title: An artificial neural network approach to automatic speech processing

abstract: Abstract An artificial neural network (ANN) is a powerful mathematical framework used to either model complex relationships between inputs and outputs or find patterns in data. It is based on an interconnected group of artificial neurons, and it employs a connectionist approach to computation when processing information. ANNs have been successfully used for a great variety of applications, such as decision making, quantum chemistry, radar systems, face identification, gesture recognition, handwritten text recognition, medical diagnosis, financial applications, robotics, data mining, and e-spam filtering. In the speech community, neural architectures have been used since the beginning of the 1980s, and ANNs have been proven useful to accomplish several speech processing tasks, e.g., to extract linguistically motivated features, to perform speech detection, and to generate local scores to be used for different goals. In recent years, there has been a renewed interest in the use of ANNs for speech applications due to a major advance made in pre-training the weights in deep neural networks (DNNs). It seems that a new trend to move the speech technology forward through the use of NNs has begun, and it can therefore be instructive to review key ANN applications to automatic speech processing. In this paper, several ANN-based applications for speech processing will be presented, ranging from speech attribute extraction to phoneme estimation and/or classification. Furthermore, it will be shown that ANNs play a key role in several important speech applications, such as large vocabulary continuous speech recognition (LVCSR) and automatic language recognition. The goal of the paper is to summarize chief ANN approaches to speech processing using the experience gathered in the last seven years in our laboratories.


title: Smart City: An Event Driven Architecture for Monitoring Public Spaces with Heterogeneous Sensors

abstract: In this paper, we present the Smart City Architecture developed in the context of the ARTEMIS JU SP3 SOFIA project. It is an Event Driven Architecture that allows the management and cooperation of heterogeneous sensors for monitoring public spaces. The main components of the architecture are implemented in a testbed on a subway scenario with the objective to demonstrate that our proposed solution, can enhance the detection of anomalous events and simplify both the operators tasks and the communications to passengers in case of emergency.


title: Fog Computing Micro Datacenter Based Dynamic Resource Estimation and Pricing Model for IoT

abstract: Pervasive and ubiquitous computing services have recently been under focus of not only the research community, but developers as well. Prevailing wireless sensor networks (WSNs), Internet of Things (IoT), and healthcare related services have made it difficult to handle all the data in an efficient and effective way and create more useful services. Different devices generate different types of data with different frequencies. Therefore, amalgamation of cloud computing with IoTs, termed as Cloud of Things (CoT) has recently been under discussion in research arena. CoT provides ease of management for the growing media content and other data. Besides this, features like: ubiquitous access, service creation, service discovery, and resource provisioning play a significant role, which comes with CoT. Emergency, healthcare, and latency sensitive services require real-time response. Also, it is necessary to decide what type of data is to be uploaded in the cloud, without burdening the core network and the cloud. For this purpose, Fog computing plays an important role. Fog resides between underlying IoTs and the cloud. Its purpose is to manage resources, perform data filtration, preprocessing, and security measures. For this purpose, Fog requires an effective and efficient resource management framework for IoTs, which we provide in this paper. Our model covers the issues of resource prediction, customer type based resource estimation and reservation, advance reservation, and pricing for new and existing IoT customers, on the basis of their characteristics. The implementation was done using Java, while the model was evaluated using CloudSim toolkit. The results and discussion show the validity and performance of our system.


title: Duchenne smile, emotional experience, and autonomic reactivity: a test of the facial feedback hypothesis.

abstract: This study examined the modulatory function of Duchenne and non-Duchenne smiles on subjective and autonomic components of emotion. Participants were asked to hold a pencil in their mouth to either facilitate or inhibit smiles and were not instructed to contract specific muscles. Five conditions-namely lips pressing, low-level non-Duchenne smiling, high-level non-Duchenne smiling, Duchenne smiling, and control-were produced while participants watched videoclips that were evocative of positive or negative affect. Participants who displayed Duchenne smiles reported more positive experience when pleasant scenes and humorous cartoons were presented. Furthermore, they tended to exhibit different patterns of autonomic arousal when viewing positive scenes. These results support the facial feedback hypothesis and suggest that facial feedback has more powerful effects when facial configurations represent valid analogs of basic emotional expressions.


title: A Span-Extraction Dataset for Chinese Machine Reading Comprehension

abstract: Machine Reading Comprehension (MRC) has become enormously popular recently and has attracted a lot of attention. However, the existing reading comprehension datasets are mostly in English. In this paper, we introduce a Span-Extraction dataset for Chinese Machine Reading Comprehension to add language diversities in this area. The dataset is composed by near 20,000 real questions annotated by human on Wikipedia paragraphs. We also annotated a challenge set which contains the questions that need comprehensive understanding and multi-sentence inference throughout the context. With the release of the dataset, we hosted the Second Evaluation Workshop on Chinese Machine Reading Comprehension (CMRC 2018). We hope the release of the dataset could further accelerate the machine reading comprehension research in Chinese language. The data is available through: this https URL


title: Efficient Convolutions for Real-Time Semantic Segmentation of 3D Point Clouds

abstract: In this work, we propose a novel voxel representation which allows for efficient, real-time processing of point clouds with deep neural networks. Our approach takes a 2D representation of a simple occupancy grid and produces fine-grained 3D segmentation. We show that our approach outperforms the state-of-the art while being an order of magnitude faster. We can perform segmentation of large outdoor scenes of size 160m x 80m in as little as 30ms. In indoor scenarios, we can segment full rooms in less than 15ms. This is crucial for robotics applications which require real-time inference for safety critical tasks.


title: Deep Discriminative Clustering Network

abstract: Deep clustering aims to cluster unlabeled data by embedding them into a subspace based on deep model. The key challenge of deep clustering is to learn discriminative representations for input data with high dimensions. In this paper, we present a deep discriminative clustering network for clustering the real-world images. We use a convolutional auto-encoder stacked with a softmax layer to predict clustering assignments. To learn a discriminative representations, the proposed approach adds discriminative loss as embedded regularization with relative entropy minimization. With the discriminative loss, the network can not only produce clustering assignments, but also learn discriminative features by reducing intra-cluster distance and increasing inter-cluster distance. We evaluate the proposed method on three datasets: MNIST-full, YTF and FRGC-v2.0. We outperform state-of-the-art results on MNIST-full and FRGC-v2.0 and achieve competitive result on YTF. The source code has been made publicly available at https://github.com/shaoxuying/DeepDiscriminativeClusteringNetwork.


title: An Interleaved Half-Bridge Three-Port Converter With Enhanced Power Transfer Capability Using Three-Leg Rectifier for Renewable Energy Applications

abstract: In this paper, an interleaved half-bridge (IHB) three-port converter (TPC) is proposed for a renewable power system. The IHB-TPC is used to interface three power ports: 1) one source port; 2) one battery port; and 3) one isolated load port. The proposed IHB-TPC is derived by integrating two half-bridge TPC modules. A parallel configuration is adopted for the primary side of the two half-bridge modules, while a parallel–series configuration is adopted for the secondary side of the two modules. The power can be transferred from the source and the battery to the load within the whole switching cycle with the proposed IHB-TPC. It means there are no additional conduction losses caused by the circulating current or the free-wheeling operation stage. Hence, the voltage gain can be extended, and the output filter can be reduced. Zero-voltage switching is realized for all the four main switches to reduce the switching losses. Two of the three ports can be tightly regulated by adopting pulsewidth modulation plus phase-shift control, while the third port is left unregulated to maintain power balance for the system. The operation principles and the performances of the proposed converter are analyzed in detail. The experimental results are given to verify the feasibility and the effectiveness of the proposed converter.


title: Active Video/Arcade Games (Exergaming) and Energy Expenditure in College Students.

abstract: Video games have become increasingly popular among young adults. The purpose of this pilot study was to determine if interactive video/arcade games, requiring physical activity to play, increase the energy expenditure (EE) and heart rate (HR) of young adults enough to elicit a training response. Thirteen male and female participants 26.6 ± 5.7 years of age were in the study. Participants were familiarized with equipment and allowed to practice with three games: (1) moving and striking lighted pads, (2) riding a bike to increase the pace of a race car, and (3) boxing against a video simulated opponent. A portable metabolic cart and HR monitor were attached to participants to measure baseline and exercise values. Participants could play any of the three games for 30 minutes while metabolic and HR data were collected. Exercise data were compared to baseline measures, and the 3 games were compared for EE. Paired sample t-tests showed baseline and exercise values differed for HR (t(12) = −18.91, p < 0.01), and EE (t(12) = −15.62, p < 0.01). The boxing game provided the highest VO2 (17.47 ± 4.79 ml·kg·−1min−1). Participants achieved 60% or better of their HR reserve (162.82 ± 10.78 beats·min−1), well within the ACSM guidelines for a training HR. Caloric expenditure during the 30-minute exercise session (226. 07 ± 48.68) is also within the ACSM recommendations for daily physical activity. Thus, interactive video/arcade games that require physical activity to play can be utilized as part of an overall aerobic exercise program.


