title: PRCP: Packet replication control based prophet routing strategy for delay tolerant network

abstract: Continuous network connectivity has always been a significant issue in the area of networks. Delay Tolerant Networks(DTNs) provides an architecture that casts upon the problems of discontinuity in the network and tries to solve it. Such dropping of connectivity is found in tremendous terrestrial atmosphere and planetary systems. Instead of traditional communication system of delivering data from source to destination through continuous connectivity, DTNs uses the intermediate nodes to relay messages from source to destination. Sometimes these nodes act selfishly and can deny to relay packets for saving their resources like energy, buffer etc and that is why they may not agree to forward data packets which leads to lack of cooperation and hence hampers transferability in the network. To combat this selfish behavior various incentives or rewards have been proposed to these nodes such they assist with each other and make the network communication smoother. Packet replication control based prophet routing strategy for DTNs (PRCP) uses replication control based incentive mechanism in prophet routing algorithm which has shown better values of parameters compared to existing ones.


title: Preliminary Study of VR and AR Applications in Medical and Healthcare Education

abstract: As technology advances, mobile devices have gradually turned into wearable devices, and Virtual Reality (VR), Augmented Reality (AR) as well as Mixed Reality (MR) have been applied more and more widely. For example, VR, AR and MR are applied in the medical fields like medical education and training, surgical simulation, neurological rehabilitation, psychotherapy, and telemedicine. Related research result has proved that VR, AR and MR ameliorate inconvenience of traditional medical care, reduce medical malpractice caused by unskilled operation, and lower the cost of medical education and training. Moreover, the application has enhanced effectiveness of medical education and training, raised the level of diagnosis and treatment, improved the doctor-patient relationship, and boosted efficiency of medical execution. This study introduces VR, AR, and MR applications in medical practices and education, and aims to help health professionals know more about these applications, becoming interested to improve the quality of medical care via the technology.


title: Recent Trend and Advance of Synthetic Aperture Radar with Selected Topics

abstract: The present article is an introductory paper in this special issue on synthetic aperture radar (SAR). A short review is presented on the recent trend and development of SAR and related techniques with selected topics, including the fields of applications, specifications of airborne and spaceborne SARs, and information contents in and interpretations of amplitude data, interferometric SAR (InSAR) data, and polarimetric SAR (PolSAR) data. The review is by no means extensive, and as such only brief summaries of of each selected topics and key references are provided. For further details, the readers are recommended to read the literature given in the references theirin.


title: BCDForest: a boosting cascade deep forest model towards the classification of cancer subtypes based on gene expression data

abstract: BACKGROUND ::: The classification of cancer subtypes is of great importance to cancer disease diagnosis and therapy. Many supervised learning approaches have been applied to cancer subtype classification in the past few years, especially of deep learning based approaches. Recently, the deep forest model has been proposed as an alternative of deep neural networks to learn hyper-representations by using cascade ensemble decision trees. It has been proved that the deep forest model has competitive or even better performance than deep neural networks in some extent. However, the standard deep forest model may face overfitting and ensemble diversity challenges when dealing with small sample size and high-dimensional biology data. ::: ::: ::: RESULTS ::: In this paper, we propose a deep learning model, so-called BCDForest, to address cancer subtype classification on small-scale biology datasets, which can be viewed as a modification of the standard deep forest model. The BCDForest distinguishes from the standard deep forest model with the following two main contributions: First, a named multi-class-grained scanning method is proposed to train multiple binary classifiers to encourage diversity of ensemble. Meanwhile, the fitting quality of each classifier is considered in representation learning. Second, we propose a boosting strategy to emphasize more important features in cascade forests, thus to propagate the benefits of discriminative features among cascade layers to improve the classification performance. Systematic comparison experiments on both microarray and RNA-Seq gene expression datasets demonstrate that our method consistently outperforms the state-of-the-art methods in application of cancer subtype classification. ::: ::: ::: CONCLUSIONS ::: The multi-class-grained scanning and boosting strategy in our model provide an effective solution to ease the overfitting challenge and improve the robustness of deep forest model working on small-scale data. Our model provides a useful approach to the classification of cancer subtypes by using deep learning on high-dimensional and small-scale biology data.


title: SDIoT: a software defined based internet of things framework

abstract: The internet of things (IoT) represent the current and future state of the Internet. The large number of things (objects), which are connected to the Internet, produce a huge amount of data that needs a lot of effort and processing operations to transfer it to useful information. Moreover, the organization and control of this large volume of data requires novel ideas in the design and management of the IoT network to accelerate and enhance its performance. The software defined systems is a new paradigm that appeared recently to hide all complexity in traditional system architecture by abstracting all the controls and management operations from the underling devices (things in the IoT) and setting them inside a middleware layer, a software layer. In this work, a comprehensive software defined based framework model is proposed to simplify the IoT management process and provide a vital solution for the challenges in the traditional IoT architecture to forward, store, and secure the produced data from the IoT objects by integrating the software defined network, software defined storage, and software defined security into one software defined based control model.


title: Four LTE low-band MIMO antennas for the smartphone

abstract: Four LTE low-band MIMO antennas in the smartphone for applications in the 4 × 4 MIMO system are presented. The four antennas are based on the open-slot antenna structure and disposed along the short edges and the side edges of the smartphone. The two short-edge antennas cover 698∼960 MHz. The two side-edge antennas cover 824∼960 MHz. The four antennas can be applied in a 4 × 4 LTE MIMO operation in the 824∼960 MHz band. The calculated channel capacities of the 4 × 4 LTE MIMO operation can reach about 17.6 bps/Hz at 20-dB signal-to-noise ratio (SNR). The effects of user's hand on the MIMO performance are also studied. The channel capacities for different handheld scenarios are presented and discussed.


title: SMOTEBoost: Improving Prediction of the Minority Class in Boosting

abstract: Many real world data mining applications involve learning from imbalanced data sets. Learning from data sets that contain very few instances of the minority (or interesting) class usually produces biased classifiers that have a higher predictive accuracy over the majority class(es), but poorer predictive accuracy over the minority class. SMOTE (Synthetic Minority Over-sampling TEchnique) is specifically designed for learning from imbalanced data sets. This paper presents a novel approach for learning from imbalanced data sets, based on a combination of the SMOTE algorithm and the boosting procedure. Unlike standard boosting where all misclassified examples are given equal weights, SMOTEBoost creates synthetic examples from the rare or minority class, thus indirectly changing the updating weights and compensating for skewed distributions. SMOTEBoost applied to several highly and moderately imbalanced data sets shows improvement in prediction performance on the minority class and overall improved F-values.


title: Unveiling the Biometric Potential of Finger-Based ECG Signals

abstract: The ECG signal has been shown to contain relevant information for human identification. Even though results validate the potential of these signals, data acquisition methods and apparatus explored so far compromise user acceptability, requiring the acquisition of ECG at the chest. In this paper, we propose a finger-based ECG biometric system, that uses signals collected at the fingers, through a minimally intrusive 1-lead ECG setup recurring to Ag/AgCl electrodes without gel as interface with the skin. The collected signal is significantly more noisy than the ECG acquired at the chest, motivating the application of feature extraction and signal processing techniques to the problem. Time domain ECG signal processing is performed, which comprises the usual steps of filtering, peak detection, heartbeat waveform segmentation, and amplitude normalization, plus an additional step of time normalization. Through a simple minimum distance criterion between the test patterns and the enrollment database, results have revealed this to be a promising technique for biometric applications.


title: Digital image authentication and encryption using digital signature

abstract: In this paper, a methodology for digital image authentication using digital signature is proposed. The hash of the original image is taken and is encrypted by RSA. The digital signature obtained is concealed in the image. Digital signature is sent along with the encrypted image which decreases the probability of meticulous attack by the intruder. The encrypted image is shuffled using Chaotic Logistic Map to get the final shuffled encrypted image. The use of Logistic Map improves the randomness in the image. For the authentication, a comparator is employed which evaluates correctness of the hash extracted. The simulations have been carried out to examine the proposed authentication and encryption technique.


title: Application-based review of GaN HFETs

abstract: Normally-off GaN-on-Si heterojunction field-effect transistors (HFETs) have been developed with up to 650 V blocking capability, fast switching, and low conduction losses in commercial devices. The natively depletion-mode device can be modified to be normally-off using a variety of techniques. For a power electronics engineer accustomed to Si-based converter design, there is inherent benefit to understanding the unique characteristics and challenges that distinguish GaN HFETs from Si MOSFETs. Dynamic Rds-on, self-commutated reverse conduction, gate voltage and current requirements, and the effects of very fast switching are explained from an applications perspective. This paper reviews available literature on commercial and near-commercial GaN HFETs, to prepare engineers with Si-based power electronics experience to effectively design GaN-based converters.


title: Study, analysis and simulation of three phase three-level, five-level and seven-level neutral-point-clamped inverters by PSIM

abstract: In this paper, a Pulse Width Modulation (PWM) control of multi levels inverters neutral-point-clamped (NPC) inverter is proposed. First, the structures of this power converter are presented. After that, a PWM control strategy is developed. Finally several electrical simulations were performed, using the software PSIM environment, to validate the theoretical analysis described previously. In particular, the three structures: three levels, five-level and seven-level inverters neutral-point-clamped (NPC) are studied.


