title: Improving Unsupervised Word-by-Word Translation with Language Model and Denoising Autoencoder

abstract: Unsupervised learning of cross-lingual word embedding offers elegant matching of words across languages, but has fundamental limitations in translating sentences. In this paper, we propose simple yet effective methods to improve word-by-word translation of cross-lingual embeddings, using only monolingual corpora but without any back-translation. We integrate a language model for context-aware search, and use a novel denoising autoencoder to handle reordering. Our system surpasses state-of-the-art unsupervised neural translation systems without costly iterative training. We also analyze the effect of vocabulary size and denoising type on the translation performance, which provides better understanding of learning the cross-lingual word embedding and its usage in translation.


title: Analysis of Voltage Doubler Behavior of 2.45-GHz Voltage Doubler-Type Rectenna

abstract: This paper describes the voltage doubler (VD) behavior of a 2.45-GHz VD-type rectenna. An important feature of the developed VD-type rectenna is the absence of the charge pump capacitor that is present in conventional VDs. Based on the circuit simulation and measurement results, the developed VD-type rectenna shows drastic improvement to the radio frequency-to-direct current (RF–dc) conversion efficiency in comparison with a half-wave rectifier (HWR)-type rectenna at output loads exceeding $100~\Omega $ . From the simulation, both the VD- and HWR-type rectennas show VD behavior, even without the charge pump capacitor. This behavior was found to be attributable to the electric charge stored in the junction and package capacitances of the Schottky barrier diode in the rectennas. Additionally, it was found that the improvement of the RF–dc conversion efficiency is caused by the voltage conservation in the series diode. This improvement is dependent on the microwave period and the electric discharge time constant. When the microwave period is comparable with the electric discharge time constant, the VD-type rectenna conserves a dc voltage even when the input microwave voltage is negative. These phenomena were observed by analyzing the voltage and current waveforms of the rectenna equivalent circuit obtained via circuit simulation.


title: Comprehension-Guided Referring Expressions

abstract: We consider generation and comprehension of natural language referring expression for objects in an image. Unlike generic image captioning which lacks natural standard evaluation criteria, quality of a referring expression may be measured by the receivers ability to correctly infer which object is being described. Following this intuition, we propose two approaches to utilize models trained for comprehension task to generate better expressions. First, we use a comprehension module trained on human-generated expressions, as a critic of referring expression generator. The comprehension module serves as a differentiable proxy of human evaluation, providing training signal to the generation module. Second, we use the comprehension model in a generate-and-rerank pipeline, which chooses from candidate expressions generated by a model according to their performance on the comprehension task. We show that both approaches lead to improved referring expression generation on multiple benchmark datasets.


title: Multi-turn QA : A RNN Contextual Approach to Intent Classification for Goal-oriented Systems

abstract: QA systems offer a human friendly interface to navigate through knowledge, which can range from encyclopedic to domain-specific. Generally, a QA system is designed to provide an answer to a specific question once (so-called single turn) and state-of-the-art systems reach nowadays robust performance in such a scenario. However, most of the interactions with QA systems are based on multiple handshakes of question/answer pairs, where the human being refines the questions further, while the system can collect the necessary information and generate a compelling final answer through multiple turns. In this paper, we investigate and experiment a multi-turn QA system that is suited to work given a particular domain of knowledge and configurable goals. Our approach models the entire dialogue as a sequence of turns, i.e. questions and answers, using a Recurrent Neural Network which is firstly trained to understand natural language, classifying entities and intents using prior knowledge of domain-specific interactions, and provide answers according to the domain used as background knowledge. We have compared our approach with state-of-the-art sequence-based intent classification using a well-known and standardized gold standard observing an increase of 17.16% of F1. Results show the robustness of the approach and the competitive results motivate the adoption in multi-turn QA scenarios.


title: Downstream changes in the transport and storage of sediment-associated contaminants (P, Cr and PCBs) in agricultural and industrialized drainage basins

abstract: Samples of suspended, floodplain and channel bed sediment have been used to examine downstream changes in sediment-associated contaminant transport and storage in contrasting rivers in Yorkshire, UK. The concentrations of phosphorus, chromium and selected PCBs associated with sediment in the River Aire and its main tributary, the River Calder, which drain an urbanized and industrialized catchment, are considerably higher than those in the relatively unpolluted River Swale, which drains an agricultural catchment. Concentrations of sediment-associated contaminants in the Aire/Calder system increase downstream, reflecting the location of urban and industrial areas in the middle and lower reaches, and the location of point source inputs, such as sewage treatment works. The contaminant concentrations associated with floodplain and channel bed sediment in the Rivers Aire and Calder are high, particularly in the lower reaches. This, combined with measurements of sediment storage on the floodplain and channel bed, indicate that significant storage of sediment-associated contaminants occurs in the Rivers Aire and Calder.


title: The Ship Movement Trajectory Prediction Algorithm Using Navigational Data Fusion

abstract: It is essential for the marine navigator conducting maneuvers of his ship at sea to know future positions of himself and target ships in a specific time span to effectively solve collision situations. This article presents an algorithm of ship movement trajectory prediction, which, through data fusion, takes into account measurements of the ship’s current position from a number of doubled autonomous devices. This increases the reliability and accuracy of prediction. The algorithm has been implemented in NAVDEC, a navigation decision support system and practically used on board ships.


title: Network A/B Testing: From Sampling to Estimation

abstract: A/B testing, also known as bucket testing, split testing, or controlled experiment, is a standard way to evaluate user engagement or satisfaction from a new service, feature, or product. It is widely used in online websites, including social network sites such as Facebook, LinkedIn, and Twitter to make data-driven decisions. The goal of A/B testing is to estimate the treatment effect of a new change, which becomes intricate when users are interacting, i.e., the treatment effect of a user may spill over to other users via underlying social connections.When conducting these online controlled experiments, it is a common practice to make the Stable Unit Treatment Value Assumption (SUTVA) that each individual's response is affected by their own treatment only. Though this assumption simplifies the estimation of treatment effect, it does not hold when network interference is present, and may even lead to wrong conclusion. In this paper, we study the problem of network A/B testing in real networks, which have substantially different characteristics from the simulated random networks studied in previous works. We first examine the existence of network effect in a recent online experiment conducted at LinkedIn; Secondly, we propose an efficient and effective estimator for Average Treatment Effect (ATE) considering the interference between users in real online experiments; Finally, we apply our method in both simulations and a real world online experiment. The simulation results show that our estimator achieves better performance with respect to both bias and variance reduction. The real world online experiment not only demonstrates that large-scale network A/B test is feasible but also further validates many of our observations in the simulation studies.


title: Unsupervised/Semi-supervised Deep Learning for Low-dose CT Enhancement

abstract: Recently, deep learning(DL) methods have been proposed for the low-dose computed tomography(LdCT) enhancement, and obtain good trade-off between computational efficiency and image quality. Most of them need large number of pre-collected ground-truth/high-dose sinograms with less noise, and train the network in a supervised end-to-end manner. This may bring major limitations on these methods because the number of such low-dose/high-dose training sinogram pairs would affect the network's capability and sometimes the ground-truth sinograms are hard to be obtained in large scale. Since large number of low-dose ones are relatively easy to obtain, it should be critical to make these sources play roles in network training in an unsupervised learning manner. To address this issue, we propose an unsupervised DL method for LdCT enhancement that incorporates unlabeled LdCT sinograms directly into the network training. The proposed method effectively considers the structure characteristics and noise distribution in the measured LdCT sinogram, and then learns the proper gradient of the LdCT sinogram in a pure unsupervised manner. Similar to the labeled ground-truth, the gradient information in an unlabeled LdCT sinogram can be used for sufficient network training. The experiments on the patient data show effectiveness of the proposed method.


Authorship Attribution with Neural Networks and Multiple Features: Notebook for PAN at CLEF 2018


title: Blockchain-based secure firmware update for embedded devices in an Internet of Things environment

abstract: Embedded devices are going to be used extremely in Internet of Things (IoT) environments. The small and tiny IoT devices will operate and communicate each other without involvement of users, while their operations must be correct and protected against various attacks. In this paper, we focus on a secure firmware update issue, which is a fundamental security challenge for the embedded devices in an IoT environment. A new firmware update scheme that utilizes a blockchain technology is proposed to securely check a firmware version, validate the correctness of firmware, and download the latest firmware for the embedded devices. In the proposed scheme, an embedded device requests its firmware update to nodes in a blockchain network and gets a response to determine whether its firmware is up-to-date or not. If not latest, the embedded device downloads the latest firmware from a peer-to-peer firmware sharing network of the nodes. Even in the case that the version of the firmware is up-to-date, its integrity, i.e., correctness of firmware, is checked. The proposed scheme guarantees that the embedded device's firmware is up-to-date while not tampered. Attacks targeting known vulnerabilities on firmware of embedded devices are thus mitigated.


