Integrated Phased Array Systems in Silicon

Silicon offers a new set of possibilities and challenges for RF, microwave, and millimeter-wave applications. While the high cutoff frequencies of the SiGe heterojunction bipolar transistors and the ever-shrinking feature sizes of MOSFETs hold a lot of promise, new design techniques need to be devised to deal with the realities of these technologies, such as low breakdown voltages, lossy substrates, low-Q passives, long interconnect parasitics, and high-frequency coupling issues. As an example of complete system integration in silicon, this paper presents the first fully integrated 24-GHz eight-element phased array receiver in 0.18-/spl mu/m silicon-germanium and the first fully integrated 24-GHz four-element phased array transmitter with integrated power amplifiers in 0.18-/spl mu/m CMOS. The transmitter and receiver are capable of beam forming and can be used for communication, ranging, positioning, and sensing applications.


Low cost Ka-band transmitter for CubeSat systems

CubeSat platforms grow increasingly popular in commercial ventures as alternative solutions for global Internet networks deep space exploration, and aerospace research endeavors. Many technology companies and system engineers plan to implement small satellite systems as part of global Low Earth Orbit (LEO) inter-satellite constellations. High performing low cost hardware is of key importance in driving these efforts. This paper presents the heterodyne architecture and performance of Ka-Band Integrated Transmitter Assembly (ITA) Module, which could be implemented in nano/microsatellite or other satellite systems as a low-cost solution for high data rate space communication systems. The module converts a 0.9 to 1.1 GHz IF input signal to deliver linear transmission of +29 dBm at 26.7 to 26.9 GHz frequency range with a built-in phase locked oscillator, integrated transmitter, polarizer, and lens corrected antenna.


Identifying Harm Events in Clinical Care through Medical Narratives

Preventable medical errors are estimated to be among the leading causes of injury and death in the United States. To prevent such errors, healthcare systems have implemented patient safety and incident reporting systems. These systems enable clinicians to report unsafe conditions and cases where patients have been harmed due to errors in medical care. These reports are narratives in natural language and while they provide detailed information about the situation, it is non-trivial to perform large scale analysis for identifying common causes of errors and harm to the patients. In this work, we present a method for identifying harm events in patient care and categorize the harm event types based on their severity level. We show that our method which is based on convolutional and recurrent networks with an attention mechanism is able to significantly improve over the existing methods on two large scale datasets of patient reports.


Quality of life in the anxiety disorders: a meta-analytic review.

There has been significant interest in the impact of anxiety disorders on quality of life. In this meta-analytic review, we empirically evaluate differences in quality of life between patients with anxiety disorders and nonclinical controls. Thirty-two patient samples from 23 separate studies (N=2892) were included in the analysis. The results yielded a large effect size indicating poorer quality of life among anxiety disorder patients vs. controls and this effect was observed across all anxiety disorders. Compared to control samples, no anxiety disorder diagnosis was associated with significantly poorer overall quality of life than was any other anxiety disorder diagnosis. Examination of specific domains of QOL suggests that impairments may be particularly prominent among patients with post-traumatic stress disorder. QOL domains of mental health and social functioning were associated with the highest levels of impairment among anxiety disorder patients. These findings are discussed in the context of future research on the assessment of quality of life in the anxiety disorders.


An Investigation into the Use of Common Libraries in Android Apps

The packaging model of Android apps requires the entire code necessary for the execution of an app to be shipped into one single apk file. Thus, an analysis of Android apps often visits code which is not part of the functionality delivered by the app. Such code is often contributed by the common libraries which are used pervasively by all apps. Unfortunately, Android analyses, e.g., for piggybacking detection and malware detection, can produce inaccurate results if they do not take into account the case of library code, which constitute noise in app features. Despite some efforts on investigating Android libraries, the momentum of Android research has not yet produced a complete set of common libraries to further support in-depth analysis of Android apps. In this paper, we leverage a dataset of about 1.5 million apps from Google Play to harvest potential common libraries, including advertisement libraries. With several steps of refinements, we finally collect by far the largest set of 1,113 libraries supporting common functionality and 240 libraries for advertisement. We use the dataset to investigates several aspects of Android libraries, including their popularity and their proportion in Android app code. Based on these datasets, we have further performed several empirical investigations to confirm the motivations behind our work.


' s personal copy Core , animal reminder , and contamination disgust : Three kinds of disgust with distinct personality , behavioral , physiological , and clinical correlates

We examined the relationships between sensitivity to three kinds of disgust (core, animalreminder, and contamination) and personality traits, behavioral avoidance, physiological responding, and anxiety disorder symptoms. Study 1 revealed that these disgusts are particularly associated with neuroticism and behavioral inhibition. Moreover, the three disgusts showed a theoretically consistent pattern of relations on four disgust-relevant behavioral avoidance tasks in Study 2. Similar results were found in Study 3 such that core disgust was significantly related to increased physiological responding during exposure to vomit, while animal-reminder disgust was specifically related to physiological responding during exposure to blood. Lastly, Study 4 revealed that each of the three disgusts showed a different pattern of relations with fear of contamination, fear of animals, and fear of blood– injury relevant stimuli. These findings provide support for the convergent and divergent validity of core, animal-reminder, and contamination disgust. These findings also highlight the possibility that the three kinds of disgust may manifest as a function of different psychological mechanisms (i.e., oral incorporation, mortality defense, disease avoidance) that may give rise to different clinical conditions. However, empirical examination of the mechanisms that underlie the three disgusts will require further refinement of the psychometric properties of the disgust scale. 2008 Elsevier Inc. All rights reserved.


A primer on spatial modeling and analysis in wireless networks

The performance of wireless networks depends critically on their spatial configuration, because received signal power and interference depend critically on the distances between numerous transmitters and receivers. This is particularly true in emerging network paradigms that may include femtocells, hotspots, relays, white space harvesters, and meshing approaches, which are often overlaid with traditional cellular networks. These heterogeneous approaches to providing high-capacity network access are characterized by randomly located nodes, irregularly deployed infrastructure, and uncertain spatial configurations due to factors like mobility and unplanned user-installed access points. This major shift is just beginning, and it requires new design approaches that are robust to spatial randomness, just as wireless links have long been designed to be robust to fading. The objective of this article is to illustrate the power of spatial models and analytical techniques in the design of wireless networks, and to provide an entry-level tutorial.


Digital Literacy: A Conceptual Framework for Survival Skills in the Digital Era

Digital literacy involves more than the mere ability to use software or operate a digital device; it includes a large variety of complex cognitive, motor, sociological, and emotional skills, which users need in order to function effectively in digital environments. The tasks required in this context include, for example, “reading” instructions from graphical displays in user interfaces; using digital reproduction to create new, meaningful materials from existing ones; constructing knowledge from a nonlinear, hypertextual navigation; evaluating the quality and validity of information; and have a mature and realistic understanding of the “rules” that prevail in the cyberspace. This newly emerging concept of digital literacy may be used as a measure of the quality of learners’ work in digital environments, and provide scholars and developers with a more effective means of communication in designing better user-oriented environments. This article proposes a holistic, refined conceptual framework for digital literacy, which includes photo-visual literacy; reproduction literacy; branching literacy; information literacy; and socioemotional literacy.


A 0.003 mm$^{2}$ 10 b 240 MS/s 0.7 mW SAR ADC in 28 nm CMOS With Digital Error Correction and Correlated-Reversed Switching

This paper describes a single-channel, calibration-free Successive-Approximation-Register (SAR) ADC with a resolution of 10 bits at 240 MS/s. A DAC switching technique and an addition-only digital error correction technique based on the non-binary search are proposed to tackle the static and dynamic non-idealities attributed to capacitor mismatch and insufficient DAC settling. The conversion speed is enhanced, and the power and area of the DAC are also reduced by 40% as a result. In addition, a switching scheme lifting the input common mode of the comparator is proposed to further enhance the speed. Moreover, the comparator employs multiple feedback paths for an enhanced regeneration strength to alleviate the metastable problem. Occupying an active area of 0.003 mm 2 and dissipating 0.68 mW from 1 V supply at 240 MS/s in 28 nm CMOS, the proposed design achieves an SNDR of 57 dB with low-frequency inputs and 53 dB at the Nyquist input. This corresponds to a conversion efficiency of 4.8 fJ/c.-s. and 7.8 fJ/c.-s. respectively. The DAC switching technique improves the INL and DNL from +1.15/-1.01 LSB and +0.92/-0.28 LSB to within +0.55/-0.45 LSB and +0.45/-0.23 LSB, respectively. This ADC is at least 80% smaller and 32% more power efficient than reported state-of-the-art ADCs of similar resolutions and Nyquist bandwidths larger than 75 MHz.


