title: A Simple Generic Attack on Text Captchas

abstract: Text-based Captchas have been widely deployed across the Internet to defend against undesirable or malicious bot programs. Many attacks have been proposed; these fine prior art advanced the scientific understanding of Captcha robustness, but most of them have a limited applicability. In this paper, we report a simple, low-cost but powerful attack that effectively breaks a wide range of text Captchas with distinct design features, including those deployed by Google, Microsoft, Yahoo!, Amazon and other Internet giants. For all the schemes, our attack achieved a success rate ranging from 5% to 77%, and achieved an average speed of solving a puzzle in less than 15 seconds on a standard desktop computer (with a 3.3GHz Intel Core i3 CPU and 2 GB RAM). This is to date the simplest generic attack on text Captchas. Our attack is based on Log-Gabor filters; a famed application of Gabor filters in computer security is John Daugman’s iris recognition algorithm. Our work is the first to apply Gabor filters for breaking Captchas.


title: Hierarchical Bayesian space-time models

abstract: Space-time data are ubiquitous in the environmental sciences. Often, as is the case with atmo- spheric and oceanographic processes, these data contain many different scales of spatial and temporal variability. Such data are often non-stationary in space and time and may involve many observation/prediction locations. These factors can limit the effectiveness of traditional space- time statistical models and methods. In this article, we propose the use of hierarchical space-time models to achieve more flexible models and methods for the analysis of environmental data distributed in space and time. The first stage of the hierarchical model specifies a measurement- error process for the observational data in terms of some 'state' process. The second stage allows for site-specific time series models for this state variable. This stage includes large-scale (e.g. seasonal) variability plus a space-time dynamic process for the ’anomalies'. Much of our interest is with this anomaly proc ess. In the third stage, the parameters of these time series models, which are distributed in space, are themselves given a joint distribution with spatial dependence (Markov random fields). The Bayesian formulation is completed in the last two stages by speci- fying priors on parameters. We implement the model in a Markov chain Monte Carlo framework and apply it to an atmospheric data set of monthly maximum temperature.


title: QoE-Driven Rate Adaptation Heuristic for Fair Adaptive Video Streaming

abstract: HTTP Adaptive Streaming (HAS) is quickly becoming the de facto standard for video streaming services. In HAS, each video is temporally segmented and stored in different quality levels. Rate adaptation heuristics, deployed at the video player, allow the most appropriate level to be dynamically requested, based on the current network conditions. It has been shown that today’s heuristics underperform when multiple clients consume video at the same time, due to fairness issues among clients. Concretely, this means that different clients negatively influence each other as they compete for shared network resources. In this article, we propose a novel rate adaptation algorithm called FINEAS (Fair In-Network Enhanced Adaptive Streaming), capable of increasing clients’ Quality of Experience (QoE) and achieving fairness in a multiclient setting. A key element of this approach is an in-network system of coordination proxies in charge of facilitating fair resource sharing among clients. The strength of this approach is threefold. First, fairness is achieved without explicit communication among clients and thus no significant overhead is introduced into the network. Second, the system of coordination proxies is transparent to the clients, that is, the clients do not need to be aware of its presence. Third, the HAS principle is maintained, as the in-network components only provide the clients with new information and suggestions, while the rate adaptation decision remains the sole responsibility of the clients themselves. We evaluate this novel approach through simulations, under highly variable bandwidth conditions and in several multiclient scenarios. We show how the proposed approach can improve fairness up to 80p compared to state-of-the-art HAS heuristics in a scenario with three networks, each containing 30 clients streaming video at the same time.


title: The prospects for psychological science in human-computer interaction

abstract: This paper discusses the prospects of psychology playing a significant role in the progress of human-computer interaction. In any field, hard science (science that is mathematical or otherwise technical) has a tendency to drive out softer sciences, even if the softer sciences have important contributions to make. It is possible that, as computer science and artificial intelligence contributions to human-computer interaction mature, this could happen to psychology. It is suggested that this trend might be prevented by hardening the applicable psychological science. This approach, however, has been critized on the grounds that the resulting body of knowledge would be too low level, too limited in scope, too late to affect computer technology, and too difficult to apply. The prospects for overcoming each of these obstacles are analyzed here.


title: A novel LDPC decoder for DVB-S2 IP

abstract: In this paper a programmable Forward Error Correction (FEC) IP for a DVB-S2 receiver is presented. It is composed of a Low-Density Parity Check (LDPC), a Bose-Chaudhuri-Hoquenghem (BCH) decoder, and pre- and postprocessing units. Special emphasis is put on LDPC decoding, since it accounts for the most complexity of the IP core by far. We propose a highly efficient LDPC decoder which applies Gauss-Seidel decoding. In contrast to previous publications, we show in detail how to solve the well known problem of superpositions of permutation matrices. The enhanced convergence speed of Gauss-Seidel decoding is used to reduce area and power consumption. Furthermore, we propose a modified version of the λ-Min algorithm which allows to further decrease the memory requirements of the decoder by compressing the extrinsic information. Compared to the latest published DVB-S2 LDPC decoders, we could reduce the clock frequency by 40% and the memory consumption by 16%, yielding large energy and area savings while offering the same throughput.


title: A printed planar helix antenna

abstract: This paper presents design, fabrication and measurement results of a novel broadband, circularly polarized, planar helix antenna operating at X-band that is fabricated using printed circuit techniques. The conventional circular helix antenna is modified into a planar helix antenna, and the effect of various parameters like pitch angle, dimensions, feed and substrate, etc. on the impedance and radiation characteristics is studied. The performance parameters under investigation are S 11 , directivity, side lobe level, and axial ratio. These parameters are obtained using CST Microwave Studio. For a design with 5 turns of the planar helix, the results of the simulations are as follows: over a frequency range of 8.3 – 11.7 GHz, S 11 is less than −10 dB, directivity ranges from 11.7 – 12.6 dBi, and the axial ratio ranges from 0.68 – 3.3 dB. The corresponding design is fabricated using RO4003C dielectric substrates. The measurement results match the simulation results reasonably well.


title: Cleansing Data for Mining and Warehousing

abstract: Given the rapid growth of data, it is important to extract, mine and discover useful information from databases and data warehouses. The process of data cleansing is crucial because of the "garbage in, garbage out" principle. "Dirty" data files are prevalent because of incorrect or missing data values, inconsistent value naming conventions, and incomplete information. Hence, we may have multiple records refering to the same real world entity. In this paper, we examine the problem of detecting and removing duplicating records. We present several effcient techniques to pre-process the records before sorting them so that potentially matching records will be brought to a close neighbourhood. Based on these techniques, we implement a data cleansing system which can detect and remove more duplicate records than existing methods.


title: On the Optical Transmission of Millimeter Waves

abstract: Optical Components, which have been developed over the past 15 years mainly for telecommunication systems, also have the potential for widespread applications in the microwave domain. One of the interesting topics is the transmlssion of microwave via optical waveguldes. Since the fibers are low loss. broad band and flexible channels, the method becomes attractive This paper reviews the state - of - the - art of the basic components to achieve a microwave transmission via optical waveguides. A special attention is given to the wide band light modulators which are now under development. A new type of light modulator is proposed eg : The Tunable Travelling Wave Electro Optic Modulator (TTWEOM)


title: A Primary-Side Control Scheme for High-Power-Factor LED Driver With TRIAC Dimming Capability

abstract: This paper proposes a primary-side control scheme for low-power light-emitting diode (LED) driver with Flyback topology. With average primary-side current signal and transformer auxiliary winding voltage signal, the output current can be derived and regulated precisely by the proposed control scheme without any isolated feedback circuit. Furthermore, the proposed primary-side control scheme can be implemented with power factor correction control to achieve high input power factor, which is usually required for LED drivers. Since the triode for alternating current (TRIAC) dimming is very popular in practical application, it is preferred that the LED driver can also be compatible with TRIAC dimming. With the proposed control scheme, the input characteristic of the LED driver is resistive like an incandescent bulb. No extra dummy load is needed to provide the TRIAC holding current. Both the output current signal and TRIAC dimming signal can be obtained at the primary side, the TRIAC dimming function can be easily implemented and linear dimming characteristic can be achieved. Detailed operation principles and design considerations of the proposed control scheme are presented in the paper. The experimental results from a 15-W prototype verify the theoretical analysis.


title: Capacitively Coupled Electrocardiogram Measuring System and Noise Reduction by Singular Spectrum Analysis

abstract: A wearable electrocardiogram (ECG) measuring system is designed based upon capacitively coupled biopotential sensing electrodes. The sensing electrodes are made from the standard printed-circuit board. Using a reversely connected diode to provide the required high-value bias resistance, the pickup circuit for biopotential sensing is implemented by a high-input-impedance instrumentation amplifier. Two different configurations of the ECG measuring system are constructed and experimentally tested. One is a floating ground configuration in which only two sensing electrodes are used for ECG signal acquisition. Another is the passive ground configuration in which an additional grounding electrode is employed as the reference for common-mode noise suppression. The ECG signal can be obtained through thin clothes for both cases but performs lower signal-to-noise ratio. In order to improve the quality of the obtained signal, an algorithm based on a singular spectrum analysis is designed for noise reduction. A matching template is introduced to pick out the critical principal components automatically. Experimental results show that, with the aid of the proposed algorithm, satisfied ECG and respiration signals can be effectively obtained outside cotton-fiber clothing.