title: Hp-Apriori: Horizontal parallel-apriori algorithm for frequent itemset mining from big data

abstract: Due to large scale and complexity of big data, mining the big data using a single personal computer is a difficult problem. With increasing in the size of databases, parallel computing systems can cause considerable advantages in the data mining applications by means of the exploitation of data mining algorithms. Parallelization of association rule mining algorithms is an important task in data mining to mine frequent patterns from transaction databases. These algorithms either distribute database horizontally or increase number of CPU to reduce execution time of frequent pattern mining. In this paper, a novel frequent itemset mining algorithm, namely Horizontal parallel-Apriori (HP-Apriori), is proposed that divides database both horizontally and vertically with partitioning mining process into four sub-processes so that all four tasks are performed in parallel way. Also the HP-Apriori tries to speed up the mining process by an index file that is generated in the first step of algorithm. The proposed algorithm has been compared with Count Distribution (CD) in terms of execution time and speedup criteria on the four real datasets. Experimental results demonstrated that the HP-Apriori outperforms over CD in terms of minimizing execution time and maximizing speedup in high scalability.


title: Table Extraction from Document Images using Fixed Point Model

abstract: The paper presents a novel learning-based framework to identify tables from scanned document images. The approach is designed as a structured labeling problem, which learns the layout of the document and labels its various entities as table header, table trailer, table cell and non-table region. We develop features which encode the foreground block characteristics and the contextual information. These features are provided to a fixed point model which learns the inter-relationship between the blocks. The fixed point model attains a contraction mapping and provides a unique label to each block. We compare the results with Condition Random Fields(CRFs). Unlike CRFs, the fixed point model captures the context information in terms of the neighbourhood layout more efficiently. Experiments on the images picked from UW-III (University of Washington) dataset, UNLV dataset and our dataset consisting of document images with multicolumn page layout, show the applicability of our algorithm in layout analysis and table detection.


title: Natural Language Processing using Hadoop and KOSHIK

abstract: Natural language processing, as a data analytics related technology, is used widely in many research areas such as artificial intelligence, human language processing, and translation. At present, due to explosive growth of data, there are many challenges for natural language processing. Hadoop is one of the platforms that can process the large amount of data required for natural language processing. KOSHIK is one of the natural language processing architectures, and utilizes Hadoop and contains language processing components such as Stanford CoreNLP and OpenNLP. This study describes how to build a KOSHIK platform with the relevant tools, and provides the steps to analyze wiki data. Finally, it evaluates and discusses the advantages and disadvantages of the KOSHIK architecture, and gives recommendations on improving the processing performance.


title: Semantic Grounding in Dialogue for Complex Problem Solving

abstract: Dialogue systems that support users in complex problem solving must interpret user utterances within the context of a dynamically changing, user-created problem solving artifact. This paper presents a novel approach to semantic grounding of noun phrases within tutorial dialogue for computer programming. Our approach performs joint segmentation and labeling of the noun phrases to link them to attributes of entities within the problem-solving environment. Evaluation results on a corpus of tutorial dialogue for Java programming demonstrate that a Conditional Random Field model performs well, achieving an accuracy of 89.3% for linking semantic segments to the correct entity attributes. This work is a step toward enabling dialogue systems to support users in increasingly complex problem-solving tasks.


title: Fear and the human amygdala

abstract: We have previously reported that bilateral amygdala damage in humans compromises the recognition of fear in facial expressions while leaving intact recognition of face identity (Adolphs et al., 1994). The present study aims at examining questions motivated by this finding. We addressed the possibility that unilateral amygdala damage might be sufficient to impair recognition of emotional expressions. We also obtained further data on our subject with bilateral amygdala damage, in order to elucidate possible mechanisms that could account for the impaired recognition of expressions of fear. The results show that bilateral, but not unilateral, damage to the human amygdala impairs the processing of fearful facial expressions. This impairment appears to result from an insensitivity to the intensity of fear expressed by faces. We also confirmed a double dissociation between the recognition of facial expressions of fear, and the recognition of identity of a face: these two processes can be impaired independently, lending support to the idea that they are subserved in part by anatomically separate neural systems. Based on our data, and on what is known about the amygdala9s connectivity, we propose that the amygdala is required to link visual representations of facial expressions, on the one hand, with representations that constitute the concept of fear, on the other. Preliminary data suggest the amygdala9s role extends to both recognition and recall of fearful facial expressions.


title: Deep learning-based classification for brain-computer interfaces

abstract: Brain-computer interface (BCI) is an emerging area of research that aims to improve the quality of human-computer applications. It has enormous scope in biomedical applications, neural rehabilitation, biometric authentication, educational programmes, and entertainment applications. A BCI system has four major components: signal acquisition, signal preprocessing, feature extraction, and classification. In this study, we provide a comparison of various traditional classification algorithms to the newer methods of deep learning. We explore two different types of deep learning methods, namely, convolutional neural networks (CNN) and recurrent neural networks (RNN) with long short-term memory (LSTM) architecture. We test the classification accuracies on a recent 5-dass steady-state visual evoked potential (SSVEP) dataset. The results prove the superiority of deep learning methods in comparison with the traditional classification algorithms. Amongst the traditional classifiers, support vector machine (SVM) with Gaussian kernel employing sequential forward selection (SFS) of features provided a better classification accuracy of 66.09%, while CNN provided the highest classification accuracy of 69.03%.


title: Syntax highlighting in business process models

abstract: Sense-making of process models is an important task in various phases of business process management initiatives. Despite this, there is currently hardly any support in business process modeling tools to adequately support model comprehension. In this paper we adapt the concept of syntax highlighting to workflow nets, a modeling technique that is frequently used for business process modeling. Our contribution is three-fold. First, we establish a theoretical argument to what extent highlighting could improve comprehension. Second, we formalize a concept for syntax highlighting in workflow nets and present a prototypical implementation with the WoPeD modeling tool. Third, we report on the results of an experiment that tests the hypothetical benefits of highlighting for comprehension. Our work can easily be transferred to other process modeling tools and other process modeling techniques.


title: IEEE 802.11 Network Anomaly Detection and Attack Classification: A Deep Learning Approach

abstract: Despite the significant advancement in wireless technologies over the years, IEEE 802.11 still emerges as the de-facto standard to achieve the required short to medium range wireless device connectivity in anywhere from offices to homes. With it being ranked the highest among all deployed wireless technologies in terms of market adoption, vulnerability exploitation and attacks targeting it have also been commonly observed. IEEE 802.11 security has thus become a key concern over the years. In this paper, we analysed the threats and attacks targeting the IEEE 802.11 network and also identified the challenges of achieving accurate threat and attack classification, especially in situations where the attacks are novel and have never been encountered by the detection and classification system before. We then proposed a solution based on anomaly detection and classification using a deep learning approach. The deep learning approach self-learns the features necessary to detect network anomalies and is able to perform attack classification accurately. In our experiments, we considered the classification as a multi-class problem (that is, legitimate traffic, flooding type attacks, injection type attacks and impersonation type attacks), and achieved an overall accuracy of 98.6688% in classifying the attacks through the proposed solution.


title: Rediscovering Workflow Models from Event-Based Data using Little Thumb

abstract: Contemporary workflow management systems are driven by explicit process models, i.e., a completely specified workflow design is required in order to enact a given workflow process. Creating a workflow design is a complicated time-consuming process and typically, there are discrepancies between the actual workflow processes and the processes as perceived by the management. Therefore, we propose a technique for rediscovering workflow models. This technique uses workflow logs to discover the workflow process as it is actually being executed. The workflow log contains information about events taking place. We assume that these events are totally ordered and each event refers to one task being executed for a single case. This information can easily be extracted from transactional information systems (e.g., Enterprise Resource Planning systems such as SAP and Baan). The rediscovering technique proposed in this paper can deal with noise and can also be used to validate workflow processes by uncovering and measuring the discrepancies between prescriptive models and actual process executions.


title: Improved Automatic Feature Selection Approach for Health Risk Prediction

abstract: With the recent advances in machines & technology, there is revolution in healthcare industry also. Thus, it gave rise to the concept of Electronic Health Record (EHR) which stores patients demographics, lab tests & results, medical history, habits etc. in electronic form. EHR is voluminous data which is difficult to store, maintain or alter. To provide extended life for people, health risk prediction model using this EHR is formulated in this work. Feature Selection is used to select only associated or relevant data from the dataset. Logistic Regression is used with improved loss functionality parameter which increases the accuracy, response time and performance of the system. Representation Learning enables formation of feature vector of the selected features thus calculating their scores. Further, risk prediction is performed by the neural network model. Deep Neural Network (DNN) is used with many hidden layers containing activation functions. Transfer learning is used to avoid re-training of the whole system every time new data enters the model. Dataset used here is of hypertension. EHR dataset is also synthetically created for analysis.


title: Volitional social interaction prevents drug addiction in rat models

abstract: Addiction treatment has not been appreciably improved by neuroscientific research. One problem is that mechanistic studies using rodent models do not incorporate volitional social factors, which play a critical role in human addiction. Here, using rats, we introduce an operant model of choice between drugs and social interaction. Independent of sex, drug class, drug dose, training conditions, abstinence duration, social housing, or addiction score in Diagnostic & Statistical Manual IV-based and intermittent access models, operant social reward prevented drug self-administration. This protection was lessened by delay or punishment of the social reward but neither measure was correlated with the addiction score. Social-choice-induced abstinence also prevented incubation of methamphetamine craving. This protective effect was associated with activation of central amygdala PKCδ-expressing inhibitory neurons and inhibition of anterior insular cortex activity. These findings highlight the need for incorporating social factors into neuroscience-based addiction research and support the wider implantation of socially based addiction treatments. Venniro et al. report that drug-addicted rats reliably choose contact with another rat over drugs, even when group-housed between tests. They also do not show the increase in drug craving that normally occurs during forced abstinence.


