title: A 24 GHz ACC Radar Sensor

abstract: The paper introduces a 24GHz radar sensor, which is applicable for the Advanced Driver Assistance Systems (ADAS) function ACC (Adaptive Cruise Control) on automobiles. Running an FMCW narrowband operational mode, it complies with most existing frequency regulations in the ISM band. Concept, technical and performance data are given and difficulties and technical solutions of the particular application are described. I Application Requirements 77GHz automotive sensors are in production since 1998. Today, they have found their way into a number of high-class models. Although being extremely valuable regarding the reduction of the number of road accidents, and allowing for a relaxed driving, the percentage of vehicles originally equipped with ACC is remarkably low. There is one main reason for this: cost. So at the beginning there was the question: would it be feasible to build a 24GHz radar having a sufficient performance to be applicable as an ACC sensor? If it was possible to reduce the cost to less than 50% of the cost of a 77GHz device, certain performance restrictions can be accepted, and the chance for higher takes rates increases significantly. During the development phase, requirements were collected from a number of OEMs. Most requirements can be met. A list of the achieved technical performance data is given in a table below. The most difficult thing was to find an optimum antenna design providing the narrowest possible beam using the smallest possible aperture. II. Frequency Regulations Any device operating in the 24GHz ISM band must meet the existing regulations, which are given (for Europe) by the harmonized standards ETSI EN 300440-1 and –2 (and others). For the UMRR sensor, operating in FMCW narrowband mode, the frequency approval for many (including automotive) purposes was granted already in 2002 by the German Frequency Allocation Authority, RegTP. By today, the sensor has been approved in a large number of countries, including France, UK, US and Canada. The spectrum of the device’s transmit waveform is depicted on the right. The UMRR sensor is not subject to the SARA restrictions for 24 GHz UWB radars. Hence the radar can be produced beyond 2013, there are no limitations for the percentage of equipped vehicles. The compatibility between UMRR and traffic enforcement devices (police radars) was demonstrated. Due to the narrow band input amplifiers, the susceptibility to mutual interference is quite low. A 24GHz ACC Radar Sensor.doc February 28, 2005 III Technology The sensor consists of 2 modules: RF module and DSP module [1]. RF Module Hardware The RF module contains a VCO, transmit and receive amplifiers, mixers, and is made all planar of discrete components. There are also means provided for the waveform generation as well as for the control of in-band emissions and waveform linearity. A monopulse principle, patch antenna type is applied. DSP Module Hardware A low cost fixed point DSP is the heart of the sensor. An optimized software was created for this device to exploit the performance in the best way. The chip already has A/D converters, a CAN module, Flash and some RAM memory on board and requires just a small number of additional components. Software Technology: Waveform and Target Detection The narrowband FMCW waveform is created by software. As an example, an FMSK signal (see [2] –[4]) is depicted. This combination of FSK and LFM waveform design principle offers the possibility of an unambiguous and simultaneous target range and velocity measurement. The transmit waveform consists in this case of at least two linear frequency modulated up-chirp or down-chirp signals (the intertwined signal sequences are called A and B). The two chirp signals will be transmitted in an intertwined sequence (ABABAB...), where the stepwise frequency modulated sequence A is used as a reference signal while the second up-chirp signal is shifted in frequency with . The received signal is down converted into base band and directly sampled at the end of each frequency step. Shift f Measurement parameters are: Upchirp: Chirp sweep dop up diff T f c R f f 2 . − = , f AB up up diff = ∆φ , , T rel dop f c v f 2 − = Downchirp: Chirp sweep dop down diff T f c R f f 2 . + = , A down ∆φ , A 24GHz ACC Radar Sensor.doc A B A B A B


title: Supporting Model---Driven Development of Object---Relational Database Schemas: A Case Study

abstract: This paper completes our proposal for automatic development of Object-Relational (OR) DataBase (DB) schemas. By means of a case study, this work focuses on presenting the tooling developed to support the whole process. As usual, the proposal starts from a conceptual data model (Platform Independent Model) depicted in a UML class diagram. Then, the conceptual data model is mapped into an OR DB model (Platform Specific Model) that represents the OR DB schema. To that end, we have implemented a set of formalized mapping rules using the ATL language. Finally, the SQL code that implements the modeled schema in Oracle 10g is automatically generated from the OR model by means of a MOFScript model to text transformation. Moreover, since the OR model could be refined along the design process, we have developed a graphical editor for OR DB models.


title: Coupled Learning for Facial Deblur

abstract: Blur in facial images significantly impedes the efficiency of recognition approaches. However, most existing blind deconvolution methods cannot generate satisfactory results due to their dependence on strong edges, which are sufficient in natural images but not in facial images. In this paper, we represent point spread functions (PSFs) by the linear combination of a set of pre-defined orthogonal PSFs, and similarly, an estimated intrinsic (EI) sharp face image is represented by the linear combination of a set of pre-defined orthogonal face images. In doing so, PSF and EI estimation is simplified to discovering two sets of linear combination coefficients, which are simultaneously found by our proposed coupled learning algorithm. To make our method robust to different types of blurry face images, we generate several candidate PSFs and EIs for a test image, and then, a non-blind deconvolution method is adopted to generate more EIs by those candidate PSFs. Finally, we deploy a blind image quality assessment metric to automatically select the optimal EI. Thorough experiments on the facial recognition technology database, extended Yale face database B, CMU pose, illumination, and expression (PIE) database, and face recognition grand challenge database version 2.0 demonstrate that the proposed approach effectively restores intrinsic sharp face images and, consequently, improves the performance of face recognition.


title: Implementing digital signature with RSA encryption algorithm to enhance the Data Security of cloud in Cloud Computing

abstract: The cloud is a next generation platform that provides dynamic resource pools, virtualization, and high availability. Today, we have the ability to utilize scalable, distributed computing environments within the confines of the Internet, a practice known as cloud computing. Cloud computing is the Concept Implemented to decipher the Daily Computing Problems, likes of Hardware Software and Resource Availability unhurried by Computer users. The cloud Computing provides an undemanding and Non ineffectual Solution for Daily Computing. The prevalent Problem Associated with Cloud Computing is the Cloud security and the appropriate Implementation of Cloud over the Network. In this Research Paper, we have tried to assess Cloud Storage Methodology and Data Security in cloud by the Implementation of digital signature with RSA algorithm


title: Application of Clustering Methods to Health Insurance Fraud Detection

abstract: Health insurance fraud detection is an important and challenging task. Traditionally, insurance companies use human inspections and heuristic rules to detect fraud. As the size of databases increases, the traditional approaches may miss a great portion of fraud for two main reasons. First, it is impossible to detect all health care fraud by manual inspection over large databases. Second, new types of health care fraud emerge constantly. SQL operations based on heuristic rules cannot identify those new emerging fraud schemes. Such a situation demands more sophisticated analytical methods and techniques that are capable of detecting fraud activities from large databases. The goal of this paper is to understand and detect suspicious health care frauds from large databases using clustering technique. Specifically, this paper applies two clustering methods, SAS EM and CLUTO, to a large real-life health insurance dataset and compares the performances of these two methods.


title: Challenges in Human-Computer Interaction Design for Mobile Devices

abstract: Designs in human-computer interaction (HCI) aim to create interactive products that are easy and enjoyable to use. However, owing to the multidisciplinary nature of HCI and the different value systems of interface users from various backgrounds and experiences, it is highly challenging for designers to create applications which are usable and affordable to such a heterogeneous set of users. Nowadays, more and more people are heard complaining about the bad interaction design of mobile devices. Is this problem caused by the bad design of products or by the users' ignorance of the logics of human- machine interaction design? This paper aims to explore the human-computer interaction challenges in designing applications in both hardware and software for mobile devices. It also makes an attempt to investigate the principles for designing mobile interfaces that result in greater user acceptance and figure out possible solutions to the problems with interaction designs for mobile devices.


title: Nuclei segmentation of microscopic breast cancer image using Gram-Schmidt and cluster validation algorithm

abstract: A combination of Gram-Schmidt method and cluster validation algorithm based Bayesian is proposed for nuclei segmentation on microscopic breast cancer image. Gram-Schmidt is applied to identify the cell nuclei on a microscopic breast cancer image and the cluster validation algorithm based Bayesian method is used for separating the touching nuclei. The microscopic image of the breast cancer cells are used as dataset. The segmented cell nuclei results on microscopic breast cancer images using Gram-Schmidt method shows that the most of MSE values are below 0.1 and the average MSE of segmented cell nuclei results is 0.08. The average accuracy of separated cell nuclei counting using cluster validation algorithm is 73% compares with the manual counting.


title: Performance Analysis of Reduced Common-Mode Voltage PWM Methods and Comparison With Standard PWM Methods for Three-Phase Voltage-Source Inverters

abstract: This paper surveys the reduced common-mode voltage pulsewidth modulation (RCMV-PWM) methods for three-phase voltage-source inverters, investigates their performance characteristics, and provides a comparison with the standard PWM methods. PWM methods are reviewed, and their pulse patterns and common-mode voltage (CMV) patterns are illustrated. The inverter input and output current ripple characteristics and output voltage linearity characteristics of each PWM method are thoroughly investigated by analytical methods, simulations, and experiments. The research results illustrate the advantages and disadvantages of the considered methods, and suggest the utilization of the near-state PWM and active zero state PWM1 methods as overall superior methods. The paper aids in the selection and application of appropriate PWM methods in inverter drives with low CMV requirements.


