title: Power electronics for renewable energy systems: Wind turbine and photovoltaic systems

abstract: The use of renewable energy sources are increased because of the depletion of natural resources and the increasing pollution level from energy production. The wind energy and the solar energy are most widely used among the renewable energy sources. Power electronics is needed in almost all kinds of renewable energy system. It controls the renewable source and interfaces with the load effectively, which can be grid-connection or working in stand-alone mode. In this paper, overview of wind and photovoltaic energy systems are introduced. Next, the power electronic circuits behind the most common wind and photovoltaic configurations are discussed. Finally, their controls and important requirements for grid connection are explained.


title: Off the wall political discourse: Facebook use in the 2008 U.S. presidential election

abstract: Both candidates and voters have increased their use of the Internet for political campaigns. Candidates have adopted many internet tools, including social networking websites, for the purposes of communicating with constituents and voters, collecting donations, fostering community, and organizing events. On the other side, voters have adopted Internet tools such as blogs and social networking sites to relate to candidates, engage in political dialogue, pursue activist causes, and share information. In this paper we examine two years of posts on the Facebook walls of the three major contenders for the U.S. Presidency in 2008: Barack Obama, Hillary Clinton, and John McCain. We analyze participation patterns of usage along dimensions of breadth and frequency, and interpret them in terms of the concept of the "public sphere".


title: Deep Convolutional Neural Network for Plant Seedlings Classification

abstract: Agriculture is vital for human survival and remains a major driver of several economies around the world; more so in underdeveloped and developing economies. With increasing demand for food and cash crops, due to a growing global population and the challenges posed by climate change, there is a pressing need to increase farm outputs while incurring minimal costs. Previous machine vision technologies developed for selective weeding have faced the challenge of reliable and accurate weed detection. We present approaches for plant seedlings classification with a dataset that contains 4,275 images of approximately 960 unique plants belonging to 12 species at several growth stages. We compare the performances of two traditional algorithms and a Convolutional Neural Network (CNN), a deep learning technique widely applied to image recognition, for this task. Our findings show that CNN-driven seedling classification applications when used in farming automation has the potential to optimize crop yield and improve productivity and efficiency when designed appropriately.


Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism


Pushing the Limits of Radiology with Joint Modeling of Visual and Textual Information


Introduction to Information Retrieval


title: Somatization vs. Psychologization of Emotional Distress: A Paradigmatic Example for Cultural Psychopathology

abstract: This paper describes the developing area of cultural psychopathology, an interdisciplinary field of study focusing on the ways in which cultural factors contribute to the experience and expression of psychological distress. We begin by outlining two approaches, often competing, in order to provide a background to some of the issues that complicate the field. The main section of the paper is devoted to a discussion of depression in Chinese culture as an example of the types of questions that can be studied. Here, we start with a review of the epidemiological literature, suggesting low rates of depression in China, and move to the most commonly cited explanation, namely that Chinese individuals with depression present this distress in a physical way. Different explanations of this phenomenon, known as somatization, are explored and reconceptualized according to an increasingly important model for cross-cultural psychologists: the cultural constitution of the self. We close by discussing some of the contributions, both theoretical and methodological, that can be made by cross-cultural psychologists to researchers in cultural psychopathology. Creative Commons License This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. This article is available in Online Readings in Psychology and Culture: http://scholarworks.gvsu.edu/orpc/vol10/iss2/3


title: Machine learning for targeted display advertising: transfer learning in action

abstract: This paper presents the design of a fully deployed multistage transfer learning system for targeted display advertising, highlighting the important role of problem formulation and the sampling of data from distributions different from that of the target environment. Notably, the machine learning system itself is deployed and has been in continual use for years for thousands of advertising campaigns--in contrast to the more common case where predictive models are built outside the system, curated, and then deployed. In this domain, acquiring sufficient data for training from the ideal sampling distribution is prohibitively expensive. Instead, data are drawn from surrogate distributions and learning tasks, and then transferred to the target task. We present the design of the transfer learning system We then present a detailed experimental evaluation, showing that the different transfer stages indeed each add value. We also present production results across a variety of advertising clients from a variety of industries, illustrating the performance of the system in use. We close the paper with a collection of lessons learned from over half a decade of research and development on this complex, deployed, and intensely used machine learning system.


title: Robust nonlinear model predictive control of a batch bioreactor using multi-stage stochastic programming

abstract: This paper presents a robust nonlinear model predictive control scheme and its application to a batch bioreactor. The approach is based on the description of the uncertainty evolution as a scenario tree. This makes it possible to take explicitly into account the future disturbances and control inputs leading to a non-conservative approach that is not based on the tracking of a nominal solution. The main challenge of the approach is that the size of the resulting optimization problem grows exponentially with the prediction horizon and with the number of uncertainties. The potential of the approach is demonstrated by simulation examples of a nonlinear penicillin fermentation process where the proposed scheme can fulfill the state and the input constraints for all the possible values of several uncertain parameters, improving the performance of existing robust approaches such as tracking of the necessary conditions of optimality.


title: Sports teams as superorganisms: Implications of sociobiological models of behaviour for research and practice in team sports performance analysis

abstract: Significant criticisms have emerged on the way that collective behaviours in team sports have been traditionally evaluated. A major recommendation has been for future research and practice to focus on the interpersonal relationships developed between team players during performance. Most research has typically investigated team game performance in subunits (attack or defence), rather than considering the interactions of performers within the whole team. In this paper, we offer the view that team performance analysis could benefit from the adoption of biological models used to explain how repeated interactions between grouping individuals scale to emergent social collective behaviours. We highlight the advantages of conceptualizing sports teams as functional integrated 'super-organisms' and discuss innovative measurement tools, which might be used to capture the superorganismic properties of sports teams. These tools are suitable for revealing the idiosyncratic collective behaviours underlying the cooperative and competitive tendencies of different sports teams, particularly their coordination of labour and the most frequent channels of communication and patterns of interaction between team players. The principles and tools presented here can serve as the basis for novel approaches and applications of performance analysis devoted to understanding sports teams as cohesive, functioning, high-order organisms exhibiting their own peculiar behavioural patterns.


title: Using Test Oracles Generated from Program Documentation

abstract: The paper illustrates how software can be described precisely using LD-relations, how these descriptions can be presented in a readable manner using tabular notations, and one way such descriptions can be used to test programs. The authors describe an algorithm that can be used to generate a test oracle from program documentation, and present the results of using a tool based on it to help test part of a commercial network management application. The results demonstrate that these methods can be effective at detecting errors and greatly increase the speed and accuracy of test evaluation when compared with manual evaluation. Such oracles can be used for unit testing, in situ testing, constructing self-checking software, and ensuring consistency between code and documentation.


title: Image Denoising Using Very Deep Fully Convolutional Encoder-Decoder Networks with Symmetric Skip Connections

abstract: Image denoising is a long-standing problem in computer vision and image processing, as well as a test bed for low-level image modeling algorithms. In this paper, we propose a very deep encoding-decoding framework for image denoising. Instead of using image priors, the proposed framework learns end-to-end fully convolutional mappings from noisy images to the clean ones. The network is composed of multiple layers of convolution and de-convolution operators. With the observation that deeper networks improve denoising performance, we propose to use significantly deeper networks than those employed previously for low-level image processing tasks such as denoising. We propose to symmetrically link convolutional and de-convolutional layers with skip-layer connections, with which the training converges much faster and attains a higher-quality local optimum. From the image processing point of view, those symmetric connections help preserve image details.


title: Multitask Learning with Low-Level Auxiliary Tasks for Encoder-Decoder Based Speech Recognition

abstract: End-to-end training of deep learning-based models allows for implicit learning of intermediate representations based on the final task loss. However, the end-to-end approach ignores the useful domain knowledge encoded in explicit intermediate-level supervision. We hypothesize that using intermediate representations as auxiliary supervision at lower levels of deep networks may be a good way of combining the advantages of end-to-end training and more traditional pipeline approaches. We present experiments on conversational speech recognition where we use lower-level tasks, such as phoneme recognition, in a multitask training approach with an encoder-decoder model for direct character transcription. We compare multiple types of lower-level tasks and analyze the effects of the auxiliary tasks. Our results on the Switchboard corpus show that this approach improves recognition accuracy over a standard encoder-decoder model on the Eval2000 test set.


title: DUAL LINEAR/CIRCULAR POLARIZED PLANNAR ANTENNA WITH LOW PROFILE DOUBLE-LAYER POLARIZER OF 45Âº TILTED METALLIC STRIPS FOR WIMAX APPLICATIONS

abstract: This work is supported by Madrid Region Government and the ::: Autonoma University of Madrid, Ref. CCG07-UAM/TIC-1794 & ::: Ref. CCG08-UAM/TIC-4315.