title: Effects of Gender and Relationship Context in Audio Narratives on Genital and Subjective Sexual Response in Heterosexual Women and Men

abstract: Previous research suggests that heterosexual women’s sexual arousal patterns are nonspecific; heterosexual women demonstrate genital arousal to both preferred and nonpreferred sexual stimuli. These patterns may, however, be related to the intense and impersonal nature of the audiovisual stimuli used. The current study investigated the gender specificity of heterosexual women’s sexual arousal in response to less intense sexual stimuli, and also examined the role of relationship context on both women’s and men’s genital and subjective sexual responses. Assessments were made of 43 heterosexual women’s and 9 heterosexual men’s genital and subjective sexual arousal to audio narratives describing sexual or neutral encounters with female and male strangers, friends, or long-term relationship partners. Consistent with research employing audiovisual sexual stimuli, men demonstrated a category-specific pattern of genital and subjective arousal with respect to gender, while women showed a nonspecific pattern of genital arousal, yet reported a category-specific pattern of subjective arousal. Heterosexual women’s nonspecific genital response to gender cues is not a function of stimulus intensity or relationship context. Relationship context did significantly affect women’s genital sexual arousal—arousal to both female and male friends was significantly lower than to the stranger and long-term relationship contexts—but not men’s. These results suggest that relationship context may be a more important factor in heterosexual women’s physiological sexual response than gender cues.


title: Ku-band 200-W Pulsed Power Amplifier Based on Waveguide Spatially Power-Combining Technique for Industrial Applications

abstract: A Ku-band 200-W pulsed solid-state power amplifier has been presented and designed by using a hybrid radial-/rectangular-waveguide spatially power-combining technique. The hybrid radial-/rectangular-waveguide power-dividing/power-combining circuit employed in this design provides not only a high power-combining efficiency over a wide bandwidth but also efficient heat sinking for the active power devices. A simple design approach of the presented power-dividing/power-combining structure has been developed. The measured small-signal gain of the pulsed power amplifier is about 51.3 dB over the operating frequency range, while the measured maximum output power at 1-dB compression is 209 W at 13.9 GHz, with an active power-combining efficiency of about 91%. Furthermore, the active power-combining efficiency is greater than 82% from 13.75 to 14.5 GHz.


title: Guided Deep Reinforcement Learning for Swarm Systems

abstract: In this paper, we investigate how to learn to control a group of cooperative agents with limited sensing capabilities such as robot swarms. The agents have only very basic sensor capabilities, yet in a group they can accomplish sophisticated tasks, such as distributed assembly or search and rescue tasks. Learning a policy for a group of agents is difficult due to distributed partial observability of the state. Here, we follow a guided approach where a critic has central access to the global state during learning, which simplifies the policy evaluation problem from a reinforcement learning point of view. For example, we can get the positions of all robots of the swarm using a camera image of a scene. This camera image is only available to the critic and not to the control policies of the robots. We follow an actor-critic approach, where the actors base their decisions only on locally sensed information. In contrast, the critic is learned based on the true global state. Our algorithm uses deep reinforcement learning to approximate both the Q-function and the policy. The performance of the algorithm is evaluated on two tasks with simple simulated 2D agents: 1) finding and maintaining a certain distance to each others and 2) locating a target.


title: Characterization of User’s Behavior Variations for Design of Replayable Mobile Workloads

abstract: Mobile systems leverage heterogeneous cores to deliver a desired user experience. However, how these cores cooperate in executing interactive mobile applications in the hands of a real user is unclear, preventing more realistic studies on mobile platforms. In this paper, we study how 33 users run applications on modern smartphones over a period of a month. We analyze the usage of CPUs, GPUs and associated memory operations in real user interactions, and develop microbenchmarks on an automated methodology which describes realistic and replayable test runs that statistically mimic user variations. Based on the generated test runs, we further empirically characterize memory bandwidth and power consumption of CPUs and GPUs to show the impact of user variations in the system, and identify user variation-aware optimization opportunities in actual mobile application uses.


title: A latent semantic analysis methodology for the identification and creation of personas

abstract: A persona represents a group of target users that share common behavioral characteristics. By using a narrative, picture, and name, a persona provides HCI practitioners with a vivid and specific design target. This research develops a new methodology for the identification and creation of personas through the application of Latent Semantic Analysis (LSA). An application of the LSA methodology is provided in the context of the design of an Institutional Repository system. The LSA methodology helps overcome some of the drawbacks of current methods for the identification and creation of personas, and makes the process less subjective, more efficient, and less reliant on specialized skills.


title: Using Short Video Lectures to Enhance Mathematics Learning - Experiences on Differential and Integral Calculus Course for Engineering Students

abstract: Mathematics' skills and knowledge lay the basis for engineering studies. However, the resources targeted to mathematics' teaching are in many cases very limited. During the past years in our university the reduction of mathematics' contact hours has been significant while at the same time the study groups have grown. However, the mathematical proficiency of incoming students has not increased. Consequently, during the contact hours, the learning of basics requires more time and the need of personalized learning is increasing. In this article a method based on short video lecturing to enhance mathematics' learning is pre- sented. The study explored how the students experienced the mathematics' studying from videos. In addition, it was investigated if the video lectures have an influence on students' motivation towards mathematics' learning. Overall, the results were encouraging and the short video lecturing seems to be worth considering while developing methods for mathematics' learning. This paper summarizes the observations during the examination.


title: Self-Similarity Driven Color Demosaicking

abstract: Demosaicking is the process by which from a matrix of colored pixels measuring only one color component per pixel, red, green, or blue, one can infer a whole color information at each pixel. This inference requires a deep understanding of the interaction between colors, and the involvement of image local geometry. Although quite successful in making such inferences with very small relative error, state-of-the-art demosaicking methods fail when the local geometry cannot be inferred from the neighboring pixels. In such a case, which occurs when thin structures or fine periodic patterns were present in the original, state-of-the-art methods can create disturbing artifacts, known as zipper effect, blur, and color spots. The aim of this paper is to show that these artifacts can be avoided by involving the image self-similarity to infer missing colors. Detailed experiments show that a satisfactory solution can be found, even for the most critical cases. Extensive comparisons with state-of-the-art algorithms will be performed on two different classic image databases.


title: Sustainability and ICT - An overview of the field

abstract: Sustainable development requires the decoupling of economic growth from environmental impacts and from the use of natural resources. This article gives an overview of existing approaches to using Information and Communication ::: Technology (ICT) in the service of sustainability: Environmental Informatics, Green ICT, and Sustainable Human-computer Interaction (HCI). These approaches are then discussed in the context of the Jevons paradox, an economic argument implying that technological efficiency alone will not produce sustainability. This consideration leads to the conclusion that a combination of efficiency and sufficiency strategies is the most effective way to stimulate innovations which will unleash ICT’s potential to support sustainability.


title: Combining multiple thresholding binarization values to improve OCR output

abstract: For noisy, historical documents, a high optical character recognition (OCR) word error rate (WER) can render the OCR ::: text unusable. Since image binarization is often the method used to identify foreground pixels, a body of research seeks ::: to improve image-wide binarization directly. Instead of relying on any one imperfect binarization technique, our method ::: incorporates information from multiple simple thresholding binarizations of the same image to improve text output. Using ::: a new corpus of 19th century newspaper grayscale images for which the text transcription is known, we observe WERs of ::: 13.8% and higher using current binarization techniques and a state-of-the-art OCR engine. Our novel approach combines ::: the OCR outputs from multiple thresholded images by aligning the text output and producing a lattice of word alternatives ::: from which a lattice word error rate (LWER) is calculated. Our results show a LWER of 7.6% when aligning two threshold ::: images and a LWER of 6.8% when aligning five. From the word lattice we commit to one hypothesis by applying the ::: methods of Lund et al. (2011) achieving an improvement over the original OCR output and a 8.41% WER result on this ::: data set.


title: Suction helps in a pinch: Improving underwater manipulation with gentle suction flow

abstract: Pinching is an important capability for mobile robots handling small items or tools. Successful pinching requires force-closure and, in underwater applications, gentle suction flow at the fingertips can dramatically improve the handling of light objects by counteracting the negative effects of water lubrication and enhancing friction. In addition, monitoring the flow gives a measure of suction-engagement and can act as a binary tactile sensor. Although a suction system adds complexity, elastic tubes can double as passive spring elements for desired finger kinematics.


