforms, algorithms, and uses in a variety of smart-world systems. Furthermore, we hope to outline recent key advancements in the technology, and provide insight into areas, in which deep learning can improve investigation, as well as highlight new areas of research that have yet to see the application of deep learning, but could nonetheless benefit immensely. We hope this survey provides a valuable reference for new deep learning practitioners, as well as those seeking to innovate in the application of deep learning.


title: Logistic Regression Setup for RTB CTR Estimation

abstract: In this paper we investigate one of the most interesting problems of Big Data user feedback prediction which is the Real-Time Bidding Click-Through Rate estimation. We evaluate experimentally the impact of the widely-referenced methods for optimization of the logistic regression - the state-of-the art Real-Time Bidding optimization method - on the quality of CTR estimation. From the perspective of this impact, we evaluate different configurations of widely-referenced regularization techniques and compare them with a simple technique of the feature generalization. On the basis of the results of the extensive experimentation, we show that in the context of the application scenario investigated herein, an optimization of the stochastic gradient descent algorithm configuration may be successfully accompanied, or even replaced, by a simple feature generalization.


title: Performance Analysis of Alamouti Code MIMO-OFDM Systems for Error Control and IQ Impairments

abstract: Bit Error Rate (BER) performance is the most important metric of any digital communication system. MIMO-OFDM systems designed with Alamouti Coding technique, using Error Control Coding schemes has been investigated under this study to achieve BER enhancement and scrutinized for Additive White Gaussian Noise (AWGN) Channel with Inphase and Quadrature-phase (IQ) impairments. This paper details out the model developed using NI LabVIEW and discusses simulation results comparing the error control coding RS, RS-CC and Convolution encoding with 4-PSK and 4-QAM. General Terms Bit Error Rate, Space Time Coding (STC).


title: Pupil-linked arousal is driven by decision uncertainty and alters serial choice bias

abstract: While judging their sensory environments, decision-makers seem to use the uncertainty about their choices to guide adjustments of their subsequent behaviour. One possible source of these behavioural adjustments is arousal: decision uncertainty might drive the brain's arousal systems, which control global brain state and might thereby shape subsequent decision-making. Here, we measure pupil diameter, a proxy for central arousal state, in human observers performing a perceptual choice task of varying difficulty. Pupil dilation, after choice but before external feedback, reflects three hallmark signatures of decision uncertainty derived from a computational model. This increase in pupil-linked arousal boosts observers' tendency to alternate their choice on the subsequent trial. We conclude that decision uncertainty drives rapid changes in pupil-linked arousal state, which shape the serial correlation structure of ongoing choice behaviour.


title: Generation of tert-Butyl-λ5-phosphanedione and Its Chemical Properties

abstract: Flash vacuum thermolysis of trimethylsilyl tert-butylphosphonohalidates involved elimination of halo(trimethyl)silane with the formation of tert-butyl-λ5-phosphanedione whose structure was confirmed by chemical reactions. tert-Butyl-λ5-phosphanedione readily undergoes trimerization, reacts with 2-phenyloxirane to give cycloaddition product, and takes up alcohols.


title: DeepQA: improving the estimation of single protein model quality with deep belief networks

abstract: Protein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem. We introduce a novel single-model quality assessment method DeepQA based on deep belief network that utilizes a number of selected features describing the quality of a model from different perspectives, such as energy, physio-chemical characteristics, and structural information. The deep belief network is trained on several large datasets consisting of models from the Critical Assessment of Protein Structure Prediction (CASP) experiments, several publicly available datasets, and models generated by our in-house ab initio method. Our experiments demonstrate that deep belief network has better performance compared to Support Vector Machines and Neural Networks on the protein model quality assessment problem, and our method DeepQA achieves the state-of-the-art performance on CASP11 dataset. It also outperformed two well-established methods in selecting good outlier models from a large set of models of mostly low quality generated by ab initio modeling methods. DeepQA is a useful deep learning tool for protein single model quality assessment and protein structure prediction. The source code, executable, document and training/test datasets of DeepQA for Linux is freely available to non-commercial users at http://cactus.rnet.missouri.edu/DeepQA/ ::: .


title: NiftyNet: a deep-learning platform for medical imaging

abstract: Abstract Background and objectives Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this domain of application requires substantial implementation effort. Consequently, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. Methods The NiftyNet infrastructure provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on the TensorFlow framework and supports features such as TensorBoard visualization of 2D and 3D images and computational graphs by default. Results We present three illustrative medical image analysis applications built using NiftyNet infrastructure: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses. Conclusions The NiftyNet infrastructure enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications.