title: Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes

abstract: Solving statistical learning problems often involves nonconvex optimization. Despite the empirical success of nonconvex statistical optimization methods, their global dynamics, especially convergence to the desirable local minima, remain less well understood in theory. In this paper, we propose a new analytic paradigm based on diffusion processes to characterize the global dynamics of nonconvex statistical optimization. As a concrete example, we study stochastic gradient descent (SGD) for the tensor decomposition formulation of independent component analysis. In particular, we cast different phases of SGD into diffusion processes, i.e., solutions to stochastic differential equations. Initialized from an unstable equilibrium, the global dynamics of SGD transit over three consecutive phases: (i) an unstable Ornstein-Uhlenbeck process slowly departing from the initialization, (ii) the solution to an ordinary differential equation, which quickly evolves towards the desirable local minimum, and (iii) a stable Ornstein-Uhlenbeck process oscillating around the desirable local minimum. Our proof techniques are based upon Stroock and Varadhan’s weak convergence of Markov chains to diffusion processes, which are of independent interest.


title: Design of an Autonomous Racecar: Perception, State Estimation and System Integration

abstract: This paper introduces fl\"uela driverless: the first autonomous racecar to win a Formula Student Driverless competition. In this competition, among other challenges, an autonomous racecar is tasked to complete 10 laps of a previously unknown racetrack as fast as possible and using only onboard sensing and computing. The key components of fl\"uela's design are its modular redundant sub-systems that allow robust performance despite challenging perceptual conditions or partial system failures. The paper presents the integration of key components of our autonomous racecar, i.e., system design, EKF-based state estimation, LiDAR-based perception, and particle filter-based SLAM. We perform an extensive experimental evaluation on real-world data, demonstrating the system's effectiveness by outperforming the next-best ranking team by almost half the time required to finish a lap. The autonomous racecar reaches lateral and longitudinal accelerations comparable to those achieved by experienced human drivers.


title: Research on stock trading strategy based on deep neural network

abstract: Deep neural network is widely concerned with the concept of deep learning. However, there are few researches focus on the application of deep neural network for stock trading strategy. Excellent trading strategies can not only help investors get high profit, but also effectively reduce the income risk. This paper studies 7 trading strategies based on a deep neural network, and uses the 2009–2015 years historical data of Shanghai Composite Index for experiments through sliding window approach, and adopts the accuracy, rate of excess return, volatility of yield and information ratio to measure the advantages and disadvantages of different trading strategies. According to the experimental results, a trading strategy suitable for the deep neural network is found. This trading strategy can not only achieve a high predictive accuracy but also have a low volatility, which can help investors reduce the risk of loss effectively while obtaining satisfactory returns.


Managing Complications of Submental Artery Involvement after Hyaluronic Acid Filler Injection in Chin Region


title: Measuring KMS success : A respecification of the DeLone and McLean ’ s model

abstract: We proposed and empirically assessed a KMS success model. This was derived through an analysis of current practice of knowledge management and review of IS success literature. Five variables (system quality, knowledge or information quality, perceived KMS benefits, user satisfaction, and system use) were used as dependent variables in evaluating KMS success, and their interrelationships were suggested and empirically tested. The results provide an expanded understanding of the factors that measure KMS success and implications of this work are discussed.


title: Identifying and classifying ambiguity for regulatory requirements

abstract: Software engineers build software systems in increasingly regulated environments, and must therefore ensure that software requirements accurately represent obligations described in laws and regulations. Prior research has shown that graduate-level software engineering students are not able to reliably determine whether software requirements meet or exceed their legal obligations and that professional software engineers are unable to accurately classify cross-references in legal texts. However, no research has determined whether software engineers are able to identify and classify important ambiguities in laws and regulations. Ambiguities in legal texts can make the difference between requirements compliance and non-compliance. Herein, we develop a ambiguity taxonomy based on software engineering, legal, and linguistic understandings of ambiguity. We examine how 17 technologists and policy analysts in a graduate-level course use this taxonomy to identify ambiguity in a legal text. We also examine the types of ambiguities they found and whether they believe those ambiguities should prevent software engineers from implementing software that complies with the legal text. Our research suggests that ambiguity is prevalent in legal texts. In 50 minutes of examination, participants in our case study identified on average 33.47 ambiguities in 104 lines of legal text using our ambiguity taxonomy as a guideline. Our analysis suggests (a) that participants used the taxonomy as intended: as a guide and (b) that the taxonomy provides adequate coverage (97.5%) of the ambiguities found in the legal text.


title: A Modern Take on the Bias-Variance Tradeoff in Neural Networks

abstract: We revisit the bias-variance tradeoff for neural networks in light of modern empirical findings. The traditional bias-variance tradeoff in machine learning suggests that as model complexity grows, variance increases. Classical bounds in statistical learning theory point to the number of parameters in a model as a measure of model complexity, which means the tradeoff would indicate that variance increases with the size of neural networks. However, we empirically find that variance due to training set sampling is roughly \textit{constant} (with both width and depth) in practice. Variance caused by the non-convexity of the loss landscape is different. We find that it decreases with width and increases with depth, in our setting. We provide theoretical analysis, in a simplified setting inspired by linear models, that is consistent with our empirical findings for width. We view bias-variance as a useful lens to study generalization through and encourage further theoretical explanation from this perspective.


title: Developments in Radar Imaging

abstract: Using range and Doppler information to produce radar images is a technique used in such diverse fields as air-to-ground imaging of objects, terrain, and oceans and ground-to-air imaging of aircraft, space objects, and planets. A review of the range-Doppler technique is presented along with a description of radar imaging forms including details of data acquisition and processing techniques.


Human-Computer Interaction And Security


title: Is it Time to Swish? Comparing Deep Learning Activation Functions Across NLP tasks

abstract: Activation functions play a crucial role in neural networks because they are the nonlinearities which have been attributed to the success story of deep learning. One of the currently most popular activation functions is ReLU, but several competitors have recently been proposed or 'discovered', including LReLU functions and swish. While most works compare newly proposed activation functions on few tasks (usually from image classification) and against few competitors (usually ReLU), we perform the first large-scale comparison of 21 activation functions across eight different NLP tasks. We find that a largely unknown activation function performs most stably across all tasks, the so-called penalized tanh function. We also show that it can successfully replace the sigmoid and tanh gates in LSTM cells, leading to a 2 percentage point (pp) improvement over the standard choices on a challenging NLP task.


Capturing Regional Variation with Distributed Place Representations and Geographic Retrofitting


Contextual Inter-modal Attention for Multi-modal Sentiment Analysis


title: Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data

abstract: We present structured domain randomization (SDR), a variant of domain randomization (DR) that takes into account the structure of the scene in order to add context to the generated data. In contrast to DR, which places objects and distractors randomly according to a uniform probability distribution, SDR places objects and distractors randomly according to probability distributions that arise from the specific problem at hand. In this manner, SDR-generated imagery enables the neural network to take the context around an object into consideration during detection. We demonstrate the power of SDR for the problem of 2D bounding box car detection, achieving competitive results on real data after training only on synthetic data. On the KITTI easy, moderate, and hard tasks, we show that SDR outperforms other approaches to generating synthetic data (VKITTI, Sim 200k, or DR), as well as real data collected in a different domain (BDD100K). Moreover, synthetic SDR data combined with real KITTI data outperforms real KITTI data alone.11Video is at http://youtu.be/1WdjWJYx9AY


title: Event Digest: A Holistic View on Past Events

abstract: For a general user, easy access to vast amounts of online information available on past events has made retrospection much harder. We propose a problem of automatic event digest generation to aid effective and efficient retrospection. For this, in addition to text, a digest should maximize the reportage of time, geolocations, and entities to present a holistic view on the past event of interest. We propose a novel divergence-based framework that selects excerpts from an initial set of pseudo-relevant documents, such that the overall relevance is maximized, while avoiding redundancy in text, time, geolocations, and named entities, by treating them as independent dimensions of an event. Our method formulates the problem as an Integer Linear Program (ILP) for global inference to diversify across the event dimensions. Relevance and redundancy measures are defined based on JS-divergence between independent query and excerpt models estimated for each event dimension. Elaborate experiments on three real-world datasets are conducted to compare our methods against the state-of-the-art from the literature. Using Wikipedia articles as gold standard summaries in our evaluation, we find that the most holistic digest of an event is generated with our method that integrates all event dimensions. We compare all methods using standard Rouge-1, -2, and -SU4 along with Rouge-NP, and a novel weighted variant of Rouge.


title: Focus+context display and navigation techniques for enhancing radial, space-filling hierarchy visualizations

abstract: Radial, space-filling visualizations can be useful for depicting information hierarchies, but they suffer from one major problem. As the hierarchy grows in size, many items become small, peripheral slices that are difficult to distinguish. We have developed three visualization/interaction techniques that provide flexible browsing of the display. The techniques allow viewers to examine the small items in detail while providing context within the entire information hierarchy. Additionally, smooth transitions between views help users maintain orientation within the complete information space.


