Enterprise Architecture for Digital Transformation

Digital transformation requires an altogether new institutional logic and effective response at a requisite organizational level. Sensing and seizing fleeting market opportunities and reconfiguring the business in line with the shifting value proposition requires increasingly specialized resources, more dynamic capabilities, and in-built resilience in the face of change. While Enterprise Architecture (EA) has been suggested to facilitate enterprise transformation, the focus has traditionally been on process standardization and integration rather than on continuous adaptation to the changing business and technological landscape. For EA to have a desired impact, more adaptive conceptualizations of EA that address the requirements of the new digital environment are called for. In this conceptual paper, we explore the implications of digital transformation on enterprise architecture. In particular, we note that existing approaches to EA address integration and coherence within a single organization but typically fall short in the face of complexities pertaining to digital ecosystems. We suggest a vertical typology of organizational capabilities and postulate that today's digital environment increasingly requires adaptive capabilities that transcend the traditional notion of dynamic capabilities. We then investigate how EA can help build flexibility and resilience in the organization.


Looking under the bonnet : factors affecting student adoption of e-learning systems in

The primary questions addressed in this paper are the following: what are the factors that affect students‟ adoption of an e-learning system and what are the relationships among these factors? This paper investigates and identifies some of the major factors affecting students‟ adoption of an e-learning system in a university in Jordan. E-learning adoption is approached from the information systems acceptance point of view. This suggests that a prior condition for learning effectively using e-learning systems is that students must actually use them. Thus, a greater knowledge of the factors that affect IT adoption and their interrelationships is a pre-cursor to a better understanding of student acceptance of e-learning systems. In turn, this will help and guide those who develop, implement, and deliver e-learning systems. In this study, an extended version of the Technology Acceptance Model (TAM) was developed to investigate the underlying factors that influence students‟ decisions to use an e-learning system. The TAM was populated using data gathered from a survey of 486 undergraduate students, who were using the Moodle based e-learning system at the Arab Open University. The model was estimated using Structural Equation Modelling (SEM). A path model was developed to analyze the relationships between the factors to explain students‟ adoption of the e-learning system. Whilst findings support existing literature about prior experience affecting perceptions, they also point to surprising group effects, which may merit future exploration.


Video semantic object segmentation by self-adaptation of DCNN

This paper proposes a new framework for semantic segmentation of objects in videos. We address the label inconsistency problem of deep convolutional neural networks (DCNNs) by exploiting the fact that videos have multiple frames; in a few frames the object is confidently-estimated (CE) and we use the information in them to improve labels of the other frames. Given the semantic segmentation results of each frame obtained from DCNN, we sample several CE frames to adapt the DCNN model to the input video by focusing on specific instances in the video rather than general objects in various circumstances. We propose offline and online approaches under different supervision levels. In experiments our method achieved great improvement over the original model and previous state-of-the-art methods. c © 2016 Elsevier Ltd. All rights reserved.


Vision: towards real time epidemic vigilance through online social networks: introducing SNEFT -- social network enabled flu trends

Our vision is to achieve faster and near real time detection and prediction of the emergence and spread of an influenza epidemic, through sophisticated data collection and analysis of Online Social Networks (OSNs) such as Facebook, MySpace, and Twitter. In particular, we present the design of a system called SNEFT (Social Network Enabled Flu Trends), which will be developed in a 12-month SBIR (Small Business Innovation Research) project funded by the National Institutes of Health (NIH). We describe the innovative technologies that will be developed in this project for collecting and aggregating OSN data, extracting information from it, and integrating it with mathematical models of influenza. One of the monitoring tools used by the Centers for Disease Control and Prevention (CDC) is reports of Influenza-Like Illness (ILI) cases; these reports are authoritative but typically have a delay of one to two weeks due to the largely manual process. We describe the SNEFT prototype in the context of predicting ILI cases well in advance of the CDC reports. We observe that OSN data is individually noisy but collectively revealing, and speculate on other applications that can potentially be enabled by OSN data collection and analysis.


Speaking in tongues: SQL access to NoSQL systems

Non-relational data stores, which are usually called NoSQL systems, have become an important class of data management systems. They often outperform the relational systems. Yet there is no common way to interface with NoSQL systems. The Structured Query Language (SQL) has already proven to be useful to provide a uniform query language for all the relational systems. We identify a subset of SQL for access to NoSQL systems. Our extensible middleware translates SQL queries to the query languages of the connected NoSQL systems. The migration between these systems is thereby greatly simplified as well as the comparison of the supported NoSQL systems.


Upgrading Wireless Home Routers for Enabling Large-Scale Deployment of Cloudlets

Smartphones become more and more popular over recent years due to their small form factors. However, such mobile systems are resource-constrained in view of computational power, storage and battery life. Offloading resource-intensive tasks (aka mobile cloud computing) to distant (e.g., cloud computing) or closely located data centers (e.g., cloudlet) overcomes these issues. Especially, cloudlets provide computational power with low latency for responsive applications due to their proximity to mobile users. However, a large-scale deployment of rangerestricted cloudlets is still an open challenge. In this paper, we propose a novel concept for a large-scale deployment of cloudlets by upgrading wireless home routers. Beside router’s native purpose of routing data packets through the network, it can now offer computing resources with low latency and high bandwidth without additional hardware. Proving our concept, we conducted comprehensive benchmark tests against existing concepts. As result, the feasibility of this concept is shown and provide a promising way to large-scale deploy cloudlets in existing infrastructures.


Sentiment expression via emoticons on social media

Emoticons (e.g., :) and :( ) have been widely used in sentiment analysis and other NLP tasks as features to machine learning algorithms or as entries of sentiment lexicons. In this paper, we argue that while emoticons are strong and common signals of sentiment expression on social media the relationship between emoticons and sentiment polarity are not always clear. Thus, any algorithm that deals with sentiment polarity should take emoticons into account but extreme caution should be exercised in which emoticons to depend on. First, to demonstrate the prevalence of emoticons on social media, we analyzed the frequency of emoticons in a large recent Twitter data set. Then we carried out four analyses to examine the relationship between emoticons and sentiment polarity as well as the contexts in which emoticons are used. The first analysis surveyed a group of participants for their perceived sentiment polarity of the most frequent emoticons. The second analysis examined clustering of words and emoticons to better understand the meaning conveyed by the emoticons. The third analysis compared the sentiment polarity of microblog posts before and after emoticons were removed from the text. The last analysis tested the hypothesis that removing emoticons from text hurts sentiment classification by training two models with and without emoticons in the text, respectively. The results confirms the arguments that: 1) a few emoticons are strong and reliable signals of sentiment polarity and one should take advantage of them in any sentiment analysis; 2) a large group of the emoticons conveys complicated sentiment hence they should be treated with extreme caution.


The Propulsion Committee Final Report and Recommendations to the 25 th ITTC 1


Thalamic relays and cortical functioning.

Studies on the visual thalamic relays, the lateral geniculate nucleus and pulvinar, provide three key properties that have dramatically changed the view that the thalamus serves as a simple relay to get information from subcortical sites to cortex. First, the retinal input, although a small minority (7%) in terms of numbers of synapses onto geniculate relay cells, dominates receptive field properties of these relay cells and strongly drives them; 93% of input thus is nonretinal and modulates the relay in dynamic and important ways related to behavioral state, including attention. We call the retinal input the driver input and the nonretinal, modulator input, and their unique morphological and functional differences allow us to recognize driver and modulator input to many other thalamic relays. Second, much of the modulation is related to control of a voltage-gated, low threshold Ca(2+) conductance that determines response properties of relay cells -burst or tonic - and this, among other things, affects the salience of information relayed. Third, the lateral geniculate nucleus and pulvinar (a massive but generally mysterious and ignored thalamic relay), are examples of two different types of relay: the LGN is a first order relay, transmitting information from a subcortical driver source (retina), while the pulvinar is mostly a higher order relay, transmitting information from a driver source emanating from layer 5 of one cortical area to another area. Higher order relays seem especially important to general corticocortical communication, and this view challenges the conventional dogma that such communication is based on direct corticocortical connections. In this sense, any new information reaching a cortical area, whether from a subcortical source or another cortical area, benefits from a thalamic relay. Other examples of first and higher order relays also exist, and generally higher order relays represent the majority of thalamus. A final property of interest emphasized in chapter 17 by Guillery (2005) is that most or all driver inputs to thalamus, whether from a subcortical source or from layer 5 of cortex, are axons that branch, with the extrathalamic branch innervating a motor or premotor region in the brainstem, or in some cases, spinal cord. This suggests that actual information relayed by thalamus to cortex is actually a copy of motor instructions (Guillery, 2005). Overall, these features of thalamic relays indicate that the thalamus not only provides a behaviorally relevant, dynamic control over the nature of information relayed, it also plays a key role in basic corticocortical communication.


The CRISPR/Cas9 system produces specific and homozygous targeted gene editing in rice in one generation.