title: MetaPAD: Meta Pattern Discovery from Massive Text Corpora

abstract: Mining textual patterns in news, tweets, papers, and many other kinds of text corpora has been an active theme in text mining and NLP research. Previous studies adopt a dependency parsing-based pattern discovery approach. However, the parsing results lose rich context around entities in the patterns, and the process is costly for a corpus of large scale. In this study, we propose a novel typed textual pattern structure, called meta pattern, which is extended to a frequent, informative, and precise subsequence pattern in certain context. We propose an efficient framework, called MetaPAD, which discovers meta patterns from massive corpora with three techniques: (1) it develops a context-aware segmentation method to carefully determine the boundaries of patterns with a learnt pattern quality assessment function, which avoids costly dependency parsing and generates high-quality patterns; (2) it identifies and groups synonymous meta patterns from multiple facets---their types, contexts, and extractions; and (3) it examines type distributions of entities in the instances extracted by each group of patterns, and looks for appropriate type levels to make discovered patterns precise. Experiments demonstrate that our proposed framework discovers high-quality typed textual patterns efficiently from different genres of massive corpora and facilitates information extraction.


title: Characteristics in information processing approaches

abstract: This paper describes and compares different information processing approaches (terms). The purpose is to identify similarities and differences in the terms, relate them to and compare them with each other, but also to identify their underlying concepts and the course of events they represent. The terms or approaches addressed are Environmental scanning, Business, Competitive, Competitor, Market and Political intelligence, Marketing research and Information management. It was concluded that all approaches have a strong future orientation and strong ties to decision-making, and advocate that information is ennobled in one way or the other. The main differences lie in their focus, and in their scope.


title: Transdermal delivery of meloxicam using niosomal hydrogels:in vitroand pharmacodynamic evaluation

abstract: AbstractNon-ionic surfactant vesicles were prepared using Span-60 and cholesterol in the mass ratios of 1:1, 2:1, 1:2 and 3:1 for transdermal delivery of an anti-inflammatory drug meloxicam (MXM). The drug encapsulation efficiencies and particle size were observed in the range of 32.9–80.7% and 56.5–133.4 nm, respectively. Three different gel bases were also prepared using Poloxamer-407, Chitosan and Carbopol-934 as polymers to study the performance of the in vitro release of the drug. Prepared gels were also converted into niosomal gels. In vitro release characteristics of MXM from different gels were carried out using dialysis membrane in phosphate buffer (pH 7.4). The poloxamer-407 gel or niosomal poloxamer-407 gel showed the superior drug release over the other formulations. The release data were treated with various mathematical models to assess the relevant parameters. The results showed that the release of MXM from the prepared gels and niosomal gels followed Higuchi’s diffusion model. The flux of ...


Instance Weighting for Neural Machine Translation Domain Adaptation


title: Learning about objects through action - initial steps towards artificial cognition

abstract: Within the field of Neuro Robotics we are driven primarily by the desire to understand how humans and animals live and grow and solve every day's problems. To this aim we adopted a "learn by doing" approach by building artificial systems, e.g. robots that not only look like human beings but also represent a model of some brain process. They should, ideally, behave and interact like human beings (being situated). The main emphasis in robotics has been on systems that act as a reaction to an external stimulus (e.g. tracking, reaching), rather than as a result of an internal drive to explore or "understand" the environment. We think it is now appropriate to try to move from acting, in the sense explained above, to "understanding". As a starting point we addressed the problem of learning about the effects and consequences of self-generated actions. How does the robot learn how to pull an object toward itself or to push it away? How does the robot learn that spherical objects roll while a cube only slides if pushed? Interacting with objects is important because it implicitly explores object representation, event understanding, and can provide definition of object-hood that could not be grasped with a mere passive observation of the world. Further, learning to understand what one's own body can do is an essential step toward learning by imitation. In this view two actions are similar not only if their kinematics and dynamics are similar but rather if the effects on the external world are the same. Along this line of research we discuss some recent experiments performed at the AI-Lab at MIT and at the LIRA-Lab at the University of Genova on COG and Babybot respectively. We show how the humanoid robots can learn how to poke and prod objects to obtain a consistently repeatable effect (e.g. sliding in a given direction), to help visual segmentation, and to interpret a poking action performed by a human manipulator.


title: The SAT Phase Transition

abstract: We describe a detailed experimental investigation of the phase transition for several different classes of randomly generated satisfiability problems. We observe a remarkable consistency of fea- tures in the phase transition despite the presence in some of the problem classes of clauses of mixed lengths. For instance, each of the problem classes considered has a sharp transition from satisfiable to unsatisfiable problems at a critical value. In addition, there is a com- mon easy-hard-easy pattern in the median difficulty of the problems, with the hardest problems being associated with the phase transition. However, the difficulty of problems of mixed clause lengths is much more variable than that of fixed clause length. Indeed, whilst the me- dian difficulty of random problems of mixed clause lengths can be orders of magnitude easier than that of equivalently sized problems of fixed clause length, the hardest problems of mixed clause lengths can be orders of magnitude harder than the hardest equivalently sized problems of fixed clause length. Such very hard random problems may be of considerable practical and theoretical use in analysing algorithm performance.


title: A new approach to the maximum-flow problem

abstract: All previously known efficient maximum-flow algorithms work by finding augmenting paths, either one path at a time (as in the original Ford and Fulkerson algorithm) or all shortest-length augmenting paths at once (using the layered network approach of Dinic). An alternative method based on the <italic>preflow</italic> concept of Karzanov is introduced. A preflow is like a flow, except that the total amount flowing into a vertex is allowed to exceed the total amount flowing out. The method maintains a preflow in the original network and pushes local flow excess toward the sink along what are estimated to be shortest paths. The algorithm and its analysis are simple and intuitive, yet the algorithm runs as fast as any other known method on dense graphs, achieving an <italic>O</italic>(<italic>n</italic><supscrpt>3</supscrpt>) time bound on an <italic>n</italic>-vertex graph. By incorporating the dynamic tree data structure of Sleator and Tarjan, we obtain a version of the algorithm running in <italic>O</italic>(<italic>nm</italic> log(<italic>n</italic><supscrpt>2</supscrpt>/<italic>m</italic>)) time on an <italic>n</italic>-vertex, <italic>m</italic>-edge graph. This is as fast as any known method for any graph density and faster on graphs of moderate density. The algorithm also admits efficient distributed and parallel implementations. A parallel implementation running in <italic>O</italic>(<italic>n</italic><supscrpt>2</supscrpt>log <italic>n</italic>) time using <italic>n</italic> processors and <italic>O</italic>(<italic>m</italic>) space is obtained. This time bound matches that of the Shiloach-Vishkin algorithm, which also uses <italic>n</italic> processors but requires <italic>O</italic>(<italic>n</italic><supscrpt>2</supscrpt>) space.


title: Determinants of promoter-specific activity by glucocorticoid receptor.

abstract: Glucocorticoid receptor (GR) is expressed at essentially equal levels in almost all tissues and cell types. Remarkably, glucocorticoids themselves regulate transcription in vivo in both a promoter- and tissue-specific manner. Thus, specific systems must be in place to regulate receptor action within certain cells and at certain promoters. To address two specific aspects of these systems, we have analyzed promoter-specific activity of GR using two different, well studied promoters (termed simple and composite promoters) from which GR activates transcription. The simple promoter depends only on the receptor for glucocorticoid-responsive transcriptional activation, while GR activity at the composite promoter depends on additional transcription factors. We have compared the action of several GR ligands at these promoters and demonstrate fundamental differences in the activities of these ligands on receptor activity. Furthermore, these compounds induce unique conformational changes in receptor, resulting in promoter-specific receptor function. We have identified critical amino acid residues within GR which, when mutated, genetically distinguish the action of GR at these promoters. Taken together, the data indicate that the presence of only the receptor and the ligand is not sufficient to allow activation of transcription. An additional system of regulation influences receptor action in both a tissue- and promoter-selective fashion, suggesting that multiple, regulated surfaces of the receptor respond to the cellular environment and determine the spectrum of GR activities. These functional surfaces may be induced or regulated by ligand binding, by the DNA sequence to which receptor is bound, or by the nonreceptor factors resident at the promoter or in the tissue.


title: Activity of the anterior cingulate cortex and ventral hippocampus underlie increases in contextual fear generalization

abstract: Abstract Memories for context become less specific with time resulting in animals generalizing fear from training contexts to novel contexts. Though much attention has been given to the neural structures that underlie the long-term consolidation of a context fear memory, very little is known about the mechanisms responsible for the increase in fear generalization that occurs as the memory ages. Here, we examine the neural pattern of activation underlying the expression of a generalized context fear memory in male C57BL/6J mice. Animals were context fear conditioned and tested for fear in either the training context or a novel context at recent and remote time points. Animals were sacrificed and fluorescent in situ hybridization was performed to assay neural activation. Our results demonstrate activity of the prelimbic, infralimbic, and anterior cingulate (ACC) cortices as well as the ventral hippocampus (vHPC) underlie expression of a generalized fear memory. To verify the involvement of the ACC and vHPC in the expression of a generalized fear memory, animals were context fear conditioned and infused with 4% lidocaine into the ACC, dHPC, or vHPC prior to retrieval to temporarily inactivate these structures. The results demonstrate that activity of the ACC and vHPC is required for the expression of a generalized fear memory, as inactivation of these regions returned the memory to a contextually precise form. Current theories of time-dependent generalization of contextual memories do not predict involvement of the vHPC. Our data suggest a novel role of this region in generalized memory, which should be incorporated into current theories of time-dependent memory generalization. We also show that the dorsal hippocampus plays a prolonged role in contextually precise memories. Our findings suggest a possible interaction between the ACC and vHPC controls the expression of fear generalization.