title: Beyond the Words: Predicting User Personality from Heterogeneous Information

abstract: An incisive understanding of user personality is not only essential to many scientific disciplines, but also has a profound business impact on practical applications such as digital marketing, personalized recommendation, mental diagnosis, and human resources management. Previous studies have demonstrated that language usage in social media is effective in personality prediction. However, except for single language features, a less researched direction is how to leverage the heterogeneous information on social media to have a better understanding of user personality. In this paper, we propose a Heterogeneous Information Ensemble framework, called HIE, to predict users' personality traits by integrating heterogeneous information including self-language usage, avatar, emoticon, and responsive patterns. In our framework, to improve the performance of personality prediction, we have designed different strategies extracting semantic representations to fully leverage heterogeneous information on social media. We evaluate our methods with extensive experiments based on a real-world data covering both personality survey results and social media usage from thousands of volunteers. The results reveal that our approaches significantly outperform several widely adopted state-of-the-art baseline methods. To figure out the utility of HIE in a real-world interactive setting, we also present DiPsy, a personalized chatbot to predict user personality through heterogeneous information in digital traces and conversation logs.


title: Nonlocality-Reinforced Convolutional Neural Networks for Image Denoising

abstract: We introduce a paradigm for nonlocal sparsity reinforced deep convolutional neural network denoising. It is a combination of a local multiscale denoising by a convolutional neural network (CNN) based denoiser and a nonlocal denoising based on a nonlocal filter (NLF) exploiting the mutual similarities between groups of patches. CNN models are leveraged with noise levels that progressively decrease at every iteration of our framework, while their output is regularized by a nonlocal prior implicit within the NLF. Unlike complicated neural networks that embed the nonlocality prior within the layers of the network, our framework is modular, it uses standard pre-trained CNNs together with standard nonlocal filters. An instance of the proposed framework, called NN3D, is evaluated over large grayscale image datasets showing state-of-the-art performance.


title: Faster R-CNN-Based Glomerular Detection in Multistained Human Whole Slide Images

abstract: The detection of objects of interest in high-resolution digital pathological images is a key part of diagnosis and is a labor-intensive task for pathologists. In this paper, we describe a Faster R-CNN-based approach for the detection of glomeruli in multistained whole slide images (WSIs) of human renal tissue sections. Faster R-CNN is a state-of-the-art general object detection method based on a convolutional neural network, which simultaneously proposes object bounds and objectness scores at each point in an image. The method takes an image obtained from a WSI with a sliding window and classifies and localizes every glomerulus in the image by drawing the bounding boxes. We configured Faster R-CNN with a pretrained Inception-ResNet model and retrained it to be adapted to our task, then evaluated it based on a large dataset consisting of more than 33,000 annotated glomeruli obtained from 800 WSIs. The results showed the approach produces comparable or higher than average F-measures with different stains compared to other recently published approaches. This approach could have practical application in hospitals and laboratories for the quantitative analysis of glomeruli in WSIs and, potentially, lead to a better understanding of chronic glomerulonephritis.


title: Fault Monitoring of Wind Turbine Generator Brushes:

abstract: Components of wind turbines are subjected to asymmetric loads caused by variable wind conditions. Carbon brushes are critical components of the wind turbine generator. Adequately maintaining and detecting abnormalities in the carbon brushes early is essential for proper turbine performance. In this paper, data-mining algorithms are applied for early prediction of carbon brush faults. Predicting generator brush faults early enables timely maintenance or replacement of brushes. The results discussed in this paper are based on analyzing generator brush faults that occurred on 27 wind turbines. The datasets used to analyze faults were collected from the supervisory control and data acquisition (SCADA) systems installed at the wind turbines. Twenty-four data-mining models are constructed to predict faults up to 12 h before the actual fault occurs. To increase the prediction accuracy of the models discussed, a data balancing approach is used. Four data-mining algorithms were studied to evaluate the quality of the models for predicting generator brush faults. Among the selected data-mining algorithms, the boosting tree algorithm provided the best prediction results. Research limitations attributed to the available datasets are discussed. [DOI: 10.1115/1.4005624]


title: Distributed Double Spending Prevention

abstract: We study the problem of preventing double spending in electronic payment schemes in a distributed fashion. This problem occurs, for instance, when the spending of electronic coins needs to be controlled by a large collection of nodes (e.g., in a peer-to-peer (P2P) system) instead of one central bank. Contrary to the commonly held belief that this is fundamentally impossible, we propose several solutions that do achieve a reasonable level of double spending prevention, and analyse their efficiency under varying assumptions.


title: Mathematical description of PCB-adapted litz wire geometry for automated layout generation of WPT coils

abstract: PCB technology provides an alternative to conventional Wireless Power Transfer (WPT) coils made of wound copper wire. The most common wires for conventional coils are either a single wire or a multistranded cable conforming a litz wire structure. On the other hand, the implementation on printed board is suitable for medium-low power applications, presenting advantages as low cost, high repetibility and compact design. Moreover, for reduced losses, the litz wire structure can be adapted to a PCB implementation. In this paper, a mathematical description of the PCB litz structure is presented together with a method to automatically generate PCB layouts of custom coils. Following this method, several prototypes were built and tested. The experimental characterization of the samples shows that coils with PCB litz structure present reduced losses.


title: Spiking neural networks , an introduction

abstract: Biological neurons use short and sudden increases in voltage to send information. These signals are more commonly known as action potentials, spikes or pulses. Recent neurological research has shown that neurons encode information in the timing of single spikes, and not only just in their average firing frequency. This paper gives an introduction to spiking neural networks, some biological background, and will present two models of spiking neurons that employ pulse coding. Networks of spiking neurons are more powerful than their non-spiking predecessors as they can encode temporal information in their signals, but therefore do also need different and biologically more plausible rules for synaptic plasticity.


title: Functional connectivity in the resting brain: A network analysis of the default mode hypothesis

abstract: Functional imaging studies have shown that certain brain regions, including posterior cingulate cortex (PCC) and ventral anterior cingulate cortex (vACC), consistently show greater activity during resting states than during cognitive tasks. This finding led to the hypothesis that these regions constitute a network supporting a default mode of brain function. In this study, we investigate three questions pertaining to this hypothesis: Does such a resting-state network exist in the human brain? Is it modulated during simple sensory processing? How is it modulated during cognitive processing? To address these questions, we defined PCC and vACC regions that showed decreased activity during a cognitive (working memory) task, then examined their functional connectivity during rest. PCC was strongly coupled with vACC and several other brain regions implicated in the default mode network. Next, we examined the functional connectivity of PCC and vACC during a visual processing task and show that the resultant connectivity maps are virtually identical to those obtained during rest. Last, we defined three lateral prefrontal regions showing increased activity during the cognitive task and examined their resting-state connectivity. We report significant inverse correlations among all three lateral prefrontal regions and PCC, suggesting a mechanism for attenuation of default mode network activity during cognitive processing. This study constitutes, to our knowledge, the first resting-state connectivity analysis of the default mode and provides the most compelling evidence to date for the existence of a cohesive default mode network. Our findings also provide insight into how this network is modulated by task demands and what functions it might subserve.


Comment on: “Association between Atopic Dermatitis and Autism Spectrum Disorders: A Systematic Review”


title: Semi-automatic selection of primary studies in systematic literature reviews: is it reasonable?

abstract: The systematic review (SR) is a methodology used to find and aggregate all relevant existing evidence about a specific research question of interest. One of the activities associated with the SR process is the selection of primary studies, which is a time consuming manual task. The quality of primary study selection impacts the overall quality of SR. The goal of this paper is to propose a strategy named "Score Citation Automatic Selection" (SCAS), to automate part of the primary study selection activity. The SCAS strategy combines two different features, content and citation relationships between the studies, to make the selection activity as automated as possible. Aiming to evaluate the feasibility of our strategy, we conducted an exploratory case study to compare the accuracy of selecting primary studies manually and using the SCAS strategy. The case study shows that for three SRs published in the literature and previously conducted in a manual implementation, the average effort reduction was 58.2 % when applying the SCAS strategy to automate part of the initial selection of primary studies, and the percentage error was 12.98 %. Our case study provided confidence in our strategy, and suggested that it can reduce the effort required to select the primary studies without adversely affecting the overall results of SR.


title: Illuminant estimation and detection using near-infrared

abstract: Digital camera sensors are sensitive to wavelengths ranging from the ultraviolet (200-400nm) to the near-infrared (700-100nm) bands. This range is, however, reduced because the aim of photographic cameras is to capture and reproduce the visible spectrum (400-700nm) only. Ultraviolet radiation is filtered out by the optical elements of the camera, while a specifically designed "hot-mirror" is placed in front of the sensor to prevent near-infrared contamination of the visible image. We propose that near-infrared data can actually prove remarkably useful in colour constancy, to estimate the incident illumination as well as providing to detect the location of different illuminants in a multiply lit scene. Looking at common illuminants spectral power distribution show that very strong differences exist between the near-infrared and visible bands, e.g., incandescent illumination peaks in the near-infrared while fluorescent sources are mostly confined to the visible band. We show that illuminants can be estimated by simply looking at the ratios of two images: a standard RGB image and a near-infrared only image. As the differences between illuminants are amplified in the near-infrared, this estimation proves to be more reliable than using only the visible band. Furthermore, in most multiple illumination situations, one of the light will be predominantly near-infrared emitting (e.g., flash, incandescent) while the other will be mostly visible emitting (e.g., fluorescent, skylight). Using near-infrared and RGB image ratios allow us to accurately pinpoint the location of diverse illuminant and recover a lighting map.


