title: First Steps towards a View Management Concept for Large-sized Head-up Displays with Continuous Depth

abstract: Windshield displays (WSDs) are the big siblings of Head-up displays (HUDs). They are assumed to cover the entire windshield and to allow displaying content at continuous depth, eventually. This creates a large and unstructured 3D space for information display -- raising the question what to display where. To address this question, we developed a view management concept for WSDs in left hand drive cars which proposes zones and areas for specific information. As driving is a safety-critical task, we designed the initial concept with the driver's perceptual abilities in mind. Subsequently, we gathered insights into the driver's needs and desires from a formative study. We asked participants where they would place different types of information after inspiring their imagination by a 3D driving scene and WSD information on a Google Cardboard. The improved concept respects both the drivers' needs and desires and their perceptual abilities and can serve as a basis for view management concepts of future WSD.


title: Fuzzy controlled fast charging system for lithium-ion batteries

abstract: A DSP is adopted to construct a fuzzy controlled lithium-ion battery charging system. By using this intelligent charging system, the data collection, calculation and peripheral circuit control are performed for the battery charging status. According to the lithium-ion battery charging specifications, two hours are required for battery charging. A fuzzy logic controller (FLC) is constructed by using the battery protection cell voltage and voltage difference among the batteries. They are used as the input variable to shorten the charging and equalizing time and assure that the battery will be operated within the safety voltage range.


title: A New Statistical Approach To Chinese Pinyin Input

abstract: Chinese input is one of the key challenges for Chinese PC users. This paper proposes a statistical approach to Pinyin-based Chinese input. This approach uses a trigram-based language model and a statistically based segmentation. Also, to deal with real input, it also includes a typing model which enables spelling correction in sentence-based Pinyin input, and a spelling model for English which enables modeless Pinyin input.


Beyond Information : Developing the Relationship between the Individual and the Group in Online Communities


title: Vehicle Detection and Width Estimation in Rain by Fusing Radar and Vision

abstract: While much effort has been devoted to deep learning object detection, relatively limited attention has been paid to object detection in bad weather, e.g. rain, snow or haze. In heavy rain, the raindrop on the front windshield can make it difficult to detect object from an in-car camera. The conventional way to cope with this has been to use radar as the main detection sensor. However, radar is highly susceptible to false positives. Furthermore, many entry level radar sensors only return the centroid of each detected object, rather than its size and extent. In addition, due to lack of texture input, radar cannot discriminate a vehicle from a non-vehicle object, e.g. roadside pole. This motivates us to detect vehicle by fusing radar and vision. In this paper, we first calibrate the radar and camera with respect to the ground plane. The radar detections are then projected to the camera image for target width estimation. Empirical evaluation on a large database shows that there is a natural synergy in both sensors, as the image based estimation is found to be greatly facilitated by the accuracy of the radar detection.


title: Exploring semi-supervised and active learning for activity recognition

abstract: In recent years research on human activity recognition using wearable sensors has enabled to achieve impressive results on real-world data. However, the most successful activity recognition algorithms require substantial amounts of labeled training data. The generation of this data is not only tedious and error prone but also limits the applicability and scalability of today's approaches. This paper explores and systematically analyzes two different techniques to significantly reduce the required amount of labeled training data. The first technique is based on semi-supervised learning and uses self-training and co-training. The second technique is inspired by active learning. In this approach the system actively asks which data the user should label. With both techniques, the required amount of training data can be reduced significantly while obtaining similar and sometimes even better performance than standard supervised techniques. The experiments are conducted using one of the largest and richest currently available datasets.


title: Minding the Source: Automatic Tagging of Reported Speech in Newspaper Articles

abstract: Reported speech in the form of direct and indirect reported speech is an important indicator of evidentiality in traditional newspaper texts, but also increasingly in the new media that rely heavily on citation and quotation of previous postings, as for instance in blogs or newsgroups. This paper details the basic processing steps for reported speech analysis and reports on performance of an implementation in form of a GATE resource.


What is the Population of Interest: Population Modeling for BayesDB


title: “Teach Me–Show Me”—End-User Personalization of a Smart Home and Companion Robot

abstract: © 2015 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications standards/publications/rights/index.html for more information.


Enhanced Refoldability and Thermoactivity of Fluorinated Phosphotriesterase


title: Chilean underground mine dataset

abstract: This article presents a robotic dataset collected from the largest underground copper mine in the world. The sensor measurements from a 3D scanning lidar, a 2D radar, and stereo cameras were recorded from an approximately two kilometer traverse of a production-active tunnel. The equipment used and the data collection process is discussed in detail, along with the format of the data. This dataset is suitable for research in robotic navigation, as well as simultaneous localization and mapping. The download instructions are available at the following website http://dataset.amtc.cl.


title: Music, memory and emotion

abstract: Because emotions enhance memory processes and music evokes strong emotions, music could be involved in forming memories, either about pieces of music or about episodes and information associated with particular music. A recent study in BMC Neuroscience has given new insights into the role of emotion in musical memory.


Inlet temperature affects spray drying quality of watermelon powder


title: 2.5D IC Micro-Bump Materials Characterization and IMCs Evolution Under Reliability Stress Conditions

abstract: High density I/O (Input/Output) with close proximity communication between dies in 2.5D heterogeneous integration of disparate ICs (Integrated Circuits) requires substantially miniaturized interconnect. Micro-bump consists of down sized Cu pillar and solder is the chosen interconnect technology for die stacking volume manufacturing. The interconnect solder bump height is shortened to less than 20µm due to significantly reduced Cu pillar diameter. In order to achieve high manufacturing yield and long term reliability of the micro-bump solder joint, a new BOM (Build of Materials) is required by optimizing Ni (Nickel), Cu (Copper), Au (Gold) and Pd (Palladium) composition within SnAg solder. The optimization can be achieved by studying the micro bump solder joint IMC (Inter-Metallic Compound) growth, and solder joint void formation due to thermal aging and thermal mechanical stress during manufacturing and reliability stress. SnAg solder alloy interaction with different composition of Ni, Cu, Au and Pd at the two terminals of the solder joint showed varied effects during die stacking, flip chip assembly, test and reliability stress tests. Different reliability performance can be observed from HTST (High Temperature Storage Test) and TCT (Thermal Cycle Test). Three different Ni, Cu, Au and Pd compositions available to high volume manufacturing environment were studied and impact to IMC, terminal metals and solder joint integrity are the three critical measures for yield, final HTST and TCT reliability. Post die stacking and assembly, three different types of solders showed significantly different IMC thickness. IMC post HTST showed the opposite growth rate compared to the initial IMC thickness post assembly. For HTST, "SnAg-3" leg showed the best solder morphology with full IMC conversion, the other two solders produced large amount of solder voids with partial IMC conversion. For TCT, SnAg-3 passed the test without void formation, cracks or delamination. SnAg-1 solder showed micro-cracks at the early TCT cycle with voids growth and crack propagation after extended TCT, SnAg-2 showed healthy joint at the early TCT read point but produced micro-cracks and voids towards end of life. SnAg-3 composition was implemented for the first high volume 2.5D interposer product in AMD Radeon R9 Fury series Graphics cards.


title: Hybrid Analog–Digital Precoding Revisited Under Realistic RF Modeling

abstract: In this paper we revisit hybrid analog-digital precoding systems with emphasis on their modelling and radio-frequency (RF) losses, to realistically evaluate their benefits in 5G system implementations. For this, we decompose the analog beamforming networks (ABFN) as a bank of commonly used RF components and formulate realistic model constraints based on their S-parameters. Specifically, we concentrate on fully-connected ABFN (FC-ABFN) and Butler networks for implementing the discrete Fourier transform (DFT) in the RF domain. The results presented in this paper reveal that the performance and energy efficiency of hybrid precoding systems are severely affected, once practical factors are considered in the overall design. In this context, we also show that Butler RF networks are capable of providing better performances than FC-ABFN for systems with a large number of RF chains.


title: RenderNet: A deep convolutional network for differentiable rendering from 3D shapes

abstract: Traditional computer graphics rendering pipeline is designed for procedurally generating 2D quality images from 3D shapes with high performance. The non-differentiability due to discrete operations such as visibility computation makes it hard to explicitly correlate rendering parameters and the resulting image, posing a significant challenge for inverse rendering tasks. Recent work on differentiable rendering achieves differentiability either by designing surrogate gradients for non-differentiable operations or via an approximate but differentiable renderer. These methods, however, are still limited when it comes to handling occlusion and restricted to particular rendering effects. We present RenderNet, a differentiable rendering convolutional network with a novel projection unit that can render 2D images from 3D shapes. Spatial occlusion and shading calculation are automatically encoded in the network. Our experiments show that RenderNet can successfully learn to implement different shaders, and can be used in inverse rendering tasks to estimate shape, pose, lighting and texture from a single image.


title: Generating EPI representations of 4D Light fields with a single lens focused plenoptic camera

abstract: In this paper, we present a novel method for the generation of Epipolar-Image (EPI) representations of 4D Light Fields (LF) from raw data captured by a single lens "Focused Plenoptic Camera". Compared to other LF representations which are usually used in the context of computational photography with Plenoptic Cameras, the EPI representation is more suitable for image analysis tasks - providing direct access to scene geometry and reflectance properties. ::: ::: The generation of EPIs requires a set of "all in focus" (full depth of field) images from different views of a scene. Hence, the main contribution of this paper is a novel algorithm for the rendering of such images from a single raw image captured with a Focused Plenoptic Camera. The main advantage of the proposed approach over existing full depth of field methods is that is able to cope with non-Lambertian reflectance in the scene.


