title: Resource management in a changing and uncertain climate

abstract: Climate change is altering ecological systems throughout the world. Managing these systems in a way that ignores climate change will likely fail to meet management objectives. The uncertainty in projected climate-change impacts is one of the greatest challenges facing managers attempting to address global change. In order to select successful management strategies, managers need to understand the uncertainty inherent in projected climate impacts and how these uncertainties affect the outcomes of management activities. Perhaps the most important tool for managing ecological systems in the face of climate change is active adaptive management, in which systems are closely monitored and management strategies are altered to address expected and ongoing changes. Here, we discuss the uncertainty inherent in different types of data on potential climate impacts and explore climate projections and potential management responses at three sites in North America. The Central Valley of California, the headwaters of the...


title: Extended SAX : Extension of Symbolic Aggregate Approximation for Financial Time Series Data Representation

abstract: Efficient and accurate similarity searching for a large amount of time series data set is an important but non-trivial problem. Many dimensionality reduction techniques have been proposed for effective representation of time series data in order to realize such similarity searching, including Singular Value Decomposition (SVD), the Discrete Fourier transform (DFT), the Adaptive Piecewise Constant Approximation (APCA), and the recently proposed Symbolic Aggregate Approximation (SAX). In this work we propose a new extended approach based on SAX, called Extended SAX in order to realize efficient and accurate discovering of important patterns, necessary for financial applications. While the original SAX approach allows a very good dimensionality reduction and distance measures to be defined on the symbolic approach, SAX is based on PAA (Piecewise Aggregate Approximation) representation for dimensionality reduction that minimizes dimensionality by the mean values of equal sized frames. This value based representation causes a high possibility to miss some important patterns in some time series data such as financial time series data. Extended SAX, proposed in the paper, uses additional two new points, that is, max and min points, in equal sized frames besides the mean value for data approximation. We show that Extended SAX can improve representation preciseness without losing symbolic nature of the original SAX representation. We empirically compare the Extended SAX with the original SAX approach and demonstrate its quality improvement.


title: Adversarial Inference for Multi-Sentence Video Description

abstract: While significant progress has been made in the image captioning task, video description is still in its infancy due to the complex nature of video data. Generating multi-sentence descriptions for long videos is even more challenging. Among the main issues are the fluency and coherence of the generated descriptions, and their relevance to the video. Recently, reinforcement and adversarial learning based methods have been explored to improve the image captioning models; however, both types of methods suffer from a number of issues, e.g. poor readability and high redundancy for RL and stability issues for GANs. In this work, we instead propose to apply adversarial techniques during inference, designing a discriminator which encourages better multi-sentence video description. In addition, we find that a multi-discriminator "hybrid" design, where each discriminator targets one aspect of a description, leads to the best results. Specifically, we decouple the discriminator to evaluate on three criteria: 1) visual relevance to the video, 2) language diversity and fluency, and 3) coherence across sentences. Our approach results in more accurate, diverse, and coherent multi-sentence video descriptions, as shown by automatic as well as human evaluation on the popular ActivityNet Captions dataset.


title: Discriminative Information Retrieval for Knowledge Discovery

abstract: We propose a framework for discriminative Information Retrieval (IR) atop linguistic features, trained to improve the recall of tasks such as answer candidate passage retrieval, the initial step in text-based Question Answering (QA). We formalize this as an instance of linear feature-based IR (Metzler and Croft, 2007), illustrating how a variety of knowledge discovery tasks are captured under this approach, leading to a 44% improvement in recall for candidate triage for QA.


title: Offline handwritten signature verification using local and global features

abstract: This paper presents a new approach to address the problem of offline handwritten signature verification. In contrast to many existing systems, we are interested in making soft decision rather than a purely binary classification for the signatures under verification. To accomplish this goal, we incorporate both types of features: finer intensity-based features and global geometry-based features. Particularly, the finer features are computed for every sample point of a signature using histogram of intensities, and the geometry-based features are extracted using an adaptation of the shape context descriptor. One of the advantages of our approach is that the extracted features are very robust to noise, rotation and scaling change without heavily relying on any complicated pre-processing steps. The extracted features are used to compute the similarity score, followed by a score calibration process to estimate the corresponding confidence score (i.e., using log-likelihood-ratio). To validate our work, we submitted our system to the signature verification competition (SignComp2011) and achieved quite good results.


title: The successive mean quantization transform

abstract: This paper presents the successive mean quantization transform (SMQT). The transform reveals the organization or structure of the data and removes properties such as gain and bias. The transform is described and applied in speech processing and image processing. The SMQT is considered as an extra processing step for the mel frequency cepstral coefficients commonly used in speech recognition. In image processing the transform is applied in automatic image enhancement and dynamic range compression.


title: Autonomous robotic system for bridge deck data collection and analysis

abstract: Bridge deck inspection is conducted to identify bridge condition deterioration and, thus, to facilitate implementation of appropriate maintenance or rehabilitation procedures. In this paper, we report the development of a robotic system for bridge deck data collection and analysis. The robotic system accurately localizes itself and autonomously maneuvers on the bridge deck to collect visual images and conduct nondestructive evaluation (NDE) measurements. The developed robotic system can reduce the cost and time of the bridge deck data collection. Crack detection and mapping algorithm to build the deck crack maps is presented in detail. The electrical resistivity (ER), impact-echo (IE) and ultrasonic surface waves (USW) data collected by the robot are analyzed to generate the corrosion, delamination and concrete elastic modulus maps of the deck. The presented robotic system has been successfully deployed to inspect numerous bridges.


title: Real-time surface light-field capture for augmentation of planar specular surfaces

abstract: A single hand-held camera provides an easily accessible but potentially extremely powerful setup for augmented reality. Capabilities which previously required expensive and complicated infrastructure have gradually become possible from a live monocular video feed, such as accurate camera tracking and, most recently, dense 3D scene reconstruction. A new frontier is to work towards recovering the reflectance properties of general surfaces and the lighting configuration in a scene without the need for probes, omni-directional cameras or specialised light-field cameras. Specular lighting phenomena cause effects in a video stream which can lead current tracking and reconstruction algorithms to fail. However, the potential exists to measure and use these effects to estimate deeper physical details about an environment, enabling advanced scene understanding and more convincing AR. In this paper we present an algorithm for real-time surface light-field capture from a single hand-held camera, which is able to capture dense illumination information for general specular surfaces. Our system incorporates a guidance mechanism to help the user interactively during capture. We then split the light-field into its diffuse and specular components, and show that the specular component can be used for estimation of an environment map. This enables the convincing placement of an augmentation on a specular surface such as a shiny book, with realistic synthesized shadow, reflection and occlusion of specularities as the viewpoint changes. Our method currently works for planar scenes, but the surface light-field representation makes it ideal for future combination with dense 3D reconstruction methods.


title: A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes

abstract: Summary Recent studies suggest that thousands of genes may contribute to breast cancer pathophysiologies when deregulated by genomic or epigenomic events. Here, we describe a model ‘‘system’’ to appraise the functional contributions of these genes to breast cancer subsets. In general, the recurrent genomic and transcriptional characteristics of 51 breast cancer cell lines mirror those of 145 primary breast tumors, although some significant differences are documented. The cell lines that comprise the system also exhibit the substantial genomic, transcriptional, and biological heterogeneity found in primary tumors. We show, using Trastuzumab (Herceptin) monotherapy as an example, that the system can be used to identify molecular features that predict or indicate response to targeted therapies or other physiological perturbations.


title: Work-stealing without the baggage

abstract: Work-stealing is a promising approach for effectively exploiting software parallelism on parallel hardware. A programmer who uses work-stealing explicitly identifies potential parallelism and the runtime then schedules work, keeping otherwise idle hardware busy while relieving overloaded hardware of its burden. Prior work has demonstrated that work-stealing is very effective in practice. However, work-stealing comes with a substantial overhead: as much as 2x to 12x slowdown over orthodox sequential code. In this paper we identify the key sources of overhead in work-stealing schedulers and present two significant refinements to their implementation. We evaluate our work-stealing designs using a range of benchmarks, four different work-stealing implementations, including the popular fork-join framework, and a range of architectures. On these benchmarks, compared to orthodox sequential Java, our fastest design has an overhead of just 15%. By contrast, fork-join has a 2.3x overhead and the previous implementation of the system we use has an overhead of 4.1x. These results and our insight into the sources of overhead for work-stealing implementations give further hope to an already promising technique for exploiting increasingly available hardware parallelism.


title: Combining Pyramid Pooling and Attention Mechanism for Pelvic MR Image Semantic Segmentaion

abstract: One of the time-consuming routine work for a radiologist is to discern anatomical structures from tomographic images. For assisting radiologists, this paper develops an automatic segmentation method for pelvic magnetic resonance (MR) images. The task has three major challenges 1) A pelvic organ can have various sizes and shapes depending on the axial image, which requires local contexts to segment correctly. 2) Different organs often have quite similar appearance in MR images, which requires global context to segment. 3) The number of available annotated images are very small to use the latest segmentation algorithms. To address the challenges, we propose a novel convolutional neural network called Attention-Pyramid network (APNet) that effectively exploits both local and global contexts, in addition to a data-augmentation technique that is particularly effective for MR images. In order to evaluate our method, we construct fine-grained (50 pelvic organs) MR image segmentation dataset, and experimentally confirm the superior performance of our techniques over the state-of-the-art image segmentation methods.