title: A robust data scaling algorithm to improve classification accuracies in biomedical data

abstract: BackgroundMachine learning models have been adapted in biomedical research and practice for knowledge discovery and decision support. While mainstream biomedical informatics research focuses on developing more accurate models, the importance of data preprocessing draws less attention. We propose the Generalized Logistic (GL) algorithm that scales data uniformly to an appropriate interval by learning a generalized logistic function to fit the empirical cumulative distribution function of the data. The GL algorithm is simple yet effective; it is intrinsically robust to outliers, so it is particularly suitable for diagnostic/classification models in clinical/medical applications where the number of samples is usually small; it scales the data in a nonlinear fashion, which leads to potential improvement in accuracy.ResultsTo evaluate the effectiveness of the proposed algorithm, we conducted experiments on 16 binary classification tasks with different variable types and cover a wide range of applications. The resultant performance in terms of area under the receiver operation characteristic curve (AUROC) and percentage of correct classification showed that models learned using data scaled by the GL algorithm outperform the ones using data scaled by the Min-max and the Z-score algorithm, which are the most commonly used data scaling algorithms.ConclusionThe proposed GL algorithm is simple and effective. It is robust to outliers, so no additional denoising or outlier detection step is needed in data preprocessing. Empirical results also show models learned from data scaled by the GL algorithm have higher accuracy compared to the commonly used data scaling algorithms.


title: Siamese neural network based gait recognition for human identification

abstract: As the remarkable characteristics of remote accessed, robust and security, gait recognition has gained significant attention in the biometrics based human identification task. However, the existed methods mainly employ the handcrafted gait features, which cannot well handle the indistinctive inter-class differences and large intra-class variations of human gait in real-world situation. In this paper, we have developed a Siamese neural network based gait recognition framework to automatically extract robust and discriminative gait features for human identification. Different from conventional deep neural network, the Siamese network can employ distance metric learning to drive the similarity metric to be small for pairs of gait from the same person, and large for pairs from different persons. In particular, to further learn effective model with limited training data, we composite the gait energy images instead of raw sequence of gaits. Consequently, the experiments on the world's largest gait database show our framework impressively outperforms state-of-the-arts.


title: An extended model of natural logic

abstract: We propose a model of natural language inference which identifies valid inferences by their lexical and syntactic features, without full semantic interpretation. We extend past work in natural logic, which has focused on semantic containment and monotonicity, by incorporating both semantic exclusion and implicativity. Our model decomposes an inference problem into a sequence of atomic edits linking premise to hypothesis; predicts a lexical semantic relation for each edit; propagates these relations upward through a semantic composition tree according to properties of intermediate nodes; and joins the resulting semantic relations across the edit sequence. A computational implementation of the model achieves 70% accuracy and 89% precision on the FraCaS test suite. Moreover, including this model as a component in an existing system yields significant performance gains on the Recognizing Textual Entailment challenge.


Improved Interpretability of the Unified Distance Matrix with Connected Components


title: An economical class of digital filters for decimation and interpolation

abstract: A class of digital linear phase finite impulse response (FIR) filters for decimation (sampling rate decrease) and interpolation (sampling rate increase) are presented. They require no multipliers and use limited storage making them an economical alternative to conventional implementations for certain applications. A digital filter in this class consists of cascaded ideal integrator stages operating at a high sampling rate and an equal number of comb stages operating at a low sampling rate. Together, a single integrator-comb pair produces a uniform FIR. The number of cascaded integrator-comb pairs is chosen to meet design requirements for aliasing or imaging error. Design procedures and examples are given for both decimation and interpolation filters with the emphasis on frequency response and register width.


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title: ALICE Chatbot: Trials and Outputs

abstract: A chatbot is a conversational agent that interacts with users using natural language. Multi chatbots are available to serve in different domains. However, the knowledge base of chatbots is hand coded in its brain. This paper presents an overview of ALICE chatbot, its AIML format, and our experiments to generate different prototypes of ALICE automatically based on a corpus approach. A description of developed software which converts readable text (corpus) into AIML format is presented alongside with describing the different corpora we used. Our trials revealed the possibility of generating useful prototypes without the need for sophisticated natural language processing or complex machine learning techniques. These prototypes were used as tools to practice different languages, to visualize corpus, and to provide answers for questions.


title: Characterizing short-term light dynamics in forested headwater streams

abstract: AbstractLight controls local- and reach-scale primary production in forested headwater streams. In terrestrial ecosystems, short periods of elevated light beneath canopies (sunflecks) are instrumental in maintaining understory autotrophic production, but sunflecks are unexplored and uncharacterized in stream systems. We described short-term light dynamics in 4 forested headwater streams in the western Cascade Mountains of Oregon. We quantified the prevalence and characteristics of sunflecks, compared the timing of light exposure at stream sites to a sensor in full sun, and evaluated whether sunfleck characteristics differed between stream sections in old-growth vs second-growth forest. Sunflecks were common in streams. A minimum of 3 individual sunflecks/d occurred at each of 47 sensor locations. Short sunflecks were more common than long sunflecks, a result consistent with findings in terrestrial studies. However, streams had longer sunflecks than upland forests in this region, probably because canopy ga...


title: Optimizing Space-Air-Ground Integrated Networks by Artificial Intelligence

abstract: It is widely acknowledged that the development of traditional terrestrial communication technologies cannot provide all users with fair and high quality services due to scarce network resources and limited coverage areas. To complement the terrestrial connection, especially for users in rural, disaster-stricken, or other difficult-to-serve areas, satellites, UAVs, and balloons have been utilized to relay communication signals. On this basis, SAGINs have been proposed to improve the users' QoE. However, compared with existing networks such as ad hoc networks and cellular networks, SAGINs are much more complex due to the various characteristics of three network segments. To improve the performance of SAGINs, researchers are facing many unprecedented challenges. In this article, we propose the AI technique to optimize SAGINs, as the AI technique has shown its predominant advantages in many applications. We first analyze several main challenges of SAGINs and explain how these problems can be solved by AI. Then, we consider the satellite traffic balance as an example and propose a deep learning based method to improve traffic control performance. Simulation results evaluate that the deep learning technique can be an efficient tool to improve the performance of SAGINs.


title: Completion and Reconstruction with Primitive Shapes

abstract: We consider the problem of reconstruction from incomplete point-clouds. To find a closed mesh the reconstruction is guided by a set of primitive shapes which has been detected on the input point-cloud (e.g. planes, cylinders etc.). With this guidance we not only continue the surrounding structure into the holes but also synthesize plausible edges and corners from the primitives' intersections. To this end we give a surface energy functional that incorporates the primitive shapes in a guiding vector field. The discretized functional can be minimized with an efficient graph-cut algorithm. A novel greedy optimization strategy is proposed to minimize the functional under the constraint that surface parts corresponding to a given primitive must be connected. From the primitive shapes our method can also reconstruct an idealized model that is suitable for use in a CAD system.


title: A Simple DFT-aided Spatial Basis Expansion Model and Channel Estimation Strategy for TDD/FDD Massive MIMO Systems

abstract: This paper proposes a new transmission strategy for the multiuser massive multiple-input multiple-output (MIMO) systems, including uplink/downlink channel estimation and user scheduling for data transmission. A discrete Fourier transform (DFT) aided spatial basis expansion model (SBEM) is first introduced to represent the uplink/downlink channels with much few parameter dimensions by exploiting angle reciprocity and the physical characteristics of the uniform linear array (ULA). With SBEM, both uplink and downlink channel estimation of multiuser can be carried out with very few amount of training resources, which significantly reduces the training overhead and feedback cost. Meanwhile, the pilot contamination problem in the uplink raining is immediately relieved by exploiting the spatial information of users. To enhance the spectral efficiency and to fully utilize the spatial resources, we also design a greedy user scheduling scheme during the data transmission period. Compared to existing low-rank models, the newly proposed SBEM offers an alternative for channel acquisition without need of channel statistics for both TDD and FDD systems based on the angle reciprocity. Moreover, the proposed method can be efficiently deployed by the fast Fourier transform (FFT). Various numerical results are provided to corroborate the proposed studies.


title: Can humans fly? Action understanding with multiple classes of actors

abstract: Can humans fly? Emphatically no. Can cars eat? Again, absolutely not. Yet, these absurd inferences result from the current disregard for particular types of actors in action understanding. There is no work we know of on simultaneously inferring actors and actions in the video, not to mention a dataset to experiment with. Our paper hence marks the first effort in the computer vision community to jointly consider various types of actors undergoing various actions. To start with the problem, we collect a dataset of 3782 videos from YouTube and label both pixel-level actors and actions in each video. We formulate the general actor-action understanding problem and instantiate it at various granularities: both video-level single- and multiple-label actor-action recognition and pixel-level actor-action semantic segmentation. Our experiments demonstrate that inference jointly over actors and actions outperforms inference independently over them, and hence concludes our argument of the value of explicit consideration of various actors in comprehensive action understanding.


title: Feature selection using support vector machines

abstract: Text categorization is the task of classifying natural language documents into a set of predefined categories. Documents are typically represented by sparse vectors under the vector space model, where each word in the vocabulary is mapped to one coordinate axis and its occurrence in the document gives rise to one nonzero component in the vector representing that document. When training classifiers on large collections of documents, both the time and memory requirements connected with processing of these vectors may be prohibitive. This calls for using a feature selection method, not only to reduce the number of features but also to increase the sparsity of document vectors. We propose a feature selection method based on linear Support Vector Machines (SVMs). First, we train the linear SVM on a subset of training data and retain only those features that correspond to highly weighted components (in absolute value sense) of the normal to the resulting hyperplane that separates positive and negative examples. This reduced feature space is then used to train a classifier over a larger training set because more documents now fit into the same amount of memory. In our experiments we compare the effectiveness of the SVM -based feature selection with that of more traditional feature selection methods, such as odds ratio and information gain, in achieving the desired tradeoff between the vector sparsity and the classification performance. Experimental results indicate that, at the same level of vector sparsity, feature selection based on SVM normals yields better classification performance than odds ratioor information gainbased feature selection when linear SVM classifiers are used.


