title: SDE: A Novel Selective, Discriminative and Equalizing Feature Representation for Visual Recognition

abstract: Bag of Words (BoW) model and Convolutional Neural Network (CNN) are two milestones in visual recognition. Both BoW and CNN require a feature pooling operation for constructing the frameworks. Particularly, the max-pooling has been validated as an efficient and effective pooling method compared with other methods such as average pooling and stochastic pooling. In this paper, we first evaluate different pooling methods, and then propose a new feature pooling method termed as selective, discriminative and equalizing pooling (SDE). The SDE representation is a feature learning mechanism by jointly optimizing the pooled representations with the target of learning more selective, discriminative and equalizing features. We use bilevel optimization to solve the joint optimization problem. Experiments on seven benchmark datasets (including both single-label and multi-label ones) well validate the effectiveness of our framework. Particularly, we achieve the state-of-the-art fused results (mAP) of 93.21 and 93.97% on the PASCAL VOC2007 and VOC2012 datasets, respectively.


title: Sharing-Aware Outlier Analytics over High-Volume Data Streams

abstract: Real-time analytics of anomalous phenomena on streaming data typically relies on processing a large variety of continuous outlier detection requests, each configured with different parameter settings. The processing of such complex outlier analytics workloads is resource consuming due to the algorithmic complexity of the outlier mining process. In this work we propose a sharing-aware multi-query execution strategy for outlier detection on data streams called SOP. A key insight of SOP is to transform the problem of handling a multi-query outlier analytics workload into a single-query skyline computation problem. We prove that the output of the skyline computation process corresponds to the minimal information needed for determining the outlier status of any point in the stream. Based on this new formulation, we design a customized skyline algorithm called K-SKY that leverages the domination relationships among the streaming data points to minimize the number of data points that must be evaluated for supporting multi-query outlier detection. Based on this K-SKY algorithm, our SOP solution achieves minimal utilization of both computational and memory resources for the processing of these complex outlier analytics workload. Our experimental study demonstrates that SOP consistently outperforms the state-of-art solutions by three orders of magnitude in CPU time, while only consuming 5% of their memory footprint - a clear win-win. Furthermore, SOP is shown to scale to large workloads composed of thousands of parameterized queries.


title: Detection of injected, dynamically generated, and obfuscated malicious code

abstract: This paper presents DOME, a host-based technique for detecting several general classes of malicious code in software executables. DOME uses static analysis to identify the locations (virtual addresses) of system calls within the software executables, and then monitors the executables at runtime to verify that every observed system call is made from a location identified using static analysis. The power of this technique is that it is simple, practical, applicable to real-world software, and highly effective against injected, dynamically generated, and obfuscated malicious code.


title: A software-defined, dual-polarized radar system

abstract: This work details the design and construction of a dual-polarization, continuous-wave, monostatic, software-defined radar system. The platform is built around the Ettus Research USRP-B210, which provides 30MHz of signal bandwidth at carrier frequencies ranging between 70MHz and 6GHz. The reconfigurable nature of the USRP is exploited to realize a radar with programmable waveforms. Radar measurements of an approximately known target response are conducted in the laboratory using an emulated target in a cabled system for validation of the system design.


Using Non-Photorealistic Rendering to Communicate Shape


title: Systematic analyses and comparison of development performance and product quality of Incremental Process and Agile Process

abstract: Context: Although Agile software development models have been widely used as a base for the software project life-cycle since 1990s, the number of studies that follow a sound empirical method and quantitatively reveal the effect of using these models over Traditional models is scarce. Objective: This article explains the empirical method of and the results from systematic analyses and comparison of development performance and product quality of Incremental Process and Agile Process adapted in two projects of a middle-size, telecommunication software development company. The Incremental Process is an adaption of the Waterfall Model whereas the newly introduced Agile Process is a combination of the Unified Software Development Process, Extreme Programming, and Scrum. Method: The method followed to perform the analyses and comparison is benefited from the combined use of qualitative and quantitative methods. It utilizes; GQM Approach to set measurement objectives, CMMI as the reference model to map the activities of the software development processes, and a pre-defined assessment approach to verify consistency of process executions and evaluate measure characteristics prior to quantitative analysis. Results: The results of the comparison showed that the Agile Process had performed better than the Incremental Process in terms of productivity (79%), defect density (57%), defect resolution effort ratio (26%), Test Execution V&V Effectiveness (21%), and effort prediction capability (4%). These results indicate that development performance and product quality achieved by following the Agile Process was superior to those achieved by following the Incremental Process in the projects compared. Conclusion: The acts of measurement, analysis, and comparison enabled comprehensive review of the two development processes, and resulted in understanding their strengths and weaknesses. The comparison results constituted objective evidence for organization-wide deployment of the Agile Process in the company.


title: Spike sorting for large, dense electrode arrays

abstract: Developments in microfabrication technology have enabled the production of neural electrode arrays with hundreds of closely spaced recording sites, and electrodes with thousands of sites are under development. These probes in principle allow the simultaneous recording of very large numbers of neurons. However, use of this technology requires the development of techniques for decoding the spike times of the recorded neurons from the raw data captured from the probes. Here we present a set of tools to solve this problem, implemented in a suite of practical, user-friendly, open-source software. We validate these methods on data from the cortex, hippocampus and thalamus of rat, mouse, macaque and marmoset, demonstrating error rates as low as 5%.


title: SearchGazer: Webcam Eye Tracking for Remote Studies of Web Search

abstract: We introduce SearchGazer, a web-based eye tracker for remote web search studies using common webcams already present in laptops and some desktop computers. SearchGazer is a pure JavaScript library that infers the gaze behavior of searchers in real time. The eye tracking model self-calibrates by watching searchers interact with the search pages and trains a mapping of eye features to gaze locations and search page elements on the screen. Contrary to typical eye tracking studies in information retrieval, this approach does not require the purchase of any additional specialized equipment, and can be done remotely in a user's natural environment, leading to cheaper and easier visual attention studies. While SearchGazer is not intended to be as accurate as specialized eye trackers, it is able to replicate many of the research findings of three seminal information retrieval papers: two that used eye tracking devices, and one that used the mouse cursor as a restricted focus viewer. Charts and heatmaps from those original papers are plotted side-by-side with SearchGazer results. While the main results are similar, there are some notable differences, which we hypothesize derive from improvements in the latest ranking technologies used by current versions of search engines and diligence by remote users. As part of this paper, we also release SearchGazer as a library that can be integrated into any search page.


title: Sensorless Direct Field-Oriented Control of Three-Phase Induction Motor Drives for Low-Cost Applications

abstract: A sensorless direct rotor field-oriented control (SDRFOC) scheme of three-phase induction motors for low-cost applications is presented in this paper. The SDRFOC algorithm is based on a sensorless closed-loop rotor flux observer whose main advantages are simplicity and robustness to motor parameter detuning. The whole algorithm has been developed and implemented on a low-cost fixed-point digital signal processor controller. Experimental results are presented for a 0.5-kW induction motor drive for a primary vacuum pump used in industry applications.


title: ShadowEth: Private Smart Contract on Public Blockchain

abstract: Blockchain is becoming popular as a distributed and reliable ledger which allows distrustful parties to transact safely without trusting third parties. Emerging blockchain systems like Ethereum support smart contracts where miners can run arbitrary user-defined programs. However, one of the biggest concerns about the blockchain and the smart contract is privacy, since all the transactions on the chain are exposed to the public. In this paper, we present ShadowEth, a system that leverages hardware enclave to ensure the confidentiality of smart contracts while keeping the integrity and availability based on existing public blockchains like Ethereum. ShadowEth establishes a confidential and secure platform protected by trusted execution environment (TEE) off the public blockchain for the execution and storage of private contracts. It only puts the process of verification on the blockchain. We provide a design of our system including a protocol of the cryptographic communication and verification and show the applicability and feasibility of ShadowEth by various case studies. We implement a prototype using the Intel SGX on the Ethereum network and analyze the security and availability of the system.


title: Towards a Design Philosophy for Interoperable Blockchain Systems

abstract: In this paper we discuss a design philosophy for interoperable blockchain systems, using the design philosophy of the Internet architecture as the basis to identify key design principles. Several interoperability challenges are discussed in the context of cross-domain transactions. We illustrate how these principles are informing the interoperability architecture of the MIT Tradecoin system.


title: A Watermarking Method for Still Images Using Modified Code Division Multiple Access

abstract: Watermarking is a potential method for copyright protection and multimedia data authentication in the internet. The watermarking process can be considered as a communication task. In this paper, a novel watermarking scheme using code division multiple access (CDMA) modulation is proposed to achieve a superior performance in terms of robustness and imperceptibility. In the proposed scheme, every three watermark bits are modulated into a five bit MCDMA code. Then the MCDMA code is concealed in the coefficients of a DCT block.To demonstrate the efficiency of the proposed scheme, simulations under various conditions are conducted. The empirical results show that our proposed scheme can stand up against most common attacks including JPEG compression, rotating, resizing, cropping, mosaicking, noising and blurring etc.


title: Data and Information Quality Research: Its Evolution and Future

abstract: Awareness of data and information quality issues has grown rapidly in light of the critical role played by the quality of information in our data-intensive, knowledge-based economy. Research in the past two decades has produced a large body of data quality knowledge and has expanded our ability to solve many data and information quality problems. We present an overview of the evolution and current landscape of data and information quality research. We introduce an integrative framework to characterize the research along two dimensions: topics and methods. Representative papers are cited for purposes of illustrating the issues addressed and the methods used. We also identify and discuss challenges to be addressed in future research.