title: Web-based quiz-game-like formative assessment: Development and evaluation

abstract: This research aims to develop a multiple-choice Web-based quiz-game-like formative assessment system, named GAM-WATA. The unique design of 'Ask-Hint Strategy' turns the Web-based formative assessment into an online quiz game. 'Ask-Hint Strategy' is composed of 'Prune Strategy' and 'Call-in Strategy'. 'Prune Strategy' removes one incorrect option and turns the original 4-option item into a 3-option one. 'Call-in Strategy' provides the rate at which other test takers choose each option when answering a question. This research also compares the effectiveness of three different types of formative assessment in an e-Learning environment: paper-and-pencil test (PPT), normal Web-based test (N-WBT) and GAM-WATA. In total, 165 fifth grade elementary students (from six classes) in central Taiwan participated in this research. The six classes of students were then divided into three groups and each group was randomly assigned one type of formative assessment. Overall results indicate that different types of formative assessment have significant impacts on e-Learning effectiveness and that the e-Learning effectiveness of the students in the GAM-WATA group appears to be better. Students in the GAM-WATA group more actively participate in Web-based formative assessment to do self-assessment than students in the N-WBT group. The effectiveness of formative assessment will not be significantly improved only by replacing the paper-and-pencil test with Web-based test. The strategies included in GAM-WATA are recommended to be taken into consideration when researchers design Web-based formative assessment systems in the future.


title: Extending market basket analysis with graph mining techniques: A real case

abstract: A common problem for many companies, like retail stores, it is to find sets of products that are sold together. The only source of information available is the history of sales transactional data. Common techniques of market basket analysis fail when processing huge amounts of scattered data, finding meaningless relationships. We developed a novel approach for market basket analysis based on graph mining techniques, able to process millions of scattered transactions. We demonstrate the effectiveness of our approach in a wholesale supermarket chain and a retail supermarket chain, processing around 238,000,000 and 128,000,000 transactions respectively compared to classical approach.


title: Women’s Knowledge of the Domestic Violence Legislation in Erbil, Iraq and their Response to Spousal Violence

abstract: This study assessed the knowledge of 82 married women about existing legislations and law enforcement structure in Iraqi Kurdistan region to combat violence against women and women’s response to spousal violence. A female physician interviewed the participants using a specially designed questionnaire. The women possessed good knowledge about the presence of legislation (76.8 %) and related law enforcement structure (70.7 % to 74.4 %) to combat violence against women, but they lacked adequate knowledge about how to contact the law enforcement agencies. Women’s response to spousal violence primarily included self-defense (37.8 %) or keeping silent (26.8 %), rather than seeking justice (4.9 %). Women’s educational status was positively associated with reporting violence to police (52.1 % vs 29.4 %, p = 0.041) and the health staff (62.5 % vs 35.3 %, p = 0.015). Further research is needed to explore the reasons behind women’s poor knowledge of the details of the existing law enforcement structure and women’s reluctance to seek justice.


title: Assessment of dicing induced damage and residual stress on the mechanical and electrical behavior of chips

abstract: Due to increased technological and economic requirements during wafer production several materials like metal structures or brittle low-k materials are placed within the dicing street. These materials have a negative effect on the quality and reliability results when using conventional separation methods like mechanical dicing. For this case a laser grooving step, prior mechanical dicing is used to remove these materials out of the dicing street. It is known that each of this separation method creates residual stress in the adjacent material in terms of specific strain fields. In the present study the influence of the strain fields on the mechanical and electrical behavior of chips was investigated. For this reason several laser grooving processes and a mechanical dicing process was compared using a dedicated dicing test chip with sensors to measure the electrical behavior. The mechanical fracture strength of the chips was investigated by 3-point bending tests. Photoelastic stress measurements were used to characterize resulting strain fields. The more extensive the strain fields, the lower the fracture strength, the greater the number of electrically conspicuous chips. The combination of strain field, electrical and mechanical strength characterization can help to optimize the process parameters get better reliability and quality of chips.


title: A More General Robust Loss Function

abstract: We present a two-parameter loss function which can be viewed as a generalization of many popular loss functions used in robust statistics: the Cauchy/Lorentzian, Geman-McClure, Welsch/Leclerc, and generalized Charbonnier loss functions (and by transitivity the L2, L1, L1-L2, and pseudo-Huber/Charbonnier loss functions). If this penalty is viewed as a negative log-likelihood, it yields a general probability distribution that includes normal and Cauchy distributions as special cases. We describe and visualize this loss and its corresponding distribution, and document several of their useful properties.


title: Applications of Text Detection and its Challenges: A Review

abstract: The rising need for automation of systems has effected the development of text detection and recognition from images to a large extent. Text recognition has a wide range of applications, each with scenario dependent challenges and complications. How can these challenges be mitigated? What image processing techniques can be applied to make the text in the image machine readable? How can text be localized and separated from non textual information? How can the text image be converted to digital text format? This paper attempts to answer these questions in chosen scenarios. The types of document images that we have surveyed include general documents such as newspapers, books and magazines, forms, scientific documents, unconstrained documents such as maps, architectural and engineering drawings, and scene images with textual information.


title: OverFlow: Multi-Site Aware Big Data Management for Scientific Workflows on Clouds

abstract: The global deployment of cloud datacenters is enabling large scale scientific workflows to improve performance and deliver fast responses. This unprecedented geographical distribution of the computation is doubled by an increase in the scale of the data handled by such applications, bringing new challenges related to the efficient data management across sites. High throughput, low latencies or cost-related trade-offs are just a few concerns for both cloud providers and users when it comes to handling data across datacenters. Existing solutions are limited to cloud-provided storage, which offers low performance based on rigid cost schemes. In turn, workflow engines need to improvise substitutes, achieving performance at the cost of complex system configurations, maintenance overheads, reduced reliability and reusability. In this paper, we introduce OverFlow, a uniform data management system for scientific workflows running across geographically distributed sites, aiming to reap economic benefits from this geo-diversity. Our solution is environment-aware, as it monitors and models the global cloud infrastructure, offering high and predictable data handling performance for transfer cost and time, within and across sites. OverFlow proposes a set of pluggable services, grouped in a data scientist cloud kit. They provide the applications with the possibility to monitor the underlying infrastructure, to exploit smart data compression, deduplication and geo-replication, to evaluate data management costs, to set a tradeoff between money and time, and optimize the transfer strategy accordingly. The system was validated on the Microsoft Azure cloud across its 6 EU and US datacenters. The experiments were conducted on hundreds of nodes using synthetic benchmarks and real-life bio-informatics applications (A-Brain, BLAST). The results show that our system is able to model accurately the cloud performance and to leverage this for efficient data dissemination, being able to reduce the monetary costs and transfer time by up to three times.


title: From Graphs to Manifolds - Weak and Strong Pointwise Consistency of Graph Laplacians

abstract: In the machine learning community it is generally believed that graph Laplacians corresponding to a finite sample of data points converge to a continuous Laplace operator if the sample size increases. Even though this assertion serves as a justification for many Laplacian-based algorithms, so far only some aspects of this claim have been rigorously proved. In this paper we close this gap by establishing the strong pointwise consistency of a family of graph Laplacians with data- dependent weights to some weighted Laplace operator. Our investigation also includes the important case where the data lies on a submanifold of ${\mathbb R}^{d}$.


title: Incremental branching adaptive radix tree

abstract: Adaptive indexing has been an area of active research in recent years. Its main concept is to create and maintain indexes adaptively and incrementally based on the incoming workload as a part of the query execution process. Database cracking is the first introduced applicable adaptive indexing paradigm. Despite the effectiveness and lightweight of database cracking, it offers slower lookups when it is compared to modern main memory index structures like adaptive radix tree (ART). ART is a recently proposed main-memory index structure which is designed to be space-efficient yet offers high performance by adapting its internal node size based on the count of keys stored in it. In this paper, we presented IBART (incremental branching adaptive radix tree), a hybrid indexing technique that takes the main concept of database cracking and applies it to ART, generating an adaptive index in terms of its creation and maintenance and also its internal nodes size. For systems of dynamic nature and unpredictable workload, IBART is proven to be more convenient than ART.


title: Design of snubber circuit to minimize switching and conduction losses in boost converter

abstract: This paper presents a zero voltage switching (ZVS) passive snubber circuit for the MOSFET used in conventional boost converter which consists of an inductor, a capacitor, a resistance and a diode. It helps in improving the high current and voltage stress. With the help of this circuit, power conversion efficiency is improved by controlling the voltage and current in the switch, thus providing zero-voltage turn-off condition for the MOSFET. By utilizing the inductor the turn-on rate is controlled, and with capacitor and diode the turn-off rate is controlled. In the paper the design considerations have been presented in detail. The conventional boost converter with the proposed snubber is able to deliver a load voltage of 300V with reduction in switching and conduction losses when it is tested at 200V input with 180 kHz frequency of switching for 300W output power.


