title: An improved two-stage non-isolated converter for on-board plug-in hybrid EV battery charger

abstract: This paper presents a single-phase two-stage non-isolated converter for on-board plug-in hybrid electric vehicle battery charger. The first stage is a conventional boost converter and the second stage is a bi-directional dc-dc converter (battery-interface) having a wide range of output voltage with low component stress in both directions compared to conventional SEPIC, Cuk, Zeta and inverting buck-boost converters. In addition, the second stage bi-directional dc-dc converter also has improved efficiency over two switches non-inverting buck-boost converter) in the buck mode from one-direction and in boost mode from another direction. Simulation results are given to demonstrate the effectiveness of the proposed system.


title: An adaptive portfolio trading system: A risk-return portfolio optimization using recurrent reinforcement learning with expected maximum drawdown

abstract: A reinforcement learning trading algorithm with expected drawdown risk is proposed.The expected maximum drawdown is shown to improve portfolio signal generation.The effectiveness of the method is validated using different transaction costs.An adaptive portfolio rebalancing system with automated retraining is recommended. Dynamic control theory has long been used in solving optimal asset allocation problems, and a number of trading decision systems based on reinforcement learning methods have been applied in asset allocation and portfolio rebalancing. In this paper, we extend the existing work in recurrent reinforcement learning (RRL) and build an optimal variable weight portfolio allocation under a coherent downside risk measure, the expected maximum drawdown, E(MDD). In particular, we propose a recurrent reinforcement learning method, with a coherent risk adjusted performance objective function, the Calmar ratio, to obtain both buy and sell signals and asset allocation weights. Using a portfolio consisting of the most frequently traded exchange-traded funds, we show that the expected maximum drawdown risk based objective function yields superior return performance compared to previously proposed RRL objective functions (i.e. the Sharpe ratio and the Sterling ratio), and that variable weight RRL long/short portfolios outperform equal weight RRL long/short portfolios under different transaction cost scenarios. We further propose an adaptive E(MDD) risk based RRL portfolio rebalancing decision system with a transaction cost and market condition stop-loss retraining mechanism, and we show that the proposed portfolio trading system responds to transaction cost effects better and outperforms hedge fund benchmarks consistently.


Ultrasonic Lamb Wave Inspection of Aircraft Components Using Integrated Optical Fibre Sensing Technology


title: A Novel Inverter-Output Passive Filter for Reducing Both Differential- and Common-Mode $dv/dt$ at the Motor Terminals in PWM Drive Systems

abstract: A novel inverter-output filter, which cannot only filter the differential-mode voltage dv/dt but also suppress the common-mode voltage dv/dt and their rms values, is proposed in this paper. The filter is in combination with a conventional RLC filter and a common-mode transformer. The main advantage is that the functions of filtering a differential-mode voltage and suppressing a common-mode voltage can be integrated into a single system. Furthermore, the structure and design of the proposed filter are rather simple because only passive components are used. Simulations and experiments are conducted to validate the performance of the proposed filter. Both of their results indicate that about 80% of the rms value of the common-mode voltage are suppressed, while the demand of differential-mode voltage filtering is still met


title: Efficient Natural Language Response Suggestion for Smart Reply

abstract: This paper presents a computationally efficient machine-learned method for natural language response suggestion. Feed-forward neural networks using n-gram embedding features encode messages into vectors which are optimized to give message-response pairs a high dot-product value. An optimized search finds response suggestions. The method is evaluated in a large-scale commercial e-mail application, Inbox by Gmail. Compared to a sequence-to-sequence approach, the new system achieves the same quality at a small fraction of the computational requirements and latency.


title: A Framework for Selecting Deep Learning Hyper-parameters

abstract: Recent research has found that deep learning architectures show significant improvements over traditional shallow algorithms when mining high dimensional datasets. When the choice of algorithm employed, hyper-parameter setting, number of hidden layers and nodes within a layer are combined, the identification of an optimal configuration can be a lengthy process. Our work provides a framework for building deep learning architectures via a stepwise approach, together with an evaluation methodology to quickly identify poorly performing architectural configurations. Using a dataset with high dimensionality, we illustrate how different architectures perform and how one algorithm configuration can provide input for fine-tuning more complex models.


title: Statistical analysis of MUSIC and ESPRIT estimates of sinusoidal frequencies

abstract: The large-sample second-order properties of multiple signal classification (MUSIC) and subspace rotation methods such as ESPRIT for sinusoidal frequency estimation are analyzed. Both MUSIC and ESPRIT are based on the eigendecomposition of a sample data covariance matrix. Explicit expressions for the covariance elements of the estimation errors associated with either method are derived. These expressions of covariances are used to analyze and compare the statistical performance of the MUSIC and ESPRIT methods. It is shown that ESPRIT is usually slightly more accurate than MUSIC. Since MUSIC is computationally more demanding than ESPRIT, it appears that the ESPRIT method for frequency estimation should be preferred to MUSIC in most cases. >


title: Weather data analysis and sensor fault detection using an extended IoT framework with semantics, big data, and machine learning

abstract: In recent years, big data and Internet of Things (IoT) implementations started getting more attention. Researchers focused on developing big data analytics solutions using machine learning models. Machine learning is a rising trend in this field due to its ability to extract hidden features and patterns even in highly complex datasets. In this study, we used our Big Data IoT Framework in a weather data analysis use case. We implemented weather clustering and sensor anomaly detection using a publicly available dataset. We provided the implementation details of each framework layer (acquisition, ETL, data processing, learning and decision) for this particular use case. Our chosen learning model within the library is Scikit-Learn based k-means clustering. The data analysis results indicate that it is possible to extract meaningful information from a relatively complex dataset using our framework.


title: Ecological responses to recent climate change

abstract: There is now ample evidence of the ecological impacts of recent climate change, from polar terrestrial to tropical marine environments. The responses of both flora and fauna span an array of ecosystems and organizational hierarchies, from the species to the community levels. Despite continued uncertainty as to community and ecosystem trajectories under global change, our review exposes a coherent pattern of ecological change across systems. Although we are only at an early stage in the projected trends of global warming, ecological responses to recent climate change are already clearly visible.


title: Smart Objects as Building Blocks for the Internet of Things

abstract: The combination of the Internet and emerging technologies such as nearfield communications, real-time localization, and embedded sensors lets us transform everyday objects into smart objects that can understand and react to their environment. Such objects are building blocks for the Internet of Things and enable novel computing applications. As a step toward design and architectural principles for smart objects, the authors introduce a hierarchy of architectures with increasing levels of real-world awareness and interactivity. In particular, they describe activity-, policy-, and process-aware smart objects and demonstrate how the respective architectural abstractions support increasingly complex application.


title: Architecture and algorithms for an IEEE 802.11-based multi-channel wireless mesh network

abstract: Even though multiple non-overlapped channels exist in the 2.4 GHz and 5 GHz spectrum, most IEEE 802.11-based multi-hop ad hoc networks today use only a single channel. As a result, these networks rarely can fully exploit the aggregate bandwidth available in the radio spectrum provisioned by the standards. This prevents them from being used as an ISP's wireless last-mile access network or as a wireless enterprise backbone network. In this paper, we propose a multi-channel wireless mesh network (WMN) architecture (called Hyacinth) that equips each mesh network node with multiple 802.11 network interface cards (NICs). The central design issues of this multi-channel WMN architecture are channel assignment and routing. We show that intelligent channel assignment is critical to Hyacinth's performance, present distributed algorithms that utilize only local traffic load information to dynamically assign channels and to route packets, and compare their performance against a centralized algorithm that performs the same functions. Through an extensive simulation study, we show that even with just 2 NICs on each node, it is possible to improve the network throughput by a factor of 6 to 7 when compared with the conventional single-channel ad hoc network architecture. We also describe and evaluate a 9-node Hyacinth prototype that Is built using commodity PCs each equipped with two 802.11a NICs.


title: Using Prerequisites to Extract Concept Maps fromTextbooks

abstract: We present a framework for constructing a specific type of knowledge graph, a concept map from textbooks. Using Wikipedia, we derive prerequisite relations among these concepts. A traditional approach for concept map extraction consists of two sub-problems: key concept extraction and concept relationship identification. Previous work for the most part had considered these two sub-problems independently. We propose a framework that jointly optimizes these sub-problems and investigates methods that identify concept relationships. Experiments on concept maps that are manually extracted in six educational areas (computer networks, macroeconomics, precalculus, databases, physics, and geometry) show that our model outperforms supervised learning baselines that solve the two sub-problems separately. Moreover, we observe that incorporating textbook information helps with concept map extraction.


title: Ion Channel Modulators and Urinary Tract Function

abstract: The membrane potential fulfils an important role in initiating smooth muscle contraction, through its depolarization and the subsequent influx of Ca2+ through voltage-gated Ca2+ channels. Changes in membrane potential can also coordinate contraction across great distances, utilizing the speed of electrical current flow through gap junctions. Hence, regulating membrane potential can greatly influence smooth muscle function. In this chapter, we will consider the influence of ion channels, as dynamic gatekeepers of membrane permeability, on urogenital function. Through their ability to act as key regulators of both the resting membrane potential and its dynamic changes, they provide important pharmacological targets for influencing urogenital function.