title: Thermo-compression bonding for 2.5D fine pitch copper pillar bump interconnections on TSV interposer

abstract: The use of portable electronic devices like smart phones and tablets results in high demand for more function, smaller dimensions and reduced power consumption requirements. To meet these challenges, electronic package design uses thinner chips with fine pitch bumping. There is active development in 2.5D and 3D IC packages with through silicon via (TSV). Tighter interconnection in addition to the increased density in the circuit in 2.5D and 3D IC systems provide higher performance with lower power consumption [1]. In addition, there is increased in the demand for fine pitch copper pillar bumping due to the lower silicon node, chip size reduction and TSV technology. Fine pitch interconnections are required in 2.5D and 3DIC integration for the demands of electrical continuity and high performance. In the existing interconnection methods, solder micro bumps have received a great deal of attentions because of its low material and process cost [2]. The major difference of copper pillar FC bonding process comparing to traditional FC bonding process is the reduction of the solder volume on each solder bump. As a result, there is no advantage of self-alignment of the solder during solder reflow process. Flip-chip bonder having accurate chip placement capability is needed to ensure good solder joint formation. Conventional reflow method is still applicable for sizable solder bump of diameter being greater than 100 μm and larger than 150μm pitch [3]. The post underfill processes such as capillary underfill (CUF) and molded underfill (MUF) can be followed after the solder joints are formed. On the other hand, when the pitch of bumps and/or the thickness of the FC go down further, FC with copper pillar bumps bonded by TC process would be one of the solutions for fine-pitch FC applications. It has been shown that the bump pitch can be reduced to as small as 50 μm (inline pitch). This process also allows for better control on the solder squeezed out effect. However this process requires tight control on (i) the planarization between the FC and the bonding substrate and (ii) the stand-off of each solder joint. Good process parameters have to be established to ensure no solder collapse or open joint.


title: Synthetic Aperture Radar

abstract: The general theory of side-looking synthetic aperture radar systems is developed. A simple circuit-theory model is developed; the geometry of the system determines the nature of the prefilter and the receiver (or processor) is the postfilter. The complex distributed reflectivity density appears as the input, and receiver noise is first considered as the interference which limits performance. Analysis and optimization are carried out for three performance criteria (resolution, signal-to-noise ratio, and least squares estimation of the target field). The optimum synthetic aperture length is derived in terms of the noise level and average transmitted power. Range-Doppler ambiguity limitations and optical processing are discussed briefly. The synthetic aperture concept for rotating target fields is described. It is observed that, for a physical aperture, a side-looking radar, and a rotating target field, the azimuth resolution is ?/? where ? is the change in aspect angle over which the target field is viewed, The effects of phase errors on azimuth resolution are derived in terms of the power density spectrum of the derivative of the phase errors and the performance in the absence of phase errors.


title: Energy-efficient MIMO underlay spectrum sharing with rate splitting

abstract: This work studies energy efficiency (EE) of multiple-input and multiple-output (MIMO) underlay spectrum sharing, where rate splitting at the secondary transmitter and successive decoding at the secondary receiver is deployed when feasible. EE of the secondary transmission is optimized while satisfying the primary rate requirement and the secondary power constraint. EE is defined as the ratio of the achievable secondary rate and the secondary power consumption, including both the transmit power and the circuit power. Numerical results show that higher EE can be achieved at the cost of achievable rate compared with that of MIMO underlay spectrum sharing with rate optimization, and that rate splitting is beneficial in terms of EE.


title: Improved latch-up immunity in junction-isolated smart power ICs with unbiased guard ring

abstract: The performance of the unbiased guard ring structure is measured and the effects of high current, emitter area, and layout of unbiased guard rings are reported and explained. Measurements show a reduction in parasitic gain by up to six orders of magnitude, while also avoiding the cross talk and power consumption of biased rings. A comparative analysis of unbiased guard ring with biased guard ring shows up to 100 times better performance at low current levels. A modification to the unbiased guard ring is also implemented and successfully tested which shows an increase in the current handling capability of the structure by an order of magnitude.


title: Time series forecasting using neural networks

abstract: Artificial neural networks are suitable for many tasks in pattern recognition and machine learning. In this paper we present an APL system for forecasting univariate time series with artificial neural networks. Unlike conventional techniques for time series analysis, an artificial neural network needs little information about the time series data and can be applied to a broad range of problems. However, the problem of network “tuning” remains: parameters of the backpropagation algorithm as well as the network topology need to be adjusted for optimal performances. For our application, we conducted experiments to find the right parameters for a forecasting network. The artificial neural networks that were found delivered a better forecasting performance than results obtained by the well known ARIMA technique.


title: Fuzzy fault tree analysis: a review of concept and application

abstract: Fault tree analysis (FTA) is a widely used method for analyzing a system’s failure logic and calculating overall reliability. However, application of conventional FTA has some shortcomings, e.g. in handling the uncertainties, allowing the use of linguistic variables, and integrating human error in failure logic model. Hence, Fuzzy set theory has been proposed to overcome the limitation of conventional FTA. Fuzzy logic provides a framework whereby basic notions such as similarity, uncertainty and preference can be modeled effectively. The aim of this paper is to present a review of the concept of fuzzy theory with fault tree analysis and their applications since 1981, to reflect the current status of Fuzzy fault tree analysis (FFTA) methodologies, their strengths, weaknesses, and their applications. This paper explains the fundamentals of fuzzy theory and describes application of fuzzy importance for using FFTA. The concept of the failure possibility and uncertainty analysis by using FFTA is discussed, and concludes with discussion on the application of FFTA in different fields. The review reveals the effectiveness of the FFTA in comparison with conventional FTA, when there is inadequate amount of accurate reliability oriented information.


A survey of secure middleware for the Internet of Things


title: A capacitive-loaded level shift circuit for improving the noise immunity of high voltage gate drive IC

abstract: A high voltage gate drive IC achieving the high dV S /dt noise immunity up to 85V/ns and the allowable negative V S swing to −12V at 15V supply voltage is proposed for the first time. The robust features are due to the presented capacitive loaded level shift circuit used in the gate driver. Measured and simulated results are performed to verify the electrical characteristics of the designed gate driver which is implemented in a 0.5um 600V Bipolar-CMOS-DMOS (BCD) technology.


title: Mapping Language to Code in Programmatic Context

abstract: Source code is rarely written in isolation. It depends significantly on the programmatic context, such as the class that the code would reside in. To study this phenomenon, we introduce the task of generating class member functions given English documentation and the programmatic context provided by the rest of the class. This task is challenging because the desired code can vary greatly depending on the functionality the class provides (e.g., a sort function may or may not be available when we are asked to "return the smallest element" in a particular member variable list). We introduce CONCODE, a new large dataset with over 100,000 examples consisting of Java classes from online code repositories, and develop a new encoder-decoder architecture that models the interaction between the method documentation and the class environment. We also present a detailed error analysis suggesting that there is significant room for future work on this task.


title: Vehicle Re-Identification Based on Coupled Dictionary Learning

abstract: Vehicle re-identification technology is a key technology to identify unlicensed vehicles in the cross-view of video. Existing vehicle re-identification methods often focuses on the extraction of vehicle license information. There is not a good way to re-identify a vehicle without a license plate or a vehicle with a blocked license plate. In this paper, a method based on feature fusion and coupled dictionary learning is proposed for vehicle re-identification in the absence of a license plate or license block. In dictionary learning, a new least squares coupled dictionary learning method (nLSCDL) is proposed. We design and join the label matrix and mapping matrix to reduce the intra-class disparity of the same vehicle and widen the inter-class disparity of different vehicles across the field of vision. The method has good robustness to illumination change, perspective change and the parameters of the camera itself. our method has good recognition rate under three different vehicle data sets and GRID data sets.


title: HyP-DESPOT: A Hybrid Parallel Algorithm for Online Planning under Uncertainty

abstract: Planning under uncertainty is critical for robust robot performance in uncertain, dynamic environments, but it incurs high computational cost. State-of-the-art online search algorithms, such as DESPOT, have vastly improved the computational efficiency of planning under uncertainty and made it a valuable tool for robotics in practice. This work takes one step further by leveraging both CPU and GPU parallelization in order to achieve near real-time online planning performance for complex tasks with large state, action, and observation spaces. Specifically, we propose Hybrid Parallel DESPOT (HyP-DESPOT), a massively parallel online planning algorithm that integrates CPU and GPU parallelism in a multi-level scheme. It performs parallel DESPOT tree search by simultaneously traversing multiple independent paths using multi-core CPUs and performs parallel Monte-Carlo simulations at the leaf nodes of the search tree using GPUs. Experimental results show that HyP-DESPOT speeds up online planning by up to several hundred times, compared with the original DESPOT algorithm, in several challenging robotic tasks in simulation.


title: Fabrik: An Online Collaborative Neural Network Editor

abstract: We present Fabrik, an online neural network editor that provides tools to visualize, edit, and share neural networks from within a browser. Fabrik provides a simple and intuitive GUI to import neural networks written in popular deep learning frameworks such as Caffe, Keras, and TensorFlow, and allows users to interact with, build, and edit models via simple drag and drop. Fabrik is designed to be framework agnostic and support high interoperability, and can be used to export models back to any supported framework. Finally, it provides powerful collaborative features to enable users to iterate over model design remotely and at scale.