A Generalized Knowledge Hunting Framework for the Winograd Schema Challenge


title: Where to Add Actions in Human-in-the-Loop Reinforcement Learning

abstract: In order for reinforcement learning systems to learn quickly in vast action spaces such as the space of all possible pieces of text or the space of all images, leveraging human intuition and creativity is key. However, a human-designed action space is likely to be initially imperfect and limited; furthermore, humans may improve at creating useful actions with practice or new information. Therefore, we propose a framework in which a human adds actions to a reinforcement learning system over time to boost performance. In this setting, however, it is key that we use human effort as efficiently as possible, and one significant danger is that humans waste effort adding actions at places (states) that aren't very important. Therefore, we propose Expected Local Improvement (ELI), an automated method which selects states at which to query humans for a new action. We evaluate ELI on a variety of simulated domains adapted from the literature, including domains with over a million actions and domains where the simulated experts change over time. We find ELI demonstrates excellent empirical performance, even in settings where the synthetic "experts" are quite poor.


title: Small-Signal Stability Analysis of an Autonomous Hybrid Renewable Energy Power Generation/Energy Storage System Part I: Time-Domain Simulations

abstract: Small-signal stability analyzed results of an autonomous hybrid renewable energy power generation/energy storage system connected to isolated loads using time-domain simulations is presented in this paper. The companion paper presents frequency-domain analyzed results of the same hybrid system. The proposed renewable energy power generation subsystems include three wind turbine generators (WTGs), a diesel engine generator, two fuel cells (FCs), and a photovoltaic system (PV) while the energy storage subsystems consist of a battery energy storage system and a flywheel energy storage system. An aqua electrolyzer absorbs a part of generated energy from PV or WTGs to generate available hydrogen for FCs. A time-domain approach based on three mathematical models for three studied cases under various operating points and disturbance conditions is performed. It can be concluded from the simulation results that the proposed hybrid power generation/energy storage system feeding isolated loads can be properly operated to achieve system power-frequency balance condition.


title: A fully-integrated dual-polarization 16-element W-band phased-array transceiver in SiGe BiCMOS

abstract: This paper presents a multi-function, dual-polarization phased array transceiver supporting both radar and communication applications at W-band. 32 receive elements and 16 transmit elements with dual outputs are integrated to support 16 dual polarized antennas in a package. The IC further includes two independent 16:1 combining networks, two receiver downconversion chains, an up-conversion chain, a 40GHz PLL, an 80GHz frequency doubler, extensive digital control circuitry, and on-chip IF/LO combining/distribution circuitry to enable scalability to arrays at the board level. The fully-integrated transceiver is fabricated in the IBM SiGe BiCMOS 0.13um process, occupies an area of 6.6×6.7mm2, and operates from 2.7V (analog/RF) and 1.5V (digital) supplies. Multiple operating modes are supported including the simultaneous reception of two polarizations with a 10GHz IF output, transmission in either polarization from an IF input, or single-polarization transmission/reception from/to I&Q base-band signals (2.5W RX, 2.9W TX). Measurement results show 8dB receiver NF and 2dBm transmitter output power per element at 94GHz in both polarizations.


title: Handwritten Digit Recognition Using Multiple Feature Extraction Techniques and Classifier Ensemble

abstract: It is herein proposed a handwritten digit recognition system which uses multiple feature extraction methods and classifier ensemble. The combination of the feature extraction methods is motivated by the observation that different feature extraction algorithms have a better discriminative power for some types of digits. Six features sets were extracted, two proposed by the authors and four published in previous works. It is shown that combining these feature sets is sufficient to achieve high recognition rates. Several combination schemes were tested, showing good results. A scheme using neural networks as a combiner achieved a recognition rate of 99.68%, the highest one on the MNIST database.


title: Cognitive development in deaf children: the interface of language and perception in neuropsychology

abstract: What does the sense of hearing contribute to human development? To answer the question, we must ask what the sense of hearing gives the child. Hearing gives the child the acoustic correlates of the physical world: approaching footsteps, dog barks, car horns, and the pitter-patter of rain. Hearing also allows the child to revel in the patterned complexity of a Beethoven symphony or a mother’s lullaby. Children who are born deaf clearly miss a great deal. However, hearing conveys much more to the growing child than the acoustics of the physical world. Hearing is the sensory modality through which children perceive speech — the universe of talk that ties individuals, families and societies together. Children born with bilateral hearing losses that are severe (70–89 dB loss) or profound (>90 dB loss) are referred to as deaf. They cannot hear conversational speech (approximately 60 dB) and consequently do not spontaneously learn to talk. Indeed, not talking at the right age is one of the first signs that a child cannot hear. The primary consequence of childhood deafness is that it blocks the development of spoken language — both the acts of speaking and comprehending. This fact leads us to ask what spoken language contributes to the child’s cognitive development. Be-


title: Pragmatic Language Interpretation as Probabilistic Inference

abstract: Understanding language requires more than the use of fixed conventions and more than decoding combinatorial structure. Instead, comprehenders make exquisitely sensitive inferences about what utterances mean given their knowledge of the speaker, language, and context. Building on developments in game theory and probabilistic modeling, we describe the rational speech act (RSA) framework for pragmatic reasoning. RSA models provide a principled way to formalize inferences about meaning in context; they have been used to make successful quantitative predictions about human behavior in a variety of different tasks and situations, and they explain why complex phenomena, such as hyperbole and vagueness, occur. More generally, they provide a computational framework for integrating linguistic structure, world knowledge, and context in pragmatic language understanding.


title: Extended Tower Number Field Sieve with Application to Finite Fields of Arbitrary Composite Extension Degree

abstract: We propose a generalization of exTNFS algorithm recently introduced by Kim and Barbulescu (CRYPTO 2016). The algorithm, exTNFS, is a state-of-the-art algorithm for discrete logarithm in \(\mathbb {F}_{p^n}\) in the medium prime case, but it only applies when \(n=\eta \kappa \) is a composite with nontrivial factors \(\eta \) and \(\kappa \) such that \(\gcd (\eta ,\kappa )=1\). Our generalization, however, shows that exTNFS algorithm can be also adapted to the setting with an arbitrary composite n maintaining its best asymptotic complexity. We show that one can compute a discrete logarithm in medium case in the running time of \(L_{p^n}(1/3, \root 3 \of {48/9})\) (resp. \(L_{p^n}(1/3, 1.71)\) if multiple number fields are used), where n is an arbitrary composite. This should be compared with a recent variant by Sarkar and Singh (Asiacrypt 2016) that has the fastest running time of \(L_{p^n}(1/3, \root 3 \of {64/9})\) (resp. \(L_{p^n}(1/3, 1.88)\)) when n is a power of prime 2. When p is of special form, the complexity is further reduced to \(L_{p^n}(1/3, \root 3 \of {32/9})\). On the practical side, we emphasize that the keysize of pairing-based cryptosystems should be updated following to our algorithm if the embedding degree n remains composite.


title: Heterogenous Uncertainty Sampling for Supervised Learning

abstract: Abstract Uncertainty sampling methods iteratively request class labels for training instances whose classes are uncertain despite the previous labeled instances. These methods can greatly reduce the number of instances that an expert need label. One problem with this approach is that the classifier best suited for an application may be too expensive to train or use during the selection of instances. We test the use of one classifier (a highly efficient probabilistic one) to select examples for training another (the C4.5 rule induction program). Despite being chosen by this heterogeneous approach, the uncertainty samples yielded classifiers with lower error rates than random samples ten times larger.


title: Shared Neural Circuits for Mentalizing about the Self and Others

abstract: Although many examples exist for shared neural representations of self and other, it is unknown how such shared representations interact with the rest of the brain. Furthermore, do high-level inference-based shared mentalizing representations interact with lower level embodied/simulation-based shared representations? We used functional neuroimaging (fMRI) and a functional connectivity approach to assess these questions during high-level inference-based mentalizing. Shared mentalizing representations in ventromedial prefrontal cortex, posterior cingulate/precuneus, and temporo-parietal junction (TPJ) all exhibited identical functional connectivity patterns during mentalizing of both self and other. Connectivity patterns were distributed across low-level embodied neural systems such as the frontal operculum/ventral premotor cortex, the anterior insula, the primary sensorimotor cortex, and the presupplementary motor area. These results demonstrate that identical neural circuits are implementing processes involved in mentalizing of both self and other and that the nature of such processes may be the integration of low-level embodied processes within higher level inference-based mentalizing.


title: Self-deception requires vagueness

abstract: The paper sets out to reveal conditions enabling diagnostic self-deception, people's tendency to deceive themselves about the diagnostic value of their own actions. We characterize different types of self-deception in terms of the distinction between intervention and observation in causal reasoning. One type arises when people intervene but choose to view their actions as observations in order to find support for a self-serving diagnosis. We hypothesized that such self-deception depends on imprecision in the environment that allows leeway to represent one's own actions as either observations or interventions. Four experiments tested this idea using a dot-tracking task. Participants were told to go as quickly as they could and that going fast indicated either above-average or below-average intelligence. Precision was manipulated by varying the vagueness in feedback about performance. As predicted, self-deception was observed only when feedback on the task used vague terms rather than precise values. The diagnosticity of the feedback did not matter.