title: Mosaics of scenes with moving objects

abstract: Image mosaics are useful for a variety of tasks in vision and computer graphics. A particularly convenient way to generate mosaics is by 'stitching' together many ordinary photographs. Existing algorithms focus on capturing static scenes. This paper presents a complete system for creating visually pleasing mosaics in the presence of moving objects. There are three primary contributions. The first component of our system is a registration method that remains unbiased by movement-the Mellin transform is extended to register images related by a projective transform. Second an efficient method for finding a globally consistent registration of all images is developed. By solving a linear system of equations, derived from many pairwise registration matrices, we find an optimal global registration. Lastly, a new method of compositing images is presented. Blurred areas due to moving objects are avoided by segmenting the mosaic into disjoint regions and sampling pixels in each region from a single source image.


title: Machine-learning method for quality of transmission prediction of unestablished lightpaths

abstract: Predicting the quality of transmission (QoT) of a lightpath prior to its deployment is a step of capital importance for an optimized design of optical networks. Due to the continuous advances in optical transmission, the number of design parameters available to system engineers (e.g., modulation formats, baud rate, code rate, etc.) is growing dramatically, thus significantly increasing the alternative scenarios for lightpath deployment. As of today, existing (pre-deployment) estimation techniques for light-path QoT belong to two categories: “exact” analytical models estimating physical-layer impairments, which provide accurate results but incur heavy computational requirements, and margined formulas, which are computationally faster but typically introduce high link margins that lead to underutilization of network resources. In this paper, we explore a third option, i.e., machine learning (ML), as ML techniques have already been successfully applied for optimization and performance prediction of complex systems where analytical models are hard to derive and/ or numerical procedures impose high computational burden. We investigate a ML classifier that predicts whether the bit error rate of unestablished lightpaths meets the required system threshold based on traffic volume, desired route, and modulation format. The classifier is trained and tested on synthetic data and its performance is assessed over different network topologies and for various combinations of classification features. Results in terms of classifier accuracy are promising and motivate further investigation over real field data.


title: When will you do what? - Anticipating Temporal Occurrences of Activities

abstract: Analyzing human actions in videos has gained increased attention recently. While most works focus on classifying and labeling observed video frames or anticipating the very recent future, making long-term predictions over more than just a few seconds is a task with many practical applications that has not yet been addressed. In this paper, we propose two methods to predict a considerably large amount of future actions and their durations. Both, a CNN and an RNN are trained to learn future video labels based on previously seen content. We show that our methods generate accurate predictions of the future even for long videos with a huge amount of different actions and can even deal with noisy or erroneous input information.


Planning and Control Models for Elevators in High-Rise Buildings


title: Reference classes and relational learning

abstract: This paper studies the connections between relational probabilistic models and reference classes, with specific focus on the ability of these models to generate the correct answers to probabilistic queries. We distinguish between relational models that represent only observed relations and those which additionally represent latent properties of individuals. We show how both types of relational models can be understood in terms of reference classes, and that learning such models correspond to different ways of identifying reference classes. Rather than examining the impact of philosophical issues associated with reference classes on relational learning, we directly assess whether relational models can represent the correct probabilities of a simple generative process for relational data. We show that models with only observed properties and relations can only represent the correct probabilities under restrictive conditions, whilst models that also represent latent properties avoids such restrictions. As such, methods for acquiring latent-property models are an attractive alternatives to traditional ways of identifying reference classes. Our experiments on synthetic as well as real-world domains support the analysis, demonstrating that models with latent relations are significantly more accurate than those without latent relations.


title: Surface reconstruction from the projection of points, curves and contours

abstract: In this paper the problem of building and reconstructing geometrical surface models from multiple calibrated images is considered. We build an appropriate statistical 3D model from the images alone and show how this a priori model can be used to automatically reconstruct new instances of the object category from one or several images. The surface reconstruction method is based on a level set representation and one of the main novel contributions lie within the level set framework. Standard methods use either image-correlation or point correspondences to achieve this goal. We show how this framework can be extended to incorporate image curves and apparent contours (i.e. the projections of silhouettes). In order to automatically obtain feature correspondences, we use a statistical shape model for the object category of interest. The model is based on the active shape model using probabilistic PCA. The scheme is applied to build and automatically reconstruct 3D surface models of faces. The resulting system is demonstrated on a database of real face images.


title: Detection and tracking of boundary of unmarked roads

abstract: This paper presents a new method of detecting and tracking the boundaries of drivable regions in road without road-markings. As unmarked roads connect residential places to public roads, the capability of autonomously driving on such a roadway is important to truly realize self-driving cars in daily driving scenarios. To detect the left and right boundaries of drivable regions, our method first examines the image region at the front of ego-vehicle and then uses the appearance information of that region to identify the boundary of the drivable region from input images. Due to variation in the image acquisition condition, the image features necessary for boundary detection may not be present. When this happens, a boundary detection algorithm working frame-by-frame basis would fail to successfully detect the boundaries. To effectively handle these cases, our method tracks, using a Bayes filter, the detected boundaries over frames. Experiments using real-world videos show promising results.


Detecting Insider Attacks on Databases using Blockchains


title: When is an example a counterexample?

abstract: In this extended abstract, we carefully examine a purported counterexample to a postulate of iterated belief revision. We suggest that the example is better seen as a failure to apply the theory of belief revision in sucient detail. The main contribution is conceptual aiming at the literature on the philosophical foundations of the AGM theory of belief revision [1]. Our discussion is centered around the observation that it is often unclear whether a specic example is a \genuine" counterexample to an abstract theory or a misapplication of that theory to a concrete case.


title: BayesOWL: Uncertainty modeling in Semantic Web ontologies

abstract: It is always essential but di±cult to capture incomplete, partial or uncertain knowledge when using ontologies to conceptualize an application domain or to achieve semantic interoperability among heterogeneous systems. This chapter presents an on-going research on developing a framework which augments and supplements the semantic web ontology language OWL for representing and reasoning with uncertainty based on Bayesian networks (BN), and its application in ontology mapping.


title: New Three-Dimensional Velocity Motion Model and Composite Odometry–Inertial Motion Model for Local Autonomous Navigation

abstract: An autonomous vehicle or a self-guided vehicle (SGV) is a vehicle (robot) that performs the desired tasks in an unstructured environment without continuous human guidance. Almost all applications of an SGV require a vehicle that is capable of moving accurately and repeatedly to a particular location within its environment while executing a specific task. The accuracy and robustness in performing a specific task are therefore very important for the SGV to achieve a high level of performance. This paper introduces a new spherical velocity motion model and a new spherical odometry-inertia motion model for 3-D local landmark-based autonomous navigation. These new models are high accuracy and low-cost models. As modeling the contents of the immediate environment is fundamental, estimation of the position of the vehicle with respect to the external world is fundamental as well. Hence, using the most powerful tools of estimation theory, i.e., the extended Kalman filter (EKF) and the unscented Kalman filter (UKF), which give the best estimations in noisy environments, will prove the accuracy and robustness of these 3-D models.


title: Online Robust PCA via Stochastic Optimization

abstract: Robust PCA methods are typically based on batch optimization and have to load all the samples into memory during optimization. This prevents them from efficiently processing big data. In this paper, we develop an Online Robust PCA (OR-PCA) that processes one sample per time instance and hence its memory cost is independent of the number of samples, significantly enhancing the computation and storage efficiency. The proposed OR-PCA is based on stochastic optimization of an equivalent reformulation of the batch RPCA. Indeed, we show that OR-PCA provides a sequence of subspace estimations converging to the optimum of its batch counterpart and hence is provably robust to sparse corruption. Moreover, OR-PCA can naturally be applied for tracking dynamic subspace. Comprehensive simulations on subspace recovering and tracking demonstrate the robustness and efficiency advantages of the OR-PCA over online PCA and batch RPCA methods.


title: Forward-Secure Searchable Encryption on Labeled Bipartite Graphs

abstract: Forward privacy is a trending security notion of dynamic searchable symmetric encryption (DSSE). It guarantees the privacy of newly added data against the server who has knowledge of previous queries. The notion was very recently formalized by Bost (CCS ’16) independently, yet the definition given is imprecise to capture how forward secure a scheme is. We further the study of forward privacy by proposing a generalized definition parametrized by a set of updates and restrictions on them. We then construct two forward private DSSE schemes over labeled bipartite graphs, as a generalization of those supporting keyword search over text files. The first is a generic construction from any DSSE, and the other is a concrete construction from scratch. For the latter, we designed a novel data structure called cascaded triangles, in which traversals can be performed in parallel while updates only affect the local regions around the updated nodes. Besides neighbor queries, our schemes support flexible edge additions and intelligent node deletions: The server can delete all edges connected to a given node, without having the client specify all the edges.