title: ORec: An Opinion-Based Point-of-Interest Recommendation Framework

abstract: As location-based social networks (LBSNs) rapidly grow, it is a timely topic to study how to recommend users with interesting locations, known as points-of-interest (POIs). Most existing POI recommendation techniques only employ the check-in data of users in LBSNs to learn their preferences on POIs by assuming a user's check-in frequency to a POI explicitly reflects the level of her preference on the POI. However, in reality users usually visit POIs only once, so the users' check-ins may not be sufficient to derive their preferences using their check-in frequencies only. Actually, the preferences of users are exactly implied in their opinions in text-based tips commenting on POIs. In this paper, we propose an opinion-based POI recommendation framework called ORec to take full advantage of the user opinions on POIs expressed as tips. In ORec, there are two main challenges: (i) detecting the polarities of tips (positive, neutral or negative), and (ii) integrating them with check-in data including social links between users and geographical information of POIs. To address these two challenges, (1) we develop a supervised aspect-dependent approach to detect the polarity of a tip, and (2) we devise a method to fuse tip polarities with social links and geographical information into a unified POI recommendation framework. Finally, we conduct a comprehensive performance evaluation for ORec using two large-scale real data sets collected from Foursquare and Yelp. Experimental results show that ORec achieves significantly superior polarity detection and POI recommendation accuracy compared to other state-of-the-art polarity detection and POI recommendation techniques.


title: Stochastic Proximal Gradient Descent with Acceleration Techniques

abstract: Proximal gradient descent (PGD) and stochastic proximal gradient descent (SPGD) are popular methods for solving regularized risk minimization problems in machine learning and statistics. In this paper, we propose and analyze an accelerated variant of these methods in the mini-batch setting. This method incorporates two acceleration techniques: one is Nesterov's acceleration method, and the other is a variance reduction for the stochastic gradient. Accelerated proximal gradient descent (APG) and proximal stochastic variance reduction gradient (Prox-SVRG) are in a trade-off relationship. We show that our method, with the appropriate mini-batch size, achieves lower overall complexity than both APG and Prox-SVRG.


title: Shared Neural Resources between Music and Language Indicate Semantic Processing of Musical Tension-Resolution Patterns

abstract: Harmonic tension-resolution patterns have long been hypothesized to be meaningful to listeners familiar with Western music. Even though it has been shown that specifically chosen musical pieces can prime meaningful concepts, the empirical evidence in favor of such a highly specific semantic pathway has been lacking. Here we show that 2 event-related potentials in response to harmonic expectancy violations, the early right anterior negativity (ERAN) and the N500, could be systematically modulated by simultaneously presented language material containing either a syntactic or a semantic violation. Whereas the ERAN was reduced only when presented concurrently with a syntactic language violation and not with a semantic language violation, this pattern was reversed for the N500. This is the first piece of evidence showing that tension- resolution patterns represent a route to meaning in music.


title: Principles of Explanatory Debugging to Personalize Interactive Machine Learning

abstract: How can end users efficiently influence the predictions that machine learning systems make on their behalf? This paper presents Explanatory Debugging, an approach in which the system explains to users how it made each of its predictions, and the user then explains any necessary corrections back to the learning system. We present the principles underlying this approach and a prototype instantiating it. An empirical evaluation shows that Explanatory Debugging increased participants' understanding of the learning system by 52% and allowed participants to correct its mistakes up to twice as efficiently as participants using a traditional learning system.


title: An agent based approach for improvised explosive device detection, public alertness and safety

abstract: One of the security challenges faced by our contemporary world is terror threats and attacks, and this is no doubt posing potential threats to lives, properties and businesses all around us; affecting the way we live and also travel. Terror attacks have been perpetrated in diverse ways whether from organized terror networks through coordinated attacks or by some lone individuals such that it is now a major concern to people and government. Indeed, there are numerous forms of terror attacks. In this proposal, we look at how the explosive substance kind of threats can be perceived and taken care of prior to potential attacks using intelligent agent systems requirement analysis. Thus, the paper demonstrates using an agent-oriented system analysis and design methodology to decompose the problem. Through defined percepts, goals and plans, agents possess capabilities to observe and perform actions. This proposal demonstrates: how agents can be situated in our cities, goal refinement for agents in the detection and rescue of potential terror attacks, and inter-agent communication for the prevention of chemical terror attack.


