title: Criteria of Successful IT Projects from Management's Perspective

abstract: The aim of this paper is to compile a model of IT project success from management's perspective. Therefore, a qualitative research approach is proposed by interviewing IT managers on how their companies evaluate the success of IT projects. The evaluation of the survey provides fourteen success criteria and four success dimensions. This paper also thoroughly analyzes which of these criteria the management considers especially important and which ones are being missed in daily practice. Additionally, it attempts to identify the relevance of the discovered criteria and dimensions with regard to the determination of IT project success. It becomes evident here that the old-fashioned Iron Triangle still plays a leading role, but some long-term strategical criteria, such as value of the project, customer perspective or impact on the organization, have meanwhile caught up or pulled even.


title: Big Data LifeCycle: Threats and Security Model

abstract: Abstract Big data is an emerging term referring to the process of managing huge amount of data from different sources, such as, DBMS, log files, postings of social media. Big data (text, number, images... etc.) could be divided into different forms: structured, semi-structured, and unstructured. Big data could be further described by some attributes like velocity, volume, variety, value, and complexity. The emerging big data technologies also raise many security concerns and challenges. In this paper, we present big data lifecycle framework. The lifecycle includes four phases, i.e., data collection, data storage, data analytics, and knowledge creation. We briefly introduce each phase. We further summarize the security threats and attacks for each phase. The big data lifecycle integrated with security threats and attacks to propose a security threat model to conduct research in big data security. Our work could be further used towards securing big data infrastructure. Keywords Big data, big data lifecycle, threats and attacks, threat model.


title: Construction and refinement of panoramic mosaics with global and local alignment

abstract: This paper presents techniques for constructing full view panoramic mosaics form sequences of images. Our representation associates a rotation matrix (and optionally a focal length) with each input image, rather than explicitly projecting all of the images onto a common surface (e.g., a cylinder). In order to reduce accumulated registration errors we apply global alignment (block adjustment) to whole sequence of images, which results in an optimal image mosaic (in the least squares sense). To compensate for small amounts of motion parallax introduced by translations of the camera and other unmodeled distortions we develop a local alignment (deghosting) technique which warps each image based on the results of pairwise local image registrations. By combining both global and local alignment we significantly improve the quality of our image mosaics thereby enabling the creation of full view panoramic mosaics with hand-held cameras.


IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, MANUSCRIPT ID 1 A Visual Inquiry System for Space-Time and Multivariate Patterns (VIS-STAMP)


title: Real-time 3D human tracking for mobile robots with multisensors

abstract: Acquiring the accurate 3-D position of a target person around a robot provides fundamental and valuable information that is applicable to a wide range of robotic tasks, including home service, navigation and entertainment. This paper presents a real-time robotic 3-D human tracking system which combines a monocular camera with an ultrasonic sensor by the extended Kalman filter (EKF). The proposed system consists of three sub-modules: monocular camera sensor tracking model, ultrasonic sensor tracking model and multi-sensor fusion. An improved visual tracking algorithm is presented to provide partial location estimation (2-D). The algorithm is designed to overcome severe occlusions, scale variation, target missing and achieve robust re-detection. The scale accuracy is further enhanced by the estimated 3-D information. An ultrasonic sensor array is employed to provide the range information from the target person to the robot and Gaussian Process Regression is used for partial location estimation (2-D). EKF is adopted to sequentially process multiple, heterogeneous measurements arriving in an asynchronous order from the vision sensor and the ultrasonic sensor separately. In the experiments, the proposed tracking system is tested in both simulation platform and actual mobile robot for various indoor and outdoor scenes. The experimental results show the superior performance of the 3-D tracking system in terms of both the accuracy and robustness.


title: FinFET-based SRAM design

abstract: Intrinsic variations and challenging leakage control in today's bulk-Si MOSFETs limit the scaling of SRAM. Design tradeoffs in six-transistor (6-T) and four-transistor (4-T) SRAM cells are presented in this work. It is found that 6-T and 4-T FinFET-based SRAM cells designed with built-in feedback achieve significant improvements in the cell static noise margin (SNM) without area penalty. Up to 2/spl times/ improvement in SNM can be achieved in 6-T FinFET-based SRAM cells. A 4-T FinFET-based SRAM cell with built-in feedback can achieve sub-100pA per-cell standby current and offer the similar improvements in SNM as the 6-T cell with feedback, making them attractive for low-power, low-voltage applications.


title: Stop-word removal algorithm for Arabic language

abstract: Summary form only given. We have designed and implemented an efficient stop-word removal algorithm for Arabic language based on a finite state machine (FSM). An efficient stop-word removal technique is needed in many natural language processing application such as: spelling normalization, stemming and stem weighting, Question answering systems and in information retrieval systems (IR). Most of the existing stop-word removal techniques bases on a dictionary that contains a list of stop-word, it is very expensive, it takes too much time for searching process and required too much space to store these stop-words. The new Arabic removal stop-word technique has been tested using a set of 242 Arabic abstracts chosen from the Proceedings of the Saudi Arabian National Computer conferences, and another set of data chosen from the holy Q'uran, and it gives impressive results that reached approximately to 98%.


title: Wearing a YouTube hat: directors, comedians, gurus, and user aggregated behavior

abstract: While existing studies on YouTube's massive user-generated video content have mostly focused on the analysis of videos, their characteristics, and network properties, little attention has been paid to the analysis of users' long-term behavior as it relates to the roles they self-define and (explicitly or not) play in the site. In this paper, we present a novel statistical analysis of aggregated user behavior in YouTube from the novel perspective of user categories, a feature that allows people to ascribe to popular roles and to potentially reach certain communities. Using a sample of 270,000 users, we found that a high level of interaction and participation is concentrated on a relatively small, yet significant, group of users, following recognizable patterns of personal and social involvement. Based on our analysis, we also show that by using simple behavioral features from user profiles, people can be automatically classified according to their category with accuracy rates of up to 73%.


title: LTCC based substrate integrated waveguide (SIW) bandpass filters with CPW transitions

abstract: In this work, substrate integrated waveguide (SIW) bandpass filters were designed and fabricated using LTCC process. The proposed scheme consists of SIW cavities with coupling slots and coplanar waveguide (CPW) transitions. To reduce the component size and surface occupation, three SIW cavities are laminated vertically. Both horizontal transition and vertical transition are used between the two CPW transmission lines. Based on the SICCAS-K70D LTCC material (er = 66, tanδ = 0.002 @3.5 GHz), an S-band bandpass filter with a center frequency of 2.59 GHz was designed and fabricated using the in-house developed LTCC material and process.


Discriminating Input Variables for Fraud Detection using Radial Basis Function Network


title: The role of media and peer influences in Australian women's attitudes towards cosmetic surgery

abstract: Abstract The study aimed to examine the influence of media and peers on attitudes towards cosmetic surgery using a sociocultural framework. A sample of 351 Australian women aged 18–69 years completed measures of media exposure, friend conversations, internalisation of appearance ideals, appearance comparison, body dissatisfaction, and attitudes towards cosmetic surgery. Correlational analysis showed that almost all media and friend variables were significantly correlated with positive attitudes towards cosmetic surgery. A structural equation model based on the sociocultural model showed a good level of fit to the data. The effects of media exposure and friend conversations on body dissatisfaction and attitudes towards cosmetic surgery were mediated by internalisation. We concluded that media exposure and friend conversations affected attitudes towards cosmetic surgery both directly and indirectly. Our results contribute to the understanding of the sociocultural mechanisms underlying women's motivations for cosmetic surgery.


title: Tree Memory Networks for Modelling Long-term Temporal Dependencies

abstract: In the domain of sequence modelling, Recurrent Neural Networks (RNN) have been capable of achieving impressive results in a variety of application areas including visual question answering, part-of-speech tagging and machine translation. However this success in modelling short term dependencies has not successfully transitioned to application areas such as trajectory prediction, which require capturing both short term and long term relationships. In this paper, we propose a Tree Memory Network (TMN) for jointly modelling both long term relationships between multiple sequences and short term relationships within a sequence, in sequence-to-sequence mapping problems. The proposed network architecture is composed of an input module, controller and a memory module. In contrast to related literature which models the memory as a sequence of historical states, we model the memory as a recursive tree structure. This structure more effectively captures temporal dependencies across both short and long term time periods through its hierarchical structure. We demonstrate the effectiveness and flexibility of the proposed TMN in two practical problems: aircraft trajectory modelling and pedestrian trajectory modelling in a surveillance setting. In both cases the proposed approach outperforms the current state-of-the-art. Furthermore, we perform an in depth analysis on the evolution of the memory module content over time and provide visual evidence on how the proposed TMN is able to map both short and long term relationships efficiently via a hierarchical structure.


title: Kernel Logistic Regression and the Import Vector Machine

abstract: The support vector machine (SVM) is known for its good performance in binary classification, but its extension to multi-class classification is still an on-going research issue. In this paper, we propose a new approach for classification, called the import vector machine (IVM), which is built on kernel logistic regression (KLR). We show that the IVM not only performs as well as the SVM in binary classification, but also can naturally be generalized to the multi-class case. Furthermore, the IVM provides an estimate of the underlying probability. Similar to the "support points" of the SVM, the IVM model uses only a fraction of the training data to index kernel basis functions, typically a much smaller fraction than the SVM. This gives the IVM a computational advantage over the SVM, especially when the size of the training data set is large.


