title: Effects of Platform and Content Attributes on Information Dissemination on We Media: A Case Study on WeChat Platform

text: The emergence of the WeChat has brought some new changes to people's ways of life and reading. Thanks to the WeChat Public Platform, many people from all walks of life have become We Media operators, creating their own brands and products. In a case study of WeChat Public Platform, this paper uses NLPIR, a tool for Chinese word segmentation, and SPSS, a data analysis software, to conduct an analysis of actual data captured by Web Spider about WeChat posts and the platform itself, exploring the effects of post content and platform attributes on users' reading and dissemination behaviors. Our regression analysis found that some content attributes, including parts of speech like adverb, punctuation marks such as question mark, and headline length and position, as well as platform attributes, such as the platform seniority, have significant impacts on page views; the genres of advertisement, joke and warning, among other content attributes, and follower count as a platform attribute, have a very significant effect on the dissemination index. This paper is helpful for operators to gain a better understanding of factors influencing the spread of information published by WeChat Official Accounts and providing valuable revelations on achieving fast and efficient dissemination of posts.


title: Mining Local Process Models

text: In this paper we describe a method to discover frequent behavioral patterns in event logs. We express these patterns as local process models. Local process model mining can be positioned in-between process discovery and episode / sequential pattern mining. The technique presented in this paper is able to learn behavioral patterns involving sequential composition, concurrency, choice and loop, like in process mining. However, we do not look at start-to-end models, which distinguishes our approach from process discovery and creates a link to episode / sequential pattern mining. We propose an incremental procedure for building local process models capturing frequent patterns based on so-called process trees. We propose five quality dimensions and corresponding metrics for local process models, given an event log. We show monotonicity properties for some quality dimensions, enabling a speedup of local process model discovery through pruning. We demonstrate through a real life case study that mining local patterns allows us to get insights in processes where regular start-to-end process discovery techniques are only able to learn unstructured, flower-like, models.


title: Marketing Theory : Overview of Ontology , Epistemology , and Axiology Aspects

text: This article discusses about the marketing theory from the perspective of philosophy of science. The discussion focused on the aspects of the ontology, epistemology, and axiology. From the aspect of the ontology, it seems that the essence of marketing is a useful science and mutually beneficial for both the marketer and the stakeholders, or in other words that marketing knowledge is useful knowledge for the benefit of humankind that can be realized through the exchange process. Side of the ontology covers what the substance of marketing knowledge, the substance of truth and reality that is inherent with marketing. Meanwhile, aspects of epistemology cover a variety of approaches, methods, sources, structure and validation or marketing truth. Finally, axiology fields are related to ethics in marketing and marketing research. Marketing ethics and ethics in marketing research is a crucial matter, including in this case is trust.


title: Adversarial Objectives for Text Generation

text: Language models can be used to generate text by iteratively sampling words conditioned on previously sampled words. In this work, we explore adversarial objectives to obtain good text generations by training a recurrent language model to keep its hidden state statistics during sampling similar to what it has sees during maximum likelihood estimation (MLE) training. We analyze the convergence of these models and discuss the effect of the adversarial objective on word-level and character-level language models. We find that using an adversarial objective assists the MLE objective and results in faster convergence and lower validation perplexities for character-level language models. We also collect sentence quality ratings from human participants and show that character-level language models with the adversarial objective generates qualitatively better sentences than the standard character-level recurrent language model.


title: A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation

text: Natural language generation lies at the core of generative dialogue systems and conversational agents. We describe an ensemble neural language generator, and present several novel methods for data representation and augmentation that yield improved results in our model. We test the model on three datasets in the restaurant, TV and laptop domains, and report both objective and subjective evaluations of our best model. Using a range of automatic metrics, as well as human evaluators, we show that our approach achieves better results than state-of-the-art models on the same datasets.


title: Metadating: Exploring the Romance and Future of Personal Data

text: We introduce Metadating -- a future-focused research and speed-dating event where single participants were invited to "explore the romance of personal data". Participants created "data profiles" about themselves, and used these to "date" other participants. In the rich context of dating, we study how personal data is used conversationally to communicate and illustrate identity. We note the manner in which participants carefully curated their profiles, expressing ambiguity before detail, illustration before accuracy. Our findings proposition a set of data services and features, each concerned with representing and curating data in new ways, beyond a focus on purely rational or analytic relationships with a quantified self. Through this, we build on emerging interest in "lived informatics" and raise questions about the experience and social reality of a "data-driven life".


title: Noise and sensitivity analysis of harmonic radar system for vital sign detection

text: Harmonic radar architectures present systems of wireless detection operating at multiple harmonic carrier frequencies. The noise and sensitivity of a harmonic radar system, which is developed at 12 GHz and 24 GHz for vital signs detection, are studied numerically and experimentally. The received signal power of the radar system is analyzed numerically as a function of the distance, taking into account the radar cross section (RCS) of the patient chest. The numerical data are compared with results obtained with single carrier frequency system. The total noise is a combination of thermal noise, residual phase noise, and flicker noise that presents the most elevated factor influencing the detection at baseband. Experimental results show that with the harmonic radar, the flicker noise can be reduced by 20dB at around 1Hz baseband frequency.


title: DNA Sequence Compression Using the Burrows-Wheeler Transform

text: We investigate off-line dictionary oriented approaches to DNA sequence compression, based on the Burrows-Wheeler Transform (BWT). The preponderance of short repeating patterns is an important phenomenon in biological sequences. Here, we propose off-line methods to compress DNA sequences that exploit the different repetition structures inherent in such sequences. Repetition analysis is performed based on the relationship between the BWT and important pattern matching data structures, such as the suffix tree and suffix array. We discuss how the proposed approach can be incorporated in the BWT compression pipeline.


title: Multichannel-Sniffing-System for Real-World Analysing of Wi-Fi-Packets

text: Wireless technologies like Wi-Fi send their data using multiple channels. To analyze an environment and all Wi-Fi packets inside, a sniffing system is needed, which can sniff on all used channels of the wireless technology at the same time. This allows catching most packets on each channel. In this paper, a way to build up a multi-channel-sniffing-system (MCSS) is described. The test system uses several single board computers (SBC) with an external Wi-Fi adapter (USB), 19 SBCs are sniffing nodes (SFN) and one SBC as sending node (SN). The sniffing SBCs are placed in a cycle around the sender so that every node has the same chance to receive the simulated packets from the SN. For the control of all 20 SBCs, a self-developed software is used, which connects from the host to the clients and is used for configuring the experiments. The configuration is sent to each client and will initiate their start, so that their times are also synchronized, for this all clients are synchronised using a time


title: Appearance Characterization of Linear Lambertian Objects, Generalized Photometric Stereo, and Illumination-Invariant Face Recognition

text: Traditional photometric stereo algorithms employ a Lambertian reflectance model with a varying albedo field and involve the appearance of only one object. In this paper, we generalize photometric stereo algorithms to handle all appearances of all objects in a class, in particular the human face class, by making use of the linear Lambertian property. A linear Lambertian object is one which is linearly spanned by a set of basis objects and has a Lambertian surface. The linear property leads to a rank constraint and, consequently, a factorization of an observation matrix that consists of exemplar images of different objects (e.g., faces of different subjects) under different, unknown illuminations. Integrability and symmetry constraints are used to fully recover the subspace bases using a novel linearized algorithm that takes the varying albedo field into account. The effectiveness of the linear Lambertian property is further investigated by using it for the problem of illumination-invariant face recognition using just one image. Attached shadows are incorporated in the model by a careful treatment of the inherent nonlinearity in Lambert's law. This enables us to extend our algorithm to perform face recognition in the presence of multiple illumination sources. Experimental results using standard data sets are presented


title: G2RHA:Group-to-Route Handover Authentication Scheme for Mobile Relays in LTE-A High-Speed Rail Networks

text: The introduction of mobile relay node (MRN) in long-term evolution advanced high-speed rail networks is an attractive method to provide uninterrupted connectivity for the group of user equipments on board. However, MRNs still suffer from frequent handovers and several security threats due to several rounds of message exchange and the insecure air interface between MRNs and donor eNBs (DeNBs). In this paper, we propose a group-to-route handover authentication scheme based on trajectory prediction for mobile relays. By our scheme, all of the DeNBs deployed along the trajectory can be formed a route-DeNB group and all of the MRNs deployed in the same train can construct a MRN group. Compared with the current 3GPP standards and other related schemes, our scheme can accomplish the mutual authentication and key agreement between the MRN group and the target DeNB with ideal efficiency in terms of authentication signaling overhead, bandwidth consumption, and computational cost. Security analysis by using the BAN logic and formal verification tool Scyther show that our scheme can work correctly and withstand several protocol attacks.


title: Using institutional theory and dynamic simulation to understand complex e-Government phenomena

text: Available online 17 May 2011


title: Color retinal image enhancement using CLAHE

text: Common method in image enhancement that's often use is histogram equalization, due to this method is simple and has low computation load. In this research, we use Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance the color retinal image. To reduce this noise effect in color retinal image due to the acquisition process, we need to enhance this image. Color retinal image has unique characteristic than other image, that is, this image has important in green (G) channel. Image enhancement has important contribution in ophthalmology. In this paper, we propose new enhancement method using CLAHE in G channel to improve the color retinal image quality. The enhancement process conduct in G channel is appropriate to enhance the color retinal image quality.