The CRISPR/Cas9 system has been demonstrated to efficiently induce targeted gene editing in a variety of organisms including plants. Recent work showed that CRISPR/Cas9-induced gene mutations in Arabidopsis were mostly somatic mutations in the early generation, although some mutations could be stably inherited in later generations. However, it remains unclear whether this system will work similarly in crops such as rice. In this study, we tested in two rice subspecies 11 target genes for their amenability to CRISPR/Cas9-induced editing and determined the patterns, specificity and heritability of the gene modifications. Analysis of the genotypes and frequency of edited genes in the first generation of transformed plants (T0) showed that the CRISPR/Cas9 system was highly efficient in rice, with target genes edited in nearly half of the transformed embryogenic cells before their first cell division. Homozygotes of edited target genes were readily found in T0 plants. The gene mutations were passed to the next generation (T1) following classic Mendelian law, without any detectable new mutation or reversion. Even with extensive searches including whole genome resequencing, we could not find any evidence of large-scale off-targeting in rice for any of the many targets tested in this study. By specifically sequencing the putative off-target sites of a large number of T0 plants, low-frequency mutations were found in only one off-target site where the sequence had 1-bp difference from the intended target. Overall, the data in this study point to the CRISPR/Cas9 system being a powerful tool in crop genome engineering.


Learning Inverse Dynamics for Robot Manipulator Control

Model-based control strategies for robot manipulators can present numerous performance advantages when an accurate model of the system dynamics is available. In practice, obtaining such a model is a challenging task which involves modeling such physical processes as friction, which may not be well understood and difficult to model. Furthermore, uncertainties in the physical parameters of a system may be introduced from significant discrepancies between the manufacturer data and the actual system. Traditionally, adaptive and robust control strategies have been developed to deal with parametric uncertainty in the dynamic model, but often require knowledge of the structure of the dynamics. Recent approaches to model-based manipulator control involve data-driven learning of the inverse dynamics relationship, eliminating the need for any a-priori knowledge of the system model. Locally Weighted Projection Regression (LWPR) has been proposed for learning the inverse dynamics function of a manipulator. Due to its use of simple local, linear models, LWPR is suitable for online and incremental learning. Although global regression techniques such as Gaussian Process Regression (GPR) have been shown to outperform LWPR in terms of accuracy, due to its heavy computational requirements, GPR has been applied mainly to offline learning of inverse dynamics. More recent efforts in making GPR computationally tractable for real-time control have resulted in several approximations which operate on a select subset, or sparse representation of the entire training data set. Despite the significant advancements that have been made in the area of learning control, there has not been much work in recent years to evaluate these newer regression techniques against traditional model-based control strategies such as adaptive control. Hence, the first portion of this thesis provides a comparison between a fixed model-based control strategy, an adaptive controller and the LWPR-based learning controller. Simulations are carried out in order to evaluate the position and orientation tracking performance of each controller under varied end effector loading, velocities and inaccuracies in the known dynamic parameters. Both the adaptive controller and LWPR controller are shown to have comparable performance in the presence of parametric uncertainty. However, it is shown that the learning controller is unable to generalize well outside of the regions in which it has been trained. Hence, achieving good performance requires significant amounts of training in the anticipated region of operation. In addition to poor generalization performance, most learning controllers commence learning entirely from ‘scratch,’ making no use of any a-priori knowledge which may be available from the well-known rigid body dynamics (RBD) formulation. The second portion of this thesis develops two techniques for online, incremental learning algorithms which incorporate prior knowledge to improve generalization performance. First, prior knowledge


The Impact of Interactive Touchscreens on Physics Education in Upper Secondary School – A systematic literature review

Interactive touchscreens such as tablet PCs (TPC) and interactive whiteboards (IWB) are becoming more and more common in classrooms around the world. To date, very little research has been conducted on the impact of the new technology on physics education. This systematic literature review aims to investigate research on what impact tablet PCs and interactive whiteboards might have on the education in upper Secondary School. The review was performed in response to the following questions: 1. What is the influence of IWBs and TPCs on students’ active participation in physics education? 2. How can an IWB or TPC improve students’ learning about physics concepts? 3. How can educational research on touchscreen technology help inform effective teaching strategies in physics education? To respond to the questions of the study, relevant research about interactive whiteboards and/or tablet PCs was consulted and analysed. Twelve articles were located, mainly through the ERIC and Scopus databases, but also through Google Scholar. The included articles reported empirical research about physics education with interactive whiteboards or tablet PCs. The results from the articles indicate that interactive touchscreens might help improve learners’ active participation in physics education. Interactive whiteboards can, for example, be used to display interactive simulations during group work, something students are found to appreciate and easily engage in. A tablet PC can be used in the same way, but also allows students to receive anonymous support and feedback from the teacher during class which seems to be beneficial for learning. Results show that it is possible to improve students’ understanding of physics concepts by using interactive whiteboards or tablet PCs. However, further research is required to compare results from students using touch technology and students taught in traditional manner to be able to draw any general conclusions about observed learning effects.


Mining Frequent Patterns by Pattern-Growth: Methodology and Implications

Mining frequent patterns has been a focused topic in data mining research in recent years, with the developmeht of numerous interesting algorithms for mining association, correlation, causality, sequential patterns, partial periodicity, constraint-based frequent pattern mining, associative classification, emerging patterns, etc. Most of the previous studies adopt an Apriori-like, candidate generation-and-test approach. However, based on our analysis, candidate generation and test may still be expensive, especially when encountering long and numerous patterns. A new methodology, called f r e q u e n t p a t t e r n g rowth , which mines frequent patterns without candidate generation, has been developed. The method adopts a divide-andconquer philosophy to project and partition databases based on the currently discovered frequent patterns and grow such patterns to longer ones in the projected databases. Moreover, efficient data structures have been developed for effective database compression and fast in-memory traversal. Such a methodology may eliminate or substantially reduce the number of candidate sets to be generated and also reduce the size of the database to be iteratively examined, and, therefore, lead to high performance. In this paper, we provide an overview of this approach and examine its methodology and implications for mining several kinds of frequent patterns, including association, frequent closed itemsets, max-patterns, sequential patterns, and constraint-based mining of frequent patterns. We show that frequent pattern growth is efficient at mining large databases and its further development may lead to scalable mining of many other kinds of patterns as well.


Alike people, alike interests? Inferring interest similarity in online social networks

Understanding how much two individuals are alike in their interests (i.e., interest similarity) has become virtually essential for many applications and services in Online Social Networks (OSNs). Since users do not always explicitly elaborate their interests in OSNs like Facebook, how to determine users’ interest similarity without fully knowing their interests is a practical problem. In this paper, we investigate how users’ interest similarity relates to various social features (e.g. geographic distance); and accordingly infer whether the interests of two users are alike or unalike where one of the users’ interests are unknown. Relying on a large Facebook dataset, which contains 479, 048 users and 5, 263, 351 user-generated interests, we present comprehensive empirical studies and verify the homophily of interest similarity across three interest domains (movies, music and TV shows). The homophily reveals that people tend to exhibit more similar tastes if they have similar demographic information (e.g., age, location), or if they are friends. It also shows that the individuals with a higher interest entropy usually share more interests with others. Based on these results, we provide a practical prediction model under a real OSN environment. For a given user with no interest information, this model can select some individuals who not only exhibit many interests but also probably achieve high interest similarities with the given user. Eventually, we illustrate a use case to demonstrate that the proposed prediction model could facilitate decision-making for OSN applications and services.


Synthesizing Training Images for Boosting Human 3D Pose Estimation

Human 3D pose estimation from a single image is a challenging task with numerous applications. Convolutional Neural Networks (CNNs) have recently achieved superior performance on the task of 2D pose estimation from a single image, by training on images with 2D annotations collected by crowd sourcing. This suggests that similar success could be achieved for direct estimation of 3D poses. However, 3D poses are much harder to annotate, and the lack of suitable annotated training images hinders attempts towards end-to-end solutions. To address this issue, we opt to automatically synthesize training images with ground truth pose annotations. Our work is a systematic study along this road. We find that pose space coverage and texture diversity are the key ingredients for the effectiveness of synthetic training data. We present a fully automatic, scalable approach that samples the human pose space for guiding the synthesis procedure and extracts clothing textures from real images. Furthermore, we explore domain adaptation for bridging the gap between our synthetic training images and real testing photos. We demonstrate that CNNs trained with our synthetic images out-perform those trained with real photos on 3D pose estimation tasks.


"Constant, constant, multi-tasking craziness": managing multiple working spheres

Most current designs of information technology are based on the notion of supporting distinct tasks such as document production, email usage, and voice communication. In this paper we present empirical results that suggest that people organize their work in terms of much larger and thematically connected units of work. We present results of fieldwork observation of information workers in three different roles: analysts, software developers, and managers. We discovered that all of these types of workers experience a high level of discontinuity in the execution of their activities. People average about three minutes on a task and somewhat more than two minutes using any electronic tool or paper document before switching tasks. We introduce the concept of working spheres to explain the inherent way in which individuals conceptualize and organize their basic units of work. People worked in an average of ten different working spheres. Working spheres are also fragmented; people spend about 12 minutes in a working sphere before they switch to another. We argue that design of information technology needs to support people's continual switching between working spheres.


A Knowledge Hunting Framework for Common Sense Reasoning

We introduce an automatic system that achieves state-of-the-art results on the Winograd Schema Challenge (WSC), a common sense reasoning task that requires diverse, complex forms of inference and knowledge. Our method uses a knowledge hunting module to gather text from the web, which serves as evidence for candidate problem resolutions. Given an input problem, our system generates relevant queries to send to a search engine, then extracts and classifies knowledge from the returned results and weighs them to make a resolution. Our approach improves F1 performance on the full WSC by 0.21 over the previous best and represents the first system to exceed 0.5 F1. We further demonstrate that the approach is competitive on the Choice of Plausible Alternatives (COPA) task, which suggests that it is generally applicable.