title: Demographic Factors, TPACK Constructs, and Teachers' Perceptions of Constructivist-Oriented TPACK

abstract: Teachers’ knowledge for implementing constructivist instruction with technology is termed as their constructivist-oriented technological pedagogical content knowledge. There are currently no available surveys for understanding teachers’ perceptions in this aspect. Therefore, teachers’ perceived knowledge gaps in terms of constructivist-oriented technology integration are not well understood. Using the Technological Pedagogical Content Knowledge for Meaningful Learning Survey, this study examines the constructivist-oriented technological pedagogical content knowledge perceptions of 354 practicing teachers. The survey was first validated through exploratory and confirmatory factor analyses. Regression analysis found that teachers’ perceptions of technological pedagogical knowledge, technological content knowledge, and technological knowledge had the largest positive relationships with their constructivist-oriented technological pedagogical content knowledge. It was not related to teachers’ age and gender but primary school teachers and those with more teaching experience tend to be less confident of their constructivist-oriented technological pedagogical content knowledge. These results show that when teachers develop intermediate forms of technological pedagogical content knowledge, it contributes to their confidence for constructivist-oriented technology integration. The specific challenges faced by experienced teachers and primary school teachers need to be better understood and considered when designing teacher technology professional development.


title: Sexual predator detection in chats with chained classifiers

abstract: This paper describes a novel approach for sexual predator detection in chat conversations based on sequences of classifiers. The proposed approach divides documents into three parts, which, we hypothesize, correspond to the different stages that a predator employs when approaching a child. Local classifiers are trained for each part of the documents and their outputs are combined by a chain strategy: predictions of a local classifier are used as extra inputs for the next local classifier. Additionally, we propose a ring-based strategy, in which the chaining process is iterated several times, with the goal of further improving the performance of our method. We report experimental results on the corpus used in the first international competition on sexual predator identification (PAN’12). Experimental results show that the proposed method outperforms a standard (global) classification technique for the different settings we consider; besides the proposed method compares favorably with most methods evaluated in the PAN’12 competition.


E-Service Quality as Antecendent to E-Satisfaction: the Moderating Effect of Culture


title: Modeling Context in Referring Expressions

abstract: Humans refer to objects in their environments all the time, especially in dialogue with other people. We explore generating and comprehending natural language referring expressions for objects in images. In particular, we focus on incorporating better measures of visual context into referring expression models and find that visual comparison to other objects within an image helps improve performance significantly. We also develop methods to tie the language generation process together, so that we generate expressions for all objects of a particular category jointly. Evaluation on three recent datasets - RefCOCO, RefCOCO+, and RefCOCOg (Datasets and toolbox can be downloaded from https://github.com/lichengunc/refer), shows the advantages of our methods for both referring expression generation and comprehension.


title: A Survey on Financial Fraud Detection Methodologies

abstract: to levitate and rapid escalation of E-Commerce, cases of financial fraud allied with it are also intensifying and which results in trouncing of billions of dollars worldwide each year. Fraud detection involves scrutinizing the behavior of populations of users in order to ballpark figure, detect, or steer clear of objectionable behavior: Undesirable behavior is a extensive term including delinquency: swindle, infringement, and account evasion. Factually, swindle transactions are speckled with genuine transactions and simple pattern matching techniques are not often sufficient to detect those frauds accurately. In this survey we, will focuses on classifying fraudulent behaviors, identifying the major sources and characteristics of the data based on which fraud detection has been conducted. This paper provide a comprehensive survey and review of different techniques to detect the financial fraud detection used in various fraud like credit card fraud detection, online auction fraud, telecommunication fraud detection, and computer intrusion detection.


title: Synergies among extinction drivers under global change.

abstract: If habitat destruction or overexploitation of populations is severe, species loss can occur directly and abruptly. Yet the final descent to extinction is often driven by synergistic processes (amplifying feedbacks) that can be disconnected from the original cause of decline. We review recent observational, experimental and meta-analytic work which together show that owing to interacting and self-reinforcing processes, estimates of extinction risk for most species are more severe than previously recognised. As such, conservation actions which only target single-threat drivers risk being inadequate because of the cascading effects caused by unmanaged synergies. Future work should focus on how climate change will interact with and accelerate ongoing threats to biodiversity, such as habitat degradation, overexploitation and invasive species.


title: Preparation of Graphene Quantum Dots for Bioimaging Application

abstract: In this communication, preparation of graphene quantum dots (GQDs) with size about 10 nm by vigorous oxidation of graphite is reported. Thus obtained GQDs exhibit good physiological solubility, high photostability, low cytotoxicity, and yellow-green fluorescence with quantum yield about 7%. Furthermore, the feasibility of the GQDs for cell imaging application is demosntrated.


title: Alpha-Beta and Gamma Rhythms Subserve Feedback and Feedforward Influences among Human Visual Cortical Areas

abstract: Primate visual cortex is hierarchically organized. Bottom-up and top-down influences are exerted through distinct frequency channels, as was recently revealed in macaques by correlating inter-areal influences with laminar anatomical projection patterns. Because this anatomical data cannot be obtained in human subjects, we selected seven homologous macaque and human visual areas, and we correlated the macaque laminar projection patterns to human inter-areal directed influences as measured with magnetoencephalography. We show that influences along feedforward projections predominate in the gamma band, whereas influences along feedback projections predominate in the alpha-beta band. Rhythmic inter-areal influences constrain a functional hierarchy of the seven homologous human visual areas that is in close agreement with the respective macaque anatomical hierarchy. Rhythmic influences allow an extension of the hierarchy to 26 human visual areas including uniquely human brain areas. Hierarchical levels of ventral- and dorsal-stream visual areas are differentially affected by inter-areal influences in the alpha-beta band.


title: Local Descriptor for Robust Place Recognition Using LiDAR Intensity

abstract: Place recognition is a challenging problem in mobile robotics, especially in unstructured environments or under viewpoint and illumination changes. Most LiDAR-based methods rely on geometrical features to overcome such challenges, as generally scene geometry is invariant to these changes, but tend to affect camera-based solutions significantly. Compared to cameras, however, LiDARs lack the strong and descriptive appearance information that imaging can provide. ::: To combine the benefits of geometry and appearance, we propose coupling the conventional geometric information from the LiDAR with its calibrated intensity return. This strategy extracts extremely useful information in the form of a new descriptor design, coined ISHOT, outperforming popular state-of-art geometric-only descriptors by significant margin in our local descriptor evaluation. To complete the framework, we furthermore develop a probabilistic keypoint voting place recognition algorithm, leveraging the new descriptor and yielding sublinear place recognition performance. The efficacy of our approach is validated in challenging global localization experiments in large-scale built-up and unstructured environments.


title: NeVAE: A Deep Generative Model for Molecular Graphs

abstract: Deep generative models have been praised for their ability to learn smooth latent representation of images, text, and audio, which can then be used to generate new, plausible data. However, current generative models are unable to work with molecular graphs due to their unique characteristics--their underlying structure is not Euclidean or grid-like, they remain isomorphic under permutation of the node labels, and they come with a different number of nodes and edges. In this paper, we propose NeVAE, a novel variational autoencoder for molecular graphs, whose encoder and decoder are specially designed to account for the above properties by means of several technical innovations. In addition, by using masking, the decoder is able to guarantee a set of valid properties in the generated molecules. Experiments reveal that our model can discover plausible, diverse and novel molecules more effectively than several state of the art methods. Moreover, by utilizing Bayesian optimization over the continuous latent representation of molecules our model finds, we can also find molecules that maximize certain desirable properties more effectively than alternatives.


title: A $2 \times 70$ W Monolithic Five-Level Class-D Audio Power Amplifier in 180 nm BCD

abstract: A $2 \times 70$ W from 24 V into 4 $\Omega $ class-D audio power amplifier in 30/40 V 180 nm bipolar CMOS DMOS is presented. The device employs a flying capacitor (FC) three-level half bridge topology to reduce switching frequency and filter/load power losses in near-idle operation. This is combined with a fourth-order analog feedback system for shaping noise introduced by the digital FC voltage control loop. A power-efficient gate drive scheme suitable for power converters with multiple floating switching devices is also presented, including a compact fast low-power dV/dt robust high-voltage level shifter circuit. Power-efficient operation from idle to full-power operation is demonstrated along with a very high audio performance of 0.003% THD + N at 10 W/1 kHz into 4 $\Omega $ .