title: A Sixth-Order PWM Modulator for Digital Input Class-D Audio Amplifiers

abstract: A digital Class-D audio amplifier is presented in this paper. This amplifier uses a sixth-order PWM modulator to generate the driver signal of the output stage of the amplifier. The transformation of pulse-amplitude-modulation (PAM) signal to pulse-width-modulation (PWM) signal is moved into the closed sigma-delta modulator loop that the noise and distortion generated during the transformation of PAM signal to PWM signal is suppressed by the high gain of the forward loop of the sigma-delta modulator. Therefore, at the output of the digital modulator, a very clean PWM signal is generated to drive the power stage of the class-D amplifier. This class-D amplifier achieves 110dB dynamic range (DR), 100dB signal to noise rate (SNR) and 0.005% total harmonic distortion plus noise (THD+N).


title: Adaptive Binarization of Unconstrained Hand-Held Camera-Captured Document Images

abstract: This paper presents a new adaptive binarization technique for degraded hand-held camera-captured document images. The state-of-the-art locally adaptive bi- narization methods are sensitive to the values of free parameter. This problem is more critical when binarizing degraded camera-captured document images because of distor- tions like non-uniform illumination, bad shading, blurring, smearing and low resolution. We demonstrate in this paper that local binarization methods are not only sensitive to the selection of free parameters values (either found manually or automatically), but also sensitive to the constant free parameters values for all pixels of a document image. Some range of values of free parameters are better for foreground regions and some other range of values are better for background regions. For overcoming this prob- lem, we present an adaptation of a state-of-the-art local binarization method such that two different set of free parameters values are used for foreground and background regions respectively. We present the use of ridges detection for rough estimation of foreground regions in a document image. This information is then used to calculate ap- propriate threshold using different set of free parameters values for the foreground and background regions respectively. The evaluation of the method using an OCR-based measure and a pixel-based measure show that our method achieves better performance as compared to state-of-the-art global and local binarization methods.


title: THE TYPE OF INFORMATION OVERLOAD AFFECTS ELECTRONIC KNOWLEDGE REPOSITORY CONTINUANCE

abstract: In the present competitive organizational environment more organizations are implementing knowledge management initiatives to gain strategic advantage. One such initiative is that of implementing electronic knowledge repositories (EKR) which often leads to a rapid increase in the quantity of information employees must process daily, raising concerns of employees being overloaded. This is especially true for current EKRs using distributive technology, enabling customizable individual workspaces which can result in loose knowledge structures. This paper identifies a new type of information overload (IO), extending the concept as occurring in both knowledge seekers and contributors and uses cognitive dissonance theory to provide evidence that IO can change employees’ perception of EKR usage. This research paper provides the first empirical evidence that overload has no sufficient affect on EKR continuance intention directly, but has a significant negative affect on the two main success measures: Perceived usefulness and satisfaction of the system.


title: Document structure meets page layout: loopy random fields for web news content extraction

abstract: Web content extraction is concerned with the automatic identification of semantically interesting web page regions. To generalize to pages from unknown sites, it is crucial to exploit not only the local characteristics of a particular web page region, but also the rich interdependencies that exist between the regions and their latent semantics. We therefore propose a loopy conditional random field which combines semantic intra-page dependencies derived from both document structure and page layout, uses a realistic set of local and relational features and is efficiently learnt in the tree-based reparameterization framework. The results of our empirical analysis on a corpus of real-world news web pages from 177 distinct sites with multiple annotations on DOM node level demonstrate that our combination of document structure and layout-driven interdependencies leads to a significant error reduction on the semantically interesting regions of a web page.


title: OPTIMIZATION OF CASH MANAGEMENT FOR ATM NETWORK

abstract: This paper presents an approach to cash management for automatic teller machine (ATM) network. This approach is based on an artificial neural network to forecast a daily cash demand for every ATM in the network and on the optimization procedure to estimate the optimal cash load for every ATM. During the optimization procedure, the most important factors for ATMs maintenance were considered: cost of cash, cost of cash uploading and cost of daily services. Simulation studies show, that in case of higher cost of cash (interest rate) and lower cost for money uploa-ding, the optimization procedure allows to decrease the ATMs maintenance costs around 15-20 %. For practical imple-mentation of the proposed ATMs’ cash management procedure, further experimental investigations are necessary.


title: A survey of video datasets for crowd density estimation

abstract: Crowd density analysis is very imperative for intellectual video surveillance to help in management and control of crowds for safety. In recent years, more and more datasets dedicated to crowd density estimation, crowd analysis and anomaly detection in crowded scenes have been created. The use of these dataset allows us to compare different crowd density estimation methods with the same input data. The survey introduced in this paper compares different crowd datasets for crowd density estimation methods.


title: BASIC: backbone-assisted successive interference cancellation

abstract: To meet the growing demand for wireless data, it is time to move away from the age-old paradigm of prohibiting interfering nodes from transmissions. Instead, through proactive management of interference among multiple colliding packets, we can design high throughput wireless systems. This is well explored in the Information Theory community and there are also a few implementational efforts that have been recently reported. The existing solutions are nontrivial to use in real systems as they require either tight time/frequency synchronization or exchange of data between transmitters prior to the transmissions. These requirements are hard to meet in practice especially for uplink transmissions. This paper proposes BASIC, a lightweight multi-user uplink transmission strategy that does not require tight synchronization or exchange of samples among nodes, which makes it an attractive alternative compared to its counterparts. BASIC exploits receiver diversity by controlling the data rates of the clients. A novel greedy algorithm is proposed for data rate selection. We implement BASIC on a software-defined radio platform. Our experiments on a real testbed show that BASIC outperforms TDMA by 48% in terms of overall throughput. Our trace-driven simulations show up to 4.8x gain in throughput with similar flow fairness.


Reinforcement Learning With Deeping Learning in Pacman


title: DAVID: Database for Annotation, Visualization, and Integrated Discovery

abstract: The distributed nature of biological knowledge poses a major challenge to the interpretation of genome-scale datasets, including those derived from microarray and proteomic studies. This report describes DAVID, a web-accessible program that integrates functional genomic annotations with intuitive graphical summaries. Lists of gene or protein identifiers are rapidly annotated and summarized according to shared categorical data for Gene Ontology, protein domain, and biochemical pathway membership. DAVID assists in the interpretation of genome-scale datasets by facilitating the transition from data collection to biological meaning.


title: Fuzzy Arden Syntax: A fuzzy programming language for medicine

abstract: Objective: The programming language Arden Syntax has been optimised for use in clinical decision support systems. We describe an extension of this language named Fuzzy Arden Syntax, whose original version was introduced in S. Tiffe's dissertation on ''Fuzzy Arden Syntax: Representation and Interpretation of Vague Medical Knowledge by Fuzzified Arden Syntax'' (Vienna University of Technology, 2003). The primary aim is to provide an easy means of processing vague or uncertain data, which frequently appears in medicine. Methods: For both propositional and number data types, fuzzy equivalents have been added to Arden Syntax. The Boolean data type was generalised to represent any truth degree between the two extremes 0 (falsity) and 1 (truth); fuzzy data types were introduced to represent fuzzy sets. The operations on truth values and real numbers were generalised accordingly. As the conditions to decide whether a certain programme unit is executed or not may be indeterminate, a Fuzzy Arden Syntax programme may split. The data in the different branches may be optionally aggregated subsequently. Results: Fuzzy Arden Syntax offers the possibility to formulate conveniently Medical Logic Modules (MLMs) based on the principle of a continuously graded applicability of statements. Furthermore, ad hoc decisions about sharp value boundaries can be avoided. As an illustrative example shows, an MLM making use of the features of Fuzzy Arden Syntax is not significantly more complex than its Arden Syntax equivalent; in the ideal case, a programme handling crisp data remains practically unchanged when compared to its fuzzified version. In the latter case, the output data, which can be a set of weighted alternatives, typically depends continuously from the input data. Conclusion: In typical applications an Arden Syntax MLM can produce a different output after only slight changes of the input; discontinuities are in fact unavoidable when the input varies continuously but the output is taken from a discrete set of possibilities. This inconvenience can, however, be attenuated by means of certain mechanisms on which the programme flow under Fuzzy Arden Syntax is based. To write a programme making use of these possibilities is not significantly more difficult than to write a programme according to the usual practice.