The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis

, doi: 10.1098/rspa.1998.0193 454 1998 Proc. R. Soc. Lond. A Yen, Chi Chao Tung and Henry H. Liu Norden E. Huang, Zheng Shen, Steven R. Long, Manli C. Wu, Hsing H. Shih, Quanan Zheng, Nai-Chyuan nonlinear and non-stationary time series analysis The empirical mode decomposition and the Hilbert spectrum for References http://rspa.royalsocietypublishing.org/content/454/1971/903#related-urls Article cited in: Email alerting service here corner of the article or click Receive free email alerts when new articles cite this article sign up in the box at the top right-hand


NFC Antenna With Nonuniform Meandering Line and Partial Coverage Ferrite Sheet for Metal Cover Smartphone Applications

This paper proposes a near-field communication (NFC) antenna solution for metal-cover smartphone applications. In this NFC antenna solution, a narrow slot is initially loaded into the metal cover, and the position of this slot can be altered (with flexibility) according to the design of the smartphone’s external appearance. Next, an unconventional six-turn coil (with a six-sided irregular hexagonal shape) is designed that has a nonuniform linewidth and a nonuniform line gap between two lines, and it is partially loaded with a rectangular ferrite composite. In this design, an enhanced magnetic line of forces can be realized in certain specific locations, and an excellent inductively coupled near-field receiver is achieved. Notably, this proposed NFC antenna can pass the tests required by NFC forum certification, and its performances are comparable with the traditional NFC antenna that has nonmetallic cover.


Miniaturized NFC Antenna Design for a Tablet PC With a Narrow Border and Metal Back-Cover

A novel structure of the near-field communication (NFC) antenna design for a tablet PC is proposed. This tablet PC has a narrow border and full metallic back-cover. A miniaturized loop antenna design is achieved by attaching ferrite sheets on both sides of the loop antenna. The ferrite sheets may reduce eddy currents induced on the adjacent metallic back-cover by the loop antenna to improve the communication range of the NFC. Only the edge of the tablet PC allows the antenna to radiate due to the full metallic back-cover. Thus, the NFC antenna needs to be narrow to be installed on the edge of the tablet PC. Therefore, we propose a miniaturized NFC antenna with the dimensions of 41.5 (L) × 7.5 (W) × 0.45 (T) mm3 only. Simulated magnetic field distributions are consistent with measured voltage distributions. This design has a good communication range more than 6 cm in front of the touchscreen panel and reaches 2 cm over the other side above the metal back-cover.


A silver inkjet printed ferrite NFC antenna

This paper presents a near field communication (NFC) antenna that is printed directly on a ferrite substrate using a silver inkjet printing process. Such a silver inkjet printed ferrite NFC antenna provides a minimum substrate thickness, a good operation on metal objects, and a small assembly effort. The NFC antenna performance is analysed by measurements based on the ISO/IEC standard 10373-6 for proximity identification cards test methods. For this, the antenna is connected to an NFC microchip. Measurements in a non-metal environment show that the ferrite antenna performs equally good a custom-built NFC antenna printed on photo paper substrate, despite additional losses in the ferrite substrate. In a metal environment the ferrite antenna clearly outperforms the photo paper antenna.


NFC Antenna Design for Low-Permeability Ferromagnetic Material

A novel structure of a near-field communication (NFC) antenna for a mobile handset is proposed by changing the sequence of loop winding and modifying the inner loop structure. In general, the sintered ferrite sheets with higher relative permeability (μr ≈ 200) have been used to reduce the performance deterioration of NFC antennas due to the eddy current in the battery pack of a mobile handset. However, their costs are high, and they are considerably breakable. In this letter, we effectively enhance the performance of an NFC antenna by employing the ferrite-polymer composite with lower relative permeability (μr ≈ 55). The proposed antenna improves up to 23% more reading range and up to 65% better load modulation performance.


Material property of on-metal magnetic sheet attached on NFC/HF-RFID antenna and research of its proper pattern and size on

When a payment system with NFC/HF-RFID is installed in smart-phone, their communication with 13.56MHz is blocked by such metal as battery, coil and PCB. To solve this problem, magnetic sheet with high permeability is set between NFC/HF-RFID antenna and near-by metal. So far thin compound magnetic sheet or thin sintered ferrite has been used. In order to make thickness of these materials thinner, we propose amorphous magnetic sheet. Besides this amorphous magnetic sheet causes to increase the transmission effect.


On neural networks and learning systems for business computing


Going wireless: behavior & practice of new mobile phone users

We report on the results of a study in which 19 new mobile phone users were closely tracked for the first six weeks after service acquisition. Results show that new users tend to rapidly modify their perceptions of social appropriateness around mobile phone use, that actual nature of use frequently differs from what users initially predict, and that comprehension of service-oriented technologies can be problematic. We describe instances and features of mobile telephony practice. When in use, mobile phones occupy multiple social spaces simultaneously, spaces with norms that sometimes conflict: the physical space of the mobile phone user and the virtual space of the conversation.


Evolution of Architectural Floor Plans

Layout planning is a process of sizing and placing rooms (e.g. in a house) while attempting to optimize various criteria. Often there are conflicting criteria such as construction cost, minimizing the distance between related activities, and meeting the area requirements for these activities. The process of layout planning has mostly been done by hand, with a handful of attempts to automate the process. This thesis explores some of these past attempts and describes several new techniques for automating the layout planning process using evolutionary computation. These techniques are inspired by the existing methods, while adding some of their own innovations. Additional experiments are done to test the possibility of allowing polygonal exteriors with rectilinear interior walls. Several multi-objective approaches are used to evaluate and compare fitness. The evolutionary representation and requirements specification used provide great flexibility in problem scope and depth and is worthy of considering in future layout and design attempts. The system outlined in this thesis is capable of evolving a variety of floor plans conforming to functional and geometric specifications. Many of the resulting plans look reasonable even when compared to a professional floor plan. Additionally polygonal and multi-floor buildings were also generated.


Overview of the TREC 2017 Real-Time Summarization Track

Jimmy Lin, Adam Roegiest, Luchen Tan, Richard McCreadie, Ellen Voorhees, and Fernando Diaz 1 David R. Cheriton School of Computer Science, University of Waterloo, Ontario, Canada 2 School of Computing Science, University of Glasgow, Scotland, the United Kingdom 3 National Institute for Standards and Technology, Maryland, USA 4 Microsoft Research, New York, USA {jimmylin, aroegies, luchen.tan}@uwaterloo.ca richard.mccreadie@glasgow.ac.uk, ellen.voorhees@nist.gov, fdiaz@microsoft.com


Human-comfortable navigation for an autonomous robotic wheelchair

Reliable autonomous navigation is an active research topic that has drawn the attention for decades, however, human factors such as navigational comfort has not received the same level of attention. This work proposes the concept of “comfortable map” and presents a navigation approach for autonomous passenger vehicles which in top of being safe and reliable is comfortable. In our approach we first extract information from users preference related to comfort while sitting on a robotic wheelchair under different conditions in an indoor corridor environment. Human-comfort factors are integrated to a geometric map generated by SLAM framework. Then a global planner computes a safe and comfortable path which is followed by the robotic wheelchair. Finally, an evaluation with 29 participants using a fully autonomous robotic wheelchair, showed that more than 90% of them found the proposed approach more comfortable than a shortest-path state of the art approach.


An ant system based on moderate search for TSP

Ant Colony Optimization (ACO) algorithms often suffer from criticism for the local optimum and premature convergence. In order to overcome these inherent shortcomings shared by most ACO algorithms, we divide the ordinary ants into two types: the utilizationoriented ants and the exploration-oriented ants. The utilization-oriented ants focus on constructing solutions based on the learned experience like ants in many other ACO algorithms. On the other hand, inspired by the adaptive behaviors of some real-world Monomorium ant species who tend to select paths with moderate pheromone concentration, a novel search strategy, that is, a completely new transition rule is designed for the exploration-oriented ants to explore more unknown solutions. In addition, a new corresponding update strategy is also employed. Moreover, applying the new search strategy and update strategy, we propose an improved version of ACO algorithm— Moderate Ant System. This improved algorithm is experimentally turned out to be effective and competitive.


A new ART-counterpropagation neural network for solving a forecasting problem

This study presents a novel Adaptive resonance theory-Counterpropagation neural network (ART-CPN) for solving forecasting problems. The network is based on the ART concept and the CPN learning algorithm for constructing the neural network. The vigilance parameter is used to automatically generate the nodes of the cluster layer for the CPN learning process. This process improves the initial weight problem and the adaptive nodes of the cluster layer (Kohonen layer). ART-CPN involves real-time learning and is capable of developing a more stable and plastic prediction model of input patterns by self-organization. The advantages of ART-CPN include the ability to cluster, learn and construct the network model for forecasting problems. The network was applied to solve the real forecasting problems. The learning algorithm revealed better learning efficiency and good prediction performance. q 2004 Elsevier Ltd. All rights reserved.