title: An Analog Integrated Circuit Beamformer for High-Frequency Medical Ultrasound Imaging

text: We designed and fabricated a dynamic receive beamformer integrated circuit (IC) in 0.35-μm CMOS technology. This beamformer IC is suitable for integration with an annular array transducer for high-frequency (30-50 MHz) intravascular ultrasound (IVUS) imaging. The beamformer IC consists of receive preamplifiers, an analog dynamic delay-and-sum beamformer, and buffers for 8 receive channels. To form an analog dynamic delay line we designed an analog delay cell based on the current-mode first-order all-pass filter topology, as the basic building block. To increase the bandwidth of the delay cell, we explored an enhancement technique on the current mirrors. This technique improved the overall bandwidth of the delay line by a factor of 6. Each delay cell consumes 2.1-mW of power and is capable of generating a tunable time delay between 1.75 ns to 2.5 ns. We successfully integrated the fabricated beamformer IC with an 8-element annular array. Experimental test results demonstrated the desired buffering, preamplification and delaying capabilities of the beamformer.


title: Multi-target threat assessment for automotive applications

text: Auto-brake systems have been on the market for a few years, but in order to continue improving their performance, multi-target threat assessment is a key component. The goal is to early be able to say that a collision can no longer be avoided by a steering maneuver, and thus braking can be started earlier. This is done by looking at all objects in the road scene, including other vehicles and barriers, and then looking for escape paths. The paper presents a computationally efficient way of determining whether an escape path can be found in a scenario with generally positioned objects, given the limited dynamics of the vehicle.


title: Managing Organizational Knowledge By Diagnosing Intellectual Capital : Framing and Advancing the State of the Field

text: Copyright © 2001, Idea Group Publishing. Since organizational knowledge is at the crux of sustainable competitive advantage, the burgeoning field of intellectual capital is an exciting area for both researchers and practitioners. Intellectual capital is conceptualized from numerous disciplines making the field a mosaic of perspectives. Accountants are interested in how to measure it on the balance sheet, information technologists want to codify it on systems, sociologists want to balance power with it, psychologists want to develop minds because of it, human resource managers want to calculate an ROI on it, and training and development officers want to make sure that they can build it. The following article represents a comprehensive literature review from a variety of managerial disciplines. In addition to highlighting the research to date, avenues for future pursuit are also offered.


title: Intellectual capital ROI : a causal map of human capital antecedents and consequents

text: This report describes the results of a ground-breaking research study that measured the antecedents and consequents of effective human capital management. The research sample consisted of 76 senior executives from 25 companies in the financial services industry. The results of the study yielded a holistic causal map that integrated constructs from the fields of intellectual capital, knowledge management, human resources, organizational behaviour, information technology and accounting. The integration of both quantitative and qualitative measures in an overall conceptual model yielded several research implications. The resulting structural equation model allows participating organizations and researchers to gauge the effectiveness of an organization’s human capital capabilities. This will allow practitioners and researchers to more efficiently allocate resources with regard to human capital management. The potential outcomes of the study are limitless, since a program of consistent re-evaluation can lead to the establishment of causal relationships between human capital management and economic and business results. Introduction Today’s knowledge-based world consists of universal dynamic change and massive information bombardment. By the year 2010, the codified information base of the world is expected to `̀ double every 11 hours’’ (Bontis, 1999, p. 435). Information storage capacities continue to expand enormously. In 1950, IBM’s Rama C tape contained 4.4 megabytes and they were able to store as many as 50 of these tapes together. At that time, 220 megabytes represented the frontiers of information storage. Many of today’s standard desktop computers are being sold with 40 gigabytes of hard disk space. It is sobering to remember that full motion video in uncompressed form requires 1 gigabyte per minute and that the 83 minutes of Snow White digitized in full colour amount to 15 terabytes of space. Unfortunately, the conscious mind is only capable of processing somewhere between 16 and 40 bits of information (ones and zeros) per second. How do we reconcile this information bombardment conundrum when it seems that human beings are the bottle-neck? The current issue and full text archive of this journal is available at http://www.emeraldinsight.com/1469-1930.htm The authors would like to acknowledge the following organizations for their financial support: Accenture, Saratoga Institute and the Institute for Intellectual Capital Research. The authors would also like to highlight the contribution of Vanessa Yeh, who administered the data collection phase of this research.


title: Producing sustainable competitive advantage through the effective management of people *

text: Executive Overview Achieving competitive success through people involves fundamentally altering how we think about the workforce and the employment relationship. It means achieving success by working with people, not by replacing them or limiting the scope of their activities. It entails seeing the workforce as a source of strategic advantage, not just as a cost to be minimized or avoided. Firms that take this different perspective are often able to successfully outmaneuver and outperform their rivals. ........................................................................................................................................................................


title: HOW MUCH DOES INDUSTRY MATTER , REALLY ?

text: In this paper, we examine the importance of year, industry, corporate-parent, and businessspecific effects on the profitability of U.S. public corporations within specific 4-digit SIC categories. Our results indicate that year, industry, corporate-parent, and business-specific effects account for 2 percent, 19 percent, 4 percent, and 32 percent, respectively, of the aggregate variance in profitability. We also find that the importance of the effects differs substantially across broad economic sectors. Industry effects account for a smaller portion of profit variance in manufacturing but a larger portion in lodging/entertainment, services, wholesale/retail trade, and transportation. Across all sectors we find a negative covariance between corporate-parent and industry effects. A detailed analysis suggests that industry, corporate-parent, and business-specific effects are related in complex ways.  1997 by John Wiley & Sons, Ltd.


title: From sunlight to phytomass: on the potential efficiency of converting solar radiation to phyto-energy.

text: The relationship between solar radiation capture and potential plant growth is of theoretical and practical importance. The key processes constraining the transduction of solar radiation into phyto-energy (i.e. free energy in phytomass) were reviewed to estimate potential solar-energy-use efficiency. Specifically, the out-put:input stoichiometries of photosynthesis and photorespiration in C(3) and C(4) systems, mobilization and translocation of photosynthate, and biosynthesis of major plant biochemical constituents were evaluated. The maintenance requirement, an area of important uncertainty, was also considered. For a hypothetical C(3) grain crop with a full canopy at 30°C and 350 ppm atmospheric [CO(2) ], theoretically potential efficiencies (based on extant plant metabolic reactions and pathways) were estimated at c. 0.041 J J(-1) incident total solar radiation, and c. 0.092 J J(-1) absorbed photosynthetically active radiation (PAR). At 20°C, the calculated potential efficiencies increased to 0.053 and 0.118 J J(-1) (incident total radiation and absorbed PAR, respectively). Estimates for a hypothetical C(4) cereal were c. 0.051 and c. 0.114 J J(-1), respectively. These values, which cannot be considered as precise, are less than some previous estimates, and the reasons for the differences are considered. Field-based data indicate that exceptional crops may attain a significant fraction of potential efficiency.


title: App Miscategorization Detection: A Case Study on Google Play

text: An ongoing challenge in the rapidly evolving app market ecosystem is to maintain the integrity of app categories. At the time of registration, app developers have to select, what they believe, is the most appropriate category for their apps. Besides the inherent ambiguity of selecting the right category, the approach leaves open the possibility of misuse and potential gaming by the registrant. Periodically, the app store will refine the list of categories available and potentially reassign the apps. However, it has been observed that the mismatch between the description of the app and the category it belongs to, continues to persist. Although some common mechanisms (e.g., a complaint-driven or manual checking) exist, they limit the response time to detect miscategorized apps and still open the challenge on categorization. We introduce FRAC+: (FR)amework for (A)pp (C)ategorization. FRAC+ has the following salient features: (i) it is based on a data-driven topic model and automatically suggests the categories appropriate for the app store, and (ii) it can detect miscategorizated apps. Extensive experiments attest to the performance of FRAC+. Experiments on Google Play shows that FRAC+’s topics are more aligned with Google’s new categories and 0.35-1.10 percent game apps are detected to be miscategorized.


title: Local Edge-Preserving Multiscale Decomposition for High Dynamic Range Image Tone Mapping

text: A novel filter is proposed for edge-preserving decomposition of an image. It is different from previous filters in its locally adaptive property. The filtered image contains local means everywhere and preserves local salient edges. Comparisons are made between our filtered result and the results of three other methods. A detailed analysis is also made on the behavior of the filter. A multiscale decomposition with this filter is proposed for manipulating a high dynamic range image, which has three detail layers and one base layer. The multiscale decomposition with the filter addresses three assumptions: 1) the base layer preserves local means everywhere; 2) every scale's salient edges are relatively large gradients in a local window; and 3) all of the nonzero gradient information belongs to the detail layer. An effective function is also proposed for compressing the detail layers. The reproduced image gives a good visualization. Experimental results on real images demonstrate that our algorithm is especially effective at preserving or enhancing local details.