title: Agent-based distributed manufacturing process planning and scheduling: a state-of-the-art survey

abstract: Manufacturing process planning is the process of selecting and sequencing manufacturing processes such that they achieve one or more goals and satisfy a set of domain constraints. Manufacturing scheduling is the process of selecting a process plan and assigning manufacturing resources for specific time periods to the set of manufacturing processes in the plan. It is, in fact, an optimization process by which limited manufacturing resources are allocated over time among parallel and sequential activities. Manufacturing process planning and scheduling are usually considered to be two separate and distinct phases. Traditional optimization approaches to these problems do not consider the constraints of both domains simultaneously and result in suboptimal solutions. Without considering real-time machine workloads and shop floor dynamics, process plans may become suboptimal or even invalid at the time of execution. Therefore, there is a need for the integration of manufacturing process-planning and scheduling systems for generating more realistic and effective plans. After describing the complexity of the manufacturing process-planning and scheduling problems, this paper reviews the research literature on manufacturing process planning, scheduling as well as their integration, particularly on agent-based approaches to these difficult problems. Major issues in these research areas are discussed, and research opportunities and challenges are identified


title: Scalable high-performance architecture for convolutional ternary neural networks on FPGA

abstract: Thanks to their excellent performances on typical artificial intelligence problems, deep neural networks have drawn a lot of interest lately. However, this comes at the cost of large computational needs and high power consumption. Benefiting from high precision at acceptable hardware cost on these difficult problems is a challenge. To address it, we advocate the use of ternary neural networks (TNN) that, when properly trained, can reach results close to the state of the art using floatingpoint arithmetic. We present a highly versatile FPGA friendly architecture for TNN in which we can vary both the number of bits of the input data and the level of parallelism at synthesis time, allowing to trade throughput for hardware resources and power consumption. To demonstrate the efficiency of our proposal, we implement high-complexity convolutional neural networks on the Xilinx Virtex-7 VC709 FPGA board. While reaching a better accuracy than comparable designs, we can target either high throughput or low power. We measure a throughput up to 27 000 fps at ≈7W or up to 8.36 TMAC/s at ≈13 W.


title: Irony and Sarcasm: Corpus Generation and Analysis Using Crowdsourcing

abstract: The ability to reliably identify sarcasm and irony in text can improve the performance of many Natural Language Processing (NLP) systems including summarization, sentiment analysis, etc. The existing sarcasm detection systems have focused on identifying sarcasm on a sentence level or for a specific phrase. However, often it is impossible to identify a sentence containing sarcasm without knowing the context. In this paper we describe a corpus generation experiment where we collect regular and sarcastic Amazon product reviews. We perform qualitative and quantitative analysis of the corpus. The resulting corpus can be used for identifying sarcasm on two levels: a document and a text utterance (where a text utterance can be as short as a sentence and as long as a whole document).


title: Suggestive contours for conveying shape

abstract: In this paper, we describe a non-photorealistic rendering system that conveys shape using lines. We go beyond contours and creases by developing a new type of line to draw: the suggestive contour. Suggestive contours are lines drawn on clearly visible parts of the surface, where a true contour would first appear with a minimal change in viewpoint. We provide two methods for calculating suggestive contours, including an algorithm that finds the zero crossings of the radial curvature. We show that suggestive contours can be drawn consistently with true contours, because they anticipate and extend them. We present a variety of results, arguing that these images convey shape more effectively than contour alone.


title: Changes in genital anatomy and microbiology in girls between age 6 and age 12 years: a longitudinal study.

abstract: STUDY OBJECTIVE ::: To study changes in genital anatomy and occurrence of human papillomavirus and Gardnerella vaginalis in girls resulting from growth and development. ::: ::: ::: DESIGN ::: At age 11-12 years, an invitation was sent to 180 girls to attend a follow-up examination. All girls had previously participated in a study exploring anogenital anatomy and microbiology in children selected for non-abuse at age 5 and 6. The genital area was examined with a colposcope and microbiological samples for Gardnerella vaginalis (GV) and human papillomavirus (HPV) were collected. GV was identified by conventional criteria and HPV by a PCR method. ::: ::: ::: RESULTS ::: Thirty-one girls were examined twice, at mean age 5.7 and 12.0 years. At first examination all were pre-pubertal. At second examination 21 girls were B2/P2 or above. Significantly more girls had developed a structure called a fossa groove. A thick and redundant hymen with a tendency of folding outward was more common at the second examination. Two girls had GV and one girl had HPV-16 identified. Another girl was classified to have a deep notch and a probable transection in her hymen, and this girl reported a painful insertion of a tampon. All girls denied sexual activity. ::: ::: ::: CONCLUSION ::: The main genital finding in girls entering puberty is the hymen becoming thick and redundant with a tendency of folding out. In the study findings associated with sexual activity were discovered in two girls, and the possibility of alternative explanations is discussed.


title: Knowledge Graph Embedding: A Survey of Approaches and Applications

abstract: Knowledge graph (KG) embedding is to embed components of a KG including entities and relations into continuous vector spaces, so as to simplify the manipulation while preserving the inherent structure of the KG. It can benefit a variety of downstream tasks such as KG completion and relation extraction, and hence has quickly gained massive attention. In this article, we provide a systematic review of existing techniques, including not only the state-of-the-arts but also those with latest trends. Particularly, we make the review based on the type of information used in the embedding task. Techniques that conduct embedding using only facts observed in the KG are first introduced. We describe the overall framework, specific model design, typical training procedures, as well as pros and cons of such techniques. After that, we discuss techniques that further incorporate additional information besides facts. We focus specifically on the use of entity types, relation paths, textual descriptions, and logical rules. Finally, we briefly introduce how KG embedding can be applied to and benefit a wide variety of downstream tasks such as KG completion, relation extraction, question answering, and so forth.


Personalized Privacy-Preserving Social Recommendation


title: Automating Change-Level Self-Admitted Technical Debt Determination

abstract: Technical debt (TD) is a metaphor to describe the situation where developers introduce suboptimal solutions during software development to achieve short-term goals that may affect the long-term software quality. Prior studies proposed different techniques to identify TD, such as identifying TD through code smells or by analyzing source code comments. Technical debt identified using comments is known as Self-Admitted Technical Debt (SATD) and refers to TD that is introduced intentionally. Compared with TD identified by code metrics or code smells, SATD is more reliable since it is admitted by developers using comments. Thus far, all of the state-of-the-art approaches identify SATD at the file-level. In essence, they identify whether a file has SATD or not. However, all of the SATD is introduced through software changes. Previous studies that identify SATD at the file-level in isolation cannot describe the TD context related to multiple files. Therefore, it is beneficial to identify the SATD once a change is being made. We refer to this type of TD identification as “Change-level SATD Determination”, which determines whether or not a change introduces SATD. Identifying SATD at the change-level can help to manage and control TD by understanding the TD context through tracing the introducing changes. To build a change-level SATD Determination model, we first identify TD from source code comments in source code files of all versions. Second, we label the changes that first introduce the SATD comments as TD-introducing changes. Third, we build the determination model by extracting 25 features from software changes that are divided into three dimensions, namely diffusion, history and message, respectively. To evaluate the effectiveness of our proposed model, we perform an empirical study on 7 open source projects containing a total of 100,011 software changes. The experimental results show that our model achieves a promising and better performance than four baselines in terms of AUC and cost-effectiveness (i.e., percentage of TD-introducing changes identified when inspecting 20 percent of changed LOC). On average across the 7 experimental projects, our model achieves AUC of 0.82, cost-effectiveness of 0.80, which is a significant improvement over the comparison baselines used. In addition, we found that “Diffusion” is the most discriminative dimension among the three dimensions of features for determining TD-introducing changes.


title: Privacy-preserving datamining on vertically partitioned databases

abstract: In a recent paper Dinur and Nissim considered a statistical database in which a trusted database administrator monitors queries and introduces noise to the responses with the goal of maintaining data privacy [5]. Under a rigorous definition of breach of privacy, Dinur and Nissim proved that unless the total number of queries is sub-linear in the size of the database, a substantial amount of noise is required to avoid a breach, rendering the database almost useless. As databases grow increasingly large, the possibility of being able to query only a sub-linear number of times becomes realistic. We further investigate this situation, generalizing the previous work in two important directions: multi-attribute databases (previous work dealt only with single-attribute databases) and vertically partitioned databases, in which different subsets of attributes are stored in different databases. In addition, we show how to use our techniques for datamining on published noisy statistics.


title: The Runner -- Recommender System of Workout and Nutrition for Runners

abstract: Recommender systems have been gaining popularity and appreciation over the past few years and they kept growing towards a semantic web. Internet users search for more and more facilities to get information and recommendations based on their preferences, experience and expectations. Nowadays, there are many recommender systems on the web for music, movies, diets, products, etc. Some of them use very efficient recommending techniques (ex. Amazon), while others are very simple, based on algorithms that do not always provide relevant or interesting recommendations. The solution we propose is a recommender system for running professionals and amateurs, which is able to provide information to users regarding the workout and the diet that best suits them, based on their profile information, preferences and declared purpose. The solution mixes a social dimension derived from an expanding community with expert knowledge defined within an ontology. Moreover, our model addresses adaptability in terms of personal profile, professional results and unfortunate events that might occur during workouts.


title: A Modified Dual Active Bridge Converter With Hybrid Phase-Shift Control for Wide Input Voltage Range

abstract: By inserting a small inductor between the transformer center tap and the midpoint of two split output capacitors in the dual active bridge (DAB) topology, this paper proposes a modified DAB converter for wide-input applications. A hybrid phase-shift (HPS) control scheme is proposed to allow all power switches to achieve practical ZVS over the full operating range; thereby, significantly minimizing the switching losses and alleviating electromagnetic interference. Moreover, the proposed control scheme does not significantly increase the conduction losses in comparison with the extended phase-shift (EPS) control. Therefore, the modified DAB can operate efficiently. The topology derivation and description are first presented. Then, the EPS and triple phase-shift (TPS) modulations are applied, and the corresponding operating principles and characteristics, including the soft-switching, power transfer, and root-mean-square current, are investigated in detail. To achieve practical ZVS operation of all switches over full operating range, while minimizing the conduction losses, the EPS and TPS are combined, and an HPS control scheme is proposed. Finally, experimental results from a 1.4-kW converter prototype with 200–400-V input and 400-V output are presented to verify the feasibility and advantages of the converter and control.