title: Road crack detection using a single stage detector based deep neural network

abstract: Condition and deterioration of public and private infrastructure is an issue that directly affects the majority of the world population. In this paper we propose the application of a Residual Neural Network to automatically detect road and pavement surface cracks. The high amount of variance in the texture of the surface and variation in illumination levels makes the task of automatically detecting defects within public and private infrastructure a difficult task. The system developed utilises a feature pyramid core with an underlying feed-forward ResNet architecture. The output from the feature pyramid then feeds into two sub-networks. One sub-network associates a class with the output from the feature pyramid. The other sub-network regresses the offset from each of the output bounding boxes of the feature pyramid to the corresponding ground truth boxes during training. The network was trained on real world data from an already established dataset. The data used to train and test on is very limited, due to the lack of available road crack datasets in the public domain. Despite the limited amount of data, the proposed method achieves a very positive results with minimal error.


title: Decentralizing Digital Identity: Open Challenges for Distributed Ledgers

abstract: Distributed Ledger Technology (DLT) has been proposed as a new way to incorporate decentralization into a wide range of digital infrastructures. Applications of DLT to digital identity are increasing in prevalence, with a recent survey reporting that 55% of DLT technologies in development track digital identity. However, while proofs of concept, open source software, and new ideas are readily available, it is still unclear the extent to which DLT can play a role to underpin new forms of digital identity. In this position paper, we situate this fast-moving application domain into the broader challenges faced in digital identity, with the aim to highlight the socio-technical nature of the challenge at hand, and to propose directions for future research.


title: Towards creating a flexible shape senor for soft robots

abstract: Recent advances in robotics have witnessed an increasing transition from designing conventional robots with rigid components to partially or completely soft ones. Soft robots are known to be highly deformable and stretchable which makes the process of registering their shape and orientation in 3D challenging. This paper presents a first step of creating a flexible shape sensor for soft robots and a calibration algorithm that can compensate for different planar deflection conditions. In this paper, we describe the design and fabrication of the proposed shape sensor prototype utilizing three segmented optical fibers along the length of a flexible continuum arm. Three experimental scenarios of deflection are investigated to validate the relation between a mechanical deflection of the prototype and the change in intensity of the optical fibers' tip outputs (15 degrees deflection to the right and left, and planar double-bending). Camera images of the intensity circles without bending are used as a reference to relate the images features (location, angles, size, and intensity) to other bending cases. This study demonstrates the potential of relating the deflection status of a soft sensor to the image samples collected through a camera for the purpose of reconstructing and calibrating the shape sensor in 2D-space using MATLAB image processing toolbox and machine learning.


title: Mechanism, control, and visual management of a jumping robot

abstract: Abstract A new robot, known as the Scoutrobot, is introduced in this paper. It is capable of moving along the ground and jumping over a surmountable obstacle. The Scoutrobot can patrol a harsh environment by rolling or jumping while delivering environmental information to the user. It requires less time and makes use of simpler mechanisms to overcome obstacles compared to conventional robots. The Scoutrobot contains several sensors, including two cameras for distance measurements and ultrasonic sensors for alignment. The jumping motion is performed by releasing a pneumatic cylinder against the ground. The jumping height is controlled with a pulse-width modulation-control mechanism on the pneumatic valve. An automatic lift-up structure is used to recover the robot’s initial pose after it jumps up or down. A robot management system provides a three-dimensional virtual display image of the robot’s location and pose.


title: Measuring the effectiveness of answers in Yahoo! Answers

abstract: Purpose – The purpose of this paper is to investigate the ways in which effectiveness of answers in Yahoo! Answers, one of the largest community question answering sites (CQAs), is related to question types and answerer reputation. Effective answers are defined as those that are detailed, readable, superior in quality and contributed promptly. Five question types that were studied include factoid, list, definition, complex interactive and opinion. Answerer reputation refers to the past track record of answerers in the community. Design/methodology/approach – The data set comprises 1,459 answers posted in Yahoo! Answers in response to 464 questions that were distributed across the five question types. The analysis was done using factorial analysis of variance. Findings – The results indicate that factoid, definition and opinion questions are comparable in attracting high quality as well as readable answers. Although reputed answerers generally fared better in offering detailed and high-quality answers, nov...