Security and Fraud Issues of E-banking


title: UltraStereo: Efficient Learning-Based Matching for Active Stereo Systems

abstract: Efficient estimation of depth from pairs of stereo images is one of the core problems in computer vision. We efficiently solve the specialized problem of stereo matching under active illumination using a new learning-based algorithm. This type of active stereo i.e. stereo matching where scene texture is augmented by an active light projector is proving compelling for designing depth cameras, largely due to improved robustness when compared to time of flight or traditional structured light techniques. Our algorithm uses an unsupervised greedy optimization scheme that learns features that are discriminative for estimating correspondences in infrared images. The proposed method optimizes a series of sparse hyperplanes that are used at test time to remap all the image patches into a compact binary representation in O(1). The proposed algorithm is cast in a PatchMatch Stereo-like framework, producing depth maps at 500Hz. In contrast to standard structured light methods, our approach generalizes to different scenes, does not require tedious per camera calibration procedures and is not adversely affected by interference from overlapping sensors. Extensive evaluations show we surpass the quality and overcome the limitations of current depth sensing technologies.


title: Experiments on Parallel Training of Deep Neural Network using Model Averaging

abstract: In this work we apply model averaging to parallel training of deep neural network (DNN). Parallelization is done in a model averaging manner. Data is partitioned and distributed to different nodes for local model updates, and model averaging across nodes is done every few minibatches. We use multiple GPUs for data parallelization, and Message Passing Interface (MPI) for communication between nodes, which allows us to perform model averaging frequently without losing much time on communication. We investigate the effectiveness of Natural Gradient Stochastic Gradient Descent (NG-SGD) and Restricted Boltzmann Machine (RBM) pretraining for parallel training in model-averaging framework, and explore the best setups in term of different learning rate schedules, averaging frequencies and minibatch sizes. It is shown that NG-SGD and RBM pretraining benefits parameter-averaging based model training. On the 300h Switchboard dataset, a 9.3 times speedup is achieved using 16 GPUs and 17 times speedup using 32 GPUs with limited decoding accuracy loss.


title: Modelling Chemical Reasoning to Predict Reactions

abstract: The ability to reason beyond established knowledge allows organic chemists to solve synthetic problems and invent novel transformations. Herein, we propose a model that mimics chemical reasoning, and formalises reaction prediction as finding missing links in a knowledge graph. We have constructed a knowledge graph containing 14.4 million molecules and 8.2 million binary reactions, which represents the bulk of all chemical reactions ever published in the scientific literature. Our model outperforms a rule-based expert system in the reaction prediction task for 180 000 randomly selected binary reactions. The data-driven model generalises even beyond known reaction types, and is thus capable of effectively (re-)discovering novel transformations (even including transition metal-catalysed reactions). Our model enables computers to infer hypotheses about reactivity and reactions by only considering the intrinsic local structure of the graph and because each single reaction prediction is typically achieved in a sub-second time frame, the model can be used as a high-throughput generator of reaction hypotheses for reaction discovery.


title: Cognitive Radio for Smart Grids: Survey of Architectures, Spectrum Sensing Mechanisms, and Networking Protocols

abstract: Traditional power grids are currently being transformed into smart grids (SGs). SGs feature multi-way communication among energy generation, transmission, distribution, and usage facilities. The reliable, efficient, and intelligent management of complex power systems requires integration of high-speed, reliable, and secure data information and communication technology into the SGs to monitor and regulate power generation and usage. Despite several challenges, such as trade-offs between wireless coverage and capacity as well as limited spectral resources in SGs, wireless communication is a promising SG communications technology. Cognitive radio networks (CRNs) in particular are highly promising for providing timely SG wireless communications by utilizing all available spectrum resources. We provide in this paper a comprehensive survey on the CRN communication paradigm in SGs, including the system architecture, communication network compositions, applications, and CR-based communication technologies. We highlight potential applications of CR-based SG systems. We survey CR-based spectrum sensing approaches with their major classifications. We also provide a survey on CR-based routing and MAC protocols, and describe interference mitigation schemes. We furthermore present open issues and research challenges faced by CR-based SG networks along with future directions.


title: Describing Locations using Tags and Images: Explorative Pattern Mining in Social Media

abstract: This paper presents an approach for explorative pattern mining in social media based on tagging information and collaborative geo-reference annotations. We utilize pattern mining techniques for obtaining sets of tags that are specific for the specified point, landmark, or region of interest. Next, we show how these candidate patterns can be presented and visualized for interactive exploration using a combination of general pattern mining visualizations and views specialized on geo-referenced tagging data. We present a case study using publicly available data from the Flickr photo sharing platform.


title: The Biology Of Physics: What The Brain Reveals About Our Understanding Of The Physical World

abstract: Fundamental concepts in physics such as Newtonian mechanics are surprisingly difficult to learn and discover. Over the past decade have been using an educational neuroscience approach to science education using a combination of ecologically naturalistic situations, classroom settings, and neuroimaging methodologies to investigate the different ways that scientific concepts are invoked or activated in different contexts. In particular, we have sought to determine how networks of brain regions that are highly sensitive to features of the context in which they are used are involved in the use of scientific concepts. We have found that some concepts in physics that are highly tuned to perception are often inhibited in experts (with increased activations in error detection and inhibitory networks of the prefrontal cortex) rather than having undergone a wholesale conceptual reorganization. Other, concepts, such as those involved in perceptual causality can activate highly diverse brain regions, depending on tas...


title: Brain tissue volume changes following weight gain in adults with anorexia nervosa

abstract: Objective: To measure brain volume deficits among underweight patients with anorexia nervosa (AN) compared to control participants and evaluate the reversibility of these deficits with shortterm weight restoration. Method: Brain volume changes in gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) were examined in 32 adult women with AN and compared to 21, age and body mass index-range matched control women. Results: Patients with AN had a significant increase in GM (p 5 .006, g 2 5 0.14) and WM volume (p 5 .001, g 2 5 0.19) fol


title: Research Commentary - Long Tails vs. Superstars: The Effect of Information Technology on Product Variety and Sales Concentration Patterns

abstract: The Internet and related information technologies are transforming the distribution of product sales across products, and the effects are likely to grow in coming years. Both the Long Tail and the Superstar effect are manifestations of these changes, yet researchers lack consistent metrics or models for integrating and extending their insights and predictions. In this paper, we begin with a taxonomy of the technological and non-technological drivers of both the Long Tails and Superstars and then define and contrast the key metrics for analyzing these phenomena. While significant research has already been done, the core the paper describes a large and promising set of questions forming a research agenda. Important opportunities exist for understanding future changes in product distribution; its impact on supply chains (including crosschannel competition, competition within the Internet channel, implications for the growth of firms, and the balance of power within the supply chain); implications for pricing, promotion and product design; and ultimately potential effects on society more generally. Our approach provides an introduction to some of the relevant research findings and allows us to identify opportunities for cross-pollination of methods and insights from related research topics.


title: Empirically Studying Participatory Sense-Making in Abstract Drawing with a Co-Creative Cognitive Agent

abstract: This paper reports on the design and evaluation of a co-creative drawing partner called the Drawing Apprentice, which was designed to improvise and collaborate on abstract sketches with users in real time. The system qualifies as a new genre of creative technologies termed "casual creators" that are meant to creatively engage users and provide enjoyable creative experiences rather than necessarily helping users make a higher quality creative product. We introduce the conceptual framework of participatory sense-making and describe how it can help model and understand open-ended collaboration. We report the results of a user study comparing human-human collaboration to human-computer collaboration using the Drawing Apprentice system. Based on insights from the user study, we present a set of design recommendations for co-creative agents.


title: A New Coupling Mechanism for Circularly Polarized Annular-Ring Patch Antenna

abstract: A novel coupling technique for circularly polarized annular-ring patch antenna is developed and discussed. The circular polarization (CP) radiation of the annular-ring patch antenna is achieved by a simple microstrip feed line through the coupling of a fan-shaped patch on the same plane of the antenna. Proper positioning of the coupling fan-shaped patch excites two orthogonal resonant modes with 90 phase difference, and a pure circular polarization is obtained. The dielectric material is a cylindrical block of ceramic with a permittivity of 25 and that reduces the size of the antenna. The prototype has been designed and fabricated and found to have an impedance bandwidth of 2.3% and a 3 dB axial-ratio bandwidth of about 0.6% at the center frequency of 2700 MHz. The characteristics of the proposed antenna have been by simulation software HFSS and experiment. The measured and simulated results are in good agreement.


title: An Automatic Method for Building a Data-to-Text Generator

abstract: We describe our contribution to the Generating from Knowledge Bases (KBgen) challenge. Our system is learned in a bottom-up fashion, by inducing a probabilistic grammar that represents alignments between strings and parts of a knowledge graph. From these alignments, we extract information about the linearization and lexical choices associated with the target knowledge base, and build a simple generate-and-rank system in the style of (Langkilde and Knight, 1998).1


title: Applying the alignment model of sustaining student motivation and active learning in a multi-cultural context.

abstract: Central to the theory and practice of teaching and learning is the search for alignment between course aims and objectives, content, structure, delivery, assessment methods and outcomes sought. The research reported in this paper was based on two broad assumptions. The first is that without alignment it is unlikely that directed teaching and learning will occur. The second is that more attention must be paid to student motivation. This paper reports an attempt to improve correlation between one particular third year business unit and the motivation of the students concerned. This largely qualitative research found that while there was an element of connection, alignment and student motivation did not necessarily always go together.