title: Verb Semantics And Lexical Selection

abstract: This paper will focus on the semantic representation of verbs in computer systems and its impact on lexical selection problems in machine translation (MT). Two groups of English and Chinese verbs are examined to show that lexical selection must be based on interpretation of the sentences as well as selection restrictions placed on the verb arguments. A novel representation scheme is suggested, and is compared to representations with selection restrictions used in transfer-based MT. We see our approach as closely aligned with knowledge-based MT approaches (KBMT), and as a separate component that could be incorporated into existing systems. Examples and experimental results will show that, using this scheme, inexact matches can achieve correct lexical selection.


title: TrueNorth: Design and Tool Flow of a 65 mW 1 Million Neuron Programmable Neurosynaptic Chip

abstract: The new era of cognitive computing brings forth the grand challenge of developing systems capable of processing massive amounts of noisy multisensory data. This type of intelligent computing poses a set of constraints, including real-time operation, low-power consumption and scalability, which require a radical departure from conventional system design. Brain-inspired architectures offer tremendous promise in this area. To this end, we developed TrueNorth, a 65 mW real-time neurosynaptic processor that implements a non-von Neumann, low-power, highly-parallel, scalable, and defect-tolerant architecture. With 4096 neurosynaptic cores, the TrueNorth chip contains 1 million digital neurons and 256 million synapses tightly interconnected by an event-driven routing infrastructure. The fully digital 5.4 billion transistor implementation leverages existing CMOS scaling trends, while ensuring one-to-one correspondence between hardware and software. With such aggressive design metrics and the TrueNorth architecture breaking path with prevailing architectures, it is clear that conventional computer-aided design (CAD) tools could not be used for the design. As a result, we developed a novel design methodology that includes mixed asynchronous–synchronous circuits and a complete tool flow for building an event-driven, low-power neurosynaptic chip. The TrueNorth chip is fully configurable in terms of connectivity and neural parameters to allow custom configurations for a wide range of cognitive and sensory perception applications. To reduce the system’s communication energy, we have adapted existing application-agnostic very large-scale integration CAD placement tools for mapping logical neural networks to the physical neurosynaptic core locations on the TrueNorth chips. With that, we have successfully demonstrated the use of TrueNorth-based systems in multiple applications, including visual object recognition, with higher performance and orders of magnitude lower power consumption than the same algorithms run on von Neumann architectures. The TrueNorth chip and its tool flow serve as building blocks for future cognitive systems, and give designers an opportunity to develop novel brain-inspired architectures and systems based on the knowledge obtained from this paper.


title: The Psychology of Confessions: A Review of the Literature and Issues

abstract: Recently, in a number of high-profile cases, defendants who were prosecuted, convicted, and sentenced on the basis of false confessions have been exonerated through DNA evidence. As a historical matter, confession has played a prominent role in religion, in psychotherapy, and in criminal law—where it is a prosecutor's most potent weapon. In recent years, psychologists from the clinical, personality, developmental, cognitive, and social areas have brought their theories and research methods to bear on an analysis of confession evidence, how it is obtained, and what impact it has on judges, juries, and other people.Drawing on individual case studies, archival reports, correlational studies, and laboratory and field experiments, this monograph scrutinizes a sequence of events during which confessions may be obtained from criminal suspects and used as evidence. First, we examine the preinterrogation interview, a process by which police target potential suspects for interrogation by making demeanor-based judgm...


title: Dialogue Natural Language Inference

abstract: Consistency is a long standing issue faced by dialogue models. In this paper, we frame the consistency of dialogue agents as natural language inference (NLI) and create a new natural language inference dataset called Dialogue NLI. We propose a method which demonstrates that a model trained on Dialogue NLI can be used to improve the consistency of a dialogue model, and evaluate the method with human evaluation and with automatic metrics on a suite of evaluation sets designed to measure a dialogue model's consistency.


46 . Fatigue , Sleepiness , and Medical Errors


title: Synthesis of activated carbon nanotube/copper oxide composites and their electrochemical performance

abstract: Abstract In this work, we synthesized copper oxide (CuO) nanoparticle-deposited activated multi-walled carbon nanotubes (ACNTs) to evaluate their potential applicability to supercapacitor electrodes. The ACNTs were prepared by chemical activation using KOH and then CuO/ACNTs composites were obtained by chemical deposition method. The structural and morphological characteristics of the CuO/ACNTs were determined by X-ray diffraction (XRD) and transmission electron microscopy (TEM), respectively. The changes of surface properties of CNTs were investigated using X-ray photoelectron spectroscopy (XPS) method. The electrochemical performance of CuO/ACNTs electrodes was examined by cyclic voltammetry (CV) and galvanostatic charge/discharge characteristics in 6 M KOH electrolyte. The results showed that the CuO/ACNTs electrodes had typical pseudo-capacitive behavior and a greater specific capacitance than those of the CuO/CNTs electrodes. This was probably due to the synergistic effect induced between CuO and ACNTs.


title: A Control Strategy for Islanded Microgrids With DC-Link Voltage Control

abstract: New opportunities for optimally integrating the increasing number of distributed-generation (DG) units in the power system rise with the introduction of the microgrid. Most DG units are connected to the microgrid via a power-electronic inverter with dc link. Therefore, new control methods for these inverters need to be developed in order to exploit the DG units as effectively as possible in case of an islanded microgrid. In the literature, most control strategies are based on the conventional transmission grid control or depend on a communication infrastructure. In this paper, on the other hand, an alternative control strategy is proposed based on the specific characteristics of islanded low-voltage microgrids. The microgrid power is balanced by using a control strategy that modifies the set value of the rms microgrid voltage at the inverter ac side as a function of the dc-link voltage. In case a certain voltage, which is determined by a constant-power band, is surpassed, this control strategy is combined with P/V -droop control. This droop controller changes the output power of the DG unit and its possible storage devices as a function of the grid voltage. In this way, voltage-limit violation is avoided. The constant-power band depends on the characteristics of the generator to avoid frequent changes of the power of certain DG units. In this paper, it is concluded that the new control method shows good results in power sharing, transient issues, and stability. This is achieved without interunit communication, which is beneficial concerning reliability issues, and an optimized integration of the renewable energy sources in the microgrid is obtained.


title: Emergency Response Information System Interoperability: Development of Chemical Incident Response Data Model *

abstract: Emergency response requires an efficient information supply chain for the smooth operations of intraand inter-organizational emergency management processes. However, the breakdown of this information supply chain due to the lack of consistent data standards presents a significant problem. In this paper, we adopt a theory driven novel approach to develop a XML-based data model that prescribes a comprehensive set of data standards (semantics and internal structures) for emergency management to better address the challenges of information interoperability. Actual documents currently being used in mitigating chemical emergencies from a large number of incidents are used in the analysis stage. The data model development is guided by Activity Theory and is validated through a RFC-like process used in standards development. This paper applies the standards to the real case of a chemical incident scenario. Further, it complies with the national leading initiatives in emergency standards (National Information Exchange Model).


title: Tracking and Short-Term Forecasting of Typhoon Structure

abstract: The tracking of wind field circulation is an important and difficult problem in typhoon forecasting. This paper studies how to catch the shape and intensity deformation characteristics of this type of severe weather system, which includes the wind field estimation and typhoon structure prediction in a short period of time. A correlation matching approach is used to retrieve typhoon wind field without the estimation of typhoon center. Firstly, an energy function is designed to dynamically restrain the tracking area instead of using a constant one. Then for each grid at previous time step, a corresponding one with maximum correlation coefficient at current time step is computed. The collection of these computed grid pairs reflects the movement of circulation structure and thus the wind field is obtained. Finally, a quality control is performed with total variation reconstruction to smooth the global wind field by eliminating local error data and filling missing data points with the average velocity in the neighborhood. With the computed wind field, the short-term forecasting of typhoon structure that represented by radar echo data is realized by the semi-Lagrangian advection approach. Experimental result shows that the proposed method could retrieve accurate and smooth typhoon wind field compared with observation data, or data computed with optical flow algorithm, and could predict relative accurate typhoon structure in 1 hour.