title: On Minimax Optimal Offline Policy Evaluation

abstract: This paper studies the off-policy evaluation problem, where one aims to estimate the value of a target policy based on a sample of observations collected by another policy. We first consider the multi-armed bandit case, establish a minimax risk lower bound, and analyze the risk of two standard estimators. It is shown, and verified in simulation, that one is minimax optimal up to a constant, while another can be arbitrarily worse, despite its empirical success and popularity. The results are applied to related problems in contextual bandits and fixed-horizon Markov decision processes, and are also related to semi-supervised learning.


title: Feedback Control of Computing Systems

abstract: Preface. PART I: BACKGROUND. 1. Introduction and Overview. PART II: SYSTEM MODELING. 2. Model Construction. 3. Z-Transforms and Transfer Functions. 4. System Modeling with Block Diagrams. 5. First-Order Systems. 6. Higher-Order Systems. 7. State-Space Models. PART III: CONTROL ANALYSIS AND DESIGN. 8. Proportional Control. 9. PID Controllers. 10. State-Space Feedback Control. 11. Advanced Topics. Appendix A: Mathematical Notation. Appendix B: Acronyms. Appendix C: Key Results. Appendix D: Essentials of Linear Algebra. Appendix E: MATLAB Basics. References. Index.


title: Persons’ Personality Traits Recognition using Machine Learning Algorithms and Image Processing Techniques

abstract: The context of this work is the development of persons’ personality recognition system using machine learning techniques. Identifying the personality traits from a face image are helpful in many situations, such as identification of criminal behavior in criminology, students’ learning attitudes in education sector and recruiting employees. Identifying the personality traits from a face image has rarely been studied. In this research identifying the personality traits from a face image includes three separate methods; ANN, SVM and deep learning. Face area of an image is identified by a color segmentation algorithm. Then that extracted image is input to personality recognition process. Features of the face are identified manually and input them to ANN and SVM. Each personality trait is valued from 1 to 9. In the second attempt m-SVM is used because outputs are multi-valued. ANN gave better results than m-SVM. In the third attempt we propose a methodology to identify personality traits using deep learning.


title: Culture and Aesthetic Preference: Comparing the Attention to Context of East Asians and Americans

abstract: Prior research indicates that East Asians are more sensitive to contextual information than Westerners. This article explored aesthetics to examine whether cultural variations were observable in art and photography. Study 1 analyzed traditional artistic styles using archival data in representative museums. Study 2 investigated how contemporary East Asians and Westerners draw landscape pictures and take portrait photographs. Study 3 further investigated aesthetic preferences for portrait photographs. The results suggest that (a) traditional East Asian art has predominantly context-inclusive styles, whereas Western art has predominantly object-focused styles, and (b) contemporary members of East Asian and Western cultures maintain these culturally shaped aesthetic orientations. The findings can be explained by the relation among attention, cultural resources, and aesthetic preference.


title: A hardware accelerator for the IEEE 802.1X-2010 key hierarchy in automotive applications

abstract: The increasing complexity of automotive networks has led to broad security issues related to human safety. On the other hand, to solve problems of network overload, the automotive world is moving in the direction of using an Ethernet backbone for in-vehicle communications. Therefore, the security of Ethernet protocols for such applications has become a significant concern. The IEEE 802.1X-2010 standard could be a suitable solution for the key management of Ethernet-based encrypted communications. In this work, an efficient hardware accelerator for the intensive sections of the IEEE 802.1X-2010 is presented. The system was synthesized both on a Stratix V FPGA and on a 40nm standard-cell library. The former gives a usage of 3% of the Adaptive Logic Modules (ALMs) while the latter shows an occupation of 55 kgates. On the FPGA, the maximum throughput is 152 Mbps and 1.14 Gbps for the two considered sections of the standard while the standard-cell technology provides a maximum throughput of 457 Mbps and 3.85 Gbps. Results show that this architecture represents a fast and low-area key management solution for Ethernet networks in automotive applications.


title: An ultra low power high sensitivity wake-up radio receiver with addressing capability

abstract: In power-limited wireless devices such as wireless sensor networks, wearable components, and Internet of Things devices energy efficiency is a critical concern. These devices are usually battery operated and have a radio transceiver that is typically their most power-hungry block. Wake-up radio schemes can be used to achieve a reasonable balance among energy consumption, range, data receiving capabilities and response time. In this paper, a high-sensitivity low power wake-up radio receiver (WUR) for wireless sensor networks is presented. The wake-up radio is comprised of a fully passive differential RF-to-DC converter that rectifies the incident RF signal, a low-power comparator and an ultra low power microcontroller to detects the envelope of the on-off keying (OOK) wake-up data used as address. We designed and implemented a novel low power tunable wake up radio with addressing capability, a minimal power consumption of only 196nW and a maximum sensitivity of -55dBm and minimal wake up time of 130μs without addressing and around 1,6ms with 2byte addressing at 10Kbit/s data rate. The flexibility of the solution makes the wake up radio suitable for both power constrained low range application (such as Body Area Network) or applications with long range needs. The wake up radio can work also at different frequencies and the addressing capability directly on board helps reduce false positives. Experimental on field results demonstrate the low power of the solution, the high sensitivity and the functionality.


title: Acceleration of Blob Detection within Images in Hardware

abstract: This report presents the implementation and evaluation of a computer vision task on a Field Programmable Gate Array (FPGA). As an experimental approach for an application-specific image-processing problem it provides reliable results to measure gained performance and precision compared with similar solutions on General Purpose Processor (GPP) architectures. ::: The project addresses the problem of detecting Binary Large OBjects (BLOBs) in a continuous video stream. For this problem a number of different solutions exist. But most of these are realized on GPP platforms, where resolution and processing speed define the performance barrier. With the opportunity of parallelization and performance abilities like in hardware, the application of FPGAs become interesting. This work belongs to the MI6 project from the Computer Vision research group of the University of Applied Sciences Bonn-Rhein-Sieg. It address the detection of the users position and orientation in relation to the virtual environment in an Immersion Square. ::: The goal is to develop a light emitting device, that points from the user towards the point of interest on the projection screen. The projected light dots are used to represent the user in the virtual environment. By detecting the light dots with video cameras, the idea is to interface the position and orientation of the relative position of the user interface. Fort that the laser dots need to be arranged in a unique pattern, which requires at least five points.[29] For a reliable estimation a robust computation of the BLOB's center-points is necessary. ::: This project has covered the development of a BLOB detection system on a FPGA platform. It detects binary spatially extended objects in a continuous video stream and computes their center points. The results are displayed to the user and where validated for their ground truth. The evaluation compares precision and performance gain against similar approaches on GPP platforms.


title: Preserving order in a forest in less than logarithmic time

abstract: We present a data structure, based upon a stratified binary tree, which enables us to manipulate on-line a priority queue whose priorities are selected from the interval 1...n, with an average and worst case processing time of O(log log n) per instruction. The structure is used to obtain a mergeable heap whose time requirements are about as good.


title: Impact of high-throughput screening in biomedical research

abstract: High-throughput screening (HTS) has been postulated in several quarters to be a contributory factor to the decline in productivity in the pharmaceutical industry. Moreover, it has been blamed for stifling the creativity that drug discovery demands. In this article, we aim to dispel these myths and present the case for the use of HTS as part of a proven scientific tool kit, the wider use of which is essential for the discovery of new chemotypes.


Electronic Biomaterials Towards Flexible Sensors: A Review


title: Context-based Online Configuration-Error Detection

abstract: Software failures due to configuration errors are commonplace as computer systems continue to grow larger and more complex. Troubleshooting these configuration errors is a major administration cost, especially in server clusters where problems often go undetected without user interference. ::: ::: This paper presents CODE-a tool that automatically detects software configuration errors. Our approach is based on identifying invariant configuration access rules that predict what access events follow what contexts. It requires no source code, application-specific semantics, or heavyweight program analysis. Using these rules, CODE can sift through a voluminous number of events and detect deviant program executions. This is in contrast to previous approaches that focus on only diagnosis. In our experiments, CODE successfully detected a real configuration error in one of our deployment machines, in addition to 20 user-reported errors that we reproduced in our test environment. When analyzing month-long event logs from both user desktops and production servers, CODE yielded a low false positive rate. The efficiency of CODE makes it feasible to be deployed as a practical management tool with low overhead.


title: Equal Partition Based Clustering Approach for Event Summarization in Videos

abstract: The rapid growth of video data demands both effective and efficient video summarization methods so that users are allowed to speedily browse and comprehend a large amount of video content. Hence, it is very challenging to store and access such audiovisual information in real time where an immense amount of recorded video content is rising within one second. In this paper we proposed an equal partition based clustering technique for summarizing the events in videos which can work better for real time applications (for e.g., surveillance video in various security systems). In clustering, the difficulty is to obtain the optimal set of clusters, which is gained by implementing Davies-Bouldin Index, a cluster validation technique which permits the users with free parameter based video summarization method for selecting the numbers of key–frames without incurring additional computational cost. The qualitative as well as quantitative evaluation is done in order to compare the performances of our proposed model and state-of-theart models. Experimental results on two benchmark datasets with various types of videos expose that the proposed method outperforms the state-of-the-art models with the best Precision and F–measure.