title: Multi-modal Score Fusion and Decision Trees for Explainable Automatic Job Candidate Screening from Video CVs

abstract: We describe an end-to-end system for explainable automatic job candidate screening from video CVs. In this application, audio, face and scene features are first computed from an input video CV, using rich feature sets. These multiple modalities are fed into modality-specific regressors to predict apparent personality traits and a variable that predicts whether the subject will be invited to the interview. The base learners are stacked to an ensemble of decision trees to produce the outputs of the quantitative stage, and a single decision tree, combined with a rule-based algorithm produces interview decision explanations based on the quantitative results. The proposed system in this work ranks first in both quantitative and qualitative stages of the CVPR 2017 ChaLearn Job Candidate Screening Coopetition.


title: Time-Elastic Generative Model for Acceleration Time Series in Human Activity Recognition

abstract: Body-worn sensors in general and accelerometers in particular have been widely used in order to detect human movements and activities. The execution of each type of movement by each particular individual generates sequences of time series of sensed data from which specific movement related patterns can be assessed. Several machine learning algorithms have been used over windowed segments of sensed data in order to detect such patterns in activity recognition based on intermediate features (either hand-crafted or automatically learned from data). The underlying assumption is that the computed features will capture statistical differences that can properly classify different movements and activities after a training phase based on sensed data. In order to achieve high accuracy and recall rates (and guarantee the generalization of the system to new users), the training data have to contain enough information to characterize all possible ways of executing the activity or movement to be detected. This could imply large amounts of data and a complex and time-consuming training phase, which has been shown to be even more relevant when automatically learning the optimal features to be used. In this paper, we present a novel generative model that is able to generate sequences of time series for characterizing a particular movement based on the time elasticity properties of the sensed data. The model is used to train a stack of auto-encoders in order to learn the particular features able to detect human movements. The results of movement detection using a newly generated database with information on five users performing six different movements are presented. The generalization of results using an existing database is also presented in the paper. The results show that the proposed mechanism is able to obtain acceptable recognition rates (F = 0.77) even in the case of using different people executing a different sequence of movements and using different hardware.


Android 7 File Based Encryption and the Attacks Against It


title: EchoBot: Facilitating data collection for robot learning with the Amazon echo

abstract: The Amazon Echo and Google Home exemplify a new class of home automation platforms that provide intuitive, low-cost, cloud-based speech interfaces. We present EchoBot, a system that interfaces the Amazon Echo to the ABB YuMi industrial robot to facilitate human-robot data collection for Learning from Demonstration (LfD). EchoBot uses the computation power of the Amazon cloud to robustly convert speech to text and provides continuous speech explanations to the user of the robot during operation. We study performance with two tasks, grasping and “Tower of Hanoi” ring stacking, with four input and output interface combinations. Our experiments vary speech and keyboard as input interfaces, and speech and monitor as output interfaces. We evaluate the effectiveness of EchoBot when collecting infrequent data in the first task, and evaluate EchoBot's effectiveness with frequent data input in the second task. Results suggest that speech has potential to provide significant improvements in demonstration times and reliability over keyboards and monitors, and we observed a 57% decrease in average time to complete a task that required two hands and frequent human input over 22 trials.


title: Dynamic selection of generative-discriminative ensembles for off-line signature verification

abstract: In practice, each writer provides only a limited number of signature samples to design a signature verification (SV) system. Hybrid generative-discriminative ensembles of classifiers (EoCs) are proposed in this paper to design an off-line SV system from few samples, where the classifier selection process is performed dynamically. To design the generative stage, multiple discrete left-to-right Hidden Markov Models (HMMs) are trained using a different number of states and codebook sizes, allowing the system to learn signatures at different levels of perception. To design the discriminative stage, HMM likelihoods are measured for each training signature, and assembled into feature vectors that are used to train a diversified pool of two-class classifiers through a specialized Random Subspace Method. During verification, a new dynamic selection strategy based on the K-nearest-oracles (KNORA) algorithm and on Output Profiles selects the most accurate EoCs to classify a given input signature. This SV system is suitable for incremental learning of new signature samples. Experiments performed with real-world signature data (composed of genuine samples, and random, simple and skilled forgeries) indicate that the proposed dynamic selection strategy can significantly reduce the overall error rates, with respect to other EoCs formed using well-known dynamic and static selection strategies. Moreover, the performance of the SV system proposed in this paper is significantly greater than or comparable to that of related systems found in the literature.


title: A chorus section detection method for musical audio signals and its application to a music listening station

abstract: This paper describes a method for obtaining a list of repeated chorus ("hook") sections in compact-disc recordings of popular music. The detection of chorus sections is essential for the computational modeling of music understanding and is useful in various applications, such as automatic chorus-preview/search functions in music listening stations, music browsers, or music retrieval systems. Most previous methods detected as a chorus a repeated section of a given length and had difficulty identifying both ends of a chorus section and dealing with modulations (key changes). By analyzing relationships between various repeated sections, our method, called RefraiD, can detect all the chorus sections in a song and estimate both ends of each section. It can also detect modulated chorus sections by introducing a perceptually motivated acoustic feature and a similarity that enable detection of a repeated chorus section even after modulation. Experimental results with a popular music database showed that this method correctly detected the chorus sections in 80 of 100 songs. This paper also describes an application of our method, a new music-playback interface for trial listening called SmartMusicKIOSK , which enables a listener to directly jump to and listen to the chorus section while viewing a graphical overview of the entire song structure. The results of implementing this application have demonstrated its usefulness


title: Improving public transit accessibility for blind riders by crowdsourcing bus stop landmark locations with Google street view

abstract: Low-vision and blind bus riders often rely on known physical landmarks to help locate and verify bus stop locations (e.g., by searching for a shelter, bench, newspaper bin). However, there are currently few, if any, methods to determine this information a priori via computational tools or services. In this paper, we introduce and evaluate a new scalable method for collecting bus stop location and landmark descriptions by combining online crowdsourcing and Google Street View (GSV). We conduct and report on three studies in particular: (i) a formative interview study of 18 people with visual impairments to inform the design of our crowdsourcing tool; (ii) a comparative study examining differences between physical bus stop audit data and audits conducted virtually with GSV; and (iii) an online study of 153 crowd workers on Amazon Mechanical Turk to examine the feasibility of crowdsourcing bus stop audits using our custom tool with GSV. Our findings reemphasize the importance of landmarks in non-visual navigation, demonstrate that GSV is a viable bus stop audit dataset, and show that minimally trained crowd workers can find and identify bus stop landmarks with 82.5% accuracy across 150 bus stop locations (87.3% with simple quality control).


title: Development of bioluminescent Edwardsiella ictaluri for noninvasive disease monitoring.

abstract: Edwardsiella ictaluri is a facultative intracellular bacterium that causes enteric septicemia of catfish (ESC). In this study, we aimed to develop bioluminescent E. ictaluri that can be monitored by noninvasive bioluminescence imaging (BLI). To accomplish this, the luxCDABE operon of Photorhabdus luminescens was cloned downstream of the lacZ promoter in the broad host range plasmid pBBR1MCS4. Edwardsiella ictaluri strain 93-146 transformed with the new plasmid, pAKlux1, was highly bioluminescent. pAKlux1 was stably maintained in E. ictaluri without any apparent effect on growth or native plasmid stability. To assess the usefulness of the bioluminescent strain in disease studies, catfish were infected with 93-146 pAKlux1 by intraperitoneal injection and by bath immersion, and in vivo bacterial dissemination was observed using BLI. This study demonstrated that bioluminescent E. ictaluri can be used for real-time monitoring of ESC in live fish, which should enable observation of pathogen attachment sites and tissue predilections.


title: Construction of rate-compatible LDPC codes utilizing information shortening and parity puncturing

abstract: This paper proposes a method for constructing rate-compatible low-density parity-check (LDPC) codes. The construction considers the problem of optimizing a family of rate-compatible degree distributions as well as the placement of bipartite graph edges. A hybrid approach that combines information shortening and parity puncturing is proposed. Local graph conditioning techniques for the suppression of error floors are also included in the construction methodology.


title: Management of Limb Ischaemia in the Neonate and Infant

abstract: Abstract Aim This article aims to provide the reader with an insight into the diagnosis and management of limb ischaemia presenting in neonates and infants by examining the published literature. Materials and methods A Pubmed search and an Ovid Medline/Embase search were both performed. The keywords were: ‘neonatal' or ‘paediatric' or ‘pediatric' and ‘limb' and ‘ischemia' or ‘ischaemia' and relevant articles were retrieved and analysed. Articles pertaining to children over the age of 3years and non-English literature articles were excluded.