title: Using neural networks and GIS to forecast land use changes : a Land Transformation Model

text: The Land Transformation Model (LTM), which couples geographic information systems (GIS) with artificial neural networks (ANNs) to forecast land use changes, is presented here. A variety of social, political, and environmental factors contribute to the model’s predictor variables of land use change. This paper presents a version of the LTM parameterized for Michigan’s Grand Traverse Bay Watershed and explores how factors such as roads, highways, residential streets, rivers, Great Lakes coastlines, recreational facilities, inland lakes, agricultural density, and quality of views can influence urbanization patterns in this coastal watershed. ANNs are used to learn the patterns of development in the region and test the predictive capacity of the model, while GIS is used to develop the spatial, predictor drivers and perform spatial analysis on the results. The predictive ability of the model improved at larger scales when assessed using a moving scalable window metric. Finally, the individual contribution of each predictor variable was examined and shown to vary across spatial scales. At the smallest scales, quality views were the strongest predictor variable. We interpreted the multi-scale influences of land use change, illustrating the relative influences of site (e.g. quality of views, residential streets) and situation (e.g. highways and county roads) variables at different scales. # 2002 Elsevier Science Ltd. All rights reserved.


title: Business & IT Alignment in Theory and Practice

text: A key success factor for a successful company in a dynamic environment is effective and efficient information technology (IT) supporting business strategies and processes. In recent surveys however it is concluded that in most companies IT is not aligned with business strategy. The alignment between business needs and IT capabilities is therefore still a prominent area of concern. This paper reports the first stages of a research program exploring the differences of business & IT alignment (BIA) in theory and in practice. The paper presents an overview of the development of theory on BIA and reports the issues with aligning IT to business in practice based on a number of focus-group discussions with CIOs and IT managers. In line with the practical approach to BIA that the CIOs and IT managers in the focus-groups took, the last part of the paper builds upon Luftman's BIA maturity model and reports the application of the model to 12 Dutch firms


title: Blockstack : A New Decentralized Internet

text: The traditional internet has many central points of failure and trust, like (a) the Domain Name System (DNS) servers, (b) public-key infrastructure, and (c) end-user data stored on centralized data stores. We present the design and implementation of a new internet, called Blockstack, where users don’t need to trust remote servers. We remove any trust points from the middle of the network and use blockchains to secure critical data bindings. Blockstack implements services for identity, discovery, and storage and can survive failures of underlying blockchains. The design of Blockstack is informed by three years of experience from a large blockchain-based production system. Blockstack gives comparable performance to traditional internet services and enables a much-needed security and reliability upgrade to the traditional internet.


title: The impact of modified Hatha yoga on chronic low back pain: a pilot study.

text:
PURPOSE
The purpose of this randomized pilot study was to evaluate a possible design for a 6-week modified hatha yoga protocol to study the effects on participants with chronic low back pain.

PARTICIPANTS
Twenty-two participants (M = 4; F = 17), between the ages of 30 and 65, with chronic low back pain (CLBP) were randomized to either an immediate yoga based intervention, or to a control group with no treatment during the observation period but received later yoga training.

METHODS
A specific CLBP yoga protocol designed and modified for this population by a certified yoga instructor was administered for one hour, twice a week for 6 weeks. Primary functional outcome measures included the forward reach (FR) and sit and reach (SR) tests. All participants completed Oswestry Disability Index (ODI) and Beck Depression Inventory (BDI) questionnaires. Guiding questions were used for qualitative data analysis to ascertain how yoga participants perceived the instructor, group dynamics, and the impact of yoga on their life.

ANALYSIS
To account for drop outs, the data were divided into better or not categories, and analyzed using chi-square to examine differences between the groups. Qualitative data were analyzed through frequency of positive responses.

RESULTS
Potentially important trends in the functional measurement scores showed improved balance and flexibility and decreased disability and depression for the yoga group but this pilot was not powered to reach statistical significance. Significant limitations included a high dropout rate in the control group and large baseline differences in the secondary measures. In addition, analysis of the qualitative data revealed the following frequency of responses (1) group intervention motivated the participants and (2) yoga fostered relaxation and new awareness/learning.

CONCLUSION
A modified yoga-based intervention may benefit individuals with CLB, but a larger study is necessary to provide definitive evidence. Also, the impact on depression and disability could be considered as important outcomes for further study. Additional functional outcome measures should be explored. This pilot study supports the need for more research investigating the effect of yoga for this population.


title: An Overview on Evaluating and Predicting Scholarly Article Impact

text: Scholarly article impact reflects the significance of academic output recognised by academic peers, and it often plays a crucial role in assessing the scientific achievements of researchers, teams, institutions and countries. It is also used for addressing various needs in the academic and scientific arena, such as recruitment decisions, promotions, and funding allocations. This article provides a comprehensive review of recent progresses related to article impact assessment and prediction. The review starts by sharing some insight into the article impact research and outlines current research status. Some core methods and recent progress are presented to outline how article impact metrics and prediction have evolved to consider integrating multiple networks. Key techniques, including statistical analysis, machine learning, data mining and network science, are discussed. In particular, we highlight important applications of each technique in article impact research. Subsequently, we discuss the open issues and challenges of article impact research. At the same time, this review points out some important research directions, including article impact evaluation by considering Conflict of Interest, time and location information, various distributions of scholarly entities, and rising stars.


title: "An Odd Kind of Pleasure": Differentiating Emotional Challenge in Digital Games

text: Recent work introduced the notion of emotional challenge as a means to afford more unique and diverse gaming experiences. However, players' experience of emotional challenge has received little empirical attention. It remains unclear whether players enjoy it and what exactly constitutes the challenge thereof. We surveyed 171 players about a challenging or an emotionally challenging experience, and analyzed their responses with regards to what made the experience challenging, their emotional response, and the relation to core player experience constructs. We found that emotional challenge manifested itself in different ways, by confronting players with difficult themes or decisions, as well as having them deal with intense emotions. In contrast to more'conventional' challenge, emotional challenge evoked a wider range of negative emotions and was appreciated significantly more by players. Our findings showcase the appeal of uncomfortable gaming experiences, and extend current conceptualizations of challenge in games.


title: RFID Systems: A Survey on Security Threats and Proposed Solutions

text: Low-cost Radio Frequency Identification (RFID) tags affixed to consumer items as smart labels are emerging as one of the most pervasive computing technology in history. This can have huge security implications. The present article surveys the most important technical security challenges of RFID systems. We first provide a brief summary of the most relevant standards related to this technology. Next, we present an overview about the state of the art on RFID security, addressing both the functional aspects and the security risks and threats associated to its use. Finally, we analyze the main security solutions proposed until date.


title: Broadband Substrate Integrated Waveguide Cavity-Backed Bow-Tie Slot Antenna

text: A novel design technique for broadband substrate integrated waveguide cavity-backed slot antenna is demonstrated in this letter. Instead of using a conventional narrow rectangular slot, a bow-tie-shaped slot is implemented to get broader bandwidth performance. The modification of the slot shape helps to induce strong loading effect in the cavity and generates two closely spaced hybrid modes that help to get a broadband response. The slot antenna incorporates thin cavity backing (height <;0.03λ0 ) in a single substrate and thus retains low-profile planar configuration while showing unidirectional radiation characteristics with moderate gain. A fabricated prototype is also presented that shows a bandwidth of 1.03 GHz (9.4%), a gain of 3.7 dBi over the bandwidth, 15 dB front-to-back ratio, and cross-polarization level below -18 dB.


title: Sparse PCA via Bipartite Matchings

text: We consider the following multi-component sparse PCA problem: given a set of data points, we seek to extract a small number of sparse components with disjoint supports that jointly capture the maximum possible variance. These components can be computed one by one, repeatedly solving the single-component problem and deflating the input data matrix, but as we show this greedy procedure is suboptimal. We present a novel algorithm for sparse PCA that jointly optimizes multiple disjoint components. The extracted features capture variance that lies within a multiplicative factor arbitrarily close to 1 from the optimal. Our algorithm is combinatorial and computes the desired components by solving multiple instances of the bipartite maximum weight matching problem. Its complexity grows as a low order polynomial in the ambient dimension of the input data matrix, but exponentially in its rank. However, it can be effectively applied on a low-dimensional sketch of the data; this allows us to obtain polynomial-time approximation guarantees via spectral bounds. We evaluate our algorithm on real data-sets and empirically demonstrate that in many cases it outperforms existing, deflationbased approaches.


title: Low-power high-speed level shifter design for block-level dynamic voltage scaling environment

text: Two novel level shifters that are suitable for block-level dynamic voltage scaling environment (namely, V/sub DD/-hopping) are proposed. In order to achieve reduction in power consumption and delay, the first proposed level shifter which is called contention mitigated level shifter (CMLS) uses a contention-reduction technique. The simulation results with 65-nm CMOS model show 24% reduction in power and 50% decrease in delay with 4% area increase compared with the conventional level shifter. The second proposed level shifter which is called bypassing enabled level shifter (BELS) implements a bypass function and it is fabricated using 0.35/spl mu/m CMOS technology. The measurement results show that the power and delay of the proposed BELS are reduced by 50% and 65%, respectively with 60% area overhead over the conventional level shifter.