title: Continuous heart rate and body temperature monitoring system using Arduino UNO and Android device

abstract: An integrated portable device for continuous heart rate and body temperature monitoring system development is presented in this paper. Heart related diseases are increasing day by day; therefore, an accurate, affordable and portable heart rate and body temperature measuring device is essential for taking action in proper time. Such a device is more essential in a situation where there is no doctor or clinic nearby (e.g., rural area) and patients are unable not recognize their actual condition. The developed system of this study consists of Arduino UNO microcontroller system, transmission system and Android based application. The system gives information of heart rate and body temperature simultaneously acquired on the portable device in real time and shows it through the connected Android application instantly. The developed system is more affordable with low price compared to other developed devices due to use of easy available Arduino UNO and smart phone as Android device. The developed device is shown acceptable outcomes when compared with other measuring devices.


title: Battery state of charge estimation by using a combination of Coulomb Counting and dynamic model with adjusted gain

abstract: Majority of electric vehicles depend on batteries that require a Battery Management System (BMS). Increasing accuracy of State of charge (SoC) estimation was crucial. The uncomplicated method for SoC estimation is Coulomb Counting (CC), however it has drawback in information of previous SoC initial value and tolerate with error propagation. Dynamic model equipped with integral controller can increase accuracy of the CC method. In this paper adjusted gain of the integral controller is proposed to further improve performance of SOC estimation. The Li-ion battery was modelled using first order RC battery model. Least square method is used to determine parameter of battery model. The simulation results show that the combination of CC and dynamic model with fixed integral gain could increase accuracy of the CC algorithm approximately to 87%, and the mixed algorithm with adjusted integral gain could further increase it to around 89%.


title: The productivity paradox of information technology

abstract: The retationship between information technology IT and productivity is widely discussed but little understood. Delivered computing power in the U.S. economy has increased by more than two orders of magnitude since 1970 (Figure 1) yet productivity, especially in the service sector, seems to have stagnated (Figure 2). Given the enormous promise of IT to usher in «the biggest technological revolution men have known» [29], disillusionment and even frustration with the technology is increasingly evident in statements like «No, computers do not boost productivity, at least not most of the time» [13]. The increased interest in the «productivity paradox,» as it has become known, has engendered a significant amount of research, but thus far, this has only deepened the mystery


title: TUNABLE AND SWITCHABLE BANDPASS FILTERS USING SLOT-LINE RESONATORS

abstract: In this paper, novel uniplanar tunable and switchable bandpass fllters are designed by using the centrally-loaded slot-line resonator. From the voltage-wave distribution along the resonator, the appropriate location for the loading element is determined to be the center of the slot-line resonator, where the voltages of the fundamental signal and second harmonic are maximum and zero, respectively. As a result, the fundamental frequency can be tuned while the second harmonic remains almost unchanged. For the flrst time, the properties of the centrally-loaded slot-line resonator are analyzed by using the even- and odd-mode method, and their respective resonant frequencies are derived. The demonstrated tunable bandpass fllter can give a 30.9% frequency tuning range with acceptable insertion loss when a varactor is used as the loading element. By replacing the loading varactors with PIN diodes, a switchable bandpass fllter is realized in which the attenuation in the fundamental passband can be controlled. In experiments, the switchable bandpass fllter exhibits a 2.13dB insertion loss in the fundamental passband when the PIN diodes are ofi and more than 49dB isolation across the passband when the PIN diodes are on.


title: Technological Discontinuities and Organizational Environments

abstract: Investigates the patterns of technological evolution and their impact on environmental conditions. Seven hypotheses are offered in order to demonstrate that technology is a central force in shaping the environments within which organizations operate. These hypotheses are: (1) technological change within a product class will be characterized by long periods of incremental change punctuated by discontinuities; (1a) technological discontinuities are either competence enhancing (build on existing skills and know-how) or competence destroying (require fundamentally new skills and competences); (2) the locus of innovation will differ for competence destroying and competence-enhancing technological changes. Competence-destroying discontinuities will be initiated by new entrants, while competence-enhancing discontinuities will be initiated by existing firms; (3) competitive uncertainty will be higher after a technological discontinuity than before discontinuity; (4) environmental munificence (i.e., resource availability and support for growth) will be higher after a technological discontinuity than before the discontinuity; (5) competence-enhancing discontinuities will be associated with decreased entry-to-exit ratios and decreased interfirm sales variability (thus strengthening product leaders and increasing barriers to entry). These patterns will be reversed for competence-destroying discontinuities; (6) successive competence-enhancing discontinuities will be associated with smaller increases in uncertainty and munificence; and (7) those organizations that initiate major technological innovations will have higher growth rates than other firms in the product class. Data were collected from U.S. firms in three product classes, domestic scheduled passenger airline transport, Portland cement manufacture, and minicomputer manufacture, from the year of the niche market's inception through 1980. Results indicate that after the three niche openings, there were six competence-enhancing technological discontinuities and two competence-destroying discontinuities in total. Each of these discontinuities had a far greater impact on a key measure of cost or performance than more incremental technological events. In addition, except for the period following the introduction of semiconductor memory in minicomputers, the ability of experienced industry observers to predict demand following technological disruptions was far worse than prior to the disruption. Demand growth following the discontinuity was significantly higher than it was immediately prior to the discontinuity, which had an enormous impact on product-class demand. Also, the ratio of entries to exits was higher in each of the five years before a competence-enhancing discontinuity than during the five subsequent years, though none of the differences is statistically significant. Though market variability in sales growth was expected, it was found that some firms' sales grew explosively while other firms experienced sales declines. It is also suggested that as technology matures, successive competence-enhancing discontinuities increase both uncertainty and munificence, but not as much as those discontinuities that preceded them in establishing the product class. Finally, early adopters of technology were found to experience more growth than other firms. Using three different product types, with a wide range of years from inception, it is shown that technology does evolve through long periods of incremental change punctuated by relatively rare innovations that radically improve the state of the art. Although these incidences of change are rare, they stand out clearly and have significantly altered competitive environments. (SFL)


title: A Robust Segmentation Approach for Noisy Medical Images Using Fuzzy Clustering With Spatial Probability

abstract: Image segmentation plays a major role in medical imaging applications. During last decades, developing robust and efficient algorithms for medical image segmentation has been a demanding area of growing research interest. The renowned unsupervised clustering method, Fuzzy C-Means (FCM) algorithm is extensively used in medical image segmentation. Despite its pervasive use, conventional FCM is highly sensitive to noise because it segments images on the basis of intensity values. In this paper, for the segmentation of noisy medical images, an effective approach is presented. The proposed approach utilizes histogram based Fuzzy C-Means clustering algorithm for the segmentation of medical images. To improve the robustness against noise, the spatial probability of the neighboring pixels is integrated in the objective function of FCM. The noisy medical images are denoised, with the help of an effective denoising algorithm, prior to segmentation, to increase further the approach's robustness. A comparative analysis is done between the conventional FCM and the proposed approach. The results obtained from the experimentation show that the proposed approach attains reliable segmentation accuracy despite of noise levels. From the experimental results, it is also clear that the proposed approach is more efficient and robust against noise when compared to that of the FCM.


title: Filling n-sided regions with NURBS patches

abstract: -sided region with Gɛ continuous NURBS patches that interpolate boundary curves and approximate given cross-boundary derivatives. The NURBS surfaces joining along inner or boundary curves have normal vectors that do not deviate more than the user-specified angular tolerance ɛ. The method is general in that there are no restrictions on the number of boundary curves, and the cross-boundary derivatives can be specified independently. To satisfy all conditions, only one degree elevation is needed.


title: A Programmable Multi-Biomarker Neural Sensor for Closed-Loop DBS

abstract: Most of the current closed-loop DBS devices use a single biomarker in their feedback loop which may limit their performance and applications. This paper presents design, fabrication, and validation of a programmable multi-biomarker neural sensor which can be integrated into closed-loop DBS devices. The device is capable of sensing a combination of low-frequency (7-45 Hz), and high-frequency (200-1000 Hz) neural signals. The signals can be amplified with a digitally programmable gain within the range 50-100 dB. The neural signals can be stored into a local memory for processing and validation. The sensing and storage functions are implemented via a combination of analog and digital circuits involving preamplifiers, filters, programmable post-amplifiers, microcontroller, digital potentiometer, and flash memory. The device is fabricated, and its performance is validated through: (i) bench tests using sinusoidal and pre-recorded neural signals, (ii) in-vitro tests using pre-recorded neural signals in saline solution, and (iii) in-vivo tests by recording neural signals from freely-moving laboratory mice. The animals were implanted with a PlasticsOne electrode, and recording was conducted after recovery from the electrode implantation surgery. The experimental results are presented and discussed confirming the successful operation of the device. The size and weight of the device enable tetherless back-mountable use in pre-clinical trials.


title: Clinical features of SMARCA2 duplication overlap with Coffin–Siris syndrome

abstract: Coffin-Siris syndrome is a rare congenital malformation and intellectual disability syndrome. Mutations in at least seven genes have been identified. Here, we performed copy number analysis in 37 patients with features of CSS in whom no causative mutations were identified by exome sequencing. We identified a patient with a 9p24.3-p22.2 duplication and another patient with the chromosome der(6)t(6;9)(p25;p21)mat. Both patients share a duplicated 15.8-Mb region containing 46 protein coding genes, including SMARCA2. Dominant negative effects of SMARCA2 mutations may contribute to Nicolaides-Baraitser syndrome. We conclude that their features better resemble Coffin-Siris syndrome, rather than Nicolaides-Baraitser syndrome and that these features likely arise from SMARCA2 over-dosage. Pure 9p duplications (not caused by unbalanced translocations) are rare. Copy number analysis in patients with features that overlap with Coffin-Siris syndrome is recommended to further determine their genetic aspects. © 2016 Wiley Periodicals, Inc.