title: An open-source research kit for the da Vinci® Surgical System

abstract: We present a telerobotics research platform that provides complete access to all levels of control via open- source electronics and software. The electronics employs an FPGA to enable a centralized computation and distributed I/O architecture in which all control computations are implemented in a familiar development environment (Linux PC) and low- latency I/O is performed over an IEEE-1394a (FireWire) bus at speeds up to 400 Mbits/sec. The mechanical components are obtained from retired first-generation da Vinci R Surgical Systems. This system is currently installed at 11 research institutions, with additional installations underway, thereby creating a research community around a common open-source hardware and software platform.


title: AXIS: Generating Explanations at Scale with Learnersourcing and Machine Learning

abstract: While explanations may help people learn by providing information about why an answer is correct, many problems on online platforms lack high-quality explanations. This paper presents AXIS (Adaptive eXplanation Improvement System), a system for obtaining explanations. AXIS asks learners to generate, revise, and evaluate explanations as they solve a problem, and then uses machine learning to dynamically determine which explanation to present to a future learner, based on previous learners' collective input. Results from a case study deployment and a randomized experiment demonstrate that AXIS elicits and identifies explanations that learners find helpful. Providing explanations from AXIS also objectively enhanced learning, when compared to the default practice where learners solved problems and received answers without explanations. The rated quality and learning benefit of AXIS explanations did not differ from explanations generated by an experienced instructor.


title: Use of metaldehyde as a molluscicide in semi-commercial and commercial milkfish ponds

abstract: We thank the following for making this study possible: Lonza Ltd, Basel, Switzerland, for funding this project; ECON Research and Evaluation Concepts for Environmental and Health Safety, GmbH, Ingelheim on Rhine, Germany for analysis of metaldehyde residues; Lucia Jimenez for technical assistance; Jose Cruz and Antonio Aguilar of AgChem Manufacturing Corporation, Manila, Philippines and Joevic Pasit and Adonis Alamban of Jardine Agchem, Philippines for assistance during the conduct of the field trials; Soledad Cruz and Concepcion Mendoza, Department of Agriculture, Philippines, also for assistance during the field trials; and Capiz Institute of Technology (c/o Victor Billanes), Benjamin Jardiolin, Raymundo Cruz, and Joey Maniquiz for allowing us to use their ponds for testing.


title: Sponges and Engines: An introduction to Keccak and Keyak

abstract: In this document we present an introductory overview of the algorithms and design components underlying the Keccac cryptographic primitive and the Keyak encryption scheme for authenticated (session-supporting) encryption. This document aims to familiarize readers with the basic principles of authenticated encryption, the Sponge and Duplex constructions (full-state, keyed as well as regular versions), the permutation functions underlying Keccak and Keyak as well as Keyak v2's Motorist mode of operation.


title: Social-ecological research in urban natural areas: an emergent process for integration

abstract: Understanding the structure and function of urban landscapes requires integrating social and ecological research. Here, we integrate parallel social and ecological assessments of natural areas within New York City. We examined social data (from a rapid assessment of park use and meaning, collected at a park zone level) alongside ecological data (from a plot-based assessment of forest structure and diversity). In-depth interviews with researchers and managers (n = 11) involved with the social and ecological assessments revealed commonly-held values considered critical for integration, including clear communication, openness, trust, and shared goals and also identified barriers to the integration process, including the scales at which each dataset was collected. We applied an informed, shared problem framing to investigate the relationships between visitor use and ecological condition in urban natural areas. We began with fuzzy cognitive modeling, where researchers developed models of defining a “healthy urban forest.” We then developed two social-ecological typologies to examine the integrated dataset in relation to how visitors may affect or perceive ecological health and threat. Typologies identify NYC natural areas where social indicators (number of visitors, diversity of park use motivations) are either high or low and ecological condition is either high or low. Examination of these typologies led to exploring correlations between social and ecological variables, to team discussions, and to developing new research questions. We conclude this paper with a discussion of tradeoffs of this type of emergent, integrative approach to social-ecological synthesis research.


title: ICT tools for enabling smart grid players' flexibility through VPP and DR services

abstract: The European Climate Change Program consists of a range of measures to fight against climate change, one of which is to reach 20% of renewable energy in the total energy supply in the EU by 2020. The increasing penetration of RES and DG introduces new business cases which require innovative ICT tools and the support of appropriate communication infrastructure. This paper focuses on the Virtual Power Plant and Demand Response cases, proposing a general architecture, as well as analyzing core functionalities, information and communication requirements, along with relative standards and technologies. The architectural analysis is based on the SGAM framework, a standardized architecture for smart grid solutions and services that enables new actors in the smart grid domain to participate in the energy markets and contribute to an optimal grid operation. The document presents the use cases' modeling and gives an overview of the pilots in which the proposed models will be materialized and validated.


title: The garbage collection advantage: improving program locality

abstract: As improvements in processor speed continue to outpace improvements in cache and memory speed, poor locality increasingly degrades performance. Because copying garbage collectors move objects, they have an opportunity to improve locality. However, no static copying order is guaranteed to match program traversal orders. This paper introduces online object reordering (OOR) which includes a new dynamic, online class analysis for Java that detects program traversal patterns and exploits them in a copying collector. OOR uses run-time method sampling that drives just-in-time (JIT) compilation. For each hot (frequently executed) method, OOR analysis identifies the hot field accesses. At garbage collection time, the OOR collector then copies referents of hot fields together with their parent. Enhancements include static analysis to exclude accesses in cold basic blocks, heuristics that decay heat to respond to phase changes, and a separate space for hot objects. The overhead of OOR is on average negligible and always less than 2% on Java benchmarks in Jikes RVM with MMTk. We compare program performance of OOR to static class-oblivious copying orders (e.g., breadth and depth first). Performance variation due to static orders is often low, but can be up to 25%. In contrast, OOR matches or improves upon the best static order since its history-based copying tunes memory layout to program traversal.


title: A novel high-order sliding mode control of magnetic levitation system

abstract: A robust high-order sliding mode control scheme, based on the super twisting control (STC) method, is proposed for the position control of the magnetic levitation (MAGLEV) system. The developed control has been proposed in [1] for second order system is extended to the third order system. The sliding surface is modified by using homogeneity principle. The proposed controller guarantees the finite-time reachability, effective disturbance rejection and continuous control without chattering. The closed loop stability and reaching time estimation of the states are analyzed by the Lyapunov stability theorem. An example of a magnetic levitation system is given and simulation results are included to verify the performance of the proposed controller.


title: Pattern discovery in data streams under the time warping distance

abstract: Subsequence matching is a basic problem in the field of data stream mining. In recent years, there has been significant research effort spent on efficiently finding subsequences similar to a query sequence. Another challenging issue in relation to subsequence matching is how we identify common local patterns when both sequences are evolving. This problem arises in trend detection, clustering, and outlier detection. Dynamic time warping (DTW) is often used for subsequence matching and is a powerful similarity measure. However, the straightforward method using DTW incurs a high computation cost for this problem. In this paper, we propose a one-pass algorithm, CrossMatch, that achieves the above goal. CrossMatch addresses two important challenges: (1) how can we identify common local patterns efficiently without any omission? (2) how can we find common local patterns in data stream processing? To tackle these challenges, CrossMatch incorporates three ideas: (1) a scoring function, which computes the DTW distance indirectly to reduce the computation cost, (2) a position matrix, which stores starting positions to keep track of common local patterns in a streaming fashion, and (3) a streaming algorithm, which identifies common local patterns efficiently and outputs them on the fly. We provide a theoretical analysis and prove that our algorithm does not sacrifice accuracy. Our experimental evaluation and case studies show that CrossMatch can incrementally discover common local patterns in data streams within constant time (per update) and space.


title: Improving Data Quality: Consistency and Accuracy

abstract: Two central criteria for data quality are consistency and accuracy. Inconsistencies and errors in a database often emerge as violations of integrity constraints. Given a dirty database D, one needs automated methods to make it consistent, i.e., find a repair D' that satisfies the constraints and "minimally" differs from D. Equally important is to ensure that the automatically-generated repair D' is accurate, or makes sense, i.e., D' differs from the "correct" data within a predefined bound. This paper studies effective methods for improving both data consistency and accuracy. We employ a class of conditional functional dependencies (CFDs) proposed in [6] to specify the consistency of the data, which are able to capture inconsistencies and errors beyond what their traditional counterparts can catch. To improve the consistency of the data, we propose two algorithms: one for automatically computing a repair D' that satisfies a given set of CFDs, and the other for incrementally finding a repair in response to updates to a clean database. We show that both problems are intractable. Although our algorithms are necessarily heuristic, we experimentally verify that the methods are effective and efficient. Moreover, we develop a statistical method that guarantees that the repairs found by the algorithms are accurate above a predefined rate without incurring excessive user interaction.


title: Deep Shading: Convolutional Neural Networks for Screen-Space Shading

abstract: In computer vision, convolutional neural networks CNNs achieve unprecedented performance for inverse problems where RGB pixel appearance is mapped to attributes such as positions, normals or reflectance. In computer graphics, screen space shading has boosted the quality of real-time rendering, converting the same kind of attributes of a virtual scene back to appearance, enabling effects like ambient occlusion, indirect light, scattering and many more. In this paper we consider the diagonal problem: synthesizing appearance from given per-pixel attributes using a CNN. The resulting Deep Shading renders screen space effects at competitive quality and speed while not being programmed by human experts but learned from example images.