title: Quantitative and Qualitative Analysis of the Medial Patellar Ligaments: An Anatomic and Radiographic Study

abstract: Background:The qualitative and quantitative anatomy of the medial patellar stabilizers has been reported; however, a quantitative analysis of the anatomic and radiographic attachments of all 4 ligaments relative to anatomic and osseous landmarks, as well as to one another, has yet to be performed.Purpose:To perform a qualitative and quantitative anatomic and radiographic evaluation of the medial patellofemoral ligament (MPFL), medial patellotibial ligament (MPTL), medial patellomeniscal ligament (MPML), and medial quadriceps tendon femoral ligament (MQTFL) attachment sites, with attention to their relationship to pertinent osseous and soft tissue landmarks.Study Design:Descriptive laboratory study.Methods:Ten nonpaired fresh-frozen human cadaveric knees were dissected, and the MPFL, MPTL, MPML, and MQTFL were identified. A coordinate measuring device quantified the attachment areas of each structure and its relationship to pertinent bony landmarks. Radiographic analysis was performed through ligament atta...


title: A spatially constrained mixture model for image segmentation

abstract: Gaussian mixture models (GMMs) constitute a well-known type of probabilistic neural networks. One of their many successful applications is in image segmentation, where spatially constrained mixture models have been trained using the expectation-maximization (EM) framework. In this letter, we elaborate on this method and propose a new methodology for the M-step of the EM algorithm that is based on a novel constrained optimization formulation. Numerical experiments using simulated images illustrate the superior performance of our method in terms of the attained maximum value of the objective function and segmentation accuracy compared to previous implementations of this approach.


title: RAPTOR: a visual programming environment for teaching algorithmic problem solving

abstract: When students are learning to develop algorithms, they very often spend more time dealing with issues of syntax rather than solving the problem. Additionally, the textual nature of most programming environments works against the learning style of the majority of students. RAPTOR is a visual programming environment, designed specifically to help students envision their algorithms and avoid syntactic baggage. RAPTOR programs are created visually and can be executed visually by tracing the execution through the program. Required syntax is kept to a minimum. Students preferred expressing their algorithms visually, and were more successful creating algorithms using RAPTOR than using a traditional language or writing flowcharts.


title: Image Recognition CAPTCHAs

abstract: CAPTCHAs are tests that distinguish humans from software robots in an online environment [3,14,7]. We propose and implement three CAPTCHAs based on naming images, distinguishing images, and identifying an anomalous image out of a set. Novel contributions include proposals for two new CAPTCHAs, the first user study on image recognition CAPTCHAs, and a new metric for evaluating CAPTCHAs.


title: Semantic Pooling for Complex Event Analysis in Untrimmed Videos

abstract: Pooling plays an important role in generating a discriminative video representation. In this paper, we propose a new semantic pooling approach for challenging event analysis tasks (e.g., event detection, recognition, and recounting) in long untrimmed Internet videos, especially when only a few shots/segments are relevant to the event of interest while many other shots are irrelevant or even misleading. The commonly adopted pooling strategies aggregate the shots indifferently in one way or another, resulting in a great loss of information. Instead, in this work we first define a novel notion of semantic saliency that assesses the relevance of each shot with the event of interest. We then prioritize the shots according to their saliency scores since shots that are semantically more salient are expected to contribute more to the final event analysis. Next, we propose a new isotonic regularizer that is able to exploit the constructed semantic ordering information. The resulting nearly-isotonic support vector machine classifier exhibits higher discriminative power in event analysis tasks. Computationally, we develop an efficient implementation using the proximal gradient algorithm, and we prove new and closed-form proximal steps. We conduct extensive experiments on three real-world video datasets and achieve promising improvements.


title: BatchFS: scaling the file system control plane with client-funded metadata servers

abstract: Parallel file systems are often characterized by a layered architecture that decouples metadata management from I/O operations, allowing file systems to facilitate fast concurrent access to file contents. However, metadata intensive workloads are still likely to bottleneck at the file system control plane due to namespace synchronization, which taxes application performance through lock contention on directories, transaction serialization, and RPC overheads. In this paper, we propose a client-driven file system metadata architecture, BatchFS, that is optimized for non-interactive, or batch, workloads. To avoid metadata bottlenecks, BatchFS features a relaxed consistency model marked by lazy namespace synchronization and optimistic metadata verification. Capable of executing namespace operations on client-provisioned resources without contacting any metadata server, BatchFS clients are able to delay namespace synchronization until synchronization is really needed. Our goal in this vision paper is to handle these delayed operations securely and efficiently with metadata verification and bulk insertion. Preliminary experiments demonstrate that our client-funded metadata architecture outperforms a traditional synchronous file system by orders of magnitude.


title: The perceptual organization of texture flows: A contextual inference approach

abstract: Locally parallel dense patterns-sometimes called texture flows-define a perceptually coherent structure of particular significance to perceptual organization. We argue that with applications ranging from image segmentation and edge classification to shading analysis and shape interpretation, texture flows deserve attention equal to edge segment grouping and curve completion. This paper develops the notion of texture flow from a geometrical point of view to argue that local measurements of such structures must incorporate two curvatures. We show how basic theoretical considerations lead to a unique model for the local behavior of the flow and to a notion of texture flow "good continuation." This, in turn, translates to a specification of consistency constraints between nearby flow measurements which we use for the computation of globally (piecewise) coherent structure through the contextual framework of relaxation labeling. We demonstrate the results on synthetic and natural images.


title: Event-driven stereo matching for real-time 3D panoramic vision

abstract: This paper presents a stereo matching approach for a novel multi-perspective panoramic stereo vision system, making use of asynchronous and non-simultaneous stereo imaging towards real-time 3D 360° vision. The method is designed for events representing the scenes visual contrast as a sparse visual code allowing the stereo reconstruction of high resolution panoramic views. We propose a novel cost measure for the stereo matching, which makes use of a similarity measure based on event distributions. Thus, the robustness to variations in event occurrences was increased. An evaluation of the proposed stereo method is presented using distance estimation of panoramic stereo views and ground truth data. Furthermore, our approach is compared to standard stereo methods applied on event-data. Results show that we obtain 3D reconstructions of 1024 × 3600 round views and outperform depth reconstruction accuracy of state-of-the-art methods on event data.


title: Rejoinder: The Dantzig selector: Statistical estimation when $p$ is much larger than $n$

abstract: First of all, we would like to thank all the discussants for their interest and comments, ::: as well as for their thorough investigation. The comments all underlie the ::: importance and timeliness of the topics discussed in our paper, namely, accurate ::: statistical estimation in high dimensions. We would also like to thank the editors ::: for this opportunity to comment briefly on a few issues raised in the discussions. ::: Of special interest is the diversity of perspectives, which include theoretical, ::: practical and computational issues. With this being said, there are two main points ::: in the discussions that are quite recurrent: ::: 1. Is it possible to extend and refine our theoretical results, and how do they compare ::: against the very recent literature? ::: 2. How does the Dantzig Selector (DS) compare with the Lasso? ::: We will address these issues in this rejoinder but before we begin, we would like ::: to restate as simply as possible the main point of our paper and put this work in a ::: broader context so as to avoid confusion about our point of view and motivations.


title: Digital Forensics to Intelligent Forensics

abstract: In this paper we posit that current investigative techniques—particularly as deployed by law enforcement, are becoming unsuitable for most types of crime investigation. The growth in cybercrime and the complexities of the types of the cybercrime coupled with the limitations in time and resources, both computational and human, in addressing cybercrime put an increasing strain on the ability of digital investigators to apply the processes of digital forensics and digital investigations to obtain timely results. In order to combat the problems, there is a need to enhance the use of the resources available and move beyond the capabilities and constraints of the forensic tools that are in current use. We argue that more intelligent techniques are necessary and should be used proactively. The paper makes the case for the need for such tools and techniques, and investigates and discusses the opportunities afforded by applying principles and procedures of artificial intelligence to digital forensics intelligence and to intelligent forensics and suggests that by applying new techniques to digital investigations there is the opportunity to address the challenges of the larger and more complex domains in which cybercrimes are taking place.


title: Ontology-based deep learning for human behavior prediction with explanations in health social networks

abstract: Abstract Human behavior modeling is a key component in application domains such as healthcare and social behavior research. In addition to accurate prediction, having the capacity to understand the roles of human behavior determinants and to provide explanations for the predicted behaviors is also important. Having this capacity increases trust in the systems and the likelihood that the systems actually will be adopted, thus driving engagement and loyalty. However, most prediction models do not provide explanations for the behaviors they predict. In this paper, we study the research problem, human behavior prediction with explanations , for healthcare intervention systems in health social networks. We propose an ontology-based deep learning model ( ORBM + ) for human behavior prediction over undirected and nodes-attributed graphs. We first propose a bottom-up algorithm to learn the user representation from health ontologies. Then the user representation is utilized to incorporate self-motivation, social influences , and environmental events together in a human behavior prediction model, which extends a well-known deep learning method, the Restricted Boltzmann Machine. ORBM + not only predicts human behaviors accurately, but also, it generates explanations for each predicted behavior. Experiments conducted on both real and synthetic health social networks have shown the tremendous effectiveness of our approach compared with conventional methods.