Bilingual Word Embeddings for Bilingual Terminology Extraction from Specialized Comparable Corpora


title: The POSSUM System of Surgical Audit

abstract: Surgical audit is not a new phenomenon. As early as 1750 BC, King Hammurabi of Babylon issued decrees for the punishment of negligent physicians, particularly surgeons. In such a decree discovered at Susa in Iran and inscribed on a 2-m-high black diorite stone, Hammurabi states that: If a doctor inflicts a serious wound with his operation knife on a free man’s slave and kills him, the doctor must replace the slave with another. If a doctor has treated a free man but caused a serious injury from which the man dies, or if he has opened an abscess and the man goes blind, the man is to cut off his hands. Not surprisingly, internal medicine rather than surgery was popular at that time. Indeed, to many surgeons today, this edict still seems to be exacted in a sublimated way. The outcome of surgical intervention, whether death or uncomplicated survival, complications, or long-term morbidity, is not solely dependent on the abilities of the surgeon in isolation. The patient’s physiological status, the disease that requires surgical correction, the nature of the operation, and the preoperative and postoperative support services have a major effect on the ultimate outcome. It is evident to surgeons worldwide that raw mortality and morbidity rates do little to expound these differences, and that the use of such statistics is at best inaccurate and at worst dangerous. When taken to an extreme, mortality rates can achieve what appears to be a self-fulfilling prophecy. The unit that selects only low-risk cases achieves a low mortality rate and therefore attracts more patients, perhaps undeservedly, whereas the unit that cannot select only low-risk cases is left with a worsening case mix, and their performance as judged by mortality rate will appear to deteriorate still further over time.


title: Optimizing Whole-Page Presentation for Web Search

abstract: Modern search engines aggregate results from different verticals: webpages, news, images, video, shopping, knowledge cards, local maps, and so on. Unlike “ten blue links,” these search results are heterogeneous in nature and not even arranged in a list on the page. This revolution directly challenges the conventional “ranked list” formulation in ad hoc search. Therefore, finding proper presentation for a gallery of heterogeneous results is critical for modern search engines. ::: ::: We propose a novel framework that learns the optimal page presentation to render heterogeneous results onto search result page (SERP). Page presentation is broadly defined as the strategy to present a set of items on SERP, much more expressive than a ranked list. It can specify item positions, image sizes, text fonts, and any other styles as long as variations are within business and design constraints. The learned presentation is content aware, i.e., tailored to specific queries and returned results. Simulation experiments show that the framework automatically learns eye-catchy presentations for relevant results. Experiments on real data show that simple instantiations of the framework already outperform leading algorithm in federated search result presentation. It means the framework can learn its own result presentation strategy purely from data, without even knowing the “probability ranking principle.”


title: Constant-Depth and Subcubic-Size Threshold Circuits for Matrix Multiplication

abstract: Boolean circuits of McCulloch-Pitts threshold gates are a classic model of neural computation studied heavily in the late 20th century as a model of general computation. Recent advances in large-scale neural computing hardware has made their practical implementation a near-term possibility. We describe a theoretical approach for multiplying two N by N matrices that integrates threshold gate logic with conventional fast matrix multiplication algorithms, that perform $O(N^omega)$ arithmetic operations for a positive constant $omega


title: High-Speed Flight of Quadrotor Despite Loss of Single Rotor

abstract: In order to achieve high-speed flight of a damaged quadrotor with complete loss of a single rotor, a multiloop hybrid nonlinear controller is designed. By fully making use of sensor measurements, the model dependence of this control method is reduced, which is conducive to handling disturbance from the unknown aerodynamic effects. This controller is tested on a quadrotor vehicle with one rotor completely removed in the high-speed condition. Free flights are performed in the Open Jet Facility, a large-scale wind tunnel. Over 9 m/s flight speed is reached for the damaged quadrotor in these tests. In addition, several high-speed spin-induced aerodynamic effects are discovered and analyzed.


title: Synthesis and NMR spectral studies of some 2,6-diarylpiperidin-4-one O-benzyloximes.

abstract: Abstract Variously substituted 2,6-diarylpiperidin-4-one O -benzyloximes were synthesized by the direct condensation of the corresponding 2,6-diarylpiperidin-4-ones with O -benzylhydroxylamine hydrochloride. All the synthesized compounds are characterized by IR, Mass and NMR spectral studies. NMR spectral assignments are made unambiguously by their one-dimensional ( 1 H NMR and 13 C NMR) and two-dimensional ( 1 H– 1 H COSY, NOESY, HSQC and HMBC) NMR spectra. All the synthesized compounds are resulted as single isomer, i.e., exclusively E isomer ( 9 – 14 ). The conformational preference of 2,6-diarylpiperidin-4-one oxime ethers with and without alkyl substituents at C-3 and C-5 has also been discussed using the spectral studies. The observed chemical shifts and coupling constants suggest that compounds 8 – 13 adopt normal chair conformation with equatorial orientation of all the substituents while compound 14 contributes significant boat conformation along with the predominant chair conformation in solution. The effect of oximination on ring carbons, their associated protons, alkyl substituents and ipso carbons are studied. Every proton in the piperidone ring of the oxime ether is observed as distinct signal due to oximination. The order of chemical shift magnitude in compound 8 is H-2a > H-6a > H-5e > H-3e > H-3a > H-5a. For 9 – 12 , the order is H-6a > H-5e > H-2a > H-3a > H-5a, for 13 , H-6a > H-2a > H-5e > H-3a > H-5a and for 14 , the order is H-2a > H-6a > H-5e > H-3a > H-5a while the 13 C chemical shift magnitude for 8 – 14 due to oximination is C-2 > C-6 > C-3 > C-5.


title: The International Classification of Functioning, Disability and Health and the version for children and youth as a tool in child habilitation/early childhood intervention – feasibility and usefulness as a common language and frame of reference for practice

abstract: Early childhood intervention and habilitation services for children with disabilities operate on an interdisciplinary basis. It requires a common language between professionals, and a shared framework for intervention goals and intervention implementation. The International Classification of Functioning, Disability and Health (ICF) and the version for children and youth (ICF-CY) may serve as this common framework and language. This overview of studies implemented by our research group is based on three research questions: Do the ICF-CY conceptual model have a valid content and is it logically coherent when investigated empirically? Is the ICF-CY classification useful for documenting child characteristics in services? What difficulties and benefits are related to using ICF-CY model as a basis for intervention when it is implemented in services? A series of studies, undertaken by the CHILD researchers are analysed. The analysis is based on data sets from published studies or master theses. Results and conclusion show that the ICF-CY has a useful content and is logically coherent on model level. Professionals find it useful for documenting children's body functions and activities. Guidelines for separating activity and participation are needed. ICF-CY is a complex classification, implementing it in services is a long-term project.


title: Mobile Low Latency Services in 5G

abstract: Networks beyond 2020 will experience 10000-fold increase in wireless traffic, connect 10-100 times more devices and support the most diverse use cases. Thus, the 5G architecture needs to be flexible and cater for both traffic volumes and diversity of service requirements. Among the set of new use cases, support of delay sensitive "mobile" applications, such as vehicular communications (V2X, where X stands for either Vehicle or Infrastructure), require architecture enhancements to natively offer low latency and high mobility. In this paper we propose the necessary technology enablers for the architectural solution to support such use cases.


title: FromDaDy: Spreading Aircraft Trajectories Across Views to Support Iterative Queries

abstract: When displaying thousands of aircraft trajectories on a screen, the visualization is spoiled by a tangle of trails. The visual analysis is therefore difficult, especially if a specific class of trajectories in an erroneous dataset has to be studied. We designed FromDaDy, a trajectory visualization tool that tackles the difficulties of exploring the visualization of multiple trails. This multidimensional data exploration is based on scatterplots, brushing, pick and drop, juxtaposed views and rapid visual design. Users can organize the workspace composed of multiple juxtaposed views. They can define the visual configuration of the views by connecting data dimensions from the dataset to Bertin's visual variables. They can then brush trajectories, and with a pick and drop operation they can spread the brushed information across views. They can then repeat these interactions, until they extract a set of relevant data, thus formulating complex queries. Through two real-world scenarios, we show how FromDaDy supports iterative queries and the extraction of trajectories in a dataset that contains up to 5 million data.


Mindfulness-Based Cognitive Therapy for Depression: A New Approach to Preventing Relapse


title: Branchio-oculo-facial syndrome (BOFS) and congenital heart defects.

abstract: We report the case of a 43-day-old boy with branchio-oculo-facial syndrome (BOFS) and congenital heart defect. On clinical examination, he presented growth retardation, epicanthal folds, small palpebral fissures, telecanthus, broadened nasal bridge, lip pseudocleft, micrognathia, dysplastic and posteriorly-rotated ears, branchial clefts, short and webbed neck, supernumerary nipple, hypotonia and decreased deep tendon reflexes. Echocardiography showed the presence of a type-A complete atrioventricular septal defect and patent ductus arteriosus. This description strengthens the possibility of congenital heart defects being part of the spectrum of anomalies seen in BOFS.


title: PTfuzz: Guided Fuzzing With Processor Trace Feedback

abstract: Greybox fuzzing, such as american fuzzy lop (AFL), is very efficient in finding software vulnerability, which makes it the state-of-the-art fuzzing technology. Greybox fuzzing leverages the branch information collected during program running as feedback to guide choosing seeds. Current greybox fuzzing generally uses two kinds of methods to collect branch information: compile-time instrumentation (AFL) and emulation [AFL extended with QEMU emulation (QAFL)]. Compile-time instrumentation is efficient, but it does not support binary programs. Meanwhile, emulation supports binary programs, but its efficiency is very low. In this paper, we propose a greybox fuzzing approach named PTfuzz, which leverages hardware mechanism (Intel Processor Trace) to collect branch information. Our approach supports binary programs, just like the emulation method, while it gains a comparable performance with the compile-time instrumentation method. Our experiments show that PTfuzz can fuzz the original binary programs without any modification, and we gain a $3\times $ performance improvement compared to QAFL.