title: An accurate algebraic solution for moving source location using TDOA and FDOA measurements

text: This paper proposes an algebraic solution for the position and velocity of a moving source using the time differences of arrival (TDOAs) and frequency differences of arrival (FDOAs) of a signal received at a number of receivers. The method employs several weighted least-squares minimizations only and does not require initial solution guesses to obtain a location estimate. It does not have the initialization and local convergence problem as in the conventional linear iterative method. The estimated accuracy of the source position and velocity is shown to achieve the Crame/spl acute/r-Rao lower bound for Gaussian TDOA and FDOA noise at moderate noise level before the thresholding effect occurs. Simulations are included to examine the algorithm's performance and compare it with the Taylor-series iterative method.


title: Towards dropout training for convolutional neural networks

text: Recently, dropout has seen increasing use in deep learning. For deep convolutional neural networks, dropout is known to work well in fully-connected layers. However, its effect in convolutional and pooling layers is still not clear. This paper demonstrates that max-pooling dropout is equivalent to randomly picking activation based on a multinomial distribution at training time. In light of this insight, we advocate employing our proposed probabilistic weighted pooling, instead of commonly used max-pooling, to act as model averaging at test time. Empirical evidence validates the superiority of probabilistic weighted pooling. We also empirically show that the effect of convolutional dropout is not trivial, despite the dramatically reduced possibility of over-fitting due to the convolutional architecture. Elaborately designing dropout training simultaneously in max-pooling and fully-connected layers, we achieve state-of-the-art performance on MNIST, and very competitive results on CIFAR-10 and CIFAR-100, relative to other approaches without data augmentation. Finally, we compare max-pooling dropout and stochastic pooling, both of which introduce stochasticity based on multinomial distributions at pooling stage.


title: Techniques and Programs Used in Bidding and Playing phases of Imperfect Information Game – an Overview

text: Bridge is an international imperfect information game played with similar rules all over the world and it is played by millions of players. It is an intelligent game; it increases creativity and knowledge of human mind. Many of the researchers analyses the Bridge bidding and playing phases, and they developed many programs for getting better results. The programs were performing well and it is also matter of time before the computer beats most human bridge players. As such, the researchers mainly focused on the techniques and computer programs which were used in bridge bidding


title: High-Gain Reconfigurable Sectoral Antenna Using an Active Cylindrical FSS Structure

text: A novel design of a high-gain reconfigurable sectoral antenna using an active cylindrical frequency selective surface (FSS) structure is presented. The FSS structure consists of metallic discontinuous strips with PIN diodes in their discontinuities, and it is placed cylindrically around an omnidirectional electromagnetically coupled coaxial dipole (ECCD) array. The cylindrical FSS structure is divided into two semi-cylinders. By controlling the state of diodes in each semi-cylinder, a directive radiation pattern is obtained that can be swept in the entire azimuth plane. The effect of the diode-state configuration and the radius of the cylindrical structure are carefully studied to obtain an optimum sectoral radiation pattern. In addition, a solution for increasing the matching bandwidth of the antenna is also proposed. An experimental prototype was fabricated, and the measured results show a beamwidth of 20° in elevation and 70° in the azimuth plane at 2.1 GHz with a gain of 13 dBi. With these features, the proposed antenna is suitable for base-station applications in wireless communication systems.


title: Methodology for customer relationship management

text: Customer relationship management (CRM) is a customer-focused business strategy that dynamically integrates sales, marketing and customer care service in order to create and add value for the company and its customers. This change towards a customer-focused strategy is leading to a strong demand for CRM solutions by companies. However, in spite of companies interest in this new management model, many CRM implementations fail. One of the main reasons for this lack of success is that the existing methodologies being used to approach a CRM project are not adequate, since they do not satisfactorily integrate and complement the strategic and technological aspects of CRM. This paper describes a formal methodology for directing the process of developing and implementing a CRM System that considers and integrates various aspects, such as defining a customer strategy, re-engineering customer-oriented business processes, human resources management, the computer system, management of change and continuous improvement. 2005 Elsevier Inc. All rights reserved.


title: Music Education and Its Impact on Students with Special Needs

text: Part of the Curriculum and Instruction Commons, Curriculum and Social Inquiry Commons, Disability and Equity in Education Commons, Educational Methods Commons, Elementary Education and Teaching Commons, Higher Education and Teaching Commons, Junior High, Intermediate, Middle School Education and Teaching Commons, Other Educational Administration and Supervision Commons, Other Teacher Education and Professional Development Commons, Pre-Elementary, Early Childhood, Kindergarten Teacher Education Commons, Scholarship of Teaching and Learning Commons, and the Special Education Administration Commons Survey: Let us know how this paper benefits you.


title: Composite Scale of Morningness: psychometric properties, validity with Munich ChronoType Questionnaire and age/sex differences in Poland.

text: The present study aimed at testing psychometric properties of the Composite Scale of Morningness (CSM) and validating it with mid sleep on free days (MSF) derived from the Munich ChronoType Questionnaire (MCTQ) in Poland, along with analyzing age and sex differences in the CSM and MSF. A sample of 952 Polish residents (62.6% females) aged between 13 and 46 was tested. Additionally, a sample of 33 university students were given MCTQ and filled in a sleep diary for 8 days. MSF derived from MCTQ was related to the one from sleep diary (r=.44). The study revealed good reliability of the CSM (α=.84) and its validity: greater morningness preference was associated with earlier MSF from MCTQ (r=-.52). CSM scores were distributed over its full range, with a mean of 34, and did not differ between sexes, although females were earlier than males by 23minutes in MSF. Regarding age, eveningness estimated with both CSM and MSF was greatest in subjects aged 16-18years, and a shift toward eveningness during puberty and a shift back toward morningness in older age was observed. The Polish version of the CSM consisted of two components of morningness. Cutoff scores were: for evening types (lower 10%) 24 or less, for morning types (upper 10%) 43 or more. The Polish CSM presents good psychometric properties, which are similar to those reported in other language versions, and also presents sex/age patterns similar to those found previously.


title: Analysis of different PM machines with concentrated windings and flux barriers in stator core

text: The new stator structure with magnetic flux-barriers in the stator yoke or tooth region represents an efficient method for reducing the sub-harmonics of electric machines with fractional slots, tooth-concentrated windings. In this paper the both flux-barriers techniques are considered during the analysis of different PM machines. The 12-teeth single-layer and double-layer concentrated winding in combination with a 10-poles and 14-poles PM rotor are investigated. For the all machine topologies the new stator design is used to improve their performances and characteristics. The flux-barrier effects on the main machine parameters, such as in the air-gap flux density harmonics, dq-machine parameters, characteristic currents, electromagnetic torque, and so on, are studied carefully. Comparisons performed with the analogous conventional machines (with conventional stator) show that, the new stator design offers significant advantages.


title: Solving the Multiple Instance Problem with Axis-Parallel Rectangles

text: The multiple instance problem arises in tasks where the training examples are ambiguous: a single example object may have many alternative feature vectors (instances) that describe it, and yet only one of those feature vectors may be responsible for the observed classification of the object. This paper describes and compares three kinds of algorithms that learn axis-parallel rectangles to solve the multiple instance problem. Algorithms that ignore the multiple instance problem perform very poorly. An algorithm that directly confronts the multiple instance problem (by attempting to identify which feature vectors are responsible for the observed classifications) performs best, giving 89% correct predictions on a musk odor prediction task. The paper also illustrates the use of artificial data to debug and compare these algorithms.


title: A survey of cost-sensitive decision tree induction algorithms

text: The past decade has seen a significant interest on the problem of inducing decision trees that take account of costs of misclassification and costs of acquiring the features used for decision making. This survey identifies over 50 algorithms including approaches that are direct adaptations of accuracy-based methods, use genetic algorithms, use anytime methods and utilize boosting and bagging. The survey brings together these different studies and novel approaches to cost-sensitive decision tree learning, provides a useful taxonomy, a historical timeline of how the field has developed and should provide a useful reference point for future research in this field.


title: Tree-structured survival analysis.

text: In this note, tree-structured recursive partitioning schemes for classification, probability class estimation, and regression are adapted to cover censored survival analysis. The only assumptions required are those which guarantee identifiability of conditional distributions of lifetime given covariates. Thus, the techniques are applicable to more general situations than are those of the famous semi-parametric model of Cox.


title: A Brief Introduction to Boosting

text: Boosting is a general method for improving the accuracy of any given learning algorithm. This short paper introduces the boosting algorithm AdaBoost, and explains the underlying theory of boosting, including an explanation of why boosting often does not suffer from overfitting. Some examples of recent applications of boosting are also described.


title: Tree-Based Batch Mode Reinforcement Learning

text: Reinforcement learning aims to determine an optimal contro l policy from interaction with a system or from observations gathered from a system. In batch mode, i t can be achieved by approximating the so-calledQ-function based on a set of four-tuples (xt ,ut , rt ,xt+1) wherext denotes the system state at timet, ut the control action taken, rt the instantaneous reward obtained and xt+1 the successor state of the system, and by determining the contro l policy from this Q-function. The Q-function approximation may be obtained from the limit of a s equence of (batch mode) supervised learning problems. Within this framework we describe the use of several classical tree-based supervised learning methods (CART, Kd-tree, tree bagging) a d two newly proposed ensemble algorithms, namelyextremelyandtotally randomized trees. We study their performances on several examples and find that the ensemble methods based on regressi on trees perform well in extracting relevant information about the optimal control policy from sets of four-tuples. In particular, the totally randomized trees give good results while ensuring the convergence of the sequence, whereas by relaxing the convergence constraint even better accurac y results are provided by the extremely randomized trees.