Detecting Fraudulent Advertisements on a Large E-Commerce Platform


Visual Analytics


title: Max-margin deepwalk: discriminative learning of network representation

abstract: DeepWalk is a typical representation learning method that learns low-dimensional representations for vertices in social networks. Similar to other network representation learning (NRL) models, it encodes the network structure into vertex representations and is learnt in unsupervised form. However, the learnt representations usually lack the ability of discrimination when applied to machine learning tasks, such as vertex classification. In this paper, we overcome this challenge by proposing a novel semi-supervised model, max-margin Deep-Walk (MMDW). MMDW is a unified NRL framework that jointly optimizes the max-margin classifier and the aimed social representation learning model. Influenced by the max-margin classifier, the learnt representations not only contain the network structure, but also have the characteristic of discrimination. The visualizations of learnt representations indicate that our model is more discriminative than unsupervised ones, and the experimental results on vertex classification demonstrate that our method achieves a significant improvement than other state-of-the-art methods. The source code can be obtained from https://github.com/thunlp/MMDW.


title: How to interpret LC3 immunoblotting.

abstract: Microtubule-associated protein light chain 3 (LC3) is now widely used to monitor autophagy. One approach is to detect LC3 conversion (LC3-I to LC3-II) by immunoblot analysis because the amount of LC3-II is clearly correlated with the number of autophagosomes. However, LC3-II itself is degraded by autophagy, making interpretation of the results of LC3 immunoblotting problematic. Furthermore, the amount of LC3 at a certain time point does not indicate autophagic flux, and therefore, it is important to measure the amount of LC3-II delivered to lysosomes by comparing LC3-II levels in the presence and absence of lysosomal protease inhibitors. Another problem with this method is that LC3-II tends to be much more sensitive to be detected by immunoblotting than LC3-I. Accordingly, simple comparison of LC3-I and LC3-II, or summation of LC3-I and LC3-II for ratio determinations, may not be appropriate, and rather, the amount of LC3-II can be compared between samples.


title: Development of the NAIST-Hand with Vision-based Tactile Fingertip Sensor

abstract: This paper introduces a multifingered robotic hand eNAIST-Handf and a grip force control by slip margin feedback. The developed prototype finger of the NAIST-hand has a new mechanism by which all 3 motors can be placed inside the palm without using wire-driven mechanisms. A method of grip force control is proposed using an incipient slip estimation. A new tactile sensor is designed to active the proposed control method by the NAIST-Hand. This sensor consists of a transparent semispherical gel, an embedded small camera, and a force sensor in order to implement the direct slip margin estimation. The structure and the principle of sensing are described.


title: Binary Opinion Dynamics with Stubborn Agents

abstract: We study binary opinion dynamics in a social network with stubborn agents who influence others but do not change their opinions. We focus on a generalization of the classical voter model by introducing nodes (stubborn agents) that have a fixed state. We show that the presence of stubborn agents with opposing opinions precludes convergence to consensus; instead, opinions converge in distribution with disagreement and fluctuations. In addition to the first moment of this distribution typically studied in the literature, we study the behavior of the second moment in terms of network properties and the opinions and locations of stubborn agents. We also study the problem of optimal placement of stubborn agents where the location of a fixed number of stubborn agents is chosen to have the maximum impact on the long-run expected opinions of agents.


title: Backpropagation through time: what it does and how to do it

abstract: Basic backpropagation, which is a simple method now being widely used in areas like pattern recognition and fault diagnosis, is reviewed. The basic equations for backpropagation through time, and applications to areas like pattern recognition involving dynamic systems, systems identification, and control are discussed. Further extensions of this method, to deal with systems other than neural networks, systems involving simultaneous equations, or true recurrent networks, and other practical issues arising with the method are described. Pseudocode is provided to clarify the algorithms. The chain rule for ordered derivatives-the theorem which underlies backpropagation-is briefly discussed. The focus is on designing a simpler version of backpropagation which can be translated into computer code and applied directly by neutral network users. >


NetQTM: Node Configuration In Network Setup By Quantum Turing Machine


title: Solution of the closed-loop inverse kinematics algorithm using the Crank-Nicolson method

abstract: The closed-loop inverse kinematics algorithm is a numerical method used to approximate the solution of the inverse kinematics problem of robot manipulators based on the explicit Euler integration, that is a simple numerical integration technique. It is known from the theory of numerical techniques that the Crank-Nicolson method gives results with better convergence properties than the explicit Euler method, however it needs the application of the implicit Euler method, that needs the solution of the differential inverse kinematics problem, which is not known in advance in the robotics problem. An iterative method is proposed to calculate the implicit Euler solution, and the results are used to carry out the numerical integration using the Crank-Nicolson method. Experimental results show that the application of the Crank-Nicolson method yields better convergence for the differential inverse positioning problem of planar nonredundant and redundant manipulators than the application of the explicit Euler method. Both the case of reaching a single end effector point and path tracking are analyzed and the results are compared to the results acquired using the explicit and implicit Euler methods separately; the convergence is faster if the goal is to reach a desired end effector configuration, while the tracking error is smaller in the case of path tracking if the numerical integration is done in the proposed way.


title: Isolated Bidirectional DC-DC Converter for Hybrid Electric Vehicle Application

abstract: Abstract : Hybrid electric vehicles (HEVs) offer many advantages, such as high fuel economy, low emissions, and silent operation. In HEVs, there are two or more different voltage buses for different purposes of the vehicle operation. There are needs of galvanically isolated bidirectional DC-DC converter to link different DC voltage bus and transfer energy back and forth. For example, on of the DC-DC converters convert the high voltage (200-300V) in the main battery to low voltage (~12V) for use in electrical equipment, while the other converts 300V battery voltage and supply the drive motor with 500V. High efficiency, compact size, lightweight, and reliability are all essential requirements for DC-DC converters for electric and hybrid vehicles. This paper introduces a bidirectional, isolated DC-DC converter for medium power applications. A dual fullbridge topology is developed to achieve the power rating. A 1kW prototype of the converter has been built and tested. The experimental results of the converter's steady state operation confirm the simulation analysis. This converter is a first step to understanding the design and build of a medium power DC-DC converter.


title: Efficient unit test case minimization

abstract: Randomized unit test cases can be very effective in detecting defects. In practice, however, failing test cases often comprise long sequences of method calls that are tiresome to reproduce and debug. We present a combination of static slicing and delta debugging that automatically minimizes the sequence of failure-inducing method calls. In a case study on the EiffelBase library, the strategy minimizes failing unit test cases on average by 96%. This approach improves on the state of the art by being far more efficient: in contrast to the approach of Lei and Andrews, who use delta debugging alone, our case study found slicing to be 50 times faster, while providing comparable results. The combination of slicing and delta debugging gives the best results and is 11 times faster.


title: How are Java software developers using the Elipse IDE?

abstract: The Eclipse integrated development environment continues to gain popularity among Java developers. Our usage monitoring approach allows tool builders to sample how developers are using their tools in the wild. The data gathered about tool use can be used to prevent feature bloat and to evolve the environments according to user needs. Information about how developers work in a development environment can also provide a baseline for assessing new software development tools. We hope this report provides a start in defining which in formation to collect and distribute on an on going basis to help improve Eclipse and other similar platforms and tools


title: DeepLayout: A Semantic Segmentation Approach to Page Layout Analysis

abstract: In this paper, we present DeepLayout, a new approach to page layout analysis. Previous work divides the problem into unsupervised segmentation and classification. Instead of a step-wise method, we adopt semantic segmentation which is an end-to-end trainable deep neural network. Our proposed segmentation model takes only document image as input and predicts per pixel saliency maps. For the post-processing part, we use connected component analysis to restore the bounding boxes from the prediction map. The main contribution is that we successfully bring RLSA into our post-processing procedures to specify the boundaries. The experimental results on ICDAR2017 POD competition dataset show that our proposed page layout analysis algorithm achieves good mAP score, outperforms most of other competition participants.


title: AVEC 2016: Depression, Mood, and Emotion Recognition Workshop and Challenge

abstract: The Audio/Visual Emotion Challenge and Workshop (AVEC 2016) “Depression, Mood and Emotion” will be the sixth competition event aimed at comparison of multimedia processing and machine learning methods for automatic audio, visual and physiological depression and emotion analysis, with all participants competing under strictly the same conditions. The goal of the Challenge is to provide a common benchmark test set for multi-modal information processing and to bring together the depression and emotion recognition communities, as well as the audio, video and physiological processing communities, to compare the relative merits of the various approaches to depression and emotion recognition under well-defined and strictly comparable conditions and establish to what extent fusion of the approaches is possible and beneficial. This paper presents the ∗The author is further affiliated with Imperial College London, Department of Computing, London, U.K. †The author is further affiliated with University of Passau, Chair of Complex & Intelligent Systems ‡The author is further affiliated with Twente University, EEMCS, Twente, The Netherlands. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org. AVEC’16 16 October 2016, Amsterdam, NL Copyright is held by the owner/author(s). Publication rights licensed to ACM. ACM 978-1-4503-3119-7/14/11. . . $15.00. http://dx.doi.org/10.1145/2661806.2661807 . challenge guidelines, the common data used, and the performance of the baseline system on the two tasks.


title: Processor Design for Soft Errors: Challenges and State of the Art

abstract: Today, soft errors are one of the major design technology challenges at and beyond the 22nm technology nodes. This article introduces the soft error problem from the perspective of processor design. This article also provides a survey of the existing soft error mitigation methods across different levels of design abstraction involved in processor design, including the device level, the circuit level, the architectural level, and the program level.


title: Generating Haptic Textures with a Vibrotactile Actuator

abstract: Vibrotactile actuation is mainly used to deliver buzzing sensations. But if vibrotactile actuation is tightly coupled to users' actions, it can be used to create much richer haptic experiences. It is not well understood, however, how this coupling should be done or which vibrotactile parameters create which experiences. To investigate how actuation parameters relate to haptic experiences, we built a physical slider with minimal native friction, a vibrotactile actuator and an integrated position sensor. By vibrating the slider as it is moved, we create an experience of texture between the sliding element and its track. We conducted a magnitude estimation experiment to map how granularity, amplitude and timbre relate to the experiences of roughness, adhesiveness, sharpness and bumpiness. We found that amplitude influences the strength of the perceived texture, while variations in granularity and timbre create distinct experiences. Our study underlines the importance of action in haptic perception and suggests strategies for deploying such tightly coupled feedback in everyday devices.