Adhesive capsulitis: review of imaging findings, pathophysiology, clinical presentation, and treatment options

Adhesive capsulitis, commonly referred to as “frozen shoulder,” is a debilitating condition characterized by progressive pain and limited range of motion about the glenohumeral joint. It is a condition that typically affects middle-aged women, with some evidence for an association with endocrinological, rheumatological, and autoimmune disease states. Management tends to be conservative, as most cases resolve spontaneously, although a subset of patients progress to permanent disability. Conventional arthrographic findings include decreased capsular distension and volume of the axillary recess when compared with the normal glenohumeral joint, in spite of the fact that fluoroscopic visualization alone is rarely carried out today in favor of magnetic resonance imaging (MRI). MRI and MR arthrography (MRA) have, in recent years, allowed for the visualization of several characteristic signs seen with this condition, including thickening of the coracohumeral ligament, axillary pouch and rotator interval joint capsule, in addition to the obliteration of the subcoracoid fat triangle. Additional findings include T2 signal hyperintensity and post-contrast enhancement of the joint capsule. Similar changes are observable on ultrasound. However, the use of ultrasound is most clearly established for image-guided injection therapy. More aggressive therapies, including arthroscopic release and open capsulotomy, may be indicated for refractory disease, with arthroscopic procedures favored because of their less invasive nature and relatively high success rate.


Issues in building general letter to sound rules

In generaltext-to-speechsystems, it is notpossibleto guaranteethat a lexiconwill containall wordsfoundin a text, thereforesomesystemfor predictingpronunciationfrom theword itself is necessary . Herewe presenta generalframework for building letter to sound (LTS) rulesfrom a word list in a language.The techniquecanbe fully automatic,thougha small amountof handseedingcangive betterresults.We have appliedthis techniqueto English(UK and US), Frenchand German. The generatedmodelsachieve, 75%, 58%, 93% and89%, respecti vely, wordscorrectfor held out data from theword lists. To testour modelson moretypical datawe alsoanalyzedgeneral text, to find which wordsdo not appearin our lexicon. Theseunknown wordswereusedasamorerealistictestcorpusfor ourmodels. We also discussthe distribution and type of suchunknown words.


Telecommunication Fraud Detection Using Data Mining techniques

This document presents the final report of the thesis “Telecommunication Fraud Detection Using Data Mining Techniques”, were a study is made over the effect of the unbalanced data, generated by the Telecommunications Industry, in the construction and performance of classifiers that allows the detection and prevention of frauds. In this subject, an unbalanced data set is characterized by an uneven class distribution where the amount of fraudulent instances (positive) is substantially smaller than the amount normal instances (negative). This will result in a classifier which is most likely to classify data has belonging to the normal class then to the fraud class. At first, an overall inspection is made over the data characteristics and the Naive Bayes model, which is the classifier selected to do the anomaly detection on these experiments. After the characteristics are presented, a feature engineering stage is done with the intent to extend the information contained in the data creating a depper relation with the data itself and model characteristics. A previously proposed solution that consists on undersampling the most abundant class (normal) before building the model, is presented and tested. In the end, the new proposals are presented. The first proposal is to study the effects of changing the intrinsic class distribution parameter in the Naive Bayes model and evaluate its performance. The second proposal consists in estimating margin values that when applied to the model output, attempt to bring more positive instances from previous negative classification. All of these suggested models are validated over a monte-carlo experiment, using data with and without the engineered features.


An Analysis of Buck Converter Efficiency in PWM / PFM Mode with Simulink

This technical paper takes a study into efficiency comparison between PWM and PFM control modes in DC-DC buck converters. Matlab Simulink Models are built to facilitate the analysis of various effects on power loss and converting efficiency, including different load conditions, gate switching frequency, setting of voltage and current thresholds, etc. From efficiency vs. load graph, a best switching frequency is found to achieve a good efficiency throughout the wide load range. This simulation point is then compared to theoretical predictions, justifying the effectiveness of computer based simulation. Efficiencies at two different control modes are compared to verify the improvement of PFM scheme.


Robust Unit Commitment Problem with Demand Response and Wind Energy

To improve the efficiency in power generation and to reduce the greenhouse gas emission, both Demand Response (DR) strategy and intermittent renewable energy have been proposed or applied in electric power systems. However, the uncertainty and the generation pattern in wind farms and the complexity of demand side management pose huge challenges in power system operations. In this paper, we analytically investigate how to integrate DR and wind energy with fossil fuel generators to (i) minimize power generation cost; (2) fully take advantage wind energy with managed demand to reduce greenhouse emission. We first build a two-stage robust unit commitment model to obtain day-ahead generator schedules where wind uncertainty is captured by a polyhedron. Then, we extend our model to include DR strategy such that both price levels and generator schedule will be derived for the next day. For these two NP-hard problems, we derive their mathematical properties and develop a novel and analytical solution method. Our computational study on a IEEE 118 system with 36 units shows that (i) the robust unit commitment model can significantly reduce total cost and fully make use of wind energy; (ii) the cutting plane method is computationally superior to known algorithms.


Learner Modeling for Integration Skills

Complex skill mastery requires not only acquiring individual basic component skills, but also practicing integrating such basic skills. However, traditional approaches to knowledge modeling, such as Bayesian knowledge tracing, only trace knowledge of each decomposed basic component skill. This risks early assertion of mastery or ineffective remediation failing to address skill integration. We introduce a novel integration-level approach to model learners' knowledge and provide fine-grained diagnosis: a Bayesian network based on a new kind of knowledge graph with progressive integration skills. We assess the value of such a model from multifaceted aspects: performance prediction, parameter plausibility, expected instructional effectiveness, and real-world recommendation helpfulness. Our experiments based on a Java programming tutor show that proposed model significantly improves two popular multiple-skill knowledge tracing models on all these four aspects.


Building a Large Annotated Corpus of English: The Penn Treebank

In this paper, we review our experience with constructing one such large annotated corpus--the Penn Treebank, a corpus consisting of over 4.5 million words of American English. During the first three-year phase of the Penn Treebank Project (1989-1992), this corpus has been annotated for part-of-speech (POS) information. In addition, over half of it has been annotated for skeletal syntactic structure. Comments University of Pennsylvania Department of Computer and Information Science Technical Report No. MSCIS-93-87. This technical report is available at ScholarlyCommons: http://repository.upenn.edu/cis_reports/237 Building A Large Annotated Corpus of English: The Penn Treebank MS-CIS-93-87 LINC LAB 260 Mitchell P. Marcus Beatrice Santorini Mary Ann Marcinkiewicz University of Pennsylvania School of Engineering and Applied Science Computer and Information Science Department Philadelphia, PA 19104-6389


INDUCING THE MORPHOLOGICAL LEXICON OF A NATURAL LANGUAGE FROM UNANNOTATED TEXT

This work presents an algorithm for the unsupervised learning, or induction, of a simple morphology of a natural language. A probabilistic maximum a posteriori model is utilized, which builds hierarchical representations for a set of morphs, which are morpheme-like units discovered from unannotated text corpora. The induced morph lexicon stores parameters related to both the “meaning” and “form” of the morphs it contains. These parameters affect the role of the morphs in words. The model is implemented in a task of unsupervised morpheme segmentation of Finnish and English words. Very good results are obtained for Finnish and almost as good results are obtained in the English task.


Unsupervised Learning of the Morphology of a Natural Language

This study reports the results of using minimum description length (MDL) analysis to model unsupervised learning of the morphological segmentation of European languages, using corpora ranging in size from 5,000 words to 500,000 words. We develop a set of heuristics that rapidly develop a probabilistic morphological grammar, and use MDL as our primary tool to determine whether the modifications proposed by the heuristics will be adopted or not. The resulting grammar matches well the analysis that would be developed by a human morphologist. In the final section, we discuss the relationship of this style of MDL grammatical analysis to the notion of evaluation metric in early generative grammar.


Unsupervised Discovery of Morphemes

We present two methods for unsupervised segmentation of words into morphemelike units. The model utilized is especially suited for languages with a rich morphology, such as Finnish. The first method is based on the Minimum Description Length (MDL) principle and works online. In the second method, Maximum Likelihood (ML) optimization is used. The quality of the segmentations is measured using an evaluation method that compares the segmentations produced to an existing morphological analysis. Experiments on both Finnish and English corpora show that the presented methods perform well compared to a current stateof-the-art system.