title: Semi-Supervised Ensemble Clustering Based on Selected Constraint Projection

abstract: Traditional cluster ensemble approaches have several limitations. (1) Few make use of prior knowledge provided by experts. (2) It is difficult to achieve good performance in high-dimensional datasets. (3) All of the weight values of the ensemble members are equal, which ignores different contributions from different ensemble members. (4) Not all pairwise constraints contribute to the final result. In the face of this situation, we propose double weighting semi-supervised ensemble clustering based on selected constraint projection(DCECP) which applies constraint weighting and ensemble member weighting to address these limitations. Specifically, DCECP first adopts the random subspace technique in combination with the constraint projection procedure to handle high-dimensional datasets. Second, it treats prior knowledge of experts as pairwise constraints, and assigns different subsets of pairwise constraints to different ensemble members. An adaptive ensemble member weighting process is designed to associate different weight values with different ensemble members. Third, the weighted normalized cut algorithm is adopted to summarize clustering solutions and generate the final result. Finally, nonparametric statistical tests are used to compare multiple algorithms on real-world datasets. Our experiments on 15 high-dimensional datasets show that DCECP performs better than most clustering algorithms.


title: Improved Line Maze Solving Algorithm for Curved and Zig-zag Track

abstract: –- Line maze solving algorithm is an algorithm used to solve a maze made of lines to be traced by a mobile robot. But it is designed only for lines with right angle intersection or turn. Meanwhile in real world, there are also curved and zig-zag turn. In this work, this algorithm is tested for curved and zig-zag track by using Arduino Uno. It turns out that line maze solving algorithm still has some deficiencies, even for the maze without curved and zig-zag line. Moreover, for the curved and zig-zag track, algorithm improvements are needed. Therefore, some of existing functions has been modified and replaced, and one new function added. When the improvements have been done, new algorithm is obtained. Then the test is done again on the mobile robot in a line maze with curved and zig-zag track. The result has proven that the new algorithm has successfully solved the maze.


title: A novel cross-modal hashing algorithm based on multimodal deep learning

abstract: With the growing popularity of multimodal data on the Web, cross-modal retrieval on large-scale multimedia databases has become an important research topic. Cross-modal retrieval methods based on hashing assume that there is a latent space shared by multimodal features. To model the relationship among heterogeneous data, most existing methods embed the data into a joint abstraction space by linear projections. However, these approaches are sensitive to noise in the data and are unable to make use of unlabeled data and multimodal data with missing values in real-world applications. To address these challenges, we proposed a novel multimodal deep-learning-based hash (MDLH) algorithm. In particular, MDLH uses a deep neural network to encode heterogeneous features into a compact common representation and learns the hash functions based on the common representation. The parameters of the whole model are fine-tuned in a supervised training stage. Experiments on two standard datasets show that the method achieves more effective results than other methods in cross-modal retrieval.


Parameterized Matching


abstract: The study for a Named Entity Recognizer for Filipino Text Using Conditional Random Field (NERF-CRF) focused creating a system which identifies and classifies named entities present in a given corpus. The named entities were classified into four, namely: person, place, date and org. Named entities that are identified but do not fall in the four classifications are tagged as etc. Different modules were created to achieve the study's purpose, including a tokenizer and a part-of-speech tagger. The conditional random field approach was used in the classification of identified named entities. Filipino biographies were the corpus used in testing the system. The results, based on solving for the F-measure, indicate that the system is 83% accurate, and best in identifying named entity Date with 0% error rate but is unsatisfactory in distinguishing named entity place and org, with 42% and 33% error rates respectively.


title: Balanced-to-Balanced Gysel Power Divider With Bandpass Filtering Response

abstract: In this paper, the balanced-to-balanced Gysel power divider is proposed with bandpass filtering response for the first time. The component is based on a ring structure and designed with an extended coupled-resonator circuit model. The characteristic impedances, electrical lengths, and isolation resistance are calculated analytically by our derived equations, according to the design specifications of the filtering power divider. The achievable bandwidth is from about 3% to 12%. As an example, a fabricated prototype occupies an area of 0.97 × 0.87 λg2, whose bandwidth is 8.4%. It has low differential-mode in-band insertion loss, good magnitude, and phase balances between two output ports, good isolation, and good suppression of common-mode noises and mode conventions. Based on stub-loaded sections, another prototype is realized for miniaturization, whose occupied area is reduced to 0.67 × 0.63 λg2. In comparison with the original prototype, the central insertion loss is increased by 0.13 dB, and the rejection band of common-mode and mode-conversion noises is narrowed by 21%.


title: Multi-tweet Summarization of Real-Time Events

abstract: Popular real-time public events often cause upsurge of traffic in Twitter while the event is taking place. These posts range from real-time update of the event's occurrences highlights of important moments thus far, personal comments and so on. A large user group has evolved who seeks these live updates to get a brief summary of the important moments of the event so far. However, major social search engines including Twitter still present the tweets satisfying the Boolean query in reverse chronological order, resulting in thousands of low quality matches agglomerated in a prosaic manner. To get an overview of the happenings of the event, a user is forced to read scores of uninformative tweets causing frustration. In this paper, we propose a method for multi-tweet summarization of an event. It allows the search users to quickly get an overview about the important moments of the event. We have proposed a graph-based retrieval algorithm that identifies tweets with popular discussion points among the set of tweets returned by Twitter search engine in response to a query comprising the event related keywords. To ensure maximum coverage of topical diversity, we perform topical clustering of the tweets before applying the retrieval algorithm. Evaluation performed by summarizing the important moments of a real-world event revealed that the proposed method could summarize the proceeding of different segments of the event with up to 81.6% precision and up to 80% recall.


title: Security slicing for auditing XML, XPath, and SQL injection vulnerabilities

abstract: XML, XPath, and SQL injection vulnerabilities are among the most common and serious security issues for Web applications and Web services. Thus, it is important for security auditors to ensure that the implemented code is, to the extent possible, free from these vulnerabilities before deployment. Although existing taint analysis approaches could automatically detect potential vulnerabilities in source code, they tend to generate many false warnings. Furthermore, the produced traces, i.e. dataflow paths from input sources to security-sensitive operations, tend to be incomplete or to contain a great deal of irrelevant information. Therefore, it is difficult to identify real vulnerabilities and determine their causes. One suitable approach to support security auditing is to compute a program slice for each security-sensitive operation, since it would contain all the information required for performing security audits (Soundness). A limitation, however, is that such slices may also contain information that is irrelevant to security (Precision), thus raising scalability issues for security audits. In this paper, we propose an approach to assist security auditors by defining and experimenting with pruning techniques to reduce original program slices to what we refer to as security slices, which contain sound and precise information. To evaluate the proposed pruning mechanism by using a number of open source benchmarks, we compared our security slices with the slices generated by a state-of-the-art program slicing tool. On average, our security slices are 80% smaller than the original slices, thus suggesting significant reduction in auditing costs.


title: Recent Advances in Treatment of Coronary Artery Disease: Role of Science and Technology

abstract: Coronary artery disease (CAD) is one of the most common causes of death worldwide. In the last decade, significant advancements in CAD treatment have been made. The existing treatment is medical, surgical or a combination of both depending on the extent, severity and clinical presentation of CAD. The collaboration between different science disciplines such as biotechnology and tissue engineering has led to the development of novel therapeutic strategies such as stem cells, nanotechnology, robotic surgery and other advancements (3-D printing and drugs). These treatment modalities show promising effects in managing CAD and associated conditions. Research on stem cells focuses on studying the potential for cardiac regeneration, while nanotechnology research investigates nano-drug delivery and percutaneous coronary interventions including stent modifications and coatings. This article aims to provide an update on the literature (in vitro, translational, animal and clinical) related to these novel strategies and to elucidate the rationale behind their potential treatment of CAD. Through the extensive and continued efforts of researchers and clinicians worldwide, these novel strategies hold the promise to be effective alternatives to existing treatment modalities.


title: Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertising

abstract: Sponsored search represents a major source of revenue for web search engines. This popular advertising model brings a unique possibility for advertisers to target users' immediate intent communicated through a search query, usually by displaying their ads alongside organic search results for queries deemed relevant to their products or services. However, due to a large number of unique queries it is challenging for advertisers to identify all such relevant queries. For this reason search engines often provide a service of advanced matching, which automatically finds additional relevant queries for advertisers to bid on. We present a novel advanced matching approach based on the idea of semantic embeddings of queries and ads. The embeddings were learned using a large data set of user search sessions, consisting of search queries, clicked ads and search links, while utilizing contextual information such as dwell time and skipped ads. To address the large-scale nature of our problem, both in terms of data and vocabulary size, we propose a novel distributed algorithm for training of the embeddings. Finally, we present an approach for overcoming a cold-start problem associated with new ads and queries. We report results of editorial evaluation and online tests on actual search traffic. The results show that our approach significantly outperforms baselines in terms of relevance, coverage, and incremental revenue. Lastly, we open-source learned query embeddings to be used by researchers in computational advertising and related fields.