E-COMMERCE AND SUPPLY CHAIN MANAGEMENT


TextFlow: A Text Similarity Measure based on Continuous Sequences


title: Circularly polarized antenna based on dual-mode circular SIW cavity

abstract: This paper describes design method of a novel circularly polarized antenna backed by low profile circular SIW cavity. The proposed antenna provides high circularly polarized radiation performance. It can be seamlessly integrated with planar circuits and more conveniently fabricated by normal PCB with low cost.


title: Diet and the Condition and Growth of Elephants in Uganda

abstract: A study was made of the nutrition of elephants collected during a limited culling operation in the Kabalega Falls and Rwenzori National Parks, Uganda. The diet and the condition and growth of the elephant populations were compared independently of seasonal fluctuations in these factors by sampling in each area in both wet and dry seasons. Growth rates and condition in Rwenzori N.P. remained relatively stable throughout the year. In Kabalega Falls N.P., however, growth rates and condition fluctuated due to inadequate food resources in the dry season. No appreciable differences in condition and growth were detected between the elephant populations to the north and south of the Victoria Nile in Kabalega Falls N.P. Some of the condition estimates frequently used to assess food resources, such as kidney fat index, are unreliable because they reflect the amount of stored food reserves rather than the diet at the time of sampling.


Development of Geotechnical Resistance Factors and Downdrag Load Factors for LRFD Foundation Strength Limit State Design Reference Manual


title: Cigarette smoking causes accumulation of polymorphonuclear leukocytes in alveolar septum

abstract: The polymorphonuclear leukocyte (PMN) may play an important role in the pathogenesis of emphysema. Cigarette smoking is associated with the accumulation of PMN in the lung as determined by bronchoalveolar lavage. We enumerated alveolar wall PMN on histologic sections from lungs of humans and hamsters with and without cigarette smoke exposure. In human non-smokers, there were 0.9 ± 0.2 PMN/mm alveolar wall. In cigarette smokers without emphysema, there were 2.1 ± 0.3 PMN/mm alveolar wall (p < 0.01), and in cigarette smokers with emphysema, there were 2.4 ± 0.7 PMN/alveolar wall (p < 0.05). There were 1.7 ± 0.3 PMN/mm alveolar wall in the lungs of hamsters unexposed to cigarette smoke compared with 3.1 ± 0.3 PMN/mm alveolar wall in smoke-exposed hamsters (p < 0.005). Although cigarette smoking causes PMN to accumulate within alveolar septa, the accumulation does not seem to be closely related to the development of emphysema. This suggests that additional or other factors are important in the pathogenesis of...


title: A Floor and Obstacle Height Map for 3D Navigation of a Humanoid Robot

abstract: With the development of biped robots, systems became able to navigate in a 3 dimensional world, walking up and down stairs, or climbing over small obstacles. We present a method for obtaining a labeled 2.5D grid map of the robot's surroundings. Each cell is marked either as floor or obstacle and contains a value telling the height of the floor or obstacle. Such height maps are useful for path planning and collision avoidance. The method uses a novel combination of a 3D occupancy grid for robust sensor data interpretation and a 2.5D height map for fine resolution floor values. We evaluate our approach using stereo vision on the humanoid robot QRIO and show the advantages over previous methods. Experimental results from navigation runs on an obstacle course demonstrate the ability of the method to generate detailed maps for autonomous navigation.


title: Parseval Networks: Improving Robustness to Adversarial Examples

abstract: We introduce Parseval networks, a form of deep neural networks in which the Lipschitz constant of linear, convolutional and aggregation layers is constrained to be smaller than 1. Parseval networks are empirically and theoretically motivated by an analysis of the robustness of the predictions made by deep neural networks when their input is subject to an adversarial perturbation. The most important feature of Parseval networks is to maintain weight matrices of linear and convolutional layers to be (approximately) Parseval tight frames, which are extensions of orthogonal matrices to non-square matrices. We describe how these constraints can be maintained efficiently during SGD. We show that Parseval networks match the state-of-the-art in terms of accuracy on CIFAR-10/100 and Street View House Numbers (SVHN) while being more robust than their vanilla counterpart against adversarial examples. Incidentally, Parseval networks also tend to train faster and make a better usage of the full capacity of the networks.


title: Advances in Lifted Importance Sampling

abstract: We consider lifted importance sampling (LIS), a previously proposed approximate inference algorithm for statistical relational learning (SRL) models. LIS achieves substantial variance reduction over conventional importance sampling by using various lifting rules that take advantage of the symmetry in the relational representation. However, it suffers from two drawbacks. First, it does not take advantage of some important symmetries in the relational representation and may exhibit needlessly high variance on models having these symmetries. Second, it uses an uninformative proposal distribution which adversely affects its accuracy. We propose two improvements to LIS that address these limitations. First, we identify a new symmetry in SRL models and define a lifting rule for taking advantage of this symmetry. The lifting rule reduces the variance of LIS. Second, we propose a new, structured approach for constructing and dynamically updating the proposal distribution via adaptive sampling. We demonstrate experimentally that our new, improved LIS algorithm is substantially more accurate than the LIS algorithm.


title: Adaptive Anomaly Detection in Performance Metric Streams

abstract: Continuous detection of performance anomalies such as service degradations has become critical in cloud and Internet services due to impact on quality of service and end-user experience. However, the volume and fast changing behavior of metric streams have rendered it a challenging task. Many diagnosis frameworks often rely on thresholding with stationarity or normality assumption, or on complex models requiring extensive offline training. Such techniques are known to be prone to spurious false-alarms in online settings as metric streams undergo rapid contextual changes from known baselines. Hence, we propose two unsupervised incremental techniques following a two-step strategy. First, we estimate an underlying temporal property of the stream via adaptive learning and, then we apply statistically robust control charts to recognize deviations. We evaluated our techniques by replaying over 40 time-series streams from the Yahoo! Webscope S5 datasets as well as four other traces of real Web service QoS and ISP traffic measurements. Our methods achieve high detection accuracy and few false-alarms, and better performance in general compared to an open-source package for time-series anomaly detection.


title: Variable Time Step Control for Six-Step Operation in Surface-Mounted Permanent Magnet Machine Drives

abstract: The six-step operation of surface-mounted permanent magnet machine drives in a flux weakening region has many advantages compared to the pulse width modulation mode, such as the reduced switching loss and fully utilized inverter output voltage. However, if the ratio of the sampling frequency to the fundamental frequency is low in fixed sampling system, the low-frequency oscillation in the current can be incurred in the six-step operation. The low-frequency current causes a system stability problem and reduces system efficiency due to an excessive heat and high power loss. Therefore, this paper proposes the variable time step controller for six-step operation. By updating an output voltage, sampling phase currents, and executing the digital controller synchronized with the variable sampling time, the turn on and off switch signals for six-step operation can be generated at the exact moment. As a result, the low-frequency oscillation in the phase current can be eliminated. In addition, the system transfer function of the proposed control method is discussed for the system stability and system dynamic analysis. The effectiveness of the proposed method is verified by the comparative simulation and experimental results.


title: Improvements of Interfacial and Electrical Properties for Ge MOS Capacitor by Using TaYON Interfacial Passivation Layer and Fluorine Incorporation

abstract: Ge metal–oxide–semiconductor capacitor with HfTiON/TaYON stacked gate dielectric treated by fluorine plasma is fabricated, and its interfacial and electrical properties are compared with its counterparts without the TaYON interfacial passivation layer or the fluorine-plasma treatment. Experimental results show that the sample exhibits excellent performances: low interface-state density ( $2.5\times 10^{11}$ cm $^{-2}$ eV $^{-1})$ , small flatband voltage (0.34 V), good capacitance–voltage behavior, small frequency dispersion, and low gate leakage current ( $2.47 \times 10^{-5}$ A/cm2 at $\text{V}_{\text {g}} = \,\, \text{V}_{\text {fb}} + 1$ V). These should be attributed to the suppressed growth of unstable Ge oxides on the Ge surface during gate dielectric annealing by the TaYON interlayer and fluorine incorporation, thus greatly reducing the defective states at/near the TaYON/Ge interface and improving the electrical properties of the device.


title: Learning Bilingual Lexicons from Monolingual Corpora

abstract: We present a method for learning bilingual translation lexicons from monolingual corpora. Word types in each language are characterized by purely monolingual features, such as context counts and orthographic substrings. Translations are induced using a generative model based on canonical correlation analysis, which explains the monolingual lexicons in terms of latent matchings. We show that high-precision lexicons can be learned in a variety of language pairs and from a range of corpus types.


title: The Effects of Acute Physical Exercise on Memory, Peripheral BDNF, and Cortisol in Young Adults

abstract: In animals, physical activity has been shown to induce functional and structural changes especially in the hippocampus and to improve memory, probably by upregulating the release of neurotrophic factors. In humans, results on the effect of acute exercise on memory are inconsistent so far. Therefore, the aim of the present study was to assess the effects of a single bout of physical exercise on memory consolidation and the underlying neuroendocrinological mechanisms in young adults. Participants encoded a list of German-Polish vocabulary before exercising for 30 minutes with either high intensity or low intensity or before a relaxing phase. Retention of the vocabulary was assessed 20 minutes after the intervention as well as 24 hours later. Serum BDNF and salivary cortisol were measured at baseline, after learning, and after the intervention. The high-intensity exercise group showed an increase in BDNF and cortisol after exercising compared to baseline. Exercise after learning did not enhance the absolute number of recalled words. Participants of the high-intensity exercise group, however, forgot less vocabulary than the relaxing group 24 hours after learning. There was no robust relationship between memory scores and the increase in BDNF and cortisol, respectively, suggesting that further parameters have to be taken into account to explain the effects of exercise on memory in humans.