title: Design of SMS commanded-and-controlled and P2P-structured mobile botnets

abstract: Botnets are one of the most serious security threats to the Internet and personal computer (PC) users. Although botnets have not yet caused major outbreaks in the mobile world, with the rapidly-growing popularity of smartphones such as Apple's iPhone and Android-based phones that store more personal data and gain more capabilities than earlier generation handsets, botnets are expected to become a severe threat to smartphones soon. In this paper, we propose the design of a mobile botnet that makes the most of mobile services and is resilient to disruption. The mobile botnet utilizes SMS messages for C&C and a P2P structure as its topology. Our simulation results demonstrate that a modified Kademlia---a structured architecture---is a better choice for the mobile botnet's topology. In addition, we discuss potential countermeasures to defend against this mobile botnet threat.


title: Estimation of the Cramer-Rao Bound for Radio Direction-Finding on the Azimuth and Elevation of the Cylindical Antenna Arrays

abstract: In this paper the problem of direction-of-arrival (DOA) estimation for conformal antenna arrays consisting of directive emitters for the azimuth and elevation cases is studied. The expression for the Cramer-Rao lower bound of the DOA estimates variance depending on the antenna directivity and the geometry is presented. The boundaries of the Cramer-Rao are evaluated across several scenarios, including the different signal sources locations in terms of the azimuth and elevation angles and signal-to-noise ratios using different antenna array configurations. The influence of the cylindrical conformal antenna array on the direction-of-arrival estimation accuracy is researched.


title: Forecasting with twitter data

abstract: The dramatic rise in the use of social network platforms such as Facebook or Twitter has resulted in the availability of vast and growing user-contributed repositories of data. Exploiting this data by extracting useful information from it has become a great challenge in data mining and knowledge discovery. A recently popular way of extracting useful information from social network platforms is to build indicators, often in the form of a time series, of general public mood by means of sentiment analysis. Such indicators have been shown to correlate with a diverse variety of phenomena. In this article we follow this line of work and set out to assess, in a rigorous manner, whether a public sentiment indicator extracted from daily Twitter messages can indeed improve the forecasting of social, economic, or commercial indicators. To this end we have collected and processed a large amount of Twitter posts from March 2011 to the present date for two very different domains: stock market and movie box office revenue. For each of these domains, we build and evaluate forecasting models for several target time series both using and ignoring the Twitter-related data. If Twitter does help, then this should be reflected in the fact that the predictions of models that use Twitter-related data are better than the models that do not use this data. By systematically varying the models that we use and their parameters, together with other tuning factors such as lag or the way in which we build our Twitter sentiment index, we obtain a large dataset that allows us to test our hypothesis under different experimental conditions. Using a novel decision-tree-based technique that we call summary tree we are able to mine this large dataset and obtain automatically those configurations that lead to an improvement in the prediction power of our forecasting models. As a general result, we have seen that nonlinear models do take advantage of Twitter data when forecasting trends in volatility indices, while linear ones fail systematically when forecasting any kind of financial time series. In the case of predicting box office revenue trend, it is support vector machines that make best use of Twitter data. In addition, we conduct statistical tests to determine the relation between our Twitter time series and the different target time series.


title: Economics of Internet of Things (IoT): An Information Market Approach

abstract: Internet of things (IoT) has been proposed to be a new paradigm of connecting devices and providing services to various applications, e.g., transportation, energy, smart city, and healthcare. In this paper, we focus on an important issue, i.e., economics of IoT, that can have a great impact to the success of IoT applications. In particular, we adopt and present the information economics approach with its applications in IoT. We first review existing economic models developed for IoT services. Then, we outline two important topics of information economics which are pertinent to IoT, i.e., the value of information and information good pricing. Finally, we propose a game theoretic model to study the price competition of IoT sensing services. Some outlooks on future research directions of applying information economics to IoT are discussed.


title: An Ultra-wideband and Polarization-independent Metasurface for RCS Reduction

abstract: In this paper, an ultra-wideband and polarization-independent metasurface for radar cross section (RCS) reduction is proposed. The unit cell of the metasurface operates in a linear cross-polarization scheme in a broad band. The phase and amplitude of cross-polarized reflection can be separately controlled by its geometry and rotation angle. Based on the diffuse reflection theory, a 3-bit coding metasurface is designed to reduce the RCS in an ultra-wide band. The wideband property of the metasurface benefits from the wideband cross polarization conversion and flexible phase modulation. In addition, the polarization-independent feature of the metasurface is achieved by tailoring the rotation angle of each element. Both the simulated and measured results demonstrate that the proposed metasurface can reduce the RCS significantly in an ultra-wide frequency band for both normal and oblique incidences, which makes it promising in the applications such as electromagnetic cloaking.


title: Learn English or die : The effects of digital games on interaction and willingness to communicate in a foreign language

abstract: In recent years there has been a lot of interest in the potential role of digital games in language education. Playing digital games is said to be motivating to students and to benefit the development of social skills, such as collaboration, and metacognitive skills such as planning and organisation. An important potential benefit is also that digital games encourage the use of the target language in a non-threatening environment. Willingness to communicate has been shown to affect second language acquisition in a number of ways and it is therefore important to investigate if there is a connection between playing games and learners’ interaction in the target language. In this article we report on the results of a pilot study that investigated the effects of playing an online multiplayer game on the quantity and quality of second language interaction in the game and on participants’ willingness to communicate in the target language. We will show that digital games can indeed affect second language interaction patterns and contribute to second language acquisition, but that this depends, like in all other teaching and learning environments, on careful pedagogic planning of the activity. Keywords: dig i ta l games , interac t ion, language t eaching se cond language acquis i t ion, wi l l ingness to communicate . Recent years have seen a growing interest in the pedagogical benefits of digital games for language learning. Gee (2003), for example, identified 36 learning principles that he found to be present in many of the games he investigated. An example of these is the ‘Active, Critical Learning Principle’. This stipulates that “All aspects of the learning environment (including the ways in which the semiotic domain is designed and presented) are set up to encourage active and critical, not passive, learning.” In other words, computer games engage learners and involve in the tasks at hand. A second principle is the ‘Regime of Competence Principle’ where “the learner gets ample opportunity to operate within, but at the outer edge of, his or her resources, so that at those points things are felt as challenging but not ‘undoable.’” (Gee, 2003, p. 36). Most games adapt to the player’s level until they succeed, at which point new challenges appear. These principles are intuitively appealing and grounded in educational research, but it is not clear how and to what extent they are related to second language acquisition. There is not much research on the effects of game play on learning a second language and the purpose of this article is to review this small body of research before describing the results of a study investigating the relationship between participation in an online multiplayer gaming environment and second language interaction patters and participants’ attitudes towards interacting in the target language (English). We limit ourselves in this study to an investigation of the acquisition of aspects of the target language. We acknowledge the importance of sociocultural and ecological views of language acquisition (Larsen-Freeman & Cameron 2008; Van Lier 2004) but these were not the focus of this study. For a “cognitive ethnography” of gaming and its effect on literacy practices, we refer the reader to Steinkuehler (2006; 2008). Learn English or die 5 The effects of game play on second language acquisition Many claims are made for the benefits of games on affective factors such as anxiety and motivation, but few studies have directly investigated the effects of digital games on second language acquisition. An example of such a study was conducted by deHaan, Reed and Kuwada (2010), who investigated the effects of playing a digital game versus watching it on immediate and delayed recall of vocabulary by Japanese learners. Participants in the study were given a music game in which the players had to complete parts of a song by pressing controller buttons at the correct time. Participants in this study did not collaborate but were interacting only with the computer (Chapelle’s human-computer interaction; 2001). An important feature of the study, and perhaps a major limitation, is that participants did not have to understand the English in order to play the game. The authors found that playing the game resulted in less vocabulary acquisition than watching it (although both resulted in learning gains), probably as a result of the greater cognitive load of having to interact with the game. A postexperimental questionnaire revealed that there was no difference between players and watchers in terms of their mental effort, so the effects were due only to their interaction with the game. The authors argue that playing digital games and interactivity are therefore not necessarily conducive to language acquisition. However, it is of course important to understand these findings in light of the fact that the language was not a focal part of participants’ experience and that they could complete the tasks without attention to the vocabulary. It is therefore important that future studies investigate gaming environments that do involve meaningful language use. Another limitation of this study was the nature of the game that was chosen. This genre of game lacks a detailed narrative component that requires comprehension in order to respond appropriately, which is common in many adventure games, for example. That noticing linguistic elements in an environment where the primary focus is not on language is possible in a gaming environment was shown by Piirainen-Marsh and Tainio (2009), who used Conversation Analysis to examine how two teenage boys repeated language elements in the game to show their involvement and to make sense of the game. Video recordings of their game interactions showed frequent repetitions both in the form of immediate imitation but also for anticipatory use and to recontextualise previously heard utterances, or to expand on them. The authors conclude: ‘On the whole, repetition offers a flexible resource through which the participants display continued attention to relevant features of the game and co-construct the collaborative play activity’ (p. 166). This study did not investigate the effects of this repetition on linguistic acquisition, however. Similarly, Zheng, Young, Wagner and Brewer (2009) focused on the effects of game play on the interaction and collaborative construction of cultural and discourse practices between native and non-native speakers in the educational game Quest Atlantis. The collaborative nature of the game required a deep exchange between the two dyads of players and encouraged the development of not only semantic and syntactic, but also pragmatic knowledge, and both from native to non-native speaker and vice versa. The authors refer to this type of interaction as negotiation for action. Chen and Johnson (2004) “modded” a commercial role playing game called Neverwinter Nights (Bioware, 2002) to investigate whether a digital game simulating a foreign language learning context could promote a state of ‘flow’ and motivate students to practise language skills (Spanish in the case of this study) outside of the classroom. The authors used questionnaires, video transcripts, field notes, and a post-game interview to investigate this but realised that there were significant differences in the