title: Evaluating LoRa energy efficiency for adaptive networks: From star to mesh topologies

abstract: The LoRa technology has emerged as an interesting solution for low power, long range loT applications by proposing multiple “degrees of freedom” at the physical layer. This flexibility provides either a long range at the cost of a lower data rate or higher throughput at the cost of low sensitivity, so a shorter range. In this paper, we analyze the flexibility of LoRa and propose various strategies to adapt its radio parameters (such as the spreading factor, bandwidth, and transmission power) to different deployment scenarios. We compute the energy consumption of LoRa transceivers using various radio configurations in both star and mesh topologies. Our simulation results show that in a star topology, we can achieve the optimal scaling-up/down strategy of LoRa radio parameters to obtain either a high data rate or a long range while respecting low energy consumption. In mesh networks, energy consumption is optimized by exploiting various radio configurations and the network topology (e.g., the number of hops, the network density, the cell coverage). Finally, we propose a strategy to take advantage of both star and mesh topologies.


title: Dynamic Attack Detection in Cyber-Physical Systems With Side Initial State Information

abstract: This paper studies the impact of side initial state information on the detectability of data deception attacks against cyber-physical systems. We assume the attack detector has access to a linear function of the initial system state that cannot be altered by an attacker. First, we provide a necessary and sufficient condition for an attack to be undetectable by any dynamic attack detector under each specific side information pattern. Second, we characterize attacks that can be sustained for arbitrarily long periods without being detected. Third, we define the zero state inducing attack, the only type of attack that remains dynamically undetectable regardless of the side initial state information available to the attack detector. Finally, we design a dynamic attack detector that detects detectable attacks.


title: Single Image Motion Deblurring Using Transparency

abstract: One of the key problems of restoring a degraded image from motion blur is the estimation of the unknown shift-invariant linear blur filter. Several algorithms have been proposed using image intensity or gradient information. In this paper, we separate the image deblurring into filter estimation and image deconvolution processes, and propose a novel algorithm to estimate the motion blur filter from a perspective of alpha values. The relationship between the object boundary transparency and the image motion blur is investigated. We formulate the filter estimation as solving a maximum a posteriori (MAP) problem with the defined likelihood and prior on transparency. Our unified approach can be applied to handle both the camera motion blur and the object motion blur.


title: Hinfinity filtering for fuzzy systems with immeasurable premise variables: An uncertain system approach

abstract: This paper discusses a design of stable filters with H"~ disturbance attenuation of Takagi-Sugeno fuzzy systems with immeasurable premise variables. When we consider the filter design of Takagi-Sugeno fuzzy systems, the selection of premise variables plays an important role. If the premise variable is the state of the system, then a fuzzy system describes a wide class of nonlinear systems. In this case, however, a filter design of fuzzy systems based on parallel distributed compensator idea is infeasible. To avoid such a difficulty, we consider the premise variables uncertainties. Then we consider a robust H"~ filtering problem for such an uncertain system. A solution of the problem is given in terms of linear matrix inequalities (LMIs). Some numerical examples are given to illustrate our theory.


title: Efficiency of algorithms for programming beginners

abstract: Introductory computer science courses often present the concept of algorithm efficiency in a rather late stage, through searching and sorting problems. We believe that the concept of efficiency can be presented much earlier. In this paper we present a novel approach in which efficiency of algorithms is presented early, gradually and intuitively. We link our approach to cognitive consequences of programming instruction and illustrate it with three problems.We implemented our approach with 10th and 11th grade high-school students. In the paper, we describe our experience with posing the three problems to the students. Student solutions varied considerably, reflecting different levels of insight into the problems. The various solutions led to fruitful class discussions, thus widening the students' repertoire of templates, and enhancing student realization as to the importance of analysis and planning.


title: High-Fidelity PWM Inverter for Digital Audio Amplification: Spectral Analysis, Real-Time DSP Implementation, and Results

abstract: A complete digital audio amplifier has been developed, implemented and tested. The process is entirely computational, and the output load and filter are the only analog components in the system. The process makes use of digital signal processing and a switching power stage to provide both high fidelity and high efficiency, beginning with a digital audio data stream. The advantages of naturally-sampled pulse-width modulation (PWM) are discussed in depth, including spectral analysis and comparisons to uniformly-sampled PWM. It is shown that natural PWM does not introduce audible distortion at switching frequencies consistent with power electronics practice. Interpolation methods for sample data conversion to natural PWM are discussed, and error analysis is presented based on Lagrange's Expansion Theorem. Noise-shaping processes are used to support high fidelity with practical values of time resolution. A counter conversion process enforces switching dead time in the inverter gate signals. The experimental full-bridge inverter implementation demonstrates that miniaturization is possible. A complete test system delivered more than 50 W into an 8 /spl Omega/ load with an efficiency of 80% and total harmonic distortion plus noise of 0.02%.


title: An Enhanced Algorithm to Predict a Future Crime using Data Mining

abstract: about national security has increased after the 26/11 Mumbai attack. In this paper we look at the use of missing value and clustering algorithm for a data mining approach to help predict the crimes patterns and fast up the process of solving crime. We will concentrate on MV algorithm and Apriori algorithm with some enhancements to aid in the process of filling the missing value and identification of crime patterns. We applied these techniques to real crime data. We also use semi- supervised learning technique in this paper for knowledge discovery from the crime records and to help increase the predictive accuracy.


title: Machine Learning of Linear Differential Equations using Gaussian Processes

abstract: This work leverages recent advances in probabilistic machine learning to discover governing equations expressed by parametric linear operators. Such equations involve, but are not limited to, ordinary and partial differential, integro-differential, and fractional order operators. Here, Gaussian process priors are modified according to the particular form of such operators and are employed to infer parameters of the linear equations from scarce and possibly noisy observations. Such observations may come from experiments or black-box computer simulations, as demonstrated in several synthetic examples and a realistic application in functional genomics. Employ probabilistic machine learning to discover governing equations expressed by parametric linear operators.Proper placement of Gaussian process priors allows one to efficiently infer model parameters via maximum likelihood estimation.A general treatment of inverse problems governed by linear operators, leading to model discovery from just a handful of noisy measurements.


title: Latent Collaborative Retrieval

abstract: Retrieval tasks typically require a ranking of items given a query. Collaborative filtering tasks, on the other hand, learn to model user's preferences over items. In this paper we study the joint problem of recommending items to a user with respect to a given query, which is a surprisingly common task. This setup differs from the standard collaborative filtering one in that we are given a query x user x item tensor for training instead of the more traditional user x item matrix. Compared to document retrieval we do have a query, but we may or may not have content features (we will consider both cases) and we can also take account of the user's profile. We introduce a factorized model for this new task that optimizes the top-ranked items returned for the given query and user. We report empirical results where it outperforms several baselines.


Generating Coherent Summaries of Scientific Articles Using Coherence Patterns


title: Test pattern generation for column compression multiplier

abstract: When used as the building cell of a parallel multiplier, the (4,2) counter tree is better suited than a Wallace tree for a VLSI implementation because of its more regular structure. In this paper test pattern generation for the CC multipliers is presented following a brief introduction to the structure of the (4,2) counter and the column compression multiplier using a (4,2) counter as its building cell. In conclusion, less test patterns are enough to exhaustively test the CC multiplier.


title: Granular Computing: Perspectives and Challenges

abstract: Granular computing, as a new and rapidly growing paradigm of information processing, has attracted many researchers and practitioners. Granular computing is an umbrella term to cover any theories, methodologies, techniques, and tools that make use of information granules in complex problem solving. The aim of this paper is to review foundations and schools of research and to elaborate on current developments in granular computing research. We first review some basic notions of granular computing. Classification and descriptions of various schools of research in granular computing are given. We also present and identify some research directions in granular computing.


title: An empirical investigation of net-enabled business value

abstract: Many traditional organizations have undertaken major initiatives to leverage the Internet to transform how they coordinate value activities with customers, suppliers, and other business partners with the objective of improving firm performance. This paper addresses processes through which business value is created through such Internet-enabled value chain activities. Relying on the resource-based view of the firm, we propose a model positing that a firm's abilities to coordinate and exploit firm resources (processes, information technology, and readiness of customers and suppliers) create online informational capabilities (a higher order resource) which then leads to improved operational and financial performance. The outcome of a firm's online informational capabilities is reflected in superior operational performance through customer and supplier-side digitization efforts, which reflect the extent to which transactions and external interactions occur electronically. We also hypothesize that increased customer and supplier-side digitization leads to better financial performance. The model is tested with data from over 1,000 firms in the manufacturing, retail, and wholesale sectors. The analysis suggests that while most firms are lagging in their supplier-side initiatives relative to the customer-side, supplier-side digitization has a strong positive impact on customer-side digitization, which, in turn, leads to better financial performance. Further, both customer and supplier readiness to engage in digital interactions are shown to be as important as a firm's internal digitization initiatives, implying that a firm's transformation-related decisions should include its customers' and suppliers' resources and incentives.


title: Dynamic complex event processing — Adaptive rule engine

abstract: Recently Complex Event Processing has emerged as a lightweight way to monitor multiple data streams to identify events or patterns of interest. Complex Event Processing is a rule based technology. Rules which represent the patterns of interest are defined by domain experts. Existing CEP engines have limitation on addition, deletion and modification of rules. Rules are configured initially once and are not changed later. Some of the engines support initial configuration and occasionally support addition of rules at runtime using the polling mechanism. This paper proposes a dynamic CEP by making the rule engine adaptive by using Event driven or push based approach for updating the rules dynamically in the Complex Event Processing System at runtime. Event driven approach facilitates Knowledgebase to update rules without polling or observing any resource and hence enhances the system performance. The resulting CEP engine with dynamic infrastructure facilitates easy addition and deletion of the rules in an easy and efficient way.