Conceptual spaces - the geometry of thought

In his book “Conceptual Spaces The Geometry of Thought”, Peter Gärdenfors [1] presents a pioneering theory for representing conceptual knowledge, the basic construct of human thinking and reasoning [4]. The conceptual level is not seen as an alternative to traditional approaches of knowledge representation in artificial intelligence, namely symbolic or subsymbolic methods. Instead, it is meant to complement both approaches. The book is highly recommendable and worth reading, as it does not only tackle many fundamental problems of knowledge representation such as grounding [3], concept formation and similarity comparisons [2], but also outlines novel and enlightening ideas how to overcome these. The book introduces the notion of a conceptual space as a framework for representing knowledge at the conceptual level. It is motivated by contrasting it to other levels of knowledge representation: The highest level, the symbolic level, conceptualizes the human brain as a computing device. Knowledge is represented based on a language consisting of a set of symbols. Logical operations can be performed on these symbols to infer new knowledge. Human reasoning is modeled as a symbol manipulation process. Classical, symbolic artificial intelligence does not very well support central cognitive processes such as the acquisition or formation of new concepts and similarity comparisons. The lowest level, the subsymbolic knowledge representation, is oriented towards the neuro-biological structure of the human brain. Concepts are implicitly represented via activation patterns within the neural network. Learning is modeled by modifying the activation of neurons. Explicit representation of knowledge and concepts is not possible. At the intersection between the symbolic and the subsymbolic level, Gärdenfors introduces the conceptual level. The theory of conceptual spaces is based on semantic spaces with a geometric structure: A conceptual space is formed by a set of quality dimensions. One or several quality dimensions model one domain. An important example used throughout the book is the color domain represented by the quality dimensions hue, saturation and brightness. Conceptual spaces have a cognitive foundation because domains can be grounded in qualities perceivable by the human sensory apparatus. Concepts are represented as conceptual regions described by their properties on the quality dimensions. The geometric structure of conceptual spaces makes it possible to determine distances and therefore provides an inherent similarity measure by taking the distance in the conceptual space as indicator of the semantic similarity. The notion of similarity is an important construct for modeling categorization and concept formation. Using similarity for reasoning can also reflect well the vagueness typical for human reasoning. The strong focus on the cognitive foundation makes the book particularly valuable. It contains many challenging claims which are related to various disciplines by giving evidence from a wide range of literature. This shows the huge and highly interdisciplinary background of the author. Unfortunately, Gärdenfors describes his theory only at a very abstract level and forbears from describing algorithms for the formalization of his theory. The realization of a computational model for conceptual spaces bears many practical problems which still have to be solved. Moreover, no empirical evidence is given for his pioneering, sometimes revolutionary ideas. However, these shortcomings should be considered as challenges to solve in the future. The target audience of the book is highly interdisciplinary: since Gärdenfors tackles the problem of cognitive knowledge representation from a psychologic and computer science perspective as well as from a philosophic, neuroscience and linguistic point of view, this book is worth reading for researchers from many different areas. It is required reading for researchers in cognitive science or artificial intelligence interested in knowledge representation. The book has a clear structure and is very well written. The convincing examples throughout the book illustrate the findings very well and make it easy to understand. Therefore I would also deem Gärdenfors’ book to be suitable for students as introducing literature to various problem fields in cognitive science. It gives readers from related areas the chance to look beyond one’s own nose and get to know an interdisciplinary way of thinking. The book certainly meets the expectations of the highly interdisciplinary research area cognitive science.


Radio Interface Technologies for Cooperative Transmission in 3GPP LTE-Advanced

This paper presents an overview of radio interface technologies for cooperative transmission in 3GPP LTE-Advanced, i.e., coordinated multi-point (CoMP) transmission, enhanced inter-cell interference coordination (eICIC) for heterogeneous deployments, and relay transmission techniques. This paper covers not only the technical components in the 3GPP specifications that have already been released, but also those that were discussed in the Study Item phase of LTE-Advanced, and those that are currently being discussed in 3GPP for potential specification in future LTE releases. key words: LTE-Advanced, CoMP, eICIC, relay


Enhancing network performance in Distributed Cognitive Radio Networks using single-agent and multi-agent Reinforcement Learning

Cognitive Radio (CR) is a next-generation wireless communication system that enables unlicensed users to exploit underutilized licensed spectrum to optimize the utilization of the overall radio spectrum. A Distributed Cognitive Radio Network (DCRN) is a distributed wireless network established by a number of unlicensed users in the absence of fixed network infrastructure such as a base station. Context awareness and intelligence are the capabilities that enable each unlicensed user to observe and carry out its own action as part of the joint action on its operating environment for network-wide performance enhancement. These capabilities can be applied in various application schemes in CR networks such as Dynamic Channel Selection (DCS), congestion control, and scheduling. In this paper, we apply Reinforcement Learning (RL), including single-agent and multi-agent approaches, to achieve context awareness and intelligence. Firstly, we show that the RL approach achieves a joint action that provides better network-wide performance in respect to DCS in DCRNs. The multi-agent approach is shown to provide higher levels of stability compared to the single-agent approach. Secondly, we show that RL achieves high level of fairness. Thirdly, we show the effects of network density and various essential parameters in RL on the network-wide performance.


The integration of the internal and external milieu in the insula during dynamic emotional experiences

Whilst external events trigger emotional responses, interoception (the perception of internal physiological states) is fundamental to core emotional experience. By combining high resolution functional neuroimaging with concurrent physiological recordings, we investigated the neural mechanisms of interoceptive integration during free listening to an emotionally salient audio film. We found that cardiac activity, a key interoceptive signal, was robustly synchronised across participants and centrally represented in the posterior insula. Effective connectivity analysis revealed that the anterior insula, specifically tuned to the emotionally salient moments of the audio stream, serves as an integration hub of interoceptive processing: interoceptive states represented in the posterior insula are integrated with exteroceptive representations by the anterior insula to highlight these emotionally salient moments. Our study for the first time demonstrates the insular hierarchy for interoceptive processing during natural emotional experience. These findings provide an ecologically-valid framework for elucidating the neural underpinnings of emotional deficits in neuropsychiatric disorders.


User Experience Design and Agile Development : From Theory to Practice

We used existing studies on the integration of user experience design and agile methods as a basis to develop a framework for integrating UX and Agile. We performed a field study in an ongoing project of a medium-sized company in order to check if the proposed framework fits in the real world, and how some aspects of the integration of UX and Agile work in a real project. This led us to some conclusions situating contributions from practice to theory and back again. The framework is briefly described in this paper and consists of a set of practices, artifacts, and techniques derived from the literature. By combining theory and practice we were able to confirm some thoughts and identify some gaps—both in the company process and in our proposed framework—and drive our attention to new issues that need to be addressed. We believe that the most important issues in our case study are: UX designers cannot collaborate closely with developers because UX designers are working on multiple projects and that UX designers cannot work up front because they are too busy with too many projects at the same time.


Turbo-Like Beamforming Based on Tabu Search Algorithm for Millimeter-Wave Massive MIMO Systems

For millimeter-wave (mmWave) massive multiple-input-multiple-output (MIMO) systems, codebook-based analog beamforming (including transmit precoding and receive combining) is usually used to compensate the severe attenuation of mmWave signals. However, conventional beamforming schemes involve complicated search among predefined codebooks to find out the optimal pair of analog precoder and analog combiner. To solve this problem, by exploring the idea of turbo equalizer together with the tabu search (TS) algorithm, we propose a Turbo-like beamforming scheme based on TS, which is called Turbo-TS beamforming in this paper, to achieve near-optimal performance with low complexity. Specifically, the proposed Turbo-TS beamforming scheme is composed of the following two key components: 1) Based on the iterative information exchange between the base station (BS) and the user, we design a Turbo-like joint search scheme to find out the near-optimal pair of analog precoder and analog combiner; and 2) inspired by the idea of the TS algorithm developed in artificial intelligence, we propose a TS-based precoding/combining scheme to intelligently search the best precoder/combiner in each iteration of Turbo-like joint search with low complexity. Analysis shows that the proposed Turbo-TS beamforming can considerably reduce the searching complexity, and simulation results verify that it can achieve near-optimal performance.


Multilingual representations for low resource speech recognition and keyword search

This paper examines the impact of multilingual (ML) acoustic representations on Automatic Speech Recognition (ASR) and keyword search (KWS) for low resource languages in the context of the OpenKWS15 evaluation of the IARPA Babel program. The task is to develop Swahili ASR and KWS systems within two weeks using as little as 3 hours of transcribed data. Multilingual acoustic representations proved to be crucial for building these systems under strict time constraints. The paper discusses several key insights on how these representations are derived and used. First, we present a data sampling strategy that can speed up the training of multilingual representations without appreciable loss in ASR performance. Second, we show that fusion of diverse multilingual representations developed at different LORELEI sites yields substantial ASR and KWS gains. Speaker adaptation and data augmentation of these representations improves both ASR and KWS performance (up to 8.7% relative). Third, incorporating un-transcribed data through semi-supervised learning, improves WER and KWS performance. Finally, we show that these multilingual representations significantly improve ASR and KWS performance (relative 9% for WER and 5% for MTWV) even when forty hours of transcribed audio in the target language is available. Multilingual representations significantly contributed to the LORELEI KWS systems winning the OpenKWS15 evaluation.