Search-Free DOD, DOA and Range Estimation for Bistatic FDA-MIMO Radar

Frequency diverse array (FDA) is an emerging technology, the hybrid of FDA and multiple-input-multiple-output (FDA-MIMO) under monostatic scenarios has received much attention in recent years. However, little work have been done for bistatic FDA-MIMO radar. In this paper, we investigate strategies on estimating direction-of-departure (DOD), direction-of-arrival and range for bistatic FDA-MIMO radar. Our strategies have two aspects. First, nonlinear frequency increments including both subarray and random modes are employed to overcome the problem that the DOD and range parameters of FDA transmitting steering vectors are coupled. Second, in order to reduce the computational complexity associated with the 3-D spectral peak searching algorithms, estimation of signal parameters via rotational invariance technique and parallel factor algorithms with their corresponding phase ambiguity resolving methods, are proposed for subarray and random modes, respectively. Both of the two algorithms perform well while the range parameter of targets satisfy a range constraint criterion. This criterion can also be used for designing frequency increments of bistatic FDA-MIMO radar. Additionally, the Cramér-Rao bound of bistatic FDA-MIMO radar and the algorithm performance analysis consist of identifiability and complexity are derived. All the proposed methods are verified by both theoretical analysis and numerical simulations. And satisfactory results are achieved.


Yoga for anxiety: a systematic review of the research evidence.

Between March and June 2004, a systematic review was carried out of the research evidence on the effectiveness of yoga for the treatment of anxiety and anxiety disorders. Eight studies were reviewed. They reported positive results, although there were many methodological inadequacies. Owing to the diversity of conditions treated and poor quality of most of the studies, it is not possible to say that yoga is effective in treating anxiety or anxiety disorders in general. However, there are encouraging results, particularly with obsessive compulsive disorder. Further well conducted research is necessary which may be most productive if focused on specific anxiety disorders.


A fully portable robot system for cleaning solar panels

Dust and dirt particles accumulating on PV panels decrease the solar energy reaching the cells, thereby reducing their overall power output. Hence, cleaning the PV panels is a problem of great practical engineering interest in solar PV power generation. In this paper, the problem is reviewed and methods for dust removal are discussed. A portable robotic cleaning device is developed and features a versatile platform which travels the entire length of a panel. An Arduino microcontroller is used to implement the robot's control system. Initial testing of the robot has provided favorable results and shows that such a system is viable. Future improvements on the design are discussed, especially the different methods of transporting the robot from one panel to another. In conclusion, it is found that robotic cleaning solution is practical and can help in maintaining the clean PV panel efficiency.


Look Deeper into Depth: Monocular Depth Estimation with Semantic Booster and Attention-Driven Loss

Monocular depth estimation benefits greatly from learning based techniques. By studying the training data, we observe that the per-pixel depth values in existing datasets typically exhibit a long-tailed distribution. However, most previous approaches treat all the regions in the training data equally regardless of the imbalanced depth distribution, which restricts the model performance particularly on distant depth regions. In this paper, we investigate the long tail property and delve deeper into the distant depth regions (i.e. the tail part) to propose an attentiondriven loss for the network supervision. In addition, to better leverage the semantic information for monocular depth estimation, we propose a synergy network to automatically learn the information sharing strategies between the two tasks. With the proposed attention-driven loss and synergy network, the depth estimation and semantic labeling tasks can be mutually improved. Experiments on the challenging indoor dataset show that the proposed approach achieves state-of-the-art performance on both monocular depth estimation and semantic labeling tasks.


Nutrition Therapy Recommendations for the Management of Adults With
Diabetes

There is no standard meal plan or eating pattern that works universally for all people with diabetes. In order to be effective, nutrition therapy should be individualized for each patient/client based on his or her individual health goals; personal and cultural preferences; health literacy and numeracy; access to healthful choices; and readiness, willingness, and ability to change. Nutrition interventions should emphasize a variety of minimally processed nutrient dense foods in appropriate portion sizes as part of a healthful eating pattern and provide the individual with diabetes with practical tools for day-to-day food plan and behavior change that can be maintained over the long term.


Impact Of Online Advertising On Consumer Attitudes And Interests Buy Online ( Survey On Students Of Internet Users In Makassar )

The rapid development of technology today makes Internet users continues to increase. This is supported by the ease of internet users access the internet either through a PC, laptop, mobile phones, tablets and other media. The increase in Internet users this makes the internet into a proper promotion using online advertising. With a wide reach and touch the various layers of the Internet media communities may be appropriate advice for company promotion. However, an increasing number of Internet users, especially in the city of Makassar is not accompanied by an increase in the number of online purchases. Based on that it is necessary to examine how the effect of online advertising on consumer buying behavior and online, as well as how to control the behavior and subjective norms influence the attitudes and interests of consumers buy online. This study aims to analyze and test the effect of online advertising on consumer attitudes and purchase interest online, a survey conducted on students of Internet users in the city of Makassar. The study was conducted on students of public and private universities in the city of Makassar. The method used was a quantitative analysis using the technique of purposive sampling method with a sample of 340 people. Testing this hypothesis using structural equation modeling (SEM). The results showed that online advertising has an influence on consumer buying behavior and online. Dimensions interactivity of online advertising provides the highest influence on the attitudes and interests of consumers purchasing online.


Bernoulli Embeddings for Graphs

Just as semantic hashing (Salakhutdinov and Hinton 2009) can accelerate information retrieval, binary valued embeddings can significantly reduce latency in the retrieval of graphical data. We introduce a simple but effective model for learning such binary vectors for nodes in a graph. By imagining the embeddings as independent coin flips of varying bias, continuous optimization techniques can be applied to the approximate expected loss. Embeddings optimized in this fashion consistently outperform the quantization of both spectral graph embeddings and various learned real-valued embeddings, on both ranking and pre-ranking tasks for a variety of datasets.


Playful bottle: a mobile social persuasion system to motivate healthy water intake

This study of mobile persuasion system explores the use of a mobile phone, when attached to an everyday object used by an everyday behavior, becomes a tool to sense and influence that behavior. This mobile persuasion system, called Playful Bottle system, makes use of a mobile phone attached to an everyday drinking mug and motivates office workers to drink healthy quantities of water. A camera and accelerometer sensors in the phone are used to build a vision/motion-based water intake tracker to detect the amount and regularity of water consumed by the user. Additionally, the phone includes hydration games in which natural drinking actions are used as game input. Two hydration games are developed: a single-user TreeGame with automated computer reminders and a multi-user ForestGame with computer-mediated social reminders from members of the group playing the game. Results from 7-week user study with 16 test subjects suggest that both hydration games are effective for encouraging adequate and regular water intake by users. Additionally, results of this study suggest that adding social reminders to the hydration game is more effective than system reminders alone.


Design of Organic TFT Pixel Electrode Circuit for Active-Matrix OLED Displays

A new current-programming pixel circuit for active-matrix organic light-emitting diode (AM-OLED) displays, composed of four organic thin-film transistors (OTFTs) and one capacitor, has been designed, simulated and evaluated. The most critical issue in realizing AMOLED displays with OTFTs is the variation and aging of the driving-TFTs and degradation of OLEDs. This can cause image sticking or degradation of image quality. These problems require compensation methods for high-quality display applications, and pixel level approach is considered to be one of the most important factors for improving display image quality. Our design shows that the current OLED and OTFT technology can be implemented for AMOLED displays, compensating the degradation of OTFT device characteristics.


Deep Optical Flow Estimation Via Multi-Scale Correspondence Structure Learning

As an important and challenging problem in computer vision, learning based optical flow estimation aims to discover the intrinsic correspondence structure between two adjacent video frames through statistical learning. Therefore, a key issue to solve in this area is how to effectively model the multi-scale correspondence structure properties in an adaptive end-to-end learning fashion. Motivated by this observation, we propose an endto-end multi-scale correspondence structure learning (MSCSL) approach for optical flow estimation. In principle, the proposed MSCSL approach is capable of effectively capturing the multi-scale inter-image-correlation correspondence structures within a multi-level feature space from deep learning. Moreover, the proposed MSCSL approach builds a spatial Conv-GRU neural network model to adaptively model the intrinsic dependency relationships among these multi-scale correspondence structures. Finally, the above procedures for correspondence structure learning and multi-scale dependency modeling are implemented in a unified end-to-end deep learning framework. Experimental results on several benchmark datasets demonstrate the effectiveness of the proposed approach.


Perspectives and Visions of Computer Science Education in Primary and Secondary (K-12) Schools

In view of the recent developments in many countries, for example, in the USA and in the UK, it appears that computer science education (CSE) in primary or secondary schools (K-12) has reached a significant turning point, shifting its focus from ICT-oriented to rigorous computer science concepts. The goal of this special issue is to offer a publication platform for soundly based in-depth experiences that have been made around the world with concepts, approaches, or initiatives that aim at supporting this shift. For this purpose, the article format was kept as large as possible, enabling the authors to explain many facets of their concepts and experiences in detail. Regarding the structure of the articles, we had encouraged the authors to lean on the Darmstadt Model, a category system that was developed to support the development, improvement, and investigation of K-12 CSE across regional or national boundaries. This model could serve as a unifying framework that might provide a proper structure for a well-founded critical discussion about the future of K-12 CSE. Curriculum designers or policy stakeholders, who have to decide, which approach an upcoming national initiative should follow, could benefit from this discussion as well as researchers who are investigating K12 CSE in any regard. With this goal in mind, we have selected six extensive and two short case studies from the UK, New Zealand, USA/Israel, France, Sweden, Georgia (USA), Russia, and Italy that provide an in-depth analysis of K-12 CSE in their respective country or state.


