title: Adversarial Transformation Networks: Learning to Generate Adversarial Examples

abstract: Multiple different approaches of generating adversarial examples have been proposed to attack deep neural networks. These approaches involve either directly computing gradients with respect to the image pixels, or directly solving an optimization on the image pixels. In this work, we present a fundamentally new method for generating adversarial examples that is fast to execute and provides exceptional diversity of output. We efficiently train feed-forward neural networks in a self-supervised manner to generate adversarial examples against a target network or set of networks. We call such a network an Adversarial Transformation Network (ATN). ATNs are trained to generate adversarial examples that minimally modify the classifier's outputs given the original input, while constraining the new classification to match an adversarial target class. We present methods to train ATNs and analyze their effectiveness targeting a variety of MNIST classifiers as well as the latest state-of-the-art ImageNet classifier Inception ResNet v2.


title: Initialization in Scratch: Seeking Knowledge Transfer

abstract: With the growing movement to use visual block-based languages (VBBLs) in elementary and middle school classrooms, questions arise about the learning outcomes of such activities. While some schools are content to use VBBLs to spark interest and motivation for the future pursuit of computing, others are asking, "Does this early exposure produce knowledge that transfers to traditional text-based languages (TBLs)?" If transfer is a goal, then a corollary is, "How do we design the transition to maximize the transfer?" This paper focuses on initialization of state and variables, exploring the differences between Scratch and two TBLs: C and Java. Based on observations of 9-12 year old students in a VBBL curriculum, we identify four "pieces of knowledge" that are critical for C and Java but are not nearly as obvious in Scratch, including whether, when, and how to perform initialization. We conclude with suggestions for instruction and development environment that may improve transfer.


title: Pragmatic analysis of malayalam sentences

abstract: Natural language processing is one of the major field in computer science. NLP is the ability of the system to process different sentences in natural language. Parts of speech tagging, pragmatic analysis, machine translation, discourse analysis etc are the different fields in NLP. Malayalam is the one of the important language in Dravidian family, where the difficult grammar structure will be the main problem. Pragmatics analysis of Malayalam sentences mainly represent how sentences are used in different situation or different context. In proposed system we can apply pragmatics for Malayalam sentence to understand the user intention about a particular sentence.


title: Design and simulation of dual rotor permanent magnet brushless DC motor

abstract: Dual-rotor permanent magnet brushless (DRPMB) machine is proposed to satisfy some special requirements, such as the drive systems of Electrical Vehicle. However, the DRPMB motor is difficult to be designed through the existing commercial programs due to its particular configuration. In this paper, an equivalent magnetic-circuit method has been given to design DRPMB motor. Then a prototype was fabricated and tested in order to prove the validity of the method and indicate the superiority of this kind of machine.


title: Context-sensitive Twitter sentiment classification using neural network

abstract: Sentiment classification on Twitter has attracted increasing research in recent years. Most existing work focuses on feature engineering according to the tweet content itself. In this paper, we propose a context-based neural network model for Twitter sentiment analysis, incorporating contextualized features from relevant Tweets into the model in the form of word embedding vectors. Experiments on both balanced and unbalanced datasets show that our proposed models outperform the current state-of-the-art.


title: A smart market for passenger road transport (SMPRT) congestion: An application of computational mechanism design

abstract: To control and price negative externalities in passenger road transport, we develop an innovative and integrated computational agent based economics (ACE) model to simulate a market oriented "cap" and trade system. (i) First, there is a computational assessment of a digitized road network model of the real world congestion hot spot to determine the "cap" of the system in terms of vehicle volumes at which traffic efficiency deteriorates and the environmental externalities take off exponentially. (ii) Road users submit bids with the market clearing price at the fixed "cap" supply of travel slots in a given time slice (peak hour) being determined by an electronic sealed bid uniform price Dutch auction. (iii) Cross-sectional demand data on car users who traverse the cordon area is used to model and calibrate the heterogeneous bid submission behaviour in order to construct the inverse demand function and demand elasticities. (iv) The willingness to pay approach with heterogeneous value of time is contrasted with the generalized cost approach to pricing congestion with homogeneous value of travel time.


title: Corrosion failure analysis of galvanized steel pipes in a water irrigation system

abstract: Abstract The premature failure of a water irrigation system made of galvanized steel pipes installed in the orange orchards of a citrus farm located in the interior of the state of Sao Paulo, Brazil, was investigated by onsite visual inspection, microstructural characterization of the Zn coatings based on SEM/EDS analysis, and chemical and physical analysis of the water used in the system. The irrigation water comes from a creek that runs through the farm, and the first pipe leakages were detected after the system had been in operation for only 2 years. The findings of this investigation indicate that the failure of the pipes before their expected service lifetime was caused by acid corrosion, which rapidly attacked the Zn coating of the inner walls of the pipes, exposing the entire steel surface in the first months of use. The deterioration of the pipes was intensified by the mechanical wear resulting from a substantial amount of suspended solids in the flowing water. In addition, the SEM/EDS analysis of the Zn coating suggested that some points of the galvanized pipes exhibited manufacturing defects, further impairing the corrosion performance of these pipes.


title: Cost Models for Future Software Life Cycle Processes: COCOMO 2.0

abstract: Current software cost estimation models, such as the 1981 Constructive Cost Model (COCOMO) for software cost estimation and its 1987 Ada COCOMO update, have been experiencing increasing difficulties in estimating the costs of software developed to new life cycle processes and capabilities. These include non-sequential and rapid-development process models; reuse-driven approaches involving commercial off-the-shelf (COTS) packages, re-engineering, applications composition, and applications generation capabilities; object-oriented approaches supported by distributed middleware; and software process maturity initiatives. This paper summarizes research in deriving a baseline COCOMO 2.0 model tailored to these new forms of software development, including rationale for the model decisions. The major new modeling capabilities of COCOMO 2.0 are a tailorable family of software sizing models, involving Object Points, Function Points, and Source Lines of Code; nonlinear models for software reuse and re-engineering; an exponentdriver approach for modeling relative software diseconomies of scale; and several additions, deletions and updates to previous COCOMO effort-multiplier cost drivers. This model is serving as a framework for an extensive current data collection and analysis effort to further refine and calibrate the model's estimation capabilities.


title: Pharmacokinetic and pharmacodynamic data and models in clinical trials

abstract: There is current emphasis for extended integration of pharmacokinetics (PK) and pharmacodynamics (PD) into all phases of new drug development, including large-scale clinical trials. In this paper, we focus on study design and data analysis issues for the investigation of pharmacokinetic/pharmacodynamic and blood level/effect relationships in patients. The application of descriptive and model-based regression statistical methodology for including sparse drug systemic concentration data in the analysis of efficacy and safety is illustrated by examples chosen from diverse therapeutic areas. The population approach, based on mixed-effects modelling, is one such methodology, which also provides new tools for analysis of response vs dose and response vs time data. The existence of a variety of statistical techniques for handling complex PK/PD time-varying data should increase the impact of such data analysis on future drug development.


title: Creativity versus the perception of creativity in computational systems

abstract: We add to the discussion of how to assess the creativity of programs which generate artefacts such as poems, theorems, paintings, melodies, etc. To do so, we first review some existing frameworks for assessing artefact generation programs. Then, drawing on our experience of building both a mathematical discovery system and an automated painter, we argue that it is not appropriate to base the assessment of a system on its output alone, and that the way it produces artefacts also needs to be taken into account. We suggest a simple framework within which the behaviour of a program can be categorised and described which may add to the perception of creativity in the system.


title: The geometry of musical chords

abstract: A musical chord can be represented as a point in a geometrical space called an orbifold. Line segments represent mappings from the notes of one chord to those of another. Composers in a wide range of styles have exploited the non-Euclidean geometry of these spaces, typically by using short line segments between structurally similar chords. Such line segments exist only when chords are nearly symmetrical under translation, reflection, or permutation. Paradigmatically consonant and dissonant chords possess different near-symmetries and suggest different musical uses.


title: In search of project classification: a non-universal approach to project success factors

abstract: Abstract In this study we attempt to answer two questions: Is there a natural way to classify projects and what are the specific factors that influence the success of various kinds of projects? Perhaps one of the major barriers to understanding the reasons behind the success of a project has been the lack of specificity of constructs applied in project management studies. Many studies of project success factors have used a universalistic approach, assuming a basic similarity among projects. Instead of presenting an initial construct, we have employed a linear discriminant analysis methodology in order to classify projects. Our results suggest that project success factors are not universal for all projects. Different projects exhibit different sets of success factors, suggesting the need for a more contingent approach in project management theory and practice. In the analysis we use multivariate methods which have been proven to be powerful in many ways, for example, enabling the ranking of different managerial factors according to their influence on project success.


title: Toward Evidence-Based Medical Statistics. 2: The Bayes Factor

abstract: Bayesian inference is usually presented as a method for determining how scientific belief should be modified by data. Although Bayesian methodology has been one of the most active areas of statistical development in the past 20 years, medical researchers have been reluctant to embrace what they perceive as a subjective approach to data analysis. It is little understood that Bayesian methods have a data-based core, which can be used as a calculus of evidence. This core is the Bayes factor, which in its simplest form is also called a likelihood ratio. The minimum Bayes factor is objective and can be used in lieu of the P value as a measure of the evidential strength. Unlike P values, Bayes factors have a sound theoretical foundation and an interpretation that allows their use in both inference and decision making. Bayes factors show that P values greatly overstate the evidence against the null hypothesis. Most important, Bayes factors require the addition of background knowledge to be transformed into inferences--probabilities that a given conclusion is right or wrong. They make the distinction clear between experimental evidence and inferential conclusions while providing a framework in which to combine prior with current evidence.