Exploring the Value of Online Reviews to Organizations: Implications for Revenue Forecasting and Planning


title: Human-Robot Collaboration: A Survey

abstract: As robots are gradually leaving highly structured factory environments and moving into human populated environments, they need to possess more complex cognitive abilities. They do not only have to operate efficiently and safely in natural, populated environments, but also be able to achieve higher levels of cooperation and communication with humans. Human–robot collaboration (HRC) is a research field with a wide range of applications, future scenarios, and potentially a high economic impact. HRC is an interdisciplinary research area comprising classical robotics, cognitive sciences, and psychology. This paper gives a survey of the state of the art of HRC. Established methods for intention estimation, action planning, joint action, and machine learning are presented together with existing guidelines to hardware design. This paper is meant to provide the reader with a good overview of technologies and methods for HRC.


title: Quantitative analysis of histological tissue image based on cytological profiles and spatial statistics

abstract: The cellular heterogeneity and complex tissue architecture of most tumor samples is a major obstacle in image analysis on standard hematoxylin and eosin-stained (H&E) tissue sections. A mixture of cancer and normal cells complicates the interpretation of their cytological profiles. Furthermore, spatial arrangement and architectural organization of cells are generally not reflected in cellular characteristics analysis. To address these challenges, first we describe an automatic nuclei segmentation of H&E tissue sections. In the task of deconvoluting cellular heterogeneity, we adopt Landmark based Spectral Clustering (LSC) to group individual nuclei in such a way that nuclei in the same group are more similar. We next devise spatial statistics for analyzing spatial arrangement and organization, which are not detectable by individual cellular characteristics. Our quantitative, spatial statistics analysis could benefit H&E section analysis by refining and complementing cellular characteristics analysis.


title: A comparison of three QRS detection algorithms over a public database

abstract: We have compared three of the best QRS detection algorithms, regarding their results, to check the performance and to elucidate which get better accuracy. In the literature these algorithms were published in a theoretical way, without offering their code, so it is difficult to check its real behaviour over different collections of ECG records. This work brings the community our source code of each algorithm and results of its validation over a public database. In addition, this software was developed as a framework in order to permit the inclusion of new QRS detection algorithms and also its testing over different databases.


title: Finding unusual review patterns using unexpected rules

abstract: In recent years, opinion mining attracted a great deal of research attention. However, limited work has been done on detecting opinion spam (or fake reviews). The problem is analogous to spam in Web search [1, 9 11]. However, review spam is harder to detect because it is very hard, if not impossible, to recognize fake reviews by manually reading them [2]. This paper deals with a restricted problem, i.e., identifying unusual review patterns which can represent suspicious behaviors of reviewers. We formulate the problem as finding unexpected rules. The technique is domain independent. Using the technique, we analyzed an Amazon.com review dataset and found many unexpected rules and rule groups which indicate spam activities.


Robust Multichannel Gender Classification from Speech in Movie Audio


title: Effects of Caffeine on Crayfish Muscle Fibers: II. Refractoriness and factors influencing recovery (repriming) of contractile responses

abstract: When caffeine evokes a contraction, and only then, crayfish muscle fibers become refractory to a second challenge with caffeine for up to 20 min in the standard saline (5 mM Ko). However, the fibers still respond with contraction to an increase in Ko, though with diminished tension. Addition of Mn slows recovery, but the latter is greatly accelerated during exposure of the fiber to high Ko, or after a brief challenge with high Ko. Neither the depolarization induced by the K, nor the repolarization after its removal accounts for the acceleration, which occurs only if the challenge with K had itself activated the contractile system; acceleration is blocked when contractile responses to K are blocked by reducing the Ca in the bath or by adding Mn. Recovery is accelerated by redistribution of intracellular Cl and by trains of intracellularly applied depolarizing pulses, but not by hyperpolarization. The findings indicate that two sources of Ca can be mobilized to activate the contractile system. Caffeine mobilizes principally the Ca store of the SR. Depolarizations that are induced by high Ko, by transient efflux of Cl, or by intracellularly applied currents mobilize another source of Ca which is strongly dependent upon the entry of Ca from the bathing medium. The sequestering mechanism of the SR apparently can utilize this second source of Ca to replenish its own store so as to accelerate recovery of responsiveness to a new challenge with caffeine.


title: Correlation of Klebsiella pneumoniae Comparative Genetic Analyses with Virulence Profiles in a Murine Respiratory Disease Model

abstract: Klebsiella pneumoniae is a bacterial pathogen of worldwide importance and a significant contributor to multiple disease presentations associated with both nosocomial and community acquired disease. ATCC 43816 is a well-studied K. pneumoniae strain which is capable of causing an acute respiratory disease in surrogate animal models. In this study, we performed sequencing of the ATCC 43816 genome to support future efforts characterizing genetic elements required for disease. Furthermore, we performed comparative genetic analyses to the previously sequenced genomes from NTUH-K2044 and MGH 78578 to gain an understanding of the conservation of known virulence determinants amongst the three strains. We found that ATCC 43816 and NTUH-K2044 both possess the known virulence determinant for yersiniabactin, as well as a Type 4 secretion system (T4SS), CRISPR system, and an acetonin catabolism locus, all absent from MGH 78578. While both NTUH-K2044 and MGH 78578 are clinical isolates, little is known about the disease potential of these strains in cell culture and animal models. Thus, we also performed functional analyses in the murine macrophage cell lines RAW264.7 and J774A.1 and found that MGH 78578 (K52 serotype) was internalized at higher levels than ATCC 43816 (K2) and NTUH-K2044 (K1), consistent with previous characterization of the antiphagocytic properties of K1 and K2 serotype capsules. We also examined the three K. pneumoniae strains in a novel BALB/c respiratory disease model and found that ATCC 43816 and NTUH-K2044 are highly virulent (LD50<100 CFU) while MGH 78578 is relatively avirulent.


title: The TUM Gait from Audio, Image and Depth (GAID) Database: Multimodal Recognition of Subjects and Traits

abstract: HighlightsPresentation of the new freely available TUM Gait from Audio, Image and Depth (GAID) database.Advancing gait based person identification by multimodal feature extraction.Gait based recognition of person traits: gender, age, height, shoe type.Baseline results and fusion for gait recognition using RGB, depth and audio. Recognizing people by the way they walk-also known as gait recognition-has been studied extensively in the recent past. Recent gait recognition methods solely focus on data extracted from an RGB video stream. With this work, we provide a means for multimodal gait recognition, by introducing the freely available TUM Gait from Audio, Image and Depth (GAID) database. This database simultaneously contains RGB video, depth and audio. With 305 people in three variations, it is one of the largest to-date. To further investigate challenges of time variation, a subset of 32 people is recorded a second time. We define standardized experimental setups for both person identification and for the assessment of the soft biometrics age, gender, height, and shoe type. For all defined experiments, we present several baseline results on all available modalities. These effectively demonstrate multimodal fusion being beneficial to gait recognition.


title: Active Learning for Classification with Maximum Model Change

abstract: Most existing active learning studies focus on designing sample selection algorithms. However, several fundamental problems deserve investigation to provide deep insight into active learning. In this article, we conduct an in-depth investigation on active learning for classification from the perspective of model change. We derive a general active learning framework for classification called maximum model change (MMC), which aims at querying the influential examples. The model change is quantified as the difference between the model parameters before and after training with the expanded training set. Inspired by the stochastic gradient update rule, the gradient of the loss with respect to a given candidate example is adopted to approximate the model change. This framework is applied to two popular classifiers: support vector machines and logistic regression. We analyze the convergence property of MMC and theoretically justify it. We explore the connection between MMC and uncertainty-based sampling to provide a uniform view. In addition, we discuss its potential usability to other learning models and show its applicability in a wide range of applications. We validate the MMC strategy on two kinds of benchmark datasets, the UCI repository and ImageNet, and show that it outperforms many state-of-the-art methods.


title: The rise and fall of network stars: Analyzing 2.5 million graphs to reveal how high-degree vertices emerge over time

abstract: Abstract Trends change rapidly in today’s world, prompting this key question: What is the mechanism behind the emergence of new trends? By representing real-world dynamic systems as complex networks, the emergence of new trends can be symbolized by vertices that “shine.” That is, at a specific time interval in a network’s life, certain vertices become increasingly connected to other vertices. This process creates new high-degree vertices, i.e., network stars. Thus, to study trends, we must look at how networks evolve over time and determine how the stars behave. In our research, we constructed the largest publicly available network evolution dataset to date, which contains 38,000 real-world networks and 2.5 million graphs. Then, we performed the first precise wide-scale analysis of the evolution of networks with various scales. Three primary observations resulted: (a) links are most prevalent among vertices that join a network at a similar time; (b) the rate that new vertices join a network is a central factor in molding a network’s topology; and (c) the emergence of network stars (high-degree vertices) is correlated with fast-growing networks. We applied our learnings to develop a flexible network-generation model based on large-scale, real-world data. This model gives a better understanding of how stars rise and fall within networks, and is applicable to dynamic systems both in nature and society. Multimedia Links ▶ Video ▶ Interactive Data Visualization ▶ Data ▶ Code Tutorials


title: Improving the Accuracy and Efficiency of MAP Inference for Markov Logic

abstract: In this work we present Cutting Plane Inference (CPI), a Maximum A Posteriori (MAP) inference method for Statistical Relational Learning. Framed in terms of Markov Logic and inspired by the Cutting Plane Method, it can be seen as a meta algorithm that instantiates small parts of a large and complex Markov Network and then solves these using a conventional MAP method. We evaluate CPI on two tasks, Semantic Role Labelling and Joint Entity Resolution, while plugging in two different MAP inference methods: the current method of choice for MAP inference in Markov Logic, MaxWalkSAT, and Integer Linear Programming. We observe that when used with CPI both methods are significantly faster than when used alone. In addition, CPI improves the accuracy of MaxWalkSAT and maintains the exactness of Integer Linear Programming.


title: A Case for Standard Non-Blocking Collective Operations

abstract: In this paper we make the case for adding standard nonblocking collective operations to the MPI standard. The nonblocking point-to-point and blocking collective operations currently defined by MPI provide important performance and abstraction benefits. To allow these benefits to be simultaneously realized, we present an application programming interface for non-blocking collective operations in MPI. Microbenchmark and application-based performance results demonstrate that non-blocking collective operations offer not only improved convenience, but improved performance as well, when compared to manual use of threads with blocking collectives.


