title: The neural correlates of maternal and romantic love

abstract: Abstract Romantic and maternal love are highly rewarding experiences. Both are linked to the perpetuation of the species and therefore have a closely linked biological function of crucial evolutionary importance. Yet almost nothing is known about their neural correlates in the human. We therefore used fMRI to measure brain activity in mothers while they viewed pictures of their own and of acquainted children, and of their best friend and of acquainted adults as additional controls. The activity specific to maternal attachment was compared to that associated to romantic love described in our earlier study and to the distribution of attachment-mediating neurohormones established by other studies. Both types of attachment activated regions specific to each, as well as overlapping regions in the brain's reward system that coincide with areas rich in oxytocin and vasopressin receptors. Both deactivated a common set of regions associated with negative emotions, social judgment and ‘mentalizing’, that is, the assessment of other people's intentions and emotions. We conclude that human attachment employs a push–pull mechanism that overcomes social distance by deactivating networks used for critical social assessment and negative emotions, while it bonds individuals through the involvement of the reward circuitry, explaining the power of love to motivate and exhilarate.


title: From popularity prediction to ranking online news

abstract: News articles are an engaging type of online content that captures the attention of a significant amount of Internet users. They are particularly enjoyed by mobile users and massively spread through online social platforms. As a result, there is an increased interest in discovering the articles that will become popular among users. This objective falls under the broad scope of content popularity prediction and has direct implications in the development of new services for online advertisement and content distribution. In this paper, we address the problem of predicting the popularity of news articles based on user comments. We formulate the prediction task as a ranking problem, where the goal is not to infer the precise attention that a content will receive but to accurately rank articles based on their predicted popularity. Using data obtained from two important news sites in France and Netherlands, we analyze the ranking effectiveness of two prediction models. Our results indicate that popularity prediction methods are adequate solutions for this ranking task and could be considered as a valuable alternative for automatic online news ranking.


title: Magnetic MIMO Signal Processing and Optimization for Wireless Power Transfer

abstract: In magnetic resonant coupling (MRC) enabled multiple-input multiple-output (MIMO) wireless power transfer (WPT) systems, multiple transmitters (TXs) each with one single coil are used to enhance the efficiency of simultaneous power transfer to multiple single-coil receivers (RXs) by constructively combining their induced magnetic fields at the RXs, a technique termed"magnetic beamforming". In this paper, we study the optimal magnetic beamforming design in a multi-user MIMO MRC-WPT system. We introduce the multi-user power region that constitutes all the achievable power tuples for all RXs, subject to the given total power constraint over all TXs as well as their individual peak voltage and current constraints. We characterize each boundary point of the power region by maximizing the sum-power deliverable to all RXs subject to their minimum harvested power constraints. For the special case without the TX peak voltage and current constraints, we derive the optimal TX current allocation for the single-RX setup in closed-form as well as that for the multi-RX setup. In general, the problem is a non-convex quadratically constrained quadratic programming (QCQP), which is difficult to solve. For the case of one single RX, we show that the semidefinite relaxation (SDR) of the problem is tight. For the general case with multiple RXs, based on SDR we obtain two approximate solutions by applying time-sharing and randomization, respectively. Moreover, for practical implementation of magnetic beamforming, we propose a novel signal processing method to estimate the magnetic MIMO channel due to the mutual inductances between TXs and RXs. Numerical results show that our proposed magnetic channel estimation and adaptive beamforming schemes are practically effective, and can significantly improve the power transfer efficiency and multi-user performance trade-off in MIMO MRC-WPT systems.


title: Dual-Polarization Slot Antenna With High Cross-Polarization Discrimination for Indoor Small-Cell MIMO Systems

abstract: We demonstrate a dual-polarization slot antenna for indoor small-cell multiple-input–multiple-output (MIMO) systems. The symmetric structure and differential feeding promotes destructive interference of cross-polarized radiation in the far field to achieve high cross-polarization discrimination (XPD) in all directions. In addition, a very similar radiation pattern is observed not only between the major planes of each polarization but also between the two polarization orientations. Therefore, the proposed antenna can be considered as a stronger candidate for indoor small-cell MIMO systems. With an average XPD of 26.4 dB in all directions, the 3-D ray-tracing simulation results show a more than 22% increase in the system throughput compared to previous dual-polarization antennas for a single-user MIMO system in a typical indoor environment.


title: Synthesizing Continuous Deployment Practices Used in Software Development

abstract: Continuous deployment speeds up the process of existing agile methods, such as Scrum, and Extreme Programming (XP) through the automatic deployment of software changes to end-users upon passing of automated tests. Continuous deployment has become an emerging software engineering process amongst numerous software companies, such as Facebook, Github, Netflix, and Rally Software. A systematic analysis of software practices used in continuous deployment can facilitate a better understanding of continuous deployment as a software engineering process. Such analysis can also help software practitioners in having a shared vocabulary of practices and in choosing the software practices that they can use to implement continuous deployment. The goal of this paper is to aid software practitioners in implementing continuous deployment through a systematic analysis of software practices that are used by software companies. We studied the continuous deployment practices of 19 software companies by performing a qualitative analysis of Internet artifacts and by conducting follow-up inquiries. In total, we found 11 software practices that are used by 19 software companies. We also found that in terms of use, eight of the 11 software practices are common across 14 software companies. We observe that continuous deployment necessitates the consistent use of sound software engineering practices such as automated testing, automated deployment, and code review.


DECADE : A Deep Metric Learning Model for Multivariate Time Series


title: Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations

abstract: High-dimensional partial differential equations (PDEs) appear in a number of models from the financial industry, such as in derivative pricing models, credit valuation adjustment models, or portfolio optimization models. The PDEs in such applications are high-dimensional as the dimension corresponds to the number of financial assets in a portfolio. Moreover, such PDEs are often fully nonlinear due to the need to incorporate certain nonlinear phenomena in the model such as default risks, transaction costs, volatility uncertainty (Knightian uncertainty), or trading constraints in the model. Such high-dimensional fully nonlinear PDEs are exceedingly difficult to solve as the computational effort for standard approximation methods grows exponentially with the dimension. In this work, we propose a new method for solving high-dimensional fully nonlinear second-order PDEs. Our method can in particular be used to sample from high-dimensional nonlinear expectations. The method is based on (1) a connection between fully nonlinear second-order PDEs and second-order backward stochastic differential equations (2BSDEs), (2) a merged formulation of the PDE and the 2BSDE problem, (3) a temporal forward discretization of the 2BSDE and a spatial approximation via deep neural nets, and (4) a stochastic gradient descent-type optimization procedure. Numerical results obtained using TensorFlow in Python illustrate the efficiency and the accuracy of the method in the cases of a 100-dimensional Black–Scholes–Barenblatt equation, a 100-dimensional Hamilton–Jacobi–Bellman equation, and a nonlinear expectation of a 100-dimensional G-Brownian motion.


title: Improved credit card churn prediction based on rough clustering and supervised learning techniques

abstract: Every process is digitized in the current society. Transfer of money from one account holder to another has become possible in seconds because of advanced technologies in information processing. Not only this in all sectors like railways, insurance, health sector, fashion technology, education sector, sales and business sectors, and advertisement sectors every firm digitized its operations. One such sector is banking where every individual based on his or her financial status will be considered for crediting loan, and credit card etc. If the credit score of the loan availing person is high banks will be ready to provide him with the loan but the availing person can opt for any one of the banks on his or her own willing. Such scenario happens in credit card churn prediction also. Hence the banks should take healthy measures to retain the existing credit card holders without any churn. Withholding existing customers of a firm plays an important role to increase the overall revenue of the firm and retains the good name of the firm in competitive market. Hence every organization takes key measures to withhold existing customers using customer management models. Because customer retention is a crucial task as it reduces the time, money and workforce needed for adding new customers to the firm. Customers retention technique in credit card churn prediction (C3P) was done using only supervised classification techniques. But it could not end with better results. So, through many proven hybrid classification techniques we can bring better accuracy in C3P. Also C3P lags in highly efficient techniques like rough set theory. Hence in this work initially we perform data processing techniques and in second stage we propose modified rough K-means algorithm used for clustering credit card holders and in next stage hold-out method divides the cluster data into testing and training clusters. At last classification is performed using various algorithms like support vector machine, random forest, decision tree, K-nearest neighbor, and Naive Bayes. Finally we evaluate the work using precision, recall (sensitivity), specification, accuracy, and misclassification error.


title: LEADER-BASED CONSENSUS

abstract: It is now well recognized that consensus is a fundamental problem one has to solve to implement reliable applications on top of unreliable asynchronous distributed systems prone to failures. It has been shown that this problem cannot be solved if the underlying asynchronous system does not satisfy additional assumptions. This paper presents a new consensus protocol based on a leader oracle (denoted Ω in the litterature). Although this protocol uses asynchronous rounds, it is not based on the rotating coordinator paradigm. As a consequence, it does not suffer from drawbacks inherent to ♢S-based consensus protocols that explicity use this paradigm. As Ω and ♢S are equivalent, the proposed protocol does not require assumptions stronger or weaker than the ones abstracted in ♢S. Hence, it also requires f < n/2 (where n is the number of processes and f an upper bound on the number of processes that may crash). From a design point of view, the proposed protocol is surprisingly simple. From an efficiency point of view, it allows the processes to agree in a single round when the oracle provides the processes with the same leader (a common case in practice). It is also shown that the time and message costs of the protocol can be reduced when f < n/3. Moreover, when, in addition to the leader oracle, the system is equipped with a random oracle, the proposed protocol can be extended to provide a hybrid consensus protocol at no additional message cost.