title: Determination of cell division axes in the early embryogenesis of Caenorhabditis elegans

abstract: The establishment of cell division axes was examined in the early embryonic divisions of Caenorhabditis elegans. It has been shown previously that there are two different patterns of cleavage during early embryogenesis. In one set of cells, which undergo predominantly determinative divisions, the division axes are established successively in the same orientation, while division axes in the other set, which divide mainly proliferatively, have an orthogonal pattern of division. We have investigated the establishment of these axes by following the movement of the centrosomes. Centrosome separation follows a reproducible pattern in all cells, and this pattern by itself results in an orthogonal pattern of cleavage. In those cells that divide on the same axis, there is an additional directed rotation of pairs of centrosomes together with the nucleus through well-defined angles. Intact microtubules are required for rotation; rotation is prevented by inhibitors of polymerization and depolymerization of microtubules. We have examined the distribution of microtubules in fixed embryos during rotation. From these and other data we infer that microtubules running from the centrosome to the cortex have a central role in aligning the centrosome-nuclear complex.


title: A framework for joint community detection across multiple related networks

abstract: Community detection in networks is an active area of research with many practical applications. However, most of the early work in this area has focused on partitioning a single network or a bipartite graph into clusters/communities. With the rapid proliferation of online social media, it has become increasingly common for web users to have noticeable presence across multiple web sites. This raises the question whether it is possible to combine information from several networks to improve community detection. In this paper, we present a framework that identifies communities simultaneously across different networks and learns the correspondences between them. The framework is applicable to networks generated from multiple web sites as well as to those derived from heterogeneous nodes of the same web site. It also allows the incorporation of prior information about the potential relationships between the communities in different networks. Extensive experiments have been performed on both synthetic and real-life data sets to evaluate the effectiveness of our framework. Our results show superior performance of simultaneous community detection over three alternative methods, including normalized cut and matrix factorization on a single network or a bipartite graph.


title: 2006b), “System configuration of intelligent parking assistant system

abstract: The configuration of the currently developed ipas (intelligent parking assistant system) is described. ipas allows driver to designate target position by three complimentary methods: monocular vision based parking slot marking recognition, ultrasonic sensor based parallel parking slot recognition, and drag&drop gui (graphical user interface). Ipas generates an optimal path to reach the designated target position. during parking operations, ipas estimates ego-vehicle pose using esp (electric stability program) sensors such as wheel speed sensors, braking pedal switch and steering angle sensor. Ipas automatically controls braking and steering to achieve planned trajectory by sending required actuations to esp and eps (electric power steering) via can. Furthermore, ipas informs driver of the on-going parking operation by showing estimated trajectory on the rear view image. The proposed system is validated by vehicle experiments.


title: Multiple orbital angular momentum (OAM) modes (De) multiplexer based on single complex phase mask

abstract: We propose and experimentally demonstrate multiple OAM modes demultiplexing with single complex phase mask. The demultiplexed position of each OAM mode can be flexibly controlled by designing the phase mask. Eight OAM modes have been well demultiplexed simultaneously.


title: The Artifact’s Theory – A Grounded Theory Perspective on Design Science Research: 10. Internationale Tagung Wirtschaftsinformatik

abstract: The need for theoretical work in IS research has been pointed out frequently in past discussions on the cumulative tradition of our discipline. In order to distinct IS research from adjacent fields, the practice of designing artifacts plays a crucial role. On the one hand, the construction and evaluation of new and innovative arti- facts solving real world problems is the core of our discipline. On the other hand, this designing aspect of IS research has been ac- cused of not being linked sufficiently to theoretical considerations in the past. To help IS design researchers to address this chal- lenge, our paper analyzes research generating theory based on the Grounded Theory Method. By identifying, analyzing, and review- ing such research we identify some process characteristics of grounded theory building that can help IS researchers to improve their design processes to also yield theoretical output. Thus, we aim to make an IS design researcher’s work clearly distinguisha- ble to the work of a software developer or a consultant. By also discussing the role of theory in this context, we intend to make a case for more theoretical work in IS design research.


title: DVB-T2 modulator design supporting multiple PLP and auxiliary streams

abstract: This paper presents the design of a DVB-T2 modulator algorithm supporting multiple PLP and auxiliary streams. The algorithm works over an asynchronous modulator design, presenting per stream scheduling policies, T2 Frames capacity management, and DFL computation. A software tool was developed in order to create system configurations, validate input parameters, and manage per stream capacity distribution, according to proposed scheduling policies.


title: A Review on Classification of the Urban Poverty Using the Artificial Intelligence Method

abstract: Poverty and how it has been assessed and measured is a frequently discussed topic by policy makers and social developers. The identification process in poverty measurement is indeed essential towards acknowledging the poor in the population; hence this needs to be clarified. Malaysia measures poverty by means of poverty line, indicating the unidimensional and inflexible distribution of poor and non-poor especially in urban areas. Many researchers have used fuzzy logic to solve the problem of rigid poor/non-poor dichotomy. This current trend has been able to augment the gap between the rigid and inflexible classification of poor and non-poor. However, there are still several shortcomings that need attention. For instance, the classification of the poor in fuzzy logic that is based on the average income of households still does not cover on the different range of disadvantage on non-monetary items. Based on these trends, ANFIS is proposed to resolve on the highlighted issues. The winning features of ANFIS, which include on simplicity in implementation, understandable explanation facilities through fuzzy rules, and ease of incorporation of both linguistic and numeric knowledge for problem solving may help in producing better result in classification of the urban poor. Essentially, the neural network is proposed to complement the fuzzy system, hence overcoming the limitations of both fuzzy systems and neural networks. As such, ANFIS method is used in this study to better classify on the poor and non-poor compared to fuzzy rule-based system which is lacking in prediction error rate due to too many variables used. However, this method deteriorates from misclassified poverty indicators; hence this study proposed on ensemble ANFIS to produce more accurate and robust classification results. An ensemble model is usually employed to address the problems of over-fitting, high dimensionality or missing features in the training data. Generally, combining multiple classification models increases predictive performance compared to the use of an individual model alone. Therefore, based on these current trends, this study is aimed to do a review on classification of the urban poverty using the artificial intelligence method.


title: Interleukin-7 receptor expression: intelligent design

abstract: Interleukin-7 (IL-7) is produced by stromal cells in lymphoid tissues and is required for the development of T cells and for their persistence in the periphery. Unlike many other cytokines that act on lymphocytes, IL-7 production by stromal cells is not substantially affected by extrinsic stimuli. So, the amount of available IL-7 protein is thought to be regulated by the rate that it is scavenged by T cells. As we review here, there is mounting evidence indicating that the amount of IL-7 receptor expressed on a cell not only determines how vigorously the cell responds to IL-7, but it can also determine how efficiently the cell consumes IL-7 and, therefore, affect the supply of this limiting resource in the niche.


title: A reliable path selection for vehicular Adhoc Network using Reliability matrix and connectivity matrix

abstract: Vehicular Ad Hoc Network (VANET) is a subgroup of mobile ad hoc network (MANET). It is an emerging new technology to exchange information between vehicles to vehicles. VANETs are considered as one of the most noticeable technologies for improving the efficiency and safety of transportation systems. VANET mainly used to exchange traffic information between the vehicles and prevent accident. In VANETs the high mobility of the nodes is the major concern. This dynamic topology makes the route unstable and unreliable for exchange of information or messages among the vehicles in the ad hoc network. To enhance the performance and throughput of the VANETs, routes between nodes must be reliable, less overhead and stable. Most of the current routing protocols use the traditional hop count as a metric to distinguish between routes, but still overhead problem is there in nodes. In this paper we proposed a reliability metrics (RM) and Connectivity metrics (CM) as a routing metrics to determine the best path from source to destination vehicle. This metrics were used to reduce the routing overhead and find the reliable path from source to destination. In VANET estimation of reliable path is a challenging task because the routing is depends upon many parameters like direction, number of hops, transmission range, node density and speed of the nodes.


title: INTELLIGENT WHEELCHAIR FOR PEOPLE WITH DISABILITIES S.Shaheen #1, A.Umamakeswari *2

abstract: People make use of different languages to express their thoughts in a different way. But, it is difficult for the people who are affected by paralysis and stroke. Hence, there is a need to develop a platform for those people who are physically challengeable. An Embedded device shall address the above said problems. Based on the ARM processor the operation takes place. The device is designed with an intelligent wheelchair and the People With Disabilities (PWD) can direct it. By pressing the keypad (buttons) in the device, people with disabilities can move the wheelchair. In this process, the line following algorithm is implemented. To direct the wheelchair, L298 motor driver is used with the help of an ultrasonic obstacle sensor. By pressing a button, an automatic call to the doctor takes place if the PWD needs critical help. The GSM modem is used to forward the call. The LCD shows the basic needs of PWD which is already assigned in the buttons. On pressing the button, a text is displayed in the LCD with a buzzer sound. This device provides a unique mobility for the people with disabilities.


title: An Efficient Green Control Algorithm in Cloud Computing for Cost Optimization

abstract: Cloud computing is a new paradigm for delivering remote computing resources through a network. However, achieving an energy-efficiency control and simultaneously satisfying a performance guarantee have become critical issues for cloud providers. In this paper, three power-saving policies are implemented in cloud systems to mitigate server idle power. The challenges of controlling service rates and applying the N-policy to optimize operational cost within a performance guarantee are first studied. A cost function has been developed in which the costs of power consumption, system congestion and server startup are all taken into consideration. The effect of energy-efficiency controls on response times, operating modes and incurred costs are all demonstrated. Our objectives are to find the optimal service rate and mode-switching restriction, so as to minimize cost within a response time guarantee under varying arrival rates. An efficient green control (EGC) algorithm is first proposed for solving constrained optimization problems and making costs/performances tradeoffs in systems with different power-saving policies. Simulation results show that the benefits of reducing operational costs and improving response times can be verified by applying the power-saving policies combined with the proposed algorithm as compared to a typical system under a same performance guarantee.