title: Distance formulas capable of unifying Euclidian space and probability space

abstract: For pattern recognition like image recognition, it has become clear that each machine-learning dictionary data actually became data in probability space belonging to Euclidean space. However, the distances in the Euclidean space and the distances in the probability space are separated and ununified when machine learning is introduced in the pattern recognition. There is still a problem that it is impossible to directly calculate an accurate matching relation between the sampling data of the read image and the learned dictionary data. In this research, we focused on the reason why the distance is changed and the extent of change when passing through the probability space from the original Euclidean distance among data belonging to multiple probability spaces containing Euclidean space. By finding the reason of the cause of the distance error and finding the formula expressing the error quantitatively, a possible distance formula to unify Euclidean space and probability space is found. Based on the results of this research, the relationship between machine-learning dictionary data and sampling data was clearly understood for pattern recognition. As a result, the calculation of collation among data and machine-learning to compete mutually between data are cleared, and complicated calculations became unnecessary. Finally, using actual pattern recognition data, experimental demonstration of a possible distance formula to unify Euclidean space and probability space discovered by this research was carried out, and the effectiveness of the result was confirmed.


title: Towards HMD-based Immersive Analytics

abstract: Advances in 3D hardware and software have led to increasingly cheaper and simple-to-use immersive virtual reality systems that can provide real-time interactive 3D data representation. The immersive analytics field is developing as the newest avatar of 3D visual analyt- ics, which may relaunch the long enduring 2D vs 3D visualization debate. However, the terms of the debate have changed: leveraging 3D human perception within virtual environments is now easier, and the immersive quality of today’s rendering is sufficient enough for researchers to concentrate on testing and designing immersive data representation and interaction rather than on technological problems. In this position paper we propose a short historical perspective on the use of immersive technologies for visual analytics and on the 2D vs 3D debate. We stress out five principles that we think should be followed to explore the HMD-based visual analytics design space, before introducing ongoing work within the IDEA project.


title: Refinement and validation of the Work Readiness Scale for graduate nurses.

abstract: The transition from student to registered nurse is often stressful and has been attributed to a lack of work readiness. Understanding what comprises work readiness for newly registered nurses, or graduate nurses as they are referred to in Australia, may reduce attrition and improve transition into the workplace. The 64-item Work Readiness Scale (WRS), developed with a generic population of graduates, has yet to be validated against specific disciplines to confirm applicability as a measure of work readiness. This study adapted the original WRS for use with a graduate nurse population (WRS-GN). The aim was to refine and validate the WRS-GN and determine whether the original four factor construct was supported. The WRS-GN was completed by 450 graduate nurses. Exploratory factor analyses supported the original four-factor solution (social intelligence, personal work characteristics, organizational acumen, and work competence). The final WRS-GN consisted of 46 items, showed excellent reliability, and explained 73.55% of the variance.


title: An improved real-time surveillance system for home security system using BeagleBoard SBC, Zigbee and FTP webserver

abstract: Real-time surveillance is an important aspect of an intelligent building with modern security demands. The proposed system implements an embedded system for monitoring wireless sensor nodes and camera installed inside a building for security surveillance. A number of surveillance devices in a Zigbee protocol (IEEE 802.15.4) based wireless network are connected to a BeagleBoard Single Board Computer (SBC) based surveillance management system. Remote alerting on fire and intruder detection are the key features of the system. When smoke or intruder movement is detected, the system sends warning messages through Short Message Service (SMS) to cell phones, starts capturing real-time video for fixed duration and makes the alarm on. The captured video clip is immediately uploaded to an FTP (File Transfer Protocol) webserver so that it can be retrieved later from anywhere around the world. The advantages of the system are that it guarantees reliability by integrating various components of a security system (sensors, alarm, camera, wireless connectivity etc.) and utilizes an FTP server for camera feeds.


Multiobjective Optimal Design of an Axial Flux Permanent Magnet Generator for Directly Coupled Wind Turbines


title: Sleep, Learning, and Dreams: Off-line Memory Reprocessing

abstract: Converging evidence and new research methodologies from across the neurosciences permit the neuroscientific study of the role of sleep in off-line memory reprocessing, as well as the nature and function of dreaming. Evidence supports a role for sleep in the consolidation of an array of learning and memory tasks. In addition, new methodologies allow the experimental manipulation of dream content at sleep onset, permitting an objective and scientific study of this dream formation and a renewed search for the possible functions of dreaming and the biological processes subserving it.


Return to Eden ? Promises and Perils on the Road to a Global Superintelligence


Generalized 3 D Object Representation using Bayesian Eigenobjects


title: What happens after an ad click?: quantifying the impact of landing pages in web advertising

abstract: Unbeknownst to most users, when a query is submitted to a search engine two distinct searches are performed: the organic or algorithmic search that returns relevant Web pages and related data (maps, images, etc.), and the sponsored search that returns paid advertisements. While an enormous amount of work has been invested in understanding the user interaction with organic search, surprisingly little research has been dedicated to what happens after an ad is clicked, a situation we aim to correct. To this end, we define and study the process of context transfer, that is, the user's transition from Web search to the context of the landing page that follows an ad-click. We conclude that in the vast majority of cases the user is shown one of three types of pages, namely, Homepage (the homepage of the advertiser), Category browse (a browse-able sub-catalog related to the original query), and Search transfer (the search results of the same query re-executed on the target site). We show that these three types of landing pages can be accurately distinguished using automatic text classification. Finally, using such an automatic classifier, we correlate the landing page type with conversion data provided by advertisers, and show that the conversion rate (i.e., users' response rate to ads) varies considerably according to the type. We believe our findings will further the understanding of users' response to search advertising in general, and landing pages in particular, and thus help advertisers improve their Web sites and help search engines select the most suitable ads.


title: CRM Scorecard - CRM Performance Measurement

abstract: Customer Relationship Management (CRM) is a strategy implemented for managing a company’s interactions with customers, clients and sales prospects. Companies have invested or planning to invest huge amounts to implement CRM strategies, tools and infrastructure in order to attract and retain profitable customers in today’s increasingly competitive markets. For that, they are recommended to have a measurement system to manage and assess the effectiveness of CRM initiatives. One of the effective performance measurement methods is the Balanced Scorecard (BSC). The purpose of this paper is to identify the variations made on BSC to become suitable to measure the performance of CRM. Then, we propose a CRM scorecard to evaluate the performance of CRM systems based on the literature review and the practice of a major bank in Saudi Arabia.


title: Monitoring of the polymerization of vinylacetate by near IR FT Raman spectroscopy

abstract: Abstract Emulsion polymerization of vinylacetate was studied to monitor the reaction on-line by the near infrared FT-Raman technique. The experimental conditions for monitoring the reaction by Raman spectroscopy were determined and discussed with regard to their applicability to industrial production of the polymer. The reaction goes to approximately 90% completion in 50 min and the polymerization can be succesfully monitored by taking full-scan measurements between 20–3500 cm−1 at every 8 min.


title: Data Structures And Other Objects Using Java

abstract: Thank you very much for downloading data structures and other objects using java. Maybe you have knowledge that, people have search hundreds times for their favorite readings like this data structures and other objects using java, but end up in harmful downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they juggled with some infectious virus inside their laptop. data structures and other objects using java is available in our digital library an online access to it is set as public so you can get it instantly. Our books collection hosts in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the data structures and other objects using java is universally compatible with any devices to read.


title: Terminology Extraction Approaches for Product Aspect Detection in Customer Reviews

abstract: In this paper, we address the problem of identifying relevant product aspects in a collection of online customer reviews. Being able to detect such aspects represents an important subtask of aspect-based review mining systems, which aim at automatically generating structured summaries of customer opinions. We cast the task as a terminology extraction problem and examine the utility of varying term acquisition heuristics, filtering techniques, variant aggregation methods, and relevance measures. We evaluate the different approaches on two distinct datasets (hotel and camera reviews). For the best configuration, we find significant improvements over a state-of-the-art baseline method.


title: Design and fabrication of an automatic window cleaning robot

abstract: The rapid growth of advanced robots has given researchers unprecedented opportunities to explore and discover new fields of research where robots can be used to assist humans in their daily life. There are many applications that use robots and automation in different aspects of life such as industry, medical, domestic machines and etc. In this paper, the work has been devoted for the use of robotics and robots in cleaning process. The window cleaning robot is one of the robots that have emerged in recent decay. This robot can be used in homes, offices and large buildings. The main target is to design a robot that can clean glass windows efficiently and rapidly even in dangerous and hazardous places. The robot will be controlled using the Programmable Logic Controller PLC. The motion will generated by three servo motors.


title: A method of entity relationship modeling of Internet of Things based on supernetwork

abstract: Currently, the methods of entity relationship modeling are mainly based on existing historical data or Complex Networks, because one-dimensional elements characterizing clusters relationship can't be a true reflection of the entity relationship and potential structure of IOT(Internet of Things). This paper proposes a method of entity relationship modeling of IOT based on Supernetwork, by considering both dominant relationship and implicit relationship of interest similarity, we build a multidimensional model of entity relationship of IOT, and use the MIT Media Lab's real datasets to verify the feasibility and effectiveness of the model. It lays a solid foundation for further study of the dynamic growth of the entity relationship network.


