title: AIXIjs: A Software Demo for General Reinforcement Learning

abstract: Reinforcement learning is a general and powerful framework with which to study and implement artificial intelligence. Recent advances in deep learning have enabled RL algorithms to achieve impressive performance in restricted domains such as playing Atari video games (Mnih et al., 2015) and, recently, the board game Go (Silver et al., 2016). However, we are still far from constructing a generally intelligent agent. Many of the obstacles and open questions are conceptual: What does it mean to be intelligent? How does one explore and learn optimally in general, unknown environments? What, in fact, does it mean to be optimal in the general sense? The universal Bayesian agent AIXI (Hutter, 2005) is a model of a maximally intelligent agent, and plays a central role in the sub-field of general reinforcement learning (GRL). Recently, AIXI has been shown to be flawed in important ways; it doesn't explore enough to be asymptotically optimal (Orseau, 2010), and it can perform poorly with certain priors (Leike and Hutter, 2015). Several variants of AIXI have been proposed to attempt to address these shortfalls: among them are entropy-seeking agents (Orseau, 2011), knowledge-seeking agents (Orseau et al., 2013), Bayes with bursts of exploration (Lattimore, 2013), MDL agents (Leike, 2016a), Thompson sampling (Leike et al., 2016), and optimism (Sunehag and Hutter, 2015). We present AIXIjs, a JavaScript implementation of these GRL agents. This implementation is accompanied by a framework for running experiments against various environments, similar to OpenAI Gym (Brockman et al., 2016), and a suite of interactive demos that explore different properties of the agents, similar to REINFORCEjs (Karpathy, 2015). We use AIXIjs to present numerous experiments illustrating fundamental properties of, and differences between, these agents.


title: Automation of Logistics Processes by Using a Universal Control

abstract: Due to the globalization companies are forced to implement efficient processes. Automation is a chance to reduce costs especially for repeative tasks. But by now it causes high costs for companies to integrate robots especially in case of individual systems for specific tasks. Due to the changing goods and working spaces within logistic processes automated systems have to be developed with respect to the task. The universal control computes an inverse kinematic based on application based data, executes an efficient path-planning and collision detection. This approach results in decreased develop costs for system-integrators, decreased costs for service and maintenance as less expert know-how is necessary and it results in shorter integration time, less system-failure due to the usage of standard components.


title: Comparison of a state cell phone law versus a fleet cell phone policy using naturalistic data

abstract: The current study used a naturalistic data set to assess the relationship between fleet cell phone policies and State cell phone laws and commercial motor vehicle (CMV) drivers use of a cell phone while driving. This research analyzed naturalistic data from commercial trucks (3-axel and tractor trailer/tanker) and buses (transit and motor coaches) over a one-year period. The data sets included 195 different truck and bus fleets comprising 13,431 trucks and buses. These 13,431 trucks and buses accounted for 230,912 safety-critical events and 211,177 non-events. The odds of a truck and bus driver using a cell phone while driving under a fleet cell phone policy were 0.83 times less, compared to no fleet cell phone policy. The data showed that a State cell phone law did not significantly impact truck and bus drivers? likelihood in using their cell phone while driving compared to a State that did not have a law prohibiting cell phone use. Moreover, CMV drivers were far more likely to obey a specific fleet cell phone policy than a State cell phone law. It may be premature to indicate that State cell phone laws are ineffective in reducing crashes; rather, the laws may be ineffective in dissuading truck and bus drivers from using cell phones while driving. This also suggests that State cell phone laws should be made more like fleet policies in terms of enforcement and consequences for violations, by having sufficient “teeth” (e.g., primary law, significant fine/points) that may impact driver compliance.


title: Programming rich interactions using the hierarchical state machine toolkit

abstract: Structured graphics models such as Scalable Vector Graphics (SVG) enable designers to create visually rich graphics for user interfaces. Unfortunately current programming tools make it difficult to implement advanced interaction techniques for these interfaces. This paper presents the Hierarchical State Machine Toolkit (HsmTk), a toolkit targeting the development of rich interactions. The key aspect of the toolkit is to consider interactions as first-class objects and to specify them with hierarchical state machines. This approach makes the resulting behaviors self-contained, easy to reuse and easy to modify. Interactions can be attached to graphical elements without knowing their detailed structure, supporting the parallel refinement of the graphics and the interaction.


title: Developing a Trojan applets in a smart card

abstract: This paper presents a method to inject a mutable Java Card applet into a smart card. This code can on demand parse the memory in order to search for a given pattern and eliminate it. One of these key features is to bypass security checks or retrieve secret data from other applets. We evaluate the countermeasures against this attack and we show how some of them can be circumvented and we propose to combine this attack with others already known.


title: An FPGA Implementation of a Polychronous Spiking Neural Network with Delay Adaptation

abstract: We present an FPGA implementation of a re-configurable, polychronous spiking neural network with a large capacity for spatial-temporal patterns. The proposed neural network generates delay paths de novo, so that only connections that actually appear in the training patterns will be created. This allows the proposed network to use all the axons (variables) to store information. Spike Timing Dependent Delay Plasticity is used to fine-tune and add dynamics to the network. We use a time-multiplexing approach allowing us to achieve 4096 (4k) neurons and up to 1.15 million programmable delay axons on a Virtex 6 FPGA. The testing results show that the proposed neural network is capable of successfully recalling more than 95% of all spikes for 96% of the stored patterns. The tests also show that the neural network is robust to noise from random input spikes.


title: Toward Adaptive VR Simulators Combining Visual, Haptic, and Brain-Computer Interfaces

abstract: The next generation of VR simulators could take into account a novel input: the user's mental state, as measured with electrodes and a brain-computer interface. One illustration of this promising path is a project that adapted a guidance system's force feedback to the user's mental workload in real time. A first application of this approach is a medical training simulator that provides virtual assistance that adapts to the trainee's mental activity. Such results pave the way to VR systems that will automatically reconfigure and adapt to their users' mental states and cognitive processes.


title: V3: Unsupervised Aspect Based Sentiment Analysis for SemEval2015 Task 12

abstract: This paper presents our participation in SemEval-2015 task 12 (Aspect Based Sentiment Analysis). We participated employing only unsupervised or weakly-supervised approaches. Our attempt is based on requiring the minimum annotated or hand-crafted content, and avoids training a model using the provided training set. We use a continuous word representations (Word2Vec) to leverage in-domain semantic similarities of words for many of the involved subtasks.


title: A Chinese Handwriting Education System with Automatic Error Detection

abstract: We build a distance education application of a Chinese handwriting education system that allows students to do practice at anytime and anywhere. As an intelligent tutor, the system can automatically check the handwriting errors, such as the stroke production errors, stroke sequence error and stroke relationship error. Then our system should provide useful feedback to the student. In this paper, attributed relational graph matching is used to locate the handwriting errors. The pruning strategy is applied to reduce the computational time. The experiment results show that our proposal can handle more handwriting error cases than existing methods with a higher accuracy.


title: Access to special care dentistry, part 2. Communication

abstract: This article considers what communication is, its elements, what helps and what hinders it, and why it matters. It also considers managing people with communication differences and when communication is affected in special care dentistry (SCD). The article focuses on patients with hearing and visual impairments and considers how communication is affected and what techniques can be used to improve the situation. It offers recommendations for communicating with patients with neurological impairments typically seen after stroke, such as aphasia and dysarthria, with tips for the listener including the use of communication aids where appropriate. Finally it will consider communicating with patients who have autistic spectrum conditions and discuss how effective techniques and a tailored approach to their specific needs and anxieties can increase the likelihood of a successful dental visit.


title: Compact Elliptic-Function Low-Pass Filter Using Defected Ground Structure

abstract: A novel elliptic-function low-pass filter (LPF) is presented, which consists of a dumb-bell-shaped defected ground structure (DB-DGS), a spiral-shaped defected ground structure (SP-DGS) and a broadened microstrip line. The DB-DGS provides the required wideband attenuation in stopband, while the SP-DGS provides steep transition from passband to stopband. A 2.4 GHz five-pole LPF is developed and experimental results show that it has a sharp cutoff frequency response. The measured passband insertion-loss is below 0.3 dB, and the rejection band over 20 dB is from 2.95 to 8.25 GHz.


title: Amygdala activity and flashbacks in PTSD – A review

abstract: Intrusive recollections are one of the core symptoms of Post-Traumatic Stress Disorder (PTSD). Flashbacks, a subcategory of intrusive memory phenomena, are considered exclusive to the disorder, indicating a direct correlation between the traumatic event, the memory encoding and the subsequently intruding flashback. The experience of a flashback is defined as the highest degree of reliving, and the memories have an ability to distort the individual’s spatiotemporal perception. Even though flashbacks are well described in PTSD patients, the neurocognitive processes underlying them are not well understood. Amygdala is one of the brain structures repeatedly targeted in the PTSD research due to its involvement in implicit and explicit emotional memory. As an intensifier for survival-related stimuli the amygdala may contribute to the generation of flashbacks and the review at hand seeks to outline processes relating flashbacks to amygdala activity. Facets include: amygdala modulation of hippocampal activity, resulting in enhanced memory for specific details of emotional events of high arousal, amygdalar influences on the visual processing areas during encoding and later retrieval, leading to more vivid memories and, finally, amygdalar influences on the prefrontal cortex during the stages of retrieval, resulting in a stronger and more vivid reconstruction of the memories. (Less)


title: Voxelisation: Modelling for volume graphics

abstract: Voxelisation is the term given to the process of converting data from one source type into a three dimensional volume of data values. The techniques known collectively as volume visualisation can then be applied to the data in order to produce a graphical representation of the object. This paper gives a new practical approach to the voxelisation of data which fills the entire volume. Its use is demonstrated on various datasets such as triangular mesh objects, CT data, and objects defined by trivariate functions.