Design and development of a mobile crawling robot with novel halbach array based magnetic wheels

Higher efficiency and the safety of hull inspection can be achieved by replacing the conventional methods with modern crawling robots. This paper consists of detailed study of the design and development of a crawling robot with a novel permanent magnetic wheel based on Halbach magnetic array. The magnetic array and the wheel assembly were designed based on the magnetic simulation results. Maintaining the adequate adhesion force as well as the friction force of the wheels were considered in designing the wheels. The crawling robot is equipped with a steering system based on Ackermann steering method hence the steering angles are controlled by an algorithm and actuated using servo motors. The driving system of the robot is equipped with four geared stepper motors and they are controlled separately by the algorithm according to the inputs of the controller. The control architecture and the components used for the robot control system are explained. The operator can remotely control the robot and inspect the hull surface via the camera feed available in the HMI (Human Machine Interface) of the robot. The prototype of the crawling robot was tested in a similar environment and the test results are included in the paper.


Security as a Service Model for Cloud Environment

Cloud computing is becoming increasingly important for provision of services and storage of data in the Internet. However there are several significant challenges in securing cloud infrastructures from different types of attacks. The focus of this paper is on the security services that a cloud provider can offer as part of its infrastructure to its customers (tenants) to counteract these attacks. Our main contribution is a security architecture that provides a flexible security as a service model that a cloud provider can offer to its tenants and customers of its tenants. Our security as a service model while offering a baseline security to the provider to protect its own cloud infrastructure also provides flexibility to tenants to have additional security functionalities that suit their security requirements. The paper describes the design of the security architecture and discusses how different types of attacks are counteracted by the proposed architecture. We have implemented the security architecture and the paper discusses analysis and performance evaluation results.


Surface-Wave Launchers for Beam Steering and Application to Planar Leaky-Wave Antennas

A linear array of surface-wave launchers (SWLs) is presented for surface-wave (SW) beam steering on a grounded dielectric slab (GDS). Specifically, a two-element array of directive Yagi-Uda like SWL antennas (operating between 21-24 GHz) is investigated. By varying the relative phase difference between SWL elements, the excited SW field distribution can be steered. In addition, a six-element linear array of SWLs with nonuniform weighting is also presented. If metallic gratings are placed on top of the GDS, the bound SW mode can be transformed into a radiated leaky-wave (LW) mode and directive beam patterns can be generated in the far field. A specific elliptical grating configuration is also investigated demonstrating single frequency cylindrical-sector LW beam steering. These slotted SWL arrays can be useful for novel SW beam scanning designs, millimeter wave power distribution systems and new LW antennas.


Holographic artificial impedance surfaces for conformal antennas

We have developed a method for generating arbitrary radiation patterns from antennas on complex objects. The object is coated with an artificial impedance surface consisting of a lattice of sub-wavelength metal patches on a grounded dielectric substrate. The effective surface impedance depends on the size of the patches, and can be varied as a function of position. Using holography, the surface impedance is designed to generate any desired radiation pattern from currents in the surface. With this technique we can create antennas with novel properties such as radiation toward angles that would otherwise be shadowed


Planar Leaky-Wave Antenna Designs Offering Conical-Sector Beam Scanning and Broadside Radiation Using Surface-Wave Launchers

Two planar antenna designs that utilize surface-waves for leaky-wave excitation are investigated. Specifically, a surface-wave launcher is implemented to excite cylindrical surface-waves, which are bound to a grounded dielectric slab. By the addition of circular metallic strip gratings, a partially reflecting surface is realized, providing appropriate conditions for 2-D leaky-wave radiation. In particular, two designs are investigated: a continuous circular strip and a segmented circular strip grating. Results illustrate conical-sector beam scanning for the continuous circular strip grating between 2022 GHz, while broadside radiation is observed at 21.2 GHz by the segmented circular strip design.


Low-Cost Phased-Array Antenna Using Compact Tunable Phase Shifters Based on Ferroelectric Ceramics

A low-cost phased-array antenna at 10 GHz is presented for a scan angle of ±50°. The array employs continuously tunable phase shifters based on a screen printed barium-strontium-titanate thick-film ceramic. Due to the use of artificial transmission line topology, the proposed phase-shifter design has a very compact size (3 mm × 2.8 mm) for 342° total phase shift. In the frequency range from 8 to 10 GHz, it exhibits a figure of merit >;52°/dB, which is among the best of phase shifters based on ferroelectric thick films. In a prototyped phased array, the RF circuit consists of a feeding network, phase shifters, and antenna elements, which are integrated into one planar metallization layer. Furthermore, a simple way for routing bias lines for phase shifters is demonstrated using high resistive electrodes. Using screen printed thick films and applying a simplified fabrication process for the RF and bias circuitry can reduce the total expense of phased arrays considerably.


Application of antenna arrays to mobile communications. II. Beam-forming and direction-of-arrival considerations

Array processing involves manipulation of signals induced on various antenna elements. Its capabilities of steering nulls to reduce cochannel interferences and pointing independent beams toward various mobiles, as well as its ability to provide estimates of directions of radiating sources, make it attractive to a mobile communications system designer. Array processing is expected to play an important role in fulfilling the increased demands of various mobile communications services. Part I of this paper showed how an array could be utilized in different configurations to improve the performance of mobile communications systems, with references to various studies where feasibility of an array system for mobile communications is considered. This paper provides a comprehensive and detailed treatment of different beam-forming schemes, adaptive algorithms to adjust the required weighting on antennas, direction-of-arrival estimation methods—including their performance comparison—and effects of errors on the performance of an array system, as well as schemes to alleviate them. This paper brings together almost all aspects of array signal processing. It is presented at a level appropriate to nonexperts in the field and contains a large reference list to probe further.


Architectural Mismatch or Why it's hard to build systems out of existing parts

Many would argue that future breakthroughs in software productivity will depend on our ability to combine existing pieces of software to produce new applications. An important step towards this goal is the development of new techniques to detect and cope with mismatches in the assembled parts. Some problems of composition are due to low-level issues of interoperability, such as mismatches in programming languages or database schemas. However, in this paper we highlight a different, and in many ways more pervasive, class of problem: architectural mismatch. Specifically, we use our experience in building a family of software design environments from existing parts to illustrate a variety of types of mismatch that center around the assumptions a reusable part makes about the structure of the application in which is to appear. Based on this experience we show how an architectural view of the mismatch problem exposes some fundamental, thorny problems for software composition and suggests possible research avenues needed to solve them.


Comparison of Discovery Service Architectures for the Internet of Things

In the emerging Internet of Things rich data on real-world objects and events will be generated in vast amounts and stored in widely distributed databases. In truly global and dynamic application scenarios, intermediate brokers are needed to find these data, even if the exact location and form of storage are initially unknown to the requester. Discovery Services are aimed to fill this gap: they respond to requests for data on specific objects with a list of corresponding data providers. In this paper, we frame functional requirements for Discovery Services, and perform an overview and analysis of five established approaches for implementing Discovery Services that are taken from literature and industrial practice. In order to compare their characteristics, we develop a quality framework based on literature review and an ISO standard for software quality.


CatchTartan: Representing and Summarizing Dynamic Multicontextual Behaviors

Representing and summarizing human behaviors with rich contexts facilitates behavioral sciences and user-oriented services. Traditional behavioral modeling represents a behavior as a tuple in which each element is one contextual factor of one type, and the tensor-based summaries look for high-order dense blocks by clustering the values (including timestamps) in each dimension. However, the human behaviors are multicontextual and dynamic: (1) each behavior takes place within multiple contexts in a few dimensions, which requires the representation to enable non-value and set-values for each dimension; (2) many behavior collections, such as tweets or papers, evolve over time. In this paper, we represent the behavioral data as a two-level matrix (temporal-behaviors by dimensional-values) and propose a novel representation for behavioral summary called Tartan that includes a set of dimensions, the values in each dimension, a list of consecutive time slices and the behaviors in each slice. We further develop a propagation method CatchTartan to catch the dynamic multicontextual patterns from the temporal multidimensional data in a principled and scalable way: it determines the meaningfulness of updating every element in the Tartan by minimizing the encoding cost in a compression manner. CatchTartan outperforms the baselines on both the accuracy and speed. We apply CatchTartan to four Twitter datasets up to 10 million tweets and the DBLP data, providing comprehensive summaries for the events, human life and scientific development.


Stronger Security of Authenticated Key Exchange

Recent work by Krawczyk [12] and Menezes [16] has highlighted the importance of understanding well the guarantees and limitations of formal security models when using them to prove the security of protocols. In this paper we focus on security models for authenticated key exchange (AKE) protocols. We observe that there are several classes of attacks on AKE protocols that lie outside the scope of the Canetti-Krawczyk model. Some of these additional attacks have already been considered by Krawczyk [12]. In an attempt to bring these attacks within the scope of the security model we extend the Canetti-Krawczyk model for AKE security by providing significantly greater powers to the adversary. Our contribution is a more compact, integrated, and comprehensive formulation of the security model. We then introduce a new AKE protocol called NAXOS and prove that it is secure against these stronger


Ensemble Coordination Approach in Multi-AGV Systems Applied to Industrial Warehouses

This paper deals with a holistic approach to coordinate a fleet of automated guided vehicles (AGVs) in an industrial environment. We propose an ensemble approach based on a two layer control architecture and on an automatic algorithm for the definition of the roadmap. The roadmap is built by considering the path planning algorithm implemented on the hierarchical architecture and vice versa. In this way, we want to manage the coordination of the whole system in order to increase the flexibility and the global efficiency. Furthermore, the roadmap is computed in order to maximize the redundancy, the coverage and the connectivity. The architecture is composed of two layers. The low-level represents the roadmap itself. The high-level describes the topological relationship among different areas of the environment. The path planning algorithm works on both these levels and the subsequent coordination among AGVs is obtained exploiting shared resource (i.e., centralized information) and local negotiation (i.e., decentralized coordination). The proposed approach is validated by means of simulations and comparison using real plants. Note to Practitioners-The motivation of this work grows from the need to increase the flexibility and efficiency of current multi-AGV systems. In particular, in the current state-of-the-art the AGVs are guided through a manually defined roadmap with ad-hoc strategies for coordination. This is translated into high setup time requested for installation and the impossibility to easily respond to dynamic changes of the environment. The proposed method aims at managing all the design, setup and control process in an automatic manner, decreasing the time for setup and installation. The flexibility is also increased by considering a coordination strategy for the fleet of AGVs not based on manual ad-hoc rules. Simulations are performed in order to compare the proposed approach to the current industrial one. In the future, industrial aspects, as the warehouse management system, will be integrated in order to achieve a real and complete industrial functionality.