A geometry-based soft shadow volume algorithm using graphics hardware

Most previous soft shadow algorithms have either suffered from aliasing, been too slow, or could only use a limited set of shadow casters and/or receivers. Therefore, we present a strengthened soft shadow volume algorithm that deals with these problems. Our critical improvements include robust penumbra wedge construction, geometry-based visibility computation, and also simplified computation through a four-dimensional texture lookup. This enables us to implement the algorithm using programmable graphics hardware, and it results in images that most often are indistinguishable from images created as the average of 1024 hard shadow images. Furthermore, our algorithm can use both arbitrary shadow casters and receivers. Also, one version of our algorithm completely avoids sampling artifacts which is rare for soft shadow algorithms. As a bonus, the four-dimensional texture lookup allows for small textured light sources, and, even video textures can be used as light sources. Our algorithm has been implemented in pure software, and also using the GeForce FX emulator with pixel shaders. Our software implementation renders soft shadows at 0.5--5 frames per second for the images in this paper. With actual hardware, we expect that our algorithm will render soft shadows in real time. An important performance measure is bandwidth usage. For the same image quality, an algorithm using the accumulated hard shadow images uses almost two orders of magnitude more bandwidth than our algorithm.


Polymer–Ceramic Composites for Microwave Applications: Fabrication and Performance Assessment

We present a novel technique to fabricate conformal and pliable substrates for microwave applications including systems-on-package. The produced materials are fabricated by combining ceramic powders with polymers to generate a high-contrast substrate that is concurrently pliable (bendable). Several such polymer-ceramic substrates are fabricated and used to examine the performance of a patch antenna and a coupled line filter. This paper presents the substrate mixing method while measurements are given to evaluate the loss performance of the substrates. Overall, the fabricated composites lead to flexible substrates with a permittivity of up to epsivr=20 and sufficiently low loss


Body Image and Personality in Aesthetic Plastic Surgery : A Case-Control Study

Introduction: The amount of research on the relationship between plastic surgery and psychological features, such as personality disorders and indexes of body image dissatisfaction, has increased significantly in the last years. Aim: The aim of the present study was to examine these psychological features among Italian patients who underwent aesthetic plastic surgery, testing the mediating role of the mass media influence on the relationship between them and the choice of aesthetic plastic surgery. The Personality Diagnostic Questionnaire 4+ (PDQ-4+) and the Body Uneasiness Test (BUT) were administered to patients who underwent aesthetic plastic surgery (N = 111) and participants who had no history of aesthetic plastic surgical procedures (N = 149). Results: Results showed that aesthetic patients reported higher indexes of body image disturbance than controls. No significant differences between aesthetic participants and controls were found in all three cluster B personality disorders. Moreover, the effect of body image dissatisfaction on the choice to undergo aesthetic plastic surgery was partially mediated by the influence of mass media. Conclusions: In conclusion, the present study confirmed the importance of body dissatisfaction as a predictor of the choice to undergo aesthetic surgery and highlighted the influence of media messages regarding physical appearance.


A DTC-Based Subsampling PLL Capable of Self-Calibrated Fractional Synthesis and Two-Point Modulation

We present an analog subsampling PLL based on a digital-to-time converter (DTC), which operates with almost no performance gap (176/198 fs RMS jitter) between the integer and the worst case fractional operation, achieving -246.6 dB FOM in the worst case fractional mode. The PLL is capable of two-point, 10 Mbit/s GMSK modulation with -40.5 dB EVM around a 10.24 GHz fractional carrier. The analog nonidealities-DTC gain, DTC nonlinearity, modulating VCO bank gain, and nonlinearity-are calibrated in the background while the system operates normally. This results in ~15 dB fractional spur improvement (from -41 dBc to -56.5 dBc) during synthesis and ~15 dB EVM improvement (from -25 dB to -40.5 dB) during modulation. The paper provides an overview of the mechanisms that contribute to performance degradation in DTC-based PLL/phase modulators and presents ways to mitigate them. We demonstrate state-of-the-art performance in nanoscale CMOS for fractional-N synthesis and phase modulation.


Broadcast Encryption

We introduce new theoretical measures for the qualitative and quantitative assessment of encryption schemes designed for broadcast transmissions. The goal is to allow a central broadcast site to broadcast secure transmissions to an arbitrary set of recipients while minimizing key management related transmissions. We present several schemes that allow a center to broadcast a secret to any subset of privileged users out of a universe of size so that coalitions of users not in the privileged set cannot learn the secret. The most interesting scheme requires every user to store keys and the center to broadcast messages regardless of the size of the privileged set. This scheme is resilient to any coalition of users. We also present a scheme that is resilient with probability against a random subset of users. This scheme requires every user to store keys and the center to broadcast messages. Preliminary version appeared in Advances in Cryptology CRYPTO ’93 Proceedings, Lecture Notes in Computer Science, Vol. 773, 1994, pp. 480–491. Dept. of Computer Science, School of Mathematics, Tel Aviv University, Tel Aviv, Israel, and Algorithmic Research Ltd. E-mail fiat@math.tau.ac.il. Incumbent of the Morris and Rose Goldman Career Development Chair, Dept. of Applied Mathematics and Computer Science, Weizmann Institute of Science, Rehovot 76100, Israel. Research supported by an Alon Fellowship and a grant from the Israel Science Foundation administered by the Israeli Academy of Sciences. E-mail: naor@wisdom.weizmann.ac.il.


A 45nm 4Gb 3-Dimensional Double-Stacked Multi-Level NAND Flash Memory with Shared Bitline Structure

Recently, 3-dimensional (3D) memories have regained attention as a potential future memory solution featuring low cost, high density and high performance. We present a 3D double stacked 4Gb MLC NAND flash memory device with shared bitline structure, with a cell size of 0.0021mum2/bit per unit feature area. The device is designed to support 3D stacking and fabricated by S3 and 45nm floating-gate CMOS technologies.


Alobar holoprosencephaly, mobile proboscis and trisomy 13 in a fetus with maternal gestational diabetes mellitus: a 2D ultrasound diagnosis and review of the literature

Alobar holoprosencephaly is a rare and severe brain malformation due to early arrest in brain cleavage and rotation. We report a congenital anomalous fetus with alobar holoprosencephaly, prenatally diagnosed by two-dimensional (2D) sonography at the 40 weeks of gestation. The mother was affected by gestational diabetes mellitus and was obese (BMI > 30 kg/m2). 2D Ultrasound depicted the cerebral malformation, cyclopy, proboscis, cardiac defects (atrial septal defect, hypoplastic left heart, anomalous communication between right ventricle and aorta) and extremities defects. The newborn died just after delivery. External examination confirmed a mobile proboscis-like nose on the normal nose position. The fetus had both claw hands. The right and left foots showed to be equine. Autopsy confirmed the ultrasound diagnosis and chromosome analysis revealed trisomy 13 (47,XY,+13). Fetopathologic examination showed cyclopy, proboscis and alobar holoprosencephaly of the fetus, which was consistent with Patau syndrome. The teratogenic effect of diabetes on fetus has been described, but no previous clinical case of a congenital anomalous fetus with trisomy 13 and maternal gestational diabetes has been reported. This case report is the first to describe 2D ultrasound diagnosis of alobar holoprosencephaly and trisomy 13 with maternal gestational diabetes mellitus.


Cloud-assisted humanoid robotics for affective interaction

In recent years, the humanoid robot is received great attention, and gradually develop to households and personal service field. The prominent progress of cloud computing, big data, and machine learning fields provides a strong support for the research of the robot. With affective interaction ability of robot has a broad market space and research value. In this paper, we propose a cloud-assisted humanoid robotics for affective interaction system architecture, and introduce the essential composition, design and implementation of related components. Finally, through an actual robot emotional interaction test platform, validating the feasibility and extendibility of proposed architecture.


Paragraph: Thwarting Signature Learning by Training Maliciously

Defending a server against Internet worms and defending a user’s email inbox against spam bear certain similarities. In both cases, a stream of samples arrives, and a classifier must automatically determine whether each sample falls into a malicious target class (e.g., worm network traffic, or spam email). A learner typically generates a classifier automatically by analyzing two labeled training pools: one of innocuous samples, and one of samples that fall in the malicious target class. Learning techniques have previously found success in settings where the content of the labeled samples used in training is either random, or even constructed by a helpful teacher, who aims to speed learning of an accurate classifier. In the case of learning classifiers for worms and spam, however, an adversary controls the content of the labeled samples to a great extent. In this paper, we describe practical attacks against learning, in which an adversary constructs labeled samples that, when used to train a learner, prevent or severely delay generation of an accurate classifier. We show that even a delusive adversary, whose samples are all correctly labeled, can obstruct learning. We simulate and implement highly effective instances of these attacks against the Polygraph [15] automatic polymorphic worm signature generation algorithms.


Diagnostic Performance of a Smartphone‐Based Photoplethysmographic Application for Atrial Fibrillation Screening in a Primary Care Setting

BACKGROUND
Diagnosing atrial fibrillation (AF) before ischemic stroke occurs is a priority for stroke prevention in AF. Smartphone camera-based photoplethysmographic (PPG) pulse waveform measurement discriminates between different heart rhythms, but its ability to diagnose AF in real-world situations has not been adequately investigated. We sought to assess the diagnostic performance of a standalone smartphone PPG application, Cardiio Rhythm, for AF screening in primary care setting.