title: Visual exploration and evaluation of climate-related simulation data

text: Large, heterogeneous volumes of simulation data are calculated and stored in many disciplines, e.g. in climate and climate impact research. To gain insight, current climate analysis applies statistical methods and model sensitivity analyzes in combination with standard visualization techniques. However, there are some obstacles for researchers in applying the full functionality of sophisticated visualization, exploiting the available interaction and visualization functionality in order to go beyond data presentation tasks. In particular, there is a gap between available and actually applied multi-variate visualization techniques. Furthermore, visual data comparison of simulation (and measured) data is still a challenging task. Consequently, this paper introduces a library of visualization techniques, tailored to support exploration and evaluation of climate simulation data. These techniques are integrated into the easy-to-use visualization framework SimEnvVis - designed as a front-end user interface to a simulation environment - which provides a high level of user support generating visual representations.


title: Meta-Path-Based Ranking with Pseudo Relevance Feedback on Heterogeneous Graph for Citation Recommendation

text: The sheer volume of scholarly publications available online significantly challenges how scholars retrieve the new information available and locate the candidate reference papers. While classical text retrieval and pseudo relevance feedback (PRF) algorithms can assist scholars in accessing needed publications, in this study, we propose an innovative publication ranking method with PRF by leveraging a number of meta-paths on the heterogeneous bibliographic graph. Different meta-paths on the graph address different ranking hypotheses, whereas the pseudo-relevant papers (from the retrieval results) are used as the seed nodes on the graph. Meanwhile, unlike prior studies, we propose "restricted meta-path" facilitated by a new context-rich heterogeneous network extracted from full-text publication content along with citation context. By using learning-to-rank, we integrate 18 different meta-path-based ranking features to derive the final ranking scores for candidate cited papers. Experimental results with ACM full-text corpus show that meta-path-based ranking with PRF on the new graph significantly (p < 0.0001) outperforms text retrieval algorithms with text-based or PageRank-based PRF.


title: Convex Formulations for Fair Principal Component Analysis

text: Though there is a growing literature on fairness for supervised learning, incorporating fairness into unsupervised learning has been less well-studied. This paper studies fairness in the context of principal component analysis (PCA). We first define fairness for dimensionality reduction, and our definition can be interpreted as saying a reduction is fair if information about a protected class (e.g., race or gender) cannot be inferred from the dimensionality-reduced data points. Next, we develop convex optimization formulations that can improve the fairness (with respect to our definition) of PCA and kernel PCA. These formulations are semidefinite programs, and we demonstrate their effectiveness using several datasets. We conclude by showing how our approach can be used to perform a fair (with respect to age) clustering of health data that may be used to set health insurance rates.


title: Feature-Rich Memory-Based Classification for Shallow NLP and Information Extraction

text: Memory-Based Learning (MBL) is based on the storage of all available training data, and similarity-based reasoning for handling new cases. By interpreting tasks such as POS tagging and shallow parsing as classification tasks, the advantages of MBL (implicit smoothing of sparse data, automatic integration and relevance weighting of information sources, handling exceptional data) contribute to state-of-the-art accuracy. However, Hidden Markov Models (HMM) typically achieve higher accuracy than MBL (and other Machine Learning approaches) for tasks such as POS tagging and chunking. In this paper, we investigate how the advantages of MBL, such as its potential to integrate various sources of information, come to play when we compare our approach to HMMs on two Information Extraction (IE) datasets: the well-known Seminar Announcement data set and a new German Curriculum Vitae data set. 1 Memory-Based Language Processing Memory-Based Learning (MBL) is a supervised classification-based learning method. A vector of feature values (an instance) is associated with a class by a classifier that lazily extrapolates from the most similar set (nearest neighbors) selected from all stored training examples. This is in contrast to eager learning methods like decision tree learning [26], rule induction [9], or Inductive Logic Programming [7], which abstract a generalized structure from the training set beforehand (forgetting the examples themselves), and use that to derive a classification for a new instance. In MBL, a distance metric on the feature space defines what are the nearest neighbors of an instance. Metrics with feature weights based on information-theory or other relevance statistics allow us to use rich representations of instances and their context, and to balance the influences of diverse information sources in computing distance. Natural Language Processing (NLP) tasks typically concern the mapping of an input representation (e.g., a series of words) into an output representation (e.g., the POS tags corresponding to each word in the input). Most NLP tasks can therefore easily be interpreted as sequences of classification 34 Zavrel, Daelemans tasks: e.g., given a word and some representation of its context, decide what tag to assign to each word in its context. By creating a separate classification instance (a “moving window” approach) for each word and its context, shallow syntactic or semantic structures can be produced for whole sentences or texts. In this paper, we argue that more semantic and complex input-output mappings, such as Information Extraction, can also effectively be modeled by such a Memory-based classification-oriented framework, and that this approach has a number of very interesting advantages over rivalling methods, most notably that each classification decision can be made dependent on a very rich and diverse set of features. The properties of MBL as a lazy, similarity-based learning method seem make a good fit to the properties of typical disambiguation problems in NLP: • Similar input representations lead to similar output. E.g., words occurring in a similar context in general have the same POS tag. Similarity-based reasoning is the core of MBL. • Many sub-generalizations and exceptions. By keeping in memory all training instances, exceptions included, an MBL approach can capture generalization from exceptional or low-frequency cases according to [12]. • Need for integration of diverse types of information. E.g., in Information Extraction, lexical features, spelling features, syntactic as well as phrasal context features, global text structure, and layout features can potentially be very relevant. • Automatic smoothing in very rich event spaces. Supervised learning of NLP tasks regularly runs into problems of sparse data; not enough training data is available to extract reliable parameters for complex models. MBL incorporates an implicit robust form of smoothing by similarity [33]. In the remainder of this Section, we will show how a memory-, similarity-, and classification-based approach can be applied to shallow syntactic parsing, and can lead to state-of-the-art accuracy. Most of the tasks discussed here can also easily be modeled using Hidden Markov Models (HMM), and often with surprising accuracy. We will discuss the strengths of the HMMs and draw a comparison between the classification-based MBL method and the sequence-optimizing HMM approach (Section 1.2). 1.1 Memory-Based Shallow Parsing Shallow parsing is an important component of most text analysis systems in Text Mining applications such as information extraction, summary generation, and question answering. It includes discovering the main constituents of sentences (NPs, VPs, PPs) and their heads, and determining syntactic relationships like subject, object, adjunct relations between verbs and heads of other constituents. This is an important first step to understanding the who, what, when, and where of sentences in a text. Feature-Rich Memory-Based Classification for Information Extraction 35 In our approach to memory-based shallow parsing, we carve up the syntactic analysis process into a number of classification tasks with input vectors representing a focus item and a dynamically selected surrounding context. These classification tasks can be segmentation tasks (e.g., decide whether a focus word or tag is the start or end of an NP) or disambiguation tasks (e.g., decide whether a chunk is the subject NP, the object NP or neither). Output of some memory-based modules is used as input by other memory-based modules (e.g., a tagger feeds a chunker and the latter feeds a syntactic relation assignment module). Similar ideas about cascading of processing steps have also been explored in other approaches to text analysis: e.g., finite state partial parsing [1,18], statistical decision tree parsing [23], and maximum entropy parsing [30]. The approach briefly described here is explained and evaluated in more detail in [10,11,6] . Chunking The phrase chunking task can be defined as a classification task by generalizing the approach of [28], who proposed to convert NP-chunking to tagging each word with I for a word inside an NP, O for outside an NP, and B for between two NPs). The decision on these so called IOB tags for a word can be made by looking at the Part-of-Speech tag and the identity of the focus word and its local context. For the more general task of chunking other non-recursive phrases, we simply extend the tag set with IOB tags for each type of phrase. To illustrate this encoding with the extended IOB tag set, we can tag the sentence: But/CC [NP the/DT dollar/NN NP] [ADVP later/RB ADVP] [VP rebounded/VBD VP] ,/, [VP finishing/VBG VP] [ADJP slightly/RB higher/R ADJP] [Prep against/IN Prep] [NP the/DT yen/NNS NP] [ADJP although/IN ADJP] [ADJP slightly/RB lower/JJR ADJP] [Prep against/IN Prep] [NP the/DT mark/NN NP] ./. as: But/CCO the/DTI−NP dollar/NNI−NP later/RBI−ADV P rebounded/VBDI−V P ,/,O finishing/VBGI−V P slightly/RBI−ADV P higher/RBRI−ADV P against/INI−Prep the/DTI−NP yen/NNSI−NP although/INI−ADJP slightly/RBB−ADJP lower/JJRI−ADJP against/INI−Prep the/DTI−NP mark/NNI−NP ./.O Table 1 (from [6]) shows the accuracy of this memory-based chunking approach when training and testing on Wall Street Journal material. We report Precision, Recall, and Fβ=1 scores, a weighted harmonic mean of Recall and Precision (Fβ = (β+1)∗P∗R β2∗P+R) ). 1 An online demonstration of the Memory-Based Shallow Parser can be found at http://ilk.kub.nl . 36 Zavrel, Daelemans type precision recall Fβ=1 NPchunks 92.5 92.2 92.3 VPchunks 91.9 91.7 91.8 ADJPchunks 68.4 65.0 66.7 ADVPchunks 78.0 77.9 77.9 Prepchunks 95.5 96.7 96.1 PPchunks 91.9 92.2 92.0 ADVFUNCs 78.0 69.5 73.5 Table 1. Results of chunking–labeling experiments. Reproduced from [6]. Grammatical Relation Finding After POS tagging, phrase chunking and labeling, the last step of the shallow parsing consists of resolving the (types of) attachment between labeled phrases. This is done by using a classifier to assign a grammatical relation (GR) between pairs of words in a sentence. In our approach, one of these words is always a verb, since this yields the most important GRs. The other word (focus) is the head of the phrase which is annotated with this grammatical relation in the treebank (e.g., a noun as head of an NP). An instance for such a pair of words is constructed by extracting a set of feature values from the sentence. The instance contains information about the verb and the focus: a feature for the word form and a feature for the POS of both. It also has similar features for the local context of the focus. Experiments on the training data suggest an optimal context width of two words to the left and one to the right. In addition to the lexical and the local context information, superficial information about clause structure was included: the distance from the verb to the focus, counted in words. A negative distance means that the focus is to the left of the verb. Other features contain the number of other verbs between the verb and the focus, and the number of intervening commas. These features were chosen by manual “feature engineering”. Table 2 shows some of the feature-value instances corresponding to the following sentence (POS tags after the slash, chunks denoted with square and curly brackets, and adverbial functions after the dash): [ADVP Not /RB surprisingly /RB ADVP] ,/, [NP Peter /NNP Miller /NNP NP] ,/, [NP who /WP NP] [VP organized /VBD VP] [NP the /DT conference /NN NP] {PP-LOC [Prep in /IN Prep] [NP New /NNP York /NNP NP] PP-LOC} ,/, [VP does /VBZ not /RB want /VB to /TO come /VB VP] {PP-DIR [Prep to /IN Prep] [NP Paris /NNP NP] PP-DIR} [Prep without /IN Prep] [VP bringing /VBG VP] [NP his /PRP$ wife /NN NP]. Table 3 shows the results of the experiments. In the first row, only POS tag features are used. Other rows show the results when adding several types of chunk informati