title: Enhancing RAN throughput by optimizec controller placement in optical metro networks

abstract: For the incoming 5G mobile communications, enhanced cell coordination is deemed as crucial to maximize network throughput. In this paper we focus on the impact that the placement of coordination controllers in the metro optical network has on the performance of cell coordination, and we aim at verifying how throughput maximization can be achieved through optimized controller placement. Specifically, we refer to Coordinated Multipoint (CoMP), a protocol that allows to coordinate cell clusters through a Radio Controller Coordinator (RCC) to enhance the mobile-network throughput by reducing interference. Several CoMP techniques have been designed, whose throughput gain is affected differently by various factors. E.g., gains increases with the size of the cluster, while it decreases for larger latencies between RCC and cells. Current metro/aggregation networks are characterized by a hierarchical architecture with different levels of central offices, so the decision of where to place RCC in order to maximize CoMP throughput gain is not trivial and depends on several factors, such as geographical dimension of the network, cluster size and latency experienced before reaching the RCC. Our results show that under certain conditions optimized placement provides benefits with respect to fixed placement of controllers.


title: Cities and Complexity: Understanding Cities with Cellular Automata, Agent-Based Models, and Fractals

abstract: As urban planning moves from a centralized, top-down approach to a decentralized, bottom-up perspective, our conception of urban systems is changing. In Cities and Complexity, Michael Batty offers a comprehensive view of urban dynamics in the context of complexity theory, presenting models that demonstrate how complexity theory can embrace a myriad of processes and elements that combine into organic wholes. He argues that bottom-up processesin which the outcomes are always uncertaincan combine with new forms of geometry associated with fractal patterns and chaotic dynamics to provide theories that are applicable to highly complex systems such as cities. Batty begins with models based on cellular automata (CA), simulating urban dynamics through the local actions of automata. He then introduces agent-based models (ABM), in which agents are mobile and move between locations. These models relate to many scales, from the scale of the street to patterns and structure at the scale of the urban region. Finally, Batty develops applications of all these models to specific urban situations, discussing concepts of criticality, threshold, surprise, novelty, and phase transition in the context of spatial developments. Every theory and model presented in the book is developed through examples that range from the simplified and hypothetical to the actual. Deploying extensive visual, mathematical, and textual material, Cities and Complexity will be read both by urban researchers and by complexity theorists with an interest in new kinds of computational models. Sample chapters and examples from the book, and other related material, can be found at http://www.complexcity.info.


title: Data, information and analytics as services

abstract: While organizations are trying to become more agile to better respond to market changes in the midst of rapidly globalizing competition by adopting service orientation-commoditization of business processes, architectures, software, infrastructures and platforms-they are also facing new challenges. In this article, we provide a conceptual framework for service oriented managerial decision making process, and briefly explain the potential impact of service oriented architecture (SOA) and cloud computing on data, information and analytics. Today, SOA, cloud computing, Web 2.0 and Web 3.0 are converging, and transforming the information technology ecosystem for the better while imposing new complexities. With this convergence, a large amount of structured and unstructured data is being created and shared over disparate networks and virtual communities. To cope and/or to take advantage of these changes, we are in need of finding new and more efficient ways to collect, store, transform, share, utilize and dispose data, information and analytics.


title: Japan Prevention Trial of Diabetes by Pitavastatin in Patients with Impaired Glucose Tolerance (the J-PREDICT study): Rationale, study design, and clinical characteristics of 1269 patients

abstract: Objective ::: The Japan Prevention Trial of Diabetes by Pitavastatin in Patients with Impaired Glucose Tolerance (J-PREDICT study) is an open-label randomized controlled study in a population with impaired glucose tolerance (IGT) to evaluate the effect of the 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitor (statin) pitavastatin on new onset of diabetes.


title: Relational learning via collective matrix factorization

abstract: Relational learning is concerned with predicting unknown values of a relation, given a database of entities and observed relations among entities. An example of relational learning is movie rating prediction, where entities could include users, movies, genres, and actors. Relations encode users' ratings of movies, movies' genres, and actors' roles in movies. A common prediction technique given one pairwise relation, for example a #users x #movies ratings matrix, is low-rank matrix factorization. In domains with multiple relations, represented as multiple matrices, we may improve predictive accuracy by exploiting information from one relation while predicting another. To this end, we propose a collective matrix factorization model: we simultaneously factor several matrices, sharing parameters among factors when an entity participates in multiple relations. Each relation can have a different value type and error distribution; so, we allow nonlinear relationships between the parameters and outputs, using Bregman divergences to measure error. We extend standard alternating projection algorithms to our model, and derive an efficient Newton update for the projection. Furthermore, we propose stochastic optimization methods to deal with large, sparse matrices. Our model generalizes several existing matrix factorization methods, and therefore yields new large-scale optimization algorithms for these problems. Our model can handle any pairwise relational schema and a wide variety of error models. We demonstrate its efficiency, as well as the benefit of sharing parameters among relations.


title: Gardening Promotes Neuroendocrine and Affective Restoration from Stress

abstract: Stress-relieving effects of gardening were hypothesized and tested in a field experiment. Thirty allotment gardeners performed a stressful Stroop task and were then randomly assigned to 30 minutes of outdoor gardening or indoor reading on their own allotment plot. Salivary cortisol levels and self-reported mood were repeatedly measured. Gardening and reading each led to decreases in cortisol during the recovery period, but decreases were significantly stronger in the gardening group. Positive mood was fully restored after gardening, but further deteriorated during reading. These findings provide the first experimental evidence that gardening can promote relief from acute stress.


title: Domain-Targeted, High Precision Knowledge Extraction

abstract: Our goal is to construct a domain-targeted, high precision knowledge base (KB), containing general (subject,predicate,object) statements about the world, in support of a downstream question-answering (QA) application. Despite recent advances in information extraction (IE) techniques, no suitable resource for our task already exists; existing resources are either too noisy, too named-entity centric, or too incomplete, and typically have not been constructed with a clear scope or purpose. To address these, we have created a domain-targeted, high precision knowledge extraction pipeline, leveraging Open IE, crowdsourcing, and a novel canonical schema learning algorithm (called CASI), that produces high precision knowledge targeted to a particular domain - in our case, elementary science. To measure the KB’s coverage of the target domain’s knowledge (it’s "comprehensiveness" with respect to science) we measure recall with respect to an independent corpus of domain text, and show that our pipeline produces output with over 80% precision and 23% recall with respect to that target, a substantially higher coverage of tuple-expressible science knowledge than other comparable resources. We have made the KB publicly available at http://data.allenai.org/tuple-kb .


title: Temporal Information Extraction

abstract: Research on information extraction (IE) seeks to distill relational tuples from natural language text, such as the contents of the WWW. Most IE work has focussed on identifying static facts, encoding them as binary relations. This is unfortunate, because the vast majority of facts are fluents, only holding true during an interval of time. It is less helpful to extract PresidentOf(Bill-Clinton, USA) without the temporal scope 1/20/93 - 1/20/01. This paper presents TIE, a novel, information-extraction system, which distills facts from text while inducing as much temporal information as possible. In addition to recognizing temporal relations between times and events, TIE performs global inference, enforcing transitivity to bound the start and ending times for each event. We introduce the notion of temporal entropy as a way to evaluate the performance of temporal IE systems and present experiments showing that TIE outperforms three alternative approaches.


A mediating influence on customer loyalty : The role of perceived value


title: Perceptions and Practices regarding Breastfeeding among Postnatal Women at a District Tertiary Referral Government Hospital in Southern India

abstract: Background. Breastfeeding is the optimal method for achieving a normal growth and development of the baby. This study aimed to study mothers' perceptions and practices regarding breastfeeding in Mangalore, India. Methodology. A cross-sectional study of 188 mothers was conducted using a structured proforma. Results. Importance of breast feeding was known to most mothers. While initiation of breast feeding within one hour of birth was done by majority of mothers, few had discarded colostrum and adopted prelacteal feeding. Mothers opined that breast feeding is healthy for their babies (96.3%) and easier than infant feeding (79.8%), does not affect marital relationship (51%), and decreases family expenditure (61.1%). However, there were poor perceptions regarding the advantages of breast milk with respect to nutritive value, immune effect, and disease protection. Few respondents reported discontinuation of breastfeeding in previous child if the baby had fever/cold (6%) or diarrhea (18%) and vomiting (26%). There was a statistically significant association between mother's educational level and perceived importance of breastfeeding and also between the mode of delivery and initiation of breast feeding (p < 0.05). Conclusion. Importance of breast feeding was known to most mothers. Few perceptions related to breast milk and feeding along with myths and disbeliefs should be rectified by health education.


title: Safe Adaptive Importance Sampling

abstract: Importance sampling has become an indispensable strategy to speed up optimization algorithms for large-scale applications. Improved adaptive variants - using importance values defined by the complete gradient information which changes during optimization - enjoy favorable theoretical properties, but are typically computationally infeasible. In this paper we propose an efficient approximation of gradient-based sampling, which is based on safe bounds on the gradient. The proposed sampling distribution is (i) provably the best sampling with respect to the given bounds, (ii) always better than uniform sampling and fixed importance sampling and (iii) can efficiently be computed - in many applications at negligible extra cost. The proposed sampling scheme is generic and can easily be integrated into existing algorithms. In particular, we show that coordinate-descent (CD) and stochastic gradient descent (SGD) can enjoy significant a speed-up under the novel scheme. The proven efficiency of the proposed sampling is verified by extensive numerical testing.