title: Lung nodules diagnosis based on evolutionary convolutional neural network

abstract: Lung cancer presents the highest cause of death among patients around the world, in addition of being one of the smallest survival rates after diagnosis. In this paper, we exploit a deep learning technique jointly with the genetic algorithm to classify lung nodules in whether malignant or benign, without computing the shape and texture features. The methodology was tested on computed tomography (CT) images from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), with the best sensitivity of 94.66%, specificity of 95.14%, accuracy of 94.78% and area under the ROC curve of 0.949.


title: Detecting Memory-Boundedness with Hardware Performance Counters

abstract: Modern processors incorporate several performance monitoring units, which can be used to count events that occur within different components of the processor. They provide access to information on hardware resource usage and can therefore be used to detect performance bottlenecks. Thus, many performance measurement tools are able to record them complementary to information about the application behavior. However, the exact meaning of the supported hardware events is often incomprehensible due to the system complexity and partially lacking or even inaccurate documentation. For most events it is also not documented whether a certain rate indicates a saturated resource usage. Therefore, it is usually difficult to draw conclusions on the performance impact from the observed event rates. In this paper, we evaluate whether hardware performance counters can be used to measure the capacity utilization within the memory hierarchy and estimate the impact of memory accesses on the achieved performance. The presented approach is based on a small selection of micro-benchmarks that constantly stress individual components in the memory subsystem, ranging from caches to main memory. These workloads are used to identify hardware performance counters that provide good estimates for the utilization of individual components in the memory hierarchy. However, since access latencies can be interleaved with computing instructions, a high utilization of the memory hierarchy does not necessarily result in low performance. We therefore also investigate which stall counters provide good estimates for the number of cycles that are actually spent waiting for the memory hierarchy.


title: Vision and Rain

abstract: The visual effects of rain are complex. Rain produces sharp intensity changes in images and videos that can severely impair the performance of outdoor vision systems. In this paper, we provide a comprehensive analysis of the visual effects of rain and the various factors that affect it. Based on this analysis, we develop efficient algorithms for handling rain in computer vision as well as for photorealistic rendering of rain in computer graphics. We first develop a photometric model that describes the intensities produced by individual rain streaks and a dynamic model that captures the spatio-temporal properties of rain. Together, these models describe the complete visual appearance of rain. Using these models, we develop a simple and effective post-processing algorithm for detection and removal of rain from videos. We show that our algorithm can distinguish rain from complex motion of scene objects and other time-varying textures. We then extend our analysis by studying how various factors such as camera parameters, rain properties and scene brightness affect the appearance of rain. We show that the unique physical properties of rain--its small size, high velocity and spatial distribution--makes its visibility depend strongly on camera parameters. This dependence is used to reduce the visibility of rain during image acquisition by judiciously selecting camera parameters. Conversely, camera parameters can also be chosen to enhance the visibility of rain. This ability can be used to develop an inexpensive and portable camera-based rain gauge that provides instantaneous rain-rate measurements. Finally, we develop a rain streak appearance model that accounts for the rapid shape distortions (i.e. oscillations) that a raindrop undergoes as it falls. We show that modeling these distortions allows us to faithfully render the complex intensity patterns that are visible in the case of raindrops that are close to the camera.


title: High Performance OCR for Camera-Captured Blurred Documents with LSTM Networks

abstract: Documents are routinely captured by digital cameras in today's age owing to the availability of high quality cameras in smart phones. However, recognition of camera-captured documents is substantially more challenging as compared to traditional flat bed scanned documents due to the distortions introduced by the cameras. One of the major performancelimiting artifacts is the motion and out-of-focus blur that is often induced in the document during the capturing process. Existing approaches try to detect presence of blur in the document to inform the user for re-capturing the image. This paper reports, for the first time, an Optical Character Recognition (OCR) system that can directly recognize blurred documents on which the stateof-the-art OCR systems are unable to provide usable results. Our presented system is based on the Long Short-Term Memory (LSTM) networks and has shown promising character recognition results on both the motion-blurred and out-of-focus blurred images. One important feature of this work is that the LSTM networks have been applied directly to the gray-scale document images to avoid error-prone binarization of blurred documents. Experiments are conducted on publicly available SmartDoc-QA dataset that contains a wide variety of image blur degradations. Our presented system achieves 12.3% character error rate on the test documents, which is an over three-fold reduction in the error rate (38.9%) of the best-performing contemporary OCR system (ABBYY Fine Reader) on the same data.


title: Multi-patch deep features for power line insulator status classification from aerial images

abstract: The status of the insulators in power line can directly affect the reliability of the power transmission systems. Computer vision aided approaches have been widely applied in electric power systems. Inspecting the status of insulators from aerial images has been challenging due to the complex background and rapid view changing under different illumination conditions. In this paper, we propose a novel approach to inspect the insulators with Deep Convolutional Neural Networks (CNN). A CNN model with multi-patch feature extraction method is applied to represent the status of insulators and a Support Vector Machine (SVM) is trained based on these features. A thorough evaluation is conducted on our insulator status dataset of six classes from real inspection videos. The experimental results show that a pre-trained model for classification is more accurate than the shallow features by hand-crafted. Our approach achieves 98.7095% mean Average Precision (mAP) in status classification. We also study the behavior of the neural activations of the convolutional layers. Different results vary with different fully connected layers, and interesting findings are discussed.


title: The MUSiC Performance Measurement Method

abstract: Abstract This paper reports a method for measuring usability in terms of task performance-achievement of frequent and critical task goals by particular users in a context simulating the work environment. The terms usability and quality in use are defined in international standards as the effectiveness, efficiency and satisfaction with which goals are achieved in a specific context of use. The performance measurement method gives measures which, in combination with measures of satisfaction, operationalize these definitions. User performance is specified and assessed by measures including task effectiveness (the quantity and quality of task performance) and User efficiency (effectiveness divided by tasktime). Measures are obtained with users performing tasks in a context of evaluation which matches the intended context of use. This can also reveal usability problems which may not become evident if the evaluator interacts with the user. The method is supported by tools which make it practical in commercial t...


title: A robust, input voltage adaptive and low energy consumption level converter for sub-threshold logic

abstract: A new level converter (LC) is proposed for logic voltage shifting between sub-threshold voltage to normal high voltage. By employing 2 PMOS diodes, the LC shows good operation robustness with sub-threshold logic input. The switching delay of the proposed LC can adapt with the input logic voltage which is more suitable for power aware systems. With a simpler circuit structure, the energy consumption of the LC is smaller than that of the existing sub-threshold LC. Simulation results demonstrate the performance improvement and energy reduction of the proposed LC. Test chip was fabricated using 0.18 mum CMOS process. Measurement results show that our proposed LC can operate correctly with an input at as low as 127 mV and an output voltage at 1.8V.


title: Real-time thresholding with Euler numbers

abstract: The problem of finding an automatic thresholding technique is well known in applications involving image differencing like visual-based surveillance systems, autonomous vehicle driving, etc. Among the algorithms proposed in the past years, the thresholding technique based on the stable Euler number method is considered one of the most promising in terms of visual results. Unfortunately its high computational complexity made it an impossible choice for real-time applications. The implementation here proposed, called fast Euler numbers, overcomes the problem since it calculates all the Euler numbers in just one single raster scan of the image. That is, it runs in O(hw), where h and w are the image's height and width, respectively. A technique for determining the optimal threshold, called zero crossing, is also proposed.


title: A Survey of Learning Causality with Data: Problems and Methods

abstract: The era of big data provides researchers with convenient access to copious data. However, people often have little knowledge about it. The increasing prevalence of big data is challenging the traditional methods of learning causality because they are developed for the cases with limited amount of data and solid prior causal knowledge. This survey aims to close the gap between big data and learning causality with a comprehensive and structured review of traditional and frontier methods and a discussion about some open problems of learning causality. We begin with preliminaries of learning causality. Then we categorize and revisit methods of learning causality for the typical problems and data types. After that, we discuss the connections between learning causality and machine learning. At the end, some open problems are presented to show the great potential of learning causality with data.


PRIME: A Novel Processing-in-Memory Architecture for Neural Network Computation in ReRAM-Based Main Memory


title: Taking a Closer Look at Domain Shift: Category-Level Adversaries for Semantics Consistent Domain Adaptation

abstract: We consider the problem of unsupervised domain adaptation in semantic segmentation. The key in this campaign consists in reducing the domain shift, i.e., enforcing the data distributions of the two domains to be similar. A popular strategy is to align the marginal distribution in the feature space through adversarial learning. However, this global alignment strategy does not consider the local category-level feature distribution. A possible consequence of the global movement is that some categories which are originally well aligned between the source and target may be incorrectly mapped. To address this problem, this paper introduces a category-level adversarial network, aiming to enforce local semantic consistency during the trend of global alignment. Our idea is to take a close look at the category-level data distribution and align each class with an adaptive adversarial loss. Specifically, we reduce the weight of the adversarial loss for category-level aligned features while increasing the adversarial force for those poorly aligned. In this process, we decide how well a feature is category-level aligned between source and target by a co-training approach. In two domain adaptation tasks, i.e., GTA5 -> Cityscapes and SYNTHIA -> Cityscapes, we validate that the proposed method matches the state of the art in segmentation accuracy.