title: Identifying quantitative trait loci via group-sparse multitask regression and feature selection

abstract: Motivation: Recent advances in high-throughput genotyping and brain imaging techniques enable new approaches to study the influence of genetic variation on brain structures and functions. Traditional association studies typically employ independent and pairwise univariate analysis, which treats single nucleotide polymorphisms (SNPs) and quantitative traits (QTs) as isolated units and ignores important underlying interacting relationships between the units. New methods are proposed here to overcome this limitation. ::: ::: Results: Taking into account the interlinked structure within and between SNPs and imaging QTs, we propose a novel Group-Sparse Multi-task Regression and Feature Selection (G-SMuRFS) method to identify quantitative trait loci for multiple disease-relevant QTs and apply it to a study in mild cognitive impairment and Alzheimer's disease. Built upon regression analysis, our model uses a new form of regularization, group2,1-norm (G2,1-norm), to incorporate the biological group structures among SNPs induced from their genetic arrangement. The new G2,1-norm considers the regression coefficients of all the SNPs in each group with respect to all the QTs together and enforces sparsity at the group level. In addition, an 2,1-norm regularization is utilized to couple feature selection across multiple tasks to make use of the shared underlying mechanism among different brain regions. The effectiveness of the proposed method is demonstrated by both clearly improved prediction performance in empirical evaluations and a compact set of selected SNP predictors relevant to the imaging QTs. ::: ::: Availability: Software is publicly available at: http://ranger.uta.edu/%7eheng/imaging-genetics/ ::: ::: Contact:[email protected]; [email protected] ::: ::: Supplementary information:Supplementary data are available at Bioinformatics online.


title: PLAG: Practical Landmark Allocation for Cloud Geolocation

abstract: Knowing the physical location of files in a cloud system is of a great importance for any user, as is it can affect the whole service drastically. However, pinpointing the exact coordinates for the location of a server is very challenging. Providers prefer not to share the location of their data centers with public for security reasons, and this fact also adds to the complexity of this concept. Researchers have recently developed delay based schemes for cloud data geolocation, some of which use proprietary landmarks for location verification. Unfortunately, such landmark-based schemes are often impractical due to high cost and latency. In this paper, we have developed a practical scheme for landmark allocation in cloud data geolocation. We augment existing approaches with a new landmark allocation modification to get the same or often better accuracy, while decreasing the cost considerably. Our approach improves the existing state of the art by introducing the concept of publicly distributed landmarks for all delay based geolocation techniques.


Convoluted convolved Fibonacci numbers


title: Emotion Recognition from Single-Trial EEG Based on Kernel Fisher's Emotion Pattern and Imbalanced Quasiconformal Kernel Support Vector Machine

abstract: Electroencephalogram-based emotion recognition (EEG-ER) has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI). However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher's discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher's emotion pattern (KFEP), and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM) serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS) are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68%) and arousal (84.79%) among all testing methods.


title: Parking lot guidance software based on MQTT Protocol

abstract: To reduce the amount of CO 2 produced from the personal cars in the world, the parking lot guidance systems are considered to be the solution in shopping malls or department stores in many countries. However, most of the current parking lot systems are located in the parking area of each shopping mall and they cannot show the parking lots information for the driver that are driving on the road. So, The drivers can see the parking lot information if and only if they arrive at the shopping mall area. This is the fact that the CO 2 are still produced to the world although we use the parking lot guidance system. From the reason as mentioned, we propose the parking lots guidance software to share a parking lot information to a large number of clients in real-time based on MQTT Protocol. The proposed software can share the real-time parking lot information to the mobile devices of the drivers in any location or when they are driving on the road. The results show that the proposed software can share the parking lot information in real-time for at least 1,000 sessions simultaneously with the high average score of usability, design and the benefits.


title: CytonMT: an Efficient Neural Machine Translation Open-source Toolkit Implemented in C++

abstract: This paper presented an open-source neural machine translation toolkit named CytonMT\footnote{this https URL}. The toolkit was built from scratch using C++ and Nvidia's GPU-accelerated libraries. The toolkit featured training efficiency, code simplicity and translation quality. Benchmarks showed that cytonMT accelerated the training speed by 64.5\% to 110.8\% and achieved a high translation quality only lower than the Google's production engine among the NMT systems of attention-based RNN encoder-decoder.


title: GraphH: High Performance Big Graph Analytics in Small Clusters

abstract: It is common for real-world applications to analyze big graphs using distributed graph processing systems. Popular in-memory systems require an enormous amount of resources to handle big graphs. While several out-of-core systems have been proposed recently for processing big graphs using secondary storage, the high disk I/O overhead could significantly reduce performance. In this paper, we propose GraphH to enable high- performance big graph analytics in small clusters. Specifically, we design a two-stage graph partition scheme to evenly divide the input graph into partitions, and propose a GAB (Gather-Apply- Broadcast) computation model to make each worker process a partition in memory at a time. We use an edge cache mechanism to reduce the disk I/O overhead, and design a hybrid strategy to improve the communication performance. GraphH can efficiently process big graphs in small clusters or even a single commodity server. Extensive evaluations have shown that GraphH could be up to 7.8x faster compared to popular in-memory systems, such as Pregel+ and PowerGraph when processing generic graphs, and more than 100x faster than recently proposed out-of-core systems, such as GraphD and Chaos when processing big graphs.


title: Comprehensive Load-Shedding System

abstract: The availability of reliable electrical power supply for petrochemical industries is extremely important not only to maintain continuous production but also from the point of view of overall plant safety. The costs, caused by production losses, due to a partial or a complete blackout easily run up to millions of dollars per blackout. If a proper load-shedding scheme is implemented, it is possible to save a plant from such occurrences. A lack of electrical power can be caused by loss of generation capacity or disconnection from the public power company supply. The load-shedding system ensures the availability of electrical power to all essential and most critical loads in the plant. This is achieved by switching off nonessential loads in the case of a lack of power in the plant electrical network, or parts of the plant electrical network. This paper discusses a comprehensive load-shedding scheme for industrial applications with a background on a similar system implemented at large integrated complexes such as refineries and petrochemical plants.


title: Feature extraction with stacked autoencoders for epileptic seizure detection

abstract: Scalp electroencephalogram (EEG), a recording of the brain's electrical activity, has been used to diagnose and detect epileptic seizures for a long time. However, most researchers have implemented seizure detectors by manually hand-engineering features from observed EEG data, and used them in seizure detection, which might not scale well to new patterns of seizures. In this paper, we investigate the possibility of utilising unsupervised feature learning, the recent development of deep learning, to automatically learn features from raw, unlabelled EEG data that are representative enough to be used in seizure detection. We develop patient-specific seizure detectors by using stacked autoencoders and logistic classifiers. A two-step training consisting of the greedy layer-wise and the global fine-tuning was used to train our detectors. The evaluation was performed by using labelled dataset from the CHB-MIT database, and the results showed that all of the test seizures were detected with a mean latency of 3.36 seconds, and a low false detection rate.


title: Spectrally optimal sampling for distribution ray tracing

abstract: Nonuniform sampling of images is a useful technique in computer graphics, because a properly designed pattern of samples can make aliasing take the form of high-frequency random noise. In this paper, the technique of nonuniform sampling is extended from two dimensions to include the extra parameter dimensions of distribution ray tracing. A condition for optimality is suggested, and algorithms for approximating optimal sampling are developed. The technique is demonstrated at low sampling densities, so the characteristics of aliasing noise are clearly visible. At supersampling rates, this technique should move noise into frequencies above the passband of the pixel-reconstruction filter.


title: Design-Oriented Analysis and Performance Evaluation of Buck PFC Front-End

abstract: In universal-line ac/dc converters that require PFC, maintaining a high efficiency across the entire load range poses a major challenge. Typically, a boost PFC front-end exhibits 1-3% lower efficiency at 100-V line compared to that at 230-V line. It is shown in this paper that a buck PFC front end with an output voltage in the 80-V range can maintain a high-efficiency across the entire line range. A thorough analysis of the buck PFC converter operation and performance along with design optimization guidelines are presented. Experimental results obtained on a 90-W notebook adapter are provided.


title: A Novel Design Methodology for High-Efficiency Current-Mode and Voltage-Mode Class-E Power Amplifiers in Wireless Power Transfer systems

abstract: The high-efficiency current-mode (CM) and voltage-mode (VM) Class-E power amplifiers (PAs) for MHz wireless power transfer (WPT) systems are first proposed in this paper and the design methodology for them is presented. The CM/VM Class-E PA is able to deliver the increasing/decreasing power with the increasing load and the efficiency maintains high even when the load varies in a wide range. The high efficiency and certain operation mode are realized by introducing an impedance transformation network with fixed components. The efficiency, output power, circuit tolerance, and robustness are all taken into consideration in the design procedure, which makes the CM and the VM Class-E PAs especially practical and efficient to real WPT systems. 6.78-MHz WPT systems with the CM and the VM Class-E PAs are fabricated and compared to that with the classical Class-E PA. The measurement results show that the output power is proportional to the load for the CM Class-E PA and is inversely proportional to the load for the VM Class-E PA. The efficiency for them maintains high, over 83%, when the load of PA varies from 10 to 100 $\Omega$ , while the efficiency of the classical Class-E is about 60% in the worst case. The experiment results validate the feasibility of the proposed design methodology and show that the CM and the VM Class-E PAs present superior performance in WPT systems compared to the traditional Class-E PA.