title: An optimization framework for cost effective design of refueling station infrastructure for alternative fuel vehicles

abstract: Green transportation Facility location models Flow interception problem Microscopic transportation models Decision analysis Alternative fuels Plug-in hybrids a b s t r a c t Historically, gasoline and diesel fuels have been used for transportation, but the possible decline of oil supplies in the future is forcing nations to consider alternative fuels. Most technically and economically feasible alternative fuels have a lower energy density than gasoline, which results in a shorter range for these vehicles. This necessitates a greater need for convenient access to refueling facilities for alternative fuel vehicles. Since infrastructure development is expensive, there is a need to direct investments towards the establishment of refueling facilities in areas which result in maximum impact. This can be addressed by locating facilities at sites which service as many vehicles as possible. This work deals with the use of mathematical programming for determining the best locations for establishing alternative transportation fuel stations. The objective was to site the refueling stations at locations which maximize the number of vehicles served, while staying within budget constraints. The model used here is a modified form of the flow interception facility location model. For the case study we used the transportation network of Alexandria, Virginia, as a test bed for our model. Origin-destination travel demand data for this city is simulated through a transportation simulator to determine the routes taken by individual vehicles. The results are then compared with the service level offered by conventional gasoline refueling stations already located in the city. This work integrates the use of transportation modeling with mathematical programming for the solution of a complex large-scale problem on a real-life transportation network.


title: Uncertain Data Mining: An Example in Clustering Location Data

abstract: Data uncertainty is an inherent property in various applications due to reasons such as outdated sources or imprecise measurement. When data mining techniques are applied to these data, their uncertainty has to be considered to obtain high quality results. We present UK-means clustering, an algorithm that enhances the K-means algorithm to handle data uncertainty. We apply UK-means to the particular pattern of moving-object uncertainty. Experimental results show that by considering uncertainty, a clustering algorithm can produce more accurate results.


title: Transformer fault diagnosis using continuous sparse autoencoder

abstract: This paper proposes a novel continuous sparse autoencoder (CSAE) which can be used in unsupervised feature learning. The CSAE adds Gaussian stochastic unit into activation function to extract features of nonlinear data. In this paper, CSAE is applied to solve the problem of transformer fault recognition. Firstly, based on dissolved gas analysis method, IEC three ratios are calculated by the concentrations of dissolved gases. Then IEC three ratios data is normalized to reduce data singularity and improve training speed. Secondly, deep belief network is established by two layers of CSAE and one layer of back propagation (BP) network. Thirdly, CSAE is adopted to unsupervised training and getting features. Then BP network is used for supervised training and getting transformer fault. Finally, the experimental data from IEC TC 10 dataset aims to illustrate the effectiveness of the presented approach. Comparative experiments clearly show that CSAE can extract features from the original data, and achieve a superior correct differentiation rate on transformer fault diagnosis.


title: Quantifying tissue loads and spine stability while performing commonly prescribed low back stabilization exercises.

abstract: STUDY DESIGN ::: A quantitative biomechanical comparison of seven different lumbar spine "stabilization exercises." ::: ::: ::: OBJECTIVES ::: The purpose of this research was to quantify lumbar spine stability resulting from the muscle activation patterns measured when performing selected stabilization exercises. ::: ::: ::: SUMMARY OF BACKGROUND DATA ::: Many exercises are termed "stabilization exercises" for the low back; however, limited attempts have been made to quantify spine stability and the resultant tissue loading. Ranking resultant stability together with spinal load is very helpful for guiding clinical decision-making and therapeutic exercise design. ::: ::: ::: METHODS ::: Eight stabilization exercises were quantified in this study. Spine kinematics, external forces, and 14 channels of torso EMG were recorded for each exercise. These data were input into a modified version of a lumbar spine model described by Cholewicki and McGill (1996) to quantify stability and L4-L5 compression. ::: ::: ::: RESULTS ::: A rank order of the various exercises was produced based on stability, muscle activation levels, and lumbar compression. ::: ::: ::: CONCLUSIONS ::: Quantification of the calibrated muscle activation levels together with low back compression and resultant stability assists clinical decisions regarding the most appropriate exercise for specific patients and specific objectives.


title: CMOS Current Reference without Resistance

abstract: A micropower current reference can be built with CMOS transistors only. It shows low sensitivity with respect to technology, supply voltage and temperature, and it works on very low supply voltages. The circuit is very simple and occupies a small surface area.


Investigating Personalized Search in E-Commerce.


title: Deep learning and the information bottleneck principle

abstract: Deep Neural Networks (DNNs) are analyzed via the theoretical framework of the information bottleneck (IB) principle. We first show that any DNN can be quantified by the mutual information between the layers and the input and output variables. Using this representation we can calculate the optimal information theoretic limits of the DNN and obtain finite sample generalization bounds. The advantage of getting closer to the theoretical limit is quantifiable both by the generalization bound and by the network's simplicity. We argue that both the optimal architecture, number of layers and features/connections at each layer, are related to the bifurcation points of the information bottleneck tradeoff, namely, relevant compression of the input layer with respect to the output layer. The hierarchical representations at the layered network naturally correspond to the structural phase transitions along the information curve. We believe that this new insight can lead to new optimality bounds and deep learning algorithms.


title: An Optimization Approach to Improving Collections of Shape Maps

abstract: Finding an informative, structure-preserving map between two shapes has been a long-standing problem in geometry processing, involving a variety of solution approaches and applications. However, in many cases, we are given not only two related shapes, but a collection of them, and considering each pairwise map independently does not take full advantage of all existing information. For example, a notorious problem with computing shape maps is the ambiguity introduced by the symmetry problem — for two similar shapes which have reflectional symmetry there exist two maps which are equally favorable, and no intrinsic mapping algorithm can distinguish between them based on these two shapes alone. Another prominent issue with shape mapping algorithms is their relative sensitivity to how “similar” two shapes are — good maps are much easier to obtain when shapes are very similar. Given the context of additional shape maps connecting our collection, we propose to add the constraint of global map consistency, requiring that any composition of maps between two shapes should be independent of the path chosen in the network. This requirement can help us choose among the equally good symmetric alternatives, or help us replace a “bad” pairwise map with the composition of a few “good” maps between shapes that in some sense interpolate the original ones. We show how, given a collection of pairwise shape maps, to define an optimization problem whose output is a set of alternative maps, compositions of those given, which are consistent, and individually at times much better than the original. Our method is general, and can work on any collection of shapes, as long as a seed set of good pairwise maps is provided. We demonstrate the effectiveness of our method for improving maps generated by state-of-the-art mapping methods on various shape databases.


title: A method of ECU software updating

abstract: The number of Electronic Control Units (ECUs) installed in vehicles is increasing owing to the implementation of automatic driving functions or advanced driver assistance systems. Moreover, the scale of software in the vehicle is becoming larger and more complicated. Therefore, the number of recalls caused by software is increasing. Furthermore, vehicles could be the targets of cyberattacks when connected cars become popular. Vehicles need their ECU software to be updated in order to fix bugs and implement countermeasures against threats. In this study, we propose an in-vehicle ECU software updating protocol and evaluate its updating time.


title: Age-Related Changes in Left Ventricular Torsion as Assessed by 2-Dimensional Ultrasound Speckle Tracking Imaging

abstract: Background and Objectives: The newly developed 2-dimensional ultrasound speckle tracking imaging (2D-STI) has enabled researchers to assess the changes of left ventricular (LV) rotation and torsion. The aims of the present study are to establish normal values and to examine the effect of advancing age on left ventricular torsion. Subjects and Methods: We enrolled 182 healthy persons in this study. After examined the standard clinical echocardiographic parameters, we obtained the degree of the LV rotation at the basal and apical levels of the short axis view with using a customized software program EchoPAC, GE. Results: Among the 182 healthy subjects, 109 healthy subjects were finally included (49 males and 60 females) due to the failure of obtaining reliable rotational patterns (feasibility: 59.8%). The basal and apical peak LV rotations during systole were 8.14±3.55 degrees and 8.48±3.70 degrees, respectively. The basal peak LV rotation and peak LV torsion had a tendency to increase with aging (r=0.277, p=0.004 and r=0.253, p=0.008, respectively). All the values of the basal LV rotation during systole tended to increase with aging. The apical LV rotation had no relationship with aging throughout the entire cardiac cycle. Conclusion: 2D-STI was a feasible methodology to measure the LV rotation. The peak LV torsion during systole shows statistically significant augmentation with advancing age, and this is mainly due to the increased basal LV rotation. (Korean Circ J 2008;38:529-535)


title: Saliency detection with color contrast based on boundary information and neighbors

abstract: Object-level saliency detection is significant in many computer vision tasks. In this paper, we propose a novel saliency detection model based on color contrast and image boundaries. The saliency of an image is defined as the contrast between the image elements (regions) and image boundaries elements (regions). We consider the saliency in two-stage procedure rather than in one stage. First of all, according to the definition of saliency, we take four boundaries of image into consideration respectively to obtain a combination coarse saliency map. Furthermore, a new energy function based on the coarse saliency map is proposed, which takes the coarse saliency map as input to yield the final full resolution saliency map. Experimental results on two public datasets demonstrate that the proposed model performs better than the state-of-the-art methods.