title: Frequency and polarization reconfigurable antenna for wireless communication

abstract: A reconfigurable antenna employing wideband frequency agile patches with simultaneous polarization reconfiguration is presented under the article. An antenna consists of a square microstrip patch which is connected to a square microstrip ring by using four varactor diodes (variable capacitor diode). These varactor diodes are used to achieve frequency reconfigurability between 1.6GHz to 2.4GHz (frequency agility bandwidth= 40%, S11 <= −10 dB). Employing two orthogonal feed points on the radiator enables switching amongst four polarizations states (two linear and two circular polarizations). These two feed points on the radiator can be excited by polarization feed network (PFN). PFN consists of a Wilkinson Power Divider and Schiffman 90° phase shifter. The simulated results agree reasonably well.


title: A family of code coverage-based heuristics for effective fault localization

abstract: Locating faults in a program can be very time-consuming and arduous, and therefore, there is an increased demand for automated techniques that can assist in the fault localization process. In this paper a code coverage-based method with a family of heuristics is proposed in order to prioritize suspicious code according to its likelihood of containing program bugs. Highly suspicious code (i.e., code that is more likely to contain a bug) should be examined before code that is relatively less suspicious; and in this manner programmers can identify and repair faulty code more efficiently and effectively. We also address two important issues: first, how can each additional failed test case aid in locating program faults; and second, how can each additional successful test case help in locating program faults. We propose that with respect to a piece of code, the contribution of the first failed test case that executes it in computing its likelihood of containing a bug is larger than or equal to that of the second failed test case that executes it, which in turn is larger than or equal to that of the third failed test case that executes it, and so on. This principle is also applied to the contribution provided by successful test cases that execute the piece of code. A tool, @gDebug, was implemented to automate the computation of the suspiciousness of the code and the subsequent prioritization of suspicious code for locating program faults. To validate our method case studies were performed on six sets of programs: Siemens suite, Unix suite, space, grep, gzip, and make. Data collected from the studies are supportive of the above claim and also suggest Heuristics III(a), (b) and (c) of our method can effectively reduce the effort spent on fault localization.


title: Levitation control design of super-speed Maglev trains

abstract: This paper deals with the levitation control design for super-speed magnetic levitation (Maglev) trains. The state feedback controller as well as a linear observer is utilized in the general framework accounting for the guideway deviation. The configuration of the control system is presented, and its characteristics are analyzed theoretically and numerically. The performances of the proposed controller are predicted by several simulations, and guidelines for designing the levitation control will be given.


title: Modeling and control aspects of a UAV with an attached manipulator

abstract: This article addresses the problem of the modeling and control of an Unmanned Aerial System (UAS) that consists of a UAV carrying a robotic manipulator. The dynamics of the UAS are derived based on the recursive Newton-Euler equations in order to handle the floating-base effect. Accordingly, the dynamics account for the force and torque transferred from the base of the manipulator to the UAV and the translational and angular velocities and accelerations from the UAV to the manipulator. The integrated nonlinear dynamic equations are symbolically derived and efficient code for execution at embedded systems is generated. An efficient nonlinear control scheme regulates the states of the UAS to a desired configuration. Simulation results are provided to evaluate the performance of the developed strategy, in comparison to the ones derived by simplifying the coupling of the UAV and manipulator dynamics by considering only the spatial coordinates of the center of mass of the manipulator.


title: Robust Lane Detection using Two-stage Feature Extraction with Curve Fitting

abstract: With the increase in the number of vehicles, many intelligent systems have been developed to help drivers to drive safely. Lane detection is a crucial element of any driver assistance system. At present, researchers working on lane detection are confronted with several major challenges, such as attaining robustness to inconsistencies in lighting and background clutter. To address these issues in this work, we propose a method named Lane Detection with Two-stage Feature Extraction (LDTFE) to detect lanes, whereby each lane has two boundaries. To enhance robustness, we take lane boundary as collection of small line segments. In our approach, we apply a modified HT (Hough Transform) to extract small line segments of the lane contour, which are then divided into clusters by using the DBSCAN (Density Based Spatial Clustering of Applications with Noise) clustering algorithm. Then, we can identify the lanes by curve fitting. The experimental results demonstrate that our modified HT works better for LDTFE than LSD (Line Segment Detector). Through extensive experiments, we demonstrate the outstanding performance of our method on the challenging dataset of road images compared with state-of-the-art lane-detection methods. HighlightsWe proposed a novel lane detection method.Our method regards lane boundary as collection of small line segments.We proposed a modified Hough Transform to detect small line segments.Small line segments are clustered based on our proposed similarity measurement.Removing interferential clusters depends on the balance of small line segments.


title: Line detection in images showing significant lens distortion and application to distortion correction

abstract: Lines are one of the basic primitives used by the perceptual system to analyze and interpret a scene. Therefore, line detection is a very important issue for the robustness and flexibility of Computer Vision systems. However, in the case of images showing a significant lens distortion, standard line detection methods fail because lines are not straight. In this paper we present a new technique to deal with this problem: we propose to extend the usual Hough representation by introducing a new parameter which corresponds to the lens distortion, in such a way that the search space is a three-dimensional space, which includes orientation, distance to the origin and also distortion. Using the collection of distorted lines which have been recovered, we are able to estimate the lens distortion, remove it and create a new distortion-free image by using a two-parameter lens distortion model. We present some experiments in a variety of images which show the ability of the proposed approach to extract lines in images showing a significant lens distortion.


title: Internet of Things: Remote Patient Monitoring Using Web Services and Cloud Computing

abstract: The focus on this paper is to build an Android platform based mobile application for the healthcare domain, which uses the idea of Internet of Things (IoT) and cloud computing. We have built an application called 'ECG Android App' which provides the end user with visualization of their Electro Cardiogram (ECG) waves and data logging functionality in the background. The logged data can be uploaded to the user's private centralized cloud or a specific medical cloud, which keeps a record of all the monitored data and can be retrieved for analysis by the medical personnel. Though the idea of building a medical application using IoT and cloud techniques is not totally new, there is a lack of empirical studies in building such a system. This paper reviews the fundamental concepts of IoT. Further, the paper presents an infrastructure for the healthcare domain, which consists of various technologies: IOIO microcontroller, signal processing, communication protocols, secure and efficient mechanisms for large file transfer, data base management system, and the centralized cloud. The paper emphasizes on the system and software architecture and design which is essential to overall IoT and cloud based medical applications. The infrastructure presented in the paper can also be applied to other healthcare domains. It concludes with recommendations and extensibilities found for the solution in the healthcare domain.


title: Network Structure Shapes Spontaneous Functional Connectivity Dynamics

abstract: The structural organization of the brain constrains the range of interactions between different regions and shapes ongoing information processing. Therefore, it is expected that large-scale dynamic functional connectivity (FC) patterns, a surrogate measure of coordination between brain regions, will be closely tied to the fiber pathways that form the underlying structural network. Here, we empirically examined the influence of network structure on FC dynamics by comparing resting-state FC (rsFC) obtained using BOLD-fMRI in macaques (Macaca fascicularis) to structural connectivity derived from macaque axonal tract tracing studies. Consistent with predictions from simulation studies, the correspondence between rsFC and structural connectivity increased as the sample duration increased. Regions with reciprocal structural connections showed the most stable rsFC across time. The data suggest that the transient nature of FC is in part dependent on direct underlying structural connections, but also that dynamic coordination can occur via polysynaptic pathways. Temporal stability was found to be dependent on structural topology, with functional connections within the rich-club core exhibiting the greatest stability over time. We discuss these findings in light of highly variable functional hubs. The results further elucidate how large-scale dynamic functional coordination exists within a fixed structural architecture.


title: A Multi-party Protocol for Constructing the Public Parameters of the Pinocchio zk-SNARK

abstract: Recent efficient constructions of zero-knowledge Succinct Non-interactive Arguments of Knowledge (zk-SNARKs), require a setup phase in which a common-reference string (CRS) with a certain structure is generated. This CRS is sometimes referred to as the public parameters of the system, and is used for constructing and verifying proofs. A drawback of these constructions is that whomever runs the setup phase subsequently possesses trapdoor information enabling them to produce fraudulent pseudoproofs.


title: Low power multi-lane MIPI CSI-2 receiver design and hardware implementations

abstract: This paper proposes a low power multi-Lane Mobile Industry Processor Interface (MIPI) Camera Serial Interface 2 (CSI-2) receiver architecture which adopts an 8-Byte parallel CSI protocol layer for hardware implementations. The proposed scheme can work in environment with 4 data Lanes and 1 Gb/s per data Lane, i.e. with maximum data rate 4 Gb/s, at 62.5 MHz which increases logic operations from 8 ns (125 MHz) to 16 ns (62.5 MHz) without throughput degradation. Therefore, the supply voltage (1.2 V) can be reduced and the power consumption can also be reduced. The proposed architecture is implemented by 0.13 μm CMOS technology and the total gate count is 32.7 K. It not only reduces the operating clock rate but also reduces more than 37%~43% logic power consumption measured in chip.