title: SMORE: A Cold Data Object Store for SMR Drives (Extended Version)

abstract: Shingled magnetic recording (SMR) increases the capacity of magnetic hard drives, but it requires that each zone of a disk be written sequentially and erased in bulk. This makes SMR a good fit for workloads dominated by large data objects with limited churn. To explore this possibility, we have developed SMORE, an object storage system designed to reliably and efficiently store large, seldom-changing data objects on an array of host-managed or host-aware SMR disks. ::: SMORE uses a log-structured approach to accommodate the constraint that all writes to an SMR drive must be sequential within large shingled zones. It stripes data across zones on separate disks, using erasure coding to protect against drive failure. A separate garbage collection thread reclaims space by migrating live data out of the emptiest zones so that they can be trimmed and reused. An index stored on flash and backed up to the SMR drives maps object identifiers to on-disk locations. SMORE interleaves log records with object data within SMR zones to enable index recovery after a system crash (or failure of the flash device) without any additional logging mechanism. ::: SMORE achieves full disk bandwidth when ingesting data---with a variety of object sizes---and when reading large objects. Read performance declines for smaller object sizes where inter- object seek time dominates. With a worst-case pattern of random deletions, SMORE has a write amplification (not counting RAID parity) of less than 2.0 at 80% occupancy. By taking an index snapshot every two hours, SMORE recovers from crashes in less than a minute. More frequent snapshots allow faster recovery.


title: A 3.2Gb/s Oversampling CDR with Improved Jitter Tolerance

abstract: A 3.2 Gbps CDR circuit employs an oversampling architecture to decouple the tradeoff between jitter generation and jitter tolerance. The test chip fabricated in a 0.13 mum CMOS process achieves a 30times increase in the jitter tolerance corner without increasing recovered clock jitter. Power consumption is 19.5 mW from a 1.4 V supply at 3.2 Gbps and die area is 0.081 mm2.


title: Unsupervised Feature Learning for Aerial Scene Classification

abstract: The rich data provided by high-resolution satellite imagery allow us to directly model aerial scenes by understanding their spatial and structural patterns. While pixel- and object-based classification approaches are widely used for satellite image analysis, often these approaches exploit the high-fidelity image data in a limited way. In this paper, we explore an unsupervised feature learning approach for scene classification. Dense low-level feature descriptors are extracted to characterize the local spatial patterns. These unlabeled feature measurements are exploited in a novel way to learn a set of basis functions. The low-level feature descriptors are encoded in terms of the basis functions to generate new sparse representation for the feature descriptors. We show that the statistics generated from the sparse features characterize the scene well producing excellent classification accuracy. We apply our technique to several challenging aerial scene data sets: ORNL-I data set consisting of 1-m spatial resolution satellite imagery with diverse sensor and scene characteristics representing five land-use categories, UCMERCED data set representing twenty one different aerial scene categories with sub-meter resolution, and ORNL-II data set for large-facility scene detection. Our results are highly promising and, on the UCMERCED data set we outperform the previous best results. We demonstrate that the proposed aerial scene classification method can be highly effective in developing a detection system that can be used to automatically scan large-scale high-resolution satellite imagery for detecting large facilities such as a shopping mall.


title: SVM based feature set analysis in dynamic malayalam handwritten character recognition

abstract: Dynamic or Online handwritten character recognition is a challenging field in Human Computer Interfaces. The classification success rate of current techniques decreases when the dataset involves the similarity and complexity in stroke styles, number of strokes and stroke characteristics variations. Malayalam is a complex south Indian language spoken about 35 million people especially in Kerala and Lakshadweep islands. In this paper, a classification scheme based on support vector machines (SVM) is proposed to improve the accuracy in classification and recognition of online malayalam handwritten characters. SVM Classifier is a popular one in academy as well as in industry. This Classifiers are more suitable in a real world applicative problem, if we have major concern on the speed of recognition per character. The contribution of various features towards the accuracy in recognition is analyzed. Performance for different kernels of SVM are also studied. A graphical user interface has developed for reading and displaying the character. Different writing styles are taken for each of the 44 alphabets. Various features are extracted and used for classification after the preprocessing of input data samples. Feature Selection is carried out by choosing of different combinations of extracted features versus accuracy. Highest recognition accuracy of 97% is obtained for the best selected features in SVM with polynomial kernel. Recognition speed of a single stroke is obtained 0.52 secs.


title: Free Material Optimization with Fundamental Eigenfrequency Constraints

abstract: The goal of this paper is to formulate and solve free material optimization problems with constraints on the smallest eigenfrequency of the optimal structure. A natural formulation of this problem as a linear semidefinite program turns out to be numerically intractable. As an alternative, we propose a new approach, which is based on a nonlinear semidefinite low-rank approximation of the semidefinite dual. We introduce an algorithm based on this approach and analyze its convergence properties. The article is concluded by numerical experiments proving the effectiveness of the new approach.


Asymmetric Cryptography for Mobile Devices


An Experience Perspective on Intuitive Interaction: Central Components and the Special Effect of Domain Transfer Distance


title: POI Popularity Prediction via Hierarchical Fusion of Multiple Social Clues

abstract: Predicting the popularity of Point of Interest (POI) has become increasingly crucial for location-based services, such as POI recommendation. Most of the existing methods can seldom achieve satisfactory performance due to the scarcity of POI's information, which tendentiously confines the recommendation to popular scenic spots, and ignores the unpopular attractions with potentially precious values. In this paper, we propose a novel approach, termed Hierarchical Multi-Clue Fusion (HMCF), for predicting the popularity of POIs. Specifically, we devise an effective hierarchy to comprehensively describe POI by integrating various types of media information (e.g., image and text) from multiple social sources. For each individual POI, we simultaneously inject semantic knowledge as well as multi-clue representative power. We collect a multi-source POI dataset from four widely-used tourism platforms. Extensive experimental results show that the proposed method can significantly improve the performance of predicting the attractions' popularity as compared to several baselines.


title: Intrusion detection in MANET using classification algorithms : The effects of cost and model selection

abstract: Intrusion detection is frequently used as a second line of defense in Mobile Ad-hoc Networks (MANETs). In this paper we examine how to properly use classification methods in intrusion detection for MANETs. In order to do so we evaluate five supervised classification algorithms for intrusion detection on a number of metrics. We measure their performance on a dataset, described in this paper, which includes varied traffic conditions and mobility patterns for multiple attacks. One of our goals is to investigate how classification performance depends on the problem cost matrix. Consequently, we examine how the use of uniform versusweighted cost matrices affects classifier performance. A second goal is to examine techniques for tuning classifiers when unknown attack subtypes are expected during testing. Frequently, when classifiers are tuned using cross-validation, data from the same types of attacks are available in all folds. This differs from real-world employment where unknown types of attacks may be present. Consequently, we develop a sequential cross-validation procedure so that not all types of attacks will necessarily be present across all folds, in the hope that this would make the tuning of classifiers more robust. Our results indicate that weighted cost matrices can be used effectively with most statistical classifiers and that sequential cross-validation can have a small, but significant effect for certain types of classifiers.


title: Sparse multinomial logistic regression: fast algorithms and generalization bounds

abstract: Recently developed methods for learning sparse classifiers are among the state-of-the-art in supervised learning. These methods learn classifiers that incorporate weighted sums of basis functions with sparsity-promoting priors encouraging the weight estimates to be either significantly large or exactly zero. From a learning-theoretic perspective, these methods control the capacity of the learned classifier by minimizing the number of basis functions used, resulting in better generalization. This paper presents three contributions related to learning sparse classifiers. First, we introduce a true multiclass formulation based on multinomial logistic regression. Second, by combining a bound optimization approach with a component-wise update procedure, we derive fast exact algorithms for learning sparse multiclass classifiers that scale favorably in both the number of training samples and the feature dimensionality, making them applicable even to large data sets in high-dimensional feature spaces. To the best of our knowledge, these are the first algorithms to perform exact multinomial logistic regression with a sparsity-promoting prior. Third, we show how nontrivial generalization bounds can be derived for our classifier in the binary case. Experimental results on standard benchmark data sets attest to the accuracy, sparsity, and efficiency of the proposed methods.


title: In-vitro platform to study ultrasound as source for wireless energy transfer and communication for implanted medical devices

abstract: A platform to study ultrasound as a source for wireless energy transfer and communication for implanted medical devices is described. A tank is used as a container for a pair of electroacoustic transducers, where a control unit is fixed to one wall of the tank and a transponder can be manually moved in three axes and rotate using a mechanical system. The tank is filled with water to allow acoustic energy and data transfer, and the system is optimized to avoid parasitic effects due to cables, reflection paths and cross talk problems. A printed circuit board is developed to test energy scavenging such that enough acoustic intensity is generated by the control unit to recharge a battery loaded to the transponder. In the same manner, a second printed circuit board is fabricated to study transmission of information through acoustic waves.


title: Planning the motion of a sliding and rolling sphere

abstract: This paper investigates the free motion of a sphere with initial velocity and angular velocity on a plane under sliding and rolling friction. The sphere will first slide along a parabolic trajectory (with a constant direction of its contact velocity), and then roll along a straight trajectory. Such a curved trajectory can be utilized for obstacle avoidance in path planning. A one-to-one correspondence exists between trajectories connecting two specified locations and pairs of sliding and rolling directions within some 2-dimensional region, called the trajectory space, which is then used for planning. A plane sweep algorithm operating in this space is presented to find all collision-free trajectories in the presence of cylindrical obstacles that are vertically oriented.