title: The effects of Doppler spreads in OFDM(A) mobile radio systems

abstract: We analyze the effects of Doppler spread in mobile channels on orthogonal frequency division multiplex (OFDM) systems. This is important, since channel variations during one OFDM symbol cause inter sub-carrier interference (ICI) in OFDM systems, which degrades the performance, since ICI can be seen as additional near-Gaussian noise. The analysis is outlined, and closed-form results given for numerous important practical Doppler spread encountered in mobile channels. We also show that for the case of asymmetrical Doppler spreads, the frequency correction of the receiver can be adjusted so as to minimize the ICI. To extend our work to OFDM(A) mobile radio systems in the uplink case where many users' contributions to the ICI overlap, we have introduced the concept of an equivalent single-user which causes the total ICI, in order to use the same analytical framework. For some channel examples, we simulated the Doppler spread of this equivalent user. Simulation results confirm our analysis and show the importance of the analytical tool to perform realistic system analysis without having to resort to time consuming time-domain simulations.


title: Semi-Supervised Truth Discovery

abstract: Accessing online information from various data sources has become a necessary part of our everyday life. Unfortunately such information is not always trustworthy, as different sources are of very different qualities and often provide inaccurate and conflicting information. Existing approaches attack this problem using unsupervised learning methods, and try to infer the confidence of the data value and trustworthiness of each source from each other by assuming values provided by more sources are more accurate. However, because false values can be widespread through copying among different sources and out-of-date data often overwhelm up-to-date data, such bootstrapping methods are often ineffective. In this paper we propose a semi-supervised approach that finds true values with the help of ground truth data. Such ground truth data, even in very small amount, can greatly help us identify trustworthy data sources. Unlike existing studies that only provide iterative algorithms, we derive the optimal solution to our problem and provide an iterative algorithm that converges to it. Experiments show our method achieves higher accuracy than existing approaches, and it can be applied on very huge data sets when implemented with MapReduce.


Guidance and Navigation for UAV Airborne Docking


title: What you ask is what you get: Understanding architecturally significant functional requirements

abstract: Software architects are responsible for designing an architectural solution that satisfies the functional and non-functional requirements of the system to the fullest extent possible. However, the details they need to make informed architectural decisions are often missing from the requirements specification. An earlier study we conducted indicated that architects intuitively recognize architecturally significant requirements in a project, and often seek out relevant stakeholders in order to ask Probing Questions (PQs) that help them acquire the information they need. This paper presents results from a qualitative interview study aimed at identifying architecturally significant functional requirements' categories from various business domains, exploring relevant PQs for each category, and then grouping PQs by type. Using interview data from 14 software architects in three countries, we identified 15 categories of architecturally significant functional requirements and 6 types of PQs. We found that the domain knowledge of the architect and her experience influence the choice of PQs significantly. A preliminary quantitative evaluation of the results against real-life software requirements specification documents indicated that software specifications in our sample largely do not contain the crucial architectural differentiators that may impact architectural choices and that PQs are a necessary mechanism to unearth them. Further, our findings provide the initial list of PQs which could be used to prompt business analysts to elicit architecturally significant functional requirements that the architects need.


title: Idiom Token Classification using Sentential Distributed Semantics

abstract: Idiom token classification is the task of deciding for a set of potentially idiomatic phrases whether each occurrence of a phrase is a literal or idiomatic usage of the phrase. In this work we explore the use of Skip-Thought Vectors to create distributed representations that encode features that are predictive with respect to idiom token classification. We show that classifiers using these representations have competitive performance compared with the state of the art in idiom token classification. Importantly, however, our models use only the sentence containing the target phrase as input and are thus less dependent on a potentially inaccurate or incomplete model of discourse context. We further demonstrate the feasibility of using these representations to train a competitive general idiom token classifier.


title: DFC: accelerating string pattern matching for network applications

abstract: Middlebox services that inspect packet payloads have become commonplace. Today, anyone can sign up for cloud-based Web application firewall with a single click. These services typically look for known patterns that might appear anywhere in the payload. The key challenge is that existing solutions for pattern matching have become a bottleneck because software packet processing technologies have advanced. The popularization of cloud-based services has made the problem even more critical. ::: ::: This paper presents an efficient multi-pattern string matching algorithm, called DFC. DFC significantly reduces the number of memory accesses and cache misses by using small and cache-friendly data structures and avoids instruction pipeline stalls by minimizing sequential data dependency. Our evaluation shows that DFC improves performance by 2.0 to 3.6 times compared to state-of-the-art on real traffic workload obtained from a commercial network. It also outperforms other algorithms even in the worst case. When applied to middlebox applications, such as network intrusion detection, anti-virus, and Web application firewalls, DFC delivers 57-160% improvement in performance.


title: Representations Systems, Perceptual Position, and Presence in Immersive Virtual Environments

abstract: This paper discusses factors that may contribute to the participant's sense of presence in immersive virtual environments. We distinguish between external factors, that is those wholly determined by the hardware and software technology employed to generate the environment, and subjective factors, that is how sensory inputs to the human participant are processed internally. The therapeutic technique known as neurolinguistic programming NLP is used as a basis for measuring such internal factors. NLP uses the idea of representation systems visual, auditory, and kinesthetic and perceptual position egocentric or exocentric to code subjective experience. The paper also considers one external factor, that is how the virtual environment represents a participant-either as a complete body, or just an arrow cursor that responds to hand movements. A case-control pilot experiment is described, where the controls have self-representation as an arrow cursor, and the experimental group subjects as a simple virtual body. Measurements of subjects' preferred representation systems and perceptual positions are obtained based on counts of types of predicates and references used in essays written after the experiment. These, together with the control variable possession/absence of a virtual body, are used as explanatory variables in a regression analysis, with reported sense of presence as the dependent variable. Although tentative and exploratory in nature, the data analysis does suggest a relationship between reported sense of presence, preferred representation system, perceptual position, and an interaction effect between these and the virtual body factor.


title: AMPK Phosphorylation of Raptor Mediates a Metabolic Checkpoint

abstract: AMPK is a highly conserved sensor of cellular energy status that is activated under conditions of low intracellular ATP. AMPK responds to energy stress by suppressing cell growth and biosynthetic processes, in part through its inhibition of the rapamycin-sensitive mTOR (mTORC1) pathway. AMPK phosphorylation of the TSC2 tumor suppressor contributes to suppression of mTORC1; however, TSC2-deficient cells remain responsive to energy stress. Using a proteomic and bioinformatics approach, we sought to identify additional substrates of AMPK that mediate its effects on growth control. We report here that AMPK directly phosphorylates the mTOR binding partner raptor on two well-conserved serine residues, and this phosphorylation induces 14-3-3 binding to raptor. The phosphorylation of raptor by AMPK is required for the inhibition of mTORC1 and cell-cycle arrest induced by energy stress. These findings uncover a conserved effector of AMPK that mediates its role as a metabolic checkpoint coordinating cell growth with energy status.


title: Empirical Validation of Commodity Spectrum Monitoring

abstract: We describe our efforts to empirically validate a distributed spectrum monitoring system built on commodity smartphones and embedded low-cost spectrum sensors. This system enables real-time spectrum sensing, identifies and locates active transmitters, and generates alarm events when detecting anomalous transmitters. To evaluate the feasibility of such a platform, we perform detailed experiments using a prototype hardware platform using smartphones and RTL dongles. We identify multiple sources of error in the sensing results and the end-user overhead (i.e. smartphone energy draw). We propose and implement a variety of techniques to identify and overcome errors and uncertainty in the data, and to reduce energy consumption. Our work demonstrates the basic viability of user-driven spectrum monitoring on commodity devices.


title: Using the focus group method in software engineering: obtaining practitioner and user experiences

abstract: This paper reflects on three cases where the focus group method was used to obtain feedback and experiences from software engineering practitioners and application users. The focus group method and its background are presented, the method's weaknesses and strengths are discussed, and guidelines are provided for how to use the method in the software engineering context. Furthermore, the results of the three studies conducted are highlighted and the paper concludes in a discussion on the applicability of the method for this type of research. In summary, the focus group method is a cost-effective and quick empirical research approach for obtaining qualitative insights and feedback from practitioners. It can be used in several phases and types of research. However, a major limitation of the method is that it is useful only in studying concepts that can be understood by participants in a limited time. We also recommend that in the software engineering context, the method should be used with sufficient empirical rigor.


title: Crawling Ajax-Based Web Applications through Dynamic Analysis of User Interface State Changes

abstract: Using JavaScript and dynamic DOM manipulation on the client side of Web applications is becoming a widespread approach for achieving rich interactivity and responsiveness in modern Web applications. At the same time, such techniques---collectively known as Ajax ---shatter the concept of webpages with unique URLs, on which traditional Web crawlers are based. This article describes a novel technique for crawling Ajax -based applications through automatic dynamic analysis of user-interface-state changes in Web browsers. Our algorithm scans the DOM tree, spots candidate elements that are capable of changing the state, fires events on those candidate elements, and incrementally infers a state machine that models the various navigational paths and states within an Ajax application. This inferred model can be used in program comprehension and in analysis and testing of dynamic Web states, for instance, or for generating a static version of the application. In this article, we discuss our sequential and concurrent Ajax crawling algorithms. We present our open source tool called Crawljax , which implements the concepts and algorithms discussed in this article. Additionally, we report a number of empirical studies in which we apply our approach to a number of open-source and industrial Web applications and elaborate on the obtained results.