title: Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks

abstract: Convolutional neural network (CNN) has been widely employed for image recognition because it can achieve high accuracy by emulating behavior of optic nerves in living creatures. Recently, rapid growth of modern applications based on deep learning algorithms has further improved research and implementations. Especially, various accelerators for deep CNN have been proposed based on FPGA platform because it has advantages of high performance, reconfigurability, and fast development round, etc. Although current FPGA accelerators have demonstrated better performance over generic processors, the accelerator design space has not been well exploited. One critical problem is that the computation throughput may not well match the memory bandwidth provided an FPGA platform. Consequently, existing approaches cannot achieve best performance due to under-utilization of either logic resource or memory bandwidth. At the same time, the increasing complexity and scalability of deep learning applications aggravate this problem. In order to overcome this problem, we propose an analytical design scheme using the roofline model. For any solution of a CNN design, we quantitatively analyze its computing throughput and required memory bandwidth using various optimization techniques, such as loop tiling and transformation. Then, with the help of rooine model, we can identify the solution with best performance and lowest FPGA resource requirement. As a case study, we implement a CNN accelerator on a VC707 FPGA board and compare it to previous approaches. Our implementation achieves a peak performance of 61.62 GFLOPS under 100MHz working frequency, which outperform previous approaches significantly.


title: A Large-Scale Evaluation of Automated Unit Test Generation Using EvoSuite

abstract: Research on software testing produces many innovative automated techniques, but because software testing is by necessity incomplete and approximate, any new technique faces the challenge of an empirical assessment. In the past, we have demonstrated scientific advance in automated unit test generation with the E VO S UITE tool by evaluating it on manually selected open-source projects or examples that represent a particular problem addressed by the underlying technique. However, demonstrating scientific advance is not necessarily the same as demonstrating practical value; even if VO S UITE worked well on the software projects we selected for evaluation, it might not scale up to the complexity of real systems. Ideally, one would use large “real-world” software systems to minimize the threats to external validity when evaluating research tools. However, neither choosing such software systems nor applying research prototypes to them are trivial tasks. In this article we present the results of a large experiment in unit test generation using the VO S UITE tool on 100 randomly chosen open-source projects, the 10 most popular open-source projects according to the SourceForge Web site, seven industrial projects, and 11 automatically generated software projects. The study confirms that VO S UITE can achieve good levels of branch coverage (on average, 71p per class) in practice. However, the study also exemplifies how the choice of software systems for an empirical study can influence the results of the experiments, which can serve to inform researchers to make more conscious choices in the selection of software system subjects. Furthermore, our experiments demonstrate how practical limitations interfere with scientific advances, branch coverage on an unbiased sample is affected by predominant environmental dependencies. The surprisingly large effect of such practical engineering problems in unit testing will hopefully lead to a larger appreciation of work in this area, thus supporting transfer of knowledge from software testing research to practice.


title: Cloud Computing: State of the Art and Security Issues

abstract: Cloud Computing is the fastest growing technology in the IT industry. It helps in providing services and resources with the help of internet. Resources are always provided by the Cloud Service Provider. Resources may be servers, storage, applications and networks. Keeping Resources in the cloud environment can be helpful in saving infrastructure cost and time for the user. Transferring the entire information of the enterprise onto the cloud contains lots of security issues and threats. This paper focuses on the various concepts related to cloud computing, its various business and service models and its entities along with several issues and challenges related to it.


title: Detecting readers with dyslexia using machine learning with eye tracking measures

abstract: Worldwide, around 10% of the population has dyslexia, a specific learning disorder. Most of previous eye tracking experiments with people with and without dyslexia have found differences between populations suggesting that eye movements reflect the difficulties of individuals with dyslexia. In this paper, we present the first statistical model to predict readers with and without dyslexia using eye tracking measures. The model is trained and evaluated in a 10-fold cross experiment with a dataset composed of 1,135 readings of people with and without dyslexia that were recorded with an eye tracker. Our model, based on a Support Vector Machine binary classifier, reaches 80.18% accuracy using the most informative features. To the best of our knowledge, this is the first time that eye tracking measures are used to predict automatically readers with dyslexia using machine learning.


title: Strategic Decision Processes in High Velocity Environments: Four Cases in the Microcomputer Industry

abstract: How do executives make strategic decisions in industries where the rate of technological and competitive change is so extreme that market information is often unavailable or obsolete, where strategic windows are opening and shutting quickly, and where the cost of error is involuntary exit? How do top management teams divide the decision making responsibility? And how is risk of strategic error mitigated? What we report here is a set of hypotheses induced from a field investigation of four microcomputer firms, where we studied how each of the top management teams went about making major decisions. Our goal was to extend prior work on strategic decision making to what we term high velocity environments. Our results consist of a set of paradoxes which the successful firms resolve and the unsuccessful firms do not. We found an imperative to make major decisions carefully, but to decide quickly; to have a powerful, decisive CEO and a simultaneously powerful top management team; to seek risk and innovation, but to execute a safe, incremental implementation. Despite the apparent paradox, effective firms do all of these simultaneously. These paradoxes are presented in the form of propositions and testable hypotheses.


title: Changing How and What Children Learn in School with Computer-Based Technologies

abstract: Schools today face ever-increasing demands in their attempts to ensure that students are well equipped to enter the workforce and navigate a complex world. Research indicates that computer technology can help support learning, and that it is especially useful in developing the higher-order skills of critical thinking, analysis, and scientific inquiry. But the mere presence of computers in the classroom does not ensure their effective use. Some computer applications have been shown to be more successful than others, and many factors influence how well even the most promising applications are implemented. This article explores the various ways computer technology can be used to improve how and what children learn in the classroom. Several examples of computer-based applications are highlighted to illustrate ways technology can enhance how children learn by supporting four fundamental characteristics of learning: (1) active engagement, (2) participation in groups, (3) frequent interaction and feedback, and (4) connections to real-world contexts. Additional examples illustrate ways technology can expand what children learn by helping them to understand core concepts in subjects like math, science, and literacy. Research indicates, however, that the use of technology as an effective learning tool is more likely to take place when embedded in a broader education reform movement that includes improvements in teacher training, curriculum, student assessment, and a school's capacity for change. To help inform decisions about the future role of computers in the classroom, the authors conclude that further research is needed to identify the uses that most effectively support learning and the conditions required for successful implementation.


title: Twitter-based Election Prediction in the Developing World

abstract: Elections are the main instrument of democracy. Citizens decide which entity or entities (a political party or a particular politician) should represent them. Traditionally, pre-election polls have been used to learn about trends and likely election outcomes. Predicting an election outcome based on user activity on Twitter has been shown to be a cheap alternative. While past research has focused on election prediction in the developed world (where its use is debatable), in this paper we provide a comprehensive argument for the use of Twitter-based election forecasting in the developing world. For our use case of Indonesia's presidential elections 2014, the most basic Twitter-predictor outperforms the majority of traditional polls, while the best performing predictor outperforms all traditional polls on the national level.


title: CRISPR Crops: Plant Genome Editing Toward Disease Resistance

abstract: Genome editing by sequence-specific nucleases (SSNs) has revolutionized biology by enabling targeted modifications of genomes. Although routine plant genome editing emerged only a few years ago, we are already witnessing the first applications to improve disease resistance. In particular, CRISPR-Cas9 has democratized the use of genome editing in plants thanks to the ease and robustness of this method. Here, we review the recent developments in plant genome editing and its application to enhancing disease resistance against plant pathogens. In the future, bioedited disease resistant crops will become a standard tool in plant breeding.


title: Graph regularized discriminative non-negative matrix factorization for face recognition

abstract: Non-negative matrix factorization (NMF) has been widely employed in computer vision and pattern recognition fields since the learned bases can be interpreted as a natural parts-based representation of the input space, which is consistent with the psychological intuition of combining parts to form a whole. In this paper, we propose a novel constrained nonnegative matrix factorization algorithm, called the graph regularized discriminative non-negative matrix factorization (GDNMF), to incorporate into the NMF model both intrinsic geometrical structure and discriminative information which have been essentially ignored in prior works. Specifically, both the graph Laplacian and supervised label information are jointly utilized to learn the projection matrix in the new model. Further we provide the corresponding multiplicative update solutions for the optimization framework, together with the convergence proof. A series of experiments are conducted over several benchmark face datasets to demonstrate the efficacy of our proposed GDNMF.


title: Compositing digital images

abstract: Most computer graphics pictures have been computed all at once, so that the rendering program takes care of all computations relating to the overlap of objects. There are several applications, however, where elements must be rendered separately, relying on compositing techniques for the anti-aliased accumulation of the full image. This paper presents the case for four-channel pictures, demonstrating that a matte component can be computed similarly to the color channels. The paper discusses guidelines for the generation of elements and the arithmetic for their arbitrary compositing .


title: Saliency detection on sampled images for tag ranking

abstract: Image saliency contributes to rank the unordered tags extracted from social media, but the existing saliency detection methods can hardly efficiently handle massive images in tag ranking. In this paper, we focus on improving the efficiency of saliency detection methods by applying them on the sampled images with suitable resolutions. We extensively investigate the influence of image resolution to saliency detection performance of the typical methods, and summarize a sampling strategy for different categories of salient object detection methods. Furthermore, we validate the effectiveness of the sampling strategy by applying the salient object detection methods on the sampled images with the selected resolutions in tag ranking. The experimental results show that sampling can significantly improve the efficiency of the existing salient object detection methods without obvious loss in effectiveness.


title: Finding time period-based most frequent path in big trajectory data

abstract: The rise of GPS-equipped mobile devices has led to the emergence of big trajectory data. In this paper, we study a new path finding query which finds the most frequent path (MFP) during user-specified time periods in large-scale historical trajectory data. We refer to this query as time period-based MFP (TPMFP). Specifically, given a time period T, a source v_s and a destination v_d, TPMFP searches the MFP from v_s to v_d during T. Though there exist several proposals on defining MFP, they only consider a fixed time period. Most importantly, we find that none of them can well reflect people's common sense notion which can be described by three key properties, namely suffix-optimal (i.e., any suffix of an MFP is also an MFP), length-insensitive (i.e., MFP should not favor shorter or longer paths), and bottleneck-free (i.e., MFP should not contain infrequent edges). The TPMFP with the above properties will reveal not only common routing preferences of the past travelers, but also take the time effectiveness into consideration. Therefore, our first task is to give a TPMFP definition that satisfies the above three properties. Then, given the comprehensive TPMFP definition, our next task is to find TPMFP over huge amount of trajectory data efficiently. Particularly, we propose efficient search algorithms together with novel indexes to speed up the processing of TPMFP. To demonstrate both the effectiveness and the efficiency of our approach, we conduct extensive experiments using a real dataset containing over 11 million trajectories.