title: Markov Logic Networks for Optical Chemical Structure Recognition

abstract: Optical chemical structure recognition is the problem of converting a bitmap image containing a chemical structure formula into a standard structured representation of the molecule. We introduce a novel approach to this problem based on the pipelined integration of pattern recognition techniques with probabilistic knowledge representation and reasoning. Basic entities and relations (such as textual elements, points, lines, etc.) are first extracted by a low-level processing module. A probabilistic reasoning engine based on Markov logic, embodying chemical and graphical knowledge, is subsequently used to refine these pieces of information. An annotated connection table of atoms and bonds is finally assembled and converted into a standard chemical exchange format. We report a successful evaluation on two large image data sets, showing that the method compares favorably with the current state-of-the-art, especially on degraded low-resolution images. The system is available as a web server at http://mlocsr.di...


title: Engineering therapeutic T cells to suppress alloimmune responses using TCRs, CARs, or BARs.

abstract: Adoptive cell therapy with therapeutic T cells has become one of the most promising strategies to stimulate or suppress immune responses. Using virus-mediated genetic manipulation, the antigen specificity of T cells can now be precisely redirected. Tailored specificity has not only overcome technical limitations and safety concerns but also considerably broadened the spectrum of therapeutic applications. Different T cell-engineering strategies have now become available to suppress alloimmune responses. We first provide an overview of the allorecognition pathways and effector mechanisms that are responsible for alloimmune injuries in the setting of vascularized organ transplantation. We then discuss the potential to use different T cell-engineering approaches to suppress alloimmune responses. Specifically, expression of allospecific T cell receptors, single-chain chimeric antigen receptors, or antigen domains recognized by B cell receptors (B cell antibody receptors) in regulatory or cytotoxic T cells are considered. The ability of these strategies to control the direct or indirect pathways of allorecognition and the cellular or humoral alloimmune responses is discussed. An intimate understanding of the complex interplay that occurs between the engineered T cells and the alloimmune players is a necessary prerequisite for the design of safe and successful strategies for precise immunomodulation in transplantation.


title: Security and Privacy Considerations for IoT Application on Smart Grids: Survey and Research Challenges

abstract: The emergence and evolution of Internet of Things (IoT) offers great advantages to improve substantially the management over electricity consumption and distribution to the benefit of consumers, suppliers and grid operators. However, introducing IoT related devices and technologies in smart grids might lead to new security and privacy challenges. Though necessary technological innovations to ensure secure communication are being developed, more work is still required towards more secure standards for communication between devices and Smart Grids. This paper provides an overview about the security and privacy challenges of IoT applications in smart grids. Furthermore, we highlight and analyze some solutions and practices being used to cope with security and privacy requirements for IoT on deployment and management of smart grid. We address three types of challenge domains: customer domain, information and communication domain, and the grid domain.


title: Secured and Robust Dual Image Steganography: A Survey

abstract: In the last few years communication technology has been improved, which increase the need of secure data communication. For this, many researchers have exerted much of their time and efforts in an attempt to find suitable ways for data hiding. There is a technique used for hiding the important information imperceptibly, which is Steganography. Steganography is the art of hiding information in such a way that prevents the detection of hidden messages. The process of using steganography in conjunction with cryptography, called as Dual Steganography. This paper tries to elucidate the basic concepts of steganography, its various types and techniques, and dual steganography. There is also some of research works done in steganography field in past few years.


title: Criterion validity of the Strengths and Difficulties Questionnaire (SDQ) with inpatient adolescents

abstract: Increasing rates of adolescent admissions to inpatient psychiatric settings and acute length of stay necessitates valid psychiatric screening tools. The Strengths and Difficulties Questionnaire (SDQ) appears to have valuable clinical application due to its brevity and strong psychometric properties. In this study we aimed to evaluate the criterion validity of the SDQ in 159 psychiatric inpatients between the ages of 12–17 against the Computerized Diagnostic Interview Schedule for Children (CDISC). In determining the criterion validity of the SDQ against the CDISC-IV, we further sought to compare its classification accuracy with the DSM-oriented scales of two widely-used clinical screeners for children and adolescents, the Child Behavior Checklist (CBCL) and Youth Self Report (YSR). Results demonstrated comparable diagnostic accuracy for the three measures in detecting common emotional and behavioral disorders. Tentative clinical cutoffs were proposed specific to American adolescents for parent and youth reports. Sensitivity and specificity values are also reported and discussed.


title: Local minima in training of neural networks

abstract: There has been a lot of recent interest in trying to characterize the error surface of deep models. This stems from a long standing question. Given that deep networks are highly nonlinear systems optimized by local gradient methods, why do they not seem to be affected by bad local minima? It is widely believed that training of deep models using gradient methods works so well because the error surface either has no local minima, or if they exist they need to be close in value to the global minimum. It is known that such results hold under very strong assumptions which are not satisfied by real models. In this paper we present examples showing that for such theorem to be true additional assumptions on the data, initialization schemes and/or the model classes have to be made. We look at the particular case of finite size datasets. We demonstrate that in this scenario one can construct counter-examples (datasets or initialization schemes) when the network does become susceptible to bad local minima over the weight space.


title: AN EFFICIENT OPTIMIZATION PROCEDURE BASED ON CUCKOO SEARCH ALGORITHM FOR PRACTICAL DESIGN OF STEEL STRUCTURES

abstract: Different kinds of meta-heuristi c algorithms have been recently utilized to overcome the complex nature of optimum design of structures. In this paper, an integrated optimization procedure with the objective of minimizing the self-weight of real size structures is simply performed interfacing SAP2000 and MATLAB ® softwares in the form of parallel computing. The meta-heuristic algorithm chosen here is Cuckoo Search (CS) recently developed as a type of population based algorithm inspired by the behavior of some Cuckoo species in combination with the Levy flight behavior. The CS algorithm performs suitable selection of sections from the American Institute of Steel Construction (AISC) wide-flange (W) shapes list. Strength constraints of the AISC load and resistance factor design specification, geometric limitations and displacement constraints are imposed on frames. Effective time-saving procedure using simple parallel computing, as well as utilizing reliable analysis and design tool are also some new features of the present study. The results show that the proposed method is effective in optimizing practical structures.


title: Exploring Consumer Adoption of Mobile Payments-A Qualitative Study

abstract: This paper presents a qualitative study on consumer adoption of mobile payments. The findings suggest that the relative advantage of mobile payments is different from that specified in adoption theories and include independence of time and place, availability, possibilities for remote payments, and queue avoidance. Furthermore, the adoption of mobile payments was found to be dynamic, depending on certain situational factors such as a lack of other payment methods or urgency. Several other barriers to adoption were also identified, including premium pricing, complexity, a lack of critical mass, and perceived risks. The findings provide foundation for an enhanced theory on mobile payment adoption and for the practical development of mobile payment services.


title: Accurate mobile robot position determination using unscented Kalman filter

abstract: The objective is to accurately determine mobile robots position and orientation by integrating information received from odometry and an inertial sensor. The position and orientation provided by odometry are subject to different types of errors. To improve the odometry, a fiber optic gyroscope is used to give the orientation information that is more reliable. The information from odometry and gyroscope are integrated using unscented Kalman filter (UKF). The position and orientation determined using the UKF is compared with the results from the extended Kalman filter (EKF)


title: Online Transfer Learning

abstract: In this paper, we propose a novel machine learning framework called "Online Transfer Learning" (OTL), which aims to attack an online learning task on a target domain by transferring knowledge from some source domain. We do not assume data in the target domain follows the same distribution as that in the source domain, and the motivation of our work is to enhance a supervised online learning task on a target domain by exploiting the existing knowledge that had been learnt from training data in source domains. OTL is in general very challenging since data in both source and target domains not only can be different in their class distributions, but also can be diverse in their feature representations. As a first attempt to this new research problem, we investigate two different settings of OTL: (i) OTL on homogeneous domains of common feature space, and (ii) OTL across heterogeneous domains of different feature spaces. For each setting, we propose effective OTL algorithms to solve online classification tasks, and show some theoretical bounds of the algorithms. In addition, we also apply the OTL technique to attack the challenging online learning tasks with concept-drifting data streams. Finally, we conduct extensive empirical studies on a comprehensive testbed, in which encouraging results validate the efficacy of our techniques.


title: Patient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Record

abstract: The wide implementation of electronic health record (EHR) systems facilitates the collection of large-scale health data from real clinical settings. Despite the significant increase in adoption of EHR systems, these data remain largely unexplored, but present a rich data source for knowledge discovery from patient health histories in tasks, such as understanding disease correlations and predicting health outcomes. However, the heterogeneity, sparsity, noise, and bias in these data present many complex challenges. This complexity makes it difficult to translate potentially relevant information into machine learning algorithms. In this paper, we propose a computational framework, Patient2Vec , to learn an interpretable deep representation of longitudinal EHR data, which is personalized for each patient. To evaluate this approach, we apply it to the prediction of future hospitalizations using real EHR data and compare its predictive performance with baseline methods. Patient2Vec produces a vector space with meaningful structure, and it achieves an area under curve around 0.799, outperforming baseline methods. In the end, the learned feature importance can be visualized and interpreted at both the individual and population levels to bring clinical insights.