Influence of Bridged Taps on Transmission Performance of VDSL2 Lines


Introducing efficient parallelism into approximate string matching and a new serial algorithm


Detection of Damage and Failure Events of Critical Public Infrastructure using Social Sensor Big Data


title: Speech-cue transmission by an algorithm to increase consonant recognition in noise for hearing-impaired listeners

abstract: Consonant recognition was assessed following extraction of speech from noise using a more efficient version of the speech-segregation algorithm described in Healy, Yoho, Wang, and Wang [(2013) J. Acoust. Soc. Am. 134, 3029-3038]. Substantial increases in recognition were observed following algorithm processing, which were significantly larger for hearing-impaired (HI) than for normal-hearing (NH) listeners in both speech-shaped noise and babble backgrounds. As observed previously for sentence recognition, older HI listeners having access to the algorithm performed as well or better than young NH listeners in conditions of identical noise. It was also found that the binary masks estimated by the algorithm transmitted speech features to listeners in a fashion highly similar to that of the ideal binary mask (IBM), suggesting that the algorithm is estimating the IBM with substantial accuracy. Further, the speech features associated with voicing, manner of articulation, and place of articulation were all transmitted with relative uniformity and at relatively high levels, indicating that the algorithm and the IBM transmit speech cues without obvious deficiency. Because the current implementation of the algorithm is much more efficient, it should be more amenable to real-time implementation in devices such as hearing aids and cochlear implants.


title: Inferring Missing Entity Type Instances for Knowledge Base Completion: New Dataset and Methods

abstract: Most of previous work in knowledge base (KB) completion has focused on the problem of relation extraction. In this work, we focus on the task of inferring missing entity type instances in a KB, a fundamental task for KB competition yet receives little attention. Due to the novelty of this task, we construct a large-scale dataset and design an automatic evaluation methodology. Our knowledge base completion method uses information within the existing KB and external information from Wikipedia. We show that individual methods trained with a global objective that considers unobserved cells from both the entity and the type side gives consistently higher quality predictions compared to baseline methods. We also perform manual evaluation on a small subset of the data to verify the effectiveness of our knowledge base completion methods and the correctness of our proposed automatic evaluation method.


title: Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue

abstract: A significant weakness of most current deep Convolutional Neural Networks is the need to train them using vast amounts of manually labelled data. In this work we propose a unsupervised framework to learn a deep convolutional neural network for single view depth prediction, without requiring a pre-training stage or annotated ground-truth depths. We achieve this by training the network in a manner analogous to an autoencoder. At training time we consider a pair of images, source and target, with small, known camera motion between the two such as a stereo pair. We train the convolutional encoder for the task of predicting the depth map for the source image. To do so, we explicitly generate an inverse warp of the target image using the predicted depth and known inter-view displacement, to reconstruct the source image; the photometric error in the reconstruction is the reconstruction loss for the encoder. The acquisition of this training data is considerably simpler than for equivalent systems, requiring no manual annotation, nor calibration of depth sensor to camera. We show that our network trained on less than half of the KITTI dataset gives comparable performance to that of the state-of-the-art supervised methods for single view depth estimation.


title: Concinnity: A Generic Platform for Big Sensor Data Applications

abstract: The drive toward smart cities alongside the increasing adoption of personal sensors is leading to big sensor data, which is so large and complex that traditional methods for utilizing it are inadequate. Although systems exist for storing and managing large-scale sensor data, the real value of such data are the insights it could enable. However, no current platforms enable sensor data to be taken from collection through use in models to produce useful data products. This article explores key challenges and introduces the Concinnity sensor data platform. Concinnity takes sensor data from collection to final product via a cloud-based data repository and easy-to-use workflow system. It supports rapid development of applications built on sensor data using data fusion and the integration and composition of models to form novel workflows. These key features enable value to be efficiently derived from sensor data.


title: LabVIEW-Based UHF RFID Tag Test and Measurement System

abstract: In this paper, we describe a UHF radio-frequency-identification tag test and measurement system based on National Instruments LabVIEW-controlled PXI RF hardware. The system operates in 800-1000-MHz frequency band with a variable output power up to 30 dBm and is capable of testing tags using Gen2 and other protocols. We explain testing methods and metrics, describe in detail the construction of our system, show its operation with real tag measurement examples, and draw general conclusions.


title: Automatic seeded region growing for color image segmentation

abstract: In this paper, we present an automatic seeded region growing algorithm for color image segmentation. First, the input RGB color image is transformed into YC"bC"r color space. Second, the initial seeds are automatically selected. Third, the color image is segmented into regions where each region corresponds to a seed. Finally, region-merging is used to merge similar or small regions. Experimental results show that our algorithm can produce good results as favorably compared to some existing algorithms.


title: Angular Quantization-based Binary Codes for Fast Similarity Search

abstract: This paper focuses on the problem of learning binary codes for efficient retrieval of high-dimensional non-negative data that arises in vision and text applications where counts or frequencies are used as features. The similarity of such feature vectors is commonly measured using the cosine of the angle between them. In this work, we introduce a novel angular quantization-based binary coding (AQBC) technique for such data and analyze its properties. In its most basic form, AQBC works by mapping each non-negative feature vector onto the vertex of the binary hypercube with which it has the smallest angle. Even though the number of vertices (quantization landmarks) in this scheme grows exponentially with data dimensionality d, we propose a method for mapping feature vectors to their smallest-angle binary vertices that scales as O(d log d). Further, we propose a method for learning a linear transformation of the data to minimize the quantization error, and show that it results in improved binary codes. Experiments on image and text datasets show that the proposed AQBC method outperforms the state of the art.


A Low power and area efficient CLA adder design using Full swing GDI technique


title: OSNR monitoring by deep neural networks trained with asynchronously sampled data

abstract: We demonstrate a use of deep neural networks (DNN) for OSNR monitoring with minimum prior knowledge. By using 5-layers DNN trained with 400,000 samples, the DNN successfully estimates OSNR in a 16-GBd DP-QPSK system.


title: Improving Text Normalization by Optimizing Nearest Neighbor Matching

abstract: Text normalization is an essential task in the processing and analysis of social media that is dominated with informal writing. It aims to map informal words to their intended standard forms. Previously proposed text normalization approaches typically require manual selection of parameters for improved performance. In this paper, we present an automatic optimizationbased nearest neighbor matching approach for text normalization. This approach is motivated by the observation that text normalization is essentially a matching problem and nearest neighbor matching with an adaptive similarity function is the most direct procedure for it. Our similarity function incorporates weighted contributions of contextual, string, and phonetic similarity, and the nearest neighbor matching involves a minimum similarity threshold. These four parameters are tuned efficiently using grid search. We evaluate the performance of our approach on two benchmark datasets. The results demonstrate that parameter tuning on small sized labeled datasets produce state-of-the-art text normalization performances. Thus, this approach allows practically easy construction of evolving domain-specific normalization lexicons


Building Information Modeling ( BIM ) : Benefits , Risks and Challenges


title: Trace Ratio Problem Revisited

abstract: Dimensionality reduction is an important issue in many machine learning and pattern recognition applications, and the trace ratio (TR) problem is an optimization problem involved in many dimensionality reduction algorithms. Conventionally, the solution is approximated via generalized eigenvalue decomposition due to the difficulty of the original problem. However, prior works have indicated that it is more reasonable to solve it directly than via the conventional way. In this brief, we propose a theoretical overview of the global optimum solution to the TR problem via the equivalent trace difference problem. Eigenvalue perturbation theory is introduced to derive an efficient algorithm based on the Newton-Raphson method. Theoretical issues on the convergence and efficiency of our algorithm compared with prior literature are proposed, and are further supported by extensive empirical results.


title: Data Mining and Analytics in the Process Industry: The Role of Machine Learning

abstract: Data mining and analytics have played an important role in knowledge discovery and decision making/supports in the process industry over the past several decades. As a computational engine to data mining and analytics, machine learning serves as basic tools for information extraction, data pattern recognition and predictions. From the perspective of machine learning, this paper provides a review on existing data mining and analytics applications in the process industry over the past several decades. The state-of-the-art of data mining and analytics are reviewed through eight unsupervised learning and ten supervised learning algorithms, as well as the application status of semi-supervised learning algorithms. Several perspectives are highlighted and discussed for future researches on data mining and analytics in the process industry.


title: Prioritization of enterprise resource planning (ERP) systems success measures: viewpoints of two organizational stakeholder groups

abstract: Business organizations worldwide are adopting Enterprise Resource Planning (ERP) systems. A number of studies deliberate the adoption and implementation of ERP, but few investigate the success of the system. To our knowledge, this is the first study of its kind to investigate the perspectives of key organizational stakeholders with respect to the success of their ERP software. Using postal surveys in Finland and Estonia, two small Northern European countries, we obtained empirical data from 44 private organizations in diverse industries. Our objective was to determine whether differences exist between two organizational stakeholder groups (top- and mid-level managers) concerning their prioritization and evaluation of measures relating to the success of their ERP software. Despite their distinct roles and influence, we did not notice any significant statistical differences between the two groups in this regard. However, each group evaluated measures such as "accuracy" and "reliability" differently. This paper discusses the implications of the study for both practice and research.