Review of visual odometry: types, approaches, challenges, and applications


title: The impact of clock gating schemes on the power dissipation of synthesizable register files

abstract: In this paper, the power dissipation of synthesizable register files with respect to different clock gating schemes is examined. Clock gating is a well-known technique for power reduction of sequential circuits. Although different clock gating schemes exist, there is no fundamental difference in the power dissipation of sequential logic because the data input signals of disabled flip-flops do not change when the clock signal is disabled. However, it is shown here that in contrast to sequential logic the clock gating scheme has significant impact on the power dissipation of register files due to signal changes of the data input port. The major result of this work is that the power dissipation of register files can be reduced significantly, if a clock gating scheme different from that one usually recommended for sequential logic is applied.


title: Deep neural networks versus support vector machines for ECG arrhythmia classification

abstract: Heart arrhythmia is a condition in which the heartbeat is too fast, too slow, or irregular. As Electrocardiography (ECG) is an efficient measurement of heart arrhythmia, lots of research efforts have been spent on the identification of heart arrhythmia by classifying ECG signals for health care. Among them, support vector machines (SVMs) and artificial neural networks (ANNs) are the most popular. However, most of the previous studies reported the performance of either the SVMs or the ANNs without in-depth comparisons between these two methods. Also, a large number of features can be extracted from ECG signals, and some may be more relevant to heart arrhythmia than the others. This paper is to enhance the performance of heart arrhythmia classification by selecting relevant features from ECG signals, applying dimension reduction on the feature vectors, and applying deep neural networks (DNNs) for classification. A holistic comparison among DNNs, SVMs, and ANNs will be provided. Experimental results suggest that DNNs outperform both SVMs and ANNs, provided that relevant features have been selected.


title: Nonintrusive Biological Signal Monitoring in a Car to Evaluate a Driver’s Stress and Health State

abstract: Nonintrusive monitoring of a driver's physiological signals was introduced and evaluated in a car as a test of extending the concept of ubiquitous healthcare to vehicles. Electrocardiogram, photoplethysmogram, galvanic skin response, and respiration were measured in the ubiquitous healthcare car (U-car) using nonintrusively installed sensors on the steering wheel, driver's seat, and seat belt. Measured signals were transmitted to the embedded computer via Bluetooth(R) communication and processed. We collected and analyzed physiological signals during driving in order to estimate a driver's stress state while using this system. In order to compare the effect of stress on physical and mental conditions, two categories of stresses were defined. Experimental results show that a driver's physiological signals were measured with acceptable quality for analysis without interrupting driving, and they were changed meaningfully due to elicited stress. This nonintrusive monitoring can be used to evaluate a driver's state of health and stress.


N achievement and entrepreneurship: A longitudinal study.


title: Estimates for eigenvalues of a clamped plate problem on Riemannian manifolds

abstract: In this paper we study eigenvalues of a clamped plate problem on a bounded domain in an n-dimensional complete Riemannian manifold. By making use of Nash’s theorem and introducing k free constants, we derive a universal bound for eigenvalues, which solves a problem proposed by Wang and Xia [16].


Facial Expression Analysis and its Visualization While Writing Messages


title: Understanding Fashion Trends from Street Photos via Neighbor-Constrained Embedding Learning

abstract: Driven by the increasing popular image-dominated social networks, such as Instagram, Pinterest and Chictopica, sharing of daily-life street photos now plays an influential role in fashion adoption between fashion trend-setters and followers. In this work, we propose a deep learning based fine-grained embedding learning approach for street fashion analysis by leveraging user-generated street fashion data. Specifically, we present QuadNet, an effective CNN based image embedding network driven by both multi-task classification loss and neighbor-constrained similarity loss. The latter loss function is computed with a novel quadruplet loss function, which considers both hard and soft positive neighbors as well as a negative neighbor for each anchor image. The embedded feature learned from co-optimization is effective for both fine-grained classification task and image retrieval task. Quantitative evaluation on a newly collected large-scale multi-task street photo dataset shows that our QuadNet outperforms the state-of-the-art triplet network by a significant margin. In order to further evaluate the effectiveness of the learned embedding, we analyze and trace the fashion trends of New York City from 2011 to 2016. In our analysis, we are able to identify some short-term and long-term fashion styles.


title: Mayavi: a package for 3D visualization of scientific data

abstract: Mayavi is a general purpose, open source 3D scientific visualization package that is tightly integrated with the rich ecosystem of Python scientific packages. Mayavi provides a continuum of tools for developing scientific applications, ranging from interactive and script-based data visualization in Python to full-blown custom end-user applications.


title: MagicToon: A 2D-to-3D creative cartoon modeling system with mobile AR

abstract: We present MagicToon, an interactive modeling system with mobile augmented reality (AR) that allows children to build 3D cartoon scenes creatively from their own 2D cartoon drawings on paper. Our system consists of two major components: an automatic 2D-to-3D cartoon model creator and an interactive model editor to construct more complicated AR scenes. The model creator can generate textured 3D cartoon models according to 2D drawings automatically and overlay them on the real world, bringing life to flat cartoon drawings. With our interactive model editor, the user can perform several optional operations on 3D models such as copying and animating in AR context through a touchscreen of a handheld device. The user can also author more complicated AR scenes by placing multiple registered drawings simultaneously. The results of our user study have shown that our system is easier to use compared with traditional sketch-based modeling systems and can give more play to children's innovations compared with AR coloring books.


title: A Stochastic Model of Human-Machine Interaction for learning dialog Strategies

abstract: We propose a quantitative model for dialog systems that can be used for learning the dialog strategy. We claim that the problem of dialog design can be formalized as an optimization problem with an objective function reflecting different dialog dimensions relevant for a given application. We also show that any dialog system can be formally described as a sequential decision process in terms of its state space, action set, and strategy. With additional assumptions about the state transition probabilities and cost assignment, a dialog system can be mapped to a stochastic model known as Markov decision process (MDP). A variety of data driven algorithms for finding the optimal strategy (i.e., the one that optimizes the criterion) is available within the MDP framework, based on reinforcement learning. For an effective use of the available training data we propose a combination of supervised and reinforcement learning: the supervised learning is used to estimate a model of the user, i.e., the MDP parameters that quantify the user's behavior. Then a reinforcement learning algorithm is used to estimate the optimal strategy while the system interacts with the simulated user. This approach is tested for learning the strategy in an air travel information system (ATIS) task. The experimental results we present in this paper show that it is indeed possible to find a simple criterion, a state space representation, and a simulated user parameterization in order to automatically learn a relatively complex dialog behavior, similar to one that was heuristically designed by several research groups.


title: Age of Second-Language Acquisition and Perception of Speech in Noise

abstract: To determine how age of acquisition influences perception of second-language speech, the Speech Perception in Noise (SPIN) test was administered to native Mexican-Spanish-speaking listeners who lea...


title: Black-box Variational Inference for Stochastic Differential Equations

abstract: Parameter inference for stochastic differential equations is challenging due to the presence of a latent diffusion process. Working with an Euler-Maruyama discretisation for the diffusion, we use variational inference to jointly learn the parameters and the diffusion paths. We use a standard mean-field variational approximation of the parameter posterior, and introduce a recurrent neural network to approximate the posterior for the diffusion paths conditional on the parameters. This neural network learns how to provide Gaussian state transitions which bridge between observations in a very similar way to the conditioned diffusion process. The resulting black-box inference method can be applied to any SDE system with light tuning requirements. We illustrate the method on a Lotka-Volterra system and an epidemic model, producing accurate parameter estimates in a few hours.


title: Integrated ultra-high impedance front-end for non-contact biopotential sensing

abstract: Non-contact ECG/EEG electrodes, which operate primarily through capacitive coupling, have been extensively studied for unobtrusive physiological monitoring. Previous implementations using discrete off-the-shelf amplifiers have been encumbered by the need for manually tuned input capacitance neutralization networks and complex DC-biasing schemes. We have designed and fabricated a custom integrated high input impedance (60 fF∥5TΩ), low noise (0.05 fA/√Hz) non-contact sensor front-end specifically to bypass these limitations. The amplifier fully bootstraps both the internal and external parasitic impedances to achieve an input capacitance of 60 fF without neutralization. To ensure DC stability and eliminate the need for external large valued resistances, a low-leakage, on-chip biasing network is included. Stable frequency response is demonstrated below 0.05 Hz even with coupling capacitances as low as 0.5 pF.


title: Detection of small objects from high-resolution panchromatic satellite imagery based on supervised image segmentation

abstract: A new concept for the detection of small objects from modular optoelectronic multispectral scanner (MOMS-02) high spatial resolution panchromatic satellite imagery is presented. The authors combine supervised shape classification with unsupervised image segmentation in an iterative procedure which allows a target-oriented search for specific object shapes.


title: V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

abstract: Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields. Despite their popularity, most approaches are only able to process 2D images while most medical data used in clinical practice consists of 3D volumes. In this work we propose an approach to 3D image segmentation based on a volumetric, fully convolutional, neural network. Our CNN is trained end-to-end on MRI volumes depicting prostate, and learns to predict segmentation for the whole volume at once. We introduce a novel objective function, that we optimise during training, based on Dice coefficient. In this way we can deal with situations where there is a strong imbalance between the number of foreground and background voxels. To cope with the limited number of annotated volumes available for training, we augment the data applying random non-linear transformations and histogram matching. We show in our experimental evaluation that our approach achieves good performances on challenging test data while requiring only a fraction of the processing time needed by other previous methods.


title: Osteoporosis in rheumatoid arthritis and ankylosing spondylitis.