Robust odometry estimation for RGB-D cameras

The goal of our work is to provide a fast and accurate method to estimate the camera motion from RGB-D images. Our approach registers two consecutive RGB-D frames directly upon each other by minimizing the photometric error. We estimate the camera motion using non-linear minimization in combination with a coarse-to-fine scheme. To allow for noise and outliers in the image data, we propose to use a robust error function that reduces the influence of large residuals. Furthermore, our formulation allows for the inclusion of a motion model which can be based on prior knowledge, temporal filtering, or additional sensors like an IMU. Our method is attractive for robots with limited computational resources as it runs in real-time on a single CPU core and has a small, constant memory footprint. In an extensive set of experiments carried out both on a benchmark dataset and synthetic data, we demonstrate that our approach is more accurate and robust than previous methods. We provide our software under an open source license.


Standard high-resolution pelvic MRI vs. low-resolution pelvic MRI in the evaluation of deep infiltrating endometriosis

To compare the capabilities of standard pelvic MRI with low-resolution pelvic MRI using fast breath-hold sequences to evaluate deep infiltrating endometriosis (DIE). Sixty-eight consecutive women with suspected DIE were studied with pelvic MRI. A double-acquisition protocol was carried out in each case. High-resolution (HR)-MRI consisted of axial, sagittal, and coronal TSE T2W images, axial TSE T1W, and axial THRIVE. Low-resolution (LR)-MRI was acquired using fast single shot (SSH) T2 and T1 images. Two radiologists with 10 and 2 years of experience reviewed HR and LR images in two separate sessions. The presence of endometriotic lesions of the uterosacral ligament (USL), rectovaginal septum (RVS), pouch of Douglas (POD), and rectal wall was noted. The accuracies of LR-MRI and HR-MRI were compared with the laparoscopic and histopathological findings. Average acquisition times were 24 minutes for HR-MRI and 7 minutes for LR-MRI. The more experienced radiologist achieved higher accuracy with both HR-MRI and LR-MRI. The values of sensitivity, specificity, PPV, NPV, and accuracy did not significantly change between HR and LR images or interobserver agreement for all of the considered anatomic sites. LR-MRI performs as well as HR-MRI and is a valuable tool for the detection of deep endometriosis extension. • High- and low-resolution MRI perform similarly in deep endometriosis evaluation • Low-resolution MRI significantly reduces the duration of the examination • Radiologist experience is fundamental for evaluating deep pelvic endometriosis


Group discussion as interactive dialogue or as serial monologue: the influence of group size.

Current models draw a broad distinction between communication as dialogue and communication as monologue. The two kinds of models have different implications for who influences whom in a group discussion. If the discussion is like interactive dialogue, group members should be influenced most by those with whom they interact in the discussion; if it is like serial monologue, they should be influenced most by the dominant speaker. The experiments reported here show that in small, 5-person groups, the communication is like dialogue and members are influenced most by those with whom they interact in the discussion. However, in large, 10-person groups, the communication is like monologue and members are influenced most by the dominant speaker. The difference in mode of communication is explained in terms of how speakers in the two sizes of groups design their utterances for different audiences.


Fuzzy Cognitive Maps Tool for Scenario Analysis and Pattern Classification

After 30 years of research, challenges and solutions, Fuzzy Cognitive Maps (FCMs) have become a suitable knowledgebased methodology for modeling and simulation. This technique is especially attractive when modeling systems that are characterized by ambiguity, complexity and non-trivial causality. FCMs are well-known due to the transparency achieved during modeling tasks. The literature reports successful studies related to the modeling of complex systems using FCMs. However, the situation is not the same when it comes to software implementations where domain experts can design FCM-based systems, run simulations or perform more advanced experiments. The existing implementations are not proficient in providing many options to adjust essential parameters during the modeling steps. The gap between the theoretical advances and the development of accurate, transparent and sound FCM-based systems advocates for the creation of more complete and flexible software products. Therefore, the goal of this paper is to introduce FCM Expert, a software tool for fuzzy cognitive modeling oriented to scenario analysis and pattern classification. The main features of FCM Expert rely on Machine Learning algorithms to compute the parameters defining the model, optimize the network topology and improve the system convergence without losing information. On the other hand, FCM Expert allows performing WHAT-IF simulations and studying the system behavior through a friendly, intuitive and easy-to-use graphical user interface.


Web-based statistical fact checking of textual documents

User generated content has been growing tremendously in recent years. This content reflects the interests and the diversity of online users. In turn, the diversity among internet users is also reflected in the quality of the content being published online. This increases the need to develop means to gauge the support available for content posted online. In this work, we aim to make use of the web-content to calculate a statistical support score for textual documents. In the proposed algorithm, phrases representing key facts are extracted to construct basic elements of the document. Search is used thereon to validate the support available for these elements online, leading to assigning an overall score for each document. Experimental results have shown a difference between the score distribution of factual news data and false facts data. This indicates that the approach seems to be a promising seed for distinguishing different articles based on the content.


Aspect identification and sentiment analysis based on NLP

This paper proposes method for aspect identification and sentiment analysis. This paper deals with the problem of sentence level. We use semantic syntax tree to extract NP nodes, a method based on slack to identify aspect and mini-support function to narrow aspect; to analyze the sentiment for the aspect we use dictionary-based method. Experimental results show that the proposed approach achieves the state-of-the-art.


The Bitcoin Backbone Protocol: Analysis and Applications

Bitcoin is the first and most popular decentralized cryptocurrency to date. In this work, we extract and analyze the core of the Bitcoin protocol, which we term the Bitcoin backbone, and prove two of its fundamental properties which we call common prefix and chain quality in the static setting where the number of players remains fixed. Our proofs hinge on appropriate and novel assumptions on the “hashing power” of the adversary relative to network synchronicity; we show our results to be tight under high synchronization. Next, we propose and analyze applications that can be built “on top” of the backbone protocol, specifically focusing on Byzantine agreement (BA) and on the notion of a public transaction ledger. Regarding BA, we observe that Nakamoto’s suggestion falls short of solving it, and present a simple alternative which works assuming that the adversary’s hashing power is bounded by 1/3. The public transaction ledger captures the essence of Bitcoin’s operation as a cryptocurrency, in the sense that it guarantees the liveness and persistence of committed transactions. Based on this notion we describe and analyze the Bitcoin system as well as a more elaborate BA protocol, proving them secure assuming high network synchronicity and that the adversary’s hashing power is strictly less than 1/2, while the adversarial bound needed for security decreases as the network desynchronizes. Finally, we show that our analysis of the Bitcoin backbone protocol for synchronous networks extends with relative ease to the recently considered “partially synchronous” model, where there is an upper bound in the delay of messages that is unknown to the honest parties.


On the use of MapReduce for imbalanced big data using Random Forest

In this age, big data applications are increasingly becoming the main focus of attention because of the enormous increment of data generation and storage that has taken place in the last years. This situation becomes a challenge when huge amounts of data are processed to extract knowledge because the data mining techniques are not adapted to the new space and time requirements. Furthermore, real-world data applications usually present a class distribution where the samples that belong to one class, which is precisely the main interest, are hugely outnumbered by the samples of the other classes. This circumstance , known as the class imbalance problem, complicates the learning process as the standard learning techniques do not correctly address this situation. In this work, we analyse the performance of several techniques used to deal with imbal-anced datasets in the big data scenario using the Random Forest classifier. Specifically, oversampling, undersampling and cost-sensitive learning have been adapted to big data using MapReduce so that these techniques are able to manage datasets as large as needed providing the necessary support to correctly identify the underrepresented class. The Random Forest classifier provides a solid basis for the comparison because of its performance, robustness and versatility. An experimental study is carried out to evaluate the performance of the diverse algorithms considered. The results obtained show that there is not an approach to imbalanced big data classification that outperforms the others for all the data considered when using Random Forest. Moreover, even for the same type of problem, the best performing method is dependent on the number of mappers selected to run the experiments. In most of the cases, when the number of splits is increased, an improvement in the running times can be observed, however, this progress in times is obtained at the expense of a slight drop in the accuracy performance obtained. This decrement in the performance is related to the lack of density problem, which is evaluated in this work from the imbalanced data point of view, as this issue degrades the performance of classifiers in the imbalanced scenario more severely than in standard learning.