title: Hybrid Brain–Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review

abstract: In this paper, hybrid brain-computer interface (hBCI) technologies for improving classification accuracy and increasing the number of commands are reviewed. Hybridization combining more than two modalities is a new trend in brain imaging and prosthesis control. Electroencephalography (EEG), due to its easy use and fast temporal resolution, is most widely utilized in combination with other brain/non-brain signal acquisition modalities, for instance, functional near-infrared spectroscopy (fNIRS), electromyography (EMG), electrooculography (EOG), eye tracker, etc. Three main purposes of hybridization are to increase the number of control commands, improve classification accuracy, and reduce the signal detection time. Currently, such combinations of EEG+fNIRS and EEG+EOG are most commonly employed. Four principal components (i.e., hardware, paradigm, classifiers, features) relevant to accuracy improvement are discussed. In the case of brain signals, motor imagination/movement tasks are combined with cognitive tasks to increase active BCI accuracy. Active and reactive tasks sometimes are combined: motor imagination with steady-state evoked visual potentials (SSVEP), and motor imagination with P300, etc. In the case of reactive tasks, SSVEP is most widely combined with P300 to increase the number of commands. Passive BCIs, however, are rare. After discussing the hardware and strategies involved in the development of hBCI, the second part examines the approaches used to increase the number of control commands and to enhance classification accuracy. The future prospects and the extension of hBCI in real-time applications for daily life scenarios are provided.


title: Attention estimation by simultaneous analysis of viewer and view

abstract: This paper introduces a system for estimating the attention of a driver wearing a first person view camera using salient objects to improve gaze estimation. A challenging data set of pedestrians crossing intersections has been captured using Google Glass worn by a driver. A challenge unique to first person view from cars is that the interior of the car can take up a large part of the image. The proposed system automatically filters out the dashboard of the car, along with other parts of the instrumentation. The remaining area is used as a region of interest for a pedestrian detector. Two cameras looking at the driver are used to determine the direction of the driver's gaze, by examining the eye corners and the center of the iris. This coarse gaze estimation is then linked to the detected pedestrians to determine which pedestrian the driver is focused on at any given time.


title: An Assessment of risk response strategies practiced in software projects

abstract: Risk management and success in projects are highly intertwined – better approaches to project risk management tend to increase chances of project success in terms of achieving scope & quality, schedule and cost targets. The process of responding to risk factors during a project’s life cycle is a crucial aspect of risk management referred to as risk response strategies, in this paper. The current research explores the status of risk response strategies applied in the software development projects in India. India provides a young IT-savvy English-speaking population, which is also cost effective. Other than the workforce, the environment for implementation of software projects in India is different from the matured economies. Risk management process is a commonly discussed theme, though its implementation in practice has a huge scope for improvement in India. The paper talks about four fundamental treatments to risk response – Avoidance, Transference, Mitigation and Acceptance (ATMA). From a primary data of 302 project managers, the paper attempts to address the risk response factors that lead to successful achievement of project scope & quality, schedule and cost targets, by using a series of regressions followed with Seemingly Unrelated Regression Equations (SURE) modelling. Mitigation emerged as the most significant risk response strategy to achieve project targets. Acceptance, transference, and avoidance of risk were mostly manifested in the forms of transparency in communication across stakeholders, careful study of the nature of risks and close coordination between project team, customers/end-users and top management.


title: Techniques for Autonomous, Off-Road Navigation

abstract: To extend autonomous navigation to unpaved and off-road scenarios, vehicles in the AutoNav program bolster a 4D perception and control architecture with area-based vision techniques. This article describes how an autonomous vehicle uses directed real-time, area-based, stereo processing to determine the vertical profile of its path.


Improving Semi-Supervised Learning with Auxiliary Deep Generative Models


title: Anatomic connections of the diaphragm: influence of respiration on the body system

abstract: The article explains the scientific reasons for the diaphragm muscle being an important crossroads for information involving the entire body. The diaphragm muscle extends from the trigeminal system to the pelvic floor, passing from the thoracic diaphragm to the floor of the mouth. Like many structures in the human body, the diaphragm muscle has more than one function, and has links throughout the body, and provides the network necessary for breathing. To assess and treat this muscle effectively, it is necessary to be aware of its anatomic, fascial, and neurologic complexity in the control of breathing. The patient is never a symptom localized, but a system that adapts to a corporeal dysfunction.


title: Design of a joint-coupled orthosis for FES-aided gait

abstract: A hybrid functional electrical stimulation (FES)/orthosis system is being developed which combines two channels of (surface-electrode-based) electrical stimulation with a computer-controlled orthosis for the purpose of restoring gait to spinal cord injured (SCI) individuals (albeit with a stability aid, such as a walker). The orthosis is an energetically passive, controllable device which 1) unidirectionally couples hip to knee flexion; 2) aids hip and knee flexion with a spring assist; and 3) incorporates sensors and modulated friction brakes, which are used in conjunction with electrical stimulation for the feedback control of joint (and therefore limb) trajectories. This paper describes the hybrid FES approach and the design of the joint coupled orthosis. Preliminary experiments are presented which test the joint coupling concept and assess the extent of quadriceps fatigue imposed by the bias spring and joint coupling.


title: Complex Concurrent Engineering and the Design Structure Matrix Method

abstract: Concurrent engineering (CE) principles have considerably matured over the last decade. However, many companies still face enormous challenges when implementing and managing CE practices. This is due to the increased complexity of engineering products and processes, on one hand, and the lack of corresponding CE models and tools, on the other hand. This paper focuses on four critical problems that challenge management while implementing CE in complex product development (PD) projects. We refer to these problems as: iteration, overlapping, decomposition and integration, and convergence problems. We describe these problems proposing a unified modeling and solution approach based on the design structure matrix (DSM) method, which is an information exchange model that allows managers to represent complex task relationships to better plan and manage CE initiatives.


title: Efficient conversion of pretreated brewer’s spent grain and wheat bran by submerged cultivation of Hericium erinaceus

abstract: Abstract Brewer’s spent grain (BSG) and wheat bran (WB) are industrial byproducts that accumulate in millions of tons per year and are typically applied as animal feed. Since both byproducts show a great potential as substrates for fermentation, the approach developed in this study consists of utilizing these lignocellulosic byproducts for biomass production of the medicinal fungus Hericium erinaceus through submerged cultivation. To increase the biological efficiency of the bioconversion, acidic pretreatment was applied yielding a bioconversion of 38.6% for pretreated BSG and 34.8% for pretreated WB. This study shows that the complete degradation of (hemi)cellulose into monosaccharides was not required for an efficient bioconversion. The produced fungal biomass was applied in a second fermentation step to induce the secondary metabolite erinacine C production. Thus, biomass was produced as a functional food ingredient with erinacine C contents of 174.8 mg/g for BSG and 99.3 mg/g for WB based bioconversions.


title: Amodal Completion and Size Constancy in Natural Scenes

abstract: We consider the problem of enriching current object detection systems with veridical object sizes and relative depth estimates from a single image. There are several technical challenges to this, such as occlusions, lack of calibration data and the scale ambiguity between object size and distance. These have not been addressed in full generality in previous work. Here we propose to tackle these issues by building upon advances in object recognition and using recently created large-scale datasets. We first introduce the task of amodal bounding box completion, which aims to infer the the full extent of the object instances in the image. We then propose a probabilistic framework for learning category-specific object size distributions from available annotations and leverage these in conjunction with amodal completions to infer veridical sizes of objects in novel images. Finally, we introduce a focal length prediction approach that exploits scene recognition to overcome inherent scale ambiguities and demonstrate qualitative results on challenging real-world scenes.


title: Kernel-based feature extraction with a speech technology application

abstract: Kernel-based nonlinear feature extraction and classification algorithms are a popular new research direction in machine learning. This paper examines their applicability to the classification of phonemes in a phonological awareness drilling software package. We first give a concise overview of the nonlinear feature extraction methods such as kernel principal component analysis (KPCA), kernel independent component analysis (KICA), kernel linear discriminant analysis (KLDA), and kernel springy discriminant analysis (KSDA). The overview deals with all the methods in a unified framework, regardless of whether they are unsupervised or supervised. The effect of the transformations on a subsequent classification is tested in combination with learning algorithms such as Gaussian mixture modeling (GMM), artificial neural nets (ANN), projection pursuit learning (PPL), decision tree-based classification (C4.5), and support vector machines (SVMs). We found, in most cases, that the transformations have a beneficial effect on the classification performance. Furthermore, the nonlinear supervised algorithms yielded the best results.


title: Data integrity attacks and their impacts on SCADA control system

abstract: Cyber threats for critical infrastructures is an area of growing concern. Data integrity attacks (e.g., manipulating sensor or control signals) on the power system through the SCADA network could have severe effects as it misleads operators into making wrong decisions. However, for an integrity attack to be successful, the malicious data should be within an acceptable range. Hence, only an attacker with intelligence or an understanding of system functionality can cause an effective attack. This paper extends cyber security attack concepts to control systems in an electric power system. Impact on the physical system is estimated by magnitude of load-generation imbalance and frequency deviation after a successful attack on the Automatic Generation Control (AGC) loop. We conduct experiments creating integrity attacks from our attack template on a sample system and evaluate the impact. Our simulation studies show that an integrity attack plan can have severe effects.


title: Cloud-deployable health data mining using secured framework for Clinical decision support system

abstract: Reliable, scalable and secured framework for predicting Heart diseases by mining big data is designed. Components of Apache Hadoop are used for processing of big data used for prediction. For increasing the performance, scalability, and reliability Hadoop clusters are deployed on Google Cloud Storage. Mapreduce based Classification via clustering method is proposed for efficient classification of instances using reduced attributes. Mapreduce based C 4.5 decision tree algorithm is improved and implemented to classify the instances. Datasets are analyzed on WEKA (Waikato Environment for Knowledge Analysis) and Hadoop. Classification via clustering method performs classification with 98.5% accuracy on WEKA with reduced attributes. On Mapreduce paradigm using this approach execution time is improved. With clustered instances 49 nodes of decision tree are reduced to 32 and execution time of Mapreduce program is reduced from 113 seconds to 84 seconds. Mapreduce based decision trees present classification of instances more accurately as compared to WEKA based decision trees.


title: HPISecure: Towards Data Confidentiality in Cloud Applications

abstract: Cloud computing has emerged over the last years as a new model of delivering computation to consumers. Due to the nature of the cloud, where the data and the computation are beyond the control of the user, data privacy and security becomes a vital factor in this new paradigm. Several research studies reported that security and privacy is cited as the biggest concern in adopting cloud computing. In this paper, we present HPISecure, a software prototype designed to facilitate the process of securing data stored on the cloud. HPISecure allows the user to store an encrypted version of his data on the cloud without breaking the functionality of the application. HPISecure intercepts the HTTP request/response objects, encrypt data before transmitting to the cloud, and decrypt data received back from the server. We have successfully tested HPISecure with Google Docs and Google Calender. Future work includes extending HPISecure to work with enterprise applications.