title: Broadband mixing of ${\cal PT}$-symmetric and ${\cal PT}$-broken phases in photonic heterostructures with a one-dimensional loss/gain bilayer

abstract: Combining loss and gain components in one photonic heterostructure opens a new route to efficient manipulation by radiation, transmission, absorption, and scattering of electromagnetic waves. Therefore, loss/gain structures enabling $${\mathscr{P}}{\mathscr{T}}$$ ::: P ::: T ::: -symmetric and $${\mathscr{P}}{\mathscr{T}}$$ ::: P ::: T ::: -broken phases for eigenvalues have extensively been studied in the last decade. In particular, translation from one phase to another, which occurs at the critical point in the two-channel structures with one-dimensional loss/gain components, is often associated with one-way transmission. In this report, broadband mixing of the $${\mathscr{P}}{\mathscr{T}}$$ ::: P ::: T ::: -symmetric and $${\mathscr{P}}{\mathscr{T}}$$ ::: P ::: T ::: -broken phases for eigenvalues is theoretically demonstrated in heterostructures with four channels obtained by combining a one-dimensional loss/gain bilayer and one or two thin polarization-converting components (PCCs). The broadband phase mixing in the four-channel case is expected to yield advanced transmission and absorption regimes. Various configurations are analyzed, which are distinguished in symmetry properties and polarization conversion regime of PCCs. The conditions necessary for phase mixing are discussed. The simplest two-component configurations with broadband mixing are found, as well as the more complex three-component configurations wherein symmetric and broken sets are not yet mixed and appear in the neighbouring frequency ranges. Peculiarities of eigenvalue behaviour are considered for different permittivity ranges of loss/gain medium, i.e., from epsilon-near-zero to high-epsilon regime.


title: Stochastic Regret Minimization via Thompson Sampling

abstract: The Thompson Sampling (TS) policy is a widely implemented algorithm for the stochastic multiarmed bandit (MAB) problem. Given a prior distribution over possible parameter settings of the underlying reward distributions of the arms, at each time instant, the policy plays an arm with probability equal to the probability that this arm has largest mean reward conditioned on the current posterior distributions of the arms. This policy generalizes the celebrated “probability matching” heuristic which has been experimentally and widely observed in human decision making. However, despite its ubiquity, the Thompson Sampling policy is poorly understood. Our goal in this paper is to make progress towards understanding the empirical success of this policy. We proceed using the lens of approximation algorithms and problem definitions from stochastic optimization. We focus on an objective function termed stochastic regret that captures the expected number of times the policy plays an arm that is not the eventual best arm, where the expectation is over the prior distribution. Given such a definition, we show that TS is a 2‐ approximation to the optimal decision policy in two extreme but canonical scenarios. One such scenario is the two-armed bandit problem which is used as a calibration point in all bandit literature. The second scenario is stochastic optimization where the outcome of a random variable is revealed in a single play to a high or low deterministic value. We show that the 2 approximation is tight in both these scenarios. We provide an uniform analysis framework that in theory is capable of proving our conjecture that the TS policy is a 2‐approximation to the optimal decision policy for minimizing stochastic regret, for any prior distribution and any time horizon.


title: Flip to Regular Triangulation and Convex Hull

abstract: Flip is a simple and local operation to transform one triangulation to another. It makes changes only to some neighboring simplices, without considering any attribute or configuration global in nature to the triangulation. Thanks to this characteristic, several flips can be independently applied to different small, non-overlapping regions of one triangulation. Such operation is favored when designing algorithms for data-parallel, massively multithreaded hardware, such as the GPU. However, most existing flip algorithms are designed to be executed sequentially, and usually need some restrictions on the execution order of flips, making them hard to be adapted to parallel computation. In this paper, we present an in depth study of flip algorithms in low dimensions, with the emphasis on the flexibility of their execution order. In particular, we propose a series of provably correct flip algorithms for regular triangulation and convex hull in 2D and 3D, with implementations for both CPUs and GPUs. Our experiment shows that our GPU implementation for constructing these structures from a given point set achieves up to two orders of magnitude of speedup over other popular single-threaded CPU implementation of existing algorithms.


title: Vehicle Velocity Observer Design Using 6-D IMU and Multiple-Observer Approach

abstract: This paper mainly focuses on the accurate estimation of the vehicle velocities of all axes, using the data received from a low-cost 6-D inertial measurement unit. The data include the vehicle linear acceleration and angular rates of all axes. In addition, the observer uses the wheel speed sensors and steering wheel angle information, which are already available on most recent production cars. Utilizing the aforementioned information, based on the combination of a bicycle model and a kinematic model, a multiple-observer system that computes the weighted sum estimation that is dependent on cornering stiffness adaptation is adopted to observe the lateral vehicle velocity, as well as longitudinal and vertical velocities. The stability of each component of the proposed observer is investigated, and a set of assessments to confirm the performance of the entire system is arranged through experiments using a real production sport utility vehicle.


title: Joint user association and green energy allocation in HetNets with hybrid energy sources

abstract: In the heterogeneous networks (HetNets) powered by hybrid energy sources, it is imperative to reduce the total on-grid energy consumption as well as minimize the peak-to-average on-grid energy consumption ratio, since the large peak-to-average on-grid energy consumption ratio will translate into the high operational expenditure (OPEX) for mobile network operators. In this paper, we propose a joint user association and green energy allocation algorithm which aims to lexicographically minimize the on-grid energy consumption in HetNets, where all the base stations (BSs) are assumed to be powered by both the power grid and renewable energy sources. The optimization problem involves both the user association optimization in space dimension, and the green energy allocation in time dimension. The independence nature of this two-dimensional optimization allows us to decompose the problem into two sub-problems. We first formulate the user association optimization in space dimension as a convex optimization problem to minimize total energy consumption via balancing the traffic across different BSs in a certain time slot. We then optimize the green energy allocation across different time slots for an individual BS to lexicographically minimize the on-grid energy consumption. Simulation results indicate the proposed algorithm achieves significant on-grid energy saving, and substantially reduces peak-to-average on-grid energy consumption ratio.


title: Optimal decision making and the anterior cingulate cortex

abstract: Learning the value of options in an uncertain environment is central to optimal decision making. The anterior cingulate cortex (ACC) has been implicated in using reinforcement information to control behavior. Here we demonstrate that the ACC's critical role in reinforcement-guided behavior is neither in detecting nor in correcting errors, but in guiding voluntary choices based on the history of actions and outcomes. ACC lesions did not impair the performance of monkeys (Macaca mulatta) immediately after errors, but made them unable to sustain rewarded responses in a reinforcement-guided choice task and to integrate risk and payoff in a dynamic foraging task. These data suggest that the ACC is essential for learning the value of actions.


title: Providing Smart Agricultural solutions to farmers for better yielding using IoT

abstract: The field of Cloud computing is helping in leaps and bounds to improvise our age old business - Agriculture. Practical applications can be built from the economic consumption of cloud computing devices that can create a whole computing ecosystem, from sensors to tools that observe data from agricultural field images and from human actors on the ground and accurately feed the data into repositories along with their location as GPS co-ordinates. In reality, sensors are now able to detect the position of water sources in a subject that is being investigated. Issues related to farmers are always hampering the course of our evolution. One of the answer to these types of problems is to help the farmers using modernization techniques. This paper proposes an approach combining the advantages of the major characteristics of emerging technologies such as Internet of Things(IoT) and Web Services inorder to construct an efficient approach to handle the enormous data involved in agrarian output. The approach uses the combination of IoT and cloud computing that promotes the fast development of agricultural modernization and helps to realize smart solution for agriculture and efficiently solve the issues related to farmers.


title: Scaling deep learning on GPU and knights landing clusters

abstract: Training neural networks has become a big bottleneck. For example, training ImageNet dataset on one Nvidia K20 GPU needs 21 days. To speed up the training process, the current deep learning systems heavily rely on the hardware accelerators. However, these accelerators have limited on-chip memory compared with CPUs. We use both self-host Intel Knights Landing (KNL) clusters and multi-GPU clusters as our target platforms. From the algorithm aspect, we focus on Elastic Averaging SGD (EASGD) to design algorithms for HPC clusters. We redesign four efficient algorithms for HPC systems to improve EASGD's poor scaling on clusters. Async EASGD, Async MEASGD, and Hogwild EASGD are faster than existing counter-part methods (Async SGD, Async MSGD, and Hogwild SGD) in all comparisons. Sync EASGD achieves 5.3X speedup over original EASGD on the same platform. We achieve 91.5% weak scaling efficiency on 4253 KNL cores, which is higher than the state-of-the-art implementation.


title: Safe mutations for deep and recurrent neural networks through output gradients

abstract: While neuroevolution (evolving neural networks) has been successful across a variety of domains from reinforcement learning, to artificial life, to evolutionary robotics, it is rarely applied to large, deep neural networks. A central reason is that while random mutation generally works in low dimensions, a random perturbation of thousands or millions of weights will likely break existing functionality. This paper proposes a solution: a family of safe mutation (SM) operators that facilitate exploration without dramatically altering network behavior or requiring additional interaction with the environment. The most effective SM variant scales the degree of mutation of each individual weight according to the sensitivity of the network's outputs to that weight, which requires computing the gradient of outputs with respect to the weights (instead of the gradient of error, as in conventional deep learning). This safe mutation through gradients (SM-G) operator dramatically increases the ability of a simple genetic algorithm-based neuroevolution method to find solutions in high-dimensional domains that require deep and/or recurrent neural networks, including domains that require processing raw pixels. By improving our ability to evolve deep neural networks, this new safer approach to mutation expands the scope of domains amenable to neuroevolution.