Event Detection with Hybrid Neural Architecture


title: Spoken Language Understanding on the Edge

abstract: We consider the problem of performing Spoken Language Understanding (SLU) on small devices typical of IoT applications. Our contributions are twofold. First, we outline the design of an embedded, private-by-design SLU system and show that it has performance on par with cloud-based commercial solutions. Second, we release the datasets used in our experiments in the interest of reproducibility and in the hope that they can prove useful to the SLU community.


title: A customer segmentation framework for targeted marketing in telecommunication

abstract: Telecommunication industry is highly competitive, and mass marketing is not applicable anymore. Moreover, Mobile customers have different behaviors that urge telecom industries to differentiate their strategies to meet customers' needs. At the same time, mobile operators have an enormous amount of customer records, and data-driven approaches can help them to draw insights from this huge amount of data. Therefore, a data-driven segmentation approach can support marketing strategies to tailor their marketing plans. In this research, we adopt behavior and beneficial segmentation in a two-dimensional framework to segment customers. The results indicate that our method has an outstanding performance for customer segmentation. Moreover, we have recommended some marketing strategies based on each segment's behavior with the aim of increasing in Average Revenue Per User (ARPU) and decreasing in marketing expenses.


title: A Survey of Semantic Segmentation

abstract: This survey gives an overview over different techniques used for pixel-level semantic segmentation. Metrics and datasets for the evaluation of segmentation algorithms and traditional approaches for segmentation such as unsupervised methods, Decision Forests and SVMs are described and pointers to the relevant papers are given. Recently published approaches with convolutional neural networks are mentioned and typical problematic situations for segmentation algorithms are examined. A taxonomy of segmentation algorithms is given.


title: 3D digital reassembling of archaeological ceramic pottery fragments based on their thickness profile

abstract: The reassembly of a broken archaeological ceramic pottery is an open and complex problem, which remains a scientific process of extreme interest for the archaeological community. Usually, the solutions suggested by various research groups and universities depend on various aspects such as the matching process of the broken surfaces, the outline of sherds or their colors and geometric characteris-tics, their axis of symmetry, the corners of their contour, the theme portrayed on the surface, the concentric circular rills that are left during the base construction in the inner pottery side by the fingers of the potter artist etc. In this work the reassembly process is based on a different and more secure idea, since it is based on the thick-ness profile, which is appropriately identified in every fragment. Specifically, our approach is based on information encapsulated in the inner part of the sherd (i.e. thickness), which is not -or at least not heavily- affected by the presence of harsh environmental conditions, but is safely kept within the sherd itself. Our method is verified in various use case experiments, using cutting edge technologies such as 3D representations and precise measurements on surfaces from the acquired 3D models.


title: Synthesis, Growth and Characterisation of Nonlinear Optical Nb-Doped L-Prolinium Picrate Single Crystals

abstract: A nonlinear optical material Niobium doped L-Prolinium Picrate was synthesized and grown as single crystals by slow evaporation method. The grown crystals were subjected to structural, elemental, thermal, optical and dielectric studies. The structural analysis reveals that Nb : LPP belongs to the monoclinic crystallographic system with space group P21. Optical transparency of the grown crystals was investigated by UV-vis-NIR spectrum. The thermal analyses reveal that Nb doped L-Prolinium Picrate is thermally stable up to 185 °C. The dielectric constant and dielectric loss of the crystal were studied as a function of frequency. The nonlinear optical property of the grown crystal was confirmed by the Kurtz-powder second harmonic generation test and the results are compared with pure LPP. Mechanical strength of the crystal was also carried out by Vicker’s micro hardness test.


Analysing the Data


title: Beltrami Representation and its applications to texture map and video compression

abstract: Surface parameterizations and registrations are important in computer graphics and imaging, where 1-1 correspondences between meshes are computed. In practice, surface maps are usually represented and stored as 3D coordinates each vertex is mapped to, which often requires lots of storage memory. This causes inconvenience in data transmission and data storage. To tackle this problem, we propose an effective algorithm for compressing surface homeomorphisms using Fourier approximation of the Beltrami representation. The Beltrami representation is a complex-valued function defined on triangular faces of the surface mesh with supreme norm strictly less than 1. Under suitable normalization, there is a 1-1 correspondence between the set of surface homeomorphisms and the set of Beltrami representations. Hence, every bijective surface map is associated with a unique Beltrami representation. Conversely, given a Beltrami representation, the corresponding bijective surface map can be exactly reconstructed using the Linear Beltrami Solver introduced in this paper. Using the Beltrami representation, the surface homeomorphism can be easily compressed by Fourier approximation, without distorting the bijectivity of the map. The storage memory can be effectively reduced, which is useful for many practical problems in computer graphics and imaging. In this paper, we proposed to apply the algorithm to texture map compression and video compression. With our proposed algorithm, the storage requirement for the texture properties of a textured surface can be significantly reduced. Our algorithm can further be applied to compressing motion vector fields for video compression, which effectively improve the compression ratio.


title: Open world classification of printed invoices

abstract: A key step in the understanding of printed documents is their classification based on the nature of information they contain and their layout. In this work we consider a dynamic scenario in which document classes are not known a priori and new classes can appear at any time. This open world setting is both realistic and highly challenging. We use an SVM-based classifier based only on image-level features and use a nearest-neighbor approach for detecting new classes. We assess our proposal on a real-world dataset composed of 562 invoices belonging to 68 different classes. These documents were digitalized after being handled by a corporate environment, thus they are quite noisy---e.g., big stamps and handwritten signatures at unfortunate positions and alike. The experimental results are highly promising.


title: Temporal Constraint Networks

abstract: Abstract This paper extends network-based methods of constraint satisfaction to include continuous variables, thus providing a framework for processing temporal constraints. In this framework, called temporal constraint satisfaction problem (TCSP), variables represent time points and temporal information is represented by a set of unary and binary constraints, each specifying a set of permitted intervals. The unique feature of this framework lies in permitting the processing of metric information, namely, assessments of time differences between events. We present algorithms for performing the following reasoning tasks: finding all feasible times that a given event can occur, finding all possible relationships between two given events, and generating one or more scenarios consistent with the information provided. We distinguish between simple temporal problems (STPs) and general temporal problems, the former admitting at most one interval constraint on any pair of time points. We show that the STP, which subsumes the major part of Vilain and Kautz's point algebra, can be solved in polynomial time. For general TCSPs, we present a decomposition scheme that performs the three reasoning tasks considered, and introduce a variety of techniques for improving its efficiency. We also study the applicability of path consistency algorithms as preprocessing of temporal problems, demonstrate their termination and bound their complexities.


title: Mechanisms of Dioxin Formation from the High-Temperature Pyrolysis of 2-Bromophenol

abstract: Brominated hydrocarbons are the most commonly used flame retardants. Materials containing brominated hydrocarbons are frequently disposed in municipal and hazardous waste incinerators as well as being subjected to thermal reaction in accidental fires. This results in the potential for formation of brominated dioxins and other hazardous combustion byproducts. In contrast to chlorinated hydrocarbons, the reactions of brominated hydrocarbons have been studied only minimally. As a model brominated hydrocarbon that may form brominated dioxins, we studied the homogeneous, gas-phase pyrolytic thermal degradation of 2-bromophenol in a 1-cm i.d., fused-silica flow reactor at a concentration of 90 ppm, with a reaction time of 2.0 s, and over a temperature range of 300 to 1000 °C. Observed products included dibenzo-p-dioxin (DD), 1-monobromodibenzo-p-dioxin (1-MBDD), 4-monobromo- dibenzofuran (4-MBDF), dibenzofuran (DF), naphthalene, bromonaphthalene, 2,4- and 2,6-dibromophenol, phenol, bromobenzene, and benzene. Th...


title: The return of Phineas Gage: clues about the brain from the skull of a famous patient

abstract: When the landmark patient Phineas Gage died in 1861, no autopsy was performed, but his skull was later recovered. The brain lesion that caused the profound personality changes for which his case became famous has been presumed to have involved the left frontal region, but questions have been raised about the involvement of other regions and about the exact placement of the lesion within the vast frontal territory. Measurements from Gage9s skull and modern neuroimaging techniques were used to reconstitute the accident and determine the probable location of the lesion. The damage involved both left and right prefrontal cortices in a pattern that, as confirmed by Gage9s modern counterparts, causes a defect in rational decision making and the processing of emotion.


title: An efficient image rectification method for parallel multi-camera arrangement

abstract: In this paper, we present an image rectification method for the multi-view image captured by a parallel multicamera arrangement. Since most multi-camera arrangements are manually adjusted, there exist misalignments with the camera positions and orientations. These misalignments cause geometric errors that can be a serious obstruction to three-dimensional (3D) image processing and applications. Moreover, different internal characteristics of cameras increase geometric errors. In order to minimize those geometric errors, we design an ideal multi-camera arrangement and then calculate the multi-view image rectifying transform (MIRT). Experimental results show that the proposed method reduces geometric errors efficiently without any visual distortion, and generates the rectified multi-view image in very short processing time.


title: Ear-EEG allows extraction of neural responses in challenging listening scenarios — A future technology for hearing aids?

abstract: Advances in brain-computer interface research have recently empowered the development of wearable sensors to record mobile electroencephalography (EEG) as an unobtrusive and easy-to-use alternative to conventional scalp EEG. One such mobile solution is to record EEG from the ear canal, which has been validated for auditory steady state responses and discrete event related potentials (ERPs). However, it is still under discussion where to place recording and reference electrodes to capture best responses to auditory stimuli. Furthermore, the technology has not yet been tested and validated for ecologically relevant auditory stimuli such as speech. In this study, Ear-EEG and conventional scalp EEG were recorded simultaneously in a discrete-tone as well as a continuous-speech design. The discrete stimuli were applied in a dichotic oddball paradigm, while continuous stimuli were presented diotically as two simultaneous talkers. Cross-correlation of stimulus envelope and Ear-EEG was assessed as a measure of ongoing neural tracking. The extracted ERPs from Ear-EEG revealed typical auditory components yet depended critically on the reference electrode chosen. Reliable neural-tracking responses were extracted from the Ear-EEG for both paradigms, albeit weaker in amplitude than from scalp EEG. In conclusion, this study shows the feasibility of extracting relevant neural features from ear-canal-recorded “Ear-EEG”, which might augment future hearing technology.