Improved Depth Map Estimation from Stereo Images based on Hybrid Method


title: Towards model-predictive control for aerial pick-and-place

abstract: This paper considers pick-and-place tasks using aerial vehicles equipped with manipulators. The main focus is on the development and experimental validation of a nonlinear model-predictive control methodology to exploit the multi-body system dynamics and achieve optimized performance. At the core of the approach lies a sequential Newton method for unconstrained optimal control and a high-frequency low-level controller tracking the generated optimal reference trajectories. A low cost quadrotor prototype with a simple manipulator extending more than twice the radius of the vehicle is designed and integrated with an on-board vision system for object tracking. Experimental results show the effectiveness of model-predictive control to motivate the future use of real-time optimal control in place of standard ad-hoc gain scheduling techniques.


title: Delving deeper into the whorl of flower segmentation

abstract: We describe an algorithm for automatically segmenting flowers in colour photographs. This is a challenging problem because of the sheer variety of flower classes, the variability within a class and within a particular flower, and the variability of the imaging conditions - lighting, pose, foreshortening, etc. The method couples two models - a colour model for foreground and background, and a light generic shape model for the petal structure. This shape model is tolerant to viewpoint changes and petal deformations, and applicable across many different flower classes. The segmentations are produced using a MRF cost function optimized using graph cuts. We show how the components of the algorithm can be tuned to overcome common segmentation errors, and how performance can be optimized by learning parameters on a training set. The algorithm is evaluated on 13 flower classes and more than 750 examples. Performance is assessed against ground truth trimap segmentations. The algorithms is also compared to several previous approaches for flower segmentation.


title: Query expansion using associated queries

abstract: Hundreds of millions of users each day use web search engines to meet their information needs. Advances in web search effectiveness are therefore perhaps the most significant public outcomes of IR research. Query expansion is one such method for improving the effectiveness of ranked retrieval by adding additional terms to a query. In previous approaches to query expansion, the additional terms are selected from highly ranked documents returned from an initial retrieval run. We propose a new method of obtaining expansion terms, based on selecting terms from past user queries that are associated with documents in the collection. Our scheme is effective for query expansion for web retrieval: our results show relative improvements over unexpanded full text retrieval of 26%--29%, and 18%--20% over an optimised, conventional expansion approach.


title: Sentiment Analysis in Twitter: From Classification to Quantification of Sentiments within Tweets

abstract: Twitter is attracting significant interests from the research community in the last few years. Sentiment analysis of tweets is among the hottest topics of research nowadays. State of the art approaches of sentiment analysis present many shortcomings when classifying tweets, in particular when the classification goes beyond the binary or ternary classification. Multi-class sentiment analysis has proven to be a very challenging task. This is mainly for the simple reason that a tweet usually does not contain a single sentiment, but many ones. In this paper, we propose a pattern-based approach for sentiment quantification in Twitter. By quantification, we refer to the detection of the existing sentiments within a tweet and the detection of the weight of these sentiments. In a first step, we classify tweets into positive, negative, or neutral. Our approach reaches an accuracy of 81%. We then perform the sentiment quantification on the sentimental tweets (i.e., positive and negative ones) to extract the sentiments within them: we define 5 positive sentiment sub-classes 5 negative ones and detect which exist in each tweet. We define 2 metrics to measure the correctness of sentiment detection, and prove that sentiment quantification can be a more meaningful task than the regular multi-class classification.


title: Biodegradability of Plastics: Challenges and Misconceptions

abstract: Plastics are one of the most widely used materials and, in most cases, they are designed to have long life times. Thus, plastics contain a complex blend of stabilizers that prevent them from degrading too quickly. Unfortunately, many of the most advantageous properties of plastics such as their chemical, physical and biological inertness and durability present challenges when plastic is released into the environment. Common plastics such as polyethylene (PE), polypropylene (PP), polystyrene (PS), and polyethylene terephthalate (PET) are extremely persistent in the environment, where they undergo very slow fragmentation (projected to take centuries) into small particles through photo-, physical, and biological degradation processes1. The fragmentation of the material into increasingly smaller pieces is an unavoidable stage of the degradation process. Ultimately, plastic materials degrade to micron-sized particles (microplastics), which are persistent in the environment and present a potential source of harm for organisms.


title: Kinematic multi-robot manipulation with no communication using force feedback

abstract: This paper proposes a novel decentralized algorithm that coordinates the forces of a group of robots during a cooperative manipulation task. The highlight of our approach is that no communication is needed between any two robots. Our underlying intuition is that every follower robot can measure the direction of the movement of the object and then applies its force along that direction to reinforce the movement. We prove that using our algorithm, all followers' forces will synchronize to the direction of the force applied by one leader robot, who guides the robotic fleet to its destination. We first verify our algorithm by simulation in a physics engine, where 20 robots transport a chair collectively. We then validate our algorithm in hardware experiments by building four low-cost robots, equipped with force and velocity sensors, to transport a cardboard box in a laboratory environment. In addition, our algorithm allows the leader to be a human, and we also demonstrate the human-swarm cooperation in our manipulation experiments.


title: The Iranian version of 12-item Short Form Health Survey (SF-12): factor structure, internal consistency and construct validity

abstract: AbstractBackgroundThe 12-item Short Form Health Survey (SF-12) as a shorter alternative of the SF-36 is largely used in health outcomes surveys. The aim of this study was to validate the SF-12 in Iran.MethodsA random sample of the general population aged 15 years and over living in Tehran, Iran completed the SF-12. Reliability was estimated using internal consistency and validity was assessed using known groups comparison and convergent validity. In addition, the factor structure of the questionnaire was extracted by performing both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). ::: Results: ::: In all, 5587 individuals were studied (2721 male and 2866 female). The mean age and formal education of the respondents were 35.1 (SD = 15.4) and 10.2 (SD = 4.4) years respectively. The results showed satisfactory internal consistency for both summary measures, that are the Physical Component Summary (PCS) and the Mental Component Summary (MCS); Cronbach's α for PCS-12 and MCS-12 was 0.73 and 0.72, respectively. Known-groups comparison showed that the SF-12 discriminated well between men and women and those who differed in age and educational status (P < 0.001). In addition, correlations between the SF-12 scales and single items showed that the physical functioning, role physical, bodily pain and general health subscales correlated higher with the PCS-12 score, while the vitality, social functioning, role emotional and mental health subscales more correlated with the MCS-12 score lending support to its good convergent validity. Finally the principal component analysis indicated a two-factor structure (physical and mental health) that jointly accounted for 57.8% of the variance. The confirmatory factory analysis also indicated a good fit to the data for the two-latent structure (physical and mental health).ConclusionIn general the findings suggest that the SF-12 is a reliable and valid measure of health related quality of life among Iranian population. However, further studies are needed to establish stronger psychometric properties for this alternative form of the SF-36 Health Survey in Iran.


title: Natural programming languages and environments

abstract: Over the last six years, we have been working to create programming languages and environments that are more natural, or closer to the way people think about their tasks. Our goal is to make it possible for people to express their ideas in the same way they think about them. To achieve this, we have performed various studies about how people think about programming tasks, both when trying to create a new program and when trying to find and fix bugs in existing programs. We then use this knowledge to develop new tools for programming and debugging. Our user studies have shown the resulting systems provide significant benefits to users.


title: PointFlowNet: Learning Representations for 3D Scene Flow Estimation from Point Clouds

abstract: Despite significant progress in image-based 3D scene flow estimation, the performance of such approaches has not yet reached the fidelity required by many applications. Simultaneously, these applications are often not restricted to image-based estimation: laser scanners provide a popular alternative to traditional cameras, for example in the context of self-driving cars, as they directly yield a 3D point cloud. In this paper, we propose to estimate 3D scene flow from such unstructured point clouds using a deep neural network. In a single forward pass, our model jointly predicts 3D scene flow as well as the 3D bounding box and rigid body motion of objects in the scene. While the prospect of estimating 3D scene flow from unstructured point clouds is promising, it is also a challenging task. We show that the traditional global representation of rigid body motion prohibits inference by CNNs, and propose a translation equivariant representation to circumvent this problem. For training our deep network, a large dataset is required. Because of this, we augment real scans from KITTI with virtual objects, realistically modeling occlusions and simulating sensor noise. A thorough comparison with classic and learning-based techniques highlights the robustness of the proposed approach.