Computational Assessment of Control Strategy for PMSG Wind Turbines aiming at Voltage Regulation on Connection Point


title: Interior Penalty Discontinuous Galerkin Time-Domain Method Based on Wave Equation for 3-D Electromagnetic Modeling

abstract: Built on the vector wave equation, an interior penalty discontinuous Galerkin time domain (IPDG-WE) method is presented for 3-D real-life electromagnetic modeling in this paper. We develop the interior penalty fluxes corresponding to different types of boundary conditions which are commonly used in electromagnetic modeling to construct the IPDG-WE system in local elements. In addition, to model the incident wave excitation, the total-field/scattered-field approach is incorporated into the proposed method. The stability of the IPDG-WE method is studied analytically and demonstrated numerically. We also compare the performance between wave equation-based finite-element time-domain method, the Maxwell’s equation-based discontinuous Galerkin time-domain method, and the proposed method, both in CPU time and memory usage, which demonstrates that the IPDG-WE method is very competitive among time-domain solvers. Besides, the presented method shows good characteristics including the energy conservation and the optimal convergence rate of $O( {h^{p+1}})$ . Numerical examples are presented to illustrate the validity, versatility, and potential of the proposed method in 3-D electromagnetic problems modeling.


title: Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia

abstract: We study the task of generating from Wikipedia articles question-answer pairs that cover content beyond a single sentence. We propose a neural network approach that incorporates coreference knowledge via a novel gating mechanism. Compared to models that only take into account sentence-level information (Heilman and Smith, 2010; Du et al., 2017; Zhou et al., 2017), we find that the linguistic knowledge introduced by the coreference representation aids question generation significantly, producing models that outperform the current state-of-the-art. We apply our system (composed of an answer span extraction system and the passage-level QG system) to the 10,000 top-ranking Wikipedia articles and create a corpus of over one million question-answer pairs. We also provide a qualitative analysis for this large-scale generated corpus from Wikipedia.


title: Selective harmonic elimination modulation technique applied on four-leg NPC

abstract: In this paper the selective harmonic elimination pulse width modulation (SHE-PWM) technique is proposed to control the three-level four-leg neutral point clamped (NPC) inverter in order to have both advantages of low switching frequency of SHE and neutral point of fourth leg in four wire systems. In this proposed method, the obtained switching angles of inverter phase legs are used to eliminate the non-triplen, 5 th to 23 th harmonics orders from the output voltage. As well, switching angles calculated for the fourth leg are considered to eliminate the triplen harmonics containing 3 th , 9 th , 15 th , 21 th and 27th orders from phase voltage. The efficiency of the proposed modulation technique is verified by simulations of a four leg NPC inverter as an UPS feeding different types of dynamic and unbalanced loads.


title: Teachers' Attitudes towards the Use of Technology in Education

abstract: Present study was done with the purpose of evaluating teachers' attitude in the city of Babol to implement technology in education. The study method was descriptive survey. Statistical population consists of all 3381 teachers of Babol in the academic year of 2011-12. The samples of 380 teachers were selected based on Morgan table according to stratified sampling method in which each academic term selected as a category related to population size. In this study in order to review teachers' attitude in implementing technology in education, a questionnaire was used. To analyze data, descriptive statistics methods such as frequency, percentage, mean and standard deviation were used. Also, in order to assess research questions, one sample T test was used. Teachers' attitude about advantages of implementing technology in education, implementing technology in education, the amount of technology efficacy in education, preconditions of implementing technology in education and effectiveness of technology in education was positive. The results indicated that teacher's attitude toward the use of technology in education, was positive. As the change made by entering information and communication technology in Education system's elements, traditional methods can't help learner's requirements in today's society and it's necessary that the main elements of education system particularly teachers subject to right alteration with global progresses and with positive attitude in this path, use this changes for upgrading their knowledge level and usage of them to improve education.


title: Effectiveness of physical therapy interventions for children with cerebral palsy: A systematic review

abstract: To assess the effectiveness of physical therapy (PT) interventions on functioning in children with cerebral palsy (CP). A search was made in Medline, Cinahl, PEDro and the Cochrane library for the period 1990 to February 2007. Only randomized controlled trials (RCTs) on PT interventions in children with diagnosed CP were included. Two reviewers independently assessed the methodological quality and extracted the data. The outcomes measured in the trials were classified using the International Classification of Functioning, Disability and Health (ICF). Twenty-two trials were identified. Eight intervention categories were distinguished. Four trials were of high methodological quality. Moderate evidence of effectiveness was established for two intervention categories: effectiveness of upper extremity treatments on attained goals and active supination, and of prehensile hand treatment and neurodevelopmental therapy (NDT) or NDT twice a week on developmental status, and of constraint-induced therapy on amount and quality of hand use. Moderate evidence of ineffectiveness was found of strength training on walking speed and stride length. Conflicting evidence was found for strength training on gross motor function. For the other intervention categories the evidence was limited due to low methodological quality and the statistically insignificant results of the studies. Due to limitations in methodological quality and variations in population, interventions and outcomes, mostly limited evidence on the effectiveness of most PT interventions is available through RCTs. Moderate evidence was found for some effectiveness of upper extremity training. Well-designed trials are needed especially for focused PT interventions.


1 Intergenerational Mobility Trends in Scandinavia and Continental Europe


title: Numerical investigation of liquid flow in two-, three- and four-stage centrifugal pumps

abstract: In this study, a liquid flow in two-, three- and four-stage centrifugal pumps was numerical investigated. The continuity and Navier-Stokes equations with the k-e turbulence model and standard wall functions were used by means of the ANSYS-CFX code. To enhance the design of the multistage pump, the impacts of the number of impeller blades, diffuser return vanes and the number of stages on the performances of a multistage centrifugal pump were analyzed. The results obtained demonstrate that the selected parameters affect the pump head, brake horsepower and efficiency in a strong yet different manner. To validate the model developed, the results of the numerical simulations were compared with the experimental results from the pump manufacturer.


GAME THEORY AND TRANSPORTATION SYSTEMS


title: Smart Parking Applications Using RFID Technology

abstract: There has been a considerable amount of reduction in transaction costs and decrease in stock shortage with the use of Radio Frequency Identification (RFID) technology in automation. Most of the RFID networks include a wide range of automation technologies. These technologies are RFID readers, RFID writers, RFID barcode scanners, RFID smart sensors and RFID controllers. In this study, a solution has been provided for the problems encountered in parking-lot management systems via RFID technology. RFID readers, RFID labels, computers, barriers and software are used as for the main components of the RFID technology. The software has been handled for the management, controlling, transaction reporting and operation tasks for parking lots located on various parts of the city. Check-ins and check-outs of the parking-lots will be under control with RFID readers, labels and barriers. Personnel costs will be reduced considerably using this technology. It will be possible to see unmanned, secure, automized parking-lots functioning with RFID technology in the future. Check-ins and check-outs will be handled in a fast manner without having to stop the cars so that traffic jam problem will be avoided during these processes. Drivers will not have to stop at the circulation points and parking tickets will be out of usage during check-ins and check-outs. It will be avoided ticket-jamming problems for the ticket processing machines as well. Vehicle owners will not have to make any payments at each check-out thus a faster traffic flow will be possible. Since there won't be any waiting during check-ins and check-outs the formation of emission gas as a result of such waiting will be avoided. An automized income tracking system, a car tracking system for charging and a central parking-car tracking system have been developed and utilized. Instead of cars' parking on streets, a more modern and a fast operating parking-lot system have been developed.


title: Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review

abstract: Abstract Standard univariate analysis of neuroimaging data has revealed a host of neuroanatomical and functional differences between healthy individuals and patients suffering a wide range of neurological and psychiatric disorders. Significant only at group level however these findings have had limited clinical translation, and recent attention has turned toward alternative forms of analysis, including Support-Vector-Machine (SVM). A type of machine learning, SVM allows categorisation of an individual's previously unseen data into a predefined group using a classification algorithm, developed on a training data set. In recent years, SVM has been successfully applied in the context of disease diagnosis, transition prediction and treatment prognosis, using both structural and functional neuroimaging data. Here we provide a brief overview of the method and review those studies that applied it to the investigation of Alzheimer's disease, schizophrenia, major depression, bipolar disorder, presymptomatic Huntington's disease, Parkinson's disease and autistic spectrum disorder. We conclude by discussing the main theoretical and practical challenges associated with the implementation of this method into the clinic and possible future directions.


Retrieve and Re-rank: A Simple and Effective IR Approach to Simple Question Answering over Knowledge Graphs


title: 3D depth estimation from a holoscopic 3D image

abstract: This paper presents an innovative technique for 3D depth estimation from a single holoscopic 3D image (H3D). The image is captured with a single aperture holoscopic 3D camera, which mimics a fly’s eye technique to acquire an optical 3D model of a true 3D scene. The proposed method works by extracting of optimum viewpoints images from a H3D image and it uses the shift and integration function in up-sampling the extracted viewpoints and then it performs the match block functions to match correspondence features between the stereoscopic 3D images. Finally, the 3D depth is estimated through a smoothing and optimizing process to produce a final 3D depth map. The proposed method estimates the full 3D depth information from a single H3D image, which makes it reliable and suitable for trend autonomous robotic applications.