JPE 11-4-3 Control and Analysis of an Integrated Bidirectional DC / AC and DC / DC Converters for Plug-In Hybrid Electric Vehicle Applications


title: Hacking Culture, Not Devices: Access and Recognition in Feminist Hackerspaces

abstract: This paper examines the motivations, activities, and ideals of people organizing feminist hackerspaces: collaborative workspaces developed to support women's creative and professional pursuits. Drawing on interviews, participant observation and archival data collected across the Pacific Northwest over nine months, we show how members of these spaces use small-scale collaborative design and acts of making to work out their place in society in ways that contest widely accepted understandings of hacking, technology, and collaboration. In designing how the space should look, feel, and run, members reframe activities seldom associated with technical work (e.g., weaving, identity workshops) as forms of hacking. In so doing, they shift concerns for women in technology from questions of access (who is included) to questions of recognition (who is visible) while grappling with productive ambiguities in between. We describe lessons these tension present for examining women's relations with technology in CSCW.


title: A Dependency Parser for Tweets

abstract: We describe a new dependency parser for English tweets, TWEEBOPARSER. The parser builds on several contributions: new syntactic annotations for a corpus of tweets (TWEEBANK), with conventions informed by the domain; adaptations to a statistical parsing algorithm; and a new approach to exploiting out-of-domain Penn Treebank data. Our experiments show that the parser achieves over 80% unlabeled attachment accuracy on our new, high-quality test set and measure the benefit of our contributions. Our dataset and parser can be found at http://www.ark.cs.cmu.edu/TweetNLP.


title: Markov Chain Monte Carlo methods and the label switching problem in Bayesian mixture modelling

abstract: In the past ten years there has been a dramatic increase of in terest in the Bayesian analysis of finite mixture models. This is primarily because of the emergence of Markov chain Monte Carlo (MCMC) methods. While MCMC provides a convenient way to draw inference from compli cated statistical models, there are many, perhaps underappreciated, problems associated with the MCMC analysis of mixtures. The problems are mainly caused by the nonidentifiability of the components under symmetric priors, which leads to so-called label switching in the MCMC output. This means that ergodic averages of component specific quantities will be identical and thus useless for inference. We review the solutions to the label switching problem, such as artificial identifiability constraints, relabelling algorithms and label invariant loss functions. We also review various MCMC sampling schemes that have been suggested for mixture models and discuss posterior sensitivity to prior specification.


title: Multiband MIMO antenna for 4G mobile terminal

abstract: A multiband MIMO antenna for a 4G mobile terminal is proposed. The antenna structure consists of a multiband main antenna element, a printed inverted-L subantenna element operating in the higher 2.5 GHz bands, and a wideband loop sub-antenna element working in lower 0.9 GHz band. In order to improve the isolation and ECC characteristics of the proposed MIMO antenna, each element is located at a different corner of the ground plane. In addition, the inductive coils are employed to reduce the antenna volume and realize the wideband property of the loop sub-antenna element. Finally, the proposed antenna covers LTE band 7/8, PCS, WiMAX, and WLAN service, simultaneously. The MIMO antenna has ECC lower than 0.15 and isolation higher than 12 dB in both lower and higher frequency bands.


title: Timeprints for identifying social media users with multiple aliases

abstract: Many people who discuss sensitive or private issues on social media services are using pseudonyms or aliases in order to not reveal their true identity, while using their usual, non-private accounts when posting messages on less sensitive issues. Previous research has shown that if those individuals post large amounts of user-generated content, stylometric techniques can be used to identify the author based on the characteristics of the textual content. In this article we show how an author’s identity can be unmasked in a similar way using various time features (e.g., period of the day and the day of the week when a user’s posts have been published). We combine several different time features into a timeprint, which can be seen as a type of fingerprint when identifying users on social media. We use supervised machine learning (i.e., author identification) and unsupervised alias matching (similarity detection) in a number of different experiments with forum data to get an understanding of to what extent timeprints can be used for identifying users in social media, both in isolation and when combined with stylometric features. The obtained results show that timeprints indeed can be a very powerful tool for both author identification and alias matching in social media.


title: Oral lichen planus and oral lichenoid lesions; a critical appraisal with emphasis on the diagnostic aspects.

abstract: Oral lichen planus (OLP) has a prevalence of approximately 1%. The etiopathogenesis is poorly understood. The annual malignant transformation is less than 0.5%. There are no effective means to either predict or to prevent such event. Oral lesions may occur that to some extent look like lichen planus but lacking the characteristic features of OLP, or that are indistinguishable from OLP clinically but having a distinct cause, e.g. amalgam restoration associated. Such lesions are referred to as oral lichenoid lesions (OLLs). The management of OLP and the various OLLs may be different. Therefore, accurate diagnosis should be aimed at.


title: Vehicular cloud computing: A survey

abstract: With the proliferation of automobile industry, vehicles are augmented with various forms of increasingly powerful computation, communication, storage and sensing resources. A vehicle therefore can be regarded as “computer-on-wheels”. With such rich resources, it is of great significance to efficiently utilize these resources. This puts forward the vision of vehicular cloud computing. In this paper, we provide an extensive survey of current vehicular cloud computing research and highlight several key issues of vehicular cloud such as architecture, inherent features, service taxonomy and potential applications.


title: A semiotic metrics suite for assessing the quality of ontologies

abstract: A suite of metrics is proposed to assess the quality of an ontology. Drawing upon semiotic theory, the metrics assess the syntactic, semantic, pragmatic, and social aspects of ontology quality. We operationalize the metrics and implement them in a prototype tool called the Ontology Auditor. An initial validation of the Ontology Auditor on the DARPA Agent Markup Language (DAML) library of domain ontologies indicates that the metrics are feasible and highlights the wide variation in quality among ontologies in the library. The contribution of the research is to provide a theory-based framework that developers can use to develop high quality ontologies and that applications can use to choose appropriate ontologies for a given task.


title: Recent Advances on Multi-Agent Patrolling

abstract: Patrolling is a complex multi-agent task, which usually requires agents to coordinate their decision-making in order to achieve optimal performance of the group as a whole. In previous work, many patrolling strategies were developed, based on different approaches: heuristic agents, negotiation mechanisms, reinforcement learning techniques, techniques based on graph-theory and others. In this paper, we complement these studies by comparing all the approaches developed so far for this domain, using patrolling problem instances that were not dealt with before (i.e. new map topologies). The final results constitute a valuable benchmark for this domain, as well as a survey of the strategies developed so far for this task.


title: Enabling the 'host jump': structural determinants of receptor-binding specificity in influenza A viruses

abstract: The shift in the receptor-binding specificity of influenza A viruses is mostly determined by mutations in viral haemagglutinin. In this Review, Gao and colleagues discuss recent crystallographic studies that provide molecular insights into haemagglutinin–host receptor interactions that have enabled several influenza A virus subtypes to 'jump' from avian to human hosts.


title: Approximate Solution for Optimal Control of Continuous-Time Switched Systems

abstract: Two approximate solutions for optimal control of switched systems with autonomous subsystems and continuous-time dynamics are developed. The proposed solutions consist of online training algorithms with recursive least squares training laws. The first solution is the classic policy iteration algorithm which imposes heavy computational burden (full back-up). In order to relax the computational burden in the policy iteration algorithm, the second algorithm is presented. The convergence of the proposed algorithms to the optimal solution in online training is investigated. Simulation results are presented to illustrate the effectiveness of the discussed algorithms.Copyright © 2016 by ASME


title: BERT: BEhavioral Regression Testing

abstract: During maintenance, it is common to run the new version of a program against its existing test suite to check whether the modifications in the program introduced unforeseen side effects. Although this kind of regression testing can be effective in identifying some change-related faults, it is limited by the quality of the existing test suite. Because generating tests for real programs is expensive, developers build test suites by finding acceptable tradeoffs between cost and thoroughness of the tests. Such test suites necessarily target only a small subset of the program's functionality and may miss many regression faults. To address this issue, we introduce the concept of behavioral regression testing, whose goal is to identify behavioral differences between two versions of a program through dynamic analysis. Intuitively, given a set of changes in the code, behavioral regression testing works by (1) generating a large number of test cases that focus on the changed parts of the code, (2) running the generated test cases on the old and new versions of the code and identifying differences in the tests' outcome, and (3) analyzing the identified differences and presenting them to the developers. By focusing on a subset of the code and leveraging differential behavior, our approach can provide developers with more (and more focused) information than traditional regression testing techniques. This paper presents our approach and performs a preliminary assessment of its feasibility.


title: Techniques of BDD/ZDD: Brief History and Recent Activity

abstract: SUMMARY Discrete structures are foundational material for computer science and mathematics, which are related to set theory, symbolic logic, inductive proof, graph theory, combinatorics, probability theory, etc. Many problems solved by computers can be decomposed into discrete structures using simple primitive algebraic operations. It is very important to represent discrete structures compactly and to execute efficiently tasks such as equivalency/validity checking, analysis of models, and optimization. Recently, BDDs (Binary Decision Diagrams) and ZDDs (Zero-suppressed BDDs) have attracted a great deal of attention, because they efficiently represent and manipulate large-scale combinational logic data, which are the basic discrete structures in various fields of application. Although a quarter of a century has passed since Bryant’s first idea, there are still a lot of interesting and exciting research topics related to BDD and ZDD. BDD/ZDD is based on in-memory data processing techniques, and it enjoys the advantage of using random access memory. Recent commodity PCs are equipped with gigabytes of main memory, and we can now solve large-scale problems which used to be impossible due to memory shortage. Thus, especially since 2000, the scope of BDD/ZDD methods has increased. This survey paper describes the history of, and recent research


title: Object tracking based on Huber loss function

abstract: In this paper we present a novel visual tracking algorithm, in which object tracking is achieved by using subspace learning and Huber loss regularization in a particle filter framework. The changing appearance of tracked target is modeled by principle component analysis basis vectors and row group sparsity. This method takes advantage of the strengths of subspace representation and explicitly takes the underlying relationship between particle candidates into consideration in the tracker. The representation of each particle is learned via the multi-task sparse learning method. Huber loss function is employed to model the error between candidates and templates, yielding robust tracking. We utilize the alternating direction method of multipliers to solve the proposed representation model. In experiments we tested sixty representative video sequences that reflect the specific challenges of tracking and used both qualitative and quantitative metrics to evaluate the performance of our tracker. The experiment results demonstrated that the proposed tracking algorithm achieves superior performance compared to nine state-of-the-art tracking methods.