title: A 24-Gb/s Double-Sampling Receiver for Ultra-Low-Power Optical Communication

abstract: This paper describes a dense, high-speed, and low-power CMOS optical receiver implemented in a 65-nm CMOS technology. High data rate is achieved using an RC double-sampling front-end and a novel dynamic offset-modulation technique. The low-voltage double-sampling technique provides high power efficiency by avoiding linear high-gain elements conventionally employed in transimpedance-amplifier (TIA) receivers. In addition, the demultiplexed output of the receiver helps save power in the following digital blocks. The receiver functionality was validated by electrical and optical measurements. The receiver achieves up to 24 Gb/s data rate with better than 160-μA current sensitivity in an experiment performed by a photodiode current emulator embedded on-chip. Optical measurements performed by a 1550-nm wire-bonded photodiode show better than - 4.7-dBm optical sensitivity at 24 Gb/s. The receiver offers peak power efficiency of 0.36 pJ/b at 20 Gb/s from a 1.2-V supply and occupies less than 0.0028 mm2 silicon area.


title: Real-time Joint Tracking of a Hand Manipulating an Object from RGB-D Input

abstract: Real-time simultaneous tracking of hands manipulating and interacting with external objects has many potential applications in augmented reality, tangible computing, and wearable computing. However, due to difficult occlusions, fast motions, and uniform hand appearance, jointly tracking hand and object pose is more challenging than tracking either of the two separately. Many previous approaches resort to complex multi-camera setups to remedy the occlusion problem and often employ expensive segmentation and optimization steps which makes real-time tracking impossible. In this paper, we propose a real-time solution that uses a single commodity RGB-D camera. The core of our approach is a 3D articulated Gaussian mixture alignment strategy tailored to hand-object tracking that allows fast pose optimization. The alignment energy uses novel regularizers to address occlusions and hand-object contacts. For added robustness, we guide the optimization with discriminative part classification of the hand and segmentation of the object. We conducted extensive experiments on several existing datasets and introduce a new annotated hand-object dataset. Quantitative and qualitative results show the key advantages of our method: speed, accuracy, and robustness.


title: Hierarchical clustering for complex data

abstract: In this paper we introduce a new tree-structured self-organizing neural network called a dynamical growing self-organizing tree (DGSOT). This DGSOT algorithm constructs a hierarchy from top to bottom by division. At each hierarchical level, the DGSOT optimizes the number of clusters, from which the proper hierarchical structure of the underlying data set can be found. We propose a K-level up distribution (KLD) mechanism. This KLD scheme increases the scope for data distribution in the hierarchy, which allows the data mis-clustered in the early stages to be re-evaluated at a later stage increasing the accuracy of the final clustering result. The DGSOT algorithm, combined with the KLD mechanism, overcomes the drawbacks of traditional hierarchical clustering algorithms (e.g., hierarchical agglomerative clustering). The DGSOT algorithm has been tested on two benchmark data sets including gene expression complex data set and we observe that our algorithm extracts patterns with different levels of abstraction. ...


title: High nucleotide divergence in a dimorphic parasite with disparate hosts

abstract: Morphologically cryptic male C. fenyesi sensu lato are found parasitic in different ant hosts over a wide geographical range. We use ribosomal DNA (rDNA) primary sequence and predicted rRNA secondary structure to compare between the second expansion segment (D2) of the nuclear large subunit rDNA (28S) and the entire nuclear small subunit rDNA (18S) of the male and the sexually dimorphic, neotenic female of Caenocholax fenyesi waloffi Kathirithamby & Johnston from Los Tuxtlas, Mexico, with that of the morphologically identical male C. f. texensis Kathirithamby & Johnston (Myrmecolacidae) from Texas, USA. In Los Tuxtlas the male C. f. waloffi parasitizes the ant, Dolichoderus bispinosus Olivier, while the female parasitizes the cricket, Macroanaxipha macilenta (Saussure). In Texas, the male C. f. texensis parasitizes the red imported fire ant, Solenopsis invicta Buren. The compared sequences show similar unusual secondary structures in the rRNA variable regions, but with approximately 14% overall divergence between Mexican and Texan specimens (15.5% divergence after correction for multiple substitutions). Our findings open a new opportunity in evolutionary biology to investigate speciation under a mechanism of morphological stasis and high genetic divergence in a unique parasite that is not only profoundly sexually dimorphic but wherein the sexes specialize in entirely different niches (hosts).


title: Scalable Algorithms for Multi-Instance Learning

abstract: Multi-instance learning (MIL) has been widely applied to diverse applications involving complicated data objects, such as images and genes. However, most existing MIL algorithms can only handle small- or moderate-sized data. In order to deal with large-scale MIL problems, we propose MIL based on the vector of locally aggregated descriptors representation (miVLAD) and MIL based on the Fisher vector representation (miFV), two efficient and scalable MIL algorithms. They map the original MIL bags into new vector representations using their corresponding mapping functions. The new feature representations keep essential bag-level information, and at the same time lead to excellent MIL performances even when linear classifiers are used. Thanks to the low computational cost in the mapping step and the scalability of linear classifiers, miVLAD and miFV can handle large-scale MIL data efficiently and effectively. Experiments show that miVLAD and miFV not only achieve comparable accuracy rates with the state-of-the-art MIL algorithms, but also have hundreds of times faster speed. Moreover, we can regard the new miVLAD and miFV representations as multiview data, which improves the accuracy rates in most cases. In addition, our algorithms perform well even when they are used without parameter tuning (i.e., adopting the default parameters), which is convenient for practical MIL applications.


title: Context-aware recommendations from implicit data via scalable tensor factorization

abstract: Albeit the implicit feedback based recommendation problem - when only the user history is available but there are no ratings - is the most typical setting in real-world applications, it is much less researched than the explicit feedback case. State-of-the-art algorithms that are efficient on the explicit case cannot be automatically transformed to the implicit case if scalability should be maintained. There are few implicit feedback benchmark data sets, therefore new ideas are usually experimented on explicit benchmarks. In this paper, we propose a generic context-aware implicit feedback recommender algorithm, coined iTALS. iTALS applies a fast, ALS-based tensor factorization learning method that scales linearly with the number of non-zero elements in the tensor. We also present two approximate and faster variants of iTALS using coordinate descent and conjugate gradient methods at learning. The method also allows us to incorporate various contextual information into the model while maintaining its computational efficiency. We present two context-aware variants of iTALS incorporating seasonality and item purchase sequentiality into the model to distinguish user behavior at different time intervals, and product types with different repetitiveness. Experiments run on six data sets shows that iTALS clearly outperforms context-unaware models and context aware baselines, while it is on par with factorization machines (beats 7 times out of 12 cases) both in terms of recall and MAP.


title: UnrealCV: Virtual Worlds for Computer Vision

abstract: UnrealCV is a project to help computer vision researchers build virtual worlds using Unreal Engine 4 (UE4). It extends UE4 with a plugin by providing (1) A set of UnrealCV commands to interact with the virtual world. (2) Communication between UE4 and an external program, such as Caffe. UnrealCV can be used in two ways. The first one is using a compiled game binary with UnrealCV embedded. This is as simple as running a game, no knowledge of Unreal Engine is required. The second is installing UnrealCV plugin to Unreal Engine 4 (UE4) and use the editor of UE4 to build a new virtual world. UnrealCV is an open-source software under the MIT license. Since the initial release in September 2016, it has gathered an active community of users, including students and researchers.


title: Information geometry and its applications

abstract: Information geometry is a new mathematical discipline which applies the methodology of differential geometry to statistics. Therefore, families of exponential distributions are considered as embedded manifolds, called statistical manifolds. This includes so important families like the multivariate normal or the gamma distributions. Fisher information — well known in information theory — becomes a metric on statistical manifolds. The Fisher information metric enables a hyperbolic structure on the multivariate normal distributions. Information geometry offers new methods for hypothesis testings, estimation theory or stochastic filtering. These can be used in engineering areas like signal processing or video processing or finance.