INTRODUCTION: What Will It Take to Address the Global Threat of Antibiotic Resistance?


title: Guiding Intelligent Surveillance System by learning-by-synthesis gaze estimation

abstract: Abstract We describe a novel learning-by-synthesis method for estimating the gaze direction of an automated intelligent surveillance system. Recently, the progress of integrated learning has proposed a training model for synthetic images, which can effectively reduce the cost of human and material resources. However, due to the different distributions between the real and synthetic images, the desired performance cannot be achieved from the synthetic image learning as compared to the real images. In order to solve this problem, the previous method was to improve the authenticity of the composite image by learning the model. However, this kind of method had the disadvantage that the distortion was not improved and the level of authenticity was unstable. In order to solve this problem, we propose a new structure to improve the composite image. By referring to the idea of style transformation, we can effectively reduce the distortion of the image and minimize the need for actual data annotation. We estimate that this can produce highly realistic images, which we have demonstrated through qualitative and user research. We quantitatively evaluate the generated images by training the gaze estimation model. We use the refined synthetic dataset to show significant improvements compared with using the raw synthetic dataset.


title: Review-Aware Answer Prediction for Product-Related Questions Incorporating Aspects

abstract: In E-commerce sites, there are platforms for users to pose product-related questions and experienced customers may provide answers voluntarily. Among the questions asked by users, a large proportion of them are yes-no questions reflecting that users wish to know whether or not the product can satisfy a certain criterion or meet a certain expectation. Both Question Answering (QA) approaches and Community Question Answering methods are not suitable for answer prediction for new questions in this setting. The reasons are that questions are product-associated and many of them are concerned about user experiences and subjective opinions. In addition to existing question-answer pairs, user written reviews can provide useful clues for answer prediction. In this paper, we propose a new framework that can tackle the task of review-aware answer prediction for product-related questions. The aspect analytics model in this framework learns latent aspects as well as aspect-specific embeddings of reviews via a 3-order Autoencoder. One advantage of this learned model is that it can generate aspect-specific representations for new questions. The predictive answer model in our framework, learned jointly from existing questions, answers, and reviews, is able to predict the answers for new yes-no questions taking into consideration of aspects. Besides, our framework can provide supportive reviews grouped by relevant aspects serving as information for explainable answers. Experiment results on 15 different product categories from a large-scale benchmark E-commence QA dataset demonstrate the effectiveness of our framework.


title: Recognition of spontaneous conversational speech using long short-term memory phoneme predictions

abstract: We present a novel continuous speech recognition framework designed to unite the principles of triphone and Long ShortTerm Memory (LSTM) modeling. The LSTM principle allows a recurrent neural network to store and to retrieve information over long time periods, which was shown to be well-suited for the modeling of co-articulation effects in human speech. Our system uses a bidirectional LSTM network to generate a phoneme prediction feature that is observed by a triphone-based large-vocabulary continuous speech recognition (LVCSR) decoder, together with conventional MFCC features. We evaluate both, phoneme prediction error rates of various network architectures and the word recognition performance of our Tandem approach using the COSINE database - a large corpus of conversational and noisy speech, and show that incorporating LSTM phoneme predictions in to an LVCSR system leads to significantly higher word accuracies. Index Terms: Long Short-Term Memory, Large-Vocabulary Continuous Speech Recognition, Context Modeling, Recurrent Neural Networks


title: Surprising properties of dropout in deep networks

abstract: We analyze dropout in deep networks with rectified linear units and the quadratic loss. Our results expose surprising differences between the behavior of dropout and more traditional regularizers like weight decay. For example, on some simple data sets dropout training produces negative weights even though the output is the sum of the inputs. This provides a counterpoint to the suggestion that dropout discourages co-adaptation of weights. We also show that the dropout penalty can grow exponentially in the depth of the network while the weight-decay penalty remains essentially linear, and that dropout is insensitive to various re-scalings of the input features, outputs, and network weights. This last insensitivity implies that there are no isolated local minima of the dropout training criterion. Our work uncovers new properties of dropout, extends our understanding of why dropout succeeds, and lays the foundation for further progress.


title: Pseudomonas infections in the thermally injured patient.

abstract: Pseudomonas aeruginosa, remains a serious cause of infection and septic mortality in burn patients, particularly when nosocomially acquired. A prototypic burn patient who developed serious nosocomially acquired Pseudomonas infection is described as an index case which initiated investigations and measures taken to identify the source of the infection. The effect of changes in wound care to avoid further nosocomial infections was measured to provide data on outcome and cost of care. The bacteriology of Pseudomonas is reviewed to increase the burn care providers understanding of the behaviour of this very common and serious pathogen in the burn care setting, before reviewing the approach to detection of the organism and treatment both medically and surgically. After controlling the nosocomial spread of Pseudomonas in our burn unit, we investigated the morbidity and mortality associated with nosocomial infection with an aminoglycoside resistant Pseudomonas and the associated costs compared to a group of case-matched control patients with similar severity of burn injury, that did not acquire resistant Pseudomonas during hospitalization at our institution. We found a significant increase in the mortality rate in the Pseudomonas group compared to controls. The morbidity in terms of length of stay, ventilator days, number of surgical procedures, and the amount of blood products used were all significantly higher in the Pseudomonas group compared to controls. Costs associated with antibiotic requirements were also significantly higher in the Pseudomonas group. Despite this increased resource consumption necessary to treat Pseudomonas infections, these efforts did not prevent significantly higher mortality rates when compared to control patients who avoided infection with the resistant organism. Thus, in addition to the specific measures required to identify and treat nosocomial Pseudomonas infections in burn patients, prevention of infection through modification of treatment protocols together with continuous infection control measures to afford early identification and eradication of nosocomial Pseudomonas infection are critical for cost-effective, successful burn care.


title: Camera-based shooting simulator using color thresholding techniques

abstract: This paper presents the camera-based shooting simulator, where the shooter shots the target using a laser pointer attached on the weapon. A camera is attached on the weapon to capture the image of laser shot and circular target. The red color thresholding techniques are employed for detecting the laser spot on the target. From the experiments, it is obtained that the proposed system could detect both circular target and laser spot under different background environments. Further the proposed algorithm is able to compute the shooting score properly.


title: Coordinated actor model of self-adaptive track-based traffic control systems

abstract: Abstract Self-adaptation is a well-known technique to handle growing complexities of software systems, where a system autonomously adapts itself in response to changes in a dynamic and unpredictable environment. With the increasing need for developing self-adaptive systems, providing a model and an implementation platform to facilitate integration of adaptation mechanisms into the systems and assuring their safety and quality is crucial. In this paper, we target Track-based Traffic Control Systems (TTCSs) in which the traffic flows through pre-specified sub-tracks and is coordinated by a traffic controller. We introduce a coordinated actor model to design self-adaptive TTCSs and provide a general mapping between various TTCSs and the coordinated actor model. The coordinated actor model is extended to build large-scale self-adaptive TTCSs in a decentralized setting. We also discuss the benefits of using Ptolemy II as a framework for model-based development of large-scale self-adaptive systems that supports designing multiple hierarchical MAPE-K feedback loops interacting with each other. We propose a template based on the coordinated actor model to design a self-adaptive TTCS in Ptolemy II that can be instantiated for various TTCSs. We enhance the proposed template with a predictive adaptation feature. We illustrate applicability of the coordinated actor model and consequently the proposed template by designing two real-life case studies in the domains of air traffic control systems and railway traffic control systems in Ptolemy II.


Headspace Gas Analysis of Salad Oils by Mass Spectrometry


Graph Convolutional Matrix Completion for Bipartite Edge Prediction


Tooth Segmentation From Cone-Beam CT Using Graph Cut


title: Research Trends in Knowledge Management: Analyzing the Past and Predicting the Future

abstract: Through a meta-data analysis of the literature over a 34-year period, this article aims to provide a review and investigation into the knowledge management field in terms of how the domain is represented along a number of dimensions including unit of analysis, research paradigm employed, and the research topics/issues investigated. Information on a series of variables was extracted after conducting a review of 1,043 articles on KM, published in various peer-reviewed journals between 1974–2008. The findings suggest that a combination of positivist, empirical, conceptual/descriptive, and multi-method approaches have been predominantly used in the area. Organizational as well as systems and environmental context-based KM research were found to be the most widely published topics within the KM domain. Further, the authors identified literature gaps that require more exploration and conceptual refinement in the context of knowledge management research.


title: Double side redistribution layer process on embedded wafer level package for package on package (PoP) applications

abstract: In this paper, an embedded wafer level package with Cu through mold via (TMV) interconnects was developed for package on package (PoP) application. Cu pillar interconnects for different heights were fabricated on daisy chain test chips and sacrificial chips. The daisy chain test chips were then stacked onto the sacrificial chips using die attach film, and the chip stacks were picked and placed onto a molding tape for mold encapsulation to form a re-configured wafer. The reconfigured wafer was mechanically backgrinded on both sides to remove sacrificial chips and to expose the Cu TMV. The thinned re-configured wafer was temporarily bonded to a stiff Si carrier using a temporary adhesive, in order to reduce the wafer warpage to enable wafer level processing of the Cu redistribution layers (RDL). After front side RDL processing, the re-configured wafer is de-bonded and re-bonded for backside RDL processing. The warpage value of the reconfigured wafer was measured during different process steps and through-scan was performed using a scanning acoustic microscope to inspect the quality of temporary bonding of re-configured wafers to a Si carrier. Electrical test shows good connectivity between front and back side RDL with Cu TMV, thus enabling embedding wafer level package for PoP application.