title: Combined acoustic echo canceller for the GSM network

abstract: When the implementation of an acoustic echo canceler is implemented in the Mobile Switching Center of a GSM network, it is necessary to take into account the non linearities introduced by the speech coder/decoder. A classical linear adaptive filter is not sufficient. The performances of a classical AEC are improved by noise reduction adaptive post-filter. The post-filter introduced, take into account implicitely the non linearity introduced by the GSM speech codecs cascaded with the linear echo path. It can reduce the quantization noise related to the coding/decoding operation. The proposed combined system presents better performances than that of the classic (linear) solution.


title: The LRP toolbox for artificial neural networks

abstract: The Layer-wise Relevance Propagation (LRP) algorithm explains a classifier's prediction specific to a given data point by attributing relevance scores to important components of the input by using the topology of the learned model itself. With the LRP Toolbox we provide platform-agnostic implementations for explaining the predictions of pretrained state of the art Caffe networks and stand-alone implementations for fully connected Neural Network models. The implementations for Matlab and python shall serve as a playing field to familiarize oneself with the LRP algorithm and are implemented with readability and transparency in mind. Models and data can be imported and exported using raw text formats, Matlab's .mat files and the .npy format for numpy or plain text.


title: Performance evaluation of iris-based recognition system implementing PCA and ICA encoding techniques

abstract: In this paper, we describe and analyze the performance of two iris-encoding techniques. The first technique is based on Principle Component Analysis (PCA) encoding method while the second technique is a combination of Principal Component Analysis with Independent Component Analysis (ICA) following it. Both techniques are applied globally. PCA and ICA are two well known methods used to process a variety of data. Though PCA has been used as a preprocessing step that reduces dimensions for obtaining ICA components for iris, it has never been analyzed in depth as an individual encoding method. In practice PCA and ICA are known as methods that extract global and fine features, respectively. It is shown here that when PCA and ICA methods are used to encode iris images, one of the critical steps required to achieve a good performance is compensation for rotation effect. ::: We further study the effect of varying the image resolution level on the performance of the two encoding methods. The major motivation for this study is the cases in practice where images of the same or different irises taken at different distances have to be compared. ::: The performance of encoding techniques is analyzed using the CASIA dataset. The original images are non-ideal and thus require a sequence of preprocessing steps prior to application of encoding methods. We plot a series of Receiver Operating Characteristics (ROCs) to demonstrate various effects on the performance of the iris-based recognition system implementing PCA and ICA encoding techniques.


title: MobTorrent: A Framework for Mobile Internet Access from Vehicles

abstract: In this paper, we present MobTorrent, an on- demand, user-driven framework designed for vehicles which have intermittent high speed access to roadside WiFi access points (AP). Mobile nodes in MobTorrent use the WWAN network as a control channel. When a mobile client wants to initiate a down- load, instead of waiting for contact with the AP, it informs one (or multiple) selected AP(s) to prefetch the content. The scheduling algorithm in MobTorrent then replicates the prefetched data on the mobile helpers so that the total amount of data transferred and the average transfer rate to the mobile clients are maximized. Therefore, instead of limiting high speed data transfer to the short contact periods between APs and mobile clients, high speed transfers among vehicles are opportunistically exploited. Evaluation based on testbed measurement and trace-driven simulation shows that MobTorrent provides substantial improve- ment over existing architectures. For the case of a single AP, its performance approximates that of an off-line optimal scheduler. In case of multiple APs, our evaluation shows that MobTorrent's performance is robust in a variety of settings.


title: Surface-normal electroabsorption reflection modulators using asymmetric Fabry-Perot structures

abstract: The authors discuss a family of surface-normal electrooptic reflection modulators using asymmetric Fabry-Perot (ASFP) structures with GaAs-AlGaAs multiple quantum wells (MQWs) as the active medium. When an optimized top mirror reflectivity ( approximately 75%) is used, a voltage swing as low as 2 V is enough to change the device reflectivity by more than 40% with a contrast ratio of 50. A comparison with various Fabry-Perot structures shows that the ASFP is the best structure in terms of its operating voltage requirement and optical bandwidth. A sensitivity analysis shows that tolerances of 2 nm in operating wavelength, 0.5 V in operating voltage, 0.3% in layer thickness control or 10 degrees C in temperature variation can be expected from ASFPs with a finesse approximately 10. >


The Automated Travel Agent : Hotel Recommendations Using Machine Learning


title: Robotic Mapping: A Survey

abstract: This article provides a comprehensive introduction into the field of robotic mapping, with a focus on indoor mapping. It describes and compares various probabilistic techniques, as they are presently being applied to a vast array of mobile robot mapping problems. The history of robotic mapping is also detailed, along with an extensive list of open research problems.


Line Drawing for Conveying Shapes in HDR Images


title: High-Order Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting

abstract: Traffic forecasting is a challenging task, due to the complicated spatial dependencies on roadway networks and the time-varying traffic patterns. To address this challenge, we learn the traffic network as a graph and propose a novel deep learning framework, High-Order Graph Convolutional Long Short-Term Memory Neural Network (HGC-LSTM), to learn the interactions between links in the traffic network and forecast the network-wide traffic state. We define the high-order traffic graph convolution based on the physical network topology. The proposed framework employs L1-norms on the graph convolution weights and L2-norms on the graph convolution features to identify the most influential links in the traffic network. We propose a novel Real-Time Branching Learning (RTBL) algorithm for the HGC-LSTM framework to accelerate the training process for spatio-temporal data. Experiments show that our HGC-LSTM network is able to capture the complex spatio-temporal dependencies efficiently present in the traffic network and consistently outperforms state-of-the-art baseline methods on two heterogeneous real-world traffic datasets. The visualization of graph convolution weights shows that the proposed framework can accurately recognize the most influential roadway segments in real-world traffic networks.


title: A single-phase grid-connected PV converter with minimal DC-link capacitor and low-frequency ripple-free maximum power point tracking

abstract: This paper presents a novel single-phase grid-connected photovoltaic (PV) converter based on current-fed dual active bridge (CF-DAB) dc-dc converter. The proposed converter allows a minimal DC-link capacitor with large voltage variation without low-frequency effect on maximum power point tracking (MPPT). An advanced control system is developed based on the converter operation principles. Minimized transformer peak current is capable to be achieved through a symmetrical dc voltage control. Simulation results are presented to validate the performance of the proposed PV converter. A 5kW laboratorial prototype is also implemented and the results will be presented later.


title: Evaluating iterative optimization across 1000 datasets

abstract: While iterative optimization has become a popular compiler optimization approach, it is based on a premise which has never been truly evaluated: that it is possible to learn the best compiler optimizations across data sets. Up to now, most iterative optimization studies find the best optimizations through repeated runs on the same data set. Only a handful of studies have attempted to exercise iterative optimization on a few tens of data sets. In this paper, we truly put iterative compilation to the test for the first time by evaluating its effectiveness across a large number of data sets. We therefore compose KDataSets, a data set suite with 1000 data sets for 32 programs, which we release to the public. We characterize the diversity of KDataSets, and subsequently use it to evaluate iterative optimization.We demonstrate that it is possible to derive a robust iterative optimization strategy across data sets: for all 32 programs, we find that there exists at least one combination of compiler optimizations that achieves 86% or more of the best possible speedup across all data sets using Intel's ICC (83% for GNU's GCC). This optimal combination is program-specific and yields speedups up to 1.71 on ICC and 2.23 on GCC over the highest optimization level (-fast and -O3, respectively). This finding makes the task of optimizing programs across data sets much easier than previously anticipated, and it paves the way for the practical and reliable usage of iterative optimization. Finally, we derive pre-shipping and post-shipping optimization strategies for software vendors.


title: Statistical Inverse Ray Tracing for Image-Based 3D Modeling

abstract: This paper proposes a new formulation and solution to image-based 3D modeling (aka “multi-view stereo”) based on generative statistical modeling and inference. The proposed new approach, named statistical inverse ray tracing, models and estimates the occlusion relationship accurately through optimizing a physically sound image generation model based on volumetric ray tracing. Together with geometric priors, they are put together into a Bayesian formulation known as Markov random field (MRF) model. This MRF model is different from typical MRFs used in image analysis in the sense that the ray clique, which models the ray-tracing process, consists of thousands of random variables instead of two to dozens. To handle the computational challenges associated with large clique size, an algorithm with linear computational complexity is developed by exploiting, using dynamic programming, the recursive chain structure of the ray clique. We further demonstrate the benefit of exact modeling and accurate estimation of the occlusion relationship by evaluating the proposed algorithm on several challenging data sets.


title: Design and control of a GaN-based, 13-level, flying capacitor multilevel inverter

abstract: Multilevel topologies are an appealing method to achieve higher power density inverters for both mobile and stationary systems. This work discusses the design and development of a 13-level, flying capacitor multilevel (FCML) inverter. Operating from an 800 V bus, this inverter requires switches with a voltage blocking capability of less than 80 V. A 120 kHz switching frequency is enabled through the use of GaN FETs and the development of custom integrated switching cells, which reduce commutation loop inductance and allow for a modular design. Additionally, the frequency multiplication effect of FCML inverters allows the output inductor of the inverter to be made exceptionally small (4.7 μH) while maintaining a 0.7 % THD due to the 1.44 MHz effective inductor ripple frequency.