METHODS AND RESULTS
Patients with hypertension, with diabetes mellitus, and/or aged ≥65 years were recruited. A single-lead ECG was recorded by using the AliveCor heart monitor with tracings reviewed subsequently by 2 cardiologists to provide the reference standard. PPG measurements were performed by using the Cardiio Rhythm smartphone application. AF was diagnosed in 28 (2.76%) of 1013 participants. The diagnostic sensitivity of the Cardiio Rhythm for AF detection was 92.9% (95% CI] 77-99%) and was higher than that of the AliveCor automated algorithm (71.4% [95% CI 51-87%]). The specificities of Cardiio Rhythm and the AliveCor automated algorithm were comparable (97.7% [95% CI: 97-99%] versus 99.4% [95% CI 99-100%]). The positive predictive value of the Cardiio Rhythm was lower than that of the AliveCor automated algorithm (53.1% [95% CI 38-67%] versus 76.9% [95% CI 56-91%]); both had a very high negative predictive value (99.8% [95% CI 99-100%] versus 99.2% [95% CI 98-100%]).

CONCLUSIONS
The Cardiio Rhythm smartphone PPG application provides an accurate and reliable means to detect AF in patients at risk of developing AF and has the potential to enable population-based screening for AF.


An indicator of research front activity: Measuring intellectual organization as uncertainty reduction in document sets

When using scientific literature to model scholarly discourse, a research specialty can be operationalized as an evolving set of related documents. Each publication can be expected to contribute to the further development of the specialty at the research front. The specific combinations of title words and cited references in a paper can then be considered as a signature of the knowledge claim in the paper: new words and combinations of words can be expected to represent variation, while each paper is at the same time selectively positioned into the intellectual organization of a field using context-relevant references. Can the mutual information among these three dimensions— title words, cited references, and sequence numbers—be used as an indicator of the extent to which intellectual organization structures the uncertainty prevailing at a research front? The effect of the discovery of nanotubes (1991) on the previously existing field of fullerenes is used as a test case. Thereafter, this method is applied to science studies with a focus on scientometrics using various sample delineations. An emerging research front about citation analysis can be indicated.


DPP-Net: Device-Aware Progressive Search for Pareto-Optimal Neural Architectures

Recent breakthroughs in Neural Architectural Search (NAS) have achieved state-of-the-art performances in applications such as image classification and language modeling. However, these techniques typically ignore device-related objectives such as inference time, memory usage, and power consumption. Optimizing neural architecture for devicerelated objectives is immensely crucial for deploying deep networks on portable devices with limited computing resources. We propose DPPNet: Device-aware Progressive Search for Pareto-optimal Neural Architectures, optimizing for both device-related (e.g., inference time and memory usage) and device-agnostic (e.g., accuracy and model size) objectives. DPP-Net employs a compact search space inspired by current state-of-the-art mobile CNNs, and further improves search efficiency by adopting progressive search (Liu et al. 2017). Experimental results on CIFAR-10 are poised to demonstrate the effectiveness of Pareto-optimal networks found by DPP-Net, for three different devices: (1) a workstation with Titan X GPU, (2) NVIDIA Jetson TX1 embedded system, and (3) mobile phone with ARM Cortex-A53. Compared to CondenseNet and NASNet (Mobile), DPP-Net achieves better performances: higher accuracy & shorter inference time on various devices. Additional experimental results show that models found by DPP-Net also achieve considerablygood performance on ImageNet as well.


Network forensic frameworks: Survey and research challenges

Network forensics is the science that deals with capture, recording, and analysis of network traffic for detecting intrusions and investigating them. This paper makes an exhaustive survey of various network forensic frameworks proposed till date. A generic process model for network forensics is proposed which is built on various existing models of digital forensics. Definition, categorization and motivation for network forensics are clearly stated. The functionality of various Network Forensic Analysis Tools (NFATs) and network security monitoring tools, available for forensics examiners is discussed. The specific research gaps existing in implementation frameworks, process models and analysis tools are identified and major challenges are highlighted. The significance of this work is that it presents an overview on network forensics covering tools, process models and framework implementations, which will be very much useful for security practitioners and researchers in exploring this upcoming and young discipline. a 2010 Elsevier Ltd. All rights reserved.


What does media use reveal about personality and mental health? An exploratory investigation among German students

The present study aimed to investigate the relationship between personality traits, mental health variables and media use among German students. The data of 633 participants were collected. Results indicate a positive association between general Internet use, general use of social platforms and Facebook use, on the one hand, and self-esteem, extraversion, narcissism, life satisfaction, social support and resilience, on the other hand. Use of computer games was found to be negatively related to these personality and mental health variables. The use of platforms that focus more on written interaction (Twitter, Tumblr) was assumed to be negatively associated with positive mental health variables and significantly positively with depression, anxiety, and stress symptoms. In contrast, Instagram use, which focuses more on photo-sharing, correlated positively with positive mental health variables. Possible practical implications of the present results for mental health, as well as the limitations of the present work are discussed.


Failure as a Service ( FaaS ) : A Cloud Service for Large-Scale , Online Failure Drills

Cloud computing is pervasive, but cloud service outages still take place. One might say that the computing forecast for tomorrow is “cloudy with a chance of failure.” One main reason why major outages still occur is that there are many unknown large-scale failure scenarios in which recovery might fail. We propose a new type of cloud service, Failure as a Service (FaaS), which allows cloud services to routinely perform large-scale failure drills in real deployments.


Young adults' experiences of seeking online information about diabetes and mental health in the age of social media

BACKGROUND
The Internet is a primary source of health information for many. Since the widespread adoption of social media, user-generated health-related content has proliferated, particularly around long-term health issues such as diabetes and common mental health disorders (CMHDs).

OBJECTIVE
To explore perceptions and experiences of engaging with health information online in a sample of young adults familiar with social media environments and variously engaged in consuming user-generated content.

METHODS
Forty semi-structured interviews were conducted with young adults, aged 18-30, with experience of diabetes or CMHDs. Data were analysed following a thematic networks approach to explore key themes around online information-seeking and content consumption practices.

RESULTS
Although participants primarily discussed well-rehearsed approaches to health information-seeking online, particularly reliance on search engines, their accounts also reflected active engagement with health-related content on social media sites. Navigating between professionally produced websites and user-generated content, many of the young adults seemed to appreciate different forms of health knowledge emanating from varied sources. Participants described negotiating health content based on social media practices and features and assessing content heuristically. Some also discussed habitual consumption of content related to their condition as integrated into their everyday social media use.

CONCLUSION
Technologies such as Facebook, Twitter and YouTube offer opportunities to consume and assess content which users deem relevant and useful. As users and organizations continue to colonize social media platforms, opportunities are increasing for health communication and intervention. However, how such innovations are adopted is dependent on their alignment with users' expectations and consumption practices.


Web-Based Virtual Learning Environments: A Research Framework and a Preliminary Assessment of Effectiveness in Basic IT Skills Training

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Computer Self-Efficacy: Development of a Measure and Initial Test