PhoneyC: A Virtual Client Honeypot

The number of client-side attacks has grown significantly in the past few years, shifting focus away from defendable positions to a broad, poorly defended space filled with vulnerable clients. Just as honeypots enabled deep research into server-side attacks, honeyclients can permit the deep study of client-side attacks. A complement to honeypots, a honeyclient is a tool designed to mimic the behavior of a userdriven network client application, such as a web browser, and be exploited by an attacker’s content. These systems are instrumented to discover what happened and how. This paper presents PhoneyC, a honeyclient tool that can provide visibility into new and complex client-side attacks. PhoneyC is a virtual honeyclient, meaning it is not a real application but rather an emulated client. By using dynamic analysis, PhoneyC is able to remove the obfuscation from many malicious pages. Furthermore, PhoneyC emulates specific vulnerabilities to pinpoint the attack vector. PhoneyC is a modular framework that enables the study of malicious HTTP pages and understands modern vulnerabilities and attacker techniques.


The treatment of osteoporotic thoracolumbar severe burst fractures with short pedicle screw fixation and vertebroplasty.

BACKGROUND
To investigate the clinical and radiological results of short pedicle screw fixation and vertebroplasty in osteoporotic thoracolumbar severe burst fractures.

METHODS
From September 2006 to August 2010, 19 consecutive patients sustained osteoporotic thoracolumbar severe burst fractures with or without neurologic deficit and were included in this prospective study. All patients underwent short pedicle screw fixation and vertebroplasty. Segmental kyphosis, AVBHr and PVBHr, and Canal compromise were calculated on radiographs pre-operatively, post-operative and at final follow up. VAS, ODI and SF-36 were calculated pre-operatively and at final follow up.

RESULTS
Mean operative time was 70.8 min (range 60~100 min) and mean blood loss was 92 ml (range 60~160 ml). The mean duration of their hospital stay was 4.5 days (range 3-7 days). The operative incisions were healing well. Average follow up time was 40.1 months (range 24~72 months). The AVBHr was corrected from preoperative (48.1 ± 6.8) % to postoperative (94.1 ± 1.7) % (P < 0.001). The PVBHr was corrected from preoperative (62.7 ± 4.8) % to postoperative (92.8 ± 1.8) % (P < 0.001). Canal compromise was corrected from preoperative (37.3 ± 5.8) % to postoperative (5.9 ± 2.3) % (P < 0.001). The segmental kyphosis was corrected from preoperative (20.6 ± 5.3) degree to postoperative (2.0 ± 3.2) degree (P < 0.001). VAS scores were reduced from preoperative 7.21 ± 0.86 to 2.21 ± 0.98 at final follow up (P < 0.001). SF-36 Bodily pain was reduced from preoperative 75.31 ± 13.85 to 13.74 ± 13.24 at final follow up (P < 0.001), and SF-36 Role Physical was reduced from preoperative 59.21 ± 26.63 to 19.74 ± 22.94 at final follow up (P < 0.001). The ODI scores were reduced from preoperative 81.68 ± 4.44 to 15.37 ± 5.54 at final follow up (P < 0.001). All 4 patients with partial neurological deficit initially had improvement. Cement leakage was observed in 3 cases (two anterior to vertebral body and one into the disc without sequela). There were no instances of instrumentation failure and no patient had persistent postoperative back pain.

CONCLUSIONS
Vertebroplasty and short pedicle screw fixation has the advantages of both radiographic and functional results for treating osteoporotic thoracolumbar severe burst fractures using a purely posterior approach.


An efficient centralized scheduling algorithm in IEEE 802.15.4e TSCH networks

IEEE 802.15.4e Time Slotted Channel Hopping (TSCH) standard has gained a lot of attention within the Industrial Internet of Things research community due to its effectiveness in improving reliability and providing ultra-low power consumption for industrial applications, and in which its communication is orchestrated by a schedule. Despite its relevance, the standard leaves out of its scope in defining how the schedule is built, updated and maintained. This open issue is one of the trending topics in the IETF 6TiSCH WG, that still need to be addressed. This work focuses on scheduling in TSCH networks in a centralized manner where the gateway makes time and frequency slot allocation. This paper formulates the scheduling problem as a throughput maximization problem and delay minimization problem. We propose a graph theoretical approach to solve the throughput maximization problem in a centralized way. The combinatorial properties of the scheduling problem are addressed by providing an equivalent maximum weighted bipartite matching (MWBM) problem to reduce the computational complexity and also adopting the Hungarian algorithm in polynomial time. Simulation results are provided to evaluate the performance of the proposed scheme.


Effects of Presowing Pulsed Electromagnetic Treatment of Tomato Seed on Growth, Yield, and Lycopene Content

The use of magnetic field as a presowing treatment has been adopted by researchers as a new environmental friendly technique. The aim of this study was to determine the effect of magnetic field exposure on tomato seeds covering a range of parameters such as transplanting percentage, plant height, shoot diameter, number of leaves per plant, fresh weight, dry weight, number of flowers, yield, and lycopene content. Pulsed electromagnetic field was used for 0, 5, 10, and 15 minutes as a presowing treatment of tomato seeds in a field experiment for two years. Papimi device (amplitude on the order of 12.5 mT) has been used. The use of pulsed electromagnetic field as a presowing treatment was found to enhance plant growth in tomato plants at certain duration of exposure. Magnetic field treatments and especially the exposure of 10 and 15 minutes gave the best results in all measurements, except plant height and lycopene content. Yield per plant was higher in magnetic field treatments, compared to control. MF-15 treatment yield was 80.93% higher than control treatment. Lycopene content was higher in magnetic field treatments, although values showed no statistically significant differences.


The mnist database of handwritten digits

please note that your browser may uncompress these files without telling you. If the files you downloaded have a larger size than the above, they have been uncompressed by your browser. Simply rename them to remove the .gz extension. Some people have asked me "my application can't open your image files". These files are not in any standard image format. You have to write your own (very simple) program to read them. The file format is described at the bottom of this page.


Statistical Pattern Recognition

This course will provide an introduction to statistical pattern recognition. The lectures will focus on different techniques including methods for feature extraction, dimensionality reduction, data clustering and pattern classification. State-of-art approaches such as ensemble learning and sparse modelling will be introduced. Selected real-world applications will illustrate how the techniques are applied in practice.


Issues and challenges of data security in a cloud computing environment

Now customers can opt for software and information technology services according to his requirements and can get these services on a leased basis from the network service provider and this has the facility to scale its requirements to up or down. This service is known as cloud computing, provided by the infrastructure provider which is a third party provider. Cloud computing provides many advantages to the customer like scalability, better economics of scaling, its ability to recover from problems, its ability to outsource non-core activities and flexibility. Cloud computing is a better option for the organizations to take as their best option without any initial investment and day by day frequent and heavy use of cloud computing is increasing but despite all the benefits a cloud offers to an organization there are certain doubts and threats regarding the security issues associated with a cloud computing platform. The security issues primarily involve the external control over organizational structure and management and personal and private data of the organization can be compromised. Personal and private data in this computing environment has a very high risk of breach of confidentiality, integrity and availability. Growth of cloud computing is mainly hampered due to these security concerns and challenges. Proper security arrangements are need to be placed before selecting the service provider for any cloud computing service and customers need to be very careful about understanding the risks of security breaches and challenges of using this new computing environment. This detailed study discusses about some of the challenges associated with cloud computing services and security issues related to this platform.


Radar target detection and Doppler ambiguity resolution

A Continuous Wave (CW) radar with a linear Frequency Modulation (FM) and a very short chirp duration is considered in this paper. A single target range and Doppler frequency measurement consits of a sequence of L chirp signals with the same chirp duration. The maximum unambiguously measurable radial velocity in this case is limited by a minimum chirp duration. Therefore fast moving targets will cause ambiguous measurement results, that have to be resolved. The technique of resolving ambiguities on the basis of several subsequent measurements with different ambiguity interval lengths is well known as Chinese Remainder Theorem (CRT). It has been extensively described in literature [2]. Two different signal processing approaches to apply this technique for the resolution of ambiguities are compared in this paper. These do not differ in terms of the ambiguity resolution itself, because they are based on the same principle. The detection performance however is different.


A direct LDA algorithm for high-dimensional data - with application to face recognition

However, for a task with very high-dimensional data such as images, the traditional LDA algorithm encounters several di$culties. Consider face recognition for example. A low-de"nition face image of size 64 64 implies a feature space of 64 64"4096 dimensions, and therefore scatter matrices of size 4096 4096"16M. First, it is computationally challenging to handle big matrices (such as computing eigenvalues). Second, those matrices are almost always singular, as the number of training images needs to be at least 16M for them to be non-degenerate. Due to these di$culties, it is commonly believed that a direct LDA solution for such high-dimensional data is infeasible. Thus, ironically, before LDA can be used to reduce dimensionality, another procedure has to be "rst applied for dimensionality reduction. In face recognition, many techniques have been proposed (for a good review, see Ref. [1]). Among them, the most notable is a two-stage PCA#LDA approach [2,3]:


A Wideband SiGe BiCMOS Frequency Doubler With 6.5-dBm Peak Output Power for Millimeter-Wave Signal Sources

This paper presents a balanced frequency doubler with 6.5-dBm peak output power at 204 GHz in 130-nm SiGe BiCMOS technology ( $f_{T}/f_{\max }=210$ /250 GHz). To convert the single-ended input signal to a differential signal for balanced operation, an on-chip transformer-based balun is employed. Detailed design procedure and compensation techniques to lower the imbalance at the output ports, based on mixed mode S parameters are proposed and verified analytically and through electromagnetic simulations. The use of optimized harmonic reflectors at the input port results in a 2-dBm increase in output power without sacrificing the bandwidth of interest. The measured conversion loss of the frequency doubler is 9 dB with 6-dBm input power at 204-GHz output. The measured peak output power is 6.5 dBm with an on-chip power amplifier stage. The 3-dB output power bandwidth is measured to be wider than 50 GHz (170–220 GHz). The total chip area of the doubler is 0.09 mm2 and the dc power consumption is 90 mW from a 1.8-V supply, which corresponds to a 5% collector efficiency.