title: From Few to many: Illumination cone models for face recognition under variable lighting and pose

abstract: We present a generative appearance-based method for recognizing human faces under variation in lighting and viewpoint. Our method exploits the fact that the set of images of an object in fixed pose, but under all possible illumination conditions, is a convex cone in the space of images. Using a small number of training images of each face taken with different lighting directions, the shape and albedo of the face can be reconstructed. In turn, this reconstruction serves as a generative model that can be used to render (or synthesize) images of the face under novel poses and illumination conditions. The pose space is then sampled and, for each pose, the corresponding illumination cone is approximated by a low-dimensional linear subspace whose basis vectors are estimated using the generative model. Our recognition algorithm assigns to a test image the identity of the closest approximated illumination cone. Test results show that the method performs almost without error, except on the most extreme lighting directions.


title: Comparison between DTC using a two-level inverters and DTC using a three level inverters of induction motor

abstract: This paper is to compare the DTC applied to an induction motor fed by two-level voltage inverters and a three level voltage inverters, and to demonstrate the great advantages of using a three-level inverters type NPC. The speed control is realised by using a PI controler. The effectiveness of this approach is shows by simulation results using Power System Blocset (PSB) of Matlab / Simulink.


title: Town Crier: An Authenticated Data Feed for Smart Contracts

abstract: Smart contracts are programs that execute autonomously on blockchains. Their key envisioned uses (e.g. financial instruments) require them to consume data from outside the blockchain (e.g. stock quotes). Trustworthy data feeds that support a broad range of data requests will thus be critical to smart contract ecosystems. We present an authenticated data feed system called Town Crier (TC). TC acts as a bridge between smart contracts and existing web sites, which are already commonly trusted for non-blockchain applications. It combines a blockchain front end with a trusted hardware back end to scrape HTTPS-enabled websites and serve source-authenticated data to relying smart contracts. TC also supports confidentiality. It enables private data requests with encrypted parameters. Additionally, in a generalization that executes smart-contract logic within TC, the system permits secure use of user credentials to scrape access-controlled online data sources. We describe TC's design principles and architecture and report on an implementation that uses Intel's recently introduced Software Guard Extensions (SGX) to furnish data to the Ethereum smart contract system. We formally model TC and define and prove its basic security properties in the Universal Composibility (UC) framework. Our results include definitions and techniques of general interest relating to resource consumption (Ethereum's "gas" fee system) and TCB minimization. We also report on experiments with three example applications. We plan to launch TC soon as an online public service.


title: A Peer-Based Financial Planning & Education Service Program: An Innovative Pedagogic Approach

abstract: This paper presents a peer-based financial planning and education program as a strategy to address the lack of financial literacy among college students and provide an experiential learning opportunity for students majoring in financial planning or other financial services-related disciplines. Benefits of such programs to campus communities are addressed by illustrating the current trends regarding student debt and financial literacy. The paper provides a specific description of a successful peer-to-peer financial planning and education program and encourages its replication at other colleges and universities. Through the review of this program, other schools may emulate effective strategies to assist in the implementation of this program as a pedagogical tool to improve students’ educational experience.


title: Learning to Generate the"Unseen"via Part Synthesis and Composition

abstract: Data-driven generative modeling has made remarkable progress by leveraging the power of deep neural networks. A reoccurring challenge is how to sample a rich variety of data from the entire target distribution, rather than only from the distribution of the training data. In other words, we would like the generative model to go beyond the observed training samples and learn to also generate"unseen"data. In our work, we present a generative neural network for shapes that is based on a part-based prior, where the key idea is for the network to synthesize shapes by varying both the shape parts and their compositions. Treating a shape not as an unstructured whole, but as a (re-)composable set of deformable parts, adds a combinatorial dimension to the generative process to enrich the diversity of the output, encouraging the generator to venture more into the"unseen". We show that our part-based model generates richer variety of feasible shapes compared with a baseline generative model. To this end, we introduce two quantitative metrics to evaluate the ingenuity of the generative model and assess how well generated data covers both the training data and unseen data from the same target distribution.


title: Design of H-plane Y junction in RSIW for C-band applications

abstract: This paper concerns the designing, considerations and analysis of H-plane Y junction in rectangular substrate integrated waveguide technology using HFSS code. The proposed design unites the advantages such as low fabrication cost, low signal losses, planer profile, easily integrated with the other microwave devices and high performance in microwave application. The transmission coefficients S 21 and S 31 shows that the input power (3W) in a waveguide is equally split into −8dB power or combined −8dB power in needed manner by using presented SIW Y junction. The divided E-fields are in same phase as the E-field at the input. The junction is optimized in C-band [4–8 GHz] at super high frequency of 7 GHz. The proposed SIW Y junction is designed on RT Duroid substrate and the measured results demonstrate that The return loss is −44.5668 dB, VSWR at the output port is 1.1 & 1.2 and reflection loss is 0.0059 dB at 7.9 GHz frequency. Thus, through the study of results, proposed SIW Y junction is very effective for signal transmission with low losses over entire C-band.


title: Key management systems for sensor networks in the context of the Internet of Things

abstract: If a wireless sensor network (WSN) is to be completely integrated into the Internet as part of the Internet of Things (IoT), it is necessary to consider various security challenges, such as the creation of a secure channel between an Internet host and a sensor node. In order to create such a channel, it is necessary to provide key management mechanisms that allow two remote devices to negotiate certain security credentials (e.g. secret keys) that will be used to protect the information flow. In this paper we will analyse not only the applicability of existing mechanisms such as public key cryptography and pre-shared keys for sensor nodes in the IoT context, but also the applicability of those link-layer oriented key management systems (KMS) whose original purpose is to provide shared keys for sensor nodes belonging to the same WSN.


title: Characterization of oakmoss products used in perfumery by high-performance liquid chromatography

abstract: Abstract An high-performance liquid chromatographic (HPLC) method was developed to identify and to quantify characteristic substances in commercially available oakmoss products. The procedure offers a rapid and reliable method for routine process and/or quality control. The identity of the registered peaks was confirmed using HPLC coupled on-line with ultraviolet—visible spectroscopy as well as by spectral analysis ( 1 H and 13 C nuclear magnetic resonance and mass spectrometry) of the isolated substances. Whereas a freshly prepared laboratory extract of Evernia prunastri contains mainly evernic acid, heating at 118°C results in decomposition products such as evernyl, orsellic acid, everninic acid and other phenol derivatives. The results indicate that the often used gas chromatographic method is not readily applicable to the study of lichen compounds, because these are not sufficiently volatile or too unstable at elevated temperatures.


title: Connectionist Music Composition Based on Melodic and Stylistic Constraints

abstract: We describe a recurrent connectionist network, called CONCERT, that uses a set of melodies written in a given style to compose new melodies in that style. CONCERT is an extension of a traditional algorithmic composition technique in which transition tables specify the probability of the next note as a function of previous context. A central ingredient of CONCERT is the use of a psychologically-grounded representation of pitch.


title: DeMIAN: Deep Modality Invariant Adversarial Network

abstract: Obtaining common representations from different modalities is important in that they are interchangeable with each other in a classification problem. For example, we can train a classifier on image features in the common representations and apply it to the testing of the text features in the representations. Existing multi-modal representation learning methods mainly aim to extract rich information from paired samples and train a classifier by the corresponding labels; however, collecting paired samples and their labels simultaneously involves high labor costs. Addressing paired modal samples without their labels and single modal data with their labels independently is much easier than addressing labeled multi-modal data. To obtain the common representations under such a situation, we propose to make the distributions over different modalities similar in the learned representations, namely modality-invariant representations. In particular, we propose a novel algorithm for modality-invariant representation learning, named Deep Modality Invariant Adversarial Network (DeMIAN), which utilizes the idea of Domain Adaptation (DA). Using the modality-invariant representations learned by DeMIAN, we achieved better classification accuracy than with the state-of-the-art methods, especially for some benchmark datasets of zero-shot learning.