title: Parameter-free modelling of 2D shapes with ellipses

abstract: Our goal is to represent a given 2D shape with an automatically determined number of ellipses, so that the total area covered by the ellipses is equal to the area of the original shape without any assumption or prior knowledge about the object structure. To solve this interesting theoretical problem, first we employ the skeleton of the 2D shape which provides important information on the parameters of the ellipses that could approximate the original shape. For a given number of such ellipses, the hard Expectation-Maximisation (EM) algorithm is employed to maximise the shape coverage under the Equal Area constraint. Different models (i.e., solutions involving different numbers of ellipses) are evaluated based on the Akaike Information Criterion (AIC). This considers a novel, entropy-based shape complexity measure that balances the model complexity and the model approximation error. In order to minimise the AIC criterion, two variants are proposed and evaluated: (a) the augmentative method that gradually increases the number of considered ellipses starting from a single one and (b) the decremental method that decreases the number of ellipses starting from a large, automatically defined set. The obtained quantitative results on more than 4000 2D shapes included in standard as well as in custom datasets, quantify the performance of the proposed methods and illustrate that their solutions agree with human intuition. Highlights2D shape representation with an automatically determined number of ellipses.We propose a novel definition of shape complexity that exploits the shape skeleton.Good balance between model complexity and shape coverage.Experiments on more than 4000 2D shapes show the effectiveness of the proposed methods.The solutions proposed by AEFA and DEFA seem to agree with human intuition.


The oldest fossil insect sensilla


DUTIR in BioNLP-ST 2016: Utilizing Convolutional Network and Distributed Representation to Extract Complicate Relations


title: Surgical and Patient Factors Affecting Marginal Bone Levels Around Dental Implants: A Comprehensive Overview of Systematic Reviews

abstract: OBJECTIVES ::: The objective of this systematic review was to perform a comprehensive overview of systematic reviews and meta-analyses of surgical and patient factors affecting marginal bone loss around osseointegrated dental implants in humans. ::: ::: ::: MATERIAL AND METHODS ::: Electronic databases were searched for systematic reviews and meta-analyses published up to November 2015. ::: ::: ::: RESULTS ::: Of the 41 articles selected, 11 evaluated implant factors, 10 evaluated patient factors, 19 evaluated surgical protocol-related factors, and one evaluated all three factors. The chosen studies were AMSTAR rated for quality. The following parameters have statistically significant effect on marginal bone loss: (1) marginal bone loss was significantly more in patients with periodontitis than in periodontally healthy patients; (2) significantly greater in generalized aggressive periodontitis patients compared with chronic periodontitis patients; (3) significantly less in alveolar socket preservation techniques; (4) significantly more in alveolar ridge augmentation sites; (5) significantly more in men than in women; (6) significantly more in smokers than in nonsmokers; and (7) smokers also have significantly more marginal bone loss in the maxilla than in the mandible. ::: ::: ::: CONCLUSION ::: Knowledge of the surgical and patient factors that affect marginal bone loss can aid the clinician in making informed choices in selecting implant treatment options that will enhance the longevity and long-term success of their implant-supported cases.


title: RGB Demultiplexer Based on Multicore Polymer Optical Fiber

abstract: Wavelength division multiplexing (WDM) is a good solution for increasing data bitrate communication of multicore polymer optical fiber (MC-POF) based visible light communication (VLC) system. However, this solution requires adding more optical components to the system which can limit its performance. In order to solve this issue, we propose a new design for an RGB demultiplexer based on polycarbonate (PC) MC-POF structure. The new structure is based on replacing several air-holes areas with PC layers over the fiber length which enables controlling the light propagation direction between the pc layers. The positions of the PC layers and the key geometrical parameters of the MC-POF were optimized and analyzed utilizing the beam propagation method (BPM). Results show that an RGB wavelength splitter can be obtained over a light propagation of 20 mm with an excellent crosstalk of −19.436 to 26.474 dB, low losses of 0.901 to 1.246 dB and a large bandwidth of 5.6 to 11.3 nm.


iPose : Instance-Aware 6 D Pose Estimation of Partly Occluded Objects Omid


title: Use of PWM Techniques for Power Quality Improvement

abstract: This paper has discussed the effect of harmonics on the power quality of the power supply. The paper also discussed the different configurations of PWM techniques for harmonic reduction and improvement of fundamental peak voltage. In addition, a comprehensive comparison of all configurations is made in terms of THD, FFT and dominating harmonics components. This paper describes the results on the basis of MATLAB simulation. It is shown how, with suitable modifications. The conventional Regular Sampled PWM technique can be simply extended to allow Harmonic Minimization and also Harmonic Elimination PWM to be closely reproduced using simple algebraic equations. This survey paper will provide the insight of trends and technology of “Power Quality Improvement using PWM Technique.


title: Link-disjoint routing algorithm under multiple additive QoS constraints

abstract: The problem of finding link-disjoint paths under multiple additive QoS constraints was studied.Since the existing algorithms depended on network’s structure and could not guarantee to find feasible solutions for arbitrary net-works,a novel algorithm called multiple constrained link-disjoint path routing algorithm(MCLPRA) was proposed.MCLPRA was based on SAMCRA and didn’t rely on the network’s structure.By introducing the parameter to control its search depth,dividing the solution space into different classes and performing searching according to the classes respectively,MCLPRA was able to obtain the feasible solutions for arbitrary networks.Theoretic analysis shows that MCLPRA can get the feasible and optimal solutions when traditional schemes can not.Comprehensive simulations also show that MCLPRA has better performances than existing algorithms in terms of higher average successful rate of getting feasible solutions with shorter average total length of the obtained path pair.


title: Is beautiful really usable? Toward understanding the relation between usability, aesthetics, and affect in HCI

abstract: This paper analyzes the relation between usability and aesthetics. In a laboratory study, 80 participants used one of four different versions of the same online shop, differing in interface-aesthetics (low vs. high) and interface-usability (low vs. high). Participants had to find specific items and rate the shop before and after usage on perceived aesthetics and perceived usability, which were assessed using four validated instruments. Results show that aesthetics does not affect perceived usability. In contrast, usability has an effect on post-use perceived aesthetics. Our findings show that the ''what is beautiful is usable'' notion, which assumes that aesthetics enhances the perception of usability can be reversed under certain conditions (here: strong usability manipulation combined with a medium to large aesthetics manipulation). Furthermore, our results indicate that the user's affective experience with the usability of the shop might serve as a mediator variable within the aesthetics-usability relation: The frustration of poor usability lowers ratings on perceived aesthetics. The significance of the results is discussed in context of the existing research on the relation between aesthetics and usability.


title: Demand Side Management in Smart Grid Using Heuristic Optimization

abstract: Demand side management (DSM) is one of the important functions in a smart grid that allows customers to make informed decisions regarding their energy consumption, and helps the energy providers reduce the peak load demand and reshape the load profile. This results in increased sustainability of the smart grid, as well as reduced overall operational cost and carbon emission levels. Most of the existing demand side management strategies used in traditional energy management systems employ system specific techniques and algorithms. In addition, the existing strategies handle only a limited number of controllable loads of limited types. This paper presents a demand side management strategy based on load shifting technique for demand side management of future smart grids with a large number of devices of several types. The day-ahead load shifting technique proposed in this paper is mathematically formulated as a minimization problem. A heuristic-based Evolutionary Algorithm (EA) that easily adapts heuristics in the problem was developed for solving this minimization problem. Simulations were carried out on a smart grid which contains a variety of loads in three service areas, one with residential customers, another with commercial customers, and the third one with industrial customers. The simulation results show that the proposed demand side management strategy achieves substantial savings, while reducing the peak load demand of the smart grid.


title: Abuse Potential of Pregabalin

abstract: Background ::: Several case reports and epidemiological studies have raised concern about the abuse potential of pregabalin, the use of which has increased substantially over the last decade. Pregabalin is, in some cases, used for recreational purposes and it has incurred attention among drug abusers for causing euphoric and dissociative effects when taken in doses exceeding normal therapeutic dosages or used by alternative routes of administration, such as nasal insufflation or venous injection. The magnitude of the abuse potential and the mechanism behind it are not fully known.


title: A Theoretical Analysis of NDCG Type Ranking Measures

abstract: A central problem in ranking is to design a ranking measure for evaluation of ranking functions. In this paper we study, from a theoretical perspective, the widely used Normalized Discounted Cumulative Gain (NDCG)-type ranking measures. Although there are extensive empirical studies of NDCG, little is known about its theoretical properties. We first show that, whatever the ranking function is, the standard NDCG which adopts a logarithmic discount, converges to 1 as the number of items to rank goes to infinity. On the first sight, this result is very surprising. It seems to imply that NDCG cannot differentiate good and bad ranking functions, contradicting to the empirical success of NDCG in many applications. In order to have a deeper understanding of ranking measures in general, we propose a notion referred to as consistent distinguishability. This notion captures the intuition that a ranking measure should have such a property: For every pair of substantially different ranking functions, the ranking measure can decide which one is better in a consistent manner on almost all datasets. We show that NDCG with logarithmic discount has consistent distinguishability although it converges to the same limit for all ranking functions. We next characterize the set of all feasible discount functions for NDCG according to the concept of consistent distinguishability. Specifically we show that whether NDCG has consistent distinguishability depends on how fast the discount decays, and 1/r is a critical point. We then turn to the cut-off version of NDCG, i.e., NDCG@k. We analyze the distinguishability of NDCG@k for various choices of k and the discount functions. Experimental results on real Web search datasets agree well with the theory.