title: An MDP-Based Winning Approach to Autonomous Power Trading: Formalization and Empirical Analysis

abstract: With the efforts of moving to sustainable and reliable energy supply, electricity markets are undergoing far-reaching changes. Due to the high-cost of failure in the real-world, it is important to test new market structures in simulation. This is the focus of the Power Trading Agent Competition (Power TAC), which proposes autonomous electricity broker agents as a means for stabilizing the electricity grid. This paper focuses on the question: how should an autonomous electricity broker agent act in competitive electricity markets to maximize its profit. We formalize the electricity trading problem as a continuous, high-dimensional Markov Decision Process (MDP), which is computationally intractable to solve. Our formalization provides a guideline for approximating the MDP's solution, and for extending existing solutions. We show that a previously champion broker can be viewed as approximating the solution using a lookahead policy. We present TacTex'15, which improves upon this previous approximation and achieves state-of-the-art performance in competitions and controlled experiments. Using thousands of experiments against 2015 finalist brokers, we analyze TacTex'15's performance and the reasons for its success. We find that lookahead policies can be effective, but their performance can be sensitive to errors in the transition function prediction, specifically demand-prediction.


title: A 140mA 90nm CMOS low drop-out regulator with −56dB power supply rejection at 10MHz

abstract: A high power supply rejection (PSR) low drop-out (LDO) voltage regulator employing a simple supply ripple cancellation adaptive technique is presented in this paper. The LDO is implemented in 90nm digital CMOS process and occupies an active area of 0.015mm2. The measured PSR is better than −50dB up to 10MHz across the load current range of 140mA with a drop-out voltage of 0.15V. The quiescent current (I Q ) varies adaptively from 33µA at no load to 145µA at maximum load with a current efficiency of 99.9%. Load regulation of 6mV for a 140mA step in load current is measured.


title: Density-based Denoising of Point Cloud

abstract: Point cloud source data for surface reconstruction is usually contaminated with noise and outliers. To overcome this deficiency, a density-based point cloud denoising method is presented to remove outliers and noisy points. First, particle-swam optimization technique is employed for automatically approximating optimal bandwidth of multivariate kernel density estimation to ensure the robust performance of density estimation. Then, mean-shift based clustering technique is used to remove outliers through a thresholding scheme. After removing outliers from the point cloud, bilateral mesh filtering is applied to smooth the remaining points. The experimental results show that this approach, comparably, is robust and efficient.


title: A new approach to wafer sawing: stealth laser dicing technology

abstract: Stealth Dicing (SD) technology has high potential to replace the conventional blade sawing and laser grooving. The dicing method has been widely researched since 2005 [1–3] especially for thin wafer (⇐ 12 mils). SD cutting has good quality because it has dry process during laser cutting, extremely narrow scribe line and multi-die sawing capability. However, along with complicated package technology, the chip quality demands fine and accurate pitch which conventional blade saw is impossible to achieve. This paper is intended as an investigation in high performance SD sawing, including multi-pattern wafer and DAF dicing tape capability. With the improvement of low-K substrate technology and min chip scale size, SD cutting is more important than other methods used before. Such sawing quality also occurs in wafer level chip scale package. With low-K substrate and small package, the SD cutting method can cut the narrow scribe line easily (15 um), which can lead the WLCSP to achieve more complicated packing method successfully.


title: Blockchain Technology: Transforming Libertarian Cryptocurrency Dreams to Finance and Banking Realities

abstract: The financial technology (FinTech) sector sees high potential value in cryptocurrency blockchain protocols, or distributed-ledger technology (DLT). However, the requirements and guarantees of blockchains for cryptocurrencies do not match those of FinTech—from transaction throughput to security primitives and privacy. The author explores how blockchain research beyond Bitcoin is closing these gaps and some of the challenges that remain.


title: Advanced Integration Techniques on Broadband Millimeter-Wave Beam Steering for 5G Wireless Networks and Beyond

abstract: Recently, the desired very high throughput of 5G wireless networks drives millimeter-wave (mm-wave) communication into practical applications. A phased array technique is required to increase the effective antenna aperture at mm-wave frequency. Integrated solutions of beamforming/beam steering are extremely attractive for practical implementations. After a discussion on the basic principles of radio beam steering, we review and explore the recent advanced integration techniques of silicon-based electronic integrated circuits (EICs), photonic integrated circuits (PICs), and antenna-on-chip (AoC). For EIC, the latest advanced designs of on-chip true time delay (TTD) are explored. Even with such advances, the fundamental loss of a silicon-based EIC still exists, which can be solved by advanced PIC solutions with ultra-broad bandwidth and low loss. Advanced PIC designs for mm-wave beam steering are then reviewed with emphasis on an optical TTD. Different from the mature silicon-based EIC, the photonic integration technology for PIC is still under development. In this paper, we review and explore the potential photonic integration platforms and discuss how a monolithic integration based on photonic membranes fits the photonic mm-wave beam steering application, especially for the ease of EIC and PIC integration on a single chip. To combine EIC, for its accurate and mature fabrication techniques, with PIC, for its ultra-broad bandwidth and low loss, a hierarchical mm-wave beam steering chip with large-array delays realized in PIC and sub-array delays realized in EIC can be a future-proof solution. Moreover, the antenna units can be further integrated on such a chip using AoC techniques. Among the mentioned techniques, the integration trends on device and system levels are discussed extensively.


title: Immune and antibody responses to an isolated capsid protein of foot-and-mouth disease virus.

abstract: The purified capsid proteins VP1, VP2, and VP3 of foot-and-mouth disease virus type A12 strain 119 emulsified with incomplete Freund's adjuvant were studied in swine and guinea pigs. Swine inoculated on days 0, 28, and 60 with 100-mug doses of VP3 were protected by day 82 against exposure to infected swine. Serums from animals inoculated with VP3 contained viral precipitating and neutralizing antibodies, but such serums recognized fewer viral antigenic determinants than did antiviral serums. Capsid proteins VP1 and VP2 did not produce detectable antiviral antibody in guinea pigs, and antiviral antibody responses in swine to a mixture of VP1, VP2, and VP3 were lower than the responses to VP3 alone. However, when swine were inoculated with VP1, VP2, and VP3 separately at different body sites, no interference with the response to VP3 was observed. Vaccine containing VP3 isolated from acetylethylenimine-treated virus appeared less protective for swine than vaccine containing VP3 from nontreated virus. Trypsinized virus, which contains the cleaved peptides VP3a and VP3b rather than intact VP3, produced approximately the same levels of antiviral antibody responses in guinea pigs as did virus. Conversely, an isolated mixture of VP3a and VP3b did not produce detectable antiviral antibody responses in guinea pigs. The VP3a-VP3b mixture did, however, sensitize guinea pigs to elicit such responses following reinoculation with a marginally effective dose of trypsinized virus.


TWO FACTOR AUTHENTICATION


Approaches for Bayesian variable selection


title: An Expression Deformation Approach to Non-rigid 3D Face Recognition

abstract: The accuracy of non-rigid 3D face recognition approaches is highly influenced by their capacity to differentiate between the deformations caused by facial expressions from the distinctive geometric attributes that uniquely characterize a 3D face, interpersonal disparities. We present an automatic 3D face recognition approach which can accurately differentiate between expression deformations and interpersonal disparities and hence recognize faces under any facial expression. The patterns of expression deformations are first learnt from training data in PCA eigenvectors. These patterns are then used to morph out the expression deformations. Similarity measures are extracted by matching the morphed 3D faces. PCA is performed in such a way it models only the facial expressions leaving out the interpersonal disparities. The approach was applied on the FRGC v2.0 dataset and superior recognition performance was achieved. The verification rates at 0.001 FAR were 98.35% and 97.73% for scans under neutral and non-neutral expressions, respectively.


title: A Nonlinear Bayesian Filtering Framework for ECG Denoising

abstract: In this paper, a nonlinear Bayesian filtering framework is proposed for the filtering of single channel noisy electrocardiogram (ECG) recordings. The necessary dynamic models of the ECG are based on a modified nonlinear dynamic model, previously suggested for the generation of a highly realistic synthetic ECG. A modified version of this model is used in several Bayesian filters, including the Extended Kalman Filter, Extended Kalman Smoother, and Unscented Kalman Filter. An automatic parameter selection method is also introduced, to facilitate the adaptation of the model parameters to a vast variety of ECGs. This approach is evaluated on several normal ECGs, by artificially adding white and colored Gaussian noises to visually inspected clean ECG recordings, and studying the SNR and morphology of the filter outputs. The results of the study demonstrate superior results compared with conventional ECG denoising approaches such as bandpass filtering, adaptive filtering, and wavelet denoising, over a wide range of ECG SNRs. The method is also successfully evaluated on real nonstationary muscle artifact. This method may therefore serve as an effective framework for the model-based filtering of noisy ECG recordings.