Aspect Based Sentiment Analysis-A Survey


abstract: We propose an algorithm called Sparse Manifold Clustering and Embedding (SMCE) for simultaneous clustering and dimensionality reduction of data lying in multiple nonlinear manifolds. Similar to most dimensionality reduction methods, SMCE finds a small neighborhood around each data point and connects each point to its neighbors with appropriate weights. The key difference is that SMCE finds both the neighbors and the weights automatically. This is done by solving a sparse optimization problem, which encourages selecting nearby points that lie in the same manifold and approximately span a low-dimensional affine subspace. The optimal solution encodes information that can be used for clustering and dimensionality reduction using spectral clustering and embedding. Moreover, the size of the optimal neighborhood of a data point, which can be different for different points, provides an estimate of the dimension of the manifold to which the point belongs. Experiments demonstrate that our method can effectively handle multiple manifolds that are very close to each other, manifolds with non-uniform sampling and holes, as well as estimate the intrinsic dimensions of the manifolds.


title: Teaching Computer Organization and Architecture Using Simulation and FPGA Applications

abstract: This paper presents the design concepts and realization of incorporating micro-operation simulation and FPGA implementation into a teaching tool for computer organization and architecture. This teaching tool helps computer engineering and computer science students to be familiarized practically with computer organization and architecture through the development of their own instruction set, computer programming and interfacing experiments. A two-pass assembler has been designed and implemented to write assembly programs in this teaching tool. In addition to the micro- operation simulation, the complete configuration can be run on Xilinx Spartan-3 FPGA board. Such implementation offers good code density, easy customization, easily developed software, small area, and high performance at low cost.


title: A framework for interpreting social media interactions in the public sector

abstract: Abstract Social media applications are extending the information and communication technology landscape in the public sector and are used to increase government transparency, participation and collaboration in the U.S. federal government. The success, impact and performance of these new forms of bi-directional and networked interactions can provide insights to understand compliance with the mandate of the Open Government Initiative. Many government agencies are experimenting with the use of social media, however very few actively measure the impact of their digital interactions. This article builds on insights from social media directors in the U.S. federal government highlighting their current lack of measurement practices for social media interactions. Based on their articulated needs for measurement, existing rules regulating the extent of measurement practices and technological features of the main social media platforms, a framework is presented that traces online interactions to mission support and the resulting social media tactics. Implications for both researchers and practitioners are discussed.


title: F-logic: a higher-order language for reasoning about objects, inheritance, and scheme

abstract: We propose a database logic which accounts in a clean declarative fashion for most of the “object-oriented” features such as object identity, complex objects, inheritance, methods, etc. Furthermore, database schema is part of the object language, which allows the user to browse schema and data using the same declarative formalism. The proposed logic has a formal semantics and a sound and complete resolution-based proof procedure, which makes it also computationally attractive.


title: KrishiPustak: A Social Networking System for Low-Literate Farmers

abstract: With the wide penetration of mobile internet, social networking (SN) systems are becoming increasingly popular in the developing world. However, most SN sites are text heavy, and are therefore unusable by low-literate populations. Here we ask what would an SN application for low-literate users look like and how would it be used? We designed and deployed KrishiPustak, an audio-visual SN mobile application for low-literate farming populations in rural India. Over a four month deployment, 306 farmers registered through the phones of eight agricultural mediators making 514 posts and 180 replies. We conducted interviews with farmers and mediators and analyzed the content to understand system usage and to drive iterative design. The context of mediated use and agricultural framing had a powerful impact on system understanding (what it was for) and usage. Overall, KrishiPustak was useful and usable, but none-the-less we identify a number of design recommendations for similar SN systems.


title: An Architectural Approach Towards the Future Internet of Things

abstract: Many of the initial developments towards the Internet of Things have focused on the combination of Auto-ID and networked infrastructures in businessto- business logistics and product life cycle applications. However, a future Internet of Things can provide a broader vision and also enable everyone to access and contribute rich information about things and locations. The success of social networks to share experience and personalised insights shows also great potential for integration with business-centric applications. The integration and interoperability with mainstream business software platforms can be enhanced and extended by real-time analytics, business intelligence and agent-based autonomous services. Information sharing may be rewarded through incentives, thus transforming the Internet of Things from a cost-focused experiment to a revenue-generating infrastructure to enable trading of enriched information and accelerate business innovation. Mash-ups and end-user programming will enable people to contribute to the Internet of Things with data, presentation and functionality. Things-generated physical world content and events from Auto-ID, sensors, actuators or meshed networks will be aggregated and combined with information from virtual worlds, such as business databases and Web 2.0 applications, and processed based on new business intelligence concepts. Direct action on the physical world will be supported through machine-interfaces and introduction of agile strategies. This chapter aims to provide a concept for a future architecture of the Internet of Things, including a definition, a review of developments, a list of key requirements and a technical design for possible implementation of the future Internet of Things. As open issues, the evaluation of usability by stakeholders in user-centric as well as business-centric scenarios is discussed and the need for quantifying costs and benefits for businesses, consumers, society and the environment is emphasised. Finally, guidelines are derived, for use by researchers as well as practitioners.


title: Automated GUI Testing of Android Apps: From Research to Practice

abstract: The last decade has seen tremendous proliferation of mobile computing in our society. Billions of users have access to millions of mobile apps that can be installed directly on their mobile devices, electrical appliances, and watches. Factors such as new monetization/revenue models, programming models, and distribution infrastructures contribute to an "attractive" movement that captivates new and traditional developers, as well as a crowd of other professionals that explore, design, and implement mobile apps. Also, the need for "enterprise apps" that support start-ups or serve as a new front-end for traditional companies is pushing software-related professionals to embrace mobile technologies. However, the nature of the economy (devices, apps, markets) imposes new challenges on how mobile apps are envisioned, designed, implemented, tested, released, and maintained. This technology briefing aims to help address the challenges of testing and maintaining mobile apps by providing participants from both academic and industrial backgrounds with information on the state-of-art and state-of-practice mobile testing techniques. Specifically, we aim to (i) highlight new techniques and methodologies for making effective automated testing of mobile apps practical and accessible to developers, and (ii) discuss open academic research questions related to such technology transfer.


title: Parked cars as a service delivery platform

abstract: We introduce a new view of parked cars as a massive, flexible resource that is currently wasted. Given the power supply in batteries as well as computing, communication, and sensing facilities in cars in conjunction with the precise localization they can provide, parked cars have the potential to serve as a service delivery platform with a wide range of possibilities. We describe diverse applications that can be implemented using parked cars to show the flexibility of the infrastructure. Potential user groups and service providers are discussed. As an illustrative example, a simulation study of the use case of localizing persons in need of assistance is presented. Finally, the need for new algorithms and their analysis adapted to the specifics of parked cars is also highlighted.


title: Clustering-based diversity improvement in top-N recommendation

abstract: The major aim of recommender algorithms has been to predict accurately the rating value of items. However, it has been recognized that accurate prediction of rating values is not the only requirement for achieving user satisfaction. One other requirement, which has gained importance recently, is the diversity of recommendation lists. Being able to recommend a diverse set of items is important for user satisfaction since it gives the user a richer set of items to choose from and increases the chance of discovering new items. In this study, we propose a novel method which can be used to give each user an option to adjust the diversity levels of their own recommendation lists. Experiments show that the method effectively increases the diversity levels of recommendation lists with little decrease in accuracy. Compared to the existing methods, the proposed method, while achieving similar diversification performance, has a very low computational time complexity, which makes it highly scalable and allows it to be used in the online phase of the recommendation process.


title: Some attacks against vehicles' passive entry security systems and their solutions

abstract: Recently, several companies have introduced passive entry systems for automotive applications. These systems are intended to increase user comfort by eliminating the requirement that the user has to reach for the customer identification device (CID), a credit card like tool, to gain access to the vehicle compartment. While this extra level of comfort is a desirable feature, especially in luxury vehicles, it introduces several key attacks against the system. This paper describes several techniques of potential attacks against the passive entry system and proposes solutions to protect the vehicle from such attacks.


title: Evaluation of Unsupervised Information Extraction

abstract: Unsupervised methods gain more and more attention nowadays in information extraction area, which allows to design more open extraction systems. In the domain of unsupervised information extraction, clustering methods are of particular importance. However, evaluating the results of clustering remains difficult at a large scale, especially in the absence of a reliable reference. On the basis of our experiments on unsupervised relation extraction, we first discuss in this article how to evaluate clustering quality without a reference by relying on internal measures. Then we propose a method, supported by a dedicated annotation tool, for building a set of reference clusters of relations from a corpus. Moreover, we apply it to our experimental framework and illustrate in this way how to build a significant reference for unsupervised relation extraction, more precisely made of 80 clusters gathering more than 4,000 relation instances, in a short time. Finally, we present how such reference is exploited for the evaluation of clustering with external measures and analyze the results of the application of these measures to the clusters of relations produced by our unsupervised relation extraction system.


title: Task-driven deep transfer learning for image classification

abstract: Transfer learning tends to be a powerful tool that can mitigate the divergence across different domains through knowledge transfer. Recent research efforts on transfer learning have exploited deep neural network (NN) structures for discriminative feature representation to better tackle cross-domain disparity. However, few of these techniques are able to jointly learn deep features and train a classifier in a unified transfer learning framework. To this end, we design a task-driven deep transfer learning framework for image classification, where the deep feature and classifier are obtained simultaneously for optimal classification performance. Therefore, the proposed deep structure can generate more discriminative features by using the classifier performance as a guide. Furthermore, the classifier performance is increased since it is optimized on a more discriminative deep feature. The developed supervised formulation is a task-driven scheme, which will provide better learned features for the classification task. By giving pseudo labels for target data, we can facilitate the knowledge transfer from source to target through the deep structures. Experimental results witness the superiority of our proposed algorithm by comparing with other ones.