Automatic guidance of a four-wheel-steering mobile robot for accurate field operations


Crime Mining: A Comprehensive Survey


title: Instance Similarity Deep Hashing for Multi-Label Image Retrieval

abstract: Hash coding has been widely used in the approximate nearest neighbor search for large-scale image retrieval. Recently, many deep hashing methods have been proposed and shown largely improved performance over traditional feature-learning-based methods. Most of these methods examine the pairwise similarity on the semantic-level labels, where the pairwise similarity is generally defined in a hard-assignment way. That is, the pairwise similarity is '1' if they share no less than one class label and '0' if they do not share any. However, such similarity definition cannot reflect the similarity ranking for pairwise images that hold multiple labels. In this paper, a new deep hashing method is proposed for multi-label image retrieval by re-defining the pairwise similarity into an instance similarity, where the instance similarity is quantified into a percentage based on the normalized semantic labels. Based on the instance similarity, a weighted cross-entropy loss and a minimum mean square error loss are tailored for loss-function construction, and are efficiently used for simultaneous feature learning and hash coding. Experiments on three popular datasets demonstrate that, the proposed method outperforms the competing methods and achieves the state-of-the-art performance in multi-label image retrieval.


title: Software Defined Networking: Redefining the Future of Internet in IoT and Cloud Era

abstract: Ubiquitous computing with smart mobile devices, Internet of Things, virtualization and cloud is changing yesteryears' static networks to dynamic networks of mobile smart devices and cloud based on-demand services. These changes are growing exponentially and network management is becoming increasingly difficult, be it in smart energy/ transport/cities or in big enterprises. The diverse nature of applications, devices, mobility of the endpoints has challenged IT to look beyond conventional applications for effective security policies, quality of service and performance. This paper describes how the emerging technology- Software Defined Networking (SDN) can be leveraged to provide scalable solutions in this diverse and rapidly changing landscape, reducing both capex and opex. Two use cases, SDN enabled IoT gateways and eNodeB, are covered in this paper. These use cases demonstrate how SDN can be effectively used to address the challenges faced by service providers/IT in managing the dynamic nature of today's networks. These solutions and other such services with help of North Bound APIs can be used in areas like wireless networks, data centers, service providers, enterprise gateways and other such deployments where there is a need to dynamically program networks to make them agile, smarter and faster.


title: Engineering multi-tenant software-as-a-service systems

abstract: Increasingly, Software-as-a-Service (SaaS) is becoming a dominant mechanism for the consumption of software by end users. From a vendor's perspective, the benefits of SaaS arise from leveraging economies of scale, by serving a large number of customers ("tenants") through a shared instance of a centrally hosted software service. Consequently, a SaaS provider would, in general, try to drive commonality amongst the requirements of different tenants, and at best, offer a fixed set of customization options. However, many tenants would also come with custom requirements, which may be a pre-requisite for them to adopt the SaaS system. These requirements should then be addressed by evolving the SaaS system in a controlled manner, while still supporting the needs of existing tenants. This need to balance tenant variability and commonality, and to optimize on development and testing effort, can make the evolution of multitenant SaaS systems an interesting engineering challenge; this has strong economic undertones as well, given the "pay-per-use" subscription model of SaaS, and the cost of incremental development and maintenance to cater to new tenant needs. In this paper, we outline a set of research issues in the design, testing and maintenance of multi-tenant SaaS systems, and highlight some of the interesting optimization questions that arise in the process. Presenting specific technical solutions is beyond the scope of this paper - instead, our goal is to help shape a research agenda for multi-tenant SaaS that can provide stimulus for further investigation into this area by the software and service engineering research community.


title: HoloR: Interactive mixed-reality rooms

abstract: Existing virtual reality technologies conver only certain areas of the mixed-reality spectrum: Augmented reality goggles are unable to provide immersion while head-mounted displays make it difficult to interact with the real world. In this paper we introduce HoloR — short for Holographic Room: A stereoscopic, multi-person, multi-viewer, spatial projected augmented reality system, which enables applications to blend between different parts of the mixed-reality spectrum. By using web-technologies like JavaScript and WebGL the operation of HoloR does not require any installation or compiling process and thus enables rapid development of applications and extensions. We present different sample applications ranging from collaborative data exploration to augmented persons and ambient information systems.


title: Direct Synthesis of van der Waals Solids

abstract: The stacking of two-dimensional layered materials, such as semiconducting transition metal dichalcogenides (TMDs), insulating hexagonal boron nitride (hBN), and semimetallic graphene, has been theorized to produce tunable electronic and optoelectronic properties. Here we demonstrate the direct growth of MoS2, WSe2, and hBN on epitaxial graphene to form large-area van der Waals heterostructures. We reveal that the properties of the underlying graphene dictate properties of the heterostructures, where strain, wrinkling, and defects on the surface of graphene act as nucleation centers for lateral growth of the overlayer. Additionally, we show that the direct synthesis of TMDs on epitaxial graphene exhibits atomically sharp interfaces. Finally, we demonstrate that direct growth of MoS2 on epitaxial graphene can lead to a 103 improvement in photoresponse compared to MoS2 alone.


Pictogrammar: an AAC device based on a semantic grammar


title: Parallel data processing with MapReduce: a survey

abstract: A prominent parallel data processing tool MapReduce is gaining significant momentum from both industry and academia as the volume of data to analyze grows rapidly. While MapReduce is used in many areas where massive data analysis is required, there are still debates on its performance, efficiency per node, and simple abstraction. This survey intends to assist the database and open source communities in understanding various technical aspects of the MapReduce framework. In this survey, we characterize the MapReduce framework and discuss its inherent pros and cons. We then introduce its optimization strategies reported in the recent literature. We also discuss the open issues and challenges raised on parallel data analysis with MapReduce.


title: Customer engagement in a Facebook brand community: An empirical study on travel industry in Vietnam

abstract: Customer Engagement Behaviors is a group of factors affecting to the Loyalty, mediating via Social Benefits. In Vietnam, former researches measuring the Loyalty of customers engaged in Social Networking sites, such as: Facebook, are still rare, especially in the travel industry. This research aims to fill this gap by testing these hypotheses in travel industry and raising recommendations for its applications in practice. The critical findings from the research are that it reveals a significantly positive impact of Community and Transaction to Loyalty, mediating via Social Benefits and Economic Benefits. In Social benefits, item "sharing knowledge" got highest score weight. In Economic Benefits, "getting vouchers" and "buying travel kits" get highest score weights. The application of this research to practice is workable, that gives much to Marketers to organize their fan page and perform marketing activities at the most efficient and economical ways.


title: High-Isolation X-Polar Antenna

abstract: Antennas with dual linear slant polarization are necessary for modern personal communication base station applications. X-Polar antenna stands for an antenna with dual linear slant polarization. The most significant feature of the proposed antenna is high isolation between two different polarizations. The structure used here includes a reflector, a U-shaped strip, a cross-slot, a patch, and a director. Simulation and measurement results show minimum 30 dB isolation between the two polarizations. This structure yields half-power beamwidth (HPBW) of 65°, a cross-polarization level of less than -15 dB, and a voltage standing wave ratio (VSWR) of better than 1.5. This structure is useful for dual-band and dual-polarized applications such as the Global System for Mobile communications (GSM) and the Universal Mobile Telecommunications System (UMTS) frequency range.


title: Evolution of traditional digital forensics in virtualization

abstract: Computer virtualization is not new; however, it has become increasingly important because of the many advantages it offers businesses and individuals to reduce costs. A company can reduce maintenance, hardware, and energy costs by running virtualized servers on a single physical machine. Although virtualization offers these advantages, it introduces new challenges to current computer forensic techniques as well as computer system defense tools. As this technology continues to be adopted by more and more companies every year, malware and hacker attacks are potentially going to affect virtualized systems as they have been affecting physical systems in the past. Therefore, the increasing growth of virtualization has created the need for a new generation of computer system defenses as well as computer forensic techniques to effectively defend these systems before or after they have been attacked. Because of the nature of how virtualization operates, new techniques to interact with these systems have become available. These techniques allow us to increase the effectiveness of current forensic and system defense tools to create new tools to defend or analyze virtualized systems. Virtual Machine Introspection (VMI) is one of these techniques that have formed the basis of a number of novel approaches in the field of Digital Forensics and Cybersecurity. In this paper, we present what VMI has offered to Digital Forensics and the new challenges it brings. Likewise, we discuss why traditional Digital Forensic techniques are not reliable to analyze virtual machines once they have been attacked.


title: FACON: A flow-aided cooperative navigation scheme

abstract: Navigation for autonomous underwater vehicles (AUVs) in mid-depth water column is notoriously challenging due to the lack of features for reference. In this paper, we propose a flow-aided cooperative navigation (FACON) strategy to improve the navigation performance of a team of AUVs when neither frequent surfacing nor persistent bottom-locking is available. Preloaded ocean current forecasts are referenced by each individual vehicle as it performs dead-reckoning with an inertial navigation system and measures relative current velocities during navigation. A marginalized particle filter is applied by each AUV to track its location, velocity, sensor biases, and local flow perturbation unresolved by the ocean forecast. Meanwhile, AUVs perform distributed cooperative localization based on relative pose measurements and asynchronized local communication when they enter the communication range of one another. Opportunistic information fusion among AUVs is realized through covariance intersection. The performance of the flow-aided navigation scheme of a single AUV is analyzed within a simulated experiment based on field test data. The feasibility of FACON is discussed through simulation in a turbulent, multi-gyre flow field.