title: Moving-object detection method for moving cameras by merging background subtraction and optical flow methods

abstract: This paper presents a novel moving-object detection method for moving cameras. The proposed method merges two scores to detect moving objects more accurately in quasi-real time. We designed two scores, anomaly and motion, for real-time application. The anomaly score is calculated based on a background subtraction and depends on the difference in pixel intensities between the current image and the background model. The anomaly score extract the moving objects pixels with high precision rate, however it tends to result in under-detection (low recall rate) in the object pixels whose intensities are close to the background. The motion score is calculated from a sparse optical-flow, which is based on the short-term tracking results of sparsely sampled points. The motion score can extract the moving object's pixels with high recall rate, even if those pixel intensities are close to the background. However, it tends to result in over-detection (low precision rate) around the object boundaries due to the sparse optical flow. We propose to merge these two complementary scores to compensate for each other, thus it can detect moving objects with robustness and high resolution. Experimental results showed that our proposed method outperforms the conventional methods in terms of F-measure.


title: An attention‐based BiLSTM‐CRF approach to document‐level chemical named entity recognition

abstract: Motivation ::: In biomedical research, chemical is an important class of entities, and chemical named entity recognition (NER) is an important task in the field of biomedical information extraction. However, most popular chemical NER methods are based on traditional machine learning and their performances are heavily dependent on the feature engineering. Moreover, these methods are sentence-level ones which have the tagging inconsistency problem. ::: ::: ::: Results ::: In this paper, we propose a neural network approach, i.e. attention-based bidirectional Long Short-Term Memory with a conditional random field layer (Att-BiLSTM-CRF), to document-level chemical NER. The approach leverages document-level global information obtained by attention mechanism to enforce tagging consistency across multiple instances of the same token in a document. It achieves better performances with little feature engineering than other state-of-the-art methods on the BioCreative IV chemical compound and drug name recognition (CHEMDNER) corpus and the BioCreative V chemical-disease relation (CDR) task corpus (the F-scores of 91.14 and 92.57%, respectively). ::: ::: ::: Availability and implementation ::: Data and code are available at https://github.com/lingluodlut/Att-ChemdNER. ::: ::: ::: Contact ::: yangzh@dlut.edu.cn or wangleibihami@gmail.com. ::: ::: ::: Supplementary information ::: Supplementary data are available at Bioinformatics online.


title: Biomimetic Tactile Sensor Array

abstract: The performance of robotic and prosthetic hands in unstructured environments is severely limited by their having little or no tactile information compared to the rich tactile feedback of the human ...


title: Understanding Convolution for Semantic Segmentation

abstract: Recent advances in deep learning, especially deep convolutional neural networks (CNNs), have led to significant improvement over previous semantic segmentation systems. Here we show how to improve pixel-wise semantic segmentation by manipulating convolution-related operations that are of both theoretical and practical value. First, we design dense upsampling convolution (DUC) to generate pixel-level prediction, which is able to capture and decode more detailed information that is generally missing in bilinear upsampling. Second, we propose a hybrid dilated convolution (HDC) framework in the encoding phase. This framework 1) effectively enlarges the receptive fields (RF) of the network to aggregate global information; 2) alleviates what we call the "gridding issue"caused by the standard dilated convolution operation. We evaluate our approaches thoroughly on the Cityscapes dataset, and achieve a state-of-art result of 80.1% mIOU in the test set at the time of submission. We also have achieved state-of-theart overall on the KITTI road estimation benchmark and the PASCAL VOC2012 segmentation task. Our source code can be found at https://github.com/TuSimple/TuSimple-DUC.


title: Consensus for multi-agent systems with inherent nonlinear dynamics under directed topologies

abstract: This paper considers the consensus problem for multi-agent systems with inherent nonlinear dynamics under directed topologies. A variable transformation method is used to convert the consensus problem to a partial stability problem. Both first-order and second-order systems are investigated under fixed and switching topologies, respectively. It is assumed that the inherent nonlinear terms satisfy the Lipshitz condition. Sufficient conditions on the feedback gains are given based on a Lyapunov function method. For first-order systems under a fixed topology, the consensus is achieved if the feedback gain related to the agents’ positions is large enough. For first-order systems under switching topologies, the effect of the minimum dwell time for the switching signal on the consensus achievement is considered. For second-order systems under a fixed topology, the consensus is achieved if the feedback gains related to the agents’ positions and velocities, respectively, are both large enough. For second-order systems under switching topologies, a switching variable transformation is given. Then, the consensus problem is investigated when all the digraphs are strongly connected and weighted balanced with a common weighted vector. Finally, numerical simulations are provided to illustrate the effectiveness of the obtained theoretical results.


Efficient Anytime Density-based Clustering


title: Literature derived reference models for the adoption of online shopping

abstract: The aim of the authors of this paper was to identify areas that would aid in developing a better understanding of the dynamics of a customer's decision to shop online. To accomplish this, a review of the empirical studies on the antecedents of online shopping was performed. From an extensive literature search, a total of 45 relevant articles were identified. The factors that have been investigated in these studies were classified according to their similarity and patterns of their findings were identified and analyzed.


title: Audio-based music structure analysis

abstract: Humans tend to organize perceived information into hierarchies and structures, a principle that also applies to music. Even musically untrained listeners unconsciously analyze and segment music with regard to various musical aspects, for example, identifying recurrent themes or detecting temporal boundaries between contrasting musical parts. This paper gives an overview of state-of-theart methods for computational music structure analysis, where the general goal is to divide an audio recording into temporal segments corresponding to musical parts and to group these segments into musically meaningful categories. There are many different criteria for segmenting and structuring music audio. In particular, one can identify three conceptually different approaches, which we refer to as repetition-based, novelty-based, and homogeneitybased approaches. Furthermore, one has to account for different musical dimensions such as melody, harmony, rhythm, and timbre. In our state-of-the-art report, we address these different issues in the context of music structure analysis, while discussing and categorizing the most relevant and recent articles in this field.


title: Public Sentiment Analysis in Twitter Data for Prediction of a Company's Stock Price Movements

abstract: There has recently been some effort to mine social media for public sentiment analysis. Studies have suggested that public emotions shown through Tweeter may well be correlated with the Dow Jones Industrial Average. However, can public sentiment be analyzed to predict the movements of the stock price of a particular company? If so, is it possible for the stock price of one company to be more predictable than that of another company? Is there a particular kind of companies whose stock price are more predictable based on analyzing public sentiments as reflected in Twitter data? In this article, we propose a method to mine Twitter data for answers to these questions. Specifically, we propose to use a data mining algorithm to determine if the price of a selection of 30 companies listed in NASDAQ and the New York Stock Exchange can actually be predicted by the given 15 million records of tweets (i.e., Twitter messages). We do so by extracting ambiguous textual tweet data through NLP techniques to define public sentiment, then make use of a data mining technique to discover patterns between public sentiment and real stock price movements. With the proposed algorithm, we manage to discover that it is possible for the stock closing price of some companies to be predicted with an average accuracy as high as 76.12%. In this paper, we describe the data mining algorithm that we use and discuss the key findings in relation to the questions posed.


title: Deep Learning for Emotional Speech Recognition

abstract: Emotional speech recognition is a multidisciplinary research area that has received increasing attention over the last few years. The present paper considers the application of restricted Boltzmann machines (RBM) and deep belief networks (DBN) to the difficult task of automatic Spanish emotional speech recognition. The principal motivation lies in the success reported in a growing body of work employing these techniques as alternatives to traditional methods in speech processing and speech recognition. Here a well-known Spanish emotional speech database is used in order to extensively experiment with, and compare, different combinations of parameters and classifiers. It is found that with a suitable choice of parameters, RBM and DBN can achieve comparable results to other classifiers.


title: Learning Multimodal Neural Network with Ranking Examples

abstract: To support cross-modal information retrieval, cross-modal learning to rank approaches utilize ranking examples (e.g., an example may be a text query and its corresponding ranked images) to learn appropriate ranking (similarity) function. However, the fact that each modality is represented with intrinsically different low-level features hinders these approaches from better reducing the heterogeneity-gap between the modalities and thus giving satisfactory retrieval results. In this paper, we consider learning with neural networks, from the perspective of optimizing the listwise ranking loss of the cross-modal ranking examples. The proposed model, named Cross-Modal Ranking Neural Network (CMRNN), benefits from the advance of both neural networks on learning high-level semantics and learning to rank techniques on learning ranking function, such that the learned cross-modal ranking function is implicitly embedded in the learned high-level representation for data objects with different modalities (e.g., text and imagery) to perform cross-modal retrieval directly. We compare CMRNN to existing state-of-the-art cross-modal ranking methods on two datasets and show that it achieves a better performance.


title: HFST—Framework for Compiling and Applying Morphologies*

abstract: HFST–Helsinki Finite-State Technology ( hfst.sf.net ) is a framework for compiling and applying linguistic descriptions with finite-state methods. HFST currently connects some of the most important finite-state tools for creating morphologies and spellers into one open-source platform and supports extending and improving the descriptions with weights to accommodate the modeling of statistical information. HFST offers a path from language descriptions to efficient language applications in key environments and operating systems. HFST also provides an opportunity to exchange transducers between different software providers in order to get the best out of each finite-state library.