title: Surveillance Video Parsing with Single Frame Supervision

abstract: Surveillance video parsing, which segments the video frames into several labels, e.g., face, pants, left-leg, has wide applications. However,pixel-wisely annotating all frames is tedious and inefficient. In this paper, we develop a Single frame Video Parsing (SVP) method which requires only one labeled frame per video in training stage. To parse one particular frame, the video segment preceding the frame is jointly considered. SVP (1) roughly parses the frames within the video segment, (2) estimates the optical flow between frames and (3) fuses the rough parsing results warped by optical flow to produce the refined parsing result. The three components of SVP, namely frame parsing, optical flow estimation and temporal fusion are integrated in an end-to-end manner. Experimental results on two surveillance video datasets show the superiority of SVP over state-of-the-arts.


title: CODA: Toward Automatically Identifying and Scheduling Coflows in the Dark

abstract: Leveraging application-level requirements using coflows has recently been shown to improve application-level communication performance in data-parallel clusters. However, existing coflow-based solutions rely on modifying applications to extract coflows, making them inapplicable to many practical scenarios. In this paper, we present CODA, a first attempt at automatically identifying and scheduling coflows without any application-level modifications. We employ an incremental clustering algorithm to perform fast, application-transparent coflow identification and complement it by proposing an error-tolerant coflow scheduler to mitigate occasional identification errors. Testbed experiments and large-scale simulations with production workloads show that CODA can identify coflows with over 90% accuracy, and its scheduler is robust to inaccuracies, enabling communication stages to complete 2.4x (5.1x) faster on average (95-th percentile) compared to per-flow mechanisms. Overall, CODA's performance is comparable to that of solutions requiring application modifications.


title: Multimodal Probabilistic Model-Based Planning for Human-Robot Interaction

abstract: This paper presents a method for constructing human-robot interaction policies in settings where multimodality, i.e., the possibility of multiple highly distinct futures, plays a critical role in decision making. We are motivated in this work by the example of traffic weaving, e.g., at highway on-ramps/off-ramps, where entering and exiting cars must swap lanes in a short distance-a challenging negotiation even for experienced drivers due to the inherent multimodal uncertainty of who will pass whom. Our approach is to learn multimodal probability distributions over future human actions from a dataset of human-human exemplars and perform real-time robot policy construction in the resulting environment model through massively parallel sampling of human responses to candidate robot action sequences. Direct learning of these distributions is made possible by recent advances in the theory of conditional variational autoencoders (CVAEs), whereby we learn action distributions simultaneously conditioned on the present interaction history, as well as candidate future robot actions in order to take into account response dynamics. We demonstrate the efficacy of this approach with a human-in-the-loop simulation of a traffic weaving scenario.


title: ListOps: A Diagnostic Dataset for Latent Tree Learning

abstract: Latent tree learning models learn to parse a sentence without syntactic supervision, and use that parse to build the sentence representation. Existing work on such models has shown that, while they perform well on tasks like sentence classification, they do not learn grammars that conform to any plausible semantic or syntactic formalism (Williams et al., 2018a). Studying the parsing ability of such models in natural language can be challenging due to the inherent complexities of natural language, like having several valid parses for a single sentence. In this paper we introduce ListOps, a toy dataset created to study the parsing ability of latent tree models. ListOps sequences are in the style of prefix arithmetic. The dataset is designed to have a single correct parsing strategy that a system needs to learn to succeed at the task. We show that the current leading latent tree models are unable to learn to parse and succeed at ListOps. These models achieve accuracies worse than purely sequential RNNs.


title: An extension of the Electre I method for group decision-making under a fuzzy environment

abstract: Many real-world decision problems involve conflicting systems of criteria, uncertainty and imprecise information. Some also involve a group of decision makers (DMs) where a reduction of different individual preferences on a given set to a single collective preference is required. Multi-criteria decision analysis (MCDA) is a widely used decision methodology that can improve the quality of group multiple criteria decisions by making the process more explicit, rational and efficient. One family of MCDA models uses what is known as “outranking relations” to rank a set of actions. The Electre method and its derivatives are prominent outranking methods in MCDA. In this study, we propose an alternative fuzzy outranking method by extending the Electre I method to take into account the uncertain, imprecise and linguistic assessments provided by a group of DMs. The contribution of this paper is fivefold: (1) we address the gap in the Electre literature for problems involving conflicting systems of criteria, uncertainty and imprecise information; (2) we extend the Electre I method to take into account the uncertain, imprecise and linguistic assessments; (3) we define outranking relations by pairwise comparisons and use decision graphs to determine which action is preferable, incomparable or indifferent in the fuzzy environment; (4) we show that contrary to the TOPSIS rankings, the Electre approach reveals more useful information including the incomparability among the actions; and (5) we provide a numerical example to elucidate the details of the proposed method.


title: An Efficient Diagnosis System for Parkinson's Disease Using Kernel-Based Extreme Learning Machine with Subtractive Clustering Features Weighting Approach

abstract: A novel hybrid method named SCFW-KELM, which integrates effective subtractive clustering features weighting and a fast classifier kernel-based extreme learning machine (KELM), has been introduced for the diagnosis of PD. In the proposed method, SCFW is used as a data preprocessing tool, which aims at decreasing the variance in features of the PD dataset, in order to further improve the diagnostic accuracy of the KELM classifier. The impact of the type of kernel functions on the performance of KELM has been investigated in detail. The efficiency and effectiveness of the proposed method have been rigorously evaluated against the PD dataset in terms of classification accuracy, sensitivity, specificity, area under the receiver operating characteristic (ROC) curve (AUC), f-measure, and kappa statistics value. Experimental results have demonstrated that the proposed SCFW-KELM significantly outperforms SVM-based, KNN-based, and ELM-based approaches and other methods in the literature and achieved highest classification results reported so far via 10-fold cross validation scheme, with the classification accuracy of 99.49%, the sensitivity of 100%, the specificity of 99.39%, AUC of 99.69%, the f-measure value of 0.9964, and kappa value of 0.9867. Promisingly, the proposed method might serve as a new candidate of powerful methods for the diagnosis of PD with excellent performance.


title: Hydraulic erosion using smoothed particle hydrodynamics

abstract: This paper presents a new technique for modification of 3D terrains by hydraulic erosion. It efficiently couples fluid simulation using a Lagrangian approach, namely the Smoothed Particle Hydrodynamics (SPH) method, and a physically-based erosion model adopted from an Eulerian approach. The eroded sediment is associated with the SPH particles and is advected both implicitly, due to the particle motion, and explicitly, through an additional velocity field, which accounts for the sediment transfer between the particles. We propose a new donor-acceptor scheme for the explicit advection in SPH. Boundary particles associated to the terrain are used to mediate sediment exchange between the SPH particles and the terrain itself. Our results show that this particle-based method is efficient for the erosion of dense, large, and sparse fluid. Our implementation provides interactive results for scenes with up to 25,000 particles.


title: Cognitive-behavioral therapy for externalizing disorders: a meta-analysis of treatment effectiveness

abstract: Externalizing disorders are the most common and persistent forms of maladjustment in childhood. The aim of this study was to conduct a meta-analysis evaluating the effectiveness of Cognitive Behavioral Therapy (CBT) to reduce externalizing symptoms in two disorders: Attention Deficit Hyperactivity Disorder (ADHD) and Oppositive Defiant Disorder (ODD). The efficacy of CBT to improve social competence and positive parenting and reduce internalizing behaviors, parent stress and maternal depression was also explored. The database PsycInfo, PsycARTICLES, Medline and PubMed were searched to identify relevant studies. Twenty-one trials met the inclusion criteria. RESULTS showed that the biggest improvement, after CBT, was in ODD symptoms (-0.879) followed by parental stress (-0.607), externalizing symptoms (-0.52), parenting skills (-0.381), social competence (-0.390) and ADHD symptoms (-0.343). CBT was also associated with improved attention (-0.378), aggressive behaviors (-0.284), internalizing symptoms (-0.272) and maternal depressive symptoms (-0.231). Overall, CBT is an effective treatment option for externalizing disorders and is also associated with reduced parental distress and maternal depressive symptoms. Multimodal treatments targeting both children and caregivers' symptoms (e.g. maternal depressive symptoms) appear important to produce sustained and generalized benefits. Language: en


title: Sentiment analysis of a document using deep learning approach and decision trees

abstract: The given paper describes modern approach to the task of sentiment analysis of movie reviews by using deep learning recurrent neural networks and decision trees. These methods are based on statistical models, which are in a nutshell of machine learning algorithms. The fertile area of research is the application of Google's algorithm Word2Vec presented by Tomas Mikolov, Kai Chen, Greg Corrado and Jeffrey Dean in 2013. The main idea of Word2Vec is the representations of words with the help of vectors in such manner that semantic relationships between words preserved as basic linear algebra operations. The extra advantage of the mentioned algorithm above the alternatives is computational efficiency. This paper focuses on using Word2Vec model for text classification by their sentiment type.


title: Snapshots in a flash with ioSnap

abstract: Snapshots are a common and heavily relied upon feature in storage systems. The high performance of flash-based storage systems brings new, more stringent, requirements for this classic capability. We present ioSnap, a flash optimized snapshot system. Through careful design exploiting common snapshot usage patterns and flash oriented optimizations, including leveraging native characteristics of Flash Translation Layers, ioSnap delivers low-overhead snapshots with minimal disruption to foreground traffic. Through our evaluation, we show that ioSnap incurs negligible performance overhead during normal operation, and that common-case operations such as snapshot creation and deletion incur little cost. We also demonstrate techniques to mitigate the performance impact on foreground I/O during intensive snapshot operations such as activation. Overall, ioSnap represents a case study of how to integrate snapshots into a modern, well-engineered flash-based storage system.