abstract: Bone is a target in many inflammatory rheumatic diseases, such as rheumatoid arthritis (RA) and ankylosing spondylitis (AS). The generalized effect of inflammation on bone may result in a decreased quality of bone and is associated with an increased risk of fractures and deformities, both in RA and AS. RA is characterized by periarticular osteopenia, systemic osteoporosis and bone erosions. Periarticular osteopenia and bone erosions are mainly correlated with disease activity. Unlike postmenopausal osteoporosis, osteoporosis in RA is more characterised by marked loss of bone in the hip and the radius, while the axial bone is relatively preserved. In general, several cross-sectional studies documented a lower bone mineral density in patients with RA, with a two-fold increase in osteoporosis compared to age- and sex-matched controls and relates to an increased fracture risk. Several factors contribute to the increased risk: older age, little exercise, long-term use of corticosteroids, and high disability index. AS is characterized by an increase in bone fragility due to reduced bone mineral density. The reported prevalence of osteoporosis in AS patients varies largely. The large variation reflects the difficulties in assessing BMD in AS due to new bone formation. Bone fragility is also due to changes in structural properties resulting from inflammation-induced bone failure in the spine in combination with reduced capacity of shock absorption leading to vertebral fractures. Different types of spinal fractures in patients with AS are described, including wedging. Wedging vertebral fractures contribute to hyperkyphosis and impaired physical function. In contrast to RA , bone loss in AS is accompanied by new bone formation. The pathophysiology of osteoporosis in RA and AS probably is fundamentally similar, but with different clinical phenotypes. The implications for therapeutically intervening in its occurrence and progression might be fundamentally different.


title: Avoiding Degradation in Deep Feed-Forward Networks by Phasing Out Skip-Connections

abstract: A widely observed phenomenon in deep learning is the degradation problem: increasing the depth of a network leads to a decrease in performance on both test and training data. Novel architectures such as ResNets and Highway networks have addressed this issue by introducing various flavors of skip-connections or gating mechanisms. However, the degradation problem persists in the context of plain feed-forward networks. In this work we propose a simple method to address this issue. The proposed method poses the learning of weights in deep networks as a constrained optimization problem where the presence of skip-connections is penalized by Lagrange multipliers. This allows for skip-connections to be introduced during the early stages of training and subsequently phased out in a principled manner. We demonstrate the benefits of such an approach with experiments on MNIST, fashion-MNIST, CIFAR-10 and CIFAR-100 where the proposed method is shown to greatly decrease the degradation effect (compared to plain networks) and is often competitive with ResNets.


title: The Penn Discourse TreeBank 2.0

abstract: We present the second version of the Penn Discourse Treebank, PDTB-2.0, describing its lexically-grounded annotations of discourse relations and their two abstract object arguments over the 1 million word Wall Street Journal corpus. We describe all aspects of the annotation, including (a) the argument structure of discourse relations, (b) the sense annotation of the relations, and (c) the attribution of discourse relations and each of their arguments. We list the differences between PDTB-1.0 and PDTB-2.0. We present representative statistics for several aspects of the annotation in the corpus.


title: Development of cloud-based Light intensity monitoring system for green house using Raspberry Pi

abstract: Light measurement with accuracy is essential in creating desired outcomes in practical daily applications such as poultry industry, traffic lighting system, gardening or farming, at emergency exits, etc. Hence, light measurement and scrutiny is an important step to ensue the efficiency as well as safety. Normally the growth of plants depends on light intensity falls on top of canopy. In this paper we have presented the real-time remote Light intensity monitoring system using Raspberry Pi, which provides the facility to monitor the lighting system remotely. The main characteristic of the proposed system is timely light intensity monitoring and storage of data in the database on the cloud for further reference in future. Because of this facility it is easy to take proper decision at proper time to obtain the required result for the growth of plant.


title: Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks

abstract: Prediction of popularity has profound impact for social media, since it offers opportunities to reveal individual preference and public attention from evolutionary social systems. Previous research, although achieves promising results, neglects one distinctive characteristic of social data, i.e., sequentiality. For example, the popularity of online content is generated over time with sequential post streams of social media. To investigate the sequential prediction of popularity, we propose a novel prediction framework called Deep Temporal Context Networks (DTCN) by incorporating both temporal context and temporal attention into account. Our DTCN contains three main components, from embedding, learning to predicting. With a joint embedding network, we obtain a unified deep representation of multi-modal user-post data in a common embedding space. Then, based on the embedded data sequence over time, temporal context learning attempts to recurrently learn two adaptive temporal contexts for sequential popularity. Finally, a novel temporal attention is designed to predict new popularity (the popularity of a new user-post pair) with temporal coherence across multiple time-scales. Experiments on our released image dataset with about 600K Flickr photos demonstrate that DTCN outperforms state-of-the-art deep prediction algorithms, with an average of 21.51% relative performance improvement in the popularity prediction (Spearman Ranking Correlation).


title: Atopic, contact, and seborrheic dermatitis in adolescents.

abstract: Atopic dermatitis, contact dermatitis, and seborrheic dermatitis are chronic or recurrent inflammatory dermatoses that commonly affect adolescents. They may present a diagnostic and therapeutic challenge for physicians, and can be frustrating for patients. This article reviews the pathogenesis, diagnosis, and management of these diseases.


title: Action-Video-Game Experience Alters the Spatial Resolution of Vision

abstract: Playing action video games enhances several different aspects of visual processing; however, the mechanisms underlying this improvement remain unclear. Here we show that playing action video games can alter fundamental characteristics of the visual system, such as the spatial resolution of visual processing across the visual field. To determine the spatial resolution of visual processing, we measured the smallest distance a distractor could be from a target without compromising target identification. This approach exploits the fact that visual processing is hindered as distractors are brought close to the target, a phenomenon known as crowding. Compared with nonplayers, action-video-game players could tolerate smaller target-distractor distances. Thus, the spatial resolution of visual processing is enhanced in this population. Critically, similar effects were observed in non-video-game players who were trained on an action video game; this result verifies a causative relationship between video-game play and augmented spatial resolution.


title: Design analysis of TuskBot: Universal stair climbing 4-wheel indoor robot

abstract: There are two major challenges in universal stair climbing: stairs without riser and with nose, and stairs with various dimensions. In this study, we proposed an indoor robot platform to overcome these challenges. First, to create an angle of attack, the Tusk, a passive, protruded element, was added in front of a 4-wheel robot. For design analysis and optimization of the Tusk, a simplified model of universal stair climbing was applied. To accommodate stairs without risers and with nose, the assistive track mechanism was applied. To climb the stair regardless of its dimension, length-adjustable mechanism was added. The results indicated the robot with these mechanisms successfully overcame each challenge. The performance was better than most conventional stair-climbing robots in terms of the range of compatible stairs. We expect these new approaches to expand the range of indoor robot operation with minimal cost.


title: Analysis of atrium's architectural aspects in office buildings under tropical sky conditions

abstract: This paper discusses atrium building typology by analysing the architectural aspects of existing atrium buildings. One hundred sixty commercial office buildings in Klang Valley were identified as the chosen building type for the initial selection process. Thirteen out of 160 office buildings surveyed were further analyzed based on the following architectural aspects: i) atrium spaces that include the description of atrium type, form and shape, physical dimensions, number of floors and height; ii) skylight design and roof fenestration system; and iii) atrium usage/activity and indoor environmental conditions. The atrium designs in these tropical office buildings are briefly described. The results show the most common atrium form is the enclosed central rectangular atrium with 4-storey average height. This study could lead to further research on design considerations for innovative applications to improve daylight performance in tropical office building atrium.


title: Examining the impact of privacy, trust and risk perceptions beyond monetary transactions: An integrated model

abstract: Much interest in privacy and trust studies is about shopping, but privacy research in other forms of online activities is beginning to emerge. This study examined the antecedents of privacy, trust and risk as well as their joint effect on two similar but fundamentally different activities: online transactions and retrieval of privileged information. Both activities involve the delivery of private user information, but the latter gives some leeway for users to control (or even falsify) their true identity. User shopping experience in the present study moderated the relationships and strengths of constructs. The effect of Internet literacy, social awareness and disposition to trust on privacy concern and trust was weaker for experienced shoppers. Privacy concern, trust and risk assessment played a lesser role on the two activity variables for those who were more experienced. Perceived privacy risk stood out as a strong antecedent for respondents in both experience groups, but the effect of Internet literacy, social awareness and disposition on trust was statistically insignificant for the same group. Further practical and managerial implications are provided.


title: Clinical named entity recognition: Challenges and opportunities

abstract: Information Extraction (IE), one of the important tasks in text analysis and Natural Language Processing (NLP), involves extracting meaningful pieces of knowledge from unstructured information sources, as unstructured data is computationally opaque. The intent of IE is to produce a knowledge base i.e. organize the information in a way that it is useful to people and arrange the information in a semantic way so that algorithms can make certain useful inferences from it. Named Entity Recognition (NER) is a sub-task of IE which finds and classifies the names/entities. Once these Named Entities (NE) are extracted, they can then be indexed and made searchable, relations can be derived, questions can be answered and many more. NER techniques are different for different domains, because of the uniqueness that exists in each of the domains, although the process depends on a number of fundamental Natural Language Processing (NLP) steps such as tokenization, part-of-speech tagging, parsing and model building. As an example, NER in the medical domain involves handling of a number of vital tasks such as identification of medical terms, attributes such as negation, severity, identification of relationships between entities and mapping terms in the document to concepts in domain specific ontologies. There is also a heavy dependence on domain specific resources such as medical dictionaries and ontologies such as the Unified Medical Language System (UMLS)[34]. In this paper, we focus on NER in the clinical domain. In particular, we will focus on the NER challenges and the qualitative analysis of clinical reports on the approaches we took for the named entities: anatomies, findings, location qualifier, and procedures.


title: Chemical origins of life: Prebiotic RNA unstuck.