title: Nonconscious effects of peculiar beliefs on consumer psychology and choice

abstract: Abstract Irrational or illogical beliefs are referred to variously as magical thinking, peculiar beliefs, superstitious beliefs, and half-beliefs. We first distinguish the various terms according to their most common and relevant usage for consumer psychologists and define a conceptual structure for the roles of conscious and nonconscious processes associated with peculiar beliefs in decision-making. We present a study that provides initial evidence of the effect of nonconscious, experiential processing on the impact of peculiar beliefs in a consumer auction-based sales scenario. We also offer propositions that extend the theory on peculiar beliefs to their nonconscious effects on consumer psychology and choice.


title: A Short Survey of Topological Data Analysis in Time Series and Systems Analysis

abstract: Topological Data Analysis (TDA) is the collection of mathematical tools that capture the structure of shapes in data. Despite computational topology and computational geometry, the utilization of TDA in time series and signal processing is relatively new. In some recent contributions, TDA has been utilized as an alternative to the conventional signal processing methods. Specifically, TDA is been considered to deal with noisy signals and time series. In these applications, TDA is used to find the shapes in data as the main properties, while the other properties are assumed much less informative. In this paper, we will review recent developments and contributions where topological data analysis especially persistent homology has been applied to time series analysis, dynamical systems and signal processing. We will cover problem statements such as stability determination, risk analysis, systems behaviour, and predicting critical transitions in financial markets.


title: Real Time Temperature Prediction Using IoT

abstract: Accurate temperature predictions play a crucial role on planning our day-to-day tasks. Farmers need information about the weather condition in order to get ready for planting as well as harvesting of their yields. This study is done to make an endeavor to predict accurate temperature by utilizing potential deep learning approach; Long Term Memory (LSTM). DHT11 digital temperature and humidity sensor were used to gather real time data for undertaking the experiment. For the contextual investigation, the prediction of the temperature in different places of Vadodara is considered.


title: Differential contribution of dorsal and ventral medial prefrontal cortex to the acquisition and extinction of conditioned fear in rats.

abstract: The emotional reactivity of rats with lesions of the dorsal portion of medial prefrontal cortex (mPFC) was examined using a classical fear conditioning paradigm. Conditioned fear behavior (freezing responses) was measured during both the acquisition and extinction phases of the task. Lesions enhanced fear reactivity to both the conditioned stimlulus (CS) and contextual stimuli during both phases, suggesting that dorsal mPFC lesions produce a general increase in fear reactivity in response to fear conditioning. M. A. Morgan, L. M. Romanski, and J. E. LeDoux (1993) found that lesions just ventral to the present lesions had no effect during acquisition of the same task and prolonged the fear response to the CS (but not the context) during extinction. Thus, both dorsal and ventral regions of mPFC are involved in the fear system, but each modulates different aspects of fear responsivity.


title: Effects of Music Training on the Child's Brain and Cognitive Development

abstract: Research has revealed structural and functional differences in the brains of adult instrumental musicians compared to those of matched nonmusician controls, with intensity/duration of instrumental training and practice being important predictors of these differences. Nevertheless, the differential contributions of nature and nurture to these differences are not yet clear. The musician-nonmusician comparison is an ideal model for examining whether and, if so, where such functional and structural brain plasticity occurs, because musicians acquire and continuously practice a variety of complex motor, auditory, and multimodal skills (e.g., translating visually perceived musical symbols into motor commands while simultaneously monitoring instrumental output and receiving multisensory feedback). Research has also demonstrated that music training in children results in long-term enhancement of visual-spatial, verbal, and mathematical performance. However, the underlying neural bases of such enhancements and whether the intensity and duration of instrumental training or other factors, such as extracurricular activities, attention, motivation, or instructional methods can contribute to or predict these enhancements are yet unknown. Here we report the initial results from our studies examining the brain and cognitive effects of instrumental music training on young children in a longitudinal study and a cross-sectional comparison in older children. Further, we present a comparison of the results in these children's studies with observations from our cross-sectional studies with adults.


Design and Evaluation of Event Detection Techniques for Social Media Data Streams


title: Efficient Label Propagation

abstract: Label propagation is a popular graph-based semi-supervised learning framework. So as to obtain the optimal labeling scores, the label propagation algorithm requires an inverse matrix which incurs the high computational cost of O(n3+cn2), where n and c are the numbers of data points and labels, respectively. This paper proposes an efficient label propagation algorithm that guarantees exactly the same labeling results as those yielded by optimal labeling scores. The key to our approach is to iteratively compute lower and upper bounds of labeling scores to prune unnecessary score computations. This idea significantly reduces the computational cost to O(cnt) where t is the average number of iterations for each label and t ≪ n in practice. Experiments demonstrate the significant superiority of our algorithm over existing label propagation methods.


title: Detecting distance-based outliers in streams of data

abstract: In this work a method for detecting distance-based outliers in data streams is presented. We deal with the sliding window model, where outlier queries are performed in order to detect anomalies in the current window. Two algorithms are presented. The first one exactly answers outlier queries, but has larger space requirements. The second algorithm is directly derived from the exact one, has limited memory requirements and returns an approximate answer based on accurate estimations with a statistical guarantee. Several experiments have been accomplished, confirming the effectiveness of the proposed approach and the high quality of approximate solutions.


title: The Law of the Platform

abstract: New digital platform companies are turning everything into an available resource: services, products, spaces, connections, and knowledge, all of which would otherwise be collecting dust. Unsurprisingly then, the platform economy defies conventional regulatory theory. Millions of people are becoming part-time entrepreneurs, disrupting established business models and entrenched market interests, challenging regulated industries, and turning ideas about consumption, work, risk, and ownership on their head. Paradoxically, as the digital platform economy becomes more established, we are also at an all-time high in regulatory permitting, licensing, and protection. The battle over law in the platform is therefore both conceptual and highly practical. New business models such as Uber, Airbnb, and Aereo have received massive amounts of support from venture capitalists but have also received immense pushback from incumbent stakeholders, regulators, and courts. This article argues that the platform economy is presenting not only a paradigm shift for business but also for legal theory. The platform economy does not only disrupt regulated industries but also demands that we inquire into the logic of their correlated regulations. It requires that we go back to first principles about public intervention and market innovation. The article thus poses a foundational inquiry: Do the regulations we have carry over to the platform economy? By unpacking the economic and social drives for the rise of the platform economy, the article develops a new framework for asking whether digital disruptions comprise loopholes akin to regulatory arbitrage, most prominently studied in the tax field, circumvention akin to controversial copyright protection reforms, or innovation-ripe negative spaces akin to design-around competition in patent law. Bringing together these different bodies of law, the article offers a contemporary account of the relevance of regulation for new business models. The article concludes that, as a default, legal disruption by the platform economy should be viewed as a feature rather than a bug of regulatory limits.


title: PFC Cuk Converter-Fed BLDC Motor Drive

abstract: This paper deals with a power factor correction (PFC)-based Cuk converter-fed brushless dc motor (BLDC) drive as a cost-effective solution for low-power applications. The speed of the BLDC motor is controlled by varying the dc-bus voltage of a voltage source inverter (VSI) which uses a low frequency switching of VSI (electronic commutation of the BLDC motor) for low switching losses. A diode bridge rectifier followed by a Cuk converter working in a discontinuous conduction mode (DCM) is used for control of dc-link voltage with unity power factor at ac mains. Performance of the PFC Cuk converter is evaluated under four different operating conditions of discontinuous and continuous conduction modes (CCM) and a comparison is made to select a best suited mode of operation. The performance of the proposed system is simulated in a MATLAB/Simulink environment and a hardware prototype of the proposed drive is developed to validate its performance over a wide range of speed with unity power factor at ac mains.


title: A graphical interval logic for specifying concurrent systems

abstract: This article describes a graphical interval logic that is the foundation of a tool set supporting formal specification and verification of concurrent software systems. Experience has shown that most software engineers find standard temporal logics difficult to understand and use. The objective of this article is to enable software engineers to specify and reason about temporal properties of concurrent systems more easily by providing them with a logic that has an intuitive graphical representation and with tools that support its use. To illustrate the use of the graphical logic, the article provides some specifications for an elevator system and proves several properties of the specifications. The article also describes the tool set and the implementation.