title: A RFID-enabled traceability system for the supply chain of live fish

abstract: Traceability is important in the food supply chain to ensure the consumerspsila food safety, especially for the fresh products. In recent years, many solutions which applied various emerging technology have been proposed to improve the traceability of fresh product. However, the traceability system needs to be customized to satisfy different requirements. The system depends on the different product properties and supply chain models. This paper proposed a RFID-enabled traceability system for live fish supply chain. The system architecture is designed according to the specific requirement gathered in the life fish processing. Likewise, it is adaptive for the small and medium enterprises. The RFID tag is put on each live fish and is regarded as the mediator which links the live fish logistic center, retail restaurants and consumers for identification. The sensors controlled by the PLC are used to collect the information in farming as well as the automatic transporting processes. The traceability information is designed to be exchanged and used on a Web-based system for farmers and consumers. The system was implemented and deployed in the live fish logistic center for trial, and the results are valuable for practical reference.


title: Shapelet Ensemble for Multi-dimensional Time Series.

abstract: Time series shapelets are small subsequences that maximally differentiate classes of time series. Since the inception of shapelets, researchers have used shapelets for various data domains including anthropology and health care, and in the process suggested many efficient techniques for shapelet discovery. However, multi-dimensional time series data poses unique challenges to shapelet discovery that are yet to be solved. We show that an ensemble of shapelet-based decision trees on individual dimensions works better than shapelets defined over multiple dimensions. Generating a shapelet ensemble for multidimensional time series is computationally expensive. Most of the existing techniques prune shapelet candidates for speed. In this paper, we propose a novel technique for shapelet discovery that evaluates remaining candidates efficiently. Our algorithm uses a multi-length approximate index for time series data to efficiently find the nearest neighbors of the candidate shapelets. We employ a simple skipping technique for additional candidate pruning and a voting based technique to improve accuracy while retaining interpretability. Not only do we find a significant speed increase, our techniques enable us to efficiently discover shapelets on datasets with multi-dimensional and long time series such as hours of brain activity recordings. We demonstrate our approach on a biomedical dataset and find significant differences between patients with schizophrenia and healthy controls.


title: Houdini, an Annotation Assistant for ESC/Java

abstract: A static program checker that performs modular checking can check one program module for errors without needing to analyze the entire program. Modular checking requires that each module be accompanied by annotations that specify the module. To help reduce the cost of writing specifications, this paper presents Houdini, an annotation assistant for the modular checker ESC/Java. To infer suitable ESC/Java annotations for a given program, Houdini generates a large number of candidate annotations and uses ESC/Java to verify or refute each of these annotations. The paper describes the design, implementation, and preliminary evaluation of Houdini.


title: Adding input controls and sensors to RFID tags to support dynamic tangible user interfaces

abstract: Providing high resolution tangible user interface components without batteries such as dials and sliders that support dynamic user interface arrangement is challenging. Previous work uses RFID to support limited resolution custom-built components. We demonstrate improved techniques using commercial off the shelf input controls incorporated into passive RFID tags using an on-off key subcarrier to encode state information into the RFID signal. Our method supports high resolution components that do not require power cables or batteries. We provide exemplars demonstrating how the technique supports a range of user interface components including buttons, dials, sliders, flex and light sensors. Compared to previous work, we obtain a higher resolution, only limited by sample time, for all components and demonstrate 115 discrete dial positions. Our technique allows the TUI components to be freely placed and rearranged without hardwiring or batteries.


title: Lighter-Communication Distributed Machine Learning via Sufficient Factor Broadcasting

abstract: Matrix-parametrized models (MPMs) are widely used in machine learning (ML) applications. In large-scale ML problems, the parameter matrix of a MPM can grow at an unexpected rate, resulting in high communication and parameter synchronization costs. To address this issue, we offer two contributions: first, we develop a computation model for a large family of MPMs, which share the following property: the parameter update computed on each data sample is a rank-1 matrix, i.e. the outer product of two "sufficient factors" (SFs). Second, we implement a decentralized, peer-to-peer system, Sufficient Factor Broadcasting (SFB), which broadcasts the SFs among worker machines, and reconstructs the update matrices locally at each worker. SFB takes advantage of small rank-1 matrix updates and efficient partial broadcasting strategies to dramatically improve communication efficiency. We propose a graph optimization based partial broadcasting scheme, which minimizes the delay of information dissemination under the constraint that each machine only communicates with a subset rather than all of machines. Furthermore, we provide theoretical analysis to show that SFB guarantees convergence of algorithms (under full broadcasting) without requiring a centralized synchronization mechanism. Experiments corroborate SFB's efficiency on four MPMs.


title: Penetration of nutrition information on food labels across the EU-27 plus Turkey

abstract: Food, Consumer Behaviour and Health Research Centre, Department ofPsychology, University of Surrey, Guildford, UKObjectives: The European Union (EU)-funded project Food Labelling to Advance Better Education for Life (FLABEL) aims tounderstand how nutrition information on food labels affects consumers’ dietary choices and shopping behaviour. The first phaseof this study consisted of assessing the penetration of nutrition labelling and related information on various food products in all27 EU Member States and Turkey.Methods: In each country, food products were audited in three different types of retailers to cover as many different products aspossible within five food and beverage categories: sweet biscuits, breakfast cereals, pre-packed chilled ready meals, carbonatedsoft drinks and yoghurts.Results: More than 37000 products were audited in a total of 84 retail stores. On average, 85% of the products contained back-of-pack (BOP) nutrition labelling or related information (from 70% in Slovenia to 97% in Ireland), versus 48% for front-of-pack(FOP) information (from 24% in Turkey to 82% in the UK). The most widespread format was the BOP tabular or linear listing ofnutrition content. Guideline daily amounts labelling was the most prevalent form of FOP information, showing an averagepenetration of 25% across all products audited. Among categories, breakfast cereals showed the highest penetration ofnutrition-related information, with 94% BOP penetration and 70% FOP penetration.Conclusions: Nutrition labelling and related information was found on a large majority of products audited. These findingsprovide the basis for subsequent phases of FLABEL involving attention, reading, liking, understanding and use by consumers ofdifferent nutrition labelling formats.European Journal of Clinical Nutrition (2010) 64, 1379–1385; doi:10.1038/ejcn.2010.179; published online 1 September 2010


title: Research on autonomous stairs climbing for the shape-shifting robot

abstract: In this paper, we present an algorithm for a shape-shifting robot climbing stairs autonomously. The robot has been used several times for search and rescue missions. Because of the special environment of tasks, the robot must have the ability to climb stairs autonomously to help the operators. Due to the characters of robot in "T" configuration, the mathematical model of force is established and the whole process of climbing stairs is analyzed. Grouser-tread hooking, track-stair edge frictional force et al. are all considered during the process of climbing. A description of its dynamic model is also presented which is involved in the controller. The controller is comprised of center control and heading control, which can help the robot climb stairs safely and quickly. The effectiveness of the algorithms proposed are verified by experiment.


title: Deep convolutional Triplet network for quantitative medical image analysis with comparative case study of gamma image classification

abstract: Alternative feature generation for quantitative image analysis is proposed. The proposed method reorganize Deep Convolutional Neural Networks to learn representation in Triplet Network. The features are compared with texture features using series of classifiers in a Gamma image classification task that contains visual information but has no known suitable features. Experiment show that features from the Triplet Network method outperform in the classification task suggesting a useful way of extracting feature for task without known suitable feature but advantageous for further investigation.


title: The Comparison and Application of Corner Detection Algorithms

abstract: Corners in images represent a lot of important information. Extracting corners accurately is significant to image processing, which can reduce much of the calculations. In this paper, two widely used corner detection algorithms, SUSAN and Harris corner detection algorithms which are both based on intensity, were compared in stability, noise immunity and complexity quantificationally via stability factor η, anti-noise factor ρ and the runtime of each algorithm. It concluded that Harris corner detection algorithm was superior to SUSAN corner detection algorithm on the whole. Moreover, SUSAN and Harris detection algorithms were improved by selecting an adaptive gray difference threshold and by changing directional differentials, respectively, and compared using these three criterions. In addition, SUSAN and Harris corner detectors were applied to an image matching experiment. It was verified that the quantitative evaluations of the corner detection algorithms were valid through calculating match efficiency, defined as correct matching corner pairs dividing by matching time, which can reflect the performances of a corner detection algorithm comprehensively. Furthermore, the better corner detector was used into image mosaic experiment, and the result was satisfied. The work of this paper can provide a direction to the improvement and the utilization of these two corner detection algorithms.


title: Fast Approximate Evaluation of Parallel Overhead from a Minimal Set of Measured Execution Times

abstract: Porting scientific key algorithms to HPC architectures requires a thorough understanding of the subtle balance between gain in performance and introduced overhead. Here we continue the development ...


of Analytical Chemistry Wearable and Implantable Sensors for Biomedical Applications Hatice


title: Revisiting K-Means and Topic Modeling, a Comparison Study to Cluster Arabic Documents

abstract: Clustering Arabic text documents is of high importance for many natural language technologies. This paper uses a combined method to cluster Arabic text documents. Mainly, we use generative models and clustering techniques. The study uses latent Dirichlet allocation and $k$ -means clustering algorithm and applies them to a news data set used in previous similar studies. The aim of this paper is twofold: it first shows that normalizing the weights in the vector space, for the document-term matrix of the text documents, dramatically improves the quality of clusters and hence the accuracy of clustering when using $k$ -means algorithm. The results are compared to a recent study on clustering Arabic text documents. Second, it shows that the combined method is superior in terms of clustering quality for Arabic text documents according to external measures, such as purity, F-measure, entropy, accuracy, and other measures. It is shown in this paper that the purity of the combined method is 0.933 compared to 0.82 for ${k}$ -means algorithm, and these figures are higher in comparison to a recent similar study. This is also confirmed by the other used validation measures. The correctness of the combined method is then confirmed using different Arabic data sets.