title: Tornado: A Distributed Spatio-Textual Stream Processing System

abstract: The widespread use of location-aware devices together with the increased popularity of micro-blogging applications (e.g., Twitter) led to the creation of large streams of spatio-textual data. In order to serve real-time applications, the processing of these large-scale spatio-textual streams needs to be distributed. However, existing distributed stream processing systems (e.g., Spark and Storm) are not optimized for spatial/textual content. In this demonstration, we introduce Tornado, a distributed in-memory spatio-textual stream processing server that extends Storm. To efficiently process spatio-textual streams, Tornado introduces a spatio-textual indexing layer to the architecture of Storm. The indexing layer is adaptive, i.e., dynamically re-distributes the processing across the system according to changes in the data distribution and/or query workload. In addition to keywords, higher-level textual concepts are identified and are semantically matched against spatio-textual queries. Tornado provides data deduplication and fusion to eliminate redundant textual data. We demonstrate a prototype of Tornado running against real Twitter streams, where the users can register continuous or snapshot spatio-textual queries using a map-assisted query-interface.


title: Shock Wave-Induced Damage and Poration in Eukaryotic Cell Membranes

abstract: Shock waves are known to permeabilize eukaryotic cell membranes, which may be a powerful tool for a variety of drug delivery applications. However, the mechanisms involved in shock wave-mediated membrane permeabilization are still poorly understood. In this study, the effects on both the permeability and the ultrastructural features of two human cell lineages were investigated after the application of underwater shock waves in vitro. Scanning Electron Microscopy of cells derived from a human embryo kidney (HEK)-293 and Michigan Cancer Foundation (MCF)-7 cells, an immortalized culture derived from human breast adenocarcinoma, showed a small amount of microvilli (as compared to control cells), the presence of hole-like structures, and a decrease in cell size after shock wave exposure. Interestingly, these effects were accompanied by the permeabilization of acid and macromolecular dyes and gene transfection. Trypan blue exclusion assays indicated that cell membranes were porated during shock wave treatment but resealed after a few seconds. Deformations of the cell membrane lasted for at least 5 min, allowing their observation in fixed cells. For each cell line, different shock wave parameters were needed to achieve cell membrane poration. This difference was correlated to successful gene transfection by shock waves. Our results demonstrate, for the first time, that shock waves induce transient micro- and submicrosized deformations at the cell membrane, leading to cell transfection and cell survival. They also indicate that ultrastructural analyses of cell surfaces may constitute a useful way to match the use of shock waves to different cells and settings.


title: A Survey on Peer-to-Peer Video Streaming Systems

abstract: Video-over-IP applications have recently attracted a large number of users on the Internet. Traditional client-server based video streaming solutions incur expensive bandwidth provision cost on the server. Peer-to-Peer (P2P) networking is a new paradigm to build distributed network applications. Recently, several P2P streaming systems have been deployed to provide live and on-demand video streaming services on the Internet at low server cost. In this paper, we provide a survey on the existing P2P solutions for live and on-demand video streaming. Representative P2P streaming systems, including tree, multi-tree and mesh based systems are introduced. We describe the challenges and solutions of providing live and on-demand video streaming in P2P environment. Open research issues on P2P video streaming are also discussed.


title: Multi-hop Communication in the Uplink for LPWANs

abstract: Abstract Low-Power Wide Area Networks (LPWANs) have arisen as a promising communication technology for supporting Internet of Things (IoT) services due to their low power operation, wide coverage range, low cost and scalability. However, most LPWAN solutions like SIGFOX™or LoRaWAN™rely on star topology networks, where stations (STAs) transmit directly to the gateway (GW), which often leads to rapid battery depletion in STAs located far from it. In this work, we analyze the impact on LPWANs energy consumption of multi-hop communication in the uplink, allowing STAs to transmit data packets in lower power levels and higher data rates to closer parent STAs, reducing their energy consumption consequently. To that aim, we introduce the Distance-Ring Exponential Stations Generator (DRESG) framework, 1 designed to evaluate the performance of the so-called optimal-hop routing model, which establishes optimal routing connections in terms of energy efficiency, aiming to balance the consumption among all the STAs in the network. Results show that enabling such multi-hop connections entails higher network lifetimes, reducing significantly the bottleneck consumption in LPWANs with up to thousands of STAs. These results lead to foresee multi-hop communication in the uplink as a promising routing alternative for extending the lifetime of LPWAN deployments.


title: Effects of active music therapy on the normal brain: fMRI based evidence

abstract: The aim of this study was to investigate the neurophysiological bases of Active Music Therapy (AMT) and its effects on the normal brain. Twelve right-handed, healthy, non-musician volunteers were recruited. The subjects underwent 2 AMT sessions based on the free sonorous-music improvisation using rhythmic and melodic instruments. After these sessions, each subject underwent 2 fMRI scan acquisitions while listening to a Syntonic (SP) and an A-Syntonic (AP) Production from the AMT sessions. A 3 T Discovery MR750 scanner with a 16-channel phased array head coil was used, and the image analysis was performed with Brain Voyager QX 2.8. The listening to SP vs AP excerpts mainly activated: (1) the right middle temporal gyrus and right superior temporal sulcus, (2) the right middle frontal gyrus and in particular the right precentral gyrus, (3) the bilateral precuneus, (4) the left superior temporal sulcus and (5) the left middle temporal gyrus. These results are consistent with the psychological bases of the AMT approach and with the activation of brain areas involved in memory and autobiographical processes, and also in personal or interpersonal significant experiences. Further studies are required to confirm these findings and to explain possible effects of AMT in clinical settings.


title: Using Camera-Equipped Mobile Phones for Interacting with Real-World Objects

abstract: The idea described in this paper is to use the built-in cameras of consumer mobile phones as sensors for 2-dimensional visual codes. Such codes can be attached to physical objects in order to retrieve object-related information and functionality. They are also suitable for display on electronic screens. The proposed visual code system allows the simultaneous detection of multiple codes, introduces a position-independent coordinate system, and provides the phone’s orientation as a parameter. The ability to detect objects in the user’s vicinity offers a natural way of interaction and strengthens the role of mobile phones in a large number of application scenarios. We describe the hardware requirements, the design of a suitable visual code, a lightweight recognition algorithm, and present some example applications.


title: ArrayTrack: a fine-grained indoor location system

abstract: With myriad augmented reality, social networking, and retail shopping applications all on the horizon for the mobile handheld, a fast and accurate location technology will become key to a rich user experience. When roaming outdoors, users can usually count on a clear GPS signal for accurate location, but indoors, GPS often fades, and so up until recently, mobiles have had to rely mainly on rather coarse-grained signal strength readings. What has changed this status quo is the recent trend of dramatically increasing numbers of antennas at the indoor access point, mainly to bolster capacity and coverage with multiple-input, multiple-output (MIMO) techniques. We thus observe an opportunity to revisit the important problem of localization with a fresh perspective. This paper presents the design and experimental evaluation of ArrayTrack, an indoor location system that uses MIMO-based techniques to track wireless clients at a very fine granularity in real time, as they roam about a building. With a combination of FPGA and general purpose computing, we have built a prototype of the ArrayTrack system. Our results show that the techniques we propose can pinpoint 41 clients spread out over an indoor office environment to within 23 centimeters median accuracy, with the system incurring just 100 milliseconds latency, making for the first time ubiquitous real-time, fine-grained location available on the mobile handset.


title: The Kappa Statistic in Reliability Studies: Use, Interpretation, and Sample Size Requirements

abstract: Purpose. This article examines and illustrates the use and interpretation of the kappa statistic in musculoskeletal research. Summary of Key Points. The reliability of clinicians' ratings is an important consideration in areas such as diagnosis and the interpretation of examination findings. Often, these ratings lie on a nominal or an ordinal scale. For such data, the kappa coefficient is an appropriate measure of reliability. Kappa is defined, in both weighted and unweighted forms, and its use is illustrated with examples from musculoskeletal research. Factors that can influence the magnitude of kappa (prevalence, bias, and nonindependent ratings) are discussed, and ways of evaluating the magnitude of an obtained kappa are considered. The issue of statistical testing of kappa is considered, including the use of confidence intervals, and appropriate sample sizes for reliability studies using kappa are tabulated. Conclusions. The article concludes with recommendations for the use and interpretation of kappa.


title: Decorrelation of Neutral Vector Variables: Theory and Applications

abstract: In this paper, we propose novel strategies for neutral vector variable decorrelation. Two fundamental invertible transformations, namely serial nonlinear transformation and parallel nonlinear transformation, are proposed to carry out the decorrelation. For a neutral vector variable, which is not multivariate Gaussian distributed, the conventional principal component analysis (PCA) cannot yield mutually independent scalar variables. With the two proposed transformations, a highly negatively correlated neutral vector can be transformed to a set of mutually independent scalar variables with the same degrees of freedom. We also evaluate the decorrelation performances for the vectors generated from a single Dirichlet distribution and a mixture of Dirichlet distributions. The mutual independence is verified with the distance correlation measurement. The advantages of the proposed decorrelation strategies are intensively studied and demonstrated with synthesized data and practical application evaluations.