title: Interest Representation, Enrichment, Dynamics, and Propagation: A Study of the Synergetic Effect of Different User Modeling Dimensions for Personalized Recommendations on Twitter

abstract: Microblogging services such as Twitter have been widely adopted due to the highly social nature of interactions they have facilitated. With the rich information generated by users on these services, user modeling aims to acquire knowledge about a user's interests, which is a fundamental step towards personalization as well as recommendations. To this end, researchers have explored different dimensions such as 1 Interest Representation, 2 Content Enrichment, 3 Temporal Dynamics of user interests, and 4 Interest Propagation using semantic information from a knowledge base such as DBpedia. However, those dimensions of user modeling have largely been studied separately, and there is a lack of research on the synergetic effect of those dimensions for user modeling. In this paper, we address this research gap by investigating 16 different user modeling strategies produced by various combinations of those dimensions. Different user modeling strategies are evaluated in the context of a personalized link recommender system on Twitter. Results show that Interest Representation and Content Enrichment play crucial roles in user modeling, followed by Temporal Dynamics. The user modeling strategy considering Interest Representation, Content Enrichment and Temporal Dynamics provides the best performance among the 16 strategies. On the other hand, Interest Propagation has little effect on user modeling in the case of leveraging a rich Interest Representation or considering Content Enrichment.


title: Sediment transport over an accretional intertidal flat with influences of reclamation, Jiangsu coast, China

abstract: This paper focuses on the hydrodynamic and suspended sediment transport processes in response to sequential reclamations over the intertidal flat, Jiangsu coast, which used to be one of the widest intertidal flats in China. Investigations into the sediment transport patterns reveal that net suspended sediment fluxes increased from 102 kg m?1 on the upper intertidal flat to 103–4 kg m?1 on the lower intertidal flat, per tidal cycle. This variation was caused mainly by the phases of the current velocity, suspended sediment concentration (SSC), local bed geomorphology, inundation patterns, and extreme weather conditions. The longshore component of tidal currents intensifies seaward over the intertidal flat. Extreme weather conditions (e.g. storms) and strong winds were important factors affecting the current velocity structures, the intensity of residual currents, and the SSCs. The SSCs were characterized by several peaks throughout the tidal cycle. These peaks were associated with strong currents, combined wave-current interactions and intense turbulences due to the initial flood surge. Furthermore, the suspended sediment was finer and contained more clay materials during the flood phase of the tidal cycle, than during the ebb. Fine particles were transported within the upper water column by landward residual currents, but coarse particles were transported within the lower water column by seaward residual currents. Overall, residual currents were apt to transport fine-grained sediment landward and coarse-grained sediment seaward. This pattern indicates that the suspended sediment fluxes are highly variable in the water column, even for very shallow tidal environments. The cross-shore component accounted only for a small percentage of the total sediment flux. However, this part of sediment supply dominated accretion over the intertidal flat. The intertidal flat has become narrower and steeper, following sequential reclamations. A negative feedback process existed to maintain the convex profile shape, although the human activities (i.e. reclamation) could have modified the natural process. The surficial sediment tended to become finer on the mid-upper intertidal flat but coarser on the lower intertidal flat following reclamations, in response to a reduction in the tidal currents over the intertidal zone and enhancement of wave action over the lower intertidal flat. Accretion, with high rates, was maintained by landward transport of sediment. In such an accretional system with influences of reclamation, the width or slope of the intertidal flat is adjusted continuously. Before the cease of reclamation, the intertidal flat is unable to achieve an equilibrium shape.


title: An empirical comparison of Big Graph frameworks in the context of network analysis

abstract: Complex networks are heterogeneous relational data sets with nontrivial substructures and statistical properties. They are typically represented as graphs consisting of vertices and edges. The analysis of their intricate structure is relevant to many areas of science and commerce, and data sets may reach sizes that require distributed storage and processing. We describe and compare programming models for distributed computing with a focus on graph algorithms for large-scale complex network analysis. Four frameworks-GraphLab, Apache Giraph, Giraph++ and Apache Flink—are used to implement algorithms for the representative problems connected components, community detection, PageRank and clustering coefficients. The implementations are executed on a computer cluster to evaluate the frameworks’ suitability in practice and to compare their performance to that of the single-machine, shared-memory parallel network analysis package NetworKit. Out of the distributed frameworks, GraphLab and Apache Giraph generally show the best performance. In our experiments a cluster of eight computers running Apache Giraph enables the analysis of a network with ca. 2 billion edges, which is too large for a single machine of the same type. However, for networks that fit into memory of one machine, the performance of the shared-memory parallel implementation is usually far better than the distributed ones. The study provides experimental evidence for selecting the appropriate framework depending on the task and data volume.


title: A game theoretic framework for incentives in P2P systems

abstract: Peer-to-peer (P2P) networks are self-organizing, distributed systems, with no centralized authority or infrastructure. Because of the voluntary participation, the availability of resources in a P2P system can be highly variable and unpredictable. We use ideas from game theory to study the interaction of strategic and rational peers, and propose a differential service-based incentive scheme to improve the system's performance.


title: Is Gratitude a Moral Affect

abstract: Gratitude is conceptualized as a moral affect that is analogous to other moral emotions such as empathy and guilt. Gratitude has 3 functions that can be conceptualized as morally relevant: (a) a moral barometer function (i.e., it is a response to the perception that one has been the beneficiary of another person's moral actions); (b) a moral motive function (i.e., it motivates the grateful person to behave prosocially toward the benefactor and other people); and (c) a moral reinforcer function (i.e., when expressed, it encourages benefactors to behave morally in the future). The personality and social factors that are associated with gratitude are also consistent with a conceptualization of gratitude as an affect that is relevant to people's cognitions and behaviors in the moral domain.


title: Toward Learning Graphical and Causal Process Models

abstract: We describe an approach to learning causal models that leverages temporal information. We posit the existence of a graphical description of a causal process that generates observations through time. We explore assumptions connecting the graphical description with the statistical process and what one can infer about the causal structure of the process under these assumptions.


title: A Compact Circularly Polarized Cross-Shaped Slotted Microstrip Antenna

abstract: A compact cross-shaped slotted microstrip patch antenna is proposed for circularly polarized (CP) radiation. A symmetric, cross shaped slot is embedded along one of the diagonal axes of the square patch for CP radiation and antenna size reduction. The structure is asymmetric (unbalanced) along the diagonal axes. The overall size of the antenna with CP radiation can be reduced by increasing the perimeter of the symmetric cross-shaped slot within the first patch quadrant of the square patch. The performance of the CP radiation is also studied by varying the size and angle variation of the cross-shaped slot. A measured 3-dB axial-ratio (AR) bandwidth of around 6.0 MHz is achieved with the CP cross-shaped slotted microstrip antenna, with an 18.0 MHz 10-dB return-loss bandwidth. The measured boresight gain is more than 3.8 dBic over the operating band, while the overall antenna volume is 0.273λo × 0.273λo × 0.013λo (λο operating wavelength at 910 MHz).


title: Likert scales, levels of measurement and the ''laws'' of statistics

abstract: Reviewers of research reports frequently criticize the choice of statistical methods. While some of these criticisms are well-founded, frequently the use of various parametric methods such as analysis of variance, regression, correlation are faulted because: (a) the sample size is too small, (b) the data may not be normally distributed, or (c) The data are from Likert scales, which are ordinal, so parametric statistics cannot be used. In this paper, I dissect these arguments, and show that many studies, dating back to the 1930s consistently show that parametric statistics are robust with respect to violations of these assumptions. Hence, challenges like those above are unfounded, and parametric methods can be utilized without concern for ''getting the wrong answer''.


title: From smart to smarter cities: Bridging the dimensions of technology and urban planning

abstract: This paper discusses the importance of urban form in smart cities. Development of urban form has been a major concern for urban planners, designers and policy makers. Sprawl is one way of urban development which is not considered good for a better living standard. Compact form arose as an opposed idea to urban sprawl. Form based codes (FBCs) is a tool for urban planning and design that attempts to mitigate the problem of urban sprawl, whereas conventional zoning strengthens this. This paper highlights the importance of physical place in smart city and how FBCs attempt to create better physical place than conventional zoning. Our study shows that FBCs can lead to smart growth which can be a solution to bridge technology and urban planning together in a single platform.


title: Uniform Ferrite-Loaded Open Waveguide Structure With CRLH Response and Its Application to a Novel Backfire-to-Endfire Leaky-Wave Antenna

abstract: A uniform ferrite-loaded open waveguide structure with composite right/left-handed (CRLH) response and its application to a novel backfire-to-endfire leaky-wave antenna are presented. The structure consists of a ferrite-filled rectangular waveguide open to free space at one of its small sides and biased perpendicularly to its large sides. Based on the fact that the leakage from the open side represents only a small perturbation in terms of guidance, an analytical dispersion relation for the approximately equivalent perfect magnetic conductor closed waveguide structure is derived. The unique dispersive properties of this structure, including its inherent CRLH balanced response (gapless nonzero group velocity transition between the left- and right-handed bands) and low-loss characteristics due to off-resonance operation, are extensively described, parametrically studied, and concretized by design guidelines. This theory is validated by both finite-element method and finite integration technique full-wave results and demonstrated experimentally. The leakage of the structure is exploited to build a full-space backfire-to-endfire scanning leaky-wave antenna, which is capable of both fixed-bias frequency scanning and fixed-frequency bias scanning, while being a perfectly uniform structure not requiring any chip tuning components. This feature, and its subsequent design simplicity, represents a fundamental advantage over previous CRLH metmaterial implementations. To the best of our knowledge, the proposed ferrite waveguide is the first and unique uniform structure exhibiting a CRLH response.


title: A Novel high-speed transistorized 8x8 Multiplier using 4-2 Compressors

abstract: In this paper, a novel implementation of 8x8 Multiplier using 4-2 Compressors is presented; which produces quick results, especially for use in Digital Signal Processors and in Microprocessors. This multiplier uses a new partial-product reduction format which consecutively reduces the maximum output delay. The new design of multiplier requires less number of MOSFET's compared to Wallace Tree Multipliers. The 4-2 Compressor used is made from high-speed and low-power XOR-XNOR module and transmission gate based Multiplexer. The delay and power-delay product (PDP) is compared with earlier Wallace and Dadda Multipliers, implemented with 4-2 Compressors and without compressors, and is proven to have minimum delay and PDP. The Simulation results were obtained using HSPICE at 0.18µm standard CMOS technology.