title: Efficient incremental near duplicate detection based on locality sensitive hashing

abstract: In this paper, we study the problem of detecting near duplicates for high dimensional data points in an incremental manner. For example, for an image sharing website, it would be a desirable feature if near-duplicates can be detected whenever a user uploads a new image into the website so that the user can take some action such as stopping the upload or reporting an illegal copy. Specifically, whenever a new point arrives, our goal is to find all points within an existing point set that are close to the new point based on a given distance function and a distance threshold before the new point is inserted into the data set. Based on a well-known indexing technique, Locality Sensitive Hashing, we propose a new approach which clearly speeds up the running time of LSH indexing while using only a small amount of extra space. The idea is to store a small fraction of near duplicate pairs within the existing point set which are found when they are inserted into the data set, and use them to prune LSH candidate sets for the newly arrived point. Extensive experiments based on three real-world data sets show that our method consistently outperforms the original LSH approach: to reach the same query response time, our method needs significantly less memory than the original LSH approach. Meanwhile, the LSH theoretical guarantee on the quality of the search result is preserved by our approach. Furthermore, it is easy to implement our approach based on LSH.


title: EEG montage analysis in the Blind Source Separation framework

abstract: Abstract Blind Source Separation (BSS) is a relatively recent technique, more and more applied in electroencephalographic (EEG) signal processing. Still, the classical mixing model of the BSS does not take into account the real recording set-up. In fact, a major problem in electrophysiological recording systems (e.g. ECG, EEG, EMG) is to find a region in the human body whose bio-potential activity can be considered as neutral as possible i.e., a quasi-inactive reference place. Nowadays, it is well known that it is impossible to find a “zero-potential” site on the human body. In particular, the most common way of performing EEG recordings is by using as a common reference an electrode placed somewhere on the head. Starting from this Common Reference Montage (CRM), several other montages can be constructed to obtain alternative interpretation or processing solutions. Regardless of the chosen montage, the reference electrode intervenes in the mixing model of the BSS. The objective of this work is to analyse the influence of the montage on the mixing matrix and the quality of the BSS solution. This paper proposes to formalize the source separation problem in a non zero-potential reference context and shows that the Average Reference Montage (ARM), augmented by a virtual “average measure”, leads to better source separation results (separability index IS ). This conclusion is supported by simulated EEGs using the most common montages i.e., Common Reference Montage, Average Reference Montage and Bipolar-Longitudinal Montage, as well as by real EEG examples.


8 Renewable Energy Opportunities in Water Desalination


title: A comparative assessment of answer quality on four question answering sites

abstract: Question answering (QA (2) answer multiplication does not always result in better information; it yields more complete and verifiable answers but does not result in higher accuracy levels; and (3) a Q&A site's popularity does not correlate with its answer quality, on all three measures.


title: Observation of plasma density dependence of electromagnetic soliton excitation by an intense laser pulse

abstract: The experimental evidence of the correlation between the initial electron density of the plasma and electromagnetic soliton excitation at the wake of an intense (1019 W/cm2) and short (1 ps) laser pulse is presented. The spatial distribution of the solitons, together with their late time evolution into post-solitons, is found to be dependent upon the background plasma parameters, in agreement with published analytical and numerical findings. The measured temporal evolution and electrostatic field distribution of the structures are consistent with their late time evolution and the occurrence of multiple merging of neighboring post-solitons.


title: An optimizing compiler for a purely functional web-application language

abstract: High-level scripting languages have become tremendously popular for development of dynamic Web applications. Many programmers appreciate the productivity benefits of automatic storage management, freedom from verbose type annotations, and so on. While it is often possible to improve performance substantially by rewriting an application in C or a similar language, very few programmers bother to do so, because of the consequences for human development effort. This paper describes a compiler that makes it possible to have most of the best of both worlds, coding Web applications in a high-level language but compiling to native code with performance comparable to handwritten C code. The source language is Ur/Web, a domain-specific, purely functional, statically typed language for the Web. Through a coordinated suite of relatively straightforward program analyses and algebraic optimizations, we transform Ur/Web programs into almost-idiomatic C code, with no garbage collection, little unnecessary memory allocation for intermediate values, etc. Our compiler is in production use for commercial Web sites supporting thousands of users, and microbenchmarks demonstrate very competitive performance versus mainstream tools.


title: Improving resolution and depth-of-field of light field cameras using a hybrid imaging system

abstract: Current light field (LF) cameras provide low spatial resolution and limited depth-of-field (DOF) control when compared to traditional digital SLR (DSLR) cameras. We show that a hybrid imaging system consisting of a standard LF camera and a high-resolution standard camera enables (a) achieve high-resolution digital refocusing, (b) better DOF control than LF cameras, and (c) render graceful high-resolution viewpoint variations, all of which were previously unachievable. We propose a simple patch-based algorithm to super-resolve the low-resolution views of the light field using the high-resolution patches captured using a high-resolution SLR camera. The algorithm does not require the LF camera and the DSLR to be co-located or for any calibration information regarding the two imaging systems. We build an example prototype using a Lytro camera (380×380 pixel spatial resolution) and a 18 megapixel (MP) Canon DSLR camera to generate a light field with 11 MP resolution (9× super-resolution) and about 1 over 9 th of the DOF of the Lytro camera. We show several experimental results on challenging scenes containing occlusions, specularities and complex non-lambertian materials, demonstrating the effectiveness of our approach.


title: A 20W/channel Class-D amplifier with significantly reduced common-mode radiated emissions

abstract: Due to their rail-to-rail switching nature, Class-D audio amplifiers are prone to generating electromagnetic interference (EMI) that is in excess of what is acceptable in many systems. Such systems typically require devices to limit their EMI to an accepted standard, such as the FCC Class-B standard. In Class-D audio amplifiers that employ Class-BD modulation [1] using a single supply, the common-mode output signal, VCM, has a significant EMI component because this signal also swings rail-to-rail at the amplifier switching frequency, typically in the hundreds of kHz. This phenomenon is illustrated in the upper signals of Fig. 26.4.1.


title: A Low-Profile High-Gain and Wideband Filtering Antenna With Metasurface

abstract: A low-profile, high-gain, and wideband metasurface (MS)-based filtering antenna with high selectivity is investigated in this communication. The planar MS consists of nonuniform metallic patch cells, and it is fed by two separated microstrip-coupled slots from the bottom. The separation between the two slots together with a shorting via is used to provide good filtering performance in the lower stopband, whereas the MS is elaborately designed to provide a sharp roll-off rate at upper band edge for the filtering function. The MS also simultaneously works as a high-efficient radiator, enhancing the impedance bandwidth and antenna gain of the feeding slots. To verify the design, a prototype operating at 5 GHz has been fabricated and measured. The reflection coefficient, radiation pattern, antenna gain, and efficiency are studied, and reasonable agreement between the measured and simulated results is observed. The prototype with dimensions of 1.3 $\lambda_{0}\times1.3\ \lambda_{0}\times0.06\ \lambda_{0}$ has a 10-dB impedance bandwidth of 28.4%, an average gain of 8.2 dBi within passband, and an out-of-band suppression level of more than 20 dB within a very wide stop-band.


title: A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation

abstract: Recent advances in deep learning, like 3D fully convolutional networks (FCNs), have improved the state-of-the-art in dense semantic segmentation of medical images. However, most network architectures require severely downsampling or cropping the images to meet the memory limitations of today's GPU cards while still considering enough context in the images for accurate segmentation. In this work, we propose a novel approach that utilizes auto-context to perform semantic segmentation at higher resolutions in a multi-scale pyramid of stacked 3D FCNs. We train and validate our models on a dataset of manually annotated abdominal organs and vessels from 377 clinical CT images used in gastric surgery, and achieve promising results with close to 90% Dice score on average. For additional evaluation, we perform separate testing on datasets from different sources and achieve competitive results, illustrating the robustness of the model and approach.


title: Cultivating Competence, Self-Efficacy, and Intrinsic Interest Through Proximal Self-Motivation

abstract: The present experiment tested the hypothesis that self-motivation through proximal goal setting serves as an effective mechanism for cultivating competencies, self-percepts of efficacy, and intrinsic interest. Children who exhibited gross deficits and disinterest in mathematical tasks pursued a program of self-directed learning under conditions involving either proximal subgoals, distal goals, or no goals. Results of the multifaceted assessment provide support for the superiority of proximal self-influence. Under proximal subgoals, children progressed rapidly in self-directed learning, achieved substantial mastery of mathematical operations, and developed a sense of personal efficacy and intrinsic interest in arithmetic activities that initially held little attraction for them. Distal goals had no demonstrable effects. In addition to its other benefits, goal proximity fostered veridical self-knowledge of capabilities as reflected in high congruence between judgments of mathematical self-efficacy and subsequent mathematical performance. Perceived self-efficacy was positively related to accuracy of mathematical performance and to intrinsic interest in arithmetic activities.


title: Enforcing access control in Web-based social networks

abstract: In this article, we propose an access control mechanism for Web-based social networks, which adopts a rule-based approach for specifying access policies on the resources owned by network participants, and where authorized users are denoted in terms of the type, depth, and trust level of the relationships existing between nodes in the network. Different from traditional access control systems, our mechanism makes use of a semidecentralized architecture, where access control enforcement is carried out client-side. Access to a resource is granted when the requestor is able to demonstrate being authorized to do that by providing a proof. In the article, besides illustrating the main notions on which our access control model relies, we present all the protocols underlying our system and a performance study of the implemented prototype.