title: The reactable: tangible and tabletop music performance

abstract: In this paper we present the Reactable, a new electronic musical instrument with a simple and intuitive tabletop interface that turns music into a tangible and visual experience, enabling musicians to experiment with sound, change its structure, control its parameters and be creative in a direct, refreshing and unseen way.


title: Backbone dynamics of a model membrane protein: measurement of individual amide hydrogen-exchange rates in detergent-solubilized M13 coat protein using 13C NMR hydrogen/deuterium isotope shifts.

abstract: Hydrogen-exchange rates have been measured for individual assigned amide protons in M13 coat protein, a 50-residue integral membrane protein, using a 13C nuclear magnetic resonance (NMR) equilibrium isotope shift technique. The locations of the more rapidly exchanging amides have been determined. In D2O solutions, a peptide carbonyl resonance undergoes a small upfield isotope shift (0.08-0.09 ppm) from its position in H2O solutions; in 1:1 H2O/D2O mixtures, the carbonyl line shape is determined by the exchange rate at the adjacent nitrogen atom. M13 coat protein was labeled biosynthetically with 13C at the peptide carbonyls of alanine, glycine, phenylalanine, proline, and lysine, and the exchange rates of 12 assigned amide protons in the hydrophilic regions were measured as a function of pH by using the isotope shift method. This equilibrium technique is sensitive to the more rapidly exchanging protons which are difficult to measure by classical exchange-out experiments. In proteins, structural factors, notably H bonding, can decrease the exchange rate of an amide proton by many orders of magnitude from that observed in the freely exposed amides of model peptides such as poly(DL-alanine). With corrections for sequence-related inductive effects [Molday, R. S., Englander, S. W., & Kallen, R. G. (1972) Biochemistry 11, 150-158], the retardation of amide exchange in sodium dodecyl sulfate solubilized coat protein has been calculated with respect to poly(DL-alanine). The most rapidly exchanging protons, which are retarded very little or not at all, are shown to occur at the N- and C-termini of the molecule.(ABSTRACT TRUNCATED AT 250 WORDS)


title: Online Grocery Shopping Around the World: Examples of Key Business Models

abstract: The grocery industry evolved from mom-and-pop stores to mega-markets to online grocery buying, with many different forms along the way. The global market for groceries in 2000 is over $2 trillion. The online grocery segment of the market is expected to reach $34 billion by 2002, a thirty-three fold increase from 1998. This tutorial examines the issues surrounding online groceries and looks closely at fourteen examples from the Americas, Europe, and Australia. The online market is currently one of growth, not profits. Two key components of profitability for online vendors are the ability to generate sufficient volume, while keeping delivery costs low. The Internet also impacts business-tobusiness relations among grocers. This tutorial describes key online grocery firms including Peapod, NetGrocer, Streamline, WebVan, Ruok@Net, Albert Heijn, Disco, Ykkoshalli, Shoplink, Coles and Woolworths. Each of these companies represents different business models, with differing organizational structure and scope of operations. Each has its own set of strengths and


title: Active Learning with Partial Feedback

abstract: In the large-scale multiclass setting, assigning labels often consists of answering multiple questions to drill down through a hierarchy of classes. Here, the labor required per annotation scales with the number of questions asked. We propose active learning with partial feedback. In this setup, the learner asks the annotator if a chosen example belongs to a (possibly composite) chosen class. The answer eliminates some classes, leaving the agent with a partial label. Success requires (i) a sampling strategy to choose (example, class) pairs, and (ii) learning from partial labels. Experiments on the TinyImageNet dataset demonstrate that our most effective method achieves a 26% relative improvement (8.1% absolute) in top1 classification accuracy for a 250k (or 30%) binary question budget, compared to a naive baseline. Our work may also impact traditional data annotation. For example, our best method fully annotates TinyImageNet with only 482k (with EDC though, ERC is 491) binary questions (vs 827k for naive method).


title: Product Kernel Interpolation for Scalable Gaussian Processes

abstract: Recent work shows that inference for Gaussian processes can be performed efficiently using iterative methods that rely only on matrix-vector multiplications (MVMs). Structured Kernel Interpolation (SKI) exploits these techniques by deriving approximate kernels with very fast MVMs. Unfortunately, such strategies suffer badly from the curse of dimensionality. We develop a new technique for MVM based learning that exploits product kernel structure. We demonstrate that this technique is broadly applicable, resulting in linear rather than exponential runtime with dimension for SKI, as well as state-of-the-art asymptotic complexity for multi-task GPs.


title: Photo forensics from JPEG dimples

abstract: Previous forensic techniques have exploited various characteristics of JPEG compression to reveal traces of manipulation in digital images. We describe a JPEG artifact that can arise depending on the choice of the mathematical operator used to convert DCT coefficients from floating-point to integer values. We show that the more commonly used floor or ceiling operators (but not the round operator) introduce a periodic artifact in the form of a single darker or brighter pixel — which we term a dimple — in 8 × 8 pixel blocks. We describe the nature of this artifact, its prevalence in commercial cameras, and how this artifact can be quantified and used to detect a wide range of digital manipulations from content-aware fill to re-sampling, airbrushing, and compositing.


title: Bottle Detection in the Wild Using Low-Altitude Unmanned Aerial Vehicles

abstract: In this paper, we propose a new dataset and benchmark for low altitude UAV object detection, aiming to find and localize waste plastic bottles in the wild, as well as to inspire the development of object detection models to be capable of detecting small and transparent objects. To this end, we collect 25, 407 UAV images of bottles with various kinds of backgrounds. Unlike traditional horizontal bounding box based annotation methods, we use the oriented bounding box to accurately and compactly annotate the bottles, which provides more detailed information for subsequent robotic grasping. The fully annotated images contain 34, 791 bottles, each of which is annotated by an arbitrary (5 d.o.f.) quadrilateral. To build a baseline for bottle detection, we evaluate several state-of-the-art object detection algorithms on our UAV-Bottle Dataset (UAV-BD), such as Faster R-CNN, SSD, YOLOv2 and RRPN. We also present an analysis of the dataset along with baseline approaches. Both the dataset and benchmark are made publicly available to the vision community on our website to advance research in the area of object detection from UAVs.


title: Bicomp: A Bilayer Scalable Nakamoto Consensus Protocol

abstract: Blockchain has received great attention in recent years and motivated innovations in different scenarios. However, many vital issues which affect its performance are still open. For example, it is widely convinced that high level of security and scalability and full decentralization are still impossible to achieve simultaneously. In this paper, we propose Bicomp, a bilayer scalable Nakamoto consensus protocol, which is an approach based on high security and pure decentralized Nakamoto consensus, and with a significant improvement on scalability. In Bicomp, two kinds of blocks are generated, i.e., microblocks for concurrent transaction packaging in network, and macroblocks for leadership competition and chain formation. A leader is elected at beginning of each round by using a macroblock header from proof-of-work. An elected leader then receives and packages multiple microblocks mined by different nodes into one macroblock during its tenure, which results in a bilayer block structure. Such design limits a leader's power and encourages as many nodes as possible to participate in the process of packaging transactions, which promotes the sharding nature of the system. Furthermore, several mechanisms are carefully designed to reduce transaction overlapping and further limit a leader's power, among which a novel transaction diversity based metric is proposed as the second level criteria besides the longest-chain-first principle on selecting a legitimate chain when fork happens. Security issues and potential attacks to Bicomp are extensively discussed and experiments for evaluation are performed. From the experimental results based on 50 nodes all over the world, Bicomp achieves significant improvement on scalability than that of Bitcoin and Ethereum, while the security and decentralization merits are still preserved.


title: Automated Epileptic Seizure Detection Methods: A Review Study

abstract: Epilepsy is a neurological disorder with prevalence of about 1-2% of the world’s population (Mormann, Andrzejak, Elger & Lehnertz, 2007). It is characterized by sudden recurrent and transient disturbances of perception or behaviour resulting from excessive synchronization of cortical neuronal networks; it is a neurological condition in which an individual experiences chronic abnormal bursts of electrical discharges in the brain. The hallmark of epilepsy is recurrent seizures termed "epileptic seizures". Epileptic seizures are divided by their clinical manifestation into partial or focal, generalized, unilateral and unclassified seizures (James, 1997; Tzallas, Tsipouras & Fotiadis, 2007a, 2009). Focal epileptic seizures involve only part of cerebral hemisphere and produce symptoms in corresponding parts of the body or in some related mental functions. Generalized epileptic seizures involve the entire brain and produce bilateral motor symptoms usually with loss of consciousness. Both types of epileptic seizures can occur at all ages. Generalized epileptic seizures can be subdivided into absence (petit mal) and tonic-clonic (grand mal) seizures (James, 1997).


title: Tutte Polynomial of Multi-Bridge Graphs

abstract: In this paper, using a well-known recursion for computing the Tutte polynomial of any graph, we found explicit formulae for the Tutte polynomials of any multi-bridge graph and some 2 tree graphs. Further, several recursive formulae for other graphs such as the fan and the wheel graphs are also discussed.