title: HairNet: Single-View Hair Reconstruction using Convolutional Neural Networks

abstract: We introduce a deep learning-based method to generate full 3D hair geometry from an unconstrained image. Our method can recover local strand details and has real-time performance. State-of-the-art hair modeling techniques rely on large hairstyle collections for nearest neighbor retrieval and then perform ad-hoc refinement. Our deep learning approach, in contrast, is highly efficient in storage and can run 1000 times faster while generating hair with 30K strands. The convolutional neural network takes the 2D orientation field of a hair image as input and generates strand features that are evenly distributed on the parameterized 2D scalp. We introduce a collision loss to synthesize more plausible hairstyles, and the visibility of each strand is also used as a weight term to improve the reconstruction accuracy. The encoder-decoder architecture of our network naturally provides a compact and continuous representation for hairstyles, which allows us to interpolate naturally between hairstyles. We use a large set of rendered synthetic hair models to train our network. Our method scales to real images because an intermediate 2D orientation field, automatically calculated from the real image, factors out the difference between synthetic and real hairs. We demonstrate the effectiveness and robustness of our method on a wide range of challenging real Internet pictures and show reconstructed hair sequences from videos.


title: Deep Learning for Imbalance Data Classification using Class Expert Generative Adversarial Network

abstract: Abstract Without any specific way for imbalance data classification, artificial intelligence algorithm cannot recognize data from minority classes easily. In general, modifying the existing algorithm by assuming that the training data is imbalanced, is the only way to handle imbalance data. However, for a normal data handling, this way mostly produces a deficient result. In this research, we propose a class expert generative adversarial network (CE-GAN) as the solution for imbalance data classification. CE-GAN is a modification in deep learning algorithm architecture that does not have an assumption that the training data is imbalance data. Moreover, CE-GAN is designed to identify more detail about the character of each class before classification step. CE-GAN has been proved in this research to give a good performance for imbalance data classification.


title: Collaborative Ranking with a Push at the Top

abstract: The goal of collaborative filtering is to get accurate recommendations at the top of the list for a set of users. From such a perspective, collaborative ranking based formulations with suitable ranking loss functions are natural. While recent literature has explored the idea based on objective functions such as NDCG or Average Precision, such objectives are difficult to optimize directly. In this paper, building on recent advances from the learning to rank literature, we introduce a novel family of collaborative ranking algorithms which focus on accuracy at the top of the list for each user while learning the ranking functions collaboratively. We consider three specific formulations, based on collaborative p-norm push, infinite push, and reverse-height push, and propose efficient optimization methods for learning these models. Experimental results illustrate the value of collaborative ranking, and show that the proposed methods are competitive, usually better than existing popular approaches to personalized recommendation.


title: Citrus polyphenol hesperidin stimulates production of nitric oxide in endothelial cells while improving endothelial function and reducing inflammatory markers in patients with metabolic syndrome

abstract: Signaling mechanisms were identified by which hesperetin stimulates endothelial nitric oxide production that informed this translational study, demonstrating that oral hesperidin treatment improves endothelial dysfunction.


title: Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion?

abstract: The Ward error sum of squares hierarchical clustering method has been very widely used since its first description by Ward in a 1963 publication. It has also been generalized in various ways. Two algorithms are found in the literature and software, both announcing that they implement the Ward clustering method. When applied to the same distance matrix, they produce different results. One algorithm preserves Ward's criterion, the other does not. Our survey work and case studies will be useful for all those involved in developing software for data analysis using Ward's hierarchical clustering method.


title: A Primer on Hardware Security: Models, Methods, and Metrics

abstract: The multinational, distributed, and multistep nature of integrated circuit (IC) production supply chain has introduced hardware-based vulnerabilities. Existing literature in hardware security assumes ad hoc threat models, defenses, and metrics for evaluation, making it difficult to analyze and compare alternate solutions. This paper systematizes the current knowledge in this emerging field, including a classification of threat models, state-of-the-art defenses, and evaluation metrics for important hardware-based attacks.


title: A Comparison of Ketamine and Paracetamol for Preventing Remifentanil Induced Hyperalgesia in Patients Undergoing Total Abdominal Hysterectomy

abstract: Background: The aim of this prospective, randomized, placebo-controlled study was to compare the effects of ketamine and paracetamol on preventing remifentanil induced hyperalgesia. ::: ::: Methods: Ninety patients undergoing total abdominal hysterectomy were randomly assigned to one of three groups to receive (I) either saline infusion; (II) 0.5 mg/kg ketamine iv bolus or (III) 1000 mg iv paracetamol infusion before induction of anesthesia. Until the skin closure, anesthesia was maintained with 0.4 µg/kg/min remifentanil infusion in all groups, additionally Group II received 5 µg/kg/min ketamine infusion. Pressure pain thresholds were measured the day before surgery during the preoperative visit for baseline measurements and repeated postoperatively at 24 and 48 hours (hrs). Pressure pain thresholds were established by digital algometer on three different peri- incisional regions for calculating mean pressure pain threshold values. The visual analogue scale (VAS), sedation scores, total morphine consumption and side effects were assessed postoperatively. ::: ::: Results: Demographic characteristics, duration of surgery and anesthesia were similar in the three groups. Pain thresholds at the incision region were significantly lower at 24 and 48 hrs postoperatively in Group I than the other Groups (p< 0.05). In Group І, pain thresholds were lower compared with preoperative baseline values. Thresholds in Group ІІ and Group ІІІ were higher compared with preoperative baseline values (p< 0.05) The VAS scores at all evaluation times were significantly higher in Group І when compared to Group ІІ and at 2, 4, 6 ,12 hrs were higher in Group I than Group ІІІ (p< 0.05). The morphine consumption was higher in Group ІІІ at 24 and 48 hrs postoperatively (p< 0.05). ::: ::: Conclusion: It was shown that ketamine and paracetamol were both effective in preventing remifentanil induced hyperalgesia.


title: Adaptive Transfer Learning

abstract: Transfer learning aims at reusing the knowledge in some source tasks to improve the learning of a target task. Many transfer learning methods assume that the source tasks and the target task be related, even though many tasks are not related in reality. However, when two tasks are unrelated, the knowledge extracted from a source task may not help, and even hurt, the performance of a target task. Thus, how to avoid negative transfer and then ensure a "safe transfer" of knowledge is crucial in transfer learning. In this paper, we propose an Adaptive Transfer learning algorithm based on Gaussian Processes (AT-GP), which can be used to adapt the transfer learning schemes by automatically estimating the similarity between a source and a target task. The main contribution of our work is that we propose a new semi-parametric transfer kernel for transfer learning from a Bayesian perspective, and propose to learn the model with respect to the target task, rather than all tasks as in multi-task learning. We can formulate the transfer learning problem as a unified Gaussian Process (GP) model. The adaptive transfer ability of our approach is verified on both synthetic and real-world datasets.


title: Innate and adaptive immune cells in the tumor microenvironment

abstract: Most tumor cells express antigens that can mediate recognition by host CD8(+) T cells. Cancers that are detected clinically must have evaded antitumor immune responses to grow progressively. Recent work has suggested two broad categories of tumor escape based on cellular and molecular characteristics of the tumor microenvironment. One major subset shows a T cell-inflamed phenotype consisting of infiltrating T cells, a broad chemokine profile and a type I interferon signature indicative of innate immune activation. These tumors appear to resist immune attack through the dominant inhibitory effects of immune system-suppressive pathways. The other major phenotype lacks this T cell-inflamed phenotype and appears to resist immune attack through immune system exclusion or ignorance. These two major phenotypes of tumor microenvironment may require distinct immunotherapeutic interventions for maximal therapeutic effect.


title: Demographics, behavior problems, and psychosexual characteristics of adolescents with gender identity disorder or transvestic fetishism.

abstract: This study provided a descriptive and quantitative comparative analysis of data from an assessment protocol for adolescents referred clinically for gender identity disorder (n = 192; 105 boys, 87 girls) or transvestic fetishism (n = 137, all boys). The protocol included information on demographics, behavior problems, and psychosexual measures. Gender identity disorder and transvestic fetishism youth had high rates of general behavior problems and poor peer relations. On the psychosexual measures, gender identity disorder patients had considerably greater cross-gender behavior and gender dysphoria than did transvestic fetishism youth and other control youth. Male gender identity disorder patients classified as having a nonhomosexual sexual orientation (in relation to birth sex) reported more indicators of transvestic fetishism than did male gender identity disorder patients classified as having a homosexual sexual orientation (in relation to birth sex). The percentage of transvestic fetishism youth and mal...