THE PERCEIVED VALUE OF BUILDING INFORMATION MODELING IN THE U.S. BUILDING INDUSTRY


title: Grasping-force optimization for multifingered robotic hands using a recurrent neural network

abstract: Grasping-force optimization of multifingered robotic hands can be formulated as a problem for minimizing an objective function subject to form-closure constraints and balance constraints of external force. This paper presents a novel recurrent neural network for real-time dextrous hand-grasping force optimization. The proposed neural network is shown to be globally convergent to the optimal grasping force. Compared with existing approaches to grasping-force optimization, the proposed neural-network approach has the advantages that the complexity for implementation is reduced, and the solution accuracy is increased, by avoiding the linearization of quadratic friction constraints. Simulation results show that the proposed neural network can achieve optimal grasping force in real time.


title: Authorship Attribution for Twitter in 140 Characters or Less

abstract: Authorship attribution is a growing field, moving from beginnings in linguistics to recent advances in text mining. Through this change came an increase in the capability of authorship attribution methods both in their accuracy and the ability to consider more difficult problems. Research into authorship attribution in the 19th century considered it difficult to determine the authorship of a document of fewer than 1000 words. By the 1990s this values had decreased to less than 500 words and in the early 21st century it was considered possible to determine the authorship of a document in 250 words. The need for this ever decreasing limit is exemplified by the trend towards many shorter communications rather than fewer longer communications, such as the move from traditional multi-page handwritten letters to shorter, more focused emails. This trend has also been shown in online crime, where many attacks such as phishing or bullying are performed using very concise language. Cybercrime messages have long been hosted on Internet Relay Chats (IRCs) which have allowed members to hide behind screen names and connect anonymously. More recently, Twitter and other short message based web services have been used as a hosting ground for online crimes. This paper presents some evaluations of current techniques and identifies some new preprocessing methods that can be used to enable authorship to be determined at rates significantly better than chance for documents of 140 characters or less, a format popularised by the micro-blogging website Twitter1. We show that the SCAP methodology performs extremely well on twitter messages and even with restrictions on the types of information allowed, such as the recipient of directed messages, still perform significantly higher than chance. Further to this, we show that 120 tweets per user is an important threshold, at which point adding more tweets per user gives a small but non-significant increase in accuracy.


title: Exploring the Opportunities and Challenges with Exercise Technologies for People who are Blind or Low-Vision

abstract: People who are blind or low-vision may have a harder time participating in exercise due to inaccessibility or lack of experience. We employed Value Sensitive Design (VSD) to explore the potential of technology to enhance exercise for people who are blind or low-vision. We conducted 20 semi-structured interviews about exercise and technology with 10 people who are blind or low-vision and 10 people who facilitate fitness for people who are blind or low-vision. We also conducted a survey with 76 people to learn about outsider perceptions of hypothetical exercise with people who are blind or low-vision. Based on our interviews and survey, we found opportunities for technology development in four areas: 1) mainstream exercise classes, 2) exercise with sighted guides, 3) rigorous outdoors activity, and 4) navigation of exercise spaces. Design considerations should include when and how to deliver auditory or haptic information based on exercise and context, and whether it is acceptable to develop less mainstream technologies if they enhance mainstream exercise. The findings of this work seek to inform the design of accessible exercise technologies.


Supplementary Materials : Signal and Noise Statistics Oblivious Orthogonal Matching Pursuit


title: Enhancing noise margins of FinFET SRAM by integrating Vth-controllable flexible-pass-gates

abstract: We propose a flexible-pass-gate (Flex-PG) FinFET SRAM to enhance both the read and write noise margins. The flip-flop in the Flex-PG SRAM cell consists of usual FinFETs while its pass gates consist of Vth-controllable four-terminal (4T) FinFETs with independent double-gates. We experimentally demonstrate that the proposed Flex-PG SRAM increases both the read and write margins by controlling the Vth of the pass gates.


title: DESIGN OF MULTI-STAGE POWER DIVIDER BASED ON THE THEORY OF SMALL REFLECTIONS

abstract: This paper presents a novel multi-way multi-stage power divider design method based on the theory of small reflections. Firstly, the application of the theory of small reflections is extended from transmission line to microwave network. Secondly, an explicit closed-form analytical formula of the input reflection coefficient, which consists of the scattering parameters of power divider elements and the lengths of interconnection lines between each element, is derived. Thirdly, the proposed formula is applied to determine the lengths of interconnection lines. A prototype of a 16-way 4-stage power divider working at 4 GHz is designed and fabricated. Both the simulation and measurement results demonstrate the validity of the proposed method.


title: A comprehensive review of cuckoo search: variants and hybrids

abstract: Cuckoo search (CS) is an efficient swarm-intelligence-based algorithm and significant developments have been made since its introduction in 2009. CS has many advantages due to its simplicity and efficiency in solving highly non-linear optimisation problems with real-world engineering applications. This paper provides a timely review of all the state-of-the-art developments in the last five years, including the discussions of theoretical background and research directions for future development of this powerful algorithm.


title: A multiresolution descriptor for deformable 3D shape retrieval

abstract: In this paper, we present a spectral graph wavelet framework for the analysis and design of efficient shape signatures for nonrigid 3D shape retrieval. Although this work focuses primarily on shape retrieval, our approach is, however, fairly general and can be used to address other 3D shape analysis problems. In a bid to capture the global and local geometry of 3D shapes, we propose a multiresolution signature via a cubic spline wavelet generating kernel. The parameters of the proposed signature can be easily determined as a trade-off between effectiveness and compactness. Experimental results on two standard 3D shape benchmarks demonstrate the much better performance of the proposed shape retrieval approach in comparison with three state-of-the-art methods. Additionally, our approach yields a higher retrieval accuracy when used in conjunction with the intrinsic spatial partition matching.


title: Large-Batch Training for LSTM and Beyond

abstract: Large-batch training approaches have enabled researchers to utilize large-scale distributed processing and greatly accelerate deep-neural net (DNN) training. For example, by scaling the batch size from 256 to 32K, researchers have been able to reduce the training time of ResNet50 on ImageNet from 29 hours to 2.2 minutes (Ying et al., 2018). In this paper, we propose a new approach called linear-epoch gradual-warmup (LEGW) for better large-batch training. With LEGW, we are able to conduct large-batch training for both CNNs and RNNs with the Sqrt Scaling scheme. LEGW enables Sqrt Scaling scheme to be useful in practice and as a result we achieve much better results than the Linear Scaling learning rate scheme. For LSTM applications, we are able to scale the batch size by a factor of 64 without losing accuracy and without tuning the hyper-parameters. For CNN applications, LEGW is able to achieve the same accuracy even as we scale the batch size to 32K. LEGW works better than previous large-batch auto-tuning techniques. LEGW achieves a 5.3X average speedup over the baselines for four LSTM-based applications on the same hardware. We also provide some theoretical explanations for LEGW.


title: Greening the internet with nano data centers

abstract: Motivated by increased concern over energy consumption in modern data centers, we propose a new, distributed computing platform called Nano Data Centers (NaDa). NaDa uses ISP-controlled home gateways to provide computing and storage services and adopts a managed peer-to-peer model to form a distributed data center infrastructure. To evaluate the potential for energy savings in NaDa platform we pick Video-on-Demand (VoD) services. We develop an energy consumption model for VoD in traditional and in NaDa data centers and evaluate this model using a large set of empirical VoD access data. We find that even under the most pessimistic scenarios, NaDa saves at least 20% to 30% of the energy compared to traditional data centers. These savings stem from energy-preserving properties inherent to NaDa such as the reuse of already committed baseline power on underutilized gateways, the avoidance of cooling costs, and the reduction of network energy consumption as a result of demand and service co-localization in NaDa.


title: Role-Based Access Control

abstract: This newly revised edition of the Artech House bestseller, Role-Based Access Control, offers you the very latest details on this sophisticated security model aimed at reducing the cost and complexity of security administration for large networked applications. The second edition provides more comprehensive and updated coverage of access control models, new Rbac standards, new in-depth case studies and discussions on role engineering and the design of role-based systems. The book shows you how Rbac simplifies security administration by using roles, hierarchies, and constraints to manage the review and control of organizational privileges. Moreover, it explains how Rbac makes it possible to specify many types of enterprise security policies. This unique resource covers all facets of Rbac, from its solid model-theoretic foundations to its implementation within commercial products. You learn how to use Rbac to emulate other access control models and find frameworks and tools for administering Rbac. Research prototypes that have incorporated Rbac into various classes of software like Wfms, Web server, Os (Unix) and Java (Jee) are reviewed. Products implementing Rbac features such as relational Dbms and Enterprise Security Administration (Esa) systems are described to serve as a guide to the state of practice of Rbac.


title: Quantum machine learning: a classical perspective

abstract: Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning (ML) techniques to impressive results in regression, classification, data generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets is motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed up classical ML algorithms. Here we review the literature in quantum ML and discuss perspectives for a mixed readership of classical ML and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in ML are identified as promising directions for the field. Practical questions, such as how to upload classical data into quantum form, will also be addressed.


title: Meet me where i'm gazing: how shared attention gaze affects human-robot handover timing

abstract: In this paper we provide empirical evidence that using humanlike gaze cues during human-robot handovers can improve the timing and perceived quality of the handover event. Handovers serve as the foundation of many human-robot tasks. Fluent, legible handover interactions require appropriate nonverbal cues to signal handover intent, location and timing. Inspired by observations of human-human handovers, we implemented gaze behaviors on a PR2 humanoid robot. The robot handed over water bottles to a total of 102 naive subjects while varying its gaze behaviour: no gaze, gaze designed to elicit shared attention at the handover location, and the shared attention gaze complemented with a turntaking cue. We compared subject perception of and reaction time to the robot-initiated handovers across the three gaze conditions. Results indicate that subjects reach for the offered object significantly earlier when a robot provides a shared attention gaze cue during a handover. We also observed a statistical trend of subjects preferring handovers with turn-taking gaze cues over the other conditions. Our work demonstrates that gaze can play a key role in improving user experience of human-robot handovers, and help make handovers fast and fluent.Categories and Subject Descriptors I.2.9 [Robotics]: Operator interfaces, Commercial robots and applications; H.1.2 [User/Machine Systems]: Human FactorsGeneral TermsExperimentation, Design, Human Factors, Verification.