Measuring the Information Content of Financial News


Congenital penile curvature presenting as unconsummated marriage. Repair by 16-dot plication with subjectively reported patient and partner satisfaction


title: Undithering using linear filtering and non-linear diffusion techniques

abstract: Data compression is a method of improving the efficiency of transmission and storage of images. Dithering, as a method of data compression, can be used to convert an 8-bit gray level image into a 1-bit / binary image. Undithering is the process of reconstruction of gray image from binary image obtained from dithering of gray image. In the present paper, I propose a method of undithering using linear filtering followed by anisotropic diffusion which brings the advantage of smoothing and edge enhancement. First-order statistical parameters, second-order statistical parameters, mean-squared error (MSE) between reconstructed image and the original image before dithering, and peak signal to noise ratio (PSNR) are evaluated at each step of diffusion. Results of the experiments show that the reconstructed image is not as sharp as the image before dithering but a large number of gray values are reproduced with reference to those of the original image prior to dithering.


title: ArabicWeb16: A New Crawl for Today's Arabic Web

abstract: Web crawls provide valuable snapshots of the Web which enable a wide variety of research, be it distributional analysis to characterize Web properties or use of language, content analysis in social science, or Information Retrieval (IR) research to develop and evaluate effective search algorithms. While many English-centric Web crawls exist, existing public Arabic Web crawls are quite limited, limiting research and development. To remedy this, we present ArabicWeb16, a new public Web crawl of roughly 150M Arabic Web pages with significant coverage of dialectal Arabic as well as Modern Standard Arabic. For IR researchers, we expect ArabicWeb16 to support various research areas: ad-hoc search, question answering, filtering, cross-dialect search, dialect detection, entity search, blog search, and spam detection. Combined use with a separate Arabic Twitter dataset we are also collecting may provide further value.


title: Autonomous indoor navigation of a stock quadcopter with off-board control

abstract: We present an enhanced autonomous indoor navigation system for a stock quadcopter drone where all navigation commands are derived off-board on a base station. The base station processes the video stream transmitted from a forward-facing camera on the drone to determine the drone's physical disposition and trajectory in building hallways to derive steering commands that are sent to the drone. Off-board processing and the lack of on-board sensors for localizing the drone permits standard mid-range quadcopters to be used and conserves the limited power source on the quadcopter. We introduce improved and new techniques, compared to our prototype system [1], to maintain stable flights, estimate distance to hallway intersections and describe algorithms to stop the drone ahead of time and turn correctly at intersections.


title: Comparative characterization of physicochemical properties and bioactivities of polysaccharides from selected medicinal mushrooms

abstract: Mushroom polysaccharides have been known to possess various pharmacological activities. However, information on their chemical and biological differences between mushrooms remains limited. In this study, we aimed to examine the differences in physicochemical characteristics of polysaccharides prepared from Antrodia cinnamomea (AC-P), Coriolus versicolor (CV-P), Grifola frondosa (GF-P), Ganoderma lucidum (GL-P), and Phellinus linteus (PL-P), followed by evaluating their inhibitory effects on nitric oxide (NO) production in lipopolysaccharide (LPS)-stimulated RAW264.7 macrophages. Results showed that under similar conditions of preparation, the monosaccharide composition of polysaccharides varied between different mushrooms, and glucose was the predominant monosaccharide, followed by galactose and mannose. AC-P and GF-P contained the highest amount of (1,3;1,6)-β-d-glucans. The degree of branching of (1,3;1,6)-β-d-glucans in all polysaccharides ranged from 0.21 to 0.26, with the exception of GF-P (0.38). The molecular weights of different polysaccharides showed diverse distributions; AC-P, CV-P, and GF-P contained two major macromolecular populations ( 200 kDa) and possessed triple-helix conformation, whereas GL-P (10.2 kDa) and PL-P (15.5 kDa) only had a low molecular weight population without triple-helix structure. These polysaccharides showed different inhibitory potency on NO production in LPS-stimulated RAW264.7 cells.


GRUV : Algorithmic Music Generation using Recurrent Neural Networks


title: Toward accurate dynamic time warping in linear time and space

abstract: Dynamic Time Warping (DTW) has a quadratic time and space complexity that limits its use to small time series. In this paper we introduce FastDTW, an approximation of DTW that has a linear time and space complexity. FastDTW uses a multilevel approach that recursively projects a solution from a coarser resolution and refines the projected solution. We prove the linear time and space complexity of FastDTW both theoretically and empirically. We also analyze the accuracy of FastDTW by comparing it to two other types of existing approximate DTW algorithms: constraints (such as Sakoe-Chiba Bands) and abstraction. Our results show a large improvement in accuracy over existing methods.


title: INSIGHT: a Semantic Visual Analytics for Programming Discussion Forums

abstract: This paper presents INSIGHT, a visual analytics web application, designed to induce & inspire programming language learning from discussion forums. The visual analytics, extracts and displays semantic content from ’Stack Exchange’ in a form of bubble chart. The bubbles represent summarized semantic concepts from the forum posts and outlines the concept specificity of each individual post. The discussion forum content are modeled as concepts based on an innovative Topic Facet Modeling algorithm (a probabilistic topic model that assumes all words in single sentence are generated from one topic facet), and aimed to provide better understanding and solicitation of the increasing large volume of discussion content. We hypothesize that by navigating and interacting (browsing, sorting, searching etc.) with the Facets, will enhance learning. A comprehensive system design rationales and preliminary qualitative study are reported in this paper.


title: Mechanisms Linking Social Ties and Support to Physical and Mental Health

abstract: Over the past 30 years investigators have called repeatedly for research on the mechanisms through which social relationships and social support improve physical and psychological well-being, both directly and as stress buffers. I describe seven possible mechanisms: social influence/social comparison, social control, role-based purpose and meaning (mattering), self-esteem, sense of control, belonging and companionship, and perceived support availability. Stress-buffering processes also involve these mechanisms. I argue that there are two broad types of support, emotional sustenance and active coping assistance, and two broad categories of supporters, significant others and experientially similar others, who specialize in supplying different types of support to distressed individuals. Emotionally sustaining behaviors and instrumental aid from significant others and empathy, active coping assistance, and role modeling from similar others should be most efficacious in alleviating the physical and emotional i...


title: Resistive random access memory with high selectivity and ON/OFF ratio amplification sensing

abstract: The suppression of sneak paths in a cross-point RRAM array, one of the most challenging tasks in high density RRAM integration, has been successfully demonstrated by utilizing a Field Assisted Superlinear Threshold (FAST) selector (1, 2). The FAST selector provides a volatile switching behavior at the critical electric field. Excellent selector performance is presented, including high selectivity of > 107, sharp switching slope of 1011. Furthermore, the volatile switch based selector offers various benefits such as small operation voltage overhead in the 1S1R (1 selector 1 resistor) integration, large sensing margin and increased memory ON/OFF ratio.


title: Learning to Read Between the Lines using Bayesian Logic Programs

abstract: Most information extraction (IE) systems identify facts that are explicitly stated in text. However, in natural language, some facts are implicit, and identifying them requires "reading between the lines". Human readers naturally use common sense knowledge to infer such implicit information from the explicitly stated facts. We propose an approach that uses Bayesian Logic Programs (BLPs), a statistical relational model combining firstorder logic and Bayesian networks, to infer additional implicit information from extracted facts. It involves learning uncertain common-sense knowledge (in the form of probabilistic first-order rules) from natural language text by mining a large corpus of automatically extracted facts. These rules are then used to derive additional facts from extracted information using BLP inference. Experimental evaluation on a benchmark data set for machine reading demonstrates the efficacy of our approach.


title: A probabilistic model to resolve diversity-accuracy challenge of recommendation systems

abstract: Recommendation systems have wide-spread applications in both academia and industry. Traditionally, performance of recommendation systems has been measured by their precision. By introducing novelty and diversity as key qualities in recommender systems, recently increasing attention has been focused on this topic. Precision and novelty of recommendation are not in the same direction, and practical systems should make a trade-off between these two quantities. Thus, it is an important feature of a recommender system to make it possible to adjust diversity and accuracy of the recommendations by tuning the model. In this paper, we introduce a probabilistic structure to resolve the diversity---accuracy dilemma in recommender systems. We propose a hybrid model with adjustable level of diversity and precision such that one can perform this by tuning a single parameter. The proposed recommendation model consists of two models: one for maximization of the accuracy and the other one for specification of the recommendation list to tastes of users. Our experiments on two real datasets show the functionality of the model in resolving accuracy---diversity dilemma and outperformance of the model over other classic models. The proposed method could be extensively applied to real commercial systems due to its low computational complexity and significant performance.