abstract: Non-enzymatic copying of an RNA template is appealing as a transition from pre-life to an RNA world, but it has been difficult to demonstrate in the laboratory. Now, two separate studies focusing on RNA's backbone connectivity offer partial solutions to some of the problems raised with this hypothesis for the origin of life.


title: A Closed-Form Design Technique for Ultra-Wideband Doherty Power Amplifiers

abstract: This paper presents an innovative architecture to drastically enlarge the bandwidth of the Doherty power amplifier (DPA). The proposed topology, based on novel input/output splitting/combining networks, allows to overcome the typical bandwidth limiting factors of the conventional DPA. A complete and rigorous theoretical investigation of the developed architecture is presented leading to a closed-form formulation suitable for a direct synthesis of ultra-wideband DPAs. The theoretical formulation is validated through the design, realization, and test of a hybrid prototype based on commercial GaN HEMT device showing a fractional bandwidth larger than 83%. From 1.05 to 2.55 GHz, experimental results with continuous-wave signals have shown efficiency levels within 83%-45% and within 58%-35% at about 42- and 36-dBm output power, respectively. The DPA has also been tested and digitally predistorted by using a 5-MHz Third Generation Partnership Project (3GPP) signal. In particular, to evaluate the ultra-wideband and the multi-mode capabilities of the prototype, f ::: 1 ::: = 1.2 GHz, f ::: 2 ::: = 1.8 GHz, and f ::: 3 ::: = 2.5 GHz have been selected as carrier frequencies for the 3GPP signal. Under these conditions and at 36-dBm average output power, the DPA shows 52%, 35%, and 52% efficiency and an adjacent channel power ratio always lower than -43 dBc.


title: Programming Curvature using Origami Tessellations

abstract: Elementary geometric constructions and constrained optimization algorithms can be used to fit origami tessellations to any curved surface.


title: Stock Market Prediction Model by Combining Numeric and News Textual Mining

abstract: This paper proposes a novel method for the prediction of stock market closing price. Many researchers have contributed in this area of chaotic forecast in their ways. Data mining techniques can be used more in financial markets to make qualitative decisions for investors. Fundamental and technical analyses are the traditional approaches so far. ANN is a popular way to identify unknown and hidden patterns in data is used for share market prediction. A multilayered feed-forward neural network is built by using combination of data and textual mining. The Neural Network is trained on the stock quotes and extracted key phrases using the Backpropagation Algorithm which is used to predict share market closing price. This paper is an attempt to determine whether the BSE market news in combination with the historical quotes can efficiently help in the calculation of the BSE closing index for a given trading day.


title: Pulsed voltage converter with bipolar output voltages up to 10 kV for Dielectric Barrier Discharge

abstract: For pulsed power applications special pulsed voltage converters are needed. This paper presents an approach for a pulsed power converter, which generates bipolar output voltages up to 10 kV with extremely fast voltage slopes and high repetition rates. The topology is based on a H-bridge with a 10 kV dc-link. The output voltage and current of the pulsed voltage converter are adapted for the operation with Dielectric Barrier Discharge. To avoid the use of spark gaps due to their limited lifetime and thus to the lifetime of the converter, series stacked MOSFETs are used to realize a switch with a high blocking voltage. A balancing network for the series stacked MOSFETs is introduced as well as an adequate gate drive circuit. A matching method for the capacitive load is described, to achieve a maximum voltage slope at this capacitive load. To validate the theoretical considerations a prototype and measurements are presented.


title: LTE Capacity Compared to the Shannon Bound

abstract: In this paper we propose a modification to Shannon capacity bound in order to facilitate accurate benchmarking of UTRAN long term evolution (LTE). The method is generally applicable to wireless communication systems, while we have used LTE air-interface technology as a case study. We introduce an adjusted Shannon capacity formula, where we take into account the system bandwidth efficiency and the SNR efficiency of LTE. Separating these issues, allows for simplified parameter extraction. We show that the bandwidth efficiency can be calculated based on system parameters, while the SNR efficiency is extracted from detailed link level studies including advanced features of MIMO and frequency domain packet scheduling (FDPS). We then use the adjusted Shannon capacity formula combined with G-factor distributions for macro and micro cell scenarios to predict LTE cell spectral efficiency (SE). Such LTE SE predictions are compared to LTE cell SE results generated by system level simulations. The results show an excellent match of less that 5-10% deviation.


title: NUMERICAL ANALYSIS OF HARBOR OSCILLATION IN HARBORS OF VARIOUS SHAPES

abstract: A numerical model based on nonlinear shallow water equations is applied to study oscillations in harbors of various shapes including L-type harbors, I-type harbors with a narrowed area, a C-type harbor, a T-type harbor, and I-type harbors with a seabed crest or trough. The second mode shows a higher amplification factor at the head of an L-type harbor as its bending position is nearer to the harbor mouth. As a narrowed area of an I-type harbor is located nearer to the harbor mouth, amplification factors of the first and second modes at the harbor head are lower and higher, respectively. A C-type harbor shows rather complicated amplification because the phase difference between waves propagating through two harbor mouths depends on the position inside the harbor. The oscillation in an actual bay, i.e., Urauchi Bay, which has a shape similar to that of a T-type harbor, is also numerically simulated; damping processes are compared between Urauchi Bay and a T-type harbor.


title: Performances of OpenFlow-Based Software- Defined Networks: An overview

abstract: Software Defined Networking (SDN) has gained significant attention from network researchers and industry in recent years. Indeed, the SDN concept provides many advantages such as programmability and easy management of the network. However, it generates new challenges as scalability and performances issues, understanding in-depth the performances and limitations of the SDN concept is a prerequisite to its implementation and deployment in real networks. In this paper, we aim to present in a comprehensive way, the most important works that focus on performances of SDN. As SDN separates the control plane from the data plane, we first present research efforts made to enhance the performances of data plane devices, then, we give an overview of different solutions proposed to improve controller performances. We provide also an overview on recent control plane architectures with multiple controllers that have been proposed to meet performances and scalability constraints. Finally, we present the different techniques and tools used in literature to evaluate the performances of software defined networks


title: Mobile augmented reality for cultural heritage: A technology acceptance study

abstract: We have developed a mobile augmented reality application with historical photographs and information about a historical street. We follow a design science research methodology and use an extended version of the technology acceptance model (TAM) to study the acceptance of this application. A prototype has been developed in accordance with general principles for usability design, and two surveys have been conducted. A web survey with 200 participants that watched a short video demonstration of the application to validate the adapted acceptance model, and a street survey, where 42 participants got the opportunity to try the application in a live setting before answering a similar questionnaire and provide more concrete feedback. The results show that both perceived usefulness and perceived enjoyment has a direct impact on the intention to use mobile augmented reality applications with historical pictures and information. Further a number of practical recommendations for the development and deployment of such systems are provided.


title: Modeling of magnetically biased graphene patch frequency selective surface (FSS)

abstract: A free-standing magnetically biased graphene patch frequency selective surface (FSS) is modelled in this paper. Its transmission coefficients of co- and cross-polarizations can be obtained with an equivalent tensorial surface conductivity. Then, the rotation angle for normal incidence is explored with different values of biasing magnetic field B 0 . The maximum rotation angle provided by the magnetically biased graphene patch FSS is 43° at 4.7 THz with B 0 =2.5 T, which is much larger than the rotation angle provided by a graphene sheet. This is very promising for THz nano-devices based on the giant Faraday rotation of graphenes.


title: Creating health typologies with random forest clustering

abstract: In this paper, we describe the creation of a health-specific geodemographic classification system for the whole city of Birmingham UK. Compared to some existing open source and commercial systems, the proposed work has a couple of distinct advantages: (i) It is particularly designed for the public health domain by combining most reliable health data and other sources accounting for the main determinants of health. (ii) A novel random forest clustering algorithm is used for generating clusters and it has several obvious advantages over the commonly used k-means algorithm in practice. These resultant health typologies will help local authorities to understand and design customized health interventions for the population. A Birmingham map illustrating the distribution of all health typologies is produced.


title: Efficient and accurate nearest neighbor and closest pair search in high-dimensional space

abstract: Nearest Neighbor (NN) search in high-dimensional space is an important problem in many applications. From the database perspective, a good solution needs to have two properties: (i) it can be easily incorporated in a relational database, and (ii) its query cost should increase sublinearly with the dataset size, regardless of the data and query distributions. Locality-Sensitive Hashing (LSH) is a well-known methodology fulfilling both requirements, but its current implementations either incur expensive space and query cost, or abandon its theoretical guarantee on the quality of query results. Motivated by this, we improve LSH by proposing an access method called the Locality-Sensitive B-tree (LSB-tree) to enable fast, accurate, high-dimensional NN search in relational databases. The combination of several LSB-trees forms a LSB-forest that has strong quality guarantees, but improves dramatically the efficiency of the previous LSH implementation having the same guarantees. In practice, the LSB-tree itself is also an effective index which consumes linear space, supports efficient updates, and provides accurate query results. In our experiments, the LSB-tree was faster than: (i) iDistance (a famous technique for exact NN search) by two orders of magnitude, and (ii) MedRank (a recent approximate method with nontrivial quality guarantees) by one order of magnitude, and meanwhile returned much better results. As a second step, we extend our LSB technique to solve another classic problem, called Closest Pair (CP) search, in high-dimensional space. The long-term challenge for this problem has been to achieve subquadratic running time at very high dimensionalities, which fails most of the existing solutions. We show that, using a LSB-forest, CP search can be accomplished in (worst-case) time significantly lower than the quadratic complexity, yet still ensuring very good quality. In practice, accurate answers can be found using just two LSB-trees, thus giving a substantial reduction in the space and running time. In our experiments, our technique was faster: (i) than distance browsing (a well-known method for solving the problem exactly) by several orders of magnitude, and (ii) than D-shift (an approximate approach with theoretical guarantees in low-dimensional space) by one order of magnitude, and at the same time, outputs better results.