title: Ontology Based System to Guide Internship Assignment Process

abstract: Internship assignment is a complicated process for universities since it is necessary to take into account a multiplicity of variables to establish a compromise between companies' requirements and student competencies acquired during the university training. These variables build up a complex relations map that requires the formulation of an exhaustive and rigorous conceptual scheme. In this research a domain ontological model is presented as support to the student's decision making for opportunities of University studies level of the University Lumiere Lyon 2 (ULL) education system. The ontology is designed and created using methodological approach offering the possibility of improving the progressive creation, capture and knowledge articulation. In this paper, we draw a balance taking the demands of the companies across the capabilities of the students. This will be done through the establishment of an ontological model of an educational learners' profile and the internship postings which are written in a free text and using uncontrolled vocabulary. Furthermore, we outline the process of semantic matching which improves the quality of query results.


title: Context-Based Collaborative Filtering for Citation Recommendation

abstract: Citation recommendation is an interesting and significant research area as it solves the information overload in academia by automatically suggesting relevant references for a research paper. Recently, with the rapid proliferation of information technology, research papers are rapidly published in various conferences and journals. This makes citation recommendation a highly important and challenging discipline. In this paper, we propose a novel citation recommendation method that uses only easily obtained citation relations as source data. The rationale underlying this method is that, if two citing papers are significantly co-occurring with the same citing paper(s), they should be similar to some extent. Based on the above rationale, an association mining technique is employed to obtain the paper representation of each citing paper from the citation context. Then, these paper representations are pairwise compared to compute similarities between the citing papers for collaborative filtering. We evaluate our proposed method through two relevant real-world data sets. Our experimental results demonstrate that the proposed method significantly outperforms the baseline method in terms of precision, recall, and F1, as well as mean average precision and mean reciprocal rank, which are metrics related to the rank information in the recommendation list.


title: Human-Computer Interaction Standards

abstract: Publisher Summary This chapter presents human-computer interaction (HCI) standards. Standard user interfaces provide the benefit of consistency; they become out of date as technology changes, and are usually only appropriate for limited types of users and tasks. Thus most work on international standards for HCI has not been about precise specification, but instead has concentrated on the principles that need to be applied in order to produce an interface, which meets user and task needs. These standards broadly fall into two categories—one is a top-down approach, which is concerned with usability as a broad quality objective: the ability to use a product for its intended purpose. The other is a product-oriented bottom-up view, which is concerned with aspects of the interface that make a system easier to use. The broad quality view originates from human factors, and standards of this type are applicable in the broad context of design and quality objectives. The product-oriented view concentrates on the design of specific attributes, and relates more closely to the needs of the interface designer and the role of usability in software engineering.


title: Cochrane crisis: secrecy, intolerance, and evidence-based values

abstract: The Cochrane Collaboration was launched in 1993 with great enthusiasm. It aimed to offer a volunteer-based, community-strong, independent, and critical effort for materializing the goals of evidence-based medicine worldwide through the production of high-quality, rigorous systematic reviews (1). In the next quarter of a century, the effort did accomplish an enormous amount and its members should be proud of their achievements. The quality, depth, and breadth of expertise of the people involved in this collaborative endeavor is unmatched. Cochrane systematic reviews gained a well-deserved reputation of excellence (2). This article is protected by copyright. All rights reserved.


title: PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network

abstract: Abstract Partial differential equations (PDEs) are commonly derived based on empirical observations. However, recent advances of technology enable us to collect and store massive amount of data, which offers new opportunities for data-driven discovery of PDEs. In this paper, we propose a new deep neural network, called PDE-Net 2.0, to discover (time-dependent) PDEs from observed dynamic data with minor prior knowledge on the underlying mechanism that drives the dynamics. The design of PDE-Net 2.0 is based on our earlier work [1] where the original version of PDE-Net was proposed. PDE-Net 2.0 is a combination of numerical approximation of differential operators by convolutions and a symbolic multi-layer neural network for model recovery. Comparing with existing approaches, PDE-Net 2.0 has the most flexibility and expressive power by learning both differential operators and the nonlinear response function of the underlying PDE model. Numerical experiments show that the PDE-Net 2.0 has the potential to uncover the hidden PDE of the observed dynamics, and predict the dynamical behavior for a relatively long time, even in a noisy environment.