title: A Deep Learning Approach for Oriented Electrical Equipment Detection in Thermal Images

abstract: Due to the high precision and non-contact characteristics, infrared thermography has been widely used in equipment inspection to ensure the safety of electric power systems. A fundamental step toward automatic inspection and diagnosis is the detection of equipment in thermal images. Therefore, this paper presents a deep learning approach to detect equipment parts in real-time. Specifically, we propose a deep convolutional neural network that predicts the coordinates, orientation angle, and class type of each equipment part. A prior concerning orientation consistency between parts is also incorporated into our model to improve the prediction results. For evaluation, we construct a large image set containing various kinds of scenarios. Experiments on the data set show that our method is robust to noise, achieving 93.7% mean average precision when the intersection over union threshold is 0.5, and running at 20 fps on GPU. We believe that our high accurate detection results can benefit the subsequent diagnosis.


title: Information processing in living systems

abstract: Life depends as much on the flow of information as on the flow of energy. Here we review the many efforts to make this intuition precise. Starting with the building blocks of information theory, we explore examples where it has been possible to measure, directly, the flow of information in biological networks, or more generally where information theoretic ideas have been used to guide the analysis of experiments. Systems of interest range from single molecules (the sequence diversity in families of proteins) to groups of organisms (the distribution of velocities in flocks of birds), and all scales in between. Many of these analyses are motivated by the idea that biological systems may have evolved to optimize the gathering and representation of information, and we review the experimental evidence for this optimization, again across a wide range of scales.


title: A goal-based framework for contextual requirements modeling and analysis

abstract: Requirements engineering (RE) research often ignores or presumes a uniform nature of the context in which the system operates. This assumption is no longer valid in emerging computing paradigms, such as ambient, pervasive and ubiquitous computing, where it is essential to monitor and adapt to an inherently varying context. Besides influencing the software, context may influence stakeholders’ goals and their choices to meet them. In this paper, we propose a goal-oriented RE modeling and reasoning framework for systems operating in varying contexts. We introduce contextual goal models to relate goals and contexts; context analysis to refine contexts and identify ways to verify them; reasoning techniques to derive requirements reflecting the context and users priorities at runtime; and finally, design time reasoning techniques to derive requirements for a system to be developed at minimum cost and valid in all considered contexts. We illustrate and evaluate our approach through a case study about a museum-guide mobile information system.


title: Pre- and post-processes for automatic colorization using a fully convolutional network

abstract: Automatic colorization is a significant task especially for Anime industry. An original trace image to be colorized contains not only outlines but also boundary contour lines of shadows and highlight areas. Unfortunately, these lines tend to decrease the consistency among all images. Thus, this paper provides a method for a cleaning pre-process of anime dataset to improve the prediction quality of a fully convolutional network, and a refinement post-process to enhance the output of the network.


title: Summarization of Related Work through Citations

abstract: Automatically generating the related work section for a writing paper is useful for researchers as it can save a lot of time and avoid missing related works. The related work section of scientific paper usually introduces other researchers' work published before and makes comparisons with the current author's work. This paper proposes an approach to automatically generating related work by comparing the main text of the writing paper with the citations to the references from other papers. Our approach firstly collects the papers that cite the reference papers of the writing paper and extracts the corresponding citation sentences to form a citation document. It then makes a summarization that makes a comparison between the citation document and the writing paper's abstract, introduction and conclusion. It extracts the representative keywords from the citation document and the writing paper, and constructs a graph of the keywords. The discriminated nodes in the graph are figured out, and then the minimum Steiner tree that covers the nodes is extracted. A summary is generated by extracting the sentences covering the Steiner tree. The experiments show that our approach outperforms three baselines MEAD, ReWoS and ARWG according to ROUGE evaluation results.


title: Vibration parameter estimation using FMCW radar

abstract: Vibration sensing is essential in many applications. Traditional vibration sensors are contact based. With the advance of low-cost and highly integrated CMOS radars, another class of non-contact vibration sensors is emerging. In this paper, we present detailed analysis on obtaining vibration parameters using frequency modulated continuous wave (FMCW) radars. We establish the Cramer Rao lower bounds (CRLB) of the parameter estimation problem and propose an estimation algorithm that achieves the bounds in simulations. These analysis show that vibration sensing using FMCW radars can easily achieve sub-Hertz frequency accuracy and micrometer level amplitude accuracy.


title: Statistical analysis of real-time PCR data

abstract: BackgroundEven though real-time PCR has been broadly applied in biomedical sciences, data processing procedures for the analysis of quantitative real-time PCR are still lacking; specifically in the realm of appropriate statistical treatment. Confidence interval and statistical significance considerations are not explicit in many of the current data analysis approaches. Based on the standard curve method and other useful data analysis methods, we present and compare four statistical approaches and models for the analysis of real-time PCR data.ResultsIn the first approach, a multiple regression analysis model was developed to derive ΔΔCt from estimation of interaction of gene and treatment effects. In the second approach, an ANCOVA (analysis of covariance) model was proposed, and the ΔΔCt can be derived from analysis of effects of variables. The other two models involve calculation ΔCt followed by a two group t- test and non-parametric analogous Wilcoxon test. SAS programs were developed for all four models and data output for analysis of a sample set are presented. In addition, a data quality control model was developed and implemented using SAS.ConclusionPractical statistical solutions with SAS programs were developed for real-time PCR data and a sample dataset was analyzed with the SAS programs. The analysis using the various models and programs yielded similar results. Data quality control and analysis procedures presented here provide statistical elements for the estimation of the relative expression of genes using real-time PCR.


title: Mathematical modeling of photovoltaic cell/module/arrays with tags in Matlab/Simulink

abstract: Background ::: Photovoltaic (PV) array which is composed of modules is considered as the fundamental power conversion unit of a PV generator system. The PV array has nonlinear characteristics and it is quite expensive and takes much time to get the operating curves of PV array under varying operating conditions. In order to overcome these obstacles, common and simple models of solar panel have been developed and integrated to many engineering software including Matlab/Simulink. However, these models are not adequate for application involving hybrid energy system since they need a flexible tuning of some parameters in the system and not easily understandable for readers to use by themselves. Therefore, this paper presents a step-by-step procedure for the simulation of PV cells/modules/arrays with Tag tools in Matlab/Simulink. A DS-100M solar panel is used as reference model. The operation characteristics of PV array are also investigated at a wide range of operating conditions and physical parameters.


title: ECG Beats Classification Using Mixture of Features

abstract: Classification of electrocardiogram (ECG) signals plays an important role in clinical diagnosis of heart disease. This paper proposes the design of an efficient system for classification of the normal beat (N), ventricular ectopic beat (V), supraventricular ectopic beat (S), fusion beat (F), and unknown beat (Q) using a mixture of features. In this paper, two different feature extraction methods are proposed for classification of ECG beats: (i) S-transform based features along with temporal features and (ii) mixture of ST and WT based features along with temporal features. The extracted feature set is independently classified using multilayer perceptron neural network (MLPNN). The performances are evaluated on several normal and abnormal ECG signals from 44 recordings of the MIT-BIH arrhythmia database. In this work, the performances of three feature extraction techniques with MLP-NN classifier are compared using five classes of ECG beat recommended by AAMI (Association for the Advancement of Medical Instrumentation) standards. The average sensitivity performances of the proposed feature extraction technique for N, S, F, V, and Q are 95.70%, 78.05%, 49.60%, 89.68%, and 33.89%, respectively. The experimental results demonstrate that the proposed feature extraction techniques show better performances compared to other existing features extraction techniques.


title: Factors Leading to the Success and Failure of Agile Projects Implemented in Traditionally Waterfall Environments

abstract: This qualitative study was undertaken to investigate the factors that result in agile project success and failure. The study particularly focused on software projects where agile methods were newly adopted, implying a transition from the Waterfall to a more light-weight and iterative approach. The study was exploratory and deductive in nature, and employed multiple-case studies as research strategy. The target population was Project Management Offices (PMO) that had moved, or attempted to move, from a traditionally waterfall approach, to an agile software development approach. Primary data was collected using semi-structured, observations of meetings and team engagements. Secondary data collected included project documentation, meeting minutes, and formal project communication. This research was aimed at making practitioners and academic researchers aware of the factors that may lead to project failure as well as project success, thereby encouraging them to find and devise coping and mitigating mechanisms to enhance successful agile adoption, use and implementation. The following constructs were identified as success and failure factors in agile project delivery: Culture, Customer Involvement & Mandate, Stakeholder Involvement & Buy-In, Team Structure & Team Logistics, Project Type & Project Planning, and Skill Level & Attitude of Team Members.


title: KNPTC: Knowledge and Neural Machine Translation Powered Chinese Pinyin Typo Correction

abstract: Chinese pinyin input methods are very important for Chinese language processing. Actually, users may make typos inevitably when they input pinyin. Moreover, pinyin typo correction has become an increasingly important task with the popularity of smartphones and the mobile Internet. How to exploit the knowledge of users typing behaviors and support the typo correction for acronym pinyin remains a challenging problem. To tackle these challenges, we propose KNPTC, a novel approach based on neural machine translation (NMT). In contrast to previous work, KNPTC is able to integrate explicit knowledge into NMT for pinyin typo correction, and is able to learn to correct a variety of typos without the guidance of manually selected constraints or languagespecific features. In this approach, we first obtain the transition probabilities between adjacent letters based on large-scale real-life datasets. Then, we construct the"ground-truth"alignments of training sentence pairs by utilizing these probabilities. Furthermore, these alignments are integrated into NMT to capture sensible pinyin typo correction patterns. KNPTC is applied to correct typos in real-life datasets, which achieves 32.77% increment on average in accuracy rate of typo correction compared against the state-of-the-art system.