title: An "AI readability" Formula for French as a Foreign Language

text: This paper present a new readability formula for French as a foreign language (FFL), which relies on 46 textual features representative of the lexical, syntactic, and semantic levels as well as some of the specificities of the FFL context. We report comparisons between several techniques for feature selection and various learning algorithms. Our best model, based on support vector machines (SVM), significantly outperforms previous FFL formulas. We also found that semantic features behave poorly in our case, in contrast with some previous readability studies on English as a first language.


title: Effect of Training Class Label Noise on Classification Performances for Land Cover Mapping with Satellite Image Time Series

text: Supervised classification systems used for land cover mapping require accurate reference databases. These reference data come generally from different sources such as field measurements, thematic maps, or aerial photographs. Due to misregistration, update delay, or land cover complexity, they may contain class label noise, i.e., a wrong label assignment. This study aims at evaluating the impact of mislabeled training data on classification performances for land cover mapping. Particularly, it addresses the random and systematic label noise problem for the classification of high resolution satellite image time series. Experiments are carried out on synthetic and real datasets with two traditional classifiers: Support Vector Machines (SVM) and Random Forests (RF). A synthetic dataset has been designed for this study, simulating vegetation profiles over one year. The real dataset is composed of Landsat-8 and SPOT-4 images acquired during one year in the south of France. The results show that both classifiers are little influenced for low random noise levels up to 25%–30%, but their performances drop down for higher noise levels. Different classification configurations are tested by increasing the number of classes, using different input feature vectors, and changing the number of training instances. Algorithm complexities are also analyzed. The RF classifier achieves high robustness to random and systematic label noise for all the tested configurations; whereas the SVM classifier is more sensitive to the kernel choice and to the input feature vectors. Finally, this work reveals that the cross-validation procedure is impacted by the presence of class label noise.


title: Apple disease classification using color, texture and shape features from images

text: The presence of diseases in several kinds of fruits is the major factor of production and the economic degradation of the agricultural industry worldwide. An approach for the apple disease classification using color, texture and shape based features is investigated and experimentally verified in this paper. The primary steps of the introduced image processing based method is as follows; 1) infected fruit part detection is done with the help of K-Means clustering method, 2) color, texture and shape based features are computed over the segmented image and combined to form the single descriptor, and 3) multi-class Support Vector Machine is used to classify the apples into one of the infected or healthy categories. Apple fruit is taken as the test case in this study with 3 categories of diseases, namely blotch, rot and scab as well as healthy apples. The experimentation points out that the introduced method based is better as compared to the individual features. It also point out that shape feature is not better suited for this purpose.


title: Beyond Triplet Loss: A Deep Quadruplet Network for Person Re-identification

text: Person re-identification (ReID) is an important task in wide area video surveillance which focuses on identifying people across different cameras. Recently, deep learning networks with a triplet loss become a common framework for person ReID. However, the triplet loss pays main attentions on obtaining correct orders on the training set. It still suffers from a weaker generalization capability from the training set to the testing set, thus resulting in inferior performance. In this paper, we design a quadruplet loss, which can lead to the model output with a larger inter-class variation and a smaller intra-class variation compared to the triplet loss. As a result, our model has a better generalization ability and can achieve a higher performance on the testing set. In particular, a quadruplet deep network using a margin-based online hard negative mining is proposed based on the quadruplet loss for the person ReID. In extensive experiments, the proposed network outperforms most of the state-of-the-art algorithms on representative datasets which clearly demonstrates the effectiveness of our proposed method.


title: Exploring the musical taste of expert listeners: musicology students reveal tendency toward omnivorous taste

text: Musicology students are engaged with music on an academic level and usually have an extensive musical background. They have a considerable knowledge of music history and theory and listening to music may be regarded as one of their primary occupations. Taken together, these factors qualify them as ≫expert listeners≪, who may be expected to exhibit a specific profile of musical taste: interest in a broad range of musical styles combined with a greater appreciation of ≫sophisticated≪ styles. The current study examined the musical taste of musicology students as compared to a control student group. Participants (n = 1003) completed an online survey regarding the frequency with which they listened to 22 musical styles. A factor analysis revealed six underlying dimensions of musical taste. A hierarchical cluster analysis then grouped all participants, regardless of their status, according to their similarity on these dimensions. The employed exploratory approach was expected to reveal potential differences between musicology students and controls. A three-cluster solution was obtained. Comparisons of the clusters in terms of musical taste revealed differences in the listening frequency and variety of appreciated music styles: the first cluster (51% musicology students/27% controls) showed the greatest musical engagement across all dimensions although with a tendency toward ≫sophisticated≪ musical styles. The second cluster (36% musicology students/46% controls) exhibited an interest in ≫conventional≪ music, while the third cluster (13% musicology students/27% controls) showed a strong liking of rock music. The results provide some support for the notion of specific tendencies in the musical taste of musicology students and the contribution of familiarity and knowledge toward musical omnivorousness. Further differences between the clusters in terms of social, personality, and sociodemographic factors are discussed.


title: Markov Logic Networks for Optical Chemical Structure Recognition

text: Optical chemical structure recognition is the problem of converting a bitmap image containing a chemical structure formula into a standard structured representation of the molecule. We introduce a novel approach to this problem based on the pipelined integration of pattern recognition techniques with probabilistic knowledge representation and reasoning. Basic entities and relations (such as textual elements, points, lines, etc.) are first extracted by a low-level processing module. A probabilistic reasoning engine based on Markov logic, embodying chemical and graphical knowledge, is subsequently used to refine these pieces of information. An annotated connection table of atoms and bonds is finally assembled and converted into a standard chemical exchange format. We report a successful evaluation on two large image data sets, showing that the method compares favorably with the current state-of-the-art, especially on degraded low-resolution images. The system is available as a web server at http://mlocsr.dinfo.unifi.it.


title: An LTE Base-Station Magnetoelectric Dipole Antenna with Anti-Interference Characteristics and Its MIMO System Application

text: In this research, a wideband long-term evolution (LTE) base-station antenna with flexible band-notch characteristics is introduced by two window-type slots etched on radiation patches of the magnetoelectric dipole. The presented antenna can provide a band-notch range with an impedance bandwidth of 2.39-2.52 GHz in the band of 1.825-2.925 GHz, which well applies the wideband antenna for LTE with the good anti-interference from wireless local area network narrow communication band. The typical parameters are investigated to show that the antenna with anti-interference characteristics exhibits large scope band-notch adjustability in the whole wideband region. Based on above element, an LTE base station antenna multiple-input, multiple-output system with anti-interference characteristics is investigated and measured to show its good performances: anti-interference, high isolation, high gain, unidirectional radiation, low back radiation, and low cross-polarization.