title: Development of vision-aided navigation for a wearable outdoor augmented reality system

abstract: This paper describes the development of vision-aided navigation (i.e., pose estimation) for a wearable augmented reality system operating in natural outdoor environments. This system combines a novel pose estimation capability, a helmet-mounted see-through display, and a wearable processing unit to accurately overlay geo-registered graphics on the user's view of reality. Accurate pose estimation is achieved through integration of inertial, magnetic, GPS, terrain elevation data, and computervision inputs. Specifically, a helmet-mounted forward-looking camera and custom computer vision algorithms are used to provide measurements of absolute orientation (i.e., orientation of the helmet with respect to the Earth). These orientation measurements, which leverage mountainous terrain horizon geometry and/or known landmarks, enable the system to achieve significant improvements in accuracy compared to GPS/INS solutions of similar size, weight, and power, and to operate robustly in the presence of magnetic disturbances. Recent field testing activities, across a variety of environments where these vision-based signals of opportunity are available, indicate that high accuracy (less than 10 mrad) in graphics geo-registration can be achieved. This paper presents the pose estimation process, the methods behind the generation of vision-based measurements, and representative experimental results.


title: Calibration of a dual-laser triangulation system for assembly line completeness inspection

abstract: In controlled industrial environments, laser triangulation is an effective technique for 3D reconstruction, which is increasingly being used for quality inspection and metrology. In this paper, we propose a method for calibrating a dual laser triangulation system - consisting of a camera, two line lasers, and a linear motion platform designed to perform completeness inspection tasks on an assembly line. Calibration of such a system involves the recovery of two sets of parameters - the plane parameters of the two line lasers, and the translational direction of the linear motion platform. First, we address these two calibration problems separately. While many solutions have been given for the former problem, the latter problem has been largely ignored. Next, we highlight an issue specific to the use of multiple lasers - that small errors in the calibration parameters can lead to misalignment between the reconstructed point clouds of the different lasers. We present two different procedures that can eliminate such misalignments by imposing constraints between the two sets of calibration parameters. Our calibration method requires only the use of planar checkerboard patterns, which can be produced easily and inexpensively.


title: A Survey on Software Fault Localization

abstract: Software fault localization, the act of identifying the locations of faults in a program, is widely recognized to be one of the most tedious, time consuming, and expensive – yet equally critical – activities in program debugging. Due to the increasing scale and complexity of software today, manually locating faults when failures occur is rapidly becoming infeasible, and consequently, there is a strong demand for techniques that can guide software developers to the locations of faults in a program with minimal human intervention. This demand in turn has fueled the proposal and development of a broad spectrum of fault localization techniques, each of which aims to streamline the fault localization process and make it more effective by attacking the problem in a unique way. In this article, we catalog and provide a comprehensive overview of such techniques and discuss key issues and concerns that are pertinent to software fault localization as a whole.


title: Storm and Cloud Dynamics: The Dynamics of Clouds and Precipitating Mesoscale Systems

abstract: This book focuses on the dynamics of clouds and of precipitating mesoscale meteorological systems. Clouds and precipitating mesoscale systems represent some of the most important and scientifically exciting weather systems in the world. These are the systems that produce torrential rains, severe winds including downburst and tornadoes, hail, thunder and lightning, and major snow storms. Forecasting such storms represents a major challenge since they are too small to be adequately resolved by conventional observing networks and numerical prediction models. This book: provides a complete treatment of clouds integrating the analysis of air motions with cloud structure, microphysics, and precipitation mechanics; describes and explains the basic types of clouds and cloud systems that occur in the atmosphere-fog, stratus, stratocumulus, altocumulus, altostratus, cirrus, thunderstorms, tornadoes, waterspouts, orographically induced clouds, mesoscale convection complexes, hurricanes, fronts, and extratropical cyclones; summarizes the fundamentals, both observational and theoretical, of atmospheric dynamics, thermodynamics, cloud microphysics, and radar meteorology, allowing each type of cloud to be examined in depth; integrates the latest field observations, numerical model simulations, and theory; and, supplies a theoretical treatment suitable for the advanced undergraduate or graduate level, as well as post-graduate.


title: Sentence Boundary Detection: A Long Solved Problem?

abstract: We review the state of the art in automated sentence boundary detection (SBD) for English and call for a renewed research interest in this foundational first step in natural language processing. We observe severe limitations in comparability and reproducibility of earlier work and a general lack of knowledge about genre- and domain-specific variations. To overcome these barriers, we conduct a systematic empirical survey of a large number of extant approaches, across a broad range of diverse corpora. We further observe that much previous work interpreted the SBD task too narrowly, leading to overly optimistic estimates of SBD performance on running text. To better relate SBD to practical NLP use cases, we thus propose a generalized definition of the task, eliminating text- or language-specific assumptions about candidate boundary points. More specifically, we quantify degrees of variation across ‘standard’ corpora of edited, relatively formal language, as well as performance degradation when moving to less formal language, viz. various samples of user-generated Web content. For these latter types of text, we demonstrate how moderate interpretation of document structure (as is now often available more or less explicitly through mark-up) can substantially contribute to overall SBD performance.


title: Robust Identification of Alzheimer’s Disease subtypes based on cortical atrophy patterns

abstract: Accumulating evidence suggests that Alzheimer's disease (AD) is heterogenous and can be classified into several subtypes. Here, we propose a robust subtyping method for AD based on cortical atrophy patterns and graph theory. We calculated similarities between subjects in their atrophy patterns throughout the whole brain, and clustered subjects with similar atrophy patterns using the Louvain method for modular organization extraction. We applied our method to AD patients recruited at Samsung Medical Center and externally validated our method by using the AD Neuroimaging Initiative (ADNI) dataset. Our method categorized very mild AD into three clinically distinct subtypes with high reproducibility (>90%); the parietal-predominant (P), medial temporal-predominant (MT), and diffuse (D) atrophy subtype. The P subtype showed the worst clinical presentation throughout the cognitive domains, while the MT and D subtypes exhibited relatively mild presentation. The MT subtype revealed more impaired language and executive function compared to the D subtype.


title: Effectiveness of physiotherapy for lateral epicondylitis: a systematic review

abstract: AIM ::: To evaluate the available evidence of the effectiveness of physiotherapy for lateral epicondylitis of the elbow. ::: ::: ::: METHOD ::: Randomised controlled trials (RCTs) identified by a highly sensitive search strategy in six databases in combination with reference checking. Two independent reviewers selected RCTs that included a physiotherapy intervention, patients with lateral epicondylitis, and at least one clinically relevant outcome measure. No language restrictions were made. Methodological quality was independently assessed by two blinded reviewers. A best evidence synthesis, including a quantitative and qualitative analysis, was conducted, weighting the studies with respect to their internal validity, statistical significance, clinical relevance, and statistical power. ::: ::: ::: RESULTS ::: 23 RCTs were included in the review, evaluating the effects of lasertherapy, ultrasound treatment, electrotherapy, and exercises and mobilisation techniques. Fourteen studies satisfied at least 50% of the internal validity criteria. Except for ultrasound, pooling of data from RCTs was not possible because of insufficient data, or clinical or statistical heterogeneity. The pooled estimate of the treatment effects of two studies on ultrasound compared to placebo ultrasound, showed statistically significant and clinically relevant differences in favour of ultrasound. There is insufficient evidence either to demonstrate benefit or lack of effect of lasertherapy, electrotherapy, exercises and mobilisation techniques for lateral epicondylitis. ::: ::: ::: CONCLUSIONS ::: Despite the large number of studies, there is still insufficient evidence for most physiotherapy interventions for lateral epicondylitis due to contradicting results, insufficient power, and the low number of studies per intervention. Only for ultrasound, weak evidence for efficacy was found. More better designed, conducted and reported RCTs are needed.


title: Data mining a diabetic data warehouse

abstract: Diabetes is a major health problem in the United States. There is a long history of diabetic registries and databases with systematically collected patient information. We examine one such diabetic data warehouse, showing a method of applying data mining techniques, and some of the data issues, analysis problems, and results. The diabetic data warehouse is from a large integrated health care system in the New Orleans area with 30,383 diabetic patients. Methods for translating a complex relational database with time series and sequencing information to a flat file suitable for data mining are challenging. We discuss two variables in detail, a comorbidity index and the HgbA1c, a measure of glycemic control related to outcomes. We used the classification tree approach in Classification and Regression Trees (CART^(R)) with a binary target variable of HgbA1c >9.5 and 10 predictors: age, sex, emergency department visits, office visits, comorbidity index, dyslipidemia, hypertension, cardiovascular disease, retinopathy, end-stage renal disease. Unexpectedly, the most important variable associated with bad glycemic control is younger age, not the comorbiditity index or whether patients have related diseases. If we want to target diabetics with bad HgbA1c values, the odds of finding them is 3.2 times as high in those <6.5 years of age than those older. Data mining can discover novel associations that are useful to clinicians and administrators.


title: Simulation of structured text language for PLC programming

abstract: Programmable Logic Controllers (PLC) have been used for long in Industry and are part of learning programs in academia. The utilization of these devices must be done with care due to their electrical connections and to the work with possible dangerous or sensitive machines. As such, learning to program and use those in a safe and effective way is recommended and desired both in industrial and academia contexts. We describe a software simulator, which simulates a PLC TSX3721 as well as its programming code, namely Structured Text (ST), and also a HMI panel. It can be used to simulate discrete-event systems, discrete-time systems and hybrid systems, at their several working stages. A description of the PLC simulation architectures and its development is provided, as well as illustrative examples.


A Survey on Data-driven Dictionary-based Methods for 3D Modeling