Pinterest Analysis and Recommendations


title: Evidence for a glutamine synthetase-chromatophore association in the phototroph Rhodospirillum rubrum: purification, properties, and regulation of the enzyme.

abstract: The characteristics of soluble and membrane-bound glutamine synthetase (GS) from Rhodospirillum rubrum were compared with those of the enzyme located in situ (measured in detergent-treated cells). The results suggest that in vivo GS may be associated with, or bound to, the chromatophore membranes. GS was found to reversibly associate and dissociate from purified chromatophores as a function of the ionic strength of the buffer or the Mg2+ concentration. Solubilized GS was purified to homogeneity and found to be similar to the GS of enteric bacteria in that its molecular weight was about 600,000 and it had one type of subunit of 51,000 molecular weight. Removal of GS from the membrane had no effect on the Km values for the substrates of the biosynthetic reaction, but it did have a substantial effect on both its Mg2+ requirement (the Km increased 10-fold) and the sensitivity of the gamma-glutamyl transferase reaction to the inhibitor methionine sulfoximine (the I0.5 decreased from 1,500 to 60 microM). Both observations suggest that the active site of GS is influenced by its association with the membrane. GS activity was shown to respond to NH4+, phosphodiesterase, Mg2+, and adenylylation cofactors in a manner identical to that of the GS of the coliform bacteria, suggesting that the former may also respond to adenylylation and deadenylylation. Finally, R. rubrum GS was also inhibited by NH4+ by a newly observed, as yet undefined, system.


title: Optimistic Bayesian sampling in contextual-bandit problems

abstract: In sequential decision problems in an unknown environment, the decision maker often faces a dilemma over whether to explore to discover more about the environment, or to exploit current knowledge. We address the exploration-exploitation dilemma in a general setting encompassing both standard and contextualised bandit problems. The contextual bandit problem has recently resurfaced in attempts to maximise click-through rates in web based applications, a task with significant commercial interest. ::: ::: In this article we consider an approach of Thompson (1933) which makes use of samples from the posterior distributions for the instantaneous value of each action. We extend the approach by introducing a new algorithm, Optimistic Bayesian Sampling (OBS), in which the probability of playing an action increases with the uncertainty in the estimate of the action value. This results in better directed exploratory behaviour. ::: ::: We prove that, under unrestrictive assumptions, both approaches result in optimal behaviour with respect to the average reward criterion of Yang and Zhu (2002). We implement OBS and measure its performance in simulated Bernoulli bandit and linear regression domains, and also when tested with the task of personalised news article recommendation on a Yahoo! Front Page Today Module data set. We find that OBS performs competitively when compared to recently proposed benchmark algorithms and outperforms Thompson's method throughout.


title: The Pleasure Principle as a Tool for Scientific Forecasting of Human Self-Evolution

abstract: The pleasure principle (PP) may be a verifiable fundamental law of the living matter in the universe, and this law might then be used for forecasting human self-evolution. I do not pretend to “prove” PP, but argue that it must be regarded as a scientific hypothesis. Accordingly, I formulate verifiable and falsifiable postulates of PP. Their confirmation would allow the construction of a new scientific discipline, hedodynamics, that would be able to forecast the future development of human civilization and even the probable structure and psychology of other rational beings within the universe. I suggest basic hedodynamical scenarios for human (posthuman) civilization and argue that the discovery of the neural correlate of pleasure would provide more detailed forecasts. In particular, I demonstrate how the studies of pleasure mechanisms might predict the degree of aggression in future societies. I conclude that PP may become a scientific basis for fundamental, not phenomenological (based on extrapolations), future forecasting on large timescales.


title: Extracting precise data on the mathematical content of PDF documents

abstract: As more and more scientific documents become available in PDF format, their automatic analysis becomes increasingly important. We present a procedure that extracts mathematical symbols from PDF documents by examining both the original PDF file and a rasterized version. This provides more precise information than is available either directly from the PDF file or by traditional character recognition techniques. The data can then be used to improve mathematical parsing methods that transform the mathematics into richer formats such as MathML.


title: Spark Versus Flink: Understanding Performance in Big Data Analytics Frameworks

abstract: Big Data analytics has recently gained increasing popularity as a tool to process large amounts of data on-demand. Spark and Flink are two Apache-hosted data analytics frameworks that facilitate the development of multi-step data pipelines using directly acyclic graph patterns. Making the most out of these frameworks is challenging because efficient executions strongly rely on complex parameter configurations and on an in-depth understanding of the underlyingarchitectural choices. Although extensive research has been devoted to improving and evaluating the performance of such analytics frameworks, most of them benchmarkthe platforms against Hadoop, as a baseline, a rather unfair comparison consideringthe fundamentally different design principles. This paper aims to bring some justice in this respect, by directly evaluating the performance of Sparkand Flink. Our goal is to identify and explain the impact of the different architecturalchoices and the parameter configurations on the perceived end-to-end performance. To this end, we develop a methodology for correlating the parameter settings and the operators execution plan with the resource usage. We use this methodologyto dissect the performance of Spark and Flink with several representative batchand iterative workloads on up to 100 nodes. Our key finding is that there none of the two framework outperforms the other for all data types, sizes and job patterns. This paper performs a fine characterization of the cases when each framework is superior, and we highlight how this performance correlates to operators, to resource usage and to the specifics of the internal framework design.


title: Design Pattern Canvas: An Introduction to Unified Serious Game Design Patterns

abstract: The aim of this article is to start a dialogue and search for a unified game design tool within the game design and research community. As a possible direction, presented paper outlines the practice and importance of design pattern use in serious game development and argues that design patterns can make serious game development more efficient by enabling knowledge exchange and better communication between different stakeholders. Furthermore, the use of design patterns provides a foundation for structured research and analysis of games. In order to help advance the state of game design the paper proposes a new method - the Serious Games Design Pattern Canvas or shorter Design Pattern Canvas (DPC). DPC is a design template for developing new or documenting existing (serious) game design patterns. It is a visual chart with elements describing a pattern's purpose, mechanic, audience, consequences, collected data, related research and ethical considerations. It assists game designer in aligning their activities by illustrating patterns characteristics and potential trade-offs. One of the goals of the DPC is to either help break larger game design problems into smaller pieces or assist in a bottom up approach of designing serious games. It is important to note, that the paper proposes the first step for co-creation of a game design tool and further research and validation of the DPC is needed.


title: The Effect of Using E-Learning Tools in Online and Campus-based Classrooms on Student Performance

abstract: Creating an integrative research framework that extends a model frequently used in the Information Systems field, the Technology Acceptance Model, together with variables used in the Education field, this empirical study investigates the factors influencing student performance as reflected by their final course grade. The Technology Acceptance Model explains computer acceptance in general terms. The model measures the impact of external variables on internal beliefs, attitudes, and intentions. Perceived Usefulness and Perceived Ease of Use, two main constructs in the model, refer to an individual’s perception of how the adoption of a new technology will increase their efficiency, and the individual’s perception of how easy the technology will be to use. The lower the perceived effort is, the easier the technology will be to adopt. Thus, Perceived Usefulness, Perceived Ease of Use, Computer Self-Efficacy, and Computer Anxiety were measured to determine their effect on student performance. The proliferation of the personal computer was possible because of the applications written for it. The continuous creation of new applications has created ample ground to test the Technology Acceptance Model to determine how a user will decide to adopt such applications. The recent escalation of delivering online education via the Internet has again sparked a new dimension of information systems. This has given rise to research using the Technology Acceptance Model for applications in the Education field. Today’s modern classroom, whether online or campus-based, uses e-learning tools and Learning Management Systems that capture student cognition and engages them in the learning process via technology, while increasing their need for self-directedness. In view of this, the present study also considers the students’ ability to work independently. The results of the statistical analysis used in this study revealed marked differences in student perceptions of e-learning tools between students who chose to take an online course and students who preferred to take the campus-based section. Additionally, Perceived Usefulness, Perceived Ease of Use, and the students’ ability to work independently were all statistically significant factors in predicting students’ final grades.


Care, Social (Re)production and Global Labour Migration: Japan's ‘Special Gift’ toward ‘Innately Gifted’ Filipino Workers


Goal reasoning for autonomous underwater vehicles: Responding to unexpected agents


title: CLP(F) module for modeling, solving and improving optimal solution in fuzzy decision-making

abstract: A tool for modeling, solving and improving the optimal decision in fuzzy decision-making is presented. It is consistent with Zadeh's extension principle and Bellman-Zadeh conception of fuzzy decision making. Module is implemented with constraint programming in order to fulfill requisite constraints and to deal with a drowning effect related to the sup-min optimization criterion. Fuzzy intervals, fuzzy constraints and systems of fuzzy constraints are represented with descriptors which assign system of constraints to decision variables for realization of fuzzy interval and the degree of satisfaction for this realization. Computational example is shown in which the improved (robust for drowning effect) schedule is computed with the presented module.


title: Knowledge representation and ontologies

abstract: Knowledge representation and reasoning aims at designing computer systems that reason about a machine-interpretable representation of the world. Knowledge-based systems have a computational model of some domain of interest in which symbols serve as surrogates for real world domain artefacts, such as physical objects, events, relationships, etc. [1]. The domain of interest can cover any part of the real world or any hypothetical system about which one desires to represent knowledge for com–putational purposes. A knowledge-based system maintains a knowledge base, which stores the symbols of the computational model in the form of statements about the domain, and it performs reasoning by manipulating these symbols. Applications can base their decisions on answers to domain-relevant questions posed to a knowledge base.


title: Induced Pluripotent Stem Cells Generated from Patients with ALS Can Be Differentiated into Motor Neurons

abstract: The generation of pluripotent stem cells from an individual patient would enable the large-scale production of the cell types affected by that patient's disease. These cells could in turn be used for disease modeling, drug discovery, and eventually autologous cell replacement therapies. Although recent studies have demonstrated the reprogramming of human fibroblasts to a pluripotent state, it remains unclear whether these induced pluripotent stem (iPS) cells can be produced directly from elderly patients with chronic disease. We have generated iPS cells from an 82-year-old woman diagnosed with a familial form of amyotrophic lateral sclerosis (ALS). These patient-specific iPS cells possess properties of embryonic stem cells and were successfully directed to differentiate into motor neurons, the cell type destroyed in ALS.