title: An AUC-based permutation variable importance measure for random forests

text: The random forest (RF) method is a commonly used tool for classification with high dimensional data as well as for ranking candidate predictors based on the so-called random forest variable importance measures (VIMs). However the classification performance of RF is known to be suboptimal in case of strongly unbalanced data, i.e. data where response class sizes differ considerably. Suggestions were made to obtain better classification performance based either on sampling procedures or on cost sensitivity analyses. However to our knowledge the performance of the VIMs has not yet been examined in the case of unbalanced response classes. In this paper we explore the performance of the permutation VIM for unbalanced data settings and introduce an alternative permutation VIM based on the area under the curve (AUC) that is expected to be more robust towards class imbalance. We investigated the performance of the standard permutation VIM and of our novel AUC-based permutation VIM for different class imbalance levels using simulated data and real data. The results suggest that the new AUC-based permutation VIM outperforms the standard permutation VIM for unbalanced data settings while both permutation VIMs have equal performance for balanced data settings. The standard permutation VIM loses its ability to discriminate between associated predictors and predictors not associated with the response for increasing class imbalance. It is outperformed by our new AUC-based permutation VIM for unbalanced data settings, while the performance of both VIMs is very similar in the case of balanced classes. The new AUC-based VIM is implemented in the R package party for the unbiased RF variant based on conditional inference trees. The codes implementing our study are available from the companion website: http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/070_drittmittel/janitza/index.html


title: SiCloPe: Silhouette-Based Clothed People

text: We introduce a new silhouette-based representation for modeling clothed human bodies using deep generative models. Our method can reconstruct a complete and textured 3D model of a person wearing clothes from a single input picture. Inspired by the visual hull algorithm, our implicit representation uses 2D silhouettes and 3D joints of a body pose to describe the immense shape complexity and variations of clothed people. Given a segmented 2D silhouette of a person and its inferred 3D joints from the input picture, we first synthesize consistent silhouettes from novel view points around the subject. The synthesized silhouettes, which are the most consistent with the input segmentation are fed into a deep visual hull algorithm for robust 3D shape prediction. We then infer the texture of the subject’s back view using the frontal image and segmentation mask as input to a conditional generative adversarial network. Our experiments demonstrate that our silhouette-based model is an effective representation and the appearance of the back view can be predicted reliably using an image-to-image translation network. While classic methods based on parametric models often fail for single-view images of subjects with challenging clothing, our approach can still produce successful results, which are comparable to those obtained from multi-view input.


title: Celeb-500K: A Large Training Dataset for Face Recognition

text: In this paper, we propose a large training dataset named Celeb-500K for face recognition, which contains 50M images from 500K persons. To better facilitate academic research, we clean Celeb-500K to obtain Celeb-500K-2R, which contains 25M aligned face images from 365K persons. Based on the developed dataset, we achieve state-of-the-art face recognition performance and reveal two important observations on face recognition study. First, metric learning methods have limited performance gain when the training dataset contains a large number of identities. Second, in order to develop an efficient training dataset, the number of identities is more important than the average image number of each identity from the perspective of face recognition performance. Extensive experimental results show the superiority of Celeb-500K and provide a strong support to the two observations.


title: Random Projection and Hashing

text: Random Projection is another class of methods used for low-rank matrix approximation. A random projection algorithm projects datapoints from a high-dimensional space R n onto a lower-dimensional subspace R r (r ⌧ n) using a random matrix S 2 R r⇥n. The key idea of random mapping comes from the Johnson-Lindenstrauss lemma[7] (we will explain later in detail) that says " if points in a vector space are projected onto a randomly selected subspace of suitably high dimension, then the distances between points are approximately preserved ". Random projection methods are computationally efficient and sufficiently accurate in practice for dimen-sionality reduction of high-dimensional datasets. Moreover, since complexity of many geometric algorithms depends significantly on the dimension, applying random projection as pre-processing is a common task in many data mining applications. As opposed to column sampling methods, that need to access data for approximating the low-rank subspace, random projections are data oblivious, as their computation involves only a random matrix S. We start with the basic concepts and definitions required to understand random projections techniques. Definition 1 (Column Space). Consider a matrix A 2 R n⇥d (n > d). Notice that as one ranges over all vectors x 2 R d , Ax ranges over all linear combinations of the columns of A and therefore defines a d-dimensional subspace of R n , which we refer to as the column space of A and denote it by C(A). Definition 2 (` 2-Subspace Embedding). A matrix S 2 R r⇥n provides a subspace embedding for C(A) if kSAxk 2 2 = (1 ± ")kAxk 2 2 , 8x 2 R d. Such matrix S provides a low distortion embedding, and is called a (1 ± ") ` 2-subspace embedding. Using an`2-subspace embedding, one can work with SA 2 R r⇥d instead of A 2 R n⇥d. Typically r ⌧ n, so we are working with a smaller matrix that reduces the time/space complexity of many algorithms. However note that definitely r needs to be larger than d if we are talking about the whole subspace R d [11]. Note that subspace embedding does not depend on a particular basis for C(A), that means if we have a matrix U that is an orthonormal basis for C(A), then Ux gives the same subspace as Ax. Therefore if S is an embedding for A, it will be an embedding for U too. Let's consider …


title: Exploring the relationship between technology acceptance model and usability test

text: In the past, most studies used the technology acceptance model (TAM) to survey the subjective perception of users in using information technology. The usability test was also used to assess the ease of use of user interfaces. This study introduces a conceptual framework to explore the relationship between user’s beliefs of TAM and usability testing attributes. Usability testing was conducted on an eCampus learning system with a mobile device. TAM data was collected from the participants for analyzing a possible relationship. The findings of this study reveal that TAM results contradict the usability test results in certain areas. The focus of our proposed research model is supported from the causality between perceived ease of use and usability; however, the correlation between perceived usefulness and usability remains unclear.


title: Non-invasive optical detection of hand gestures

text: In this paper we present a novel type of sensing technology for hand and finger gesture recognition that utilizes light in the invisible spectrum to detect changes in position and form of body tissue like tendons and muscles. The proposed system can be easily integrated with existing wearable devices. Our approach not only enables gesture recognition but it could potentially double to perform a variety of health related monitoring tasks (e.g. heart rate, stress).


title: Micro bubble manipulation towards single cell handling tool

text: We report a series of micro air bubble manipulations (transporting, merging and eliminating of air bubbles) in two-dimensional microchannels filled with a water solution. Air bubbles (~500 nano liters in volume) are driven by electrowetting-on-dielectric (EWOD) principle. By sequentially energizing an array of electrodes covered with dielectric layers, air bubbles can be transported along a programmed path, merged into a single larger one, and eliminated out of a bulk water solution. This bubble manipulation technique can be applied to efficiently handle micro objects. As a proof of concept, we successfully demonstrate in a millimeter scale that a fish egg and a sesame husk are attached to an air bubble and then driven by the bubble in a programmed way. The moving air bubble can push and pull the attached objects of which surface is hydrophilic or hydrophobic. This handling technique will bring a novel, versatile, cost-effective, high throughput tool, enabling us to efficiently transport, trap, and isolate individual micro bio-entities such as cells, functional particles and possibly molecules


title: Learning and Collective Knowledge Construction With Social Media: A Process-Oriented Perspective

text: Social media are increasingly being used for educational purposes. The first part of this article briefly reviews literature that reports on educational applications of social media tools. The second part discusses theories that may provide a basis for analyzing the processes that are relevant for individual learning and collective knowledge construction. We argue that a systems-theoretical constructivist approach is appropriate to examine the processes of educational social media use, namely, self-organization, the internalization of information, the externalization of knowledge, and the interplay of externalization and internalization providing the basis of a co-evolution of cognitive and social systems. In the third part we present research findings that illustrate and support this systems-theoretical framework. Concluding, we discuss the implications for educational design and for future research on learning and collective knowledge construction with social media.


title: Low Drop-Out Voltage Regulators: Capacitor-less Architecture Comparison

text: Demand for system-on-chip solutions has increased the interest in low drop-out (LDO) voltage regulators which do not require a bulky off-chip capacitor to achieve stability, also called capacitor-less LDO (CL-LDO) regulators. Several architectures have been proposed; however comparing these reported architectures proves difficult, as each has a distinct process technology and specifications. This paper compares CL-LDOs in a unified matter. We designed, fabricated, and tested five illustrative CL-LDO regulator topologies under common design conditions using 0.6?m CMOS technology. We compare the architectures in terms of (1) line/load regulation, (2) power supply rejection, (3) line/load transient, (4) total on-chip compensation capacitance, (5) noise, and (6) quiescent power consumption. Insights on what optimal topology to choose to meet particular LDO specifications are provided.


title: Towards Cognitive Exploration through Deep Reinforcement Learning for Mobile Robots

text: Exploration in an unknown environment is the core functionality for mobile robots. Learning-based exploration methods, including convolutional neural networks, provide excellent strategies without human-designed logic for the feature extraction [1]. But the conventional supervised learning algorithms cost lots of efforts on the labeling work of datasets inevitably. Scenes not included in the training set are mostly unrecognized either. We propose a deep reinforcement learning method for the exploration of mobile robots in an indoor environment with the depth information from an RGB-D sensor only. Based on the Deep Q-Network framework [2], the raw depth image is taken as the only input to estimate the Q values corresponding to all moving commands. The training of the network weights is end-to-end. In arbitrarily constructed simulation environments, we show that the robot can be quickly adapted to unfamiliar scenes without any man-made labeling. Besides, through analysis of receptive fields of feature representations, deep reinforcement learning motivates the convolutional networks to estimate the traversability of the scenes. The test results are compared with the exploration strategies separately based on deep learning [1] or reinforcement learning [3]. Even trained only in the simulated environment, experimental results in real-world environment demonstrate that the cognitive ability of robot controller is dramatically improved compared with the supervised method. We believe it is the first time that raw sensor information is used to build cognitive exploration strategy for mobile robots through end-to-end deep reinforcement learning.