Python: A Programming Language For Software Integration And Development


title: Fast and robust face recognition via coding residual map learning based adaptive masking

abstract: Robust face recognition (FR) is an active topic in computer vision and biometrics, while face occlusion is one of the most challenging problems for robust FR. Recently, the representation (or coding) based FR schemes with sparse coding coefficients and coding residual have demonstrated good robustness to face occlusion; however, the high complexity of l1-minimization makes them less useful in practical applications. In this paper we propose a novel coding residual map learning scheme for fast and robust FR based on the fact that occluded pixels usually have higher coding residuals when representing an occluded face image over the non-occluded training samples. A dictionary is learned to code the training samples, and the distribution of coding residuals is computed. Consequently, a residual map is learned to detect the occlusions by adaptive thresholding. Finally the face image is identified by masking the detected occlusion pixels from face representation. Experiments on benchmark databases show that the proposed scheme has much lower time complexity but comparable FR accuracy with other popular approaches. A fast and robust face recognition method is proposed.A residual map is learned to effectively enhance the robustness to occlusion.The proposed method is much faster than RSC with slightly lower accuracy.


title: End-to-End Non-Factoid Question Answering with an Interactive Visualization of Neural Attention Weights

abstract: Advanced attention mechanisms are an important part of successful neural network approaches for non-factoid answer selection because they allow the models to focus on few important segments within rather long answer texts. Analyzing attention mechanisms is thus crucial for understanding strengths and weaknesses of particular models. We present an extensible, highly modular service architecture that enables the transformation of neural network models for non-factoid answer selection into fully featured end-to-end question answering systems. The primary objective of our system is to enable researchers a way to interactively explore and compare attention-based neural networks for answer selection. Our interactive user interface helps researchers to better understand the capabilities of the different approaches and can aid qualitative analyses.


title: Principled Multilayer Network Embedding

abstract: Multilayer network analysis has become a vital tool for understanding different relationships and their interactions in a complex system, where each layer in a multilayer network depicts the topological structure of a group of nodes corresponding to a particular relationship. The interactions among different layers imply how the interplay of different relations on the topology of each layer. For a single-layer network, network embedding methods have been proposed to project the nodes in a network into a continuous vector space with a relatively small number of dimensions, where the space embeds the social representations among nodes. These algorithms have been proved to have a better performance on a variety of regular graph analysis tasks, such as link prediction, or multi-label classification. In this paper, by extending a standard graph mining into multilayer network, we have proposed three methods ("network aggregation," "results aggregation" and "layer co-analysis") to project a multilayer network into a continuous vector space. From the evaluation, we have proved that comparing with regular link prediction methods, "layer co-analysis" achieved the best performance on most of the datasets, while "network aggregation" and "results aggregation" also have better performance than regular link prediction methods.


title: Conditional Anomaly Detection

abstract: When anomaly detection software is used as a data analysis tool, finding the hardest-to-detect anomalies is not the most critical task. Rather, it is often more important to make sure that those anomalies that are reported to the user are in fact interesting. If too many unremarkable data points are returned to the user labeled as candidate anomalies, the software can soon fall into disuse. One way to ensure that returned anomalies are useful is to make use of domain knowledge provided by the user. Often, the data in question includes a set of environmental attributes whose values a user would never consider to be directly indicative of an anomaly. However, such attributes cannot be ignored because they have a direct effect on the expected distribution of the result attributes whose values can indicate an anomalous observation. This paper describes a general purpose method called conditional anomaly detection for taking such differences among attributes into account, and proposes three different expectation-maximization algorithms for learning the model that is used in conditional anomaly detection. Experiments with more than 13 different data sets compare our algorithms with several other more standard methods for outlier or anomaly detection


title: Acupuncture and endorphins

abstract: Acupuncture and electroacupuncture (EA) as complementary and alternative medicine have been accepted worldwide mainly for the treatment of acute and chronic pain. Studies on the mechanisms of action have revealed that endogenous opioid peptides in the central nervous system play an essential role in mediating the analgesic effect of EA. Further studies have shown that different kinds of neuropeptides are released by EA with different frequencies. For example, EA of 2 Hz accelerates the release of enkephalin, beta-endorphin and endomorphin, while that of 100 Hz selectively increases the release of dynorphin. A combination of the two frequencies produces a simultaneous release of all four opioid peptides, resulting in a maximal therapeutic effect. This finding has been verified in clinical studies in patients with various kinds of chronic pain including low back pain and diabetic neuropathic pain.


title: Occurrence-Oriented Design Strategy for Developing Business Process Monitoring Systems

abstract: In a competitive market, business process improvement is a requirement for any organization. This improvement can only be achieved with the support of comprehensive systems that fully monitor business processes. We propose an occurrence—based approach to business process monitoring that provides a holistic perspective of system dynamics, lending support to evolution aspects. More specifically, we present a three-dimensional artifact, called Occurrence, in which structure, behavior, and guidance are considered simultaneously. Based on this, we define more complex structures, namely Occurrence Base and Occurrence Management System, which serve as scaffolding to develop business process monitoring systems. We also present a specific occurrence-based design strategy that, using an MDE approach, has been applied by our research group for the development of successful monitoring applications.


title: A wearable human activity recognition system on a chip

abstract: The availability of cheap wearable motion and biometric sensors has favoured the research on wearable human activity recognition (HAR) systems. However, a HAR system comprehends many complex signal processing stages that usually require some computationally demanding operations which can hardly be directly performed in an embedded system. Modern FPGA technologies and the system-on-chip (SoC) approach open the door to the implementation of complex single-chip signal processing systems to produce tiny, wearable and autonomous embedded HAR systems. However, compared to a pure embedded software approach, the potentially higher performance-to-power ratio of FPGAs can only be exploited in very demanding applications and by a careful design of the implemented system. In this work we describe a first step in the consecution of an FPGA-based completely autonomous singlechip HAR system which can be adapted and optimized to the user with no need of external computing means neither of human intervention. The system includes all stages in a HAR process, i.e., signal segmentation, signal processing for feature extraction, input space dimensionality reduction (feature selection), and activity estimation by means of a neural classifier. A physical activity recognition example is used as a reference design to evaluate the performance of the system and to draw conclusions on the potential benefits of using FPGAs in future wearable HAR applications.


title: Portability of an RF fingerprint of a wireless transmitter

abstract: In conventional wireless networks, security issues are primarily considered above the physical layer and are usually based on bit-level algorithms to establish the identity of a legitimate wireless device. Physical layer security is a new paradigm in which features extracted from an analog signal can be used to establish the unique identity of a transmitter. Our previous research work into RF fingerprinting has shown that every transmitter has a unique RF fingerprint owing to imperfections in the analog components present in the RF front end. Generally, it is believed that the RF fingerprint of a specific transmitter is same across all receivers. That is, a fingerprint created in one receiver can be transported to another receiver to establish the identity of a transmitter. However, to the best of the author’s knowledge, no such example is available in the literature in which an RF fingerprint generated in one receiver is used for identification in other receivers. This paper presents the results of experiments, and analyzing the feasibility of using an universal RF fingerprint of a transmitter for identification across different receivers.


title: Accelerating the Lee-Seung Algorithm for Nonnegative Matrix Factorization

abstract: Approximate nonnegative matrix factorization is an emerging technique with a wide spectrum of potential applications in data analysis. Currently, the most-used algorithms for this problem are those proposed by Lee and Seung [7]. In this paper we present a variation of one of the Lee-Seung algorithms with a notably improved performance. We also show that algorithms of this type do not necessarily converge to local minima.