title: Improving Automated Bug Triaging with Specialized Topic Model

abstract: Bug triaging refers to the process of assigning a bug to the most appropriate developer to fix. It becomes more and more difficult and complicated as the size of software and the number of developers increase. In this paper, we propose a new framework for bug triaging, which maps the words in the bug reports (i.e., the term space) to their corresponding topics (i.e., the topic space). We propose a specialized topic modeling algorithm named multi-feature topic model (MTM) which extends Latent Dirichlet Allocation (LDA) for bug triaging. MTM considers product and component information of bug reports to map the term space to the topic space. Finally, we propose an incremental learning method named TopicMiner which considers the topic distribution of a new bug report to assign an appropriate fixer based on the affinity of the fixer to the topics. We pair TopicMiner with MTM ( TopicMiner $^{MTM}$ ). We have evaluated our solution on 5 large bug report datasets including GCC, OpenOffice, Mozilla, Netbeans, and Eclipse containing a total of 227,278 bug reports. We show that TopicMiner $^{MTM}$ can achieve top-1 and top-5 prediction accuracies of 0.4831-0.6868, and 0.7686-0.9084, respectively. We also compare TopicMiner $^{MTM}$ with Bugzie, LDA-KL, SVM-LDA, LDA-Activity, and Yang et al.'s approach. The results show that TopicMiner $^{MTM}$ on average improves top-1 and top-5 prediction accuracies of Bugzie by 128.48 and 53.22 percent, LDA-KL by 262.91 and 105.97 percent, SVM-LDA by 205.89 and 110.48 percent, LDA-Activity by 377.60 and 176.32 percent, and Yang et al.'s approach by 59.88 and 13.70 percent, respectively.


title: Deep Reinforcement Learning for Image Hashing

abstract: Deep hashing methods have received much attention recently, which achieve promising results by taking advantage of the strong representation power of deep networks. However, most existing deep hashing methods learn a whole set of hashing functions independently, while ignore the correlations between different hashing functions that can promote the retrieval accuracy greatly. Inspired by the sequential decision ability of deep reinforcement learning, we propose a new Deep Reinforcement Learning approach for Image Hashing (DRLIH). Our proposed DRLIH approach models the hashing learning problem as a sequential decision process, which learns each hashing function by correcting the errors imposed by previous ones and promotes retrieval accuracy. To the best of our knowledge, this is the first work to address hashing problem from deep reinforcement learning perspective. The main contributions of our proposed DRLIH approach can be summarized as follows: (1) We propose a deep reinforcement learning hashing network. In the proposed network, we utilize recurrent neural network (RNN) as agents to model the hashing functions, which take actions of projecting images into binary codes sequentially, so that the current hashing function learning can take previous hashing functions' error into account. (2) We propose a sequential learning strategy based on proposed DRLIH. We define the state as a tuple of internal features of RNN's hidden layers and image features, which can reflect history decisions made by the agents. We also propose an action group method to enhance the correlation of hash functions in the same group. Experiments on three widely-used datasets demonstrate the effectiveness of our proposed DRLIH approach.


title: Let's Dance: Learning From Online Dance Videos

abstract: In recent years, deep neural network approaches have naturally extended to the video domain, in their simplest case by aggregating per-frame classifications as a baseline for action recognition. A majority of the work in this area extends from the imaging domain, leading to visual-feature heavy approaches on temporal data. To address this issue we introduce "Let's Dance", a 1000 video dataset (and growing) comprised of 10 visually overlapping dance categories that require motion for their classification. We stress the important of human motion as a key distinguisher in our work given that, as we show in this work, visual information is not sufficient to classify motion-heavy categories. We compare our datasets' performance using imaging techniques with UCF-101 and demonstrate this inherent difficulty. We present a comparison of numerous state-of-the-art techniques on our dataset using three different representations (video, optical flow and multi-person pose data) in order to analyze these approaches. We discuss the motion parameterization of each of them and their value in learning to categorize online dance videos. Lastly, we release this dataset (and its three representations) for the research community to use.


Big data and data science methods for management research


title: Boxed out: blocking cellular interconnect bypass fraud at the network edge

abstract: The high price of incoming international calls is a common method of subsidizing telephony infrastructure in the developing world. Accordingly, international telephone system interconnects are regulated to ensure call quality and accurate billing. High call tariffs create a strong incentive to evade such interconnects and deliver costly international calls illicitly. Specifically, adversaries use VoIP-GSM gateways informally known as "simboxes" to receive incoming calls over wired data connections and deliver them into a cellular voice network through a local call that appears to originate from a customer's phone. This practice is not only extremely profitable for simboxers, but also dramatically degrades network experience for legitimate customers, violates telecommunications laws in many countries, and results in significant revenue loss. In this paper, we present a passive detection technique for combating simboxes at a cellular base station. Our system relies on the raw voice data received by the tower during a call to distinguish errors in GSM transmission from the distinct audio artifacts caused by delivering the call over a VoIP link. Our experiments demonstrate that this approach is highly effective, and can detect 87% of real simbox calls in only 30 seconds of audio with no false positives. Moreover, we demonstrate that evading our detection across multiple calls is only possible with a small probability. In so doing, we demonstrate that fraud that degrades network quality and costs telecommunications billions of dollars annually can easily be detected and counteracted in real time.


Neural Bag-of-Ngrams.


title: Online speaker diarization using adapted i-vector transforms

abstract: Many speaker diarization systems operate in an off-line mode. Such systems typically find homogeneous segments and then cluster these segments according to speaker. Such algorithms, like bottom-up clustering, k-means or spectral clustering, generally require the registration of all segments before clustering can begin. However, for real-time applications such as with multi-person voice interactive systems, there is a need to perform online speaker assignment in a strict left-to-right fashion. In this paper we propose a novel Maximum a Posteriori (MAP) adapted transform within an i-vector speaker diarization framework, that operates in a strict left-to-right fashion. Previous work by the community has shown that the principal components of variation of fixed dimensional i-vectors learned across segments tend to indicate a strong basis by which to separate speakers. However, determining this basis can be problematic when there are few segments or when operating in an online manner. The proposed method blends the prior with the estimated subspace as more i-vectors are observed. Given oracle SAD segments, with adaptation we achieve 3.2% speaker diarization error for a strict left-to-right constraint on the LDC Callhome English Corpus compared to 4.8% without adaptation.


title: Optic disk feature extraction via modified deformable model technique for glaucoma analysis

abstract: A deformable-model based approach is presented in this paper for robust detection of optic disk and cup boundaries. Earlier work on disk boundary detection up to now could not effectively solve the problem of vessel occlusion. The method proposed here improves and extends the original snake, which is essentially a deforming-only technique, in two aspects: knowledge-based clustering and smoothing update. The contour deforms to the location with minimum energy, and then self-clusters into two groups, i.e., edge-point group and uncertain-point group, which are finally updated by the combination of both local and global information. The modifications enable the proposed algorithm to become more accurate and robust to blood vessel occlusions, noises, ill-defined edges and fuzzy contour shapes. The comparative results on the 100 testing images show that the proposed method achieves better success rate (94%) when compared to those obtained by GVF-snake (12%) and modified ASM (82%). The proposed method is extended to detect the cup boundary and then extract the disk parameters for clinical application, which is a relatively new task in fundus image processing. The resulted cup-to-disk (C/D) ratio shows good consistency and compatibility when compared with the results from Heidelberg Retina Tomograph (HRT) under clinical validation.


title: Learning in Quantum Control: High-Dimensional Global Optimization for Noisy Quantum Dynamics

abstract: Abstract Quantum control is valuable for various quantum technologies such as high-fidelity gates for universal quantum computing, adaptive quantum-enhanced metrology, and ultra-cold atom manipulation. Although supervised machine learning and reinforcement learning are widely used for optimizing control parameters in classical systems, quantum control for parameter optimization is mainly pursued via gradient-based greedy algorithms. Although the quantum fitness landscape is often compatible with greedy algorithms, sometimes greedy algorithms yield poor results, especially for large-dimensional quantum systems. We employ differential evolution algorithms to circumvent the stagnation problem of non-convex optimization. We improve quantum control fidelity for noisy system by averaging over the objective function. To reduce computational cost, we introduce heuristics for early termination of runs and for adaptive selection of search subspaces. Our implementation is massively parallel and vectorized to reduce run time even further. We demonstrate our methods with two examples, namely quantum phase estimation and quantum-gate design, for which we achieve superior fidelity and scalability than obtained using greedy algorithms.


title: String Similarity Joins: An Experimental Evaluation

abstract: String similarity join is an important operation in data integration and cleansing that finds similar string pairs from two collections of strings. More than ten algorithms have been proposed to address this problem in the recent two decades. However, existing algorithms have not been thoroughly compared under the same experimental framework. For example, some algorithms are tested only on specific datasets. This makes it rather difficult for practitioners to decide which algorithms should be used for various scenarios. To address this problem, in this paper we provide a comprehensive survey on a wide spectrum of existing string similarity join algorithms, classify them into different categories based on their main techniques, and compare them through extensive experiments on a variety of real-world datasets with different characteristics. We also report comprehensive findings obtained from the experiments and provide new insights about the strengths and weaknesses of existing similarity join algorithms which can guide practitioners to select appropriate algorithms for various scenarios.


title: HypDB: A Demonstration of Detecting, Explaining and Resolving Bias in OLAP queries

abstract: On line analytical processing (OLAP) is an essential element of decision-support systems. However, OLAP queries can be biased and lead to perplexing and incorrect insights. In this demo, we present HypDB, the first system to detect, explain and resolve bias in OLAP queries. Our demonstration, shows several examples of OLAP queries from real world datasets that are biased and could lead to statistical anomalies such as Simpson's paradox. Then, we demonstrate step-by-step how HypDB: (1) detects whether an OLAP query is biased, (2) explains the root causes of the bias and reveals illuminating insights about the domain and the data collection process and (3) eliminates the bias via query rewriting and generates decision-support insights.


title: Belief Formation Under Signal Correlation

abstract: Using a set of incentivized laboratory experiments, we characterize how people form beliefs about a random variable based on independent and correlated signals. First, we theoretically show that, while pure correlation neglect always leads to overvaluing of correlated signals, that may not happen if people also exhibit overprecision perceiving signals to be more precise than they actually are. Our experimental results reveal that, while subjects do overvalue moderately or strongly correlated signals, they undervalue weakly correlated signals, suggesting concurrent presence of correlation neglect and overprecision. Estimated parameters of our model suggest that subjects show a nearly complete level of correlation neglect and also suffer from a high level of overprecision. Additionally, we find that subjects do not fully benefit from wisdom of the crowd-they undervalue aggregated information about othersi¯ actions in favor of their private information. This is consistent with models of overprecision where people do not properly incorporate the variance reducing power of averages.