title: Active varactor tunable bandpass filter

abstract: A microstrip active varactor tunable bandpass filter is presented. The filter is based upon the negative resistance method using a microstrip combline network. A 1.8-GHz, two-pole device with a bandwidth of 2% was constructed. 0.6 octave tuning with significant improvement in passband performance was achieved compared with its passive counterpart. >


title: Smart city and the applications

abstract: Based on the “digital city”, “smart city” is widely used in daily livelihood, environmental protection, public security, city services and other fields. In this paper, we mainly focus on recent research and the concept of “smart city”, summarizing the relationship between "smart city" and "digital city", putting forward the main content of application systems as well as the importance and difficulty of the construction of “smart city”, and making a brief statement of the influence of developing smart city in China.


title: Continuous 3D scan-matching with a spinning 2D laser

abstract: Scan-matching is a technique that can be used for building accurate maps and estimating vehicle motion by comparing a sequence of point cloud measurements of the environment taken from a moving sensor. One challenge that arises in mapping applications where the sensor motion is fast relative to the measurement time is that scans become locally distorted and difficult to align. This problem is common when using 3D laser range sensors, which typically require more scanning time than their 2D counterparts. Existing 3D mapping solutions either eliminate sensor motion by taking a “stop-and-scan” approach, or attempt to correct the motion in an open-loop fashion using odometric or inertial sensors. We propose a solution to 3D scan-matching in which a continuous 6DOF sensor trajectory is recovered to correct the point cloud alignments, producing locally accurate maps and allowing for a reliable estimate of the vehicle motion. Our method is applied to data collected from a 3D spinning lidar sensor mounted on a skid-steer loader vehicle to produce quality maps of outdoor scenes and estimates of the vehicle trajectory during the mapping sequences.


title: Patterns of folder use and project popularity: a case study of github repositories

abstract: Context: Every software development project uses folders to organize software artifacts. Goal: We would like to understand how folders are used and what ramifications different uses may have. Method: In this paper we study the frequency of folders used by 140k Github projects and use regression analysis to model how folder use is related to project popularity, i.e., the extent of forking. Results: We find that the standard folders, such as document, testing, and examples, are not only among the most frequently used, but their presence in a project is associated with increased chances that a project's code will be forked (i.e., used by others) and an increased number of forks. Conclusions: This preliminary study of folder use suggests opportunities to quantify (and improve) file organization practices based on folder use patterns of large collections of repositories.


title: Bridgeless SEPIC PFC Rectifier With Reduced Components and Conduction Losses

abstract: In this paper, a new bridgeless single-ended primary inductance converter power-factor-correction rectifier is introduced. The proposed circuit provides lower conduction losses with reduced components simultaneously. In conventional PFC converters (continuous-conduction-mode boost converter), a voltage loop and a current loop are required for PFC. In the proposed converter, the control circuit is simplified, and no current loop is required while the converter operates in discontinuous conduction mode. Theoretical analysis and simulation results are provided to explain circuit operation. A prototype of the proposed converter is realized, and the results are presented. The measured efficiency shows 1% improvement in comparison to conventional SEPIC rectifier.


title: Hierarchical Control and Learning for Markov Decision Processes

abstract: This dissertation investigates the use of hierarchy and problem decomposition as a means of solving large, stochastic, sequential decision problems. These problems are framed as Markov decision problems (MDPs). The new technical content of this dissertation begins with a discussion of the concept of temporal abstraction. Temporal abstraction is shown to be equivalent to the transformation of a policy defined over a region of an MDP to an action in a semi-Markov decision problem (SMDP). Several algorithms are presented for performing this transformation efficiently. ::: This dissertation introduces the HAM for generating hierarchical, temporally abstract actions. This method permits the partial specification of abstract actions in a way that corresponds to an abstract plan or strategy. Abstract actions specified as HAMs can be optimally refined for new tasks by solving a reduced SMDP. The formal results show that traditional MDP algorithms can be used to optimally refine HAMs for new tasks. This can be achieved in much less time than it would take to learn a new policy for the task from scratch. ::: HAMs complement some novel decomposition algorithms that are presented in this dissertation. These algorithms work by constructing a cache of policies for different regions of the MDP and then optimally combining the cached solution to produce a global solution that is within provable bounds of the optimal solution. ::: Together, the methods developed in this dissertation provide important tools for producing good policies for large MDPs. Unlike some ad-hoc methods, these methods provide strong formal guarantees. They use prior knowledge in a principled way, and they reduce larger MDPs into smaller ones while maintaining a well-defined relationship between the smaller problem and the larger problem.


title: Longitudinal Stability of the Leadership Scale for Sports

abstract: This study evaluated the invariance properties of the Leadership Scale for Sport in a sample of 219 female netball players over four time points within a 10-week playing season. Support was found for Chelladurai and Saleh's (1980) >hypothesized 5-factor structure of the Leadership Scale for Sport. Furthermore, differential stability and partial invariance was found for the Leadership Scale for Sport when all four time periods were included. Players perceived slight changes in their coach's autocratic behavior and social support over the season; however, the three other leadership dimensions showed larger changes. The motivational aspects of training and instruction and positive feedback behavior were perceived to increase, while democratic behavior simultaneously decreased in the second half of the season. Furthermore, perceptions of leadership within teams showed a high level of homogeneity with the exception of positive feedback behavior.


title: Green Internet of Things for Smart World

abstract: Smart world is envisioned as an era in which objects (e.g., watches, mobile phones, computers, cars, buses, and trains) can automatically and intelligently serve people in a collaborative manner. Paving the way for smart world, Internet of Things (IoT) connects everything in the smart world. Motivated by achieving a sustainable smart world, this paper discusses various technologies and issues regarding green IoT, which further reduces the energy consumption of IoT. Particularly, an overview regarding IoT and green IoT is performed first. Then, the hot green information and communications technologies (ICTs) (e.g., green radio-frequency identification, green wireless sensor network, green cloud computing, green machine to machine, and green data center) enabling green IoT are studied, and general green ICT principles are summarized. Furthermore, the latest developments and future vision about sensor cloud, which is a novel paradigm in green IoT, are reviewed and introduced, respectively. Finally, future research directions and open problems about green IoT are presented. Our work targets to be an enlightening and latest guidance for research with respect to green IoT and smart world.


title: Optical font recognition for multi-font OCR and document processing

abstract: In this paper we present a multi-font OCR system to be employed for document processing, which performs, at the same time, both the character recognition and the font-style detection of the digits belonging to a subset of the existing fonts. The detection of the font-style of the document words can guide a rough automatic classification of documents, and can also be used to improve the character recognition. The system uses the tangent distance as a classification function in a nearest neighbour approach. We have to discriminate among different digits and, for the same character, we have to discriminate among different font-styles. The nearest neighbour approach is always able to recognize the digit, but the performance in font detection is not optimal. To improve the performance of the system, we have used a discriminant model, the TD-Neuron, which is employed to discriminate between two similar classes. Some experimental results and prospective use in document processing applications are presented.


title: Gynecologic pathology on multidetector CT: a pictorial review

abstract: The purpose of this pictorial essay is to review the imaging appearance of the spectrum of gynecologic pathology that may be visualized by multidetector computed tomography (CT). Although ultrasound and magnetic resonance imaging remain the primary imaging modalities for evaluating female patients with suspected obstetric and gynecologic pathology, CT is frequently performed as the initial imaging modality in the evaluation of abdominal and pelvic pain of unknown etiology. Pelvic pain in women due to a gynecologic condition may also mimic numerous other conditions such as appendicitis and diverticulitis, resulting in initial evaluation by CT—particularly in the emergency setting. The radiologist should, therefore, be familiar with the spectrum of gynecologic and obstetric pathology that may be present on a CT evaluation of the abdomen and pelvis regardless of the study indication, particularly because CT is often the most readily available imaging modality in the emergency setting on a 24/7 basis.


title: Mechatronic design and position control of a novel ball and plate system

abstract: The mechatronic design of a novel mechanism for ball and plate system is discussed. A webcam provides the system with feedback of the ball's position. A heuristic technique for image processing is introduced. The kinematics and dynamics of the system are also described. Supervisory fuzzy control and sliding control techniques are proposed to perform position control for the system. Experimental results corroborate the validity of the controllers.


title: Microfluidics and Nanotechnology for Detection of Global Infectious Diseases

abstract: Infectious diseases remain among the world's leading causes of mortality and years of life lost. Signifi- cant attention has been paid to the ''Big Three'' infectious pathogensVhuman immunodeficiency virus (HIV), malaria, and tuberculosis (TB)Vbut other conditions such as Chagas' disease, dengue, Ebola, and typhoid, as well as multipathogen processes such as viral hepatitis, pneumonia, and diarrhea, also have major global impact. Addressing these significant di- sease burdens, which disproportionately impact the world's poorest regions, is a multifaceted grand challenge, requiring new solutions and new technologies. Diagnostics enabled by advances in microfluidics and nanotechnologies can be an im- portant part of the solution. Advantages such as smaller sample size, increased sensitivity, and new multiplexed sensing moda- lities in a point-of-care format can allow rapid dissemination of test results in remote and resource-limited regions. In this re- view, we provide a critical assessment of the state-of-the-art in use of these technologies for detection of HIV, malaria, and TB. In addition to discussing opportunities and future prospects, we also discuss the need for additional governmental and non- governmental funding sources to develop these technologies to their fullest potential, and the need for new business models to enable their commercialization and deployment.


title: Signature segmentation and recognition from scanned documents

abstract: Signature as a query is important for content-based document image retrieval from a scanned document repository. This paper presents a two-stage approach towards automatic signature segmentation and recognition from scanned document images. In the first stage, signature blocks are segmented from the document using word-wise component extraction and classification. Gradient based features are extracted from each component at the word level to perform the classification task. In the 2nd stage, SIFT (Scale-Invariant Feature Transform) descriptors and Spatial Pyramid Matching (SPM)-based approaches are used for signature recognition. Support Vector Machines (SVMs) are employed as the classifier for both levels in this experiment. The experiments are performed on the publicly available “Tobacco-800” and GPDS [1] datasets and the results obtained from the experiments are promising.