Using Knowledge Representation and Reasoning Tools in the Design of Robots


title: All-inside Arthroscopic Lateral Collateral Ligament Repair for Ankle Instability With a Knotless Suture Anchor Technique

abstract: Background:Recently, arthroscopic-assisted techniques have been described to treat lateral ankle instability with excellent results. However, complications including neuritis of the superficial peroneal or sural nerve, and pain or discomfort due to a prominent anchor or suture knot have been reported. The aim of this study was to describe a novel technique, the “all-inside arthroscopic lateral collateral ankle ligament repair,” and its results for treating patients with ankle instability.Methods:Sixteen patients (10 men and 6 women, mean age 29.3 years, 17-46) with lateral ankle instability were treated with an arthroscopic procedure. Using a suture passer and a knotless anchor, the ligaments were repaired with an all-inside technique. The right ankle was affected in 10 cases. Mean follow-up was 22.3 (12-35) months.Results:On arthroscopic examination, 13 patients had an isolated anterior talofibular ligament (ATFL) injury, and in 3 patients, both the ATFL and calcaneofibular ligament (CFL) were affected. ...


title: Atmospheric effects in interferometric synthetic aperture radar surface deformation and topographic maps

abstract: Interferogram images derived from repeat-pass spaceborne synthetic aperture radar systems exhibit artifacts due to the time and space variations of atmospheric water vapor. Other tropospheric variations, such as pressure and temperature, also induce distortions, but the effects are smaller in magnitude and more evenly distributed throughout the interferogram than the wet troposphere term. Spatial and temporal changes of 20% in relative humidity lead to 10 cm errors in deformation products, and perhaps 100 m of error in derived topographic maps for those pass pairs with unfavorable baseline geometries. In wet regions such as Hawaii, these are by far the dominant errors in the Spaceborne Imaging Radar-C and X Band Synthetic Aperature Radar (SIR-C/X-SAR) interferometric products. The unknown time delay from tropospheric distortion is independent of frequency, and thus multiwavelength measurements, such as those commonly used to correct radar altimeter and Global Positioning System (GPS) ionospheric biases, cannot be used to rectify the error. In the topographic case, the errors may be mitigated by choosing interferometric pairs with relatively long baselines, as the error amplitude is inversely proportional to the perpendicular component of the interferometer baseline. For the SIR-C/X-SAR Hawaii data we found that the best (longest) baseline pair produced a map supporting 100 m contouring, whereas the poorest baseline choice yielded an extremely noisy topographic map even at this coarse contour interval. In the case of deformation map errors the result is either independent of baseline parameters or else very nearly so. Here the only solution is averaging of independent interferograms, so in order to create accurate deformation products in wet regions many multiple passes may be required. Rules for designing optimal data acquisition and processing sequences for interferometric analyses in nondesert parts of the world are (1) to use the longest radar wavelengths possible, within ionospheric scintillation and Faraday rotation limits, (2) for topography, maximize interferometer baseline within decorrelation limits* and (3) for surface deformation, use multiple observations and average the derived products. Following the above recipe yields accuracies of 10 m for digital elevation models and 1 cm for deformation maps even in very wet regions, such as Hawaii.


Digit Recognition via Unsupervised Learning


title: The challenges of creating sustainable competitive advantage in large B2B multidivisional corporations

abstract: It addresses the challenges that large multi-divisional B2B corporations face each day to take advantage of their corporate strategy and ensure sustainable competitive advantages. The synergies and interrelations that would give them such a position are not always simple to be achieved or even perceived by customers. The chosen approach was focused on B2B companies, because processes and communication tend to be more truncated, as they are usually more technical in the way of doing business. This can make the challenges of interrelation and synergy even greater.


title: Increasing the Robustness of the Montgomery kP-Algorithm Against SCA by Modifying Its Initialization

abstract: The Montgomery kP-algorithm using Lopez-Dahab projective coordinates is a well-known method for performing the scalar multiplication in elliptic curve crypto-systems (ECC). It is considered resistant against simple power analysis (SPA) since each key bit is processed by the same type, amount and sequence of operations, independently of the key bit’s value. Nevertheless, its initialization phase affects this algorithm’s robustness against side channel analysis (SCA) attacks. We describe how the first iteration of the kP processing loop reveals information about the key bit being processed, i.e. bit \(k_{l-2}\). We explain how the value of this bit can be extracted with SPA and how the power profile of its processing can reveal details about the implementation of the algorithm. We propose a modification of the algorithm’s initialization phase and of the processing of bit \(k_{l-2}\), in order to hinder the extraction of its value using SPA. Our proposed modifications increase the algorithm’s robustness against SCA and even reduce the time needed for the initialization phase and for processing \(k_{l-2}\). Compared to the original design, our new implementation needs only 0.12 % additional area, while its energy consumption is almost the same, i.e. we improved the security of the design at no cost.