From Discourse Structures To Text Summaries


Circuit Implementation of a 300-MHz 64-bit Second-generation CMOS Alpha CPU


title: Dataset Construction via Attention for Aspect Term Extraction with Distant Supervision

abstract: Aspect Term Extraction (ATE) detects opinionated aspect terms in sentences or text spans, with the end goal of performing aspect-based sentiment analysis. The small amount of available datasets for supervised ATE and the fact that they cover only a few domains raise the need for exploiting other data sources in new and creative ways. Publicly available review corpora contain a plethora of opinionated aspect terms and cover a larger domain spectrum. In this paper, we first propose a method for using such review corpora for creating a new dataset for ATE. Our method relies on an attention mechanism to select sentences that have a high likelihood of containing actual opinionated aspects. We thus improve the quality of the extracted aspects. We then use the constructed dataset to train a model and perform ATE with distant supervision. By evaluating on human annotated datasets, we prove that our method achieves a significantly improved performance over various unsupervised and supervised baselines. Finally, we prove that sentence selection matters when it comes to creating new datasets for ATE. Specifically, we show that, using a set of selected sentences leads to higher ATE performance compared to using the whole sentence set.


title: Data analysis and data mining: current issues in biomedical informatics.

abstract: Background: Medicine and biomedical sciences have become data-intensive fields, which, at the same time, enable the application of data-driven approaches and require sophisticated data analysis and data mining methods. Biomedical informatics provides a proper interdisciplinary context to integrate data and knowledge when processing available information, with the aim of giving effective decision-making support in clinics and translational research. Objectives: To reflect on different perspectives related to the role of data analysis and data mining in biomedical informatics. Methods: On the occasion of the 50th year of Methods of Information in Medicine a symposium was organized, which reflected on opportunities, challenges and priorities of organizing, representing and analysing data, information and knowledge in biomedicine and health care. The contributions of experts with a variety of backgrounds in the area of biomedical data analysis have been collected as one outcome of this symposium, in order to provide a broad, though coherent, overview of some of the most interesting aspects of the field. Results: The paper presents sections on data accumulation and data-driven approaches in medical informatics, data and knowledge integration, statistical issues for the evaluation of data mining models, translational bioinformatics and bioinformatics aspects of genetic epidemiology. Conclusions: Biomedical informatics represents a natural framework to properly and effectively apply data analysis and data mining methods in a decision-making context. In the future, it will be necessary to preserve the inclusive nature of the field and to foster an increasing sharing of data and methods between researchers.


title: Direct Sparse Odometry

abstract: Direct Sparse Odometry (DSO) is a visual odometry method based on a novel, highly accurate sparse and direct structure and motion formulation. It combines a fully direct probabilistic model (minimizing a photometric error) with consistent, joint optimization of all model parameters, including geometry-represented as inverse depth in a reference frame-and camera motion. This is achieved in real time by omitting the smoothness prior used in other direct methods and instead sampling pixels evenly throughout the images. Since our method does not depend on keypoint detectors or descriptors, it can naturally sample pixels from across all image regions that have intensity gradient, including edges or smooth intensity variations on essentially featureless walls. The proposed model integrates a full photometric calibration, accounting for exposure time, lens vignetting, and non-linear response functions. We thoroughly evaluate our method on three different datasets comprising several hours of video. The experiments show that the presented approach significantly outperforms state-of-the-art direct and indirect methods in a variety of real-world settings, both in terms of tracking accuracy and robustness.


title: A rotation and scale invariance face Recognition Method Based on Complex Network and Image Contour

abstract: The Method based on Image Contour and shape can achieve high identify efficiency with simple process. But the change of the facial expression, light intensity or position of shelters will reduce the recognition effect. To solve these problems, a Face Recognition Method Based on Complex Network and Image Contour is discussed in this paper. The main idea of the approach is to extract the face image contour, model them into graphs and use complex network methodology to extract a feature vector for face recognition. Experiments show that the proposed method could effectively control the scale of the complex network and adapt to the changes in the boundary shape with efficient power of face recognition. The proposed method is also proved to be scale invariant and rotation invariant.


title: A review on the oxidative and nitrosative stress (O&NS) pathways in major depression and their possible contribution to the (neuro)degenerative processes in that illness

abstract: Abstract This paper reviews the body of evidence that major depression is accompanied by a decreased antioxidant status and by induction of oxidative and nitrosative (ION and increased 8-hydroxy-2-deoxyguanosine, indicating oxidative DNA damage. There is also evidence in major depression, that ON and increased IgM-mediated immune responses against membrane fatty acids, like phosphatidyl inositol (Pi); oleic, palmitic, and myristic acid; and NO modified amino-acids, e.g. NO-tyrosine, NO-tryptophan and NO-arginine; and NO-albumin. There is a significant association between depression and polymorphisms in O&NS genes, like manganese superoxide dismutase, catalase, and myeloperoxidase. Animal models of depression very consistently show lowered antioxidant defences and activated O&NS pathways in the peripheral blood and the brain. In animal models of depression, antidepressants consistently increase lowered antioxidant levels and normalize the damage caused by O&NS processes. Antioxidants, such as N-acetyl-cysteine, compounds that mimic GPX activity, and zinc exhibit antidepressive effects. This paper reviews the pathways by which lowered antioxidants and O&NS may contribute to depression, and the (neuro)degenerative processes that accompany that illness. It is concluded that aberrations in O&NS pathways are - together with the inflammatory processes - key components of depression. All in all, the results suggest that depression belongs to the spectrum of (neuro)degenerative disorders.


title: Sampling from Multimodal Distributions Using Tempered Transitions

abstract: I present a new Markov chain sampling method appropriate for distributions with isolated modes. Like the recently developed method of ‘simulated tempering’, the ‘tempered transition’ method uses a series of distributions that interpolate between the distribution of interest and a distribution for which sampling is easier. The new method has the advantage that it does not require approximate values for the normalizing constants of these distributions, which are needed for simulated tempering, and can be tedious to estimate. Simulated tempering performs a random walk along the series of distributions used. In contrast, the tempered transitions of the new method move systematically from the desired distribution, to the easily-sampled distribution, and back to the desired distribution. This systematic movement avoids the inefficiency of a random walk, an advantage that is unfortunately cancelled by an increase in the number of interpolating distributions required. Because of this, the sampling efficiency of the tempered transition method in simple problems is similar to that of simulated tempering. On more complex distributions, however, simulated tempering and tempered transitions may perform differently. Which is better depends on the ways in which the interpolating distributions are ‘deceptive’.


title: f3.js: A Parametric Design Tool for Physical Computing Devices for Both Interaction Designers and End-users

abstract: Although the exploration of design alternatives is crucial for interaction designers and customization is required for end-users, the current development tools for physical computing devices have focused on single versions of an artifact. We propose the parametric design of devices including their enclosure layouts and programs to address this issue. A Web-based design tool called f3.js is presented as an example implementation, which allows devices assembled from laser-cut panels with sensors and actuator modules to be parametrically created and customized. It enables interaction designers to write code with dedicated APIs, declare parameters, and interactively tune them to produce the enclosure layouts and programs. It also provides a separate user interface for end-users that allows parameter tuning and dynamically generates instructions for device assembly. The parametric design approach and the tool were evaluated through two user studies with interaction designers, university students, and end-users.


title: Video Inter-Frame Forgery Identification Based on Consistency of Correlation Coefficients of Gray Values

abstract: Identifying inter-frame forgery is a hot ::: topic in video forensics. In this paper, we propose a method based on the assumption ::: that the correlation coefficients of gray values is consistent in an original ::: video, while in forgeries the consistency will be destroyed. We first extract ::: the consistency of correlation coefficients of gray values (CCCoGV for short) ::: after normalization and quantization as distinguishing feature to identify ::: interframe forgeries. Then we test the CCCoGV in a large database with the ::: help of SVM (Support Vector Machine). Experimental results show that the proposed ::: method is efficient in classifying original videos and forgeries. Furthermore, the ::: proposed method performs also pretty well in classifying frame insertion and ::: frame deletion forgeries.


title: A survey of longest common subsequence algorithms

abstract: The aim of this paper is to give a comprehensive comparison of well-known longest common subsequence algorithms (for two input strings) and study their behaviour in various application environments. The performance of the methods depends heavily on the properties of the problem instance as well as the supporting data structures used in the implementation. We want to make also a clear distinction between methods that determine the actual lcs and those calculating only its length, since the execution time and more importantly, the space demand depends crucially on the type of the task. To our knowledge, this is the first time this kind of survey has been done. Due to the page limits, the paper gives only a coarse overview of the performance of the algorithms; more detailed studies are reported elsewhere.


An overview of legged and wheeled robotic locomotion


title: Co-Training And Self-Training For Word Sense Disambiguation

abstract: This paper investigates the application of co-training and self-training to word sense disambiguation. Optimal and empirical parameter selection methods for co-training and self-training are investigated, with various degrees of error reduction. A new method that combines co-training with majority voting is introduced, with the effect of smoothing the bootstrapping learning curves, and improving the average performance.