Nucleosides. XXI. Synthesis of Some 3'-Substituted 2',3'-Dideoxyribonucleosides of Thymine and 5-Methylcytosine1


title: Intonation in Spanish Classroom-style Didactic Speech

abstract: Work on intonation, especially related to Spanish, has not focused extensively on second language acquisition in general. Within this context, little to no research has addressed how language instructors use rhythm and melody to facilitate the oral comprehension of language learners. This study begins to enter this space by analyzing recordings of Spanish language instructors reading passages directed at first year language students. The main results reveal that the these language instructors indeed employ intonational strategies such as pitch range expansion, early peak alignment, increased time and extent of pitch rises, less pitch reduction, and stress addition. All of these characteristics corroborate and expand upon previous literature on narrow focus and emphatic speech styles in Spanish. In this case, phonetic cues are produced to create special emphasis on words that are of particular interest to students when answering oral comprehension questions on a written exam. The concluding remarks hope to spark future perceptual studies that will help us gain a more complete picture of how didactic speech benefits learners with low levels of proficiency.


title: Learning to Count with CNN Boosting

abstract: In this paper, we address the task of object counting in images. We follow modern learning approaches in which a density map is estimated directly from the input image. We employ CNNs and incorporate two significant improvements to the state of the art methods: layered boosting and selective sampling. As a result, we manage both to increase the counting accuracy and to reduce processing time. Moreover, we show that the proposed method is effective, even in the presence of labeling errors. Extensive experiments on five different datasets demonstrate the efficacy and robustness of our approach. Mean Absolute error was reduced by 20 % to 35 %. At the same time, the training time of each CNN has been reduced by 50 %.


title: Learning Frames from Text with an Unsupervised Latent Variable Model

abstract: We develop a probabilistic latent-variable model to discover semantic frames---types of events and their participants---from corpora. We present a Dirichlet-multinomial model in which frames are latent categories that explain the linking of verb-subject-object triples, given document-level sparsity. We analyze what the model learns, and compare it to FrameNet, noting it learns some novel and interesting frames. This document also contains a discussion of inference issues, including concentration parameter learning; and a small-scale error analysis of syntactic parsing accuracy.


title: Automatic histopathology image analysis with CNNs

abstract: We define Pathomics as the process of high throughput generation, interrogation, and mining of quantitative features from high-resolution histopathology tissue images. Analysis and mining of large volumes of imaging features has great potential to enhance our understanding of tumors. The basic Pathomics workflow consists of several steps: segmentation of tissue images to delineate the boundaries of nuclei, cells, and other structures; computation of size, shape, intensity, and texture features for each segmented object; classification of images and patients based on imaging features; and correlation of classification results with genomic signatures and clinical outcome. Executing a Pathomics workflow on a dataset of thousands of very high resolution (gigapixels) and heterogeneous histopathology images is a computationally challenging problem. In this paper, we use Convolutional Neural Networks (CNN) for automatic recognition of nuclear morphological attributes in histopathology images of glioma, the most common malignant brain tumor. We constructed a comprehensive multi-label dataset of glioma nuclei and applied two CNN based methods on this dataset. Both methods perform well recognizing some but not all morphological attributes and are complementary with each other.


title: Picture: A probabilistic programming language for scene perception

abstract: Recent progress on probabilistic modeling and statistical learning, coupled with the availability of large training datasets, has led to remarkable progress in computer vision. Generative probabilistic models, or “analysis-by-synthesis” approaches, can capture rich scene structure but have been less widely applied than their discriminative counterparts, as they often require considerable problem-specific engineering in modeling and inference, and inference is typically seen as requiring slow, hypothesize-and-test Monte Carlo methods. Here we present Picture, a probabilistic programming language for scene understanding that allows researchers to express complex generative vision models, while automatically solving them using fast general-purpose inference machinery. Picture provides a stochastic scene language that can express generative models for arbitrary 2D/3D scenes, as well as a hierarchy of representation layers for comparing scene hypotheses with observed images by matching not simply pixels, but also more abstract features (e.g., contours, deep neural network activations). Inference can flexibly integrate advanced Monte Carlo strategies with fast bottom-up data-driven methods. Thus both representations and inference strategies can build directly on progress in discriminatively trained systems to make generative vision more robust and efficient. We use Picture to write programs for 3D face analysis, 3D human pose estimation, and 3D object reconstruction - each competitive with specially engineered baselines.


Automated Detection of Diabetic Retinopathy using Fluorescein Angiography Photographs


title: Single phase NPC inverter controller with integrated MPPT for PV grid connection

abstract: This paper presents a single-stage three-level Neutral Point Clamped (NPC) inverter for connection to the electrical power grid, with integrated Maximum Power Point Tracking (MPPT) algorithm to extract the maximum power available from solar photovoltaic (PV) panels. This single-stage topology is more compact than the traditional topology, it was chosen because with the proper control strategy. It is suitable to connect the PV panels to the power grid. The paper describes the design of a 5 kW NPC inverter for the interface of PV panels with the power grid, presenting the circuit parameters and the description of the control algorithms. A phase locked loop control is used to connect the inverter into the grid. Then, a proposed DC Link voltage control to regulate the input voltage of the inverter. Although an MPPT algorithm was used to optimize the energy extraction and the system efficiency. Inverter Output Current control to produce an output current (current injected in the power grid) with low Total Harmonic Distortion (THD) implemented in a DSP. Simulation and experimental results verify the correct operation of the proposed system, even with fluctuations in the solar radiation.


title: Training Set Debugging Using Trusted Items

abstract: Training set bugs are flaws in the data that adversely affect machine learning. The training set is usually too large for man- ual inspection, but one may have the resources to verify a few trusted items. The set of trusted items may not by itself be adequate for learning, so we propose an algorithm that uses these items to identify bugs in the training set and thus im- proves learning. Specifically, our approach seeks the smallest set of changes to the training set labels such that the model learned from this corrected training set predicts labels of the trusted items correctly. We flag the items whose labels are changed as potential bugs, whose labels can be checked for veracity by human experts. To find the bugs in this way is a challenging combinatorial bilevel optimization problem, but it can be relaxed into a continuous optimization problem. Ex- periments on toy and real data demonstrate that our approach can identify training set bugs effectively and suggest appro- priate changes to the labels. Our algorithm is a step toward trustworthy machine learning.


title: Sentiment Flow - A General Model of Web Review Argumentation

abstract: Web reviews have been intensively studied in argumentation-related tasks such as sentiment analysis. However, due to their focus on content-based features, many sentiment analysis approaches are effective only for reviews from those domains they have been specifically modeled for. This paper puts its focus on domain independence and asks whether a general model can be found for how people argue in web reviews. Our hypothesis is that people express their global sentiment on a topic with similar sequences of local sentiment independent of the domain. We model such sentiment flow robustly under uncertainty through abstraction. To test our hypothesis, we predict global sentiment based on sentiment flow. In systematic experiments, we improve over the domain independence of strong baselines. Our findings suggest that sentiment flow qualifies as a general model of web review argumentation.


title: Premier 'Smokeless Cigarettes' Can Be Used to Deliver Crack

abstract: To the Editor.— The recent commercial introduction into the US market of the Premier "smokeless cigarette" by R. J. Reynolds Tobacco Company is the newest entry by the tobacco industry of nicotine delivery systems. The device is a tube that has the superficial appearance of a conventional cigarette but that contains very little tobacco. 1 Its primary means of nicotine delivery is an aluminum cylinder in which a charcoal tip has been partially inserted. The cylinder (4.5 mm in diameter and 29 mm in length) is filled with aluminum oxide beads that have been treated with glycerol and nicotine-laden substances. The smoker ignites the charcoal tip and draws hot gases through the aluminum chamber, causing volatile components to form an aerosol and be drawn into the user's lungs. A patent (No. 4756318) application by the company claims that the device "may be used or may be modified for use as


title: A Low-Power Convolutional Neural Network Face Recognition Processor and a CIS Integrated With Always-on Face Detector

abstract: A Low-power convolutional neural network (CNN)-based face recognition system is proposed for the user authentication in smart devices. The system consists of two chips: an always-on CMOS image sensor (CIS)-based face detector (FD) and a low-power CNN processor. For always-on FD, analog–digital Hybrid Haar-like FD is proposed to improve the energy efficiency of FD by 39%. For low-power CNN processing, the CNN processor with 1024 MAC units and 8192-bit-wide local distributed memory operates at near threshold voltage, 0.46 V with 5-MHz clock frequency. In addition, the separable filter approximation is adopted for the workload reduction of CNN, and transpose-read SRAM using 7T SRAM cell is proposed to reduce the activity factor of the data read operation. Implemented in 65-nm CMOS technology, the $3.30 \times 3.36$ mm2 CIS chip and the $4 \times 4$ mm2 CNN processor consume 0.62 mW to evaluate one face at 1 fps and achieved 97% accuracy in LFW dataset.


OPFE: Outsourcing Computation for Private Function Evaluation


title: Treatment planning of the edentulous maxilla

abstract: Implants ::: The predictability of successful osseointegrated implant rehabilitation of the edentulous jaw as described by Branemark et al.,1 introduced a new era of management for the edentulous predicament. Implant rehabilitation of the edentulous maxilla remains one of the most complex restorative challenges because of the number of variables that affect both the aesthetic and functional aspect of the prosthesis.2 Among the prosthesis designs used to treat the edentulous maxilla are fixed or removable implant-supported restorations. Since the aesthetic requirements and preoperative situation of each patient varies, considerable time must be spent on accurate diagnosis to ensure patient desires are satisfied and predictable outcomes are achieved. The purpose of this article is to compare the treatment options and prosthesis designs for the edentulous maxilla. Emphasis will be placed on diagnosis and treatment planning. Criteria will be given to guide the practitioner in deciding whether a fixed or removable restoration should be placed. This objective will be accomplished through the review of cases with regard to varying design considerations and factors that influence the decision-making process.


title: Remote plethysmographic imaging using ambient light.

abstract: Plethysmographic signals were measured remotely (>1m) using ambient light and a simple consumer level digital camera in movie mode. Heart and respiration rates could be quantified up to several harmonics. Although the green channel featuring the strongest plethysmographic signal, corresponding to an absorption peak by (oxy-) hemoglobin, the red and blue channels also contained plethysmographic information. The results show that ambient light photo-plethysmography may be useful for medical purposes such as characterization of vascular skin lesions (e.g., port wine stains) and remote sensing of vital signs (e.g., heart and respiration rates) for triage or sports purposes.