This paper discusses the role of individuals’ beliefs about their abilities to competently use computers (computer self-efficacy) in the determination of computer use. A survey of Canadian managers and professionals was conducted to develop and validate a measure of computer self-efficacy and to assess both its impacts and antecedents. Computer selfefficacy was found to exert a significant influence on individuals’ expectations of the outcomes of using computers, their emotional reactions to computers (affect and anxiety), well as their actual computer use. An individual’s self-efficacy and outcome expectations were found to be positively influenced by the encouragement of others in their work group, as well as others’ use of computers. Thus, self-efficacy represents an important individual trait, which moderates organizational influences (such as encouragement and support) on an individual’s decision to use computers. Understanding self-efficacy, then, is important o the successful implementation of systems in Introduction Understanding the factors that influence an individual’s use of information technology has been a goal of MIS research since the mid-1970s, when organizations and researchers began to find that adoption of new technology was not living up to expectations. Lucas (1975, 1978) provides some of the earliest evidence of the individual or behavioral factors that influenced IT adoption. The first theoretical perspective to gain widespread acceptance in this research was the Theory of Reasoned Action (Fishbein and Ajzen, 1975). This theory maintains that individuals would use computers if they could see that there would be positive benefits (outcomes) associated with using them. This theory is still widely used today in the IS literature and has demonstrated validity. However, there is also a growing recognition that additional explanatory variables are needed (e.g., Thompson, et al., 1991; Webster and Martocchio, 1992). One such variable, examined in this research, comes from the writings of Albert Bandura and his work on Social Cognitive Theory (Bandura, 1986). Self-efficacy, the belief that one has the capability to perform a particular behavior, is an important construct in social psychology. Self-efficacy perceptions have been found to influence decisions about what behaviors to undertake (e.g., Bandura, et al., 1977; Betz and Hackett, 1981), the effort exerted and persistence in attempting those behaviors (e.g., Barling and Beattie, 1983; Brown and Inouye, 1978), the emotional responses (including stress and anxiety) of the individual performing the behaviors (e.g., Bandura, et al., 1977; Stumpf, et a1.,1987), and the actual performance attainments of the individual with respect to the behavior (e.g., Barling M/S Quarterly/June 1995 189 Computer Self-Efficacy---Measurement and Beattie, 1983; Collins, 1985; Locke, et al., 1984; Schunk, 1981; Wood and Bandura, 1989). These effects have been shown for a wide variety of behaviors in both clinical and managerial settings. Within a management context, self-efficacy has been found to be related to attendance (Frayne and Latham, 1987; Latham and Frayne, 1989), career choice and dev,elopment (Betz and Hackett, 1981; Jones, 1986), research productivity (Taylor, et al., 1984), and sales performance (Barling and Beattie, 1983). Several more recent studies (Burkhardt and Brass, 1990; Gist, et a1.,1989; Hill, et al., 1986; 1987; Webster and Martocohio, 1992; 1993) have examined the relationship between self-efficacy with respect to using computers and a variety of computer behaviors. These studies found evidence of a relationship between selfefficacy and registration in computer courses at universities (Hill, et al., 1987), adoption of high technology products (Hill, et al., 1986) and innovations (Burkhardt and Brass, 1990), as well performance in software training (Gist, et al., 1989; Webster and Martocchio, 1992; 1993). All of the studies argue the need for further research to explore fully the role of self-efficacy in computing behavior. This paper describes the first study in a program of research aimed at understanding the impact of self-efficacy on individual reactions to computing technology. The study involves the development of a measure for computer self-efficacy and a test of its reliability and validity. The measure was evaluated by examining its performance in a nomological network, through structural equations modeling. A research model for this purpose was developed with reference to literature from social psychology, as well as prior IS research. The paper is organized as follows. The next section presents the theoretical foundation for this research. The third section discusses the development of the self-efficacy measure. The research model is described and the hypotheses are presented in the following section. Then, the research methodology is outlined, and the results of the analyses are presented. The paper concludes with a discussion of the implications of these findings and the strengths and limitations of the research. Theoretical Background Social Cognitive Theory Social Cognitive Theory (Bandura, 1977; 1978; 1982; 1986) is a widely accepted, empirically validated model of individual behavior. It is based on the premise that environmental influences such as social pressures or unique situational characteristics, cognitive and other personal factors including personality as well as demographic haracteristics, and behavior are reciprocally determined. Thus, individuals choose the environments in which they exist in


Judo strategy. The competitive dynamics of Internet time.

Competition on the Internet is creating fierce battles between industry giants and small-scale start-ups. Smart start-ups can avoid those conflicts by moving quickly to uncontested ground and, when that's no longer possible, turning dominant players' strengths against them. The authors call this competitive approach judo strategy. They use the Netscape-Microsoft battles to illustrate the three main principles of judo strategy: rapid movement, flexibility, and leverage. In the early part of the browser wars, for instance, Netscape applied the principle of rapid movement by being the first company to offer a free stand-alone browser. This allowed Netscape to build market share fast and to set the market standard. Flexibility became a critical factor later in the browser wars. In December 1995, when Microsoft announced that it would "embrace and extend" competitors' Internet successes, Netscape failed to give way in the face of superior strength. Instead it squared off against Microsoft and even turned down numerous opportunities to craft deep partnerships with other companies. The result was that Netscape lost deal after deal when competing with Microsoft for common distribution channels. Netscape applied the principle of leverage by using Microsoft's strengths against it. Taking advantage of Microsoft's determination to convert the world to Windows or Windows NT, Netscape made its software compatible with existing UNIX systems. While it is true that these principles can't replace basic execution, say the authors, without speed, flexibility, and leverage, very few companies can compete successfully on Internet time.


Forward Reasoning and Dependency-Directed Backtracking in a System for Computer-Aided Circuit Analysis

We present a rule-based system for computer-aided circuit analysis. The set of rules, called EL, is written in a rule language called ARS. Rules are implemented by ARS as pattern-directed invocation demons monitoring an associative data base. Deductions are performed in an antecedent manner, giving EL's analysis a catch-as-catch-can flavour suggestive of the behavior of expert circuit analyzers. We call this style of circuit analysis propagation of constraints. The system threads deduced fact~ with justifications which mention the antecedent facts attd the rule used. These justifications may be examined by the user to gain in right into the operation of the set of rules as they apply to a problem. ~q~e same justifications are used by the system to determine the currently active dat~-base context Jor reasoning in hypothetical situations. They are also used by the system in the at~alysis of failures to reduce the search space. This leads to effective control of combinatorial search which we call dependency-directed backtracking.


Gender Differences in the Perception and Use of E-Mail: An Extension to the Technology Acceptance Model

This study extends Davis' (1989) TAM model and Straub's (1994) SPIR addendum by adding gender to an IT diffusion model. The Technology Acceptance Model (TAM) has been widely studied in IS research as an explanation of the use of information systems across IS-types and nationalities. While this line of research has found significant cross-cultural differences, it has ignored the effects of gender, even though in socio-linguistic research, gender is a fundamental aspect of culture. Indeed, sociolinguistic research that has shown that men tend to focus discourse on hierarchy and independence while women focus on intimacy and solidarity. This literature provides a solid grounding for conceptual extensions to the IT diffusion research and the Technology Acceptance Model. Testing gender differences that might relate to beliefs and use of computer-based media, the present study sampled 392 female and male responses via a cross-sectional survey instrument. The sample drew from comparable groups of knowledge workers using E-mail systems in the airline industry in North America, Asia, and Europe. Study findings indicate that women and men differ in their perceptions but not use of E-mail. These findings suggest that researchers should include gender in IT diffusion models along with other cultural effects. Managers and co-workers, moreover, need to realize that the same mode of communication may be perceived differently by the sexes, suggesting that more favorable communications environments might be created, environments that take into account not only organizational contextual factors, but also the gender of users. The creation of these environments involves not only the actual deployment of communication media, but also organizational training on communications media.


EDUCATIONAL DATA MINING

Computer-based learning systems can now keep detailed logs of user-system interactions, including key clicks, eye-tracking, and video data, opening up new opportunities to study how students learn with technology. Educational Data Mining (EDM; Romero, Ventura, Pechenizkiy, & Baker, 2010) is concerned with developing, researching, and applying computerized methods to detect patterns in large collections of educational data – patterns that would otherwise be hard or impossible to analyze due to the enormous volume of data they exist within. Data of interest is not restricted to interactions of individual students with an educational system (e.g., navigation behavior, input to quizzes and interactive exercises) but might also include data from collaborating students (e.g., text chat), administrative data (e.g., school, school district, teacher), and demographic data (e.g., gender, age, school grades). Data on student affect (e.g., motivation, emotional states) has also been a focus, which can be inferred from physiological sensors (e.g., facial expression, seat posture and perspiration). EDM uses methods and tools from the broader field of Data Mining (Witten & Frank, 2005), a sub-field of Computer Science and Artificial Intelligence that has been used for purposes as diverse as credit card fraud detection, analysis of gene sequences in bioinformatics, or the analysis of purchasing behaviors of customers. Distinguishing EDM features are its particular focus on educational data and problems, both theoretical (e.g., investigating a learning hypothesis) and practical (e.g., improving a learning tool). Furthermore, EDM makes a methodological contribution by developing and researching data mining techniques for educational applications. Typical steps in an EDM project include data acquisition, data preprocessing (e.g., data “cleaning”), data mining, and validation of results.


Why Do Men Get More Attention? Exploring Factors Behind Success in an Online Design Community

Online platforms are an increasingly popular tool for people to produce, promote or sell their work. However recent studies indicate that social disparities and biases present in the real world might transfer to online platforms and could be exacerbated by seemingly harmless design choices on the site (e.g., recommendation systems or publicly visible success measures). In this paper we analyze an exclusive online community of teams of design professionals called Dribbble and investigate apparent differences in outcomes by gender. Overall, we find that men produce more work, and are able to show it to a larger audience thus receiving more likes. Some of this effect can be explained by the fact that women have different skills and design different images. Most importantly however, women and men position themselves differently in the Dribbble community. Our investigation of users’ position in the social network shows that women have more clustered and gender homophilous following relations, which leads them to have smaller and more closely knit social networks. Overall, our study demonstrates that looking behind the apparent patterns of gender inequalities in online markets with the help of social networks and product differentiation helps us to better understand gender differences in success and failure.