Block-VN: A Distributed Blockchain Based Vehicular Network Architecture in Smart City.


title: Frame Structure Design and Analysis for Millimeter Wave Cellular Systems

abstract: The millimeter-wave (mmWave) frequencies have attracted considerable attention for fifth generation (5G) cellular communication as they offer orders of magnitude greater bandwidth than current cellular systems. However, the medium access control (MAC) layer may need to be significantly redesigned to support the highly directional transmissions, ultra-low latencies and high peak rates expected in mmWave communication. To address these challenges, we present a novel mmWave MAC layer frame structure with a number of enhancements including flexible, highly granular transmission times, dynamic control signal locations, extended messaging and ability to efficiently multiplex directional control signals. Analytic formulae are derived for the utilization and control overhead as a function of control periodicity, number of users, traffic statistics, signal-to-noise ratio and antenna gains. Importantly, the analysis can incorporate various front-end MIMO capability assumptions -- a critical feature of mmWave. Under realistic system and traffic assumptions, the analysis reveals that the proposed flexible frame structure design offers significant benefits over designs with fixed frame structures similar to current 4G long-term evolution (LTE). It is also shown that fully digital beamforming architectures offer significantly lower overhead compared to analog and hybrid beamforming under equivalent power budgets.


title: The Impact of Timing on the Salience of Smartphone App Privacy Notices

abstract: In a series of experiments, we examined how the timing impacts the salience of smartphone app privacy notices. In a web survey and a field experiment, we isolated different timing conditions for displaying privacy notices: in the app store, when an app is started, during app use, and after app use. Participants installed and played a history quiz app, either virtually or on their phone. After a distraction or delay they were asked to recall the privacy notice's content. Recall was used as a proxy for the attention paid to and salience of the notice. Showing the notice during app use significantly increased recall rates over showing it in the app store. In a follow-up web survey, we tested alternative app store notices, which improved recall but did not perform as well as notices shown during app use. The results suggest that even if a notice contains information users care about, it is unlikely to be recalled if only shown in the app store.


title: On the Diagonal Distribution of a Complex Wishart Matrix and its Application to the Analysis of MIMO Systems

abstract: The statistical properties of Wishart matrices have been extensively used to analyze the performance of multiple-input multiple-output (MIMO) systems. In particular, the signal-to-noise ratio (SNR) output statistics of several MIMO systems depends on the diagonal distribution of a complex Wishart matrix. In this paper, we derive the joint density of the diagonal elements of a complex Wishart matrix, which follows a multivariate chi-square distribution. The density expression is in the form of an infinite series representation which converges rapidly and is easy to compute. This expression is used to obtain the distribution of the maximum of the diagonal elements, which allows analyzing the performance of two different MIMO systems under practical conditions. First, our statistical results are applied to the outage probability characterization of MIMO systems with receive antenna selection in spatially correlated Rayleigh fading. Then, the same results are used to analyze the outage probability of transmit beamforming systems under limited-rate feedback.


title: Variable-Angle Phase-Shifted PWM for Multilevel Three-Cell Cascaded H-Bridge Converters

abstract: Multilevel cascaded H-bridge converters have become a mature technology for applications where high-power medium ac voltages are required. Normal operation of multilevel cascaded H-bridge converters assumes that all power cells have the same dc voltage, and each power cell generates the same voltage averaged over a sampling period using a conventional phase-shifted pulse width modulation (PWM) technique. However, this modulation method does not achieve good results under unbalanced operation per H-bridge in the power converter, which may happen in grid-connected applications such as photovoltaic or battery energy storage systems. In the paper, a simplified mathematical analysis of the phase-shifted PWM technique is presented. In addition, a modification of this conventional modulation method using variable shift angles between the power cells is introduced. This modification leads to the elimination of harmonic distortion of low-order harmonics due to the switching (triangular carrier frequency and its multiples) even under unbalanced operational conditions. The analysis is particularized for a three-cell cascaded H-bridge converter, and experimental results are presented to demonstrate the good performance of the proposed modulation method.


title: An Open Framework for Participatory PM2.5 Monitoring in Smart Cities

abstract: As the population in cities continues to increase rapidly, air pollution becomes a serious issue from public health to social economy. Among all pollutants, fine particulate matters (PM2.5) directly related to various serious health concerns, e.g., lung cancer, premature death, asthma, and cardiovascular and respiratory diseases. To enhance the quality of urban living, sensors are deployed to create smart cities. In this paper, we present a participatory urban sensing framework for PM2.5 monitoring with more than 2500 devices deployed in Taiwan and 29 other countries. It is one of the largest deployment project for PM2.5 monitor in the world as we know until May 2017. The key feature of the framework is its open system architecture, which is based on the principles of open hardware, open source software, and open data. To facilitate the deployment of the framework, we investigate the accuracy issue of low-cost particle sensors with a comprehensive set of comparison evaluations to identify the most reliable sensor. By working closely with government authorities, industry partners, and maker communities, we can construct an effective eco-system for participatory urban sensing of PM2.5 particles. Based on our deployment achievements to date, we provide a number of data services to improve environmental awareness, trigger on-demand responses, and assist future government policymaking. The proposed framework is highly scalable and sustainable with the potential to facilitate the Internet of Things, smart cities, and citizen science in the future.


title: A system for the collaborative reading of digital books with the partially sighted: project proposal

abstract: We propose a collaborative reading environment for digital books for use by and with individuals with failing vision. The system would record the audio of the person reading aloud and synchronize it with the text. The proposal includes two major components; the design of a tablet-based recording and reading system that feels like reading a book rather than using a complex application, and an environment for the intuitive re-reading of these digital books that has few to no visual requirements for the partially sighted individual. The DAISY standard would allow these audio-enhanced books to be interoperable with a variety of existing digital book systems.


title: Knowing When to Look: Adaptive Attention via a Visual Sentinel for Image Captioning

abstract: Attention-based neural encoder-decoder frameworks have been widely adopted for image captioning. Most methods force visual attention to be active for every generated word. However, the decoder likely requires little to no visual information from the image to predict non-visual words such as the and of. Other words that may seem visual can often be predicted reliably just from the language model e.g., sign after behind a red stop or phone following talking on a cell. In this paper, we propose a novel adaptive attention model with a visual sentinel. At each time step, our model decides whether to attend to the image (and if so, to which regions) or to the visual sentinel. The model decides whether to attend to the image and where, in order to extract meaningful information for sequential word generation. We test our method on the COCO image captioning 2015 challenge dataset and Flickr30K. Our approach sets the new state-of-the-art by a significant margin.


title: Video streaming in autonomous Mobile Robot using Wi-Fi

abstract: An Autonomous Mobile Robot is used to travel from one source to destination using two or more wheels. In order to reach a target where the person could not reach that particular destination, they must depend on some non-human called Mobile Robots. The Proposed work contains Autonomous Arduino Yun four wheeled mobile robot is allowed to move from source to destination position for finding a path, avoiding the obstacles and Video Streaming. These are achieved using a Wi-Fi technology, by avoiding the existing work with Bluetooth technology which is less secure and covers only shorter range. The best path is identified by using an Aggrandized Genetic Algorithm (AGA) which is best compared to few other algorithms. In order to achieve a secure communication and travelling to a longer distance when critical situation occurs to the person for reaching a destination from source, the Wi-Fi (IEEE 802.11 b/g/n) is useful for reaching the task.


title: Geometrically-Correct Projection-Based Texture Mapping onto a Deformable Object

abstract: Projection-based Augmented Reality commonly employs a rigid substrate as the projection surface and does not support scenarios where the substrate can be reshaped. This investigation presents a projection-based AR system that supports deformable substrates that can be bent, twisted or folded. We demonstrate a new invisible marker embedded into a deformable substrate and an algorithm that identifies deformations to project geometrically correct textures onto the deformable object. The geometrically correct projection-based texture mapping onto a deformable marker is conducted using the measurement of the 3D shape through the detection of the retro-reflective marker on the surface. In order to achieve accurate texture mapping, we propose a marker pattern that can be partially recognized and can be registered to an object's surface. The outcome of this work addresses a fundamental vision recognition challenge that allows the underlying material to change shape and be recognized by the system. Our evaluation demonstrated the system achieved geometrically correct projection under extreme deformation conditions. We envisage the techniques presented are useful for domains including prototype development, design, entertainment and information based AR systems.


title: Detecting seasonal queries by time-series analysis

abstract: Seasonal events such as Halloween and Christmas repeat every year and initiate several temporal information needs. The impact of such events on users is often reflected in search logs in form of seasonal spikes in the frequency of related queries (e.g. "halloween costumes", "where is santa"). Many seasonal queries such as "sigir conference" mainly target fresh pages (e.g. sigir2011.org) that have less usage data such as clicks and anchor-text compared to older alternatives (e.g.sigir2009.org). Thus, it is important for search engines to correctly identify seasonal queries and make sure that their results are temporally reordered if necessary. In this poster, we focus on detecting seasonal queries using time-series analysis. We demonstrate that the seasonality of a query can be determined with high accuracy according to its historical frequency distribution.


title: Arabic Text Recognition

abstract: The issue of handwritten character recognition is still a big challenge to the scientific community. Several approaches to address this challenge have been attempted in the last years, mostly focusing on the English pre-printed or handwritten characters space. Thus, the need to attempt a research related to Arabic handwritten text recognition. Algorithms based on neural networks have proved to give better results than conventional methods when applied to problems where the decision rules of the classification problem are not clearly defined. Two neural networks were built to classify already segmented characters of handwritten Arabic text. The two neural networks correctly recognized 73% of the characters. However, one hurdle was encountered in the above scenario, which can be summarized as follows: there are a lot of handwritten characters that can be segmented and classified into two or more different classes depending on whether they are looked at separately, or in a word, or even in a sentence. In other words, character classification, especially handwritten Arabic characters, depends largely on contextual information, not only on topographic features extracted from these characters.