Carbon dioxide toxicity and climate change: a major unapprehended risk for human health


title: Fault-tolerant techniques for the Internet of Military Things

abstract: Some obtained results related to an ongoing project which aims at providing a comprehensive approach for implementation of Internet of Things concept into the military domain are presented. A comprehensive approach to fault diagnosis within the Internet of Military Things was outlined. Particularly a method of fault detection which is based on a network partitioning into clusters was proposed. Also, some solutions proposed for the experimentally constructed network called EFTSN was conducted.


title: Heterogeneous Feature Selection With Multi-Modal Deep Neural Networks and Sparse Group LASSO

abstract: Heterogeneous feature representations are widely used in machine learning and pattern recognition, especially for multimedia analysis. The multi-modal, often also high- dimensional , features may contain redundant and irrelevant information that can deteriorate the performance of modeling in classification. It is a challenging problem to select the informative features for a given task from the redundant and heterogeneous feature groups. In this paper, we propose a novel framework to address this problem. This framework is composed of two modules, namely, multi-modal deep neural networks and feature selection with sparse group LASSO. Given diverse groups of discriminative features, the proposed technique first converts the multi-modal data into a unified representation with different branches of the multi-modal deep neural networks. Then, through solving a sparse group LASSO problem, the feature selection component is used to derive a weight vector to indicate the importance of the feature groups. Finally, the feature groups with large weights are considered more relevant and hence are selected. We evaluate our framework on three image classification datasets. Experimental results show that the proposed approach is effective in selecting the relevant feature groups and achieves competitive classification performance as compared with several recent baseline methods.


title: Design and Evaluation of the LOPES Exoskeleton Robot for Interactive Gait Rehabilitation

abstract: This paper introduces a newly developed gait rehabilitation device. The device, called LOPES, combines a freely translatable and 2-D-actuated pelvis segment with a leg exoskeleton containing three actuated rotational joints: two at the hip and one at the knee. The joints are impedance controlled to allow bidirectional mechanical interaction between the robot and the training subject. Evaluation measurements show that the device allows both a "patient-in-charge" and "robot-in-charge" mode, in which the robot is controlled either to follow or to guide a patient, respectively. Electromyography (EMG) measurements (one subject) on eight important leg muscles, show that free walking in the device strongly resembles free treadmill walking; an indication that the device can offer task-specific gait training. The possibilities and limitations to using the device as gait measurement tool are also shown at the moment position measurements are not accurate enough for inverse-dynamical gait analysis.


title: Severe short stature due to 3-M syndrome with a novel OBSL1 gene mutation

abstract: 3-M syndrome is an underdiagnosed autosomal recessive disorder characterized by severe pre- and postnatal growth retardation with minimal dysmorphic features and distinguishing radiological findings. We report a patient who was first admitted at 7.5 years of age. He was born to consanguineous parents with a birth weight of 2250 g. Physical examination revealed a severe short stature (height, 95 cm; SD score -5.64) and minimal dysmorphic features. Biochemistry, endocrine work-up, and karyotype were normal. Reevaluation at 16.5 years of age revealed a height of 128.5 cm (SD score -5.27), prominent forehead, anteverted nasal openings, fleshy nasal tip, full lips, malar hypoplasia, hyperlordosis, prominent heels, testicular volumes 8-10 mL, and pubic hair consistent with Tanner stage II. Growth hormone trial for a year resulted in inadequate height gain (3 cm). The diagnosis of 3-M syndrome was made upon typical findings (thin long bones with diaphyseal narrowing and tall lumbar vertebrae) in a recent skeletal survey. Genetic analysis disclosed a homozygote frame shift mutation in exon 2: c.457_458delinsT resulting in p.Gly153fs.


title: Effects of venous needle turbulence during ex vivo hemodialysis on endothelial morphology and nitric oxide formation.

abstract: Arteriovenous grafts used for hemodialysis frequently develop intimal hyperplasia (IH), which ultimately leads to graft failure. Although the turbulent jet from the dialysis needle may contribute to vessel wall injury, its role in the pathogenesis of IH is relatively unexplored. In the current study, using bovine aortic endothelial cells (BAEC) cultured on the inner surface of a compliant tube, we evaluated the effects of simulated hemodialysis conditions on morphology and nitric oxide (NO) production. The flows via the graft and needle were 500 ml/min (Reynolds number=819) and 100ml/min (Reynolds number=954), respectively. In the presence of the needle jet for 6h, 19.3% (+/-1.53%) of BAEC were sheared off, whereas no loss of BAEC was observed in the presence of graft flow alone (P<0.05). In the presence of graft flow alone, assessment of cell orientation by the Saltykov method revealed that BAEC were oriented along the flow direction. This alignment, however, was lost in the presence of needle flow. Finally, NO production was also significantly decreased in the presence of the needle flow compared to the presence of graft flow alone (16+/-3.1 vs 34.7+/-1.9 nmol/10(6)cells/h, P<0.05). NO is a key player in vascular homeostasis mechanisms modulating vasomotor tone, inhibiting inflammation and smooth muscle cell proliferation. Thus, the loss of NO signaling and the loss of endothelial integrity caused by needle jet turbulence may contribute to the cascade of events leading to IH formation during hemodialysis.


title: Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements

abstract: Increasing numbers of methodologies are available to find functional genomic clusters in RNA expression data. We describe a technique that computes comprehensive pair-wise mutual information for all genes in such a data set. An association with a high mutual information means that one gene is non-randomly associated with another; we hypothesize this means the two are related biologically. By picking a threshold mutual information and using only associations at or above the threshold, we show how this technique was used on a public data set of 79 RNA expression measurements of 2,467 genes to construct 22 clusters, or Relevance Networks. The biological significance of each Relevance Network is explained.


Managing Personal Information : A Telco Perspective


title: Test input generation with java PathFinder

abstract: We show how model checking and symbolic execution can be used to generate test inputs to achieve structural coverage of code that manipulates complex data structures. We focus on obtaining branch-coverage during unit testing of some of the core methods of the red-black tree implementation in the Java TreeMap library, using the Java PathFinder model checker. Three different test generation techniques will be introduced and compared, namely, straight model checking of the code, model checking used in a black-box fashion to generate all inputs up to a fixed size, and lastly, model checking used during white-box test input generation. The main contribution of this work is to show how efficient white-box test input generation can be done for code manipulating complex data, taking into account complex method preconditions.


title: Overview of the IBM Java just-in-time compiler

abstract: We present the design and implementation of several optimizations and techniques included in the latest IBM JavaTM Just-in-Time (JIT) Compiler. We first discuss some of the modifications we have applied to Sun Microsystems' reference implementation of the Java Virtual Machine (JVMTM) Specification to increase the performance, including a change in the object layout. We then describe each of the optimizations, referring to what had to be taken into account because of both the just-in-time nature of the compiler and the requirements of the Java language specification, such as exception checking. We also present code generation techniques targeting Intel architectures, describing the register allocation schemes, exception handling, and code scheduling. Finally we report on the performance of the IBM JIT compiler, showing both the effectiveness of the individual optimizations and the competitive overall performance of the JIT compiler in comparison with a competitor, using industry-standard benchmarking programs. All the techniques presented here are included in the official product (JIT Compiler version 3.0), which has been integrated into the IBM Developer Kit for WindowsTM, Java Technology Edition, Version 1.1.7.


title: Trends in the evolution of the database curriculum

abstract: This paper focuses on the current database curriculum and how it reflects changes in the practice of data management. The content of the database course is determined using a series of three surveys given to groups of database educators over a period of four years. The results of these surveys are presented in the paper and are used to indicate trends in database courses as they are actually taught. The paper examines the different focus choices used in the introductory database course and describes how these have changed during the period, 1999-2002. The paper also examines how actual database curricula compare with the CC2001 curriculum recommendations.


Magnetoacoustic tomography with magnetic induction for low conductivity objects


A class of exponentially fitted second derivative extended backward differentiation formula for solving stiff problems


title: Globalization and fruitfly invasion and expansion: the medfly paradigm

abstract: The phytophagous insects of the Tephritidae family commonly referred to as "true fruit flies" offer different case histories of successful invasions. Mankind has played an important role in altering the distributions of some of the more polyphagous and oligophagous species. However, the question arises why only a few species have become major invaders. The understanding of traits underlying adaptation in different environments is a major topic in invasion biology. Being generalists or specialists, along the K-r gradient of the growth curve, make a difference in term of food resources exploitation and interspecies competition and displacement. The species of the genus Ceratitis are good examples of r-strategists. The genetic and biological data of the most notorious Ceratitis species, the Mediterranean fruit fly Ceratitis capitata (medfly), are reviewed to investigate the traits and behaviours that make the medfly an important invader. It can be learnt from medfly, that invasions in a modern global trade network tend to be due to multiple introductions. This fact allows a maintenance or enhancement of genetic variability in the adventive populations, which in turn increases their potential invasiveness. Our current knowledge of the medfly genome opens the way for future studies on functional genomics.


title: Development of Wall-climbing Robots with Sliding Suction Cups

abstract: This paper investigates the development of wall-climbing robots (WCRs) with sliding suction cups (SSCs) based on the authors' recent research work. The merits and the challenges of the SSCs are analyzed in detail aiming at wheeled WCRs (WWCRs). The dynamic model of WWCRs is presented. It should be noted that the suction force derived from SSCs has great influence on the motion characteristics of WCRs. To utilize the merits, get over the weaknesses of SSCs and increase the service abilities of wall-cleaning robots, a series of tether supported wall sliding robots (WSRs) with large SSCs are designed. The control strategies of suction force are investigated to improve the adaptability to complex wall surfaces


title: Critical Success Factors for Shared Services: Results from Two Case Studies

abstract: Shared services have been heralded as a means of enhancing services and improving the efficiency of their delivery. As such they have been embraced by the private, and increasingly, the public sectors. Yet implementation has proved to be difficult and the number of success stories has been limited. Which factors are critical to success in the development of shared services arrangements is not yet well understood. The current paper examines existing research in the area of critical success factors (CSFs) and suggests that there are actually three distinct types of CSF: outcome, implementation process and operating environment characteristic. Two case studies of public sector shared services in Australia and the Netherlands are examined through a lens that both incorporates all three types of CSF and distinguishes between them.