title: Effects of support and job demands on social media use and work outcomes

abstract: We investigated some organizational factors that determine social media use at work.We investigated if social media use at work affects job-related outcomes.Coworker support and job demands positively associate with social media use at work.Supervisor support negatively correlates with social media use at work.Social media use at work positively correlates with job-related outcomes. Studies related to the use of social media in the workplace are still somewhat scarce despite their increasing popularity in social media research. This paper aims to investigate how employee perceptions of a workplace related to coworker support, supervisor support, and job-related demands can determine the degree of attachment some employees feel to social media use at work. The study also explores some consequences of social media use at work by analyzing its associations with job satisfaction, job performance, and cognitive absorption. The data was collected through the snowball sampling technique of 170 employees in Thailand and analyzed using partial least squares regression. For the factors predicted to influence social media use at work, the analysis found that coworker support and job demands are positively associated with social media use intensity, while supervisor support is negatively associated with it. The analysis also found a positive association between job satisfaction and job performance and social media use at work. An indirect relationship between social media use and cognitive absorption was also found through the mediating effect of job satisfaction. Overall, the evidence suggests that social media use at work may not necessarily lead to negative job-related outcomes.


title: Parallel worlds: immersion in location-based experiences

abstract: This paper analyses the stages and circumstances for immersion based on quantitative and qualitative feedback from 700 people who took part in a three week long public trial of a location-based audio drama. Ratings of enjoyment, immersion and how much history came alive all scored highly and people often spent up to an hour in the experience. A model of immersion as a cycle of transient states triggered by events in the overall experience is defined. This model can be used to design for immersion in future experiences.


title: Advancements in OCV Measurement and Analysis for Lithium-Ion Batteries

abstract: Incremental open-circuit voltage (OCV) curves and low-current charge/discharge voltage profiles of a lithium-ion (Li-ion) battery are compared and evaluated for optimizing measurement time and resolution. Since these curves are often used for further analysis, minimizing kinetic contributions is crucial for approximating battery OCV behavior. In this context, an incremental OCV measurement is characterized by state of charge (SOC) intervals and relaxation times. Various constant low C-rates, SOC intervals, and relaxation times are tested for approximating OCV behavior. Differential capacity and voltage analysis is used to check whether the main electrode features can be resolved satisfactorily. An interpolation method yields additional data points for the differential analysis of incremental OCV curves. It is shown that incremental OCV measurements are suitable for an approximation of battery OCV behavior, rather than low current-voltage profiles. Furthermore, extrapolation of voltage relaxation enables the estimation of fully relaxed OCV.


title: Analysis of Five Different Dielectric Substrates on Microstrip Patch Antenna

abstract: The study of microstrip patch antennas has made great progress in recent years. Compared with conventional antennas, microstrip patch antennas have more advantages and better prospects. Different researchers have used different dielectric substrates to fabricate microstrip patch antenna. So a question arises that which dielectric substrate among the common substrates available gives better performance and what are the properties of the dielectric substrates which affects antenna performance. So a comparative study has been performed to know the dielectric properties of five different substrates which affect antenna performance. The aim of the study to design and fabricate five triangular microstrip patch antennas on five different substrates and analyze their radiation characteristics. The antenna is designed to work in X-band applications. The resonant frequency is taken to be 10 GHz and height of the dielectric substrate is kept constant i.e.,1.5mm for all the five antennas. This study will help for authors and researchers to get a fair idea of which substrate should be given preference and why for fabricating microstrip patch antenna.


title: Development of Simulator and Analysis of Walking for Hexapod Robots

abstract: The control of legs for hexapod robot is complicated and it has been attracted many researchers' concern. Recently, such unmanned electric vehicles as automatic guided vehicles, drones with many rotors, and mobilities with crawlers, etc. are rapidly put into practical use. However, hexapod robots are far from the practical use although some toy robots are available. The hexapod robot would be much more useful for walking on wasteland, muddy land or slippery land, etc. and it is required to design the large-scale hexapod robot with optimized control of legs. A simulator for the walking of hexapod robot is developed for the first time and the walking behaviors are analyzed. The application of Model Based Design method to the development of simulator and the straight walking stability is discussed.


title: The endocrinology of stress in fish: An environmental perspective

abstract: Much of the understanding of the endocrine basis of stress in fish comes from studies of cultured stocks of teleosts; there is comparatively little information on stress responses in wild stock, and less still on chondrosteans and elasmobranchs. This understanding is being refined through increasing understanding of molecular processes underlying endocrine events, with molecular tools offering ready examination of parts of the endocrine pathway that have been resistant to easy measurement of hormone products. An assessment of the timecourse of activation of the hypothalamic-pituitary-interrenal axis shows generally strong independence of temperature, with most teleosts showing measurable increase in plasma cortisol within 10 min of stress. Chondrostean and elasmobranch responses are less well described, but in chondrosteans at least, the response pattern appears to be similar to teleosts. The short latency for increases in corticosteroids following exposure to a stressor means that sampling of wild fish needs to occur rapidly after encounter. Several techniques including underwater sampling and rapid line capture are suitable for this, as is measurement of steroid release to the water by undisturbed fish, albeit possibly with a reduced range of applications. Basal cortisol values in wild teleosts are typically <10 ng mL(-1), but a number of species show values orders of magnitude higher in unstressed fish. Variability in corticosteroid levels arises from a range of factors in addition to stress including, sex and maturity, time of day or since feeding, and season. These factors need to be understood for the sensible assessment of stress responses in wild fish. Studies on free-living birds suggest that environmental stress resides mainly around unpredictable change, and the limited data available for fish support this view. The effect of unpredictable event such as floods or storms are difficult to assess in wild fish due to the difficulty in sampling at these times, and would be predicted to impose environmental stress as in terrestrial systems; however, this has yet to be demonstrated. There is scope for use of stress responses to be used as a measure of environmental quality but only if the basic response to environmental stress is well understood first. Development of this understanding remains a priority for this field of research.


title: Towards an extensible context ontology for Ambient Intelligence

abstract: To realise an Ambient Intelligence environment, it is paramount that applications can dispose of information about the context in which they operate, preferably in a very general manner. For this purpose various types of information should be assembled to form a representation of the context of the device on which aforementioned applications run. To allow interoperability in an Ambient Intelligence environment, it is necessary that the context terminology is commonly understood by all participating devices. In this paper we propose an adaptable and extensible context ontology for creating context-aware computing infrastructures, ranging from small embedded devices to high-end service platforms. The ontology has been designed to solve several key challenges in Ambient Intelligence, such as application adaptation, automatic code generation and code mobility, and generation of device specific user interfaces.


title: Certain factor analysis for extra pulmonary tuberculosis diagnosis

abstract: Tuberculosis is an infectious disease that is easily transmitted through the air. Tuberculosis mostly attacks the lungs, but this disease can also attack other organs called extra-pulmonary. The Extra pulmonary tuberculosis can endanger the safety of community and slow in initial diagnosis because of the lack of knowledge. The purpose of this research is to build expert system of the extra pulmonary tuberculosis disease diagnosis. The system can facilitate the user in making the initial diagnosis of the disease, so patients can get the right handling. The system uses forward chaining inference and a Web-based Certainty Factor method with an accuracy rate of 85% has been utilized to assist health and community.


title: Transporter-mediated tissue targeting of therapeutic molecules in drug discovery

abstract: Tissue concentrations of endogenous chemicals and nutrients are in large part regulated by membrane transporters through their substrate specificity and differential tissue distributions. These transporters also play a key role in the disposition of therapeutic agents thus affecting their efficacy and safety profile. A transporter-mediated tissue targeting strategy, where the structural features recognized by the transporters are incorporated into the therapeutic molecule, is emerging as an effective approach in drug discovery. In this digest, we review this phenomenon and highlight recent cases in the design of liver and kidney targeted drug molecules.


title: Who are my neighbors?: A perception model for selecting neighbors of pedestrians in crowds

abstract: Pedestrian trajectory prediction is a challenging problem. One of the aspects that makes it so challenging is the fact that the future positions of an agent are not only determined by its previous positions, but also by the interaction of the agent with its neighbors. Previous methods, like Social Attention have considered the interactions with all agents as neighbors. However, this ends up assigning high attention weights to agents who are far away from the queried agent and/or moving in the opposite direction, even though, such agents might have little to no impact on the queried agent's trajectory. Furthermore, trajectory prediction of a queried agent involving all agents in a large crowded scenario is not efficient. In this paper, we propose a novel approach for selecting neighbors of an agent by modeling its perception as a combination of a location and a locomotion model. We demonstrate the performance of our method by comparing it with the existing state-of-the-art method on publicly available datasets. The results show that our neighbor selection model overall improves the accuracy of trajectory prediction and enables prediction in scenarios with large numbers of agents in which other methods do not scale well.