title: Reducing DRAM Refresh Overheads with Refresh-Access Parallelism

abstract: This article summarizes the idea of"refresh-access parallelism,"which was published in HPCA 2014, and examines the work's significance and future potential. The overarching objective of our HPCA 2014 paper is to reduce the significant negative performance impact of DRAM refresh with intelligent memory controller mechanisms. To mitigate the negative performance impact of DRAM refresh, our HPCA 2014 paper proposes two complementary mechanisms, DARP (Dynamic Access Refresh Parallelization) and SARP (Subarray Access Refresh Parallelization). The goal is to address the drawbacks of state-of-the-art per-bank refresh mechanism by building more efficient techniques to parallelize refreshes and accesses within DRAM. First, instead of issuing per-bank refreshes in a round-robin order, as it is done today, DARP issues per-bank refreshes to idle banks in an out-of-order manner. Furthermore, DARP proactively schedules refreshes during intervals when a batch of writes are draining to DRAM. Second, SARP exploits the existence of mostly-independent subarrays within a bank. With minor modifications to DRAM organization, it allows a bank to serve memory accesses to an idle subarray while another subarray is being refreshed. Our extensive evaluations on a wide variety of workloads and systems show that our mechanisms improve system performance (and energy efficiency) compared to three state-of-the-art refresh policies, and their performance bene ts increase as DRAM density increases.


Mining Implicit Data Association from Tripadvisor Hotel Reviews


title: Retrieving address-based locations from the web

abstract: Geospatial search for the Web determines the relation of documents' contents to a location within a region. For some pedestrian scenarios, information at a higher granularity down to individual buildings is necessary. In this paper, we describe a process for the extraction and simultaneous verification of precise addresses on German Web pages by a validating parser. We describe how an address-level location extraction can be aided by an extensive use of previous geographic knowledge and the use of its structure. The analysis of address structure, components and dependencies leads to the design of a geoparser that determines valid addresses within unstructured Web content. We further discuss some noteworthy issues that arise within the process.


title: Nonlocal Image and Movie Denoising

abstract: Abstract ::: Neighborhood filters are nonlocal image and movie filters which reduce the noise by averaging similar pixels. The first object of the paper is to present a unified theory of these filters and reliable criteria to compare them to other filter classes. A CCD noise model will be presented justifying the involvement of neighborhood filters. A classification of neighborhood filters will be proposed, including classical image and movie denoising methods and discussing further a recently introduced neighborhood filter, NL-means. In order to compare denoising methods three principles will be discussed. The first principle, “method noise”, specifies that only noise must be removed from an image. A second principle will be introduced, “noise to noise”, according to which a denoising method must transform a white noise into a white noise. Contrarily to “method noise”, this principle, which characterizes artifact-free methods, eliminates any subjectivity and can be checked by mathematical arguments and Fourier analysis. “Noise to noise” will be proven to rule out most denoising methods, with the exception of neighborhood filters. This is why a third and new comparison principle, the “statistical optimality”, is needed and will be introduced to compare the performance of all neighborhood filters. ::: ::: The three principles will be applied to compare ten different image and movie denoising methods. It will be first shown that only wavelet thresholding methods and NL-means give an acceptable method noise. Second, that neighborhood filters are the only ones to satisfy the “noise to noise” principle. Third, that among them NL-means is closest to statistical optimality. A particular attention will be paid to the application of the statistical optimality criterion for movie denoising methods. It will be pointed out that current movie denoising methods are motion compensated neighborhood filters. This amounts to say that they are neighborhood filters and that the ideal neighborhood of a pixel is its trajectory. Unfortunately the aperture problem makes it impossible to estimate ground true trajectories. It will be demonstrated that computing trajectories and restricting the neighborhood to them is harmful for denoising purposes and that space-time NL-means preserves more movie details.


title: Circularly Polarized U-Slot Antenna

abstract: Circularly polarized single-layer U-slot microstrip patch antenna has been proposed. The suggested asymmetrical U-slot can generate the two orthogonal modes for circular polarization without chamfering any corner of the probe-fed square patch microstrip antenna. A parametric study has been carried out to investigate the effects caused by different arm lengths of the U-slot. The thickness of the foam substrate is about 8.5% of the wavelength at the operating frequency. The 3 dB axial ratio bandwidth of the antenna is 4%. Both experimental and theoretical results of the antenna have been presented and discussed. Circular polarization, printed antennas, U-slot.


title: Pairwise Constraint-Guided Sparse Learning for Feature Selection

abstract: Feature selection aims to identify the most informative features for a compact and accurate data representation. As typical supervised feature selection methods, Lasso and its variants using L1-norm-based regularization terms have received much attention in recent studies, most of which use class labels as supervised information. Besides class labels, there are other types of supervised information, e.g., pairwise constraints that specify whether a pair of data samples belong to the same class (must-link constraint) or different classes (cannot-link constraint). However, most of existing L1-norm-based sparse learning methods do not take advantage of the pairwise constraints that provide us weak and more general supervised information. For addressing that problem, we propose a pairwise constraint-guided sparse (CGS) learning method for feature selection, where the must-link and the cannot-link constraints are used as discriminative regularization terms that directly concentrate on the local discriminative structure of data. Furthermore, we develop two variants of CGS, including: 1) semi-supervised CGS that utilizes labeled data, pairwise constraints, and unlabeled data and 2) ensemble CGS that uses the ensemble of pairwise constraint sets. We conduct a series of experiments on a number of data sets from University of California-Irvine machine learning repository, a gene expression data set, two real-world neuroimaging-based classification tasks, and two large-scale attribute classification tasks. Experimental results demonstrate the efficacy of our proposed methods, compared with several established feature selection methods.


title: BOOK REVIEW Objects, Components, and Frameworks with UML: The Catalysis Approach