title: Optical Acceleration for Motion Description in Videos

abstract: Modern techniques for describing motion in videos are centred around velocity descriptors based on optical flow. Realizing that acceleration is as important as velocity for describing motion information, in this paper first we propose two different algorithms to compute optical acceleration. Delving deeper into the concept of optical acceleration, we use two descriptors: histogram of optical acceleration (HOA) and histogram of spatial gradient of acceleration (HSGA), to effectively encode the motion information. To assess the effectiveness of these descriptors for motion encoding, we applied it for human action recognition and abnormal event detection in videos. In fact, we used acceleration descriptors in conjunction with velocity descriptors to get a better description of motion in videos. Our experiments reveal that acceleration descriptors could provide additional information that velocity descriptors missed and hence combining them results in a superior motion descriptor.


title: Design of linear-phase IIR filters via impulse-response gramians

abstract: A new method for the design of a linear-phase infinite-impulse-response (IIR) filter is presented. It involves designing a finite-impulse-response (FIR) filter satisfying the given frequency response specifications and subsequently obtaining a significantly lower order IIR filter using model reduction based on impulse-response gramians. The general outline of the method and a brief overview of the existing linear-phase FIR filter design and model-reduction techniques are presented. The impulse-response gramian and the model-reduction algorithm used are presented. The method is illustrated by design examples and is compared with other methods for the design of linear-phase IIR filters using equalizers. >


title: Ship Rotated Bounding Box Space for Ship Extraction From High-Resolution Optical Satellite Images With Complex Backgrounds

abstract: Extracting ships from complex backgrounds is the bottleneck of ship detection in high-resolution optical satellite images. In this letter, we propose a nearly closed-form ship rotated bounding box space used for ship detection and design a method to generate a small number of highly potential candidates based on this space. We first analyze the possibility of accurately covering all ships by labeling rotated bounding boxes. Moreover, to reduce search space, we construct a nearly closed-form ship rotated bounding box space. Then, by scoring for each latent candidate in the space using a two-cascaded linear model followed by binary linear programming, we select a small number of highly potential candidates. Moreover, we also propose a fast version of our method. Experiments on our data set validate the effectiveness of our method and the efficiency of its fast version, which achieves a close detection rate in near real time.


Discovering Stylistic Variations in Distributional Vector Space Models via Lexical Paraphrases


title: Modeling the Dropbox client behavior

abstract: Cloud storage systems are currently very popular, generating a large amount of traffic. Indeed, many companies offer this kind of service, including worldwide providers such as Dropbox, Microsoft and Google. These companies, as well as new providers entering the market, could greatly benefit from knowing typical workload patterns that their services have to face in order to develop more cost-effective solutions. However, despite recent analyses of typical usage patterns and possible performance bottlenecks, no previous work investigated the underlying client processes that generate workload to the system. In this context, this paper proposes a hierarchical two-layer model for representing the Dropbox client behavior. We characterize the statistical parameters of the model using passive measurements gathered in 3 different network vantage points. Our contributions can be applied to support the design of realistic synthetic workloads, thus helping in the development and evaluation of new, well-performing personal cloud storage services.


The real value of microarray technology.


title: A Body Sensor Network With Electromyogram and Inertial Sensors: Multimodal Interpretation of Muscular Activities

abstract: The evaluation of the postural control system (PCS) has applications in rehabilitation, sports medicine, gait analysis, fall detection, and diagnosis of many diseases associated with a reduction in balance ability. Standing involves significant muscle use to maintain balance, making standing balance a good indicator of the health of the PCS. Inertial sensor systems have been used to quantify standing balance by assessing displacement of the center of mass, resulting in several standardized measures. Electromyogram (EMG) sensors directly measure the muscle control signals. Despite strong evidence of the potential of muscle activity for balance evaluation, less study has been done on extracting unique features from EMG data that express balance abnormalities. In this paper, we present machine learning and statistical techniques to extract parameters from EMG sensors placed on the tibialis anterior and gastrocnemius muscles, which show a strong correlation to the standard parameters extracted from accelerometer data. This novel interpretation of the neuromuscular system provides a unique method of assessing human balance based on EMG signals. In order to verify the effectiveness of the introduced features in measuring postural sway, we conduct several classification tests that operate on the EMG features and predict significance of different balance measures.


Attention , I ’ m Trying to Speak CS 224 n Project : Speech Synthesis


title: A Survey of Techniques for Approximate Computing

abstract: Approximate computing trades off computation quality with effort expended, and as rising performance demands confront plateauing resource budgets, approximate computing has become not merely attractive, but even imperative. In this article, we present a survey of techniques for approximate computing (AC). We discuss strategies for finding approximable program portions and monitoring output quality, techniques for using AC in different processing units (e.g., CPU, GPU, and FPGA), processor components, memory technologies, and so forth, as well as programming frameworks for AC. We classify these techniques based on several key characteristics to emphasize their similarities and differences. The aim of this article is to provide insights to researchers into working of AC techniques and inspire more efforts in this area to make AC the mainstream computing approach in future systems.


title: Hyperspectral Unmixing via $L_{1/2}$ Sparsity-Constrained Nonnegative Matrix Factorization

abstract: Hyperspectral unmixing is a crucial preprocessing step for material classification and recognition. In the last decade, nonnegative matrix factorization (NMF) and its extensions have been intensively studied to unmix hyperspectral imagery and recover the material end-members. As an important constraint for NMF, sparsity has been modeled making use of the L1 regularizer. Unfortunately, the L1 regularizer cannot enforce further sparsity when the full additivity constraint of material abundances is used, hence limiting the practical efficacy of NMF methods in hyperspectral unmixing. In this paper, we extend the NMF method by incorporating the L1/2 sparsity constraint, which we name L1/2 -NMF. The L1/2 regularizer not only induces sparsity but is also a better choice among Lq(0 <; q <; 1) regularizers. We propose an iterative estimation algorithm for L1/2-NMF, which provides sparser and more accurate results than those delivered using the L1 norm. We illustrate the utility of our method on synthetic and real hyperspectral data and compare our results to those yielded by other state-of-the-art methods.


title: Kryptein: a compressive-sensing-based encryption scheme for the internet of things

abstract: Internet of Things (IoT) is flourishing and has penetrated deeply into people's daily life. With the seamless connection to the physical world, IoT provides tremendous opportunities to a wide range of applications. However, potential risks exist when the IoT system collects sensor data and uploads it to the cloud. The leakage of private data can be severe with curious database administrator or malicious hackers who compromise the cloud. In this work, we propose Kryptein, a compressive-sensing-based encryption scheme for cloud-enabled IoT systems to secure the interaction between the IoT devices and the cloud. Kryptein supports random compressed encryption, statistical decryption, and accurate raw data decryption. According to our evaluation based on two real datasets, Kryptein provides strong protection to the data. It is 250 times faster than other state-of-the-art systems and incurs 120 times less energy consumption. The performance of Kryptein is also measured on off-the-shelf IoT devices, and the result shows Kryptein can run efficiently on IoT devices.


title: SysML and safety analysis for mechatronic systems

abstract: Model-based system engineering is an efficient approach to specifying, designing, simulating and validating mechatronic systems. This approach allows errors to be detected as soon as possible in the design process, and thus reduces the overall cost of the product. Uniformity in a complex mechatronic project, which is by definition multidisciplinary, is achieved by expressing the models in a common modeling language such as SysML. This paper presents the state of the art of integrating risk and reliability studies with SysML in the design process of safety-critical systems. An Electro Mechanical Actuator system for light aircraft is used to illustrate the integration process, showing how a failure modes and effects analysis is automatically carried out from SysML structural and behavioral diagrams. Through our industry-relevant case study, the advantages and drawbacks of the employed integration methodology are analyzed.


title: All About VLAD

abstract: The objective of this paper is large scale object instance retrieval, given a query image. A starting point of such systems is feature detection and description, for example using SIFT. The focus of this paper, however, is towards very large scale retrieval where, due to storage requirements, very compact image descriptors are required and no information about the original SIFT descriptors can be accessed directly at run time. We start from VLAD, the state-of-the art compact descriptor introduced by Jegou et al. for this purpose, and make three novel contributions: first, we show that a simple change to the normalization method significantly improves retrieval performance, second, we show that vocabulary adaptation can substantially alleviate problems caused when images are added to the dataset after initial vocabulary learning. These two methods set a new state-of-the-art over all benchmarks investigated here for both mid-dimensional (20k-D to 30k-D) and small (128-D) descriptors. Our third contribution is a multiple spatial VLAD representation, MultiVLAD, that allows the retrieval and localization of objects that only extend over a small part of an image (again without requiring use of the original image SIFT descriptors).