Hierarchical Multi-Agent Reinforcement Learning through Communicative Actions for Human-Robot Collaboration


Efficient Construction of Approximate Ad-Hoc ML models Through Materialization and Reuse


title: QuickCSG: Fast Arbitrary Boolean Combinations of N Solids

abstract: QuickCSG computes the result for general N-polyhedron boolean expressions without an intermediate tree of solids. We propose a vertex-centric view of the problem, which simplifies the identification of final geometric contributions, and facilitates its spatial decomposition. The problem is then cast in a single KD-tree exploration, geared toward the result by early pruning of any region of space not contributing to the final surface. We assume strong regularity properties on the input meshes and that they are in general position. This simplifying assumption, in combination with our vertex-centric approach, improves the speed of the approach. Complemented with a task-stealing parallelization, the algorithm achieves breakthrough performance, one to two orders of magnitude speedups with respect to state-of-the-art CPU algorithms, on boolean operations over two to dozens of polyhedra. The algorithm also outperforms GPU implementations with approximate discretizations, while producing an output without redundant facets. Despite the restrictive assumptions on the input, we show the usefulness of QuickCSG for applications with large CSG problems and strong temporal constraints, e.g. modeling for 3D printers, reconstruction from visual hulls and collision detection.


title: Background subtraction for static & moving camera

abstract: Background subtraction is one of the most commonly used components in machine vision systems. Despite the numerous algorithms proposed in the literature and used in practical applications, key challenges remain in designing a single system that can handle diverse environmental conditions. In this paper we present Multiple Background Model based Background Subtraction Algorithm as such a candidate. The algorithm was originally designed for handling sudden illumination changes. The new version has been refined with changes at different steps of the process, specifically in terms of selecting optimal color space, clustering of training images for Background Model Bank and parameter for each channel of color space. This has allowed the algorithm's applicability to wide variety of challenges associated with change detection including camera jitter, dynamic background, Intermittent Object Motion, shadows, bad weather, thermal, night videos etc. Comprehensive evaluation demonstrates the superiority of algorithm against state of the art.


title: Blocking online advertising - A state of the art

abstract: Online advertising has emerged as one of the major business models on the Internet. Publishers rely on the online revenue generated from advertising to offer many free services. However, it has become evident that online advertisements are now becoming quite intrusive and also consume a lot of valuable bandwidth to download zero-caching ads and flash or video ads. As a result many users are now using ad-blocking software to make their Internet surfing more enjoyable. The main contribution of this paper is as follows - it attempts to identify the main reasons why internet users want to block online ads; it also critically evaluates several existing ad-blocking techniques and conducts an experiment to measure the amount of bandwidth used by online advertisements relative to the actual content. The paper concludes by outlining future research directions.


title: Towards Multi-view and Partially-Occluded Face Alignment

abstract: We present a robust model to locate facial landmarks under different views and possibly severe occlusions. To build reliable relationships between face appearance and shape with large view variations, we propose to formulate face alignment as an L1-induced Stagewise Relational Dictionary (SRD) learning problem. During each training stage, the SRD model learns a relational dictionary to capture consistent relationships between face appearance and shape, which are respectively modeled by the pose-indexed image features and the shape displacements for current estimated landmarks. During testing, the SRD model automatically selects a sparse set of the most related shape displacements for the testing face and uses them to refine its shape iteratively. To locate facial landmarks under occlusions, we further propose to learn an occlusion dictionary to model different kinds of partial face occlusions. By deploying the occlusion dictionary into the SRD model, the alignment performance for occluded faces can be further improved. Our algorithm is simple, effective, and easy to implement. Extensive experiments on two benchmark datasets and two newly built datasets have demonstrated its superior performances over the state-of-the-art methods, especially for faces with large view variations and/or occlusions.


title: Relevant Subspace Clustering: Mining the Most Interesting Non-redundant Concepts in High Dimensional Data

abstract: Subspace clustering aims at detecting clusters in any subspace projection of a high dimensional space. As the number of possible subspace projections is exponential in the number of dimensions, the result is often tremendously large. Recent approaches fail to reduce results to relevant subspace clusters. Their results are typically highly redundant, i.e. many clusters are detected multiple times in several projections. In this work, we propose a novel model for relevant subspace clustering (RESCU). We present a global optimization which detects the most interesting non-redundant subspace clusters. We prove that computation of this model is NP-hard. For RESCU, we propose an approximative solution that shows high accuracy with respect to our relevance model. Thorough experiments on synthetic and real world data show that RESCU successfully reduces the result to manageable sizes. It reliably achieves top clustering quality while competing approaches show greatly varying performance.


title: Orthographic Star Coordinates

abstract: Star coordinates is a popular projection technique from an nD data space to a 2D/3D visualization domain. It is defined by setting n coordinate axes in the visualization domain. Since it generally defines an affine projection, strong distortions can occur: an nD sphere can be mapped to an ellipse of arbitrary size and aspect ratio. We propose to restrict star coordinates to orthographic projections which map an nD sphere of radius r to a 2D circle of radius r. We achieve this by formulating conditions for the coordinate axes to define orthographic projections, and by running a repeated non-linear optimization in the background of every modification of the coordinate axes. This way, we define a number of orthographic interaction concepts as well as orthographic data tour sequences: a scatterplot tour, a principle component tour, and a grand tour. All concepts are illustrated and evaluated with synthetic and real data.


title: Anatomy of High-Performance Many-Threaded Matrix Multiplication

abstract: BLIS is a new framework for rapid instantiation of the BLAS. We describe how BLIS extends the "GotoBLAS approach" to implementing matrix multiplication (GEMM). While GEMM was previously implemented as three loops around an inner kernel, BLIS exposes two additional loops within that inner kernel, casting the computation in terms of the BLIS micro-kernel so that porting G E M M becomes a matter of customizing this micro-kernel for a given architecture. We discuss how this facilitates a finer level of parallelism that greatly simplifies the multithreading of GEMM as well as additional opportunities for parallelizing multiple loops. Specifically, we show that with the advent of many-core architectures such as the IBM PowerPC A2 processor (used by Blue Gene/Q) and the Intel Xeon Phi processor, parallelizing both within and around the inner kernel, as the BLIS approach supports, is not only convenient, but also necessary for scalability. The resulting implementations deliver what we believe to be the best open source performance for these architectures, achieving both impressive performance and excellent scalability.


title: Stock market prediction using Hidden Markov Models

abstract: Stock market prediction is a classic problem which has been analyzed extensively using tools and techniques of Machine Learning. Interesting properties which make this modeling non-trivial is the time dependence, volatility and other similar complex dependencies of this problem. To incorporate these, Hidden Markov Models (HMM's) have recently been applied to forecast and predict the stock market. We present the Maximum a Posteriori HMM approach for forecasting stock values for the next day given historical data. In our approach, we consider the fractional change in Stock value and the intra-day high and low values of the stock to train the continuous HMM. This HMM is then used to make a Maximum a Posteriori decision over all the possible stock values for the next day. We test our approach on several stocks, and compare the performance to some of the existing methods using HMMs and Artificial Neural Networks using Mean Absolute Percentage Error (MAPE).


title: Blind Source Separation by Sparse Decomposition in a Signal Dictionary

abstract: The blind source separation problem is to extract the underlying source signals from a set of linear mixtures, where the mixing matrix is unknown. This situation is common in acoustics, radio, medical signal and image processing, hyperspectral imaging, and other areas. We suggest a two-stage separation process: a priori selection of a possibly overcomplete signal dictionary (for instance, a wavelet frame or a learned dictionary) in which the sources are assumed to be sparsely representable, followed by unmixing the sources by exploiting the their sparse representability. We consider the general case of more sources than mixtures, but also derive a more efficient algorithm in the case of a nonovercomplete dictionary and an equal numbers of sources and mixtures. Experiments with artificial signals and musical sounds demonstrate significantly better separation than other known techniques.


title: Semiautomatic White Blood Cell Segmentation Based on Multiscale Analysis

abstract: This paper approaches novel methods to segment the nucleus and cytoplasm of white blood cells (WBC). This information is the basis to perform higher level tasks such as automatic differential counting, which plays an important role in the diagnosis of different diseases. We explore the image simplification and contour regularization resulting from the application of the selfdual multiscale morphological toggle (SMMT), an operator with scale-space properties. To segment the nucleus, the image preprocessing with SMMT has shown to be essential to ensure the accuracy of two well-known image segmentations techniques, namely, watershed transform and Level-Set methods. To identify the cytoplasm region, we propose two different schemes, based on granulometric analysis and on morphological transformations. The proposed methods have been successfully applied to a large number of images, showing promising segmentation and classification results for varying cell appearance and image quality, encouraging future works.