title: Multi-arm snake-like robot

abstract: Since the minimally invasive surgery has significant benefits for the patient, special equipment and techniques are constantly enhanced in this relatively new surgical field, so that more challenging surgical applications can be achieved. Thereby, the major aim is to enable the surgeon to manipulate instruments inside the human body intuitively, despite restricted access to the operating field and limited workspace. With our spine like overtube system for use with standard endoscopes and standard endoscopic instruments, we provide a system facilitating tissue manipulation with sufficient triangulation. In this paper we describe our 3D-printed manipulator system for minimally invasive applications and its pure mechanical control and actuation concepts. Following our aim of an individualized disposable system, in a first step we demonstrate the feasibility of a 3D-printed surgical robotic system by laboratory experiments and a clinical evaluation on a porcine model.


title: Using Cooccurrence Statistics and the Web to Discover Synonyms in a Technical Language

abstract: Turney (2001) has shown that computing the mutual information of a pair of words by using cooccurrence counts obtained via queries to the AltaVista search engine performs very effectively in a synonym detection task. Since manual synonym detection is a challenging task for terminologists, we investigate whether the AltaVista-based Mutual Information (AVMI) method can be applied to the task of finding pairs of synonyms in the lexicon of a specialized sub-language. In particular, we experiment with synonyms in the field of nautical terminology. Our results indicate that AVMI is very good at spotting synonym couples among pairs of unrelated terms (with precision close to 90% at 62.5% recall) and that it outperforms more standard methods based on contextual cosine similarity. However, AVMI is not able to distinguish between synonyms and other semantically related terms. Thus, AVMI can be used for synonym mining only if it is combined with techniques to filter out other semantic relations.


SEMANTIC UNDERSPECIFICATION: WHICH TECHNIQUE FOR WHAT PURPOSE?


title: A Learning Scheme for Microgrid Islanding and Reconnection

abstract: This paper introduces a potential learning scheme that can dynamically predict the stability of the reconnection of sub-networks to a main grid. As the future electrical power systems tend towards smarter and greener technology, the deployment of self sufficient networks, or microgrids, becomes more likely. Microgrids may operate on their own or synchronized with the main grid, thus control methods need to take into account islanding and reconnecting of said networks. The ability to optimally and safely reconnect a portion of the grid is not well understood and, as of now, limited to raw synchronization between interconnection points. A support vector machine (SVM) leveraging real-time data from phasor measurement units (PMUs) is proposed to predict in real time whether the reconnection of a sub-network to the main grid would lead to stability or instability. A dynamics simulator fed with pre-acquired system parameters is used to create training data for the SVM in various operating states. The classifier was tested on a variety of cases and operating points to ensure diversity. Accuracies of approximately 85% were observed throughout most conditions when making dynamic predictions of a given network.


IDEA: Planning at the Core of Autonomous Reactive Agents


title: Sustainable, efficient livestock production with high biodiversity and good welfare for animals

abstract: What is the future for livestock agriculture in the world? Consumers have concerns about sustainability but many widely used livestock production methods do not satisfy consumers' requirements for a sustainable system. However, production can be sustainable, occurring in environments that: supply the needs of the animals resulting in good welfare, allow coexistence with a wide diversity of organisms native to the area, minimize carbon footprint and provide a fair lifestyle for the people working there. Conservation need not just involve tiny islands of natural vegetation in a barren world of agriculture, as there can be great increases in biodiversity in farmed areas. Herbivores, especially ruminants that consume materials inedible by humans, are important for human food in the future. However, their diet should not be just ground-level plants. Silvopastoral systems, pastures with shrubs and trees as well as herbage, are described which are normally more productive than pasture alone. When compared with widely used livestock production systems, silvopastoral systems can provide efficient feed conversion, higher biodiversity, enhanced connectivity between habitat patches and better animal welfare, so they can replace existing systems in many parts of the world and should be further developed.


title: Training Sparse Neural Networks

abstract: The emergence of Deep neural networks has seen human-level performance on large scale computer vision tasks such as image classification. However these deep networks typically contain large amount of parameters due to dense matrix multiplications and convolutions. As a result, these architectures are highly memory intensive, making them less suitable for embedded vision applications. Sparse Computations are known to be much more memory efficient. In this work, we train and build neural networks which implicitly use sparse computations. We introduce additional gate variables to perform parameter selection and show that this is equivalent to using a spike-and-slab prior. We experimentally validate our method on both small and large networks which result in highly sparse neural network models.


title: Online Multi-Modal Distance Metric Learning with Application to Image Retrieval

abstract: Distance metric learning (DML) is an important technique to improve similarity search in content-based image retrieval. Despite being studied extensively, most existing DML approaches typically adopt a single-modal learning framework that learns the distance metric on either a single feature type or a combined feature space where multiple types of features are simply concatenated. Such single-modal DML methods suffer from some critical limitations: (i) some type of features may significantly dominate the others in the DML task due to diverse feature representations; and (ii) learning a distance metric on the combined high-dimensional feature space can be extremely time-consuming using the naive feature concatenation approach. To address these limitations, in this paper, we investigate a novel scheme of online multi-modal distance metric learning (OMDML), which explores a unified two-level online learning scheme: (i) it learns to optimize a distance metric on each individual feature space; and (ii) then it learns to find the optimal combination of diverse types of features. To further reduce the expensive cost of DML on high-dimensional feature space, we propose a low-rank OMDML algorithm which not only significantly reduces the computational cost but also retains highly competing or even better learning accuracy. We conduct extensive experiments to evaluate the performance of the proposed algorithms for multi-modal image retrieval, in which encouraging results validate the effectiveness of the proposed technique.


title: The System Evaluation Model of Mobile Navigation Devices (MPD) Using Integrated DEMATEL Approach

abstract: The development of mobile devices (such as mobile phone) has moved into fast pace and provides multiple-functions (such as camera and GPS) and works more like a personal computer in now a days. In this study, we intend to explore the features and services needs for the next generation mobile navigation devices. In order to find future strategy for mobile navigation devices, handheld navigation devices (PND), three lithographic navigation device (Pad) and smart phones (Smart phone) mobile navigation device were included in the survey for this empirical analysis. In accordance with its competitive advantage dimension to further develop a new generation of mobile navigation device development strategy. We then recommend service development strategies based on the findings. The conclusions are a good reference for mobile navigation devices companies to apply to improve their service quality.


title: prefuse: a toolkit for interactive information visualization

abstract: Although information visualization (infovis) technologies have proven indispensable tools for making sense of complex data, wide-spread deployment has yet to take hold, as successful infovis applications are often difficult to author and require domain-specific customization. To address these issues, we have created prefuse, a software framework for creating dynamic visualizations of both structured and unstructured data. prefuse provides theoretically-motivated abstractions for the design of a wide range of visualization applications, enabling programmers to string together desired components quickly to create and customize working visualizations. To evaluate prefuse we have built both existing and novel visualizations testing the toolkit's flexibility and performance, and have run usability studies and usage surveys finding that programmers find the toolkit usable and effective.


title: Arabic speech recognition using recurrent neural networks

abstract: In this paper, a novel approach for implementing Arabic isolated speech recognition is described. While most of the literature on speech recognition (SR) is based on hidden Markov models (HMM), the present system is implemented by modular recurrent Elman neural networks (MRENN). The promising results obtained through this design show that this new neural networks approach can compete with the traditional HMM-based speech recognition approaches.


title: P-Rank: a comprehensive structural similarity measure over information networks

abstract: With the ubiquity of information networks and their broad applications, the issue of similarity computation between entities of an information network arises and draws extensive research interests. However, to effectively and comprehensively measure "how similar two entities are within an information network" is nontrivial, and the problem becomes even more challenging when the information network to be examined is massive and diverse. In this paper, we propose a new similarity measure, P-Rank (Penetrating Rank), toward effectively computing the structural similarities of entities in real information networks. P-Rank enriches the well-known similarity measure, SimRank, by jointly encoding both in- and out-link relationships into structural similarity computation. P-Rank is proven to be a unified structural similarity framework, under which all state-of-the-art similarity measures, including CoCitation, Coupling, Amsler and SimRank, are just its special cases. Based on its recursive nature of P-Rank, we propose a fixed point algorithm to reinforce structural similarity of vertex pairs beyond the localized neighborhood scope toward the entire information network. Our experimental studies demonstrate the power of P-Rank as an effective similarity measure in different information networks. Meanwhile, under the same time/space complexity, P-Rank outperforms SimRank as a comprehensive and more meaningful structural similarity measure, especially in large real information networks.


title: Improving Shape Deformation in Unsupervised Image-to-Image Translation

abstract: Unsupervised image-to-image translation techniques are able to map local texture between two domains, but they are typically unsuccessful when the domains require larger shape change. Inspired by semantic segmentation, we introduce a discriminator with dilated convolutions that is able to use information from across the entire image to train a more context-aware generator. This is coupled with a multi-scale perceptual loss that is better able to represent error in the underlying shape of objects. We demonstrate that this design is more capable of representing shape deformation in a challenging toy dataset, plus in complex mappings with significant dataset variation between humans, dolls, and anime faces, and between cats and dogs.