title: LFTF: A Framework for Efficient Tensor Analytics at Scale

abstract: Tensors are higher order generalizations of matrices to model multi-aspect data, e.g., a set of purchase records with the schema (user_id, product_id, timestamp, feedback). Tensor factorization is a powerful technique for generating a model from a tensor, just like matrix factorization generates a model from a matrix, but with higher accuracy and richer information as more attributes are available in a higher- order tensor than a matrix. The data model obtained by tensor factorization can be used for classification, recommendation, anomaly detection, and so on. Though having a broad range of applications, tensor factorization has not been popularly applied compared with matrix factorization that has been widely used in recommender systems, mainly due to the high computational cost and poor scalability of existing tensor factorization methods. Efficient and scalable tensor factorization is particularly challenging because real world tensor data are mostly sparse and massive. In this paper, we propose a novel distributed algorithm, called Lock-Free Tensor Factorization (LFTF), which significantly improves the efficiency and scalability of distributed tensor factorization by exploiting asynchronous execution in a re-formulated problem. Our experiments show that LFTF achieves much higher CPU and network throughput than existing methods, converges at least 17 times faster and scales to much larger datasets.


title: Convolutional Neural Network-Based Human Detection in Nighttime Images Using Visible Light Camera Sensors

abstract: Because intelligent surveillance systems have recently undergone rapid growth, research on accurately detecting humans in videos captured at a long distance is growing in importance. The existing research using visible light cameras has mainly focused on methods of human detection for daytime hours when there is outside light, but human detection during nighttime hours when there is no outside light is difficult. Thus, methods that employ additional near-infrared (NIR) illuminators and NIR cameras or thermal cameras have been used. However, in the case of NIR illuminators, there are limitations in terms of the illumination angle and distance. There are also difficulties because the illuminator power must be adaptively adjusted depending on whether the object is close or far away. In the case of thermal cameras, their cost is still high, which makes it difficult to install and use them in a variety of places. Because of this, research has been conducted on nighttime human detection using visible light cameras, but this has focused on objects at a short distance in an indoor environment or the use of video-based methods to capture multiple images and process them, which causes problems related to the increase in the processing time. To resolve these problems, this paper presents a method that uses a single image captured at night on a visible light camera to detect humans in a variety of environments based on a convolutional neural network. Experimental results using a self-constructed Dongguk night-time human detection database (DNHD-DB1) and two open databases (Korea advanced institute of science and technology (KAIST) and computer vision center (CVC) databases), as well as high-accuracy human detection in a variety of environments, show that the method has excellent performance compared to existing methods.


title: Integrating TAM, TPB and TOE frameworks and expanding their characteristic constructs for e-commerce adoption by SMEs

abstract: Purpose – This paper aims to propose a framework that integrated the technology acceptance model (TAM), theory of planned behaviour (TPB) and technology-organization-environment (TOE) and extended the constructs to enrich literature and capture some peculiarities of small and medium-scale enterprises (SMEs). Individually, the frameworks of extant TAM, TPB and TOE are insightful to the understanding of e-commerce adoption but a bit parochial in their constructs and so, can rarely provide clear lenses to deal with SMEs. Design/methodology/approach – The adoption of e-commerce depends largely on the users’ conscious assessment of the influencing constructs as proposed, among others, in theories of reasoned action, TAM, TPB and TOE. This paper reviewed, synthesized and extended the constructs of these models in an integrated framework. The proposed integrated framework led to 18 propositions to promote and facilitate future research, and to guide explanation and prediction of e-commerce adoption in an organiz...


title: Enumerating possible Sudoku grids

abstract: In some of these boxes, the setter puts some of the digits 1–9; the aim of the solver is to complete the grid by filling in a digit in every box in such a way that each row, each column, and each 3× 3 box contains each of the digits 1–9 exactly once. In this note, we discuss the problem of enumerating all possible Sudoku grids. This is a very natural problem, but, perhaps surprisingly, it seems unlikely that the problem should have a simple combinatorial answer. Indeed, Sudoku grids are simply special cases of Latin squares, and the enumeration of Latin squares is itself a difficult problem, with no general combinatorial formulae known. Latin squares of sizes up to 11 × 11 have been enumerated, and the methods are broadly brute force calculations, much like the approach we sketch for Sudoku grids below. See [1], [2] and [3] for more details. It is known that the number of 9× 9 Latin squares is 5524751496156892842531225600 ≈ 5.525× 10. Since this answer is enormous, we need to refine our search considerably in order to be able to get an answer in a sensible amount of computing time.


title: DeepExposure: Learning to Expose Photos with Asynchronously Reinforced Adversarial Learning

abstract: The accurate exposure is the key of capturing high-quality photos in computational photography, especially for mobile phones that are limited by sizes of camera modules. Inspired by exposure blending with luminosity masks usually applied by professional photographers, in this paper, we develop a novel algorithm for learning local exposures with deep reinforcement learning and adversarial learning. To be specific, we segment an image into sub-images that can reflect variations of dynamic range exposures according to raw low-level features. Based on these sub-images, a local exposure for each sub-image is automatically learned by virtue of policy network sequentially while the reward of learning is globally designed for striking a balance of overall exposures. The aesthetic evaluation function is approximated by discriminator in generative adversarial networks. The reinforcement learning and the adversarial learning are trained collaboratively by asynchronous deterministic policy gradient and generative loss approximation. To further simplify the algorithmic architecture, we also prove the feasibility of leveraging the discriminator as the value function. Further more, we employ each local exposure to retouch the raw input image respectively, thus delivering multiple retouched images under different exposures which are fused with exposure blending. The extensive experiments verify that our algorithms are superior to state-of-the-art methods in terms of quantitative accuracy and visual illustration.


title: Optimal Trajectory Control of Burst Mode for LLC Resonant Converter

abstract: The LLC resonant converter has drawn much attention for high efficiency. However, its light-load efficiency still cannot meet the ever increasing requirements. This paper proposes a novel burst-mode control for the LLC resonant converter to improve the light-load efficiency. Based on the state-plane trajectory analysis, during the burst-on time, a three-pulse switching pattern is implemented. By optimizing the first pulse width, the state variables are settled to the steady state of the highest efficiency load in the minimal time, and then, the next two pulse operation follows this steady-state trajectory to achieve high burst efficiency. Moreover, when the load changes, the burst-on time with the optimal three-pulse switching pattern is maintained, but the burst-off time is modulated. As a result, the output-voltage low-frequency ripple minimally keeps constant, and no additional filter is needed. Experimental results validate the proposed techniques.


System using Data Mining for theUniversity of Hong Kong Libraries


title: LIVING LAB: user‐driven innovation for sustainability

abstract: Purpose – The purpose of this paper is to summarize and discuss the results from the LIVING LAB design study, a project within the 7th Framework Programme of the European Union. The aim of this project was to develop the conceptual design of the LIVING LAB Research Infrastructure that will be used to research human interaction with, and stimulate the adoption of, sustainable, smart and healthy innovations around the home.Design/methodology/approach – A LIVING LAB is a combined lab‐/household system, analysing existing product‐service‐systems as well as technical and socioeconomic influences focused on the social needs of people, aiming at the development of integrated technical and social innovations and simultaneously promoting the conditions of sustainable development (highest resource efficiency, highest user orientation, etc.). This approach allows the development and testing of sustainable domestic technologies, while putting the user on centre stage.Findings – As this paper discusses the design stud...


title: Detecting medley of iris spoofing attacks using DESIST

abstract: Human iris is considered a reliable and accurate modality for biometric recognition due to its unique texture information. However, similar to other biometric modalities, iris recognition systems are also vulnerable to presentation attacks (commonly called spoofing) that attempt to conceal or impersonate identity. Examples of typical iris spoofing attacks are printed iris images, textured contact lenses, and synthetic creation of iris images. It is critical to note that majority of the algorithms proposed in the literature are trained to handle a specific type of spoofing attack. These algorithms usually perform very well on that particular attack. However, in real-world applications, an attacker may perform different spoofing attacks. In such a case, the problem becomes more challenging due to inherent variations in different attacks. In this paper, we focus on a medley of iris spoofing attacks and present a unified framework for detecting such attacks. We propose a novel structural and textural feature based iris spoofing detection framework (DESIST). Multi-order dense Zernike moments are calculated across the iris image which encode variations in structure of the iris image. Local Binary Pattern with Variance (LBPV) is utilized for representing textural changes in a spoofed iris image. The highest classification accuracy of 82.20% is observed by the proposed framework for detecting normal and spoofed iris images on a combined iris spoofing database.


title: Thermal effects in friction welding

abstract: Abstract Friction welding is a complicated process, which involves the interaction of thermal, mechanical and metallurgical phenomena. A finite element model to simulate this coupled process is developed to represent the workpieces and surface contact conditions. Predictions of the temperature distribution, thermal expansion and thermo-plastic stresses are obtained from this model. Comparison of the analytic results to test data are presented and discussed.


title: Fruit disease detection using color, texture analysis and ANN

abstract: Now-a-days as there is prohibitive demand for agricultural industry, effective growth and improved yield of fruit is necessary and important. For this purpose farmers need manual monitoring of fruits from harvest till its progress period. But manual monitoring will not give satisfactory result all the times and they always need satisfactory advice from expert. So it requires proposing an efficient smart farming technique which will help for better yield and growth with less human efforts. We introduce a technique which will diagnose and classify external disease within fruits. Traditional system uses thousands of words which lead to boundary of language. Whereas system that we have come up with, uses image processing techniques for implementation as image is easy way for conveying. In the proposed work, OpenCV library is applied for implementation. K-means clustering method is applied for image segmentation, the images are catalogue and mapped to their respective disease categories on basis of four feature vectors color, morphology, texture and structure of hole on the fruit. The system uses two image databases, one for implementation of query images and the other for training of already stored disease images. Artificial Neural Network (ANN) concept is used for pattern matching and classification of diseases.


Factored Neural Machine Translation Architectures