title: Generating Holistic 3D Scene Abstractions for Text-Based Image Retrieval

abstract: Spatial relationships between objects provide important information for text-based image retrieval. As users are more likely to describe a scene from a real world perspective, using 3D spatial relationships rather than 2D relationships that assume a particular viewing direction, one of the main challenges is to infer the 3D structure that bridges images with users text descriptions. However, direct inference of 3D structure from images requires learning from large scale annotated data. Since interactions between objects can be reduced to a limited set of atomic spatial relations in 3D, we study the possibility of inferring 3D structure from a text description rather than an image, applying physical relation models to synthesize holistic 3D abstract object layouts satisfying the spatial constraints present in a textual description. We present a generic framework for retrieving images from a textual description of a scene by matching images with these generated abstract object layouts. Images are ranked by matching object detection outputs (bounding boxes) to 2D layout candidates (also represented by bounding boxes) which are obtained by projecting the 3D scenes with sampled camera directions. We validate our approach using public indoor scene datasets and show that our method outperforms baselines built upon object occurrence histograms and learned 2D pairwise relations.


title: Accelerometer-Based Detection of Food Intake in Free-Living Individuals

abstract: The goal of this pilot study is to evaluate the feasibility of using a 3-axis accelerometer attached to the frame of eyeglasses for automatic detection of food intake. A 3D acceleration sensor was attached to the temple of the regular eyeglasses. Ten participants wore the device in two visits (first, laboratory; second, free-living) on different days, reporting the food intake episodes using a pushbutton. Hold-one-out procedure was used to test the algorithm for food intake detection. The accelerometer signal was split into epochs of varying durations (3s, 5s, 10s 15s, 20s, 25s, and 30s); 152 time and frequency domain features were computed for each epoch. A two-stage procedure was used for finding the best feature set suitable for classification. The first stage used minimum Redundancy and Maximum Relevance (mRMR) to get the 30 top-ranked features and the second stage used forward feature selection along with a kNN classifier to get the optimum feature set for each hold-one-out set. The best average F1-score combined from laboratory and free-living experiments was 87.9 +/- 13.8% (Mean±Standard Deviation) for 20s epochs; and 84.7 +/- 7.95% for the shortest epoch of 3s. The results suggest that accelerometer may provide a compelling alternative to other sensor modalities, as the proposed sensor does not require direct attachment to the body and, therefore, significantly improves user comfort and social acceptability of the food intake monitoring system.


title: Cost-Aware Resource Management for Federated Clouds Using Resource Sharing Contracts

abstract: Cloud computing and its pay-as-you-go model continue to provide significant cost benefits and a seamless service delivery model for cloud consumers. The evolution of small-scale and large-scale geo-distributed datacenters operated and managed by individual cloud service providers raises new challenges in terms of effective global resource sharing and management of autonomously-controlled individual datacenter resources. Earlier solutions for geo-distributed clouds have focused primarily on achieving global efficiency in resource sharing that results in significant inefficiencies in local resource allocation for individual datacenters leading to unfairness in revenue and profit earned. In this paper, we propose a new contracts-based resource sharing model for federated geo-distributed clouds that allows cloud service providers to establish resource sharing contracts with individual datacenters apriori for defined time intervals during a 24 hour time period. Based on the established contracts, individual cloud service providers employ a cost-aware job scheduling and provisioning algorithm that enables tasks to complete and meet their response time requirements. The proposed techniques are evaluated through extensive experiments using realistic workloads and the results demonstrate the effectiveness, scalability and resource sharing efficiency of the proposed model.


title: Cooperative communication in wireless networks

abstract: Transmit diversity generally requires more than one antenna at the transmitter. However, many wireless devices are limited by size or hardware complexity to one antenna. Recently, a new class of methods called cooperative communication has been proposed that enables single-antenna mobiles in a multi-user environment to share their antennas and generate a virtual multiple-antenna transmitter that allows them to achieve transmit diversity. This article presents an overview of the developments in this burgeoning field.


title: Planar Luneburg Lens Based on the High Impedance Surface for Effective Ku-Band Wave Focusing

abstract: In this paper, a planar Ku-band Luneburg lens is designed, fabricated, and measured. Based on the high impedance surface, metamaterial units are designed and arranged in an array with gradient equivalent refractive index to achieve good wave focusing effects in the Ku band. The proposed 2-D lens is superior at its compact size and more feasible fabrication process when compared with the conventional 3-D lens. Both theoretical analysis and measurement results have been provided for a systematic study on this lens’ performance, and good agreement between them has been achieved. Benefiting from flexible beam steering, the proposed lens has helpful applications in estimating the direction of arrival in the passive direction finding system, improving the directive radiation for an antenna, and simulating increased target’s radar cross-section characteristic by adding a reflector.


title: Demand forecast sharing in a dual-channel supply chain

abstract: Abstract We assess the benefits of sharing demand forecast information in a manufacturer–retailer supply chain, consisting of a traditional retail channel and a direct channel. The demand is a linear function of price with a Gaussian primary demand (i.e., zero-price market potential). Both the manufacturer and the retailer set their price based on their forecast of the primary demand. In this setting, we investigate the value of sharing demand forecasts. We analyze the ‘make-to-order’ scenario, in which prices are set before and production takes place after the primary demand is known, and the ‘make-to-stock’ scenario, in which production takes place and prices are set before the primary demand is known. We also compare the supply chain performance with and without the direct channel under some assumptions (production cost is zero, and each demand function has the same slope of price). We find that the direct channel has a negative impact on the retailer’s performance, and, under some conditions, the manufacturer and the whole supply chain are better off. Our research extends and complements prior research that has investigated only the inventory and replenishment-related benefits of information sharing.


title: Effective self-training author name disambiguation in scholarly digital libraries

abstract: Name ambiguity in the context of bibliographic citation records is a hard problem that affects the quality of services and content in digital libraries and similar systems. Supervised methods that exploit training examples in order to distinguish ambiguous author names are among the most effective solutions for the problem, but they require skilled human annotators in a laborious and continuous process of manually labeling citations in order to provide enough training examples. Thus, addressing the issues of (i) automatic acquisition of examples and (ii) highly effective disambiguation even when only few examples are available, are the need of the hour for such systems. In this paper, we propose a novel two-step disambiguation method, SAND (Self-training Associative Name Disambiguator), that deals with these two issues. The first step eliminates the need of any manual labeling effort by automatically acquiring examples using a clustering method that groups citation records based on the similarity among coauthor names. The second step uses a supervised disambiguation method that is able to detect unseen authors not included in any of the given training examples. Experiments conducted with standard public collections, using the minimum set of attributes present in a citation (i.e., author names, work title and publication venue), demonstrated that our proposed method outperforms representative unsupervised disambiguation methods that exploit similarities between citation records and is as effective as, and in some cases superior to, supervised ones, without manually labeling any training example.


title: How far does a tweet travel?: Information brokers in the twitterverse

abstract: In this paper, I present evidence on the geographic diffusion patterns of information of Twitter users. I identify three possible information diffusion patterns: random, local and information brokerage and show that the information brokerage pattern describes best how users of Twitter diffuse information through the act of retweeting.


title: Real-time specular highlight removal using bilateral filtering. ECCV

abstract: In this paper, we propose a simple but effective specular highlight removal method using a single input image. Our method is based on a key observation - the maximum fraction of the diffuse color component (so called maximum diffuse chromaticity in the literature) in local patches in color images changes smoothly. Using this property, we can estimate the maximum diffuse chromaticity values of the specular pixels by directly applying low-pass filter to the maximum fraction of the color components of the original image, such that the maximum diffuse chromaticity values can be propagated from the diffuse pixels to the specular pixels. The diffuse color at each pixel can then be computed as a nonlinear function of the estimated maximum diffuse chromaticity. Our method can be directly extended for multi-color surfaces if edge-preserving filters (e.g., bilateral filter) are used such that the smoothing can be guided by the maximum diffuse chromaticity. But maximum diffuse chromaticity is to be estimated. We thus present an approximation and demonstrate its effectiveness. Recent development in fast bilateral filtering techniques enables our method to run over 200× faster than the state-of-the-art on a standard CPU and differentiates our method from previous work.


title: The Role of Energy Storage in Development of Smart Grids

abstract: The adoption of Smart Grid devices throughout utility networks will effect tremendous change in grid operations and usage of electricity over the next two decades. The changes in ways to control loads, coupled with increased penetration of renewable energy sources, offer a new set of challenges in balancing consumption and generation. Increased deployment of energy storage devices in the distribution grid will help make this process happen more effectively and improve system performance. This paper addresses the new types of storage being utilized for grid support and the ways they are integrated into the grid.