Crowdfunding: A Spimatic application of digital fandom

The digital environment has opened up new opportunities for fans to engage with the production process of their favoured texts. The successful Kickstarter campaigns for Rob Thomas and Kristen Bell’s Veronica Mars film and Zach Braff’s film Wish I Was Here, and the failed Kickstarter campaign for Melissa Joan Hart’s film Darci’s Walk of Shame, each illustrate how the entertainers drew (or did not draw) on specific support from his or her own fan communities to generate funds and word-of-mouth publicity. In this paper I augment studies of digital fandom by integrating the technosocial concept of the Spime into the relationships forged between the technology of crowdfunding and the affect of particular audiences. I illustrate how ‘crowdfunding fandom’ changes the unique posthuman experience of 21 century participatory culture by interpreting fandom in a Spimatic network. This ultimately more fully integrates the fan into the production process, creating a truly ‘participatory culture’.


A computational cognition model of perception, memory, and judgment

The mechanism of human cognition and its computability provide an important theoretical foundation to intelligent computation of visual media. This paper focuses on the intelligent processing of massive data of visual media and its corresponding processes of perception, memory, and judgment in cognition. In particular, both the human cognitive mechanism and cognitive computability of visual media are investigated in this paper at the following three levels: neurophysiology, cognitive psychology, and computational modeling. A computational cognition model of Perception, Memory, and Judgment (PMJ model for short) is proposed, which consists of three stages and three pathways by integrating the cognitive mechanism and computability aspects in a unified framework. Finally, this paper illustrates the applications of the proposed PMJ model in five visual media research areas. As demonstrated by these applications, the PMJ model sheds some light on the intelligent processing of visual media, and it would be innovative for researchers to apply human cognitive mechanism to computer science.


A Flexible Architecture for Ray Tracing Terrain Heightfields

High-quality interactive rendering of terrain surfaces is a challenging task, which requires compromises between rendering quality, rendering time and available resources. However, current solutions typically provide optimized strategies tailored to particular constraints. In this paper we propose a more scalable approach based on functional programming and introduce a flexible ray tracer for rendering terrain heightfields. This permits the dynamic composition of complex and recursive shaders. In order to exploit the concurrency of the GPU for a large number of dynamically created tasks with inter-dependencies, the functional model is represented as a token stream and is iteratively rewritten via pattern matching on multiple shader cores in parallel. A first prototype demonstrates the feasibility of our approach.


Presurgical evaluation of epilepsy.

An overview of the following six cortical zones that have been defined in the presurgical evaluation of candidates for epilepsy surgery is given: the symptomatogenic zone; the irritative zone; the seizure onset zone; the epileptogenic lesion; the epileptogenic zone; and the eloquent cortex. The stepwise historical evolution of these different zones is described. The current diagnostic techniques used in the definition of these cortical zones, such as video-EEG monitoring, MRI and ictal single photon emission computed tomography, are discussed. Established diagnostic tests are set apart from procedures that should still be regarded as experimental, such as magnetoencephalography, dipole source localization and spike-triggered functional MRI. Possible future developments that might lead to a more direct definition of the epileptogenic zone are presented.


Detecting Encrypted Traffic: A Machine Learning Approach

Detecting encrypted traffic is increasingly important for deep packet inspection (DPI) to improve the performance of intrusion detection systems. We propose a machine learning approach with several randomness tests to achieve high accuracy detection of encrypted traffic while requiring low overhead incurred by the detection procedure. To demonstrate how effective the proposed approach is, the performance of four classification methods (Näıve Bayesian, Support Vector Machine, CART and AdaBoost) are explored. Our recommendation is to use CART which is not only capable of achieving an accuracy of 99.9% but also up to about 2.9 times more efficient than the second best candidate (Näıve Bayesian).


Topic Modeling using Topics from Many Domains, Lifelong Learning and Big Data

Topic modeling has been commonly used to discover topics from document collections. However, unsupervised models can generate many incoherent topics. To address this problem, several knowledge-based topic models have been proposed to incorporate prior domain knowledge from the user. This work advances this research much further and shows that without any user input, we can mine the prior knowledge automatically and dynamically from topics already found from a large number of domains. This paper first proposes a novel method to mine such prior knowledge dynamically in the modeling process, and then a new topic model to use the knowledge to guide the model inference. What is also interesting is that this approach offers a novel lifelong learning algorithm for topic discovery, which exploits the big (past) data and knowledge gained from such data for subsequent modeling. Our experimental results using product reviews from 50 domains demonstrate the effectiveness of the proposed approach.


Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design

Flow-based generative models are powerful exact likelihood models with efficient sampling and inference. Despite their computational efficiency, flowbased models generally have much worse density modeling performance compared to state-of-the-art autoregressive models. In this paper, we investigate and improve upon three limiting design choices employed by flow-based models in prior work: the use of uniform noise for dequantization, the use of inexpressive affine flows, and the use of purely convolutional conditioning networks in coupling layers. Based on our findings, we propose Flow++, a new flow-based model that is now the state-of-the-art non-autoregressive model for unconditional density estimation on standard image benchmarks. Our work has begun to close the significant performance gap that has so far existed between autoregressive models and flow-based models. Our implementation is available at https://github.com/aravind0706/flowpp.


Engineering exosomes as refined biological nanoplatforms for drug delivery

Exosomes, a subgroup of extracellular vesicles (EVs), have been recognized as important mediators of long distance intercellular communication and are involved in a diverse range of biological processes. Because of their ideal native structure and characteristics, exosomes are promising nanocarriers for clinical use. Exosomes are engineered at the cellular level under natural conditions, but successful exosome modification requires further exploration. The focus of this paper is to summarize passive and active loading approaches, as well as specific exosome modifications and examples of the delivery of therapeutic and imaging molecules. Examples of exosomes derived from a variety of biological origins are also provided. The biocompatible characteristics of exosomes, with suitable modifications, can increase the stability and efficacy of imaging probes and therapeutics while enhancing cellular uptake. Challenges in clinical translation of exosome-based platforms from different cell sources and the advantages of each are also reviewed and discussed.


A Compositional Interpretation of Biomedical Event Factuality

We propose a compositional method to assess the factuality of biomedical events extracted from the literature. The composition procedure relies on the notion of semantic embedding and a fine-grained classification of extrapropositional phenomena, including modality and valence shifting, and a dictionary based on this classification. The event factuality is computed as a product of the extra-propositional operators that have scope over the event. We evaluate our approach on the GENIA event corpus enriched with certainty level and polarity annotations. The results indicate that our approach is effective in identifying the certainty level component of factuality and is less successful in recognizing the other element, negative polarity.


CIM: Community-Based Influence Maximization in Social Networks

Given a social graph, the problem of influence maximization is to determine a set of nodes that maximizes the spread of influences. While some recent research has studied the problem of influence maximization, these works are generally too time consuming for practical use in a large-scale social network. In this article, we develop a new framework, community-based influence maximization (CIM), to tackle the influence maximization problem with an emphasis on the time efficiency issue. Our proposed framework, CIM, comprises three phases: (i) community detection, (ii) candidate generation, and (iii) seed selection. Specifically, phase (i) discovers the community structure of the network; phase (ii) uses the information of communities to narrow down the possible seed candidates; and phase (iii) finalizes the seed nodes from the candidate set. By exploiting the properties of the community structures, we are able to avoid overlapped information and thus efficiently select the number of seeds to maximize information spreads. The experimental results on both synthetic and real datasets show that the proposed CIM algorithm significantly outperforms the state-of-the-art algorithms in terms of efficiency and scalability, with almost no compromise of effectiveness.


A Dictionary Approach to EBSD Indexing

We propose a framework for indexing of grain and sub-grain structures in electron backscatter diffraction (EBSD) images of polycrystalline materials. The framework is based on a previously introduced physics-based forward model by Callahan and De Graef (2013) relating measured patterns to grain orientations (Euler angle). The forward model is tuned to the microscope and the sample symmetry group. We discretize the domain of the forward model onto a dense grid of Euler angles and for each measured pattern we identify the most similar patterns in the dictionary. These patterns are used to identify boundaries, detect anomalies, and index crystal orientations. The statistical distribution of these closest matches is used in an unsupervised binary decision tree (DT) classifier to identify grain boundaries and anomalous regions. The DT classifies a pattern as an anomaly if it has an abnormally low similarity to any pattern in the dictionary. It classifies a pixel as being near a grain boundary if the highly ranked patterns in the dictionary differ significantly over the pixels 3× 3 neighborhood. Indexing is accomplished by computing the mean orientation of the closest dictionary matches to each pattern. The mean orientation is estimated using a maximum likelihood approach that models the orientation distribution as a mixture of Von MisesFisher distributions over the quaternionic 3-sphere. The proposed dictionary matching approach permits segmentation, anomaly detection, and indexing to be performed in a unified manner with the additional benefit of uncertainty quantification. We demonstrate the proposed dictionary-based approach on a Ni-base IN100 alloy. c1 c1 Part of this work was reported in the Proceedings of the IEEE International Conference on Image Processing (ICIP), Melbourne Australia, Sept 2013.