title: Automatic Grading and Feedback using Program Repair for Introductory Programming Courses

abstract: We present GradeIT, a system that combines the dual objectives of automated grading and program repairing for introductory programming courses (CS1). Syntax errors pose a significant challenge for testcase-based grading as it is difficult to differentiate between a submission that is almost correct and has some minor syntax errors and another submission that is completely off-the-mark. GradeIT also uses program repair to help in grading submissions that do not compile. This enables running testcases on submissions containing minor syntax errors, thereby awarding partial marks for these submissions (which, without repair, do not compile successfully and, hence, do not pass any testcase). Our experiments on 15613 submissions show that GradeIT results are comparable to manual grading by teaching assistants (TAs), and do not suffer from unintentional variability that happens when multiple TAs grade the same assignment. The repairs performed by GradeIT enabled successful compilation of 56% of the submissions having compilation errors, and resulted in an improvement in marks for 11% of these submissions.


title: From cars to bikes – The effect of an intervention providing access to different bike types: A randomized controlled trial

abstract: INTRODUCTION ::: We aimed to investigate whether providing parents with children in kindergarten with access to different bicycle types could influence (i) travel behavior and cycling amount, and (ii) intrinsic motivation for cycling and psychological constructs related to car use. ::: ::: ::: METHODS ::: A randomized, controlled trial was conducted in Southern Norway from September 2017 to June 2018. In total 36 parents were recruited and randomly drawn into an intervention (n = 18) or control group (n = 18). The intervention group was in random order equipped with an e-bike with trailer (n = 6), a cargo (longtail) bike (n = 6) and a traditional bike with trailer (n = 6). ::: ::: ::: RESULTS ::: At follow-up, more participants from the intervention group (vs. the control group) were classified as cyclists to the workplace (n = 7 (38.9%) vs. n = 1 (5.9%), p = 0.04), but not to the kindergarten (n = 6 (33.3%) vs. n = 2 (11.8%), p = 0.23) or to the grocery store (n = 2 (11.1%) vs. n = 0 (0%), p = 0.49). A significant (p = ≤0.05) increase in cycling frequency (0.1 to 2.0 days/week) from baseline to follow-up was found in the intervention group for all destinations and seasons, except to the grocery store during winter (p = 0.16). A decrease in frequency of car driving (-0.2 to -1.7 days/week) was found to be apparent in terms of travelling to the workplace and the kindergarten for all seasons, yet not to the grocery store for any season (p = 0.15-0.49). The intervention group (vs. the control group) reported significantly higher "intrinsic regulation" for cycling (p = 0.01) at follow-up. ::: ::: ::: CONCLUSION ::: Access to different bike types for parents with children attending kindergarten resulted in overall increased cycling, decreased car use and higher intrinsic motivation for cycling. E-bikes obtained the greatest cycling amount in total, with the smallest sample variability. Hence, providing parents with children in kindergarten with access to e-bikes might result in increased and sustained cycling, also during the winter season.


title: A 10MHz 2mA-800mA 0.5V-1.5V 90% peak efficiency time-based buck converter with seamless transition between PWM/PFM modes

abstract: We present a 10MHz buck converter with enhanced light load efficiency achieved by combining time-based PWM control with PFM. The proposed seamless transition technique provides additional degree of freedom to extend high efficiency regime across a wider range of load current. Fabricated in a 65nm CMOS, the prototype achieves 90% peak efficiency and > 80% efficiency over I load range of 2mA to 800mA. Vo changes by less than 40mV during PWM to PFM transitions.


title: Community Detection and Mining in Social Media

abstract: This book, from a data mining perspective, introduces characteristics of social media, reviews representative tasks of computing with social media, and illustrates associated challenges. It introduces basic concepts, presents state-of-the-art algorithms with easy-to-understand examples, and recommends effective evaluation methods. In particular, we discuss graph-based community detection techniques and many important extensions that handle dynamic, heterogeneous networks in social media. We also demonstrate how discovered patterns of communities can be used for social media mining. The concepts, algorithms, and methods presented in this lecture can help harness the power of social media and support building socially-intelligent systems. This book is an accessible introduction to the study of \emph{community detection and mining in social media}. It is an essential reading for students, researchers, and practitioners in disciplines and applications where social media is a key source of data that piques our curiosity to understand, manage, innovate, and excel. This book is supported by additional materials, including lecture slides, the complete set of figures, key references, some toy data sets used in the book, and the source code of representative algorithms. The readers are encouraged to visit the book website http://dmml.asu.edu/cdm/ for the latest information.


title: Development of a wearable plantar force measurement device for gait analysis in remote conditions

abstract: The pressure field that exists between the foot and the supporting surface is identified as the foot plantar pressure. The information obtained from foot plantar pressure measurements has useful applications that include diagnosis of gait disturbances, optimization of footwear design, sport biomechanics and prevention of injury. Using wearable technology to measure foot plantar pressure continuously allows the collection of comprehensive real-life data sets while interfering minimally with the subject's daily activities. This paper presents the design of a wearable device to measure foot plantar pressure. Mechanical and electrical design considerations as well as data analysis are discussed. A pilot study involving 20 physically fit volunteers (15 males and 5 females, ageing from 20 – 45) performing a variety of physical activities (such as standing, walking, jumping and climbing up and down stairs) illustrate the potential of the device in terms of its wearability, and suitability for unobtrusive long-term monitoring.


title: AC Power Theory From Poynting Theorem: Identification of the Power Components of Magnetic Saturating and Hysteretic Circuits

abstract: This paper is the continuation of an earlier work on narrowing the theoretical gap with power theory (or “power definitions”) for nonlinear circuits. The underlying idea is to compute the active and reactive powers of a general single-phase circuit from only the knowledge of the instantaneous voltage and current at the circuit terminals. This is done to shed light on the long-standing controversy on the physical meaning of the various types of powers defined for nonsinusoidal waveforms. Previously, only the power components of nonlinear switching circuits consisting of constant resistive and inductive elements were properly identified. The method is now extended to be suitable for the identification of the power components of general single-phase nonlinear circuits including magnetic nonlinear components with saturation and hysteresis. The Poynting Theorem is applied to correctly identify the energy consumed in electrical elements (Joule losses) as well as the energy stored/restored and consumed in the hysteresis loop of magnetic elements. Numerous examples are presented for illustration and validation of the method.


title: A Need for Speed: Adapting Agent Action Speed to Improve Task Learning from Non-Expert Humans

abstract: As robots become pervasive in human environments, it is important to enable users to effectively convey new skills without programming. Most existing work on Interactive Reinforcement Learning focuses on interpreting and incorporating non-expert human feedback to speed up learning; we aim to design a better representation of the learning agent that is able to elicit more natural and effective communication between the human trainer and the learner, while treating human feedback as discrete communication that depends probabilistically on the trainer's target policy. This work entails a user study where participants train a virtual agent to accomplish tasks by giving reward and/or punishment in a variety of simulated environments. We present results from 60 participants to show how a learner can ground natural language commands and adapt its action execution speed to learn more efficiently from human trainers. The agent's action execution speed can be successfully modulated to encourage more explicit feedback from a human trainer in areas of the state space where there is high uncertainty. Our results show that our novel adaptive speed agent dominates different fixed speed agents on several measures of performance. Additionally, we investigate the impact of instructions on user performance and user preference in training conditions.


title: Wide-Bandwidth Power-Combining and Inverse Class-F GaN Power Amplifier at X-Band

abstract: In this paper, we present a wide bandwidth power amplifier (PA) utilizing a Marchand balun to achieve power combining and inverse class-F (class-F-1) matching. We have used a 4-to-1 balun and its out-of-band characteristics to achieve matching at the second and third harmonic for differential class-F-1 operation. Thus, the balun simultaneously acts as a combiner and harmonic matching network. The experimental results demonstrate that the prototype PA achieves an output power of 41.5 dBm at the 1-dB compression point (P1 dB), power-added efficiency of 55%, and gain of 10 dB over a fractional bandwidth of 40% from 8 to 12 GHz. Due to the differential operation, the class-F-1 PA has also achieved a measured second harmonic distortion of -50 dBc.


title: Supporting Exploratory Search Through User Modeling

abstract: Exploratory search is becoming more common as the web is used more increasingly as a medium for learning and discovery. Compared to traditional known-item search, exploratory search is more challenging and difficult to support because it initiates with poorly defined search goals, while the user knowledge and information-needs constantly change throughout the search process. Although information-retrieval algorithms have improved greatly in providing users with relevant information to specific queries, there is still room to improve our understanding of users’ exploratory information-seeking strategies as well as design of systems supporting exploratory information-seeking. Modeling the user behavior and predicting dynamically changing information-needs in exploratory search is hard. Over the past decade there has been increasing attention on rich user interfaces, retrieval techniques, and studies of exploratory search. However, existing work does not yet support the dynamic aspects of exploratory search. The aim of this research is to explore different aspects of how to understand and support exploratory search, including user studies, intent visualization and user modeling.