Gait parameter adaptation for lower-limb exoskeletons


title: Single-layered complex-valued neural network for real-valued classification problems

abstract: This paper presents a model of complex-valued neuron (CVN) for real-valued classification problems, introducing two new activation functions. In this CVN model, each real-valued input is encoded into a phase between 0 and @p of a complex number of unity magnitude, and multiplied by a complex-valued weight. The weighted sum of inputs is then fed to an activation function. Both the proposed activation functions map complex values into real values, and their role is to divide the net-input (weighted sum) space into multiple regions representing the classes of input patterns. Gradient-based learning rules are derived for each of the activation functions. The ability of such CVN is discussed and tested with two-class problems, such as two- and three-input Boolean problems, and the symmetry detection in binary sequences. We show here that the CVN with both activation functions can form proper boundaries for these linear and nonlinear problems. For solving n-class problems, a complex-valued neural network (CVNN) consisting of n CVNs is also studied. We defined the one exhibiting the largest output among all the neurons as representing the output class. We tested such single-layered CVNNs on several real world benchmark problems. The results show that the classification ability of single-layered CVNN on unseen data is comparable to the conventional real-valued neural network (RVNN) having one hidden layer. Moreover, convergence of the CVNN is much faster than that of the RVNN in most cases.


title: A design of phased array antenna based on the Vivaldi antenna

abstract: A new design process of a phased array antenna at 2GHz is presented by the simulation method. First a suitable antenna pattern for the radiator of Vivaldi is selected as a slot antenna with light weight, small volume, low cost and high gain. A kind of the analysis software of high frequency structure simulation CST MWS is optimized for the VSWR and radiation pattern. However, several antennas with certain arrangement are often used to make up antenna array to realize beam synthesis. 1*10H-plane linear array and 16*10 planar arrays are designed and simulated in this paper. Since behavior of element is not directly relevant to their behavior in the array, mutual coupling effect is discussed after the radiator design. The influence on the radiation pattern caused by the number of the array, the distance of the adjacent elements and the phase and the amplitude of the source current are all analyzed, and a simulation result for the parameter design of antenna array is analyzed either.


title: IIITH at BioASQ Challange 2015 Task 3b: Bio-Medical Question Answering System

abstract: In this paper, we describe our participation in the 2015 BioASQ challenge on Bio-Medical Question Answering. For Question Answering task (Task 3b), teams were provided with natural language questions and asked to retrieve responses from PubMed corpus in the form of documents, snippets, concepts and RDF triplets (Phase A) and direct answers (Phase B). For Phase A, we took the support of PubMed search engine and our snippet extraction technique. In our QA system, apart from the standard techniques discussed in literature, we tried the following novel techniques to - a) leverage web search results for improv- ing question processing and b) identify domain words and dene a new answer ranking function based on number of common domain words. We scored an F-measure of 0.193 for document extraction and F-measure of 0.0717 in snippet generation.


of Domain Adaptation with Unlabeled Data


title: Community detection in networks: A user guide

abstract: Community detection in networks is one of the most popular topics of modern network science. Communities, or clusters, are usually groups of vertices having higher probability of being connected to each other than to members of other groups, though other patterns are possible. Identifying communities is an ill-defined problem. There are no universal protocols on the fundamental ingredients, like the definition of community itself, nor on other crucial issues, like the validation of algorithms and the comparison of their performances. This has generated a number of confusions and misconceptions, which undermine the progress in the field. We offer a guided tour through the main aspects of the problem. We also point out strengths and weaknesses of popular methods, and give directions to their use.


title: Graph-based Semi-supervised Learning for Text Classification

abstract: In this paper, we propose a graph-based representation of document collections in which both documents and features are represented by nodes. The nodes are connected with weights based on word order, context similarity and word frequency. Graph-based representations can overcome the limitations of bag-of-words based representations that suffer from sparseness for collections with short documents. In a series of experiments, we evaluate multiple types of graph-based text features in the context of semi-supervised text classification, and investigate the effect of the number of labeled documents in the collection. We find that graph-based semi-supervised learning outperforms bag-of-words semi-supervised learning but not bag-of-words supervised learning in 20-class text categorization. A large asset of graph-based representations is that they are flexible in the types of nodes and relations that are included.


title: Stigma and its public health implications

abstract: The publication of Erving Goffmans Stigma: Notes on the Management of Spoiled Identity in 1963 generated a profusion of research on the nature sources and consequences of stigma--albeit with considerable variation on how stigma was defined. In our conceptualisation stigma is the result of a process in which a series of five interrelated components combine to generate stigma. In the first component people identify and label human differences. Although most human differences are socially irrelevant differences such as skin colour IQ and sexual preferences are highly salient in many social contexts. The point is that there is a social selection process determining which differences are deemed relevant and consequential and which are not. Medical conditions vary dramatically in the extent to which they are socially significant. Compare hypertension bone fractures and melanoma for example with incontinence AIDS and schizophrenia. (excerpt)


title: aBCD18 - An advanced 0.18um BCD technology for PMIC application

abstract: We present a new advanced 0.18um BCD(Bipolar-CMOS-DMOS) technology with the key features being a 40V HV-MOS and an SSTC(Sidewall Selective Transistor Cell) type EEPROM as well as complimentary available analog devices such as a high gain BJT, 4fF/um2 MIM capacitor, and 10k Ω/sq. poly resistor. To reduce device area and enhance latch up immunity, a 15um depth deep trench isolation process has been developed, which will help to significantly reduce the chip size.


title: Boosting in the presence of noise

abstract: Boosting algorithms are procedures that "boost" low accuracy weak learning algorithms to achieve arbitrarily high accuracy. Over the past decade boosting has been widely used in practice and has become a major research topic in computational learning theory. In this paper we study boosting in the presence of random classification noise, giving both positive and negative results.We show that a modified version of a boosting algorithm due to Mansour and McAllester [14] can achieve accuracy arbitrarily close to the noise rate. We also give a matching lower bound by showing that no efficient black-box boosting algorithm can boost accuracy beyond the noise rate (assuming that one-way functions exist). Finally, we consider a variant of the standard scenario for boosting in which the "weak learner" satisfies a slightly stronger condition than the usual weak learning guarantee. We give an efficient algorithm in this framework which can boost to arbitrarily high accuracy in the presence of classification noise.


title: Should I Protect You? Understanding Developers' Behavior to Privacy-Preserving APIs

abstract: There have been many proposals and developments to improve smartphone users’ location privacy with respect to mobile applications. These include user-centric application permission models and disclosures. However, little attention has been paid to how application developers could build privacypreserving apps. In this paper, we present a laboratory study (N=25) to understand developers’ behavior to enhanced APIs, which are a privacy-preserving modification of the existing Android Location API. In contrast to the existing methods, the studied API facilitates acquiring coarse location information without accessing the geocoordinates. Our results indicate that by offering a redesigned API, programmers can be nudged into making choices with their programming that help to preserve privacy of their users.


Embodied conversational interface agents


title: Graph Database For collaborative Communities

abstract: Data manipulated in an enterprise context are structured data as well as unstructured data such as emails, documents, and social networks. Graphs are a natural way of representing and modeling such data (structured, semistructured, and unstructured ones) in a unified manner. The main advantage of such a structure relies on the dynamic aspect and the capability to represent relations, even multiple ones, between objects. Recent database research work shows a growing interest in the definition of graph models and languages to allow a natural way of presenting data. In this chapter, we provide a survey of the main graph database models and the associated graph query languages. We then present an application using a graph database to extract social networks.


title: Personality trait structure as a human universal.

abstract: Patterns of covariation among personality traits in English-speaking populations can be summarized by the five-factor model (FFM). To assess the cross-cultural generalizability of the FFM, data from studies using 6 translations of the Revised NEO Personality Inventory (P. T. Costa & R. R. McCrae, 1992) were compared with the American factor structure. German, Portuguese, Hebrew, Chinese, Korean, and Japanese samples (N = 7,134) showed similar structures after varimax rotation of 5 factors. When targeted rotations were used, the American factor structure was closely reproduced, even at the level of secondary loadings. Because the samples studied represented highly diverse cultures with languages from 5 distinct language families, these data strongly suggest that personality trait structure is universal.


title: A hybrid method for table detection from document image

abstract: In this paper, we present a hybrid method consisting of three main stages for detecting tables in document images. Based on table structure, our system separates table into two main categories, ruling line table and non-ruling line table. In the first stage, the text and non-text elements in document are classified by a heuristic filter. Then, the white space analysis is used to group the text elements into text lines, while ruling line table candidates are identified from non-text elements. In the second stage, based on the text lines, text and non-text elements, a hybrid method which consist of the alternative bottom-up and top-down approaches is implemented to find the table region candidates. In the final stage, these candidates are examined to get the table regions by analyzing text lines and spare lines. Experimental results with the document database from the ICDAR2013 table competition show that the proposed method works better than the previous ones.


title: On some artificial intelligence methods and technologies for cloud-computing protection

abstract: An overview of data-mining technologies used in applied information security systems is presented. The focus is made on a new and actively developing trend, cloud-computing media (including the socalled fog computing). The status and promising opportunities of using artificial intelligence models and methods to solve information security problems are also discussed.


title: Medical image registration

abstract: Radiological images are increasingly being used in healthcare and medical research. There is, consequently, widespread interest in accurately relating information in the different images for diagnosis, treatment and basic science. This article reviews registration techniques used to solve this problem, and describes the wide variety of applications to which these techniques are applied. Applications of image registration include combining images of the same subject from different modalities, aligning temporal sequences of images to compensate for motion of the subject between scans, image guidance during interventions and aligning images from multiple subjects in cohort studies. Current registration algorithms can, in many cases, automatically register images that are related by a rigid body transformation (i.e. where tissue deformation can be ignored). There has also been substantial progress in non-rigid registration algorithms that can compensate for tissue deformation, or align images from different subjects. Nevertheless many registration problems remain unsolved, and this is likely to continue to be an active field of research in the future.