Invariant Representation and Classification of Maized Wheat Kernels from X-ray Images


CNN for Text-Based Multiple Choice Question Answering


title: Epistemology in the Era of Fake News: An Exploration of Information Verification Behaviors among Social Networking Site Users

abstract: Fake news has recently garnered increased attention across the world. Digital collaboration technologies now enable individuals to share information at unprecedented rates to advance their own ideologies. Much of this sharing occurs via social networking sites (SNSs), whose members may choose to share information without consideration for its authenticity. This research advances our understanding of information verification behaviors among SNS users in the context of fake news. Grounded in literature on the epistemology of testimony and theoretical perspectives on trust, we develop a news verification behavior research model and test six hypotheses with a survey of active SNS users. The empirical results confirm the significance of all proposed hypotheses. Perceptions of news sharers' network (perceived cognitive homogeneity, social tie variety, and trust), perceptions of news authors (fake news awareness and perceived media credibility), and innate intentions to share all influence information verification behaviors among SNS members. Theoretical implications, as well as implications for SNS users and designers, are presented in the light of these findings.


#MeToo Alexa: How Conversational Systems Respond to Sexual Harassment


title: Pre-conditions for Inflation Targeting in an Emerging Economy: The Case of India

abstract: This article looks at the preconditions that an emerging economy needs to fulfill, before it can adopt inflation targeting as a monetary policy regime. The study is conducted using the Indian economy as a case study. We conduct an in-depth sector-wise analysis of the Indian economy to evaluate the independence of India's monetary policy from fiscal, external, structural and financial perspectives. Dominance from any of these sectors may divert monetary policy from the objective of maintaining price stability in the economy. Our analysis suggests that among the four dominance issues, the issue of 'structural dominance??? is the most acute for India. Supply shocks, hitting the economy due to structural bottlenecks, pose a major threat to the independent conduct of monetary policy. This study concludes that inflation band targeting with a wide target range would be a feasible monetary policy option for India.


A Reference Model Architecture for Intelligent Systems Design


title: Inverse Problem Theory and Methods for Model Parameter Estimation

abstract: 1. The general discrete inverse problem 2. Monte Carol methods 3. The least-squares criterion 4. Least-absolute values criterion and minimax criterion 5. Functional inverse problems 6. Appendices 7. Problems References Index.


title: 94 GHz Substrate Integrated Monopulse Antenna Array

abstract: A planar W-band monopulse antenna array is designed based on the substrate integrated waveguide (SIW) technology. The sum-difference comparator, 16-way divider and 32 × 32 slot array antenna are all integrated on a single dielectric substrate in the compact layout through the low-cost PCB process. Such a substrate integrated monopulse array is able to operate over 93 ~ 96 GHz with narrow-beam and high-gain. The maximal gain is measured to be 25.8 dBi, while the maximal null-depth is measured to be - 43.7 dB. This SIW monopulse antenna not only has advantages of low-cost, light, easy-fabrication, etc., but also has good performance validated by measurements. It presents an excellent candidate for W-band directional-finding systems.


title: Using feedback in collaborative reinforcement learning to adaptively optimize MANET routing

abstract: Designers face many system optimization problems when building distributed systems. Traditionally, designers have relied on optimization techniques that require either prior knowledge or centrally managed runtime knowledge of the system's environment, but such techniques are not viable in dynamic networks where topology, resource, and node availability are subject to frequent and unpredictable change. To address this problem, we propose collaborative reinforcement learning (CRL) as a technique that enables groups of reinforcement learning agents to solve system optimization problems online in dynamic, decentralized networks. We evaluate an implementation of CRL in a routing protocol for mobile ad hoc networks, called SAMPLE. Simulation results show how feedback in the selection of links by routing agents enables SAMPLE to adapt and optimize its routing behavior to varying network conditions and properties, resulting in optimization of network throughput. In the experiments, SAMPLE displays emergent properties such as traffic flows that exploit stable routes and reroute around areas of wireless interference or congestion. SAMPLE is an example of a complex adaptive distributed system.


title: Dual Decomposition with Many Overlapping Components

abstract: Dual decomposition has been recently proposed as a way of combining complementary models, with a boost in predictive power. However, in cases where lightweight decompositions are not readily available (e.g., due to the presence of rich features or logical constraints), the original subgradient algorithm is inefficient. We sidestep that difficulty by adopting an augmented Lagrangian method that accelerates model consensus by regularizing towards the averaged votes. We show how first-order logical constraints can be handled efficiently, even though the corresponding subproblems are no longer combinatorial, and report experiments in dependency parsing, with state-of-the-art results.


title: Generating eating plans for athletes using the particle swarm optimization

abstract: This paper presents the automatic generation of optimal eating plans for athletes. The automatic generation of the eating plans is introduced as an optimization problem, where particle swarm optimization is taken as the problem solver. Inputs for the proposed particle swarm optimization algorithm are generated training plan and list of the potential meals, while the output of the algorithm represents a list of meals that should be consumed by the athletes. The first practical experiments showed that this solution is very promising.


title: TimeNet: Pre-trained deep recurrent neural network for time series classification

abstract: Inspired by the tremendous success of deep Convolutional Neural Networks as generic feature extractors for images, we propose TimeNet: a deep recurrent neural network (RNN) trained on diverse time series in an unsupervised manner using sequence to sequence (seq2seq) models to extract features from time series. Rather than relying on data from the problem domain, TimeNet attempts to generalize time series representation across domains by ingesting time series from several domains simultaneously. Once trained, TimeNet can be used as a generic off-the-shelf feature extractor for time series. The representations or embeddings given by a pre-trained TimeNet are found to be useful for time series classification (TSC). For several publicly available datasets from UCR TSC Archive and an industrial telematics sensor data from vehicles, we observe that a classifier learned over the TimeNet embeddings yields significantly better performance compared to (i) a classifier learned over the embeddings given by a domain-specific RNN, as well as (ii) a nearest neighbor classifier based on Dynamic Time Warping.


title: Evaluation of LBP and HOG Descriptors for Clothing Attribute Description

abstract: In this work an experimental study about the capability of the LBP, HOG descriptors and color for clothing attribute classification is presented. Two different variants of the LBP descriptor are considered, the original LBP and the uniform LBP. Two classifiers, Linear SVM and Random Forest, have been included in the comparison because they have been frequently used in clothing attributes classification. The experiments are carried out with a public available dataset, the clothing attribute dataset, that has 26 attributes in total. The obtained accuracies are over 75 % in most cases, reaching 80 % for the necktie or sleeve length attributes.


title: Sponge: portable stream programming on graphics engines

abstract: Graphics processing units (GPUs) provide a low cost platform for accelerating high performance computations. The introduction of new programming languages, such as CUDA and OpenCL, makes GPU programming attractive to a wide variety of programmers. However, programming GPUs is still a cumbersome task for two primary reasons: tedious performance optimizations and lack of portability. First, optimizing an algorithm for a specific GPU is a time-consuming task that requires a thorough understanding of both the algorithm and the underlying hardware. Unoptimized CUDA programs typically only achieve a small fraction of the peak GPU performance. Second, GPU code lacks efficient portability as code written for one GPU can be inefficient when executed on another. Moving code from one GPU to another while maintaining the desired performance is a non-trivial task often requiring significant modifications to account for the hardware differences. In this work, we propose Sponge, a compilation framework for GPUs using synchronous data flow streaming languages. Sponge is capable of performing a wide variety of optimizations to generate efficient code for graphics engines. Sponge alleviates the problems associated with current GPU programming methods by providing portability across different generations of GPUs and CPUs, and a better abstraction of the hardware details, such as the memory hierarchy and threading model. Using streaming, we provide a write-once software paradigm and rely on the compiler to automatically create optimized CUDA code for a wide variety of GPU targets. Sponge's compiler optimizations improve the performance of the baseline CUDA implementations by an average of 3.2x.


title: Social Interactions in Massively Multiplayer Online Role-Playing Gamers

abstract: To date, most research into massively multiplayer online role-playing games (MMORPGs) has examined the demographics of play. This study explored the social interactions that occur both within and outside of MMORPGs. The sample consisted of 912 self-selected MMORPG players from 45 countries. MMORPGs were found to be highly socially interactive environments providing the opportunity to create strong friendships and emotional relationships. The study demonstrated that the social interactions in online gaming form a considerable element in the enjoyment of playing. The study showed MMORPGs can be extremely social games, with high percentages of gamers making life-long friends and partners. It was concluded that virtual gaming may allow players to express themselves in ways they may not feel comfortable doing in real life because of their appearance, gender, sexuality, and/or age. MMORPGs also offer a place where teamwork, encouragement, and fun can be experienced.


title: Branch regulation: low-overhead protection from code reuse attacks

abstract: Code reuse attacks (CRAs) are recent security exploits that allow attackers to execute arbitrary code on a compromised machine. CRAs, exemplified by return-oriented and jump-oriented programming approaches, reuse fragments of the library code, thus avoiding the need for explicit injection of attack code on the stack. Since the executed code is reused existing code, CRAs bypass current hardware and software security measures that prevent execution from data or stack regions of memory. While software-based full control flow integrity (CFI) checking can protect against CRAs, it includes significant overhead, involves non-trivial effort of constructing a control flow graph, relies on proprietary tools and has potential vulnerabilities due to the presence of unintended branch instructions in architectures such as x86---those branches are not checked by the software CFI. We propose branch regulation (BR), a lightweight hardware-supported protection mechanism against the CRAs that addresses all limitations of software CFI. BR enforces simple control flow rules in hardware at the function granularity to disallow arbitrary control flow transfers from one function into the middle of another function. This prevents common classes of CRAs without the complexity and run-time overhead of full CFI enforcement. BR incurs a slowdown of about 2% and increases the code footprint by less than 1% on the average for the SPEC 2006 benchmarks.