Removing ECG noise from surface EMG signals using adaptive filtering

Surface electromyograms (EMGs) are valuable in the pathophysiological study and clinical treatment for dystonia. These recordings are critically often contaminated by cardiac artefact. Our objective of this study was to evaluate the performance of an adaptive noise cancellation filter in removing electrocardiogram (ECG) interference from surface EMGs recorded from the trapezius muscles of patients with cervical dystonia. Performance of the proposed recursive-least-square adaptive filter was first quantified by coherence and signal-to-noise ratio measures in simulated noisy EMG signals. The influence of parameters such as the signal-to-noise ratio, forgetting factor, filter order and regularization factor were assessed. Fast convergence of the recursive-least-square algorithm enabled the filter to track complex dystonic EMGs and effectively remove ECG noise. This adaptive filter procedure proved a reliable and efficient tool to remove ECG artefact from surface EMGs with mixed and varied patterns of transient, short and long lasting dystonic contractions.


Forecasting the behavior of multivariate time series using neural networks

Abstract--This pal~er presents a neural network approach to multivariate time-series anal.lwis. Real world observations ~?/flottr /.'ices in three cities have been used as a benchmark in our ev~eriments, l"eed/orward connectionist networks have bt,t,tl ~k,.signed to model flottr l , ices over the period/iom ..lltgttsl 1972 to Novt,mher 1980./or the cities ~?[Blt[]ith~. :lliltneapoli.s, and Kansas Cit): Remarkable sttccess has heen achieved ill training the lletworks to leatvt the price CllrlV [i," each t?/these cities and in m a k i l l g acc l t ra le price predictions. Our r¢,~lt/l.s show that the it¢ltral network al~proat'h is a leading contender with the statistical modeling apl,oaches.


Dose-response relationship in music therapy for people with serious mental disorders: systematic review and meta-analysis.

Serious mental disorders have considerable individual and societal impact, and traditional treatments may show limited effects. Music therapy may be beneficial in psychosis and depression, including treatment-resistant cases. The aim of this review was to examine the benefits of music therapy for people with serious mental disorders. All existing prospective studies were combined using mixed-effects meta-analysis models, allowing to examine the influence of study design (RCT vs. CCT vs. pre-post study), type of disorder (psychotic vs. non-psychotic), and number of sessions. Results showed that music therapy, when added to standard care, has strong and significant effects on global state, general symptoms, negative symptoms, depression, anxiety, functioning, and musical engagement. Significant dose-effect relationships were identified for general, negative, and depressive symptoms, as well as functioning, with explained variance ranging from 73% to 78%. Small effect sizes for these outcomes are achieved after 3 to 10, large effects after 16 to 51 sessions. The findings suggest that music therapy is an effective treatment which helps people with psychotic and non-psychotic severe mental disorders to improve global state, symptoms, and functioning. Slight improvements can be seen with a few therapy sessions, but longer courses or more frequent sessions are needed to achieve more substantial benefits.


Javalanche: efficient mutation testing for Java

To assess the quality of a test suite, one can use mutation testing - seeding artificial defects (mutations) into the program and checking whether the test suite finds them. Javalanche is an open source framework for mutation testing Java programs with a special focus on automation, efficiency, and effectiveness. In particular, Javalanche assesses the impact of individual mutations to effectively weed out equivalent mutants; it has been demonstrated to work on programs with up to 100,000 lines of code.


Embedded Trench Redistribution Layers (RDL) by Excimer Laser Ablation and Surface Planer Processes

This paper reports the demonstration of 2-5 µm embedded trench formation in dry film polymer dielectrics such as Ajinomoto build-up film (ABF) and Polyimide without using chemical mechanical polishing (CMP) process. The trenches in these dielectrics were formed by excimer laser ablation, followed by metallization of trenches by copper plating processes and overburden removal with surface planer tool. The materials and processes integrated in this work are scalable to large panel fabrication at much higher throughput, for interposers and high density fan-out packaging at lower cost and higher performance than silicon interposers.


ESPRIT-estimation of signal parameters via rotational invariance techniques

High-resolution signal parameter estimation is a problem of significance in many signal processing applications. Such applications include direction-of-arrival (DOA) estimation, system identification, and time series analysis. A novel approach to the general problem of signal parameter estimation is described. Although discussed in the context of direction-of-arrival estimation, ESPRIT can be applied to a wide variety of problems including accurate detection and estimation of sinusoids in noise. It exploits an underlying rotational invariance among signal subspaces induced by an array of sensors with a translational invariance structure. The technique, when applicable, manifests significant performance and computational advantages over previous algorithms such as MEM, Capon's MLM, and MUSIC.


Assessment of Knowledge , Perception and Practice of Maternal Nutrition Among Pregnant Mother Attending Antenatal Care in Selected Health

Nutrition during preconception as well as throughout pregnancy has a major impact on the outcome of pregnancy. Therefore, the aim of this study was designed to assess knowledge, perception and practices of maternal nutrition among pregnant women during antenatal care in selected health center of Horo Guduru Wollega zone. A facility-based cross-sectional study design was conducted on total 405 pregnant mothers from January to June, 2017. A semi structured interview and questionnaires was used to collect information in the areas of socio-demographic, knowledge, perception, and practices towards maternal nutrition among pregnant mothers. Statistical Package for Social Sciences (SPSS) version 20.0 was used to perform descriptive statistics. The result obtained during study indicates that 63.5%, 70.6% and74.6% of pregnant mother good knowledge, perception and practices respectively while 36.5%, 29.4% and 25.4% was poor knowledge, perception and practice respectively. This study clearly indicated that less than half of pregnant mother’s attending antenatal care in the study area had poor knowledge, perception and practices. Therefore, nutrition education should be intensified to improve the overall knowledge, perception, and practices of pregnant mothers towards maternal nutrition in different villages, health centers, health posts and hospitals.


Novelty and diversity metrics for recommender systems: choice, discovery and relevance

There is an increasing realization in the Recommender Systems (RS) field that novelty and diversity are fundamental qualities of recommendation effectiveness and added-value. We identify however a gap in the formalization of novelty and diversity metrics –and a consensus around them– comparable to the recent proposals in IR diversity. We study a formal characterization of different angles that RS novelty and diversity may take from the end-user viewpoint, aiming to contribute to a formal definition and understanding of different views and meanings of these magnitudes under common groundings. Building upon this, we derive metric schemes that take item position and relevance into account, two aspects not generally addressed in the novelty and diversity metrics reported in the RS literature.


Comparative evaluation of soft-switching concepts for bi-directional buck+boost dc-dc converters

Soft-switching techniques are an enabling technology to further reduce the losses and the volume of automotive dc-dc converters, utilized to interconnect the high voltage battery or ultra-capacitor to the dc-link of a Hybrid Electrical Vehicle (HEV) or a Fuel Cell Vehicle (FCV). However, as the performance indices of a power electronics converter, such as efficiency and power density, are competing and moreover dependent on the underlying specifications and technology node, a comparison of different converter topologies naturally demands detailed analytical models. Therefore, to investigate the performance of the ARCP, CF-ZVS-M, SAZZ and ZCT-QZVT soft-switching converters, the paper discusses in detail the advantages and drawbacks of each concept, and the impact of the utilized semiconductor technology and silicon area on the converter efficiency. The proposed analytical models that correlate semiconductor, capacitor and inductor losses with the component volume furthermore allow for a comparison of power density and to find the η-ρ-Pareto-Front of the CF-ZVS-M converter.


Development of a robotic finger with an active dual-mode twisting actuation and a miniature tendon tension sensor

A robot finger is developed with enhanced grasping force and speed using active dual-mode twisting actuation, which is a type of twisted string actuation. This actuation system has two twisting modes (Speed Mode and Force Mode) with different radii of the twisted string, and the twisting mode can be changed automatically. Therefore, the actuator operates like an automatic transmission having two transmission ratios, and the robot finger can generate a large grasping force or fast motion depending on the twisting mode. In addition, a miniature tendon tension sensor is developed and embedded in the fingertip for automatic mode change and grasping force control. A relation between the fingertip force and the tension of the tendon is derived by kinematic and kinetic analyses of the robot finger. The performance of the robotic finger and the tension sensor are verified by experimental results.


Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM

Sentiment analysis on large-scale social media data is important to bridge the gaps between social media contents and real world activities including political election prediction, individual and public emotional status monitoring and analysis, and so on. Although textual sentiment analysis has been well studied based on platforms such as Twitter and Instagram, analysis of the role of extensive emoji uses in sentiment analysis remains light. In this paper, we propose a novel scheme for Twitter sentiment analysis with extra attention on emojis. We first learn bi-sense emoji embeddings under positive and negative sentimental tweets individually, and then train a sentiment classifier by attending on these bi-sense emoji embeddings with an attention-based long short-term memory network (LSTM). Our experiments show that the bi-sense embedding is effective for extracting sentiment-aware embeddings of emojis and outperforms the state-of-the-art models. We also visualize the attentions to show that the bi-sense emoji embedding provides better guidance on the attention mechanism to obtain a more robust understanding of the semantics and sentiments.