title: Intrusion-Tolerant Autonomous Driving

text: Fully autonomous driving is one if not the killer application for the upcoming decade of real-time systems. However, in the presence of increasingly sophisticated attacks by highly skilled and well equipped adversarial teams, autonomous driving must not only guarantee timeliness and hence safety. It must also consider the dependability of the software concerning these properties while the system is facing attacks. For distributed systems, fault-and-intrusion tolerance toolboxes already offer a few solutions to tolerate partial compromise of the system behind a majority of healthy components operating in consensus. In this paper, we present a concept of an intrusion-tolerant architecture for autonomous driving. In such a scenario, predictability and recovery challenges arise from the inclusion of increasingly more complex software on increasingly less predictable hardware. We highlight how an intrusion tolerant design can help solve these issues by allowing timeliness to emerge from a majority of complex components being fast enough, often enough while preserving safety under attack through pre-computed fail safes.


title: Modern FMCW radar - techniques and applications

text: The range of practical applications for FMCW radars has increased significantly in the last decade or so. This paper renews some of the recent applications and also some of the techniques which are now being applied to improve the versatility and performance of FMCW radars. Significant system developments include the use of moving-target detection techniques, the development of the automotive radar market, and the building of prototypes of active phased arrays using this modulation. Significant technical developments include improved ways of generating the sweep signal, using direct digital synthesis, or quieter oscillators and an improved understanding of the significance of the term 'Low Probability of Intercept.'.


title: A Literature Review on the Design of Spherical Rolling Robots

text: There are many advantages to the use of spherical robot designs. The current status of the design of spherical rolling robots is reviewed.


title: SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning

text: Visual attention has been successfully applied in structural prediction tasks such as visual captioning and question answering. Existing visual attention models are generally spatial, i.e., the attention is modeled as spatial probabilities that re-weight the last conv-layer feature map of a CNN encoding an input image. However, we argue that such spatial attention does not necessarily conform to the attention mechanism — a dynamic feature extractor that combines contextual fixations over time, as CNN features are naturally spatial, channel-wise and multi-layer. In this paper, we introduce a novel convolutional neural network dubbed SCA-CNN that incorporates Spatial and Channel-wise Attentions in a CNN. In the task of image captioning, SCA-CNN dynamically modulates the sentence generation context in multi-layer feature maps, encoding where (i.e., attentive spatial locations at multiple layers) and what (i.e., attentive channels) the visual attention is. We evaluate the proposed SCA-CNN architecture on three benchmark image captioning datasets: Flickr8K, Flickr30K, and MSCOCO. It is consistently observed that SCA-CNN significantly outperforms state-of-the-art visual attention-based image captioning methods.


title: CIDEr: Consensus-based image description evaluation

text: Automatically describing an image with a sentence is a long-standing challenge in computer vision and natural language processing. Due to recent progress in object detection, attribute classification, action recognition, etc., there is renewed interest in this area. However, evaluating the quality of descriptions has proven to be challenging. We propose a novel paradigm for evaluating image descriptions that uses human consensus. This paradigm consists of three main parts: a new triplet-based method of collecting human annotations to measure consensus, a new automated metric that captures consensus, and two new datasets: PASCAL-50S and ABSTRACT-50S that contain 50 sentences describing each image. Our simple metric captures human judgment of consensus better than existing metrics across sentences generated by various sources. We also evaluate five state-of-the-art image description approaches using this new protocol and provide a benchmark for future comparisons. A version of CIDEr named CIDEr-D is available as a part of MS COCO evaluation server to enable systematic evaluation and benchmarking.


title: Mind's eye: A recurrent visual representation for image caption generation

text: In this paper we explore the bi-directional mapping between images and their sentence-based descriptions. Critical to our approach is a recurrent neural network that attempts to dynamically build a visual representation of the scene as a caption is being generated or read. The representation automatically learns to remember long-term visual concepts. Our model is capable of both generating novel captions given an image, and reconstructing visual features given an image description. We evaluate our approach on several tasks. These include sentence generation, sentence retrieval and image retrieval. State-of-the-art results are shown for the task of generating novel image descriptions. When compared to human generated captions, our automatically generated captions are equal to or preferred by humans 21.0% of the time. Results are better than or comparable to state-of-the-art results on the image and sentence retrieval tasks for methods using similar visual features.


title: Enhanced Simulated Annealing for Globally Minimizing Functions of Many-Continuous Variables

text: A new global optimization algorithm for functions of many continuous variables is presented, derived from the basic Simulated annealing method. Our main contribution lies in dealing with high-dimensionality minimization problems, which are often difficult to solve by all known minimization methods with or without gradient. In this article we take a special interest in the variables discretization issue. We also develop and implement several complementary stopping criteria. The original Metropolis iterative random search, which takes place in a Euclidean space Rn, is replaced by another similar exploration, performed within a succession of Euclidean spaces Rp, with p <<n. This Enhanced Simulated Annealing (ESA) algorithm was validated first on classical highly multimodal functions of 2 to 100 variables. We obtained significant reductions in the number of function evaluations compared to six other global optimization algorithms, selected according to previously published computational results for the same set of test functions. In most cases, ESA was able to closely approximate known global optima. The reduced ESA computational cost helped us to refine further the obtained global results, through the use of some local search. We have used this new minimizing procedure to solve complex circuit design problems, for which the objective function evaluation can be exceedingly costly.


title: Microbial Prevalence and Antimicrobial Resistance in Children and Adolescents with Chronic Rhinosinusitis in South Indian Population

text: Objective: Chronic rhinosinusitis (CRS) is a common multifactorial upper respiratory disease with a key role of microbes in worsening of disease and its associated co-morbidities. Further, significant region specific variation in patient demographics and antibiotic resistance of causative bacteria are reported to pose difficulty in diagnosis and treatment. In India, studies on the etiology and antibiotic resistance in chronic rhinosinusitis are very meager, especially in children. The present study aimed to determine the prevalence of common causative microbes and their antibiotic resistance in children and adolescents with chronic rhinosinusitis in South Indian population. Subjects and methods: The present study was conducted on 89 children and 99 adolescents with chronic rhinosinusitis who visited MAA ENT Institute, Hyderabad, South India. The study samples were collected under the nasal endoscopic guidance from the middle meatus at first visit and sinuses at surgery. Conventional and VITEK-2 methods were used for identification and antibiotic sensitivity of the microbes. Chi-square test and multinomial logistic regression was applied to determine statistical differences between the variable using PASW v. 18.0 software (SPSS Inc., Chicago, IL). Results: The male-female ratio was 2:1 with an average children age of 8.9 ± 3.65 years and 16.1 ± 1.23 years in adolescents. The risk for adenoids was seen in 49.2 % of children (OR; 2.6: 95% CI: 1.63-4.06) while allergic fungal sinusitis (18.1%, OR: 2.7; 95% CI: 1.12-6.57) and nasal polyps (26.6%, OR: 2.3; 95% CI: 1.07-4.86) was commonly seen in adolescents. About 26.6% of adolescents with fungal positivity also showed bacterial infection. Aspergillus flavus (68%) was the most common fungi identified. Bacterial culture rate was positive in 46.8% of the total subjects of which Streptococcus aureus was the most common bacteria (59.1%) followed by Streptococcus pnuemoniae (21.2%), Klebsiella sp. (11.4%), Pseudomonas aeruginosa (11.4%) and β hemolytic streptococci (1.1%). No Methicillin-resistant Staphylococcus aureus strains could be identified. Streptococcus pneumonia (63.2%) was commonly identified in younger children and Pseudomonas aeruginosa (80%) was mostly seen in adolescents. The frequency of bacterial positivity in adolescents with CRS when compared to CRS children was high and varied between different associated co-morbidities. High antibiotic resistance in Staphylococcus aureus was seen towards gentamicin (73%) and co-trimoxazole (64%), Streptococcus pnuemoniae to gentamicin (58%), cotrimoxazole (68%) and meropenem (32%), Pseudomonas aeruginosa to co-trimoxazole (100%), cefatoximine (60%) and cefatazidime (50%) while Klebsiella sp. to gentamicin (80%) and co-trimoxazole (60%). Streptococcus aureus showed high sensitivity to cefatoximine (95.8%) and Streptococcus pnuemoniae for ofloxacin (100%), ciprofloxacin (89.5%) and cefazolin (89.5%). Pseudomonas aeruginosa showed high sensitivity for amikacin (100%) and ciprofloxacin (80%) and Klebsiella sp. for amikacin (100%) Conclusion: Significant regional specific variation in bacterial etiology that differed with age, severity and comorbidities was observed in children and adolescents with chronic rhinosinusitis. High antimicrobial resistance in the cultures of chronic rhinosinusitis patients at their first visit and also at sinus surgery warrants urgent need for early initiation of personalized interventions for better management of the infectious disease.