title: Subspace Hierarchical Particle Filter

abstract: Particle filtering has become a standard tool for nonparametric estimation in computer vision tracking applications. It is an instance of stochastic search. Each particle represents a possible state of the system. Higher concentration of particles at any given region of the search space implies higher probabilities. One of its major drawbacks is the exponential growth in the number of particles for increasing dimensions in the search space. We present a graph based filtering framework for hierarchical model tracking that is capable of substantially alleviate this issue. The method relies on dividing the search space in subspaces that can be estimated separately. Low correlated subspaces may be estimated with parallel, or serial, filters and have their probability distributions combined by a special aggregator filter. We describe a new algorithm to extract parameter groups, which define the subspaces, from the system model. We validate our method with different graph structures withing a simple hand tracking experiment with both synthetic and real data.


title: pdc: AnRPackage for Complexity-Based Clustering of Time Series

abstract: Permutation distribution clustering is a complexity-based approach to clustering time series. The dissimilarity of time series is formalized as the squared Hellinger distance between the permutation distribution of embedded time series. The resulting distance measure has linear time complexity, is invariant to phase and monotonic transformations, and robust to outliers. A probabilistic interpretation allows the determination of the number of significantly different clusters. An entropy-based heuristic relieves the user of the need to choose the parameters of the underlying time-delayed embedding manually and, thus, makes it possible to regard the approach as parameter-free. This approach is illustrated with examples on empirical data.


title: Channel polarization: A method for constructing capacity-achieving codes for symmetric binary-input memoryless channels

abstract: A method is proposed, called channel polarization, to construct code sequences that achieve the symmetric capacity $I(W)$ of any given binary-input discrete memoryless channel (B-DMC) $W$. The symmetric capacity is the highest rate achievable subject to using the input letters of the channel with equal probability. Channel polarization refers to the fact that it is possible to synthesize, out of $N$ independent copies of a given B-DMC $W$, a second set of $N$ binary-input channels $\{W_N^{(i)}:1\le i\le N\}$ such that, as $N$ becomes large, the fraction of indices $i$ for which $I(W_N^{(i)})$ is near 1 approaches $I(W)$ and the fraction for which $I(W_N^{(i)})$ is near 0 approaches $1-I(W)$. The polarized channels $\{W_N^{(i)}\}$ are well-conditioned for channel coding: one need only send data at rate 1 through those with capacity near 1 and at rate 0 through the remaining. Codes constructed on the basis of this idea are called polar codes. The paper proves that, given any B-DMC $W$ with $I(W)>0$ and any target rate $R<I(W)$, there exists a sequence of polar codes $\{{\mathscr C}_n;n\ge 1\}$ such that ${\mathscr C}_n$ has block-length $N=2^n$, rate $\ge R$, and probability of block error under successive cancellation decoding bounded as $P_{e}(N,R) \le \bigoh(N^{-\frac14})$ independently of the code rate. This performance is achievable by encoders and decoders with complexity $O(N\log N)$ for each.


title: Clinical signs caused by the use of electric training collars on dogs in everyday life situations

abstract: The use of electric shock collars for training dogs is the subject of considerable controversy. Supporters claim that they are a reliable means of eliminating self-rewarding behaviour and that they can be used over greater distances and with less risk of stress and injury than mechanical devices, such as choke chains. Opponents cite the risk of incorrect or abusive use and temptation to use electric training collars without thought or time given to alternative training methods, regardless of the fact that their use may be associated with pain and fear. The aim of this study was to investigatewhether anystress is caused by the use of electric shock collars or not and in this way to contribute to their evaluation with respect to animal welfare. Fourteen laboratory-bred Beagles were used to ensure standardised breeding, raising and training. Heart rate and saliva cortisol were used as stress parameters. The research project lasted for 7 months, during which each dog was trained for 1.5 h per day. To exclude circadian deviations of salivary cortisol values, each individual was allocated a rigid timeslot. Training as well as the experiments themselves were conducted in a secluded storage building to exclude the influence of external stressors. Three experimental groups were used. Group A (Aversion) received the electric shock when the dogs touched the prey—a rabbit dummy fixed to a motion device. Group H (Here) received the electric shock when they did not obey a previously trained recall command during hunting. Animals of group R (Random) received the electric shock arbitrarily, i.e. the shock was administered unpredictably and out of context. The main experiment lasted for 17 days. All animals were allowed to hunt unimpeded for the first 5 days. For the next 5 days the dogs were stopped from hunting by a leash. Every day, the stress parameters were determined. These values were compared with the values that were obtained during the use of the electric training collars. The collars were used over a period of 7 days as described previously. After 4 weeks the dogs were brought back into the research area without receiving an electric pulse.


title: Jerk-bounded manipulator trajectory planning: design for real-time applications

abstract: An online method for obtaining smooth, jerk-bounded trajectories has been developed and implemented. Jerk limitation is important in industrial robot applications, since it results in improved path tracking and reduced wear on the robot. The method described herein uses a concatenation of fifth-order polynomials to provide a smooth trajectory between two way points. The trajectory approximates a linear segment with parabolic blends trajectory. A sine wave template is used to calculate the end conditions (control points) for ramps from zero acceleration to nonzero acceleration. Joining these control points with quintic polynomials results in a controlled quintic trajectory that does not oscillate, and is near time optimal for the jerk and acceleration limits specified. The method requires only the computation of the quintic control points, up to a maximum of eight points per trajectory way point. This provides hard bounds for online motion algorithm computation time. A method for blending these straight-line trajectories over a series of way points is also discussed. Simulations and experimental results on an industrial robot are presented.


title: Recurrent convolutional neural networks for text classification

abstract: Text classification is a foundational task in many NLP applications. Traditional text classifiers often rely on many human-designed features, such as dictionaries, knowledge bases and special tree kernels. In contrast to traditional methods, we introduce a recurrent convolutional neural network for text classification without human-designed features. In our model, we apply a recurrent structure to capture contextual information as far as possible when learning word representations, which may introduce considerably less noise compared to traditional window-based neural networks. We also employ a max-pooling layer that automatically judges which words play key roles in text classification to capture the key components in texts. We conduct experiments on four commonly used datasets. The experimental results show that the proposed method outperforms the state-of-the-art methods on several datasets, particularly on document-level datasets.


title: VFDB 2012 update: toward the genetic diversity and molecular evolution of bacterial virulence factors

abstract: The virulence factor database (VFDB, http://www.mgc.ac.cn/VFs/) has served as a comprehensive repository of bacterial virulence factors (VFs) for >7 years. Bacterial virulence is an exciting and dynamic field, due to the availability of complete sequences of bacterial genomes and increasing sophisticated technologies for manipulating bacteria and bacterial genomes. The intricacy of virulence mechanisms offers a challenge, and there exists a clear need to decipher the ‘language’ used by VFs more effectively. In this article, we present the recent major updates of VFDB in an attempt to summarize some of the most important virulence mechanisms by comparing different compositions and organizations of VFs from various bacterial pathogens, identifying core components and phylogenetic clades and shedding new light on the forces that shape the evolutionary history of bacterial pathogenesis. In addition, the 2012 release of VFDB provides an improved user interface.


title: Poster: A SDN/NFV-Based IoT Network Slicing Creation System

abstract: With the emergency of IoT, there are many IoT network slices with different network requirements. Most of the current IoT system are specific and non-programmable and therefore their slices are difficult to reuse. It is difficult to meet different QoS requirements especially in IoT system because there are plenty of IoT sensors in IoT system. In this paper, we propose a novel IoT network slicing creation system which based on two emerging SDN and NFV technologies. It provides an easily-operating service creation environment and a service execution environment based on micro service architecture. We implement an IoT muti-flow transmission scenario. After adding subservices and QoS policies into a business process at the design plane, the IoT scenario can run automatically at the execution plane. Experiment results on the scenario show that the numbers of packets per second of different flows are changing gradually depend on QoS policies.


title: Artificial Intelligence in Drug Design

abstract: Artificial Intelligence (AI) plays a pivotal role in drug discovery. In particular artificial neural networks such as deep neural networks or recurrent networks drive this area. Numerous applications in property or activity predictions like physicochemical and ADMET properties have recently appeared and underpin the strength of this technology in quantitative structure-property relationships (QSPR) or quantitative structure-activity relationships (QSAR). Artificial intelligence in de novo design drives the generation of meaningful new biologically active molecules towards desired properties. Several examples establish the strength of artificial intelligence in this field. Combination with synthesis planning and ease of synthesis is feasible and more and more automated drug discovery by computers is expected in the near future.


title: Revisions to Radiocarbon Dates from the Sydney University Radiocarbon Laboratory

abstract: A new and previously unsuspected systematic error, associated with the vials used in the older counter in the laboratory, has been discovered recently. Unfortunately, this means that some of the radiocarbon ages previously reported by this laboratory have to be corrected, by a few hundred years for the dates most severely affected.


title: RDF Graph Visualization by Interpreting Linked Data as Knowledge

abstract: It is known that Semantic Web and Linked Open Data (LOD) are powerful technologies for knowledge management, and explicit knowledge is expected to be presented by RDF format (Resource Description Framework), but normal users are far from RDF due to technical skills required. As we learn, a concept-map or a node-link diagram can enhance the learning ability of learners from beginner to advanced user level, so RDF graph visualization can be a suitable tool for making users be familiar with Semantic technology. However, an RDF graph generated from the whole query result is not suitable for reading, because it is highly connected like a hairball and less organized. To make a graph presenting knowledge be more proper to read, this research introduces an approach to sparsify a graph using the combination of three main functions: graph simplification, triple ranking, and property selection. These functions are mostly initiated based on the interpretation of RDF data as knowledge units together with statistical analysis in order to deliver an easily-readable graph to users. A prototype is implemented to demonstrate the suitability and feasibility of the approach. It shows that the simple and flexible graph visualization is easy to read, and it creates the impression of users. In addition, the attractive tool helps to inspire users to realize the advantageous role of linked data in knowledge management.