title: A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization

abstract: In this paper, we introduce an embedding model, named CapsE, exploring a capsule network to model relationship triples (subject, relation, object). Our CapsE represents each triple as a 3-column matrix where each column vector represents the embedding of an element in the triple. This 3-column matrix is then fed to a convolution layer where multiple filters are operated to generate different feature maps. These feature maps are used to construct capsules in the first capsule layer. Capsule layers are connected via dynamic routing mechanism. The last capsule layer consists of only one capsule to produce a vector output. The length of this vector output is used to measure the plausibility of the triple. Our proposed CapsE obtains state-of-the-art link prediction results for knowledge graph completion on two benchmark datasets: WN18RR and FB15k-237, and outperforms strong search personalization baselines on SEARCH17 dataset.


title: Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much

abstract: Gibbs sampling is a Markov Chain Monte Carlo sampling technique that iteratively samples variables from their conditional distributions. There are two common scan orders for the variables: random scan and systematic scan. Due to the benefits of locality in hardware, systematic scan is commonly used, even though most statistical guarantees are only for random scan. While it has been conjectured that the mixing times of random scan and systematic scan do not differ by more than a logarithmic factor, we show by counterexample that this is not the case, and we prove that that the mixing times do not differ by more than a polynomial factor under mild conditions. To prove these relative bounds, we introduce a method of augmenting the state space to study systematic scan using conductance.


title: Toward Diverse Text Generation with Inverse Reinforcement Learning

abstract: Text generation is a crucial task in NLP. Recently, several adversarial generative models have been proposed to improve the exposure bias problem in text generation. Though these models gain great success, they still suffer from the problems of reward sparsity and mode collapse. In order to address these two problems, in this paper, we employ inverse reinforcement learning (IRL) for text generation. Specifically, the IRL framework learns a reward function on training data, and then an optimal policy to maximum the expected total reward. Similar to the adversarial models, the reward and policy function in IRL are optimized alternately. Our method has two advantages: (1) the reward function can produce more dense reward signals. (2) the generation policy, trained by"entropy regularized"policy gradient, encourages to generate more diversified texts. Experiment results demonstrate that our proposed method can generate higher quality texts than the previous methods.


title: Design of Multi-Grip Patterns Prosthetic Hand With Single Actuator

abstract: In hand prosthesis design, the important characteristics that directly affect hand performance are the ability to grasp various type of objects, grasping force that provides stability for holding objects, and cosmetic appearance that resembles the human hand. The presence of all of these characteristics is currently a challenging task for prosthesis design. This paper presents the design of a five-fingered prosthetic hand that has multiple grip patterns with the use of only one actuator in order to perform important tasks in daily life and which achieves significant grip force from the large size of the actuator. The prosthetic hand is capable of performing one neutral position and two grip patterns that are dominant in daily life tasks. Different move patterns are achieved through the use of multiple sets of rigid four-bar linkages which provide different motions to fingers and thumb when the mechanism is actuated to the opposite direction. This paper describes the design of the prosthesis, mechanism synthesis, and achieved performance. The prosthetic hand developed here, having one degree of freedom, is an improvement from conventional single-actuator hands, which can only perform open/close motion. Whereas achieved grip force (34.5 N) is higher than multiple-actuator hands in market. Thus, this design could be an alternative answer of improvement between conventional and multiple degree of freedom prosthetic hands.


title: Multi-pose face recognition by dynamic loss weights

abstract: This paper explores a face recognition method that combines the dynamic changes of loss weights with face poses. As opposed to current techniques which either expect a single model to learn a variety of poses through massive amounts of training data, or normalize images to a single frontal pose, our method explicitly tackles pose variations by using multiple poses specific models, not only the identity information, but also the facial pose information of the training data is fully utilized. Firstly, we use the solvePnP function in opencv library to classify the facial poses in training dataset. Secondly, we set different loss weights according to the different pose labels. Thirdly, we propose to take into account pose variability by training multiple pose specific models. We leverage deep Convolutional Neural Network (CNN) to learn models with discriminative facial features which we call Dynamic Loss Weights Models(DLW models). We test our models respectively in the LFW dataset and our multi-pose face dataset which contains 50 number of identities. The experiment results show that our designed loss function can improve the performances of profile face recognition.


title: Pg-Trajectory: A PostgreSQL/PostGIS Based Data Model for Spatiotemporal Trajectories

abstract: Due to the tremendous advances in GPS and location-based web services, highly available spatiotemporal trajectory data poses an important challenge - knowledge discovery from trajectories. Knowledge discovery tasks on trajectory big data such as classification, clustering and outlier detection require a dedicated data model, which can support various utility functions and provide a robust object-relational mapping. This paper introduces PG-TRAJECTORY, a data model extension for the popular open source database management system PostgreSQL. Our data model is built on PostGIS, the spatial database extender of PostgreSQL. Apart from providing a data model, PG-TRAJECTORY contains a wide range of functions for storing and manipulating spatiotemporal trajectories. Throughout the paper, we discuss the basic structure of our data model with the working principles of the functions, and show a set of real life query examples. Finally, we present the results of our experimental evaluation to demonstrate the scalability and effectiveness of our data model.


title: Gain Scheduling of PID Controller Based on Fuzzy Systems

abstract: This paper aims to utilize fuzzy rules and reasoning to determine the controller parameters and the PID controller generates the control signal. The objective of this study is to simulate the proposed scheme on various processes and arrive at results providing better response of the system when compared with best industrial auto-tuning technique: Ziegler-Nichols. The proposed scheme is based upon the Ultimate Gain (Ku) and the Period (Tu) of the system. The error and rate of change in error gains are tuned manually to get the desired response using LabVIEW. This can also be done with various optimization techniques. A thumb rule for choosing the ranges for Kc, Kd and Ki has been obtained experimentally.


title: Efficiency-enhanced cost-volume filtering featuring coarse-to-fine strategy

abstract: Cost-volume filtering (CVF) is one of the most widely used techniques for solving general multi-labeling problems based on a Markov random field (MRF). However it is inefficient when the label space size (i.e., the number of labels) is large. This paper presents a coarse-to-fine strategy for cost-volume filtering that efficiently and accurately addresses multi-labeling problems with a large label space size. Based on the observation that true labels at the same coordinates in images of different scales are highly correlated, we truncate unimportant labels for cost-volume filtering by leveraging the labeling output of lower scales. Experimental results show that our algorithm achieves much higher efficiency than the original CVF method while maintaining a comparable level of accuracy. Although we performed experiments that deal with only stereo matching and optical flow estimation, the proposed method can be employed in many other applications because of the applicability of CVF to general discrete pixel-labeling problems based on an MRF.


title: VFP: a virtual switch platform for host sdn in the public cloud

abstract: Many modern scalable cloud networking architectures rely on host networking for implementing VM network policy - e.g. tunneling for virtual networks, NAT for load balancing, stateful ACLs, QoS, and more. We present the Virtual Filtering Platform (VFP) - a programmable virtual switch that powers Microsoft Azure, a large public cloud, and provides this policy. We define several major goals for a programmable virtual switch based on our operational experiences, including support for multiple independent network controllers, policy based on connections rather than only on packets, efficient caching and classification algorithms for performance, and efficient offload of flow policy to programmable NICs, and demonstrate how VFP achieves these goals. VFP has been deployed on <1M hosts running IaaS and PaaS workloads for over 4 years. We present the design of VFP and its API, its flow language and compiler used for flow processing, performance results, and experiences deploying and using VFP in Azure over several years.


title: An Improved Approximation Algorithm for the Column Subset Selection Problem

abstract: We consider the problem of selecting the best subset of exactly $k$ columns from an $m \times n$ matrix $A$. We present and analyze a novel two-stage algorithm that runs in $O(\min\{mn^2,m^2n\})$ time and returns as output an $m \times k$ matrix $C$ consisting of exactly $k$ columns of $A$. In the first (randomized) stage, the algorithm randomly selects $\Theta(k \log k)$ columns according to a judiciously-chosen probability distribution that depends on information in the top-$k$ right singular subspace of $A$. In the second (deterministic) stage, the algorithm applies a deterministic column-selection procedure to select and return exactly $k$ columns from the set of columns selected in the first stage. Let $C$ be the $m \times k$ matrix containing those $k$ columns, let $P_C$ denote the projection matrix onto the span of those columns, and let $A_k$ denote the best rank-$k$ approximation to the matrix $A$. Then, we prove that, with probability at least 0.8, $$ \FNorm{A - P_CA} \leq \Theta(k \log^{1/2} k) \FNorm{A-A_k}. $$ This Frobenius norm bound is only a factor of $\sqrt{k \log k}$ worse than the best previously existing existential result and is roughly $O(\sqrt{k!})$ better than the best previous algorithmic result for the Frobenius norm version of this Column Subset Selection Problem (CSSP). We also prove that, with probability at least 0.8, $$ \TNorm{A - P_CA} \leq \Theta(k \log^{1/2} k)\TNorm{A-A_k} + \Theta(k^{3/4}\log^{1/4}k)\FNorm{A-A_k}. $$ This spectral norm bound is not directly comparable to the best previously existing bounds for the spectral norm version of this CSSP. Our bound depends on $\FNorm{A-A_k}$, whereas previous results depend on $\sqrt{n-k}\TNorm{A-A_k}$; if these two quantities are comparable, then our bound is asymptotically worse by a $(k \log k)^{1/4}$ factor.


title: A 16TX/16RX 60 GHz 802.11ad Chipset With Single Coaxial Interface and Polarization Diversity

abstract: The IEEE 802.11ad standard supports PHY rates up to 6.7Gb/s on four 2GHz-wide channels from 57 to 64GHz. A 60GHz system offers higher throughput than existing 802.11ac solutions but has several challenges for high-volume production including: integration in the host platform, automated test, and high link loss due to blockage and polarization mismatch. This paper presents a full-featured 802.11ad chipset capable of SC and OFDM modulation using a 16TX-16RX beamforming RF front-end, complete with an antenna array that supports polarization diversity. To aid low-cost integration in PC platforms, a single coaxial cable interface is used between chips. The chipset includes MAC, PHY, and RF with a PCIeTM interface and is capable of maintaining a link of 4.6Gb/s (PHY rate) at 10m.