title: Deep MIMO detection

abstract: In this paper, we consider the use of deep neural networks in the context of Multiple-Input-Multiple-Output (MIMO) detection. We give a brief introduction to deep learning and propose a modern neural network architecture suitable for this detection task. First, we consider the case in which the MIMO channel is constant, and we learn a detector for a specific system. Next, we consider the harder case in which the parameters are known yet changing and a single detector must be learned for all multiple varying channels. We demonstrate the performance of our deep MIMO detector using numerical simulations in comparison to competing methods including approximate message passing and semidefinite relaxation. The results show that deep networks can achieve state of the art accuracy with significantly lower complexity while providing robustness against ill conditioned channels and mis-specified noise variance.


title: Malicious Shellcode Detection with Virtual Memory Snapshots

abstract: Malicious shellcodes are segments of binary code disguised as normal input data. Such shellcodes can be injected into a target process's virtual memory. They overwrite the process's return addresses and hijack control flow. Detecting and filtering out such shellcodes is vital to prevent damage. In this paper, we propose a new malicious shellcode detection methodology in which we take snapshots of the process's virtual memory before input data are consumed, and feed the snapshots to a malicious shellcode detector. These snapshots are used to instantiate a runtime environment that emulates the target process's input data consumption to monitor shellcodes' behaviors. The snapshots can also be used to examine the system calls that shellcodes invoke, these system call parameters, and the process's execution flow. We implement a prototype system in Debian Linux with kernel version 2.6.26. Our extensive experiments with real traces and thousands of malicious shellcodes illustrate our system's performance with low overhead and few false negatives and few false positives.


title: Coupling CCG And Hybrid Logic Dependency Semantics

abstract: Categorial grammar has traditionally used the λ-calculus to represent meaning. We present an alternative, dependency-based perspective on linguistic meaning and situate it in the computational setting. This perspective is formalized in terms of hybrid logic and has a rich yet perspicuous propositional ontology that enables a wide variety of semantic phenomena to be represented in a single meaning formalism. Finally, we show how we can couple this formalization to Combinatory Categorial Grammar to produce interpretations compositionally.


title: Security in cloud computing: Opportunities and challenges

abstract: The cloud computing exhibits, remarkable potential to provide cost effective, easy to manage, elastic, and powerful resources on the fly, over the Internet. The cloud computing, upsurges the capabilities of the hardware resources by optimal and shared utilization. The above mentioned features encourage the organizations and individual users to shift their applications and services to the cloud. Even the critical infrastructure, for example, power generation and distribution plants are being migrated to the cloud computing paradigm. However, the services provided by third-party cloud service providers entail additional security threats. The migration of user's assets (data, applications, etc.) outside the administrative control in a shared environment where numerous users are collocated escalates the security concerns. This survey details the security issues that arise due to the very nature of cloud computing. Moreover, the survey presents the recent solutions presented in the literature to counter the security issues. Furthermore, a brief view of security vulnerabilities in the mobile cloud computing are also highlighted. In the end, the discussion on the open issues and future research directions is also presented.


VLSI Design and Implementation of Ternary Logic Gates and Ternary SRAM Cell


title: A Simple Relational Classifier

abstract: Abstract : We analyze a Relational Neighbor (RN) classifier, a simple relational predictive model the predicts only based on class labels of related neighbors, using no learning and no inherent attributes. We show that it performs surprisingly well by comparing it to more complex models such as Probabilistic Relational Models and Relational Probability Trees on three data sets from published work. We argue that a simple model such as this should be used as a baseline to assess the performance of relational learners.


title: Effect of fenugreek seeds on glycemia and dyslipidemia in patients with type 2 diabetes mellitus

abstract: Background: Fenugreek is an aromatic plant widely grown worldwide. Two fairly distinct types of plants are recognized—the dwarf type grown for culinary purposes and the tall type grown for medicinal purposes. It has small round leaves sold as vegetables (fresh leaves, sprouts) commonly known as methi and long pods that contained seeds known as menthe, which are used as such or in powdered form. These seeds are rich in soluble fibers and many phytochemical compounds. In addition to its hypoglycemic effect, the hypolipidemic effect of fenugreek seeds has also been documented. Therefore, fenugreek seeds have a dual role in the management of diabetes. It has been used as a herbal medicine in the management of glycemia and dyslipidemia since a long time in India. ::: ::: Objective: To evaluate the effect of fenugreek seeds taken orally in patients with type 2 diabetes mellitus (DM) with dyslipidemia. ::: ::: Materials and Methods: This prospective study was conducted on patients with type 2 DM with dyslipidemia after meeting inclusion and exclusion criteria. The patients were divided into two groups (age matched). One group received fenugreek seeds and other group received drugs (oral hypoglycemic and hypolipidemic agents). Lipid profile and HbA1C were measured initially as well as after 8 weeks of treatment period. Blood sugar (fasting) was recorded weekly. Student’s t-test was applied for statistical analysis. ::: ::: Results: After 8 weeks of treatment of fenugreek seeds in group 1 patients, there was a significant improvement in blood glucose Fasting level (178±72.4 to 104±28.2; p < 0.0001), T-cholesterol (350±20.6 to 176±17.2; p < 0.0001), triglycerides (280±18.2 to 132±16.8; p < 0.0001), low-density cholesterol (220±21.4 to 96±14.2; p < 0.0001), and high-density cholesterol (27.0±13.4 to 58±32.2; p < 0.0002). Although there was an improvement in HbA1C status but it was not significant. ::: ::: Conclusion: This study supports beneficial effects of fenugreek seeds on glycemia and dyslipidemia in patients with type 2 DM and can be used as an adjuvant/alternative in management of diabetes and its complications.


title: Usability heuristics for touchscreen-based mobile devices: update

abstract: The idea of usability as something optional and only applicable in latest stages of the development process is obsolete. Usability is very valuable for users, reason why counting with proper tools for assessing usability in products like touchscreen-based mobile devices is needed. One traditional usability evaluation method is the heuristic evaluation, where evaluators through the use of usability heuristics detect usability issues. In this paper we introduce a revised version of our previously proposed set of 12 usability heuristics for touchscreen-based mobile devices. Three types of validation experiments were performed: inquiry tests, heuristic evaluations and experts' opinion. Results supported and checked the utility of the proposal.


title: Analysis of Named Entity Recognition and Linking for Tweets

abstract: Applying natural language processing for mining and intelligent information access to tweets (a form of microblog) is a challenging, emerging research area. Unlike carefully authored news text and other longer content, tweets pose a number of new challenges, due to their short, noisy, context-dependent, and dynamic nature. Information extraction from tweets is typically performed in a pipeline, comprising consecutive stages of language identification, tokenisation, part-of-speech tagging, named entity recognition and entity disambiguation (e.g. with respect to DBpedia). In this work, we describe a new Twitter entity disambiguation dataset, and conduct an empirical analysis of named entity recognition and disambiguation, investigating how robust a number of state-of-the-art systems are on such noisy texts, what the main sources of error are, and which problems should be further investigated to improve the state of the art.


title: Performance of Alternating Least Squares in a Distributed Approach Using GraphLab and MapReduce

abstract: Automated recommendation systems have been increasingly adopted by companies that aim to draw people attention about products and services on Internet. In this sense, development of distributed model abstractions such as MapReduce and GraphLab has brought new possibilities for recommendation research tasks due to allow us to perform Big Data analysis. Thus, this paper investigates the suitability of these two approaches for massive recommendation. In order to do so, the Alternating Least Squares (ALS), which is a Collaborative Filtering algorithm, has been tested using recommendation benchmark datasets. Results on RMSE show a preliminary comparative performance analysis.


title: Arb: A software environment for sequence data

abstract: The ARB (from Latin arbor, tree) project was initiated almost 10 years ago. The ARB program package comprises a variety of directly interacting software tools for sequence database maintenance and analysis which are controlled by a common graphical user interface. Although it was initially designed for ribosomal RNA data, it can be used for any nucleic and amino acid sequence data as well. A central database contains processed (aligned) primary structure data. Any additional descriptive data can be stored in database fields assigned to the individual sequences or linked via local or worldwide networks. A phylogenetic tree visualized in the main window can be used for data access and visualization. The package comprises additional tools for data import and export, sequence alignment, primary and secondary structure editing, profile and filter calculation, phylogenetic analyses, specific hybridization probe design and evaluation and other components for data analysis. Currently, the package is used by numerous working groups worldwide.


title: Forensic mitochondrial DNA analysis: two years of commercial casework experience in the United States.

abstract: AIM ::: To describe mitochondrial DNA (mtDNA) forensic casework experience in a commercial laboratory in the United States. ::: ::: ::: METHODS ::: Frequency statistics were kept for two years on all aspects of mtDNA forensic cases, including types of clientele, types of samples, levels of sample success and failure, site heteroplasmy, length heteroplasmy, contamination, rates of failures to exclude, and match statistics using a mtDNA sequence database. ::: ::: ::: RESULTS ::: Low sample failure rate was observed, especially since an "ancient DNA" approach was used for samples with degraded DNA. Levels of contamination were low, and the observed site and length heteroplasmy did not confound the interpretation of results. The data collected from mtDNA haplotypes developed in casework showed extremely high diversity of haplotypes consistent with other formally developed databases. ::: ::: ::: CONCLUSIONS ::: MtDNA forensic analysis in the private sector was successfully applied to many different types of samples overall, with minimal rates of complication due to sample handling challenges (degraded DNA, minimal samples, contamination) and sequence-specific phenomena (site and length heteroplasmy).