title: Models for retrieval with probabilistic indexing

abstract: Abstract In this article three retrieval models for probabilistic indexing are described along with evaluation results for each. First is the binary independence indexing (BII) model, which is a generalized version of the Maron and Kuhns indexing model. In this model, the indexing weight of a descriptor in a document is an estimate of the probability of relevance of this document with respect to queries using this descriptor. Second is the retrieval-with-probabilistic-indexing (RPI) model, which is suited to different kinds of probabilistic indexing. For that we assume that each indexing scheme has its own concept of “correctness” to which the probabilities relate. In addition to the probabilistic indexing weights, the RPI model provides the possibility of relevance weighting of search terms. A third model that is similar was proposed by Croft some years ago as an extension of the binary independence retrieval model but it can be shown that this model is not based on the probabilistic ranking principle. The probabilistic indexing weights required for any of these models can be provided by an application of the Darmstadt indexing approach (DIA) for indexing with descriptors from a controlled vocabulary. The experimental results show significant improvements over retrieval with binary indexing. Finally, suggestions are made regarding how the DIA can be applied to probabilistic indexing with free text terms.


title: Continuum thinking and the contexts of personal information management

abstract: Introduction. Recent personal information management literature has underlined the significance of the contextuality of personal information and its use. The present article discusses the applicability of the records continuum model and its generalisation, continuum thinking, as a theoretical framework for explicating the overlap and evolution of the diverse contexts of personal information and their implications for personal information management practices. Method. This article is based on three empirical case studies on 1) the personal information management practices of sound artists (qualitative semi-structured interviews), 2) graduate students' management of scholarly articles (web survey of Swedish students) and 3) the management of personal archives at Swedish archival institutions (qualitative semi-structured interviews). Analysis. The empirical material was analysed using the constant comparative method (case 1), nonparametric statistical tests including Friedman's test, Kruskal-Wallis analysis, Wilcoxon-Signed Rank test, Mann-Whitney-U test and Chi-square test in SPSS version 20.0 (case 2), and thematic analysis (case 3). Results. The continuum approach and the conceptualisation of diverse contextual aspects of personal information management as axes on a Giddensian spatio-temporal continuum can help to understand the contextual changes and continuities of personal information management and use. Conclusions. The records continuum model provides a useful theoretical basis for explicating personal information management as a process of Giddensian time-space structuration similarly to how Upward argued that the theory is useful in the context of explicating the record-keeping process and, in more general terms, providing a framework for theorising particular types of document-centric management processes.


title: A flexible capacitive tactile sensor array for prosthetic hand real-time contact force measurement

abstract: This paper develops a novel flexible capacitive tactile sensor array for prosthetic hand gripping force measurement. The sensor array has 8 × 8 (= 64) sensing units, each sensing unit has a four-layered structure: two thick PET layers with embedded copper electrodes generates a capacitor, a PDMS film with line-structure used as an insulation layer, and a top PDMS bump layer to concentrate external force. The structural design, working principle, and fabrication process of this sensor array are presented. The fabricated tactile sensor array features high flexibility has a spatial resolution of 2 mm. This is followed by the characterization of the sensing unit for normal force measurement and found that the sensing unit has two sensitivities: 4.82 ‰/mN for small contact force and 0.23 ‰/mN for large gripping force measurements. Finally, the tactile sensor array is integrated into a prosthetic hand for gripping force measurement. Results showed that the developed flexible capacitive tactile sensor array could be utilized for tactile sensing and real-time contact force visualization for prosthetic hand gripping applications.


title: Cognition as a dynamic system: Principles from embodiment

abstract: Traditional approaches to cognitive development concentrate on the stability of cognition and explain that stability via concepts segregated from perceiving acting. A dynamic systems approach in contrast focuses on the self-organization of behavior in tasks. This article uses recent results concerning the embodiment of cognition to argue for a dynamic systems approach. The embodiment hypothesis is the idea that intelligence emerges in the interaction of an organism with an environment and as a result of sensory-motor activity. The continual coupling of cognition to the world through the body both adapts cognition to the idiosyncrasies of the here and now, makes it relevant, and provides the mechanism for developmental change.


title: Micro-blogging as online word of mouth branding

abstract: In this paper, we report research results investigating micro-blogging as a form of online word of mouth branding. We analyzed 149,472 micro-blog postings containing branding comments, sentiments, and opinions. We investigated the overall structure of these micro-blog postings, types of expressions, and sentiment fluctuations. Of the branding micro-blogs, nearly 20 percent contained some expressions of branding sentiments. Of these tweets with sentiments, more than 50 percent were positive and 33 percent critical of the company or product. We discuss the implications for organizations in using micro-blogging as part of their overall marketing strategy and branding campaigns.


title: A combined inductive-capacitive proximity sensor and its application to seat occupancy sensing

abstract: This paper presents a simple yet efficient proximity sensor obtained by combining inductive and capacitive sensing principles. Capacitive proximity sensors based on shielding of electric field are known for detecting presence of humans in its vicinity. Such systems may fail to distinguish the proximity of a human from grounded conductive objects. A novel, cost effective, sensing scheme that uses a single sensor, which performs both the inductive and capacitive proximity mode of operation is proposed in this paper. The measurement system uses a signal conditioning unit based on a carrier frequency principle. A prototype sensing system has been built and its application as a seat occupancy sensing system in a vehicle has been verified. The developed system successfully senses human proximity and distinguishes it from other conductive objects.


title: Migrating Service-Oriented System to Cloud Computing: An Experience Report

abstract: Cloud computing has gained significant attention of industry and academic sectors which are interested in adopting or experimenting with this technology. An increasing number of companies are expected to migrate their systems to cloud enabled infrastructures. However, there has not been much attention paid to provide sufficient process support. Since migration projects are likely to encounter several kinds of challenges, it is important to identify and share the process and logistical requirements of migration projects in order to build a body of knowledge of appropriate process, methods, and tools. This paper purports to contribute to the growing knowledge of how to migrate existing systems to cloud computing by reporting our effort aimed at migrating an Open Source Software (OSS) framework, Hackystat, to cloud computing. We report the main steps followed, the process and technical challenges faced, and some of the strategies that helped us to address those challenges. We expect the reported experiences can provide readers with useful insights into the process and technical aspects that should be considered when migrating existing software systems to cloud computing infrastructures.


title: Entity Resolution: Theory, Practice & Open Challenges

abstract: This tutorial brings together perspectives on ER from a variety of fields, including databases, machine learning, natural language processing and information retrieval, to provide, in one setting, a survey of a large body of work. We discuss both the practical aspects and theoretical underpinnings of ER. We describe existing solutions, current challenges, and open research problems.


title: Accurate, Compact, and Power-Efficient Li-Ion Battery Charger Circuit

abstract: A novel, accurate, compact, and power-efficient lithium-ion (Li-Ion) battery charger designed to yield maximum capacity, cycle life, and therefore runtime is presented and experimentally verified. The proposed charger uses a diode to smoothly (i.e., continuously) transition between two high-gain linear feedback loops and control a single power MOS device, automatically charging the battery with constant current and then constant voltage. An adaptive power-efficient charging scheme in the form of a cascaded switching regulator supply ensures the voltage across the charging power-intensive pMOS remains low, thereby reducing its power losses and yielding up to 27% better overall power efficiency. An 83% power-efficient printed circuit board prototype was built and used to charge several Li-Ion batteries to within plusmn0.43% of their optimum full-charge voltage and therefore within a negligibly small fraction of their full capacity


title: GPS‐denied Indoor and Outdoor Monocular Vision Aided Navigation and Control of Unmanned Aircraft

abstract: GPS-denied closed-loop autonomous control of unstable Unmanned Aerial Vehicles (UAVs) such as rotorcraft using information from a monocular camera has been an open problem. Most proposed Vision aided Inertial Navigation Systems (V-INSs) have been too computationally intensive or do not have sufficient integrity for closed-loop flight. We provide an affirmative answer to the question of whether V-INSs can be used to sustain prolonged real-world GPS-denied flight by presenting a V-INS that is validated through autonomous flight-tests over prolonged closed-loop dynamic operation in both indoor and outdoor GPS-denied environments with two rotorcraft unmanned aircraft systems (UASs). The architecture efficiently combines visual feature information from a monocular camera with measurements from inertial sensors. Inertial measurements are used to predict frame-to-frame transition of online selected feature locations, and the difference between predicted and observed feature locations is used to bind in real-time the inertial measurement unit drift, estimate its bias, and account for initial misalignment errors. A novel algorithm to manage a library of features online is presented that can add or remove features based on a measure of relative confidence in each feature location. The resulting V-INS is sufficiently efficient and reliable to enable real-time implementation on resource-constrained aerial vehicles. The presented algorithms are validated on multiple platforms in real-world conditions: through a 16-min flight test, including an autonomous landing, of a 66 kg rotorcraft UAV operating in an unconctrolled outdoor environment without using GPS and through a Micro-UAV operating in a cluttered, unmapped, and gusty indoor environment. © 2013 Wiley Periodicals, Inc.


title: A distributed multi-UAV path planning approach for 3D environments

abstract: During rescue scenarios it is indispensable to obtain an overview of the situation. Unmanned aerial vehicles (UAVs) can gather the necessary information in a fast and efficient way. This paper presents an approach for path planning in 3D environments offering a solution to explore disaster areas including, e. g., partially or completely destroyed buildings. Using multiple UAVs decreases the time needed to receive a complete overview if the problem of coordination and task allocation is solved. We present an approach for the use of multiple UAVs. The UAVs work in a distributed manner without any central coordination instance and cover the exploration of terrains as well as goal-oriented path planning. When using multiple UAVs redundant exploration is avoided through the use of inter-UAV-communication. The approach is based on potential fields and uses the simplicity of the gradient method to calculate paths for fast exploration of the terrain.