PHYSICAL INTERNET, CONVENTIONAL, AND HYBRID LOGISTIC SYSTEMS: AN OPTIMIZATION BASED COMPARISON


The Productions of Time


title: Maintenance versus Manipulation of Information Held in Working Memory: An Event-Related fMRI Study

abstract: One model of the functional organization of lateral prefrontal cortex (PFC) in primates posits that this region is organized in a dorsal/ventral fashion subserving spatial and object working memory, respectively. Alternatively, it has been proposed that a dorsal/ventral subdivision of lateral PFC instead reflects the type of processing performed upon information held in working memory. We tested this hypothesis using an event-related fMRI method that can discriminate among functional changes occurring during temporally separated behavioral subcomponents of a single trial. Subjects performed a delayed-response task with two types of trials in which they were required to: (1) retain a sequence of letters across the delay period ( maintenance) or (2) reorder the sequence into alphabetical order across the delay period (manipulation). In each subject, activity during the delay period was found in both dorsolateral and ventrolateral PFC in both types of trials. However, dorsolateral PFC activity was greater in manipulation trials. These findings are consistent with the processing model of the functional organization of working memory in PFC. ≈ 1999 Academic Press


title: Study on Constructing Strict- and Quasi-optimal (v, k, 1) Optical Orthogonal Codes

abstract: (v, k, 1) strict-optimal optical orthogonal code (SO-OOC) and (v, k, 1) quasi-optimal optical orthogonal code (QO-OOC) are introduced. One simple and practical construction for (v, k, 1) SO-OOC and QO-OOC with assistance design by computer is proposed, here, code weight k=4, 5 and 6. Some (v, k, 1) SO-OOCs and QO-OOCs are constructed. Based on all these codes we obtained and the recursive method, more (v, k, l) SO-OOCs and QO-OOCs are obtained. Finally, simulation results of correlation properties are given.


title: Error Feedback Fixes SignSGD and other Gradient Compression Schemes

abstract: Sign-based algorithms (e.g. signSGD) have been proposed as a biased gradient compression technique to alleviate the communication bottleneck in training large neural networks across multiple workers. We show simple convex counter-examples where signSGD does not converge to the optimum. Further, even when it does converge, signSGD may generalize poorly when compared with SGD. These issues arise because of the biased nature of the sign compression operator. We then show that using error-feedback, i.e. incorporating the error made by the compression operator into the next step, overcomes these issues. We prove that our algorithm EF-SGD with arbitrary compression operator achieves the same rate of convergence as SGD without any additional assumptions. Thus EF-SGD achieves gradient compression for free. Our experiments thoroughly substantiate the theory and show that error-feedback improves both convergence and generalization. Code can be found at \url{https://github.com/epfml/error-feedback-SGD}.


title: Stiffness: A New Perspective on Generalization in Neural Networks

abstract: We investigate neural network training and generalization using the concept of stiffness. We measure how stiff a network is by looking at how a small gradient step on one example affects the loss on another example. In particular, we study how stiffness varies with 1) class membership, 2) distance between data points (in the input space as well as in latent spaces), 3) training iteration, and 4) learning rate. We empirically study the evolution of stiffness on MNIST, FASHION MNIST, CIFAR-10 and CIFAR-100 using fully-connected and convolutional neural networks. Our results demonstrate that stiffness is a useful concept for diagnosing and characterizing generalization. We observe that small learning rates lead to initial learning of more specific features that do not translate well to improvements on inputs from all classes, whereas high learning rates initially benefit all classes at once. We measure stiffness as a function of distance between data points and observe that higher learning rates induce positive correlation between changes in loss further apart, pointing towards a regularization effect of learning rate. When training on CIFAR-100, the stiffness matrix exhibits a coarse-grained behavior suggestive of the model's awareness of super-class membership.


title: Simulated Gear-Tooth Contacts: Some Experiments upon Their Lubrication and Subsurface Deformations

abstract: This paper describes some experiments with disk machines in which the electrical resistance between the disks was measured at various stages of running. It also describes some experiments with mild...


title: An Efficient Homomorphic Encryption Protocol for Multi-User Systems

abstract: The homomorphic encryption problem has been an open one for three decades. Recently, Gentry has proposed a full solution. Subsequent works have made improvements on it. However, the time complexities of these algorithms are still too high for practical use. For example, Gentry’s homomorphic encryption scheme takes more than 900 seconds to add two 32 bit numbers, and more than 67000 seconds to multiply them. In this paper, we develop a non-circuit based symmetric-key homomorphic encryption scheme. It is proven that the security of our encryption scheme is equivalent to the large integer factorization problem, and it can withstand an attack with up to lnpoly chosen plaintexts for any predetermined , where is the security parameter. Multiplication, encryption, and decryption are almost linear in , and addition is linear in . Performance analyses show that our algorithm runs multiplication in 108 milliseconds and addition in a tenth of a millisecond for = 1024 and = 16. We further consider practical multiple-user data-centric applications. Existing homomorphic encryption schemes only consider one master key. To allow multiple users to retrieve data from a server, all users need to have the same key. In this paper, we propose to transform the master encryption key into different user keys and develop a protocol to support correct and secure communication between the users and the server using different user keys. In order to prevent collusion between some user and the server to derive the master key, one or more key agents can be added to mediate the interaction.


title: X-ray image classification using domain transferred convolutional neural networks and local sparse spatial pyramid

abstract: The classification of medical images is a critical step for imaging-based clinical decision support systems. Existing classification methods for X-ray images, however, generally represent the image using only local texture or generic image features (e.g. color or shape) derived from predefined feature spaces. This limits the ability to quantify the image characteristics using general data-derived features learned from image datasets. In this study we present a new algorithm to improve the performance of X-ray image classification, where we propose a late-fusion of domain transferred convolutional neural networks (DT-CNNs) with sparse spatial pyramid (SSP) features derived from a local image dictionary. Our method is robust as it exploits the rich generic information provided by the DT-CNNs and uses the specific local features and characteristics inherent in the X-ray images. Our method was evaluated on a public dataset of X-ray images and was compared to several state-of-the-art approaches. Experimental results show that our method was the most accurate for classification.


title: Multiple microRNAs Regulate the Floral Development and Sex Differentiation in the Dioecious Cucurbit Coccinia grandis (L.) Voigt

abstract: Despite having a small genome and heteromorphic sex chromosomes, the molecular basis of sex differentiation in the dioecious cucurbit, Coccinia grandis, largely remains unclear. Increasing evidences indicate a vital regulatory role of microRNAs in the reproductive development of plants. In this study, we used a combination of high-throughput small RNA sequencing and computational analysis to identify sex-specific miRNAomes from male and female buds of C. grandis. Ninety-eight conserved miRNAs from 22 families and 44 novel miRNAs specific to C. grandis were detected. Comparative profiling together with Northern blot and qRT-PCR analysis revealed 41 significantly differentially expressed (DE) miRNAs, of which 16 could be fundamental to the regulation of sexual dimorphism. One hundred six target genes were predicted for 35 DE miRNAs that were significantly involved in flower organogenesis, phytohormone signaling, metabolism, transcription regulation, and DNA methylation. Temporal analysis of a set of 16 target genes at three stages of the bud development revealed a reciprocal alteration in their expression pattern with the complementary miRNAs. Further, the miR167a-1, miR393a, miR398b, and miRn9 mediated down-regulation of four predicted targets associated with reproductive organ development in C. grandis was confirmed through transient co-expression in Nicotiana benthaminana. Taken together, the present study represents the first report suggesting that multiple miRNA-mediated gene silencing cascade could be involved in regulating the molecular basis of sex differentiation in C. grandis.


title: Infant's cry sound classification using Mel-Frequency Cepstrum Coefficients feature extraction and Backpropagation Neural Network

abstract: Crying is a communication method used by infants given the limitations of language. Parents or nannies who have never had the experience to take care of the baby will experience anxiety when the infant is crying. Therefore, we need a way to understand about infant's cry and apply the formula. This research develops a system to classify the infant's cry sound using MACF (Mel-Frequency Cepstrum Coefficients) feature extraction and BNN (Backpropagation Neural Network) based on voice type. It is classified into 3 classes: hungry, discomfort, and tired. A voice input must be ascertained as infant's cry sound which using 3 features extraction (pitch with 2 approaches: Modified Autocorrelation Function and Cepstrum Pitch Determination, Energy, and Harmonic Ratio). The features coefficients of MFCC are furthermore classified by Backpropagation Neural Network. The experiment shows that the system can classify the infant's cry sound quite well, with 30 coefficients and 10 neurons in the hidden layer.


title: Service Science

abstract: This paper is a first exploration of the relationship between service science and Grid computing. Service science is the study of value co-creation interactions among entities, known as service systems. Within the emerging service science community, service is often defined as the application of competences (resources) for the benefit of another. Grid computing is the study of resource sharing among entities, known as virtual organizations, which solve complex business, societal, scientific, and engineering problems. Within the Grid computing community, service is sometimes defined as protocols plus behavior. Both Grid computing and service science are connecting academic, industry, government, and volunteer sector collaborators on a range of projects including eScience, healthcare, environmental sustainability, and more. This paper compares and contrasts the notions of resource, entity, service, interaction, and success criteria for the two areas of study. In conclusion, new areas for collaborative inquiry are proposed.