title: The ATILF-LLF System for Parseme Shared Task: a Transition-based Verbal Multiword Expression Tagger

abstract: We describe the ATILF-LLF system built for the MWE 2017 Shared Task on automatic identification of verbal multiword expressions. We participated in the closed track only, for all the 18 available languages. Our system is a robust greedy transition-based system, in which MWE are identified through a MERGE transition. The system was meant to accommodate the variety of linguistic resources provided for each language, in terms of accompanying morphological and syntactic information. Using per-MWE Fscore, the system was ranked first 1 for all but two languages (Hungarian and Romanian).


title: Soul Searching: Why Psychotherapy Must Promote Moral Responsibility

abstract: What is going on here? I am asked to review a book on modern psychotherapy practice, and it contains a value-laden, almost foreign language. Words such as fairness, truthfulness, moral responsibility, moral character, courage, commitment, caring, community, prudence , and justice are found throughout. The author of such nonneutral syntax is a practicing family psychotherapist and academician whose purposes are to examine morality in psychotherapy practice and to emphasize the need for understanding the moral stance of a therapist. The author traces the history of psychotherapy through the present. He points out how the practice has changed and is undergoing a significant crisis of skepticism, confidence, and funding. He suggests that such developments are due in part to two factors. One is an erosion of the moral center of mainstream culture. The second is a growing recognition of psychotherapy practice as an extension of white, middle-class, North American male culture. Whereas


title: Agricultural Robotics: The Future of Robotic Agriculture

abstract: Agri-Food is the largest manufacturing sector in the UK. It supports a food chain that generates over £108bn p.a., with 3.9m employees in a truly international industry and exports £20bn of UK manufactured goods. However, the global food chain is under pressure from population growth, climate change, political pressures affecting migration, population drift from rural to urban regions and the demographics of an aging global population. These challenges are recognised in the UK Industrial Strategy white paper and backed by significant investment via a Wave 2 Industrial Challenge Fund Investment ("Transforming Food Production: from Farm to Fork"). Robotics and Autonomous Systems (RAS) and associated digital technologies are now seen as enablers of this critical food chain transformation. To meet these challenges, this white paper reviews the state of the art in the application of RAS in Agri-Food production and explores research and innovation needs to ensure these technologies reach their full potential and deliver the necessary impacts in the Agri-Food sector.


title: Crisis prevention: how to gear up your board.

abstract: Today's critics of corporate boardrooms have plenty of ammunition. The two crucial responsibilities of boards-oversight of long-term company strategy and the selection, evaluation, and compensation of top management--were reduced to damage control during the 1980s. Walter Salmon, a longtime director, notes that while boards have improved since he began serving on them in 1961, they haven't kept pace with the need for real change. Based on over 30 years of boardroom experience, Salmon recommends against government reform of board practices. But he does prescribe a series of incremental changes as a remedy. To begin with, he suggests limiting the size of boards and increasing the number of outside directors on them. In fact, according to Salmon, only three insiders belong on a board: the CEO, the COO, and the CFO. Changing how committees function is also necessary for gearing up today's boards. The audit committee, for example, can periodically review "high-exposure areas" of a business, perhaps helping to prevent embarrassing drops in future profits. Compensation committees can structure incentive compensation for executives to emphasize long-term rather than short-term performance. And nominating committees should be responsible for finding new, independent directors--not the CEO. In general, boards as a whole must spot problems early and blow the whistle, exercising what Salmon calls, "constructive dissatisfaction." On a revitalized board, directors have enough confidence in the process to vigorously challenge one another, including the company's chief executive.


title: Adversarially Regularized Autoencoders for Generating Discrete Structures

abstract: Generative adversarial networks are an effective approach for learning rich latent representations of continuous data, but have proven difficult to apply directly to discrete structured data, such as text sequences or discretized images. Ideally we could encode discrete structures in a continuous code space to avoid this problem, but it is difficult to learn an appropriate general-purpose encoder. In this work, we consider a simple approach for handling these two challenges jointly, employing a discrete structure autoencoder with a code space regularized by generative adversarial training. The model learns a smooth regularized code space while still being able to model the underlying data, and can be used as a discrete GAN with the ability to generate coherent discrete outputs from continuous samples. We demonstrate empirically how key properties of the data are captured in the model's latent space, and evaluate the model itself on the tasks of discrete image generation, text generation, and semi-supervised learning.


title: Dual-band antenna array with beam steering for mm-waves 5G networks

abstract: This work reports an innovative structure and preliminary results of a four-elements antenna array with beam steering for 5G access cellular networks, operating in the underutilized millimetre wave (mm-wave) frequency spectrum. Its structure is based on four slotted-waveguide antenna arrays, which enables to simultaneously perform radiation pattern reconfiguration in the 28 GHz and 38 GHz bands, providing scanning range of 75° and 55° for the lower and higher frequency bands, respectively. The bandwidth varies from 24.19 GHz to 31.42GHz and from 36.61 GHz to 39.22GHz.


title: Scanning LIDAR in Advanced Driver Assistance Systems and Beyond: Building a road map for next-generation LIDAR technology

abstract: Advanced driver assistance systems (ADASs) are accelerating the growth of infrared (IR) devices used in automotive applications. Some of these are well-known applications, such as ambient light sensing, steering wheel sensors, and rain sensors. However, many new technologies for ADASs, such as LIDAR, driver monitoring, adaptive beam, and matrix lighting, are still in the early evolution phase of the product life cycle. Figure 1 provides a snapshot of some of the IR devices from OSRAM Opto Semiconductors (Regensburg, Germany) and the applications for which they can be used.


title: Computing Least Common Subsumers in Description Logics with Existential Restrictions

abstract: Computing the least common subsunier (les) is an inference task that can be used to support, the "bottom-up" construction of knowledge bases for KR systems based on description logics. Previous work on how to compute the lcs has concentrated on description logics that allow for universal value restrictions, but not for existential restrictions. The main new contribution of this paper is the treatment of description logics with existential restrictions. Our approach for computing the lcs is based on an appropriate representation of concept descriptions by certain trees, and a characterization of subsumption by homomorphisins between these trees. The lcs operation then corresponds to the product operation on trees.


title: A feedback based scheme for improving TCP performance in ad-hoc wireless networks

abstract: Ad-hoc networks are completely wireless networks of mobile hosts, in which the topology rapidly changes due to the movement of mobile hosts. This frequent topology may lead to sudden packet losses and delays. Transport protocols like TCP have been built mainly for reliable, fixed networks. Hence, when used in ad-hoc networks, TCP misinterprets this loss as congestion and invokes congestion control. This leads to unnecessary retransmissions and loss of throughput. To overcome this problem, a feedback scheme is proposed, so that the source can distinguish between route failure and network congestion. When a route is disrupted, the source is sent a route failure notification (RFN) packet, allowing it to freeze its timers and stop sending packets. When the route is re-established, the source is informed through a route re-establishment notification (RRN) packet, upon which it resumes by unfreezing timers and continuing packet transmissions. The simulated performance of TCP on ad-hoc networks with and without feedback is compared and reported. It is observed that in the event of route failures, as the route re-establishment time increases, the use of feedback provides significant gains in throughput as well as savings in unnecessary packet transmissions. Several further enhancements and directions for future work are also sketched.


title: Automatic nesting seabird detection based on boosted HOG-LBP descriptors

abstract: Seabird populations are considered an important and accessible indicator of the health of marine environments: variations have been linked with climate change and pollution [1]. However, manual monitoring of large populations is labour-intensive, and requires significant investment of time and effort. In this paper, we propose a novel detection system for monitoring a specific population of Common Guillemots on Skomer Island, West Wales (UK). We incorporate two types of features, Histograms of Oriented Gradients (HOG) and Local Binary Pattern (LBP), to capture the edge/local shape information and the texture information of nesting seabirds. Optimal features are selected from a large HOG-LBP feature pool by boosting techniques, to calculate a compact representation suitable for the SVM classifier. A comparative study of two kinds of detectors, i.e., whole-body detector, head-beak detector, and their fusion is presented. When the proposed method is applied to the seabird detection, consistent and promising results are achieved.


title: Bioinformatic and functional analysis of RNA secondary structure elements among different genera of human and animal caliciviruses

abstract: The mechanism and role of RNA structure elements in the replication and translation of Caliciviridae remains poorly understood. Several algorithmically independent methods were used to predict secondary structures within the Norovirus, Sapovirus, Vesivirus and Lagovirus genera. All showed profound suppression of synonymous site variability (SSSV) at genomic 5' ends and the start of the sub-genomic (sg) transcript, consistent with evolutionary constraints from underlying RNA structure. A newly developed thermodynamic scanning method predicted RNA folding mapping precisely to regions of SSSV and at the genomic 3' end. These regions contained several evolutionarily conserved RNA secondary structures, of variable size and positions. However, all caliciviruses contained 3' terminal hairpins, and stem-loops in the anti-genomic strand invariably six bases upstream of the sg transcript, indicating putative roles as sg promoters. Using the murine norovirus (MNV) reverse-genetics system, disruption of 5' end stem-loops produced approximately 15- to 20-fold infectivity reductions, while disruption of the RNA structure in the sg promoter region and at the 3' end entirely destroyed replication ability. Restoration of infectivity by repair mutations in the sg promoter region confirmed a functional role for the RNA secondary structure, not the sequence. This study provides comprehensive bioinformatic resources for future functional studies of MNV and other caliciviruses.


Addiction and Depression , Anxiety and Stress