title: Cloud-DLS : Dynamic trusted scheduling for Cloud computing

abstract: Highlights? This paper proposed a novel Bayesian method based cognitive trust model. ? A trust dynamic level scheduling algorithm named Cloud-DLS is proposed. ? A benchmark is structured for evaluation of the proposed method. ? Experiments and case studies are carried out to evaluate our proposed method. Clouds are rapidly becoming an important platform for scientific applications. In the Cloud environment with uncountable numeric nodes, resource is inevitably unreliable, which has a great effect on task execution and scheduling. In this paper, inspired by Bayesian cognitive model and referring to the trust relationship models of sociology, we first propose a novel Bayesian method based cognitive trust model, and then we proposed a trust dynamic level scheduling algorithm named Cloud-DLS by integrating the existing DLS algorithm. Moreover, a benchmark is structured to span a range of Cloud computing characteristics for evaluation of the proposed method. Theoretical analysis and simulations prove that the Cloud-DLS algorithm can efficiently meet the requirement of Cloud computing workloads in trust, sacrificing fewer time costs, and assuring the execution of tasks in a security way.


title: Calculating web service similarity using ontology learning with machine learning

abstract: The Web is a popular, easy and common way to propagate information today and according to the growth of the Web, Web service discovery has become a challenging task. Clustering Web services into similar clusters through calculating the semantic similarity of Web services is one way for overcome this issue. Several methods are used for current similarity calculation process such as knowledge based, information-retrieval based, text mining, ontology based and context-aware based methods. Through this paper, present a method for calculating Web service similarity using both ontology learning and machine learning that uses a support vector machine for similarity calculation in generated ontology instead of edge count base method. Experimental results show that our hybrid approach of combining ontology learning and machine learning works efficiently and give accurate results than previous two approaches.


title: Wavelet entropy: a new tool for analysis of short duration brain electrical signals

abstract: Since traditional electrical brain signal analysis is mostly qualitative, the development of new quantitative methods is crucial for restricting the subjectivity in the study of brain signals. These methods are particularly fruitful when they are strongly correlated with intuitive physical concepts that allow a better understanding of brain dynamics. Here, new method based on orthogonal discrete wavelet transform (ODWT) is applied. It takes as a basic element the ODWT of the EEG signal, and defines the relative wavelet energy, the wavelet entropy (WE) and the relative wavelet entropy (RWE). The relative wavelet energy provides information about the relative energy associated with different frequency bands present in the EEG and their corresponding degree of importance. The WE carries information about the degree of order:disorder associated with a multi-frequency signal response, and the RWE measures the degree of similarity between different segments of the signal. In addition, the time evolution of the WE is calculated to give information about the dynamics in the EEG records. Within this framework, the major objective of the present work was to characterize in a quantitative way functional dynamics of order:disorder microstates in short duration EEG signals. For that aim, spontaneous EEG signals under different physiological conditions were analyzed. Further, specific quantifiers were derived to characterize how stimulus affects electrical events in terms of frequency synchronization (tuning) in the event related potentials. © 2001 Published by Elsevier Science B.V.


title: Enhanced Nearest Neighbour Search on the R-tree

abstract: Multimedia databases usually deal with huge amounts of data and it is necessary to have an indexing structure such that efficient retrieval of data can be provided. R-Tree with its variations, is a commonly cited indexing method. In this paper we propose an improved nearest neighbor search algorithm on the R-tree and its variants. The improvement lies in the removal of two hueristics that have been used in previous R*-tree work, which we prove cannot improve on the pruning power during a search.


title: Pylearn2: a machine learning research library

abstract: Pylearn2 is a machine learning research library. This does not just mean that it is a collection of machine learning algorithms that share a common API; it means that it has been designed for flexibility and extensibility in order to facilitate research projects that involve new or unusual use cases. In this paper we give a brief history of the library, an overview of its basic philosophy, a summary of the library's architecture, and a description of how the Pylearn2 community functions socially.


Teacher leadership : perspectives, perception and readiness of educators on the Eden and Central Karoo education district in the Western Cape province


title: College Student Technology Use and Academic Performance

abstract: As technology use continues to rise, especially among young individuals, there are concerns that excessive use of technology may impact academic performance. Researchers have started to investigate the possible negative effects of technology use on college academic performance, but results have been mixed. The following study seeks to expand upon previous studies by exploring the relationship among the use of a wide variety of technology forms and an objective measure of academic performance (GPA) using a 7-day time diary data collection method. The current study also seeks to examine both underclassmen and upperclassmen to see if these groups differ in how they use technology. Upperclassmen spent significantly more time using technology for academic and workrelated purposes, whereas underclassmen spent significantly more time using cell phones, online chatting, and social networking sites. Significant negative correlations with GPA emerged for television, online gaming, adult site, and total technology use categories. Keyword: Technology use, academic performance, post-secondary education.


title: Cannabis Oil: chemical evaluation of an upcoming cannabis-based medicine

abstract: Concentrated cannabis extracts, also known as Cannabis oils because of their sticky and viscous appearance, are becoming increasingly popular among self-medicating patients as a claimed cure for cancer. In general, preparation methods for Cannabis oils are relatively simple and do not require particular instruments. The most well-known example of such a product is called ‘Simpson oil’. The purpose of the extraction, often followed by a solvent evaporation step, is to make cannabinoids and other beneficial components such as terpenes available in a highly concentrated form. Although various preparation methods have been recommended for Cannabis oils, so far no studies have reported on the chemical composition of such products. Recognizing the need for more information on quality and safety issues regarding Cannabis oils, an analytical study was performed to compare several generally used preparation methods on the basis of content of cannabinoids, terpenes, and residual solvent components. Solvents used include ethanol, naphtha, petroleum ether, and olive oil. The obtained results are not intended to support or deny the therapeutic properties of these products, but may be useful for better understanding the experiences of self-medicating patients through chemical analysis of this popular medicine.


title: Conditional Adversarial Network for Semantic Segmentation of Brain Tumor

abstract: Automated brain lesion detection is an important and very challenging clinical diagnostic task, due to the lesions’different sizes, shapes, contrasts, and locations. Recently deep learning has shown promising progresses in many application fields, thereby motivating us to apply this technique for such an important problem. In this paper, we propose an automatic end-to-end trainable architecture for heterogeneous brain tumor segmentation through adversarial training for the BraTS-2017 challenge. Inspired by classical generative adversarial network, the proposed network has two components: the “Discriminator” and the “Generator”. We use a patient-wise fully convolutional neural networks (FCNs) as the segmentor network to generate segmentation label maps. The discriminator network is patient-wise fully convolutional neural networks (FCNs) with L1 loss that discriminates segmentation maps coming from the ground truth or from the segmentor network. We propose an end-to-end trainable CNNs for survival day prediction based on deep learning techniques. The experimental results demonstrate the ability of the propose approaches for both tasks of BraTS-2017 challenge. Our patient-wise cGAN achieved competitive results in the BraTS-2017 challenges.


title: Common frequency pattern for music preference identification using frontal EEG

abstract: In this paper, we investigate the use of 2-channel frontal EEG signal to classify two music preferences: like and dislike. The hypothesis for this investigation is that the frontal EEG signal contains sufficient information on the mental state of a subject for discriminating the preference of music of the subject. An experiment is performed to collect 2-channel frontal EEG data from 12 subjects by playing various types of music pieces and asking whether they like or dislike the music in order to obtain the true labels of their music preferences. We then propose a frequency band optimization method called common frequency pattern (CFP) for feature extraction and Linear SVM for classification to identify the music preference of the subjects from the 2-channel frontal EEG. The results of using the proposed method yield an average classification accuracy of 74.77% for a trial length of 30 s over the 12 subjects. Hence the experimental results show evidence that frontal EEG signal contains sufficient information to discriminate preference of music. Furthermore, the frequency band optimization results indicate that gamma band is essential for EEG-based music preference identification.


title: Automatic Selection of Context Configurations for Improved Class-Specific Word Representations

abstract: Recent work has demonstrated that state-of-the-art word embedding models require different context types to produce high-quality representations for different word classes such as adjectives (A), verbs (V), and nouns (N). This paper is concerned with identifying contexts useful for learning A/V/N-specific representations. We introduce a simple yet effective framework for selecting class-specific context configurations that yield improved representations for each class. We propose an automatic A* style selection algorithm that effectively searches only a fraction of the large configuration space. The results on predicting similarity scores for the A, V, and N subsets of the benchmarking SimLex-999 evaluation set indicate that our method is useful for each class: the improvements are 6% (A), 6% (V), and 5% (N) over the best previously proposed context type for each class. At the same time, the model trains on only 14% (A), 26.2% (V), and 33.6% (N) of all dependency-based contexts, resulting in much shorter training time.


title: Automatic Text Input Generation for Mobile Testing

abstract: Many designs have been proposed to improve the automated mobile testing. Despite these improvements, providing appropriate text inputs remains a prominent obstacle, which hinders the large-scale adoption of automated testing approaches. The key challenge is how to automatically produce the most relevant text in a use case context. For example, a valid website address should be entered in the address bar of a mobile browser app to continue the testing of the app, a singer's name should be entered in the search bar of a music recommendation app. Without the proper text inputs, the testing would get stuck. We propose a novel deep learning based approach to address the challenge, which reduces the problem to a minimization problem. Another challenge is how to make the approach generally applicable to both the trained apps and the untrained apps. We leverage the Word2Vec model to address the challenge. We have built our approaches as a tool and evaluated it with 50 iOS mobile apps including Firefox and Wikipedia. The results show that our approach significantly outperforms existing automatic text input generation methods.