title: A wireless sensor network For structural monitoring

abstract: A Wireless Sensor Network For Structural Monitoring ∗ Ning Xu Sumit Rangwala Alan Broad Krishna Kant Chintalapudi Deepak Ganesan Ramesh Govindan Deborah Estrin ABSTRACT Structural monitoring—the collection and analysis of structural re- sponse to ambient or forced excitation–is an important application of networked embedded sensing with signiﬁcant commercial po- tential. The ﬁrst generation of sensor networks for structural mon- itoring are likely to be data acquisition systems that collect data at a single node for centralized processing. In this paper, we dis- cuss the design and evaluation of a wireless sensor network sys- tem (called Wisden) for structural data acquisition. Wisden in- corporates two novel mechanisms, reliable data transport using a hybrid of end-to-end and hop-by-hop recovery, and low-overhead data time-stamping that does not require global clock synchroniza- tion. We also study the applicability of wavelet-based compression techniques to overcome the bandwidth limitations imposed by low- power wireless radios. We describe our implementation of these mechanisms on the Mica-2 motes and evaluate the performance of our implementation. We also report experiences from deploying Wisden on a large structure. General Terms Reliability, Design Keywords Sensor Network, Structural Health Monitoring, Wisden INTRODUCTION Categories and Subject Descriptors C.2.1 [Computer Communication Networks]: Wireless commu- nication; C.3 [Special-Purpose and Application-Based Systems]: Embedded Systems ∗ This material is based upon work supported by the National Sci- ence Foundation under Grants No. 0121778 (Center for Embedded Networked Systems) and 0325875 (ITR: Structural Health Moni- toring Using Local Excitations and Dense Sensing). Any opinions, ﬁndings and conclusions or recomendations expressed in this ma- terial are those of the author(s) and do not necessarily reﬂect the views of the National Science Foundation (NSF). † Computer Science Department, University of Southern California, {nxu, srangwal, chintala, ramesh}@usc.edu ‡ Current Afﬁliation - Center for Embedded Networked Sensing, Los Angeles § Computer Science Department, University of California, Los An- geles {deepak, destrin}@cs.ucla.edu ¶ Crossbow Technology Inc. abroad@xbow.com Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for proﬁt or commercial advantage and that copies bear this notice and the full citation on the ﬁrst page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior speciﬁc permission and/or a fee. SenSys’04, November 3–5, 2004, Baltimore, Maryland, USA. Copyright 2004 ACM 1-58113-879-2/04/0011 ... $ 5.00. Structural health monitoring systems seek to detect and local- ize damage in buildings, bridges, ships, and aircraft. The design of such systems is an active and well-established area of research. When built, such systems would infer the existence and location of damage by measuring structural response to ambient or forced excitation. Wireless sensor networks are a natural candidate for structural health monitoring systems, since they enable dense in- situ sensing and simplify deployment of instrumentation. However, techniques for damage assessment are quite complex, and practical wireless networked structural health monitoring systems are sev- eral years away. Wireless sensor networks do have a more immediate role to play in structural monitoring. Advances in structural engineering de- pend upon the availability of many detailed data sets that record the response of different structures to ambient vibration (caused, for example, by earthquakes, wind, or passing vehicles) or forced excitation (delivered by large special-purpose shakers). Currently, structural engineers use wired or single-hop wireless data acqui- sition systems to acquire such data sets. These systems consist of a device that collects and stores vibration measurements from a small number of sensors. However, power and wiring constraints imposed by these systems can increase the cost of acquiring these data sets, impose signiﬁcant setup delays, and limit the number and location of sensors. Wireless sensor networks can help address these issues. In this paper, we describe the design of Wisden, a wireless sen- sor network system for structural-response data acquisition. Wis- den continuously collects structural response data from a multi-hop network of sensor nodes, and displays and stores the data at a base station. Wisden can be thought of as a ﬁrst-generation wireless structural monitoring system; it incorporates some in-network pro- cessing, but later systems will move more processing into the net- work once the precise structural monitoring applications are better understood. In being essentially a data collection system, Wisden resembles other early sensor networks such as those being deployed for habitat monitoring [10]. While the architecture of Wisden is simple—a base station cen- trally collecting data—its design is a bit more challenging than that of other sensor networks built till date. Structural response data is generated at higher data rates than most sensing applications


title: Egovernment and web accessibility in South America

abstract: The number of e-government websites has increased greatly in recent years. Many countries have laws to ensure that e-government sites satisfy web accessibility requirements. The objective of web accessibility is to ensure that people with disabilities can access websites just like everyone else. However, laws that enforce web accessibility do not automatically guarantee compliance: e-government websites are not always prepared to provide a correct service to persons with disabilities. This paper analyses the accessibility of a group of e-government websites of all South American countries and Spain. Three official websites from each country has been analysed: the government, the Parliament and the Senate websites. Different automatic evaluation tools have been used to perform the analysis. The preliminary results of our research show that the majority of e-government websites do not provide adequate levels of web accessibility.


title: Deep convolutional neural network based species recognition for wild animal monitoring

abstract: We proposed a novel deep convolutional neural network based species recognition algorithm for wild animal classification on very challenging camera-trap imagery data. The imagery data were captured with motion triggered camera trap and were segmented automatically using the state of the art graph-cut algorithm. The moving foreground is selected as the region of interests and is fed to the proposed species recognition algorithm. For the comparison purpose, we use the traditional bag of visual words model as the baseline species recognition algorithm. It is clear that the proposed deep convolutional neural network based species recognition achieves superior performance. To our best knowledge, this is the first attempt to the fully automatic computer vision based species recognition on the real camera-trap images. We also collected and annotated a standard camera-trap dataset of 20 species common in North America, which contains 14, 346 training images and 9, 530 testing images, and is available to public for evaluation and benchmark purpose.


title: Forecasting Crime Using the ARIMA Model

abstract: In this paper, time series model of ARIMA is used to make short-term forecasting of property crime for one city of China. With the given data of property crime for 50 weeks, an ARIMA model is determined and the crime amount of 1 week ahead is predicted. The modelpsilas fitting and forecasting results are compared with the SES and HES. It is shown that the ARIMA model has higher fitting and forecasting accuracy than exponential smoothing. This work will be helpful for the local police stations and municipal governments in decision making and crime suppression.


title: Application Of A Stream-Based SAR-Backprojection Approach For Automotive Environment Perception

abstract: This paper presents the vehicle integration of a synthetic aperture radar (SAR) algorithm to produce SAR images for automotive scenarios while driving. The focus of this work is on two parts. At the beginning, the hardware setup embedded in the test vehicle is presented as well as the sensor synchronization to provide SAR raw data. Subsequently, a pixel-based parallelization concept for stream-based SAR image calculation on a GPU is introduced. Our experimental results prove that the complete SAR imaging process can be integrated into a vehicle to produce online highly resolved amplitude maps for automated driving applications.


title: A Novel Method for Detecting Image Forgery Based on Convolutional Neural Network

abstract: Detection of image forgery is an important part of digital forensics and has attracted a lot of attention in the past few years. Previous research has examined residual pattern noise, wavelet transform and statistics, image pixel value histogram and other features of images to authenticate the primordial nature. With the development of neural network technologies, some effort has recently applied convolutional neural networks to detecting image forgery to achieve high-level image representation. This paper proposes to build a convolutional neural network different from the related work in which we try to understand extracted features from each convolutional layer and detect different types of image tampering through automatic feature learning. The proposed network involves five convolutional layers, two full-connected layers and a Softmax classifier. Our experiment has utilized CASIA v1.0, a public image set that contains authentic images and splicing images, and its further reformed versions containing retouching images and re-compressing images as the training data. Experimental results can clearly demonstrate the effectiveness and adaptability of the proposed network.


title: On the Design of a Blockchain-Based System to Facilitate Healthcare Data Sharing

abstract: Blockchain technology though originally designed for keeping financial ledgers, recently has found applications in many different fields including healthcare. Sharing healthcare data for research purposes will boost research innovation in this area. That being said, healthcare data sharing raises many privacy and security issues for the Patients who share their data. In this work, we present the potential of Blockchain technology to facilitate (i) private and auditable healthcare data sharing and (ii) healthcare data access permission handling by proposing a blockchain-based system architecture design.


title: Enhanced Web Application and Browsing Performance through Service-Worker Infusion Framework

abstract: With the evolution of mobile devices and app eco system, all major content providers develop services in native apps and web-apps. Web-apps have an inherent advantage of platform independent and uniform experience across devices, but the page load time, battery usage, and bandwidth consumption have to be improved. To solve some of these issues, there has been an introduction of Progressive Web Apps (PWA) by content providers. PWA apps use an effective caching policy on web resources based on its property. But the webpages are overwhelmingly non-PWA compliant. There is a need for non-PWA web pages to be effective in bandwidth utilization so that we can avoid the exchange of needless resources. This paper proposes a new method to inject a service worker into a webpage at Proxy Servers. This service worker is packaged along with non-PWA webpage as a response to web browser requests. The web engine then runs the service worker to identify stale web resources which in turn will be helpful to avoid transfer of redundant web resources. The proposed approach was evaluated using top 25 Non-PWA sites from Alexa 100 websites for one month. It saved an average of 25% data traffic and also provided an offline experience of these websites. This proposal can be further extended to create a generalized framework for seamlessly converting Non-PWA apps to PWA apps.