Machine Vision for Counting Fruit on Mango Tree Canopies


title: BubbleNet: a cyber security dashboard for visualizing patterns

abstract: The field of cyber security is faced with ever-expanding amounts of data and a constant barrage of cyber attacks. Within this space, we have designed BubbleNet as a cyber security dashboard to help network analysts identify and summarize patterns within the data. This design study faced a range of interesting constraints from limited time with various expert users and working with users beyond the network analyst, such as network managers. To overcome these constraints, the design study employed a user-centered design process and a variety of methods to incorporate user feedback throughout the design of BubbleNet. This approach resulted in a successfully evaluated dashboard with users and further deployments of these ideas in both research and operational environments. By explaining these methods and the process, it can benefit future visualization designers to help overcome similar challenges in cyber security or alternative domains.


Predicting Learning by Analyzing Eye-Gaze Data of Reading Behavior


title: Adaptive Neighborhood Propagation by Joint L2,1-Norm Regularized Sparse Coding for Representation and Classification

abstract: We propose a new transductive label propagation method, termed Adaptive Neighborhood Propagation (Adaptive-NP) by joint L2,1-norm regularized sparse coding, for semi-supervised classification. To make the predicted soft labels more accurate for predicting the labels of samples and to avoid the tricky process of choosing the optimal neighborhood size or kernel width for graph construction, Adaptive-NP seamlessly integrates sparse coding and neighborhood propagation into a unified framework. That is, the sparse reconstruction error and classification error are combined for joint minimization, which clearly differs from traditional methods that explicitly separate graph construction and label propagation into independent steps, which may result in inaccurate predictions. Note that our Adaptive-NP alternately optimize the sparse codes and soft labels matrices, where the sparse codes are used as adaptive weights for neighborhood propagation at each iteration, so the tricky process of determining neighborhood size or kernel width is avoided. Besides, for enhancing sparse coding, we use the L2,1-norm constraint on the sparse coding coefficients and the reconstruction error at the same time for delivering more accurate and robust representations. Extensive simulations show that our model can deliver state-of-the-art performances on several public datasets for classification.


title: Ensemble Based Extreme Learning Machine

abstract: Extreme learning machine (ELM) was proposed as a new class of learning algorithm for single-hidden layer feedforward neural network (SLFN). To achieve good generalization performance, ELM minimizes training error on the entire training data set, therefore it might suffer from overfitting as the learning model will approximate all training samples well. In this letter, an ensemble based ELM (EN-ELM) algorithm is proposed where ensemble learning and cross-validation are embedded into the training phase so as to alleviate the overtraining problem and enhance the predictive stability. Experimental results on several benchmark databases demonstrate that EN-ELM is robust and efficient for classification.


title: Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) Utilizing Free-Text Clinical Narratives

abstract: We propose a deep learning model - Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) for estimating short-term life expectancy (>3 months) of the patients by analyzing free-text clinical notes in the electronic medical record, while maintaining the temporal visit sequence. In a single framework, we integrated semantic data mapping and neural embedding technique to produce a text processing method that extracts relevant information from heterogeneous types of clinical notes in an unsupervised manner, and we designed a recurrent neural network to model the temporal dependency of the patient visits. The model was trained on a large dataset (10,293 patients) and validated on a separated dataset (1818 patients). Our method achieved an area under the ROC curve (AUC) of 0.89. To provide explain-ability, we developed an interactive graphical tool that may improve physician understanding of the basis for the model’s predictions. The high accuracy and explain-ability of the PPES-Met model may enable our model to be used as a decision support tool to personalize metastatic cancer treatment and provide valuable assistance to the physicians.


title: Electromyography Pattern-Recognition-Based Control of Powered Multifunctional Upper-Limb Prostheses

abstract: The human history has been accompanied by accidental trauma, war, and congenital anomalies. Consequently, amputation and deformity have been dealt with, one way or another, throughout the ages. More than one million individuals in the United States today are living with limb amputations (Adams et al., 1999), in which there are approximately 100,000 patients with an upper limb amputation. The wars in Iraq and Afghanistan have added to this number. According to the survey results of the Second China National Sample Survey on Disables (SCNSSD 2006) led by the National Statistics Bureau in 2006, approximately 8% of physical disables, or 2.26 million people, live with limb amputations in China alone. Natural disasters and accidents have been making this number increase. The massive earthquakes that occurred in May 2008, Sichuan Province, China, recently increased about 20 thousand of new limb amputees. Expectations for control of upper limb prostheses have always been high because of the standard established by able-bodied dexterity. Most commercially available upper limb prostheses are either body-powered or electrical motor powered. The body-powered prostheses are operated by certain movements of the amputees’ body through a system of cables, harnesses, and sometimes, manual control. In order to operate a body-powered prosthesis, the upper limb amputees have to possess significant strength and control over various body parts, including the shoulders, chest, and residual limb which must have sufficient residual limb length, musculature, and range of motion. Exaggerated movements of the body are captured by harness systems and are transferred through cables to operate the hand, wrist, or elbow movements of a prosthesis. With some advantages such as low cost, high reliability, and some kinesthetic feedback provided by the harness system, body-powered prostheses are still widely accepted by the upper limb amputees worldwide, especially in some developing countries. However, with this inappropriate control approach, body-powered upper limb prostheses are limited in utility, frustratingly slow to operate, awkward to maintain, and can operate only one joint at a time. Myoelectric signals detected with electrodes placed on the skin surface overlying the muscles, well-known as electromyography (EMG), have been used in control of motorized upper-limb prostheses for several decades (Kay & Newman, 1975; Parker & Scott, 1986). The


title: A Low Cost Sensors Approach for Accurate Vehicle Localization and Autonomous Driving Application

abstract: Autonomous driving in public roads requires precise localization within the range of few centimeters. Even the best current precise localization system based on the Global Navigation Satellite System (GNSS) can not always reach this level of precision, especially in an urban environment, where the signal is disturbed by surrounding buildings and artifacts. Laser range finder and stereo vision have been successfully used for obstacle detection, mapping and localization to solve the autonomous driving problem. Unfortunately, Light Detection and Ranging (LIDARs) are very expensive sensors and stereo vision requires powerful dedicated hardware to process the cameras information. In this context, this article presents a low-cost architecture of sensors and data fusion algorithm capable of autonomous driving in narrow two-way roads. Our approach exploits a combination of a short-range visual lane marking detector and a dead reckoning system to build a long and precise perception of the lane markings in the vehicle’s backwards. This information is used to localize the vehicle in a map, that also contains the reference trajectory for autonomous driving. Experimental results show the successful application of the proposed system on a real autonomous driving situation.


title: 3D Object Dense Reconstruction from a Single Depth View

abstract: In this paper, we propose a novel approach, 3D-RecGAN++, which reconstructs the complete 3D structure of a given object from a single arbitrary depth view using generative adversarial networks. Unlike existing work which typically requires multiple views of the same object or class labels to recover the full 3D geometry, the proposed 3D-RecGAN++ only takes the voxel grid representation of a depth view of the object as input, and is able to generate the complete 3D occupancy grid with a high resolution of 256^3 by recovering the occluded/missing regions. The key idea is to combine the generative capabilities of autoencoders and the conditional Generative Adversarial Networks (GAN) framework, to infer accurate and fine-grained 3D structures of objects in high-dimensional voxel space. Extensive experiments on large synthetic datasets and real-world Kinect datasets show that the proposed 3D-RecGAN++ significantly outperforms the state of the art in single view 3D object reconstruction, and is able to reconstruct unseen types of objects.


title: Satellite imagery analysis for operational damage assessment in Emergency situations

abstract: When major disaster occurs the questions are raised how to estimate the damage in time to support the decision making process and relief efforts by local authorities or humanitarian teams. In this paper we consider the use of Machine Learning and Computer Vision on remote sensing imagery to improve time efficiency of assessment of damaged buildings in disaster affected area. We propose a general workflow that can be useful in various disaster management applications, and demonstrate the use of the proposed workflow for the assessment of the damage caused by the wildfires in California in 2017.


Abstract Interpreting focus


title: Adaptive control of a multi-robot system for transportation of large-sized objects based on reinforcement learning

abstract: This paper describes models and algorithms for intelligent control of a group of autonomous mobile robots, which perform large-sized object transportation in a complex environment. The proposed models allow the multi-robot system to reach its target position while avoiding obstacles and maintaining object orientation with coordinated motion of several robots. We use neural based Q-learning to provide robots adaptability to unknown environments. The inputs of the learning subsystem are 2d-map data collected during system operation and target misalignments of multi-robot system. The primary output is a control decision with a maximum value of estimated efficiency. Experimental results presented in the paper fully confirm the reliability of the proposed approach.