abstract: Preface. I. OVERVIEW. 1. A Tour of Catalysis. Objects and Actions. Refinement: Objects and Actions at Different Scales. Development Layers. Business Modeling. Model Frameworks as Templates. Zooming In on the Software: System Context. Requirements Specification Models. Components. Assigning Responsibilities. Object-Oriented Design. The Development Process. Three Constructs Plus Frameworks. Three Levels of Modeling. Three Principles. Summary. II. MODELING WITH OBJECTS. 2. Static Models: Object Attributes and Invariants. What Is a Static Model? Object State: Objects and Attributes. Implementations of Object State. Modeling Object State: Types, Attributes, and Associations. Static Invariants. The Dictionary. Models of Business Models of Components. Static Models: Summary. 3. Behavior Models: Object Types and Operations. Object Behavior: Objects and Actions. More Precise Action Specifications. Two Java Implementations of a Calendar. Type Specification of Calendar. Actions with Invariants. Interpreting an Action Specification. Subtypes and Type Extension. Factoring Action Specifications. State Charts. Outputs of Actions. Subjective Model: The Meaning of Containment. Type Specifications: Summary. Programming Language: Classes and Types. 4. Interaction Models: Use Cases, Actions, and Collaborations. Designing Object Collaborations. Actions (Use Cases) Abstract Complex Interactions. Use Cases Are Joint Actions. Actions and Effects. Concurrent Actions. Collaborations. Uses of Collaborations. Collaboration Specification. Collaborations: Summary. 5. Effective Documentation. What's It All For? Documentation Is Easy and Fun, and It Speeds Design. Reaching the Documentation Audience. The Main Documents: Specification and Implementation. Documenting Business Models. Documenting Component Specifications. Documenting Component Implementations. Summary. III. FACTORING MODELS AND DESIGNS. 6. Abstraction, Refinement, and Testing. Zooming In and Out: Why Abstract and Refine? Documenting Refinement and Conformance. Spreadsheet: A Refinement Example. Spreadsheet: Model Refinement. Spreadsheet: Action Refinement. Spreadsheet: Object Refinement. Spreadsheet: Operation Refinement. Refinement of State Charts. Summary. Process Patterns for Refinement. Pattern 6.1 The OO Golden Rule (Seamlessness or Continuity). Pattern 6.2 The Golden Rule versus Other Optimizations. Pattern 6.3 Orthogonal Abstractions and Refinement. Pattern 6.4 Refinement Is a Relation, Not a Sequence. Pattern 6.5 Recursive Refinement. 7. Using Packages. What Is a Package? Package Imports. How to Use Packages and Imports. Decoupling with Packages. Nested Packages. Encapsulation with Packages. Multiple Imports and Name Conflicts. Publication, Version Control, and Builds. Programming Language Packages. Summary. 8. Composing Models and Specifications. Sticking Pieces Together. Joining and Subtyping. Combining Packages and Their Definitions. Action Exceptions and Composing Specs. Summary. 9. Model Frameworks and Template Packages. Model Framework Overview. Model Frameworks of Types and Attributes. Collaboration Frameworks. Refining Frameworks. Composing Frameworks. Templates as Packages of Properties. Templates for Equality and Copying. Package Semantics. Down to Basics with Templates. Summary of Model Framework Concepts. IV. IMPLEMENTATION BY ASSEMBLY. 10. Components and Connectors. Overview of Component-Based Development. The Evolution of Components. Building Components with Java. Components with COM+. Components with CORBA. Component Kit: Pluggable Components Library. Component Architecture. Defining Cat One-A Component Architecture. Specifying Cat One Components. Connecting Cat One Components. Heterogeneous Components. Pattern 10.1 Extracting Generic Code Components. Pattern 10.2 Componentware Management. Pattern 10.3 Build Models from Frameworks. Pattern 10.4 Plug Conformance. Pattern 10.5 Using Legacy or Third-Party Components. Summary. 11. Reuse and Pluggable Design Frameworks in Code. Reuse and the Development Process. Generic Components and Plug-Points. The Framework Approach to Code Reuse. Frameworks: Specs to Code. Basic Plug Technology. Summary. Pattern 11.1 Role Delegation. Pattern 11.2 Pluggable Roles. 12. Architecture. What Is Architecture? Why Architect? Architecture Evaluation with Scenarios. Architecture Builds on Defined Elements. Architecture Uses Consistent Patterns. Application versus Technical Architecture. Typical Four-Tier Business Architecture. User Interfaces. Objects and Databases. Summary. V. HOW TO APPLY CATALYSIS. 13. Process Overview. Model, Design, Implement, and Test-Recursively. General Notes on the Process. Typical Project Evolution. Typical Package Structure. Main Process Patterns. Pattern 13.1 Object Development from Scratch. Pattern 13.2 Reengineering. Pattern 13.3 Short-Cycle Development. Pattern 13.4 Parallel Work. 14. How to Build a Business Model. Business Modeling Process Patterns. Pattern 14.1 Business Process Improvement. Pattern 14.2 Make a Business Model. Pattern 14.3 Represent Business Vocabulary and Rules. Pattern 14.4 Involve Business Experts. Pattern 14.5 Creating a Common Business Model. Pattern 14.6 Choose a Level of Abstraction. Modeling Patterns. Pattern 14.7 The Type Model Is a Glossary. Pattern 14.8 Separation of Concepts: Normalization. Pattern 14.9 Items and Descriptors. Pattern 14.10 Generalize and Specialize. Pattern 14.11 Recursive Composite. Pattern 14.12 Invariants from Association Loops. Video Case Study: Abstract Business Model. Video Business: Use Case Refinement. Pattern 14.13 Action Reification. 15. How to Specify a Component. Patterns for Specifying Components. Pattern 15.1 Specify Components. Pattern 15.2 Bridge Requirements and Specifications. Pattern 15.3 Use-Case-Led System Specification. Pattern 15.4 Recursive Decomposition: Divide and Conquer. Pattern 15.5 Make a Context Model with Use Cases. Pattern 15.6 Storyboards. Pattern 15.7 Construct a System Behavior Spec. Pattern 15.8 Specifying a System Action. Pattern 15.9 Using State Charts in System Type Models. Pattern 15.10 Specify Component Views. Pattern 15.11 Compose Component Views. Pattern 15.12 Avoid Miracles, Refine the Spec. Pattern 15.13 Interpreting Models for Clients. Video Case Study: System Specifications. System Context Diagram. System Specification. Using Model Frameworks. 16. How to Implement a Component. Designing to Meet a Specification. Pattern 16.1 Decoupling. Pattern 16.2 High-Level Component Design. Pattern 16.3 Reifying Major Concurrent Use Cases. Pattern 16.4 Separating Facades. Pattern 16.5 Platform Independence. Pattern 16.6 Separate Middleware from Business Components. Pattern 16.7 Implement Technical Architecture. Pattern 16.8 Basic Design. Pattern 16.9 Generalize after Basic Design. Pattern 16.10 Collaborations and Responsibilities. Pattern 16.11 Link and Attribute Ownership. Pattern 16.12 Object Locality and Link Implementation. Pattern 16.13 Optimization. Detailed Design Patterns. Pattern 16.14 Two-Way Link. Pattern 16.15 Role Decoupling. Pattern 16.16 Factories. Pattern 16.17 Observer. Pattern 16.18 Plug-Points and Plug-Ins. Video Case Study: Component-Based Design. Appendix A: Object Constraint Language. Appendix B: UML Perspective. Appendix C: Catalysis Support Tools, Services, and Experiences. Notes. Glossary. Index. 0201310120T04062001


title: Climate change and human health: present and future risks

abstract: Summary There is near unanimous scientific consensus that greenhouse gas emissions generated by human activity will change Earth's climate. The recent (globally averaged) warming by 0·5°C is partly attributable to such anthropogenic emissions. Climate change will affect human health in many ways—mostly adversely. Here, we summarise the epidemiological evidence of how climate variations and trends affect various health outcomes. We assess the little evidence there is that recent global warming has already affected some health outcomes. We review the published estimates of future health effects of climate change over coming decades. Research so far has mostly focused on thermal stress, extreme weather events, and infectious diseases, with some attention to estimates of future regional food yields and hunger prevalence. An emerging broader approach addresses a wider spectrum of health risks due to the social, demographic, and economic disruptions of climate change. Evidence and anticipation of adverse health effects will strengthen the case for pre-emptive policies, and will also guide priorities for planned adaptive strategies.


title: Research on SAR oil spill image classification based on DBN in small sample space

abstract: SAR has become one of the important means of oil spill monitoring. However, oil spills and lookalikes are characterized by dark spots on SAR images. They have similar or identical backscattering coefficients and gray values, which are easy to produce confusion. Aiming at this problem, this paper proposes a deep learning model-Deep Belief Network (DBN), which uses DBN to distinguish oil spills, lookalikes and water. In the experiment, 900 images were collected from the three SAR oil spill images to form a small sample space dataset. The two kinds of texture features such as Tamura and Gray Level-Gradient Co-occurrence Matrix are extracted, and the feature vector with good distinguishing features is selected as the input data of the model. Finally, the classification results are compared with the traditional machine learning method (BP, SVM). The experimental results shows that the DBN model proposed in this paper is superior to these classifiers in classification accuracy.


title: Links between perceptrons, MLPs and SVMs

abstract: We propose to study links between three important classification algorithms: Perceptrons, Multi-Layer Perceptrons (MLPs) and Support Vector Machines (SVMs). We first study ways to control the capacity of Perceptrons (mainly regularization parameters and early stopping), using the margin idea introduced with SVMs. After showing that under simple conditions a Perceptron is equivalent to an SVM, we show it can be computationally expensive in time to train an SVM (and thus a Perceptron) with stochastic gradient descent, mainly because of the margin maximization term in the cost function. We then show that if we remove this margin maximization term, the learning rate or the use of early stopping can still control the margin. These ideas are extended afterward to the case of MLPs. Moreover, under some assumptions it also appears that MLPs are a kind of mixture of SVMs, maximizing the margin in the hidden layer space. Finally, we present a very simple MLP based on the previous findings, which yields better performances in generalization and speed than the other models.


title: CrossNets: Cross-Information Flow in Deep Learning Architectures

abstract: We propose a novel neural network structure called CrossNets, which considers architectures on directed acyclic graphs. This structure builds on previous generalization of sequential feed-forward models, such as ResNets, by allowing for all forward cross-connections between both adjacent and non-adjacent layers. The addition of cross-connections within the network increases the information flow across the whole network, leading to better training and testing performances. The superior performance of the network is tested against both image classification and compression tasks using various datasets, such as MNIST, FER, CIFAR-10, CIFAR-100, and SVHN. We conclude with a proof of convergence for CrossNets to a local minimum for error, where weights for connections are chosen through backpropagation with momentum.


title: Building a unified framework for the practice of experience design

abstract: This workshop challenges design practitioners and researchers to begin creating a unified framework for the practice of experience design.


title: Design of PID controller for inverted pendulum using stability boundary locus

abstract: In this paper, PID controller has been designed for cart inverted pendulum system. The proposed approach is based on newly developed concept of stability boundary locus. The main advantage of this approach is that the cart oscillations has been minimized and angle of pendulum is stabilizes with minimum settling time. The results are also compared with the recently proposed approach of PID and LQR controller. The results are simulated in Matlab environment.


title: Predicting Trust Relations Within a Social Network: A Case Study on Emergency Response

abstract: Trust is a fundamental construct underpinning modern society and the social exchanges it contains. The rise of Web 2.0 technologies and the increased use of online social networks, promotes the study of trust among users. Drawing on social and psychological theory, we detect pairwise and global trust relations between users in the context of emergent real-world crisis scenarios. In such situations and scale, seeking explicit pairwise trust assessments between users is impractical. Instead, in an unsupervised manner we integrate the implicit factors of social influence exerted by each user over the network, the underlying network structural topology and the affective valence expressed by the users in the textual content they communicate. A key finding is the importance of modeling influence and affective valence in such exchanges and their role in detecting stable trust relationships. We extensively evaluate these ideas and demonstrate significant gains over competitive baselines across multiple datasets drawn from both crisis and non-crisis scenarios, including those with normative ground truth.


title: Quantitative Synthesis in Systematic Reviews

abstract: The final common pathway for most systematic reviews is a statistical summary of the data, or meta-analysis . The com plex methods used in meta-analyses should always be complemented by clinical acumen and common sense in designing the protocol of a systematic review, deciding which data can be combined, and determining whether data should be combined. Both continuous and binary data can be pooled. Most meta-analyses summarize data from randomized trials, but other applications, such as the evaluation of diagnostic test performance and observa tional studies, have also been developed. The statistical methods of meta-analysis aim at evaluating the diversity (heterogeneity) among the results of different studies, exploring and explaining observed heterogeneity, and es timating a common pooled effect with increased precision. Fixed-effects models assume that an intervention has a single true effect, whereas random-effects models assume that an effect may vary across studies. Meta-regression analyses, by using each study rather than each patient as a unit of observation, can help to evaluate the effect of individual variables on the magnitude of an observed effect and thus may sometimes explain why study results differ. It is also important to assess the robustness of con clusions through sensitivity analyses and a formal evalua tion of potential sources of bias, including publication bias and the effect of the quality of the studies on the observed effect.