CAGAN: Consistent Adversarial Training Enhanced GANs


SentiGAN: Generating Sentimental Texts via Mixture Adversarial Networks


title: Feasibility and safety of shared EEG/EOG and vision-guided autonomous whole-arm exoskeleton control to perform activities of daily living

abstract: Arm and finger paralysis, e.g. due to brain stem stroke, often results in the inability to perform activities of daily living (ADLs) such as eating and drinking. Recently, it was shown that a hybrid electroencephalography/electrooculography (EEG/EOG) brain/neural hand exoskeleton can restore hand function to quadriplegics, but it was unknown whether such control paradigm can be also used for fluent, reliable and safe operation of a semi-autonomous whole-arm exoskeleton restoring ADLs. To test this, seven abled-bodied participants (seven right-handed males, mean age 30 ± 8 years) were instructed to use an EEG/EOG-controlled whole-arm exoskeleton attached to their right arm to perform a drinking task comprising multiple sub-tasks (reaching, grasping, drinking, moving back and releasing a cup). Fluent and reliable control was defined as average ‘time to initialize’ (TTI) execution of each sub-task below 3 s with successful initializations of at least 75% of sub-tasks within 5 s. During use of the system, no undesired side effects were reported. All participants were able to fluently and reliably control the vision-guided autonomous whole-arm exoskeleton (average TTI 2.12 ± 0.78 s across modalities with 75% successful initializations reached at 1.9 s for EOG and 4.1 s for EEG control) paving the way for restoring ADLs in severe arm and hand paralysis.


title: Integrating Algorithmic Planning and Deep Learning for Partially Observable Navigation

abstract: We propose to take a novel approach to robot system design where each building block of a larger system is represented as a differentiable program, i.e. a deep neural network. This representation allows for integrating algorithmic planning and deep learning in a principled manner, and thus combine the benefits of model-free and model-based methods. We apply the proposed approach to a challenging partially observable robot navigation task. The robot must navigate to a goal in a previously unseen 3-D environment without knowing its initial location, and instead relying on a 2-D floor map and visual observations from an onboard camera. We introduce the Navigation Networks (NavNets) that encode state estimation, planning and acting in a single, end-to-end trainable recurrent neural network. In preliminary simulation experiments we successfully trained navigation networks to solve the challenging partially observable navigation task.


title: Preserving Model Privacy for Machine Learning in Distributed Systems

abstract: Machine Learning based data classification is a widely used data mining technique. By learning massive data collected from the real world, data classification helps learners discover hidden data patterns. These hidden data patterns are represented by the learned model in different machine learning schemes. Based on such models, a user can classify whether the new incoming data belongs to an existing class; or, multiple entities may test the similarity of their datasets. However, due to data locality and privacy concerns, it is infeasible for large-scale distributed systems to share each individual’s datasets for classifying or testing. On the one hand, the learned model is an entity’s private asset and may leak private information, which should be well protected from all other non-collaborative entities. On the other hand, the new incoming data may contain sensitive information which cannot be disclosed directly for classification. To address the above privacy issues, we propose an approach to preserve the model privacy of the data classification and similarity evaluation for distributed systems. With our scheme, neither new data nor learned models are directly revealed during the classification and similarity evaluation procedures. Based on extensive real-world experiments, we have evaluated the privacy preservation, feasibility, and efficiency of the proposed scheme.


title: Hierarchical motion history images for recognizing human motion

abstract: There has been increasing interest in computer analysis and recognition of human motion. Previously we presented an efficient real-time approach for representing human motion using a compact "motion history image" (MHI). Recognition was achieved by statistically matching moment-based features. To address previous problems related to global analysis and limited recognition, we present a hierarchical extension to the original MHI framework to compute dense (local) motion flow directly from the MHI. A hierarchical partitioning of motions by speed in an MHI pyramid enables efficient calculation of image motions using fixed-size gradient operators. To characterize the resulting motion field, a polar histogram of motion orientations is described. The hierarchical MHI approach remains a computationally inexpensive method for analysis of human motions.


A white paper on the benefits of chipkill-correct ecc for pc server main memory


title: Collective Transport of Complex Objects by Simple Robots: Theory and Experiments

abstract: Ants show an incredible ability to collectively transport complex irregular-shaped objects with seemingly simple coordination. Achieving similarly effective collective transport with robots has potential applications in many settings, from agriculture to construction to disaster relief. In this paper we investigate a simple decentralized strategy for collective transport in which each agent acts independently without explicit coordination. Using a physics-based model, we prove that this strategy is guaranteed to successfully transport a complex object to a target location, even though each agent only knows the target direction and does not know the object shape, weight, its own position, or the position and number of other agents. Using two robot hardware platforms, and a wide variety of complex objects, we validate the strategy through extensive experiments. Finally, we present a set of experiments to demonstrate the versatility of the simple strategy, including transport by 100 robots, transport of an actively moving object, adaptation to change in goal location, and dealing with partially observable goals.


title: Model learning for robot control: a survey

abstract: Models are among the most essential tools in robotics, such as kinematics and dynamics models of the robot’s own body and controllable external objects. It is widely believed that intelligent mammals also rely on internal models in order to generate their actions. However, while classical robotics relies on manually generated models that are based on human insights into physics, future autonomous, cognitive robots need to be able to automatically generate models that are based on information which is extracted from the data streams accessible to the robot. In this paper, we survey the progress in model learning with a strong focus on robot control on a kinematic as well as dynamical level. Here, a model describes essential information about the behavior of the environment and the influence of an agent on this environment. In the context of model-based learning control, we view the model from three different perspectives. First, we need to study the different possible model learning architectures for robotics. Second, we discuss what kind of problems these architecture and the domain of robotics imply for the applicable learning methods. From this discussion, we deduce future directions of real-time learning algorithms. Third, we show where these scenarios have been used successfully in several case studies.


title: On the Evaluation of Dialogue Systems with Next Utterance Classification

abstract: An open challenge in constructing dialogue systems is developing methods for automatically learning dialogue strategies from large amounts of unlabelled data. Recent work has proposed Next-Utterance-Classification (NUC) as a surrogate task for building dialogue systems from text data. In this paper we investigate the performance of humans on this task to validate the relevance of NUC as a method of evaluation. Our results show three main findings: (1) humans are able to correctly classify responses at a rate much better than chance, thus confirming that the task is feasible, (2) human performance levels vary across task domains (we consider 3 datasets) and expertise levels (novice vs experts), thus showing that a range of performance is possible on this type of task, (3) automated dialogue systems built using state-of-the-art machine learning methods have similar performance to the human novices, but worse than the experts, thus confirming the utility of this class of tasks for driving further research in automated dialogue systems.


title: Survey of Wireless Communications Applications in the Railway Industry

abstract: Advances in information and communications technology have enabled the adoption of wireless communication techniques in all sectors for the transmission of information in all forms between any two points. Wireless communications and distributed computing have promoted the development of vehicle- monitoring systems to reduce the maintenance and inspection requirements of railway systems while maintaining safety and reliability. This paper surveys existing wireless techniques used in the railway industry for both communications and signalling purposes. Finally we present our work in progress on low-cost, low-power wireless sensor networking architecture to monitor the health of railway wagons attached to a moving locomotive.


title: Parsing to Programs: A Framework for Situated QA

abstract: This paper introduces Parsing to Programs, a framework that combines ideas from parsing and probabilistic programming for situated question answering. As a case study, we build a system that solves pre-university level Newtonian physics questions. Our approach represents domain knowledge of Newtonian physics as programs. When presented with a novel question, the system learns a formal representation of the question by combining interpretations from the question text and any associated diagram. Finally, the system uses this formal representation to solve the questions using the domain knowledge. We collect a new dataset of Newtonian physics questions from a number of textbooks and use it to train our system. The system achieves near human performance on held-out textbook questions and section 1 of AP Physics C mechanics - both on practice questions as well as on freely available actual exams held in 1998 and 2012.


title: An efficient incremental nearest neighbor algorithm for processing k -nearest neighbor queries with visal and semantic predicates in multimedia information retrieval system

abstract: Recently, advanced multimedia applications, such as geographic information system, and content-based image/video retrieval system, require the efficient processing of k-nearest neighbor queries with semantic predicates as well as visual predicates. In this paper, we propose an integrated index structure, so-called SPY-TEC+, that provides an efficient method for indexing visual and semantic feature information at the same time using the SPY-TEC and the signature file. We also propose an efficient incremental nearest neighbor algorithm for processing the k-nearest neighbor queries with visual and semantic predicates on the SPY-TEC+.