A Look into 30 Years of Malware Development from a Software Metrics Perspective

During the last decades, the problem of malicious and unwanted software (malware) has surged in numbers and sophistication. Malware plays a key role in most of today’s cyber attacks and has consolidated as a commodity in the underground economy. In this work, we analyze the evolution of malware since the early 1980s to date from a software engineering perspective. We analyze the source code of 151 malware samples and obtain measures of their size, code quality, and estimates of the development costs (effort, time, and number of people). Our results suggest an exponential increment of nearly one order of magnitude per decade in aspects such as size and estimated effort, with code quality metrics similar to those of regular software. Overall, this supports otherwise confirmed claims about the increasing complexity of malware and its production progressively becoming an industry.


Maturity Models for Systems Thinking

Recent decades have seen a rapid increase in the complexity of goods, products, and services that society has come to demand. This has necessitated a corresponding growth in the requirements demanded of organizational systems and the people who work in them. The competence a person requires to be effective in working in such systems has become an area of increased interest to scholars and practitioners in many disciplines. How can we assess the degree to which a person is executing the competencies required to do good systems work? Several industries now utilize maturity models in the attempt to evaluate and cultivate people’s ability to effectively execute complex tasks. This paper will examine current thought regarding the value and pitfalls of maturity models. It will identify principles and exemplars that could guide the development of a Maturity Model of Systems Thinking Competence (MMSTC) for the varied roles people inhabit in systems contexts.


Learning Attractor Dynamics for Generative Memory

A central challenge faced by memory systems is the robust retrieval of a stored pattern in the presence of interference due to other stored patterns and noise. A theoretically well-founded solution to robust retrieval is given by attractor dynamics, which iteratively clean up patterns during recall. However, incorporating attractor dynamics into modern deep learning systems poses difficulties: attractor basins are characterised by vanishing gradients, which are known to make training neural networks difficult. In this work, we avoid the vanishing gradient problem by training a generative distributed memory without simulating the attractor dynamics. Based on the idea of memory writing as inference, as proposed in the Kanerva Machine, we show that a likelihood-based Lyapunov function emerges from maximising the variational lower-bound of a generative memory. Experiments shows it converges to correct patterns upon iterative retrieval and achieves competitive performance as both a memory model and a generative model.


Artificial intelligence theory (Basic concepts)

Taking the bionic approach as a basis, the article discusses the main concepts of the theory of artificial intelligence as a field of knowledge, which studies the principles of creation and functioning of intelligent systems based on multidimensional neural-like growing networks. The general theory of artificial intelligence includes the study of neural-like elements and multidimensional neural-like growing networks, temporary and long term memory, study of the functional organization of the “brain” of the artificial intelligent systems, of the sensor system, modulating system, motor system, conditioned and unconditioned reflexes, reflexes arc (ring), motivation, purposeful behavior, of “thinking”, “consciousness”, “subconscious and artificial personality developed as a result of training and education”.


Dual-band bandpass filter using helical resonators

This paper demonstrates non-uniform pitch helical resonators for realization of dual-band bandpass filters. By modifying the pitch and coil diameter of conventional uniform pitch helical resonators, the desired dual-band frequencies can be obtained. The external coupling and inter-resonator coupling structure are also illustrated. A non-uniform pitch helical resonator was designed and fabricated with unloaded quality-factor (>900) for both passbands. A 2nd order Butterworth filter that has passbands at 840 MHz and 2590 MHz has been designed. The simulation results show that the filter has a good selectivity with 6% and 4.3% fractional bandwidth at the passbands, respectively. The good power handling capability also suggests that the filter is applicable for microcell/picocell LTE base stations.


Automation of Attendances in Classrooms using RFID

This paper automates the design and implementation of students’ attendance management system, taken into consideration easy access and time saving. The system will be used in faculty of computing and information system (FCIT) at King Abdulaziz University (KAU). Radio Frequency Identification (RFID) and wireless technology are two technologies which will be applied as infrastructure in the indoor environment. University Based Services (UBS) and tag IDs of the RFID are being used in this paper, in order to determine the attendance list for academic staff. Once the academic staff enters a classroom, he will be able to register student’s presence. Therefore, an academic system for identifying and tracking attendance at computing college at KAU will be described, and hybrid RFID and Wireless LAN (WLAN) technologies will be implemented in FCIT academic environment.


Got Flow?: Using Machine Learning on Physiological Data to Classify Flow

As information technologies (IT) are both, drivers of highly engaging experiences and sources of disruptions at work, the phenomenon of flow - defined as "the holistic sensation that people feel when they act with total involvement" [5, p. 36] - has been suggested as promising vehicle to understand and enhance user behavior. Despite the growing relevance of flow at work, contemporary measurement approaches of flow are of subjective and retrospective nature, limiting our possibilities to investigate and support flow in a reliable and timely manner. Hence, we require objective and real-time classification of flow. To address this issue, this article combines recent theoretical considerations from psychology and experimental research on the physiology of flow with machine learning (ML). The overall aim is to build classifiers to distinguish flow states (i.e., low and high flow). Our results indicate that flow-classifiers can be derived from physiological signals. Cardiac features seem to play an important role in this process resulting in an accuracy of 72.3%. Hereby, our findings may serve as foundation for future work aiming to build flow-aware IT-systems.


Measuring Universal Intelligence in Agent-Based Systems Using the Anytime Intelligence Test

This paper aims to quantify and analyze the intelligence of artificial agent collectives. A universal metric is proposed and used to empirically measure intelligence for several different agent decision controllers. Accordingly, the effectiveness of various algorithms is evaluated on a per-agent basis over a selection of abstracted, canonical tasks of different algorithmic complexities. Results reflect the different settings over which cooperative multiagent systems can be significantly more intelligent per agent than others. We identify and discuss some of the factors influencing the collective performance of these systems.


Creating Constructivist Learning Environments on the Web: The Challenge in Higher Education

Australian universities have traditionally relied on government funding to support undergraduate teaching. As the government has adopted the ‘user-pays’ principle, universities have been forced to look outside their traditional market to expand the undergraduate, post-graduate and international offerings. Alternate delivery methods in many universities have utilised web-based instruction as a basis for this move because of three perceptions: access by the target market is reasonably significant, it is a cost-effective method of delivery, and it provides global access. Since the mid sixties, the trend for both on-campus teaching and teaching at a distance has been to use behaviourist instructional strategies for subject development, which rely on the development of a set of instructional sequences with predetermined outcomes. These models, whilst applicable in a behaviourist environment, are not serving instructional designers well when the theoretical foundation for the subject outcomes is based on a constructivist approach to learning, since the constructivist group of theories places less emphasis on the sequence of instruction and more emphasis on the design of the learning environment. (Jonassen, 1994. p 35). In a web-based environment this proves to be even more challenging. This paper will review current research in design goals for web-based constructivist learning environments, and a move towards the development of models. The design of two web-based subjects will be explored in the context of the design goals developed by Duffy and Cunningham (1996 p 177) who have produced some basic assumptions that they call “metaphors we teach by”. The author seeks to examine the seven goals for their relevance to the instructional designer through the examination of their relevance to the web-based subjects, both of which were framed in constructivist theory.


Robust road marking detection using convex grouping method in around-view monitoring system

As the around-view monitoring (AVM) system becomes one of the essential components for advanced driver assistance systems (ADAS), many applications using AVM such as parking guidance system are actively being developed. As a key step for such applications, detecting road markings robustly is a very important issue to be solved. However, compared to the lane marking detection methods, detection of non-lane markings, such as text marks painted on the road, has been less studied so far. While some of methods for detecting non-lane markings exist, many of them are restricted to roadways only, or work poorly on AVM images. In this paper, we propose an algorithm which can robustly detect non-lane road markings on AVM images. We first propose a difference-of-Gaussian based method for extracting a connected component set, followed by a novel grouping method for grouping connected components based on convexity condition. For a classification task, we exploit the Random Forest classifier. We demonstrate the robustness and detection accuracy of our methods through various experiments by using the dataset collected from various environments.


Focused Crawling: A New Approach to Topic-Specific Web Resource Discovery

The rapid growth of the World-Wide Web poses unprecedented scaling challenges for general-purpose crawlers and search engines. In this paper we describe a new hypertext resource discovery system called a Focused Crawler. The goal of a focused crawler is to selectively seek out pages that are relevant to a pre-defined set of topics. The topics are specified not using keywords, but using exemplary documents. Rather than collecting and indexing all accessible Web documents to be able to answer all possible ad-hoc queries, a focused crawler analyzes its crawl boundary to find the links that are likely to be most relevant for the crawl, and avoids irrelevant regions of the Web. This leads to significant savings in hardware and network resources, and helps keep the crawl more up-to-date. To achieve such goal-directed crawling, we designed two hypertext mining programs that guide our crawler: a classifier that evaluates the relevance of a hypertext document with respect to the focus topics, and a distiller that identifies hypertext nodes that are great access points to many relevant pages within a few links. We report on extensive focused-crawling experiments using several topics at different levels of specificity. Focused crawling acquires relevant pages steadily while standard crawling quickly loses its way, even though they are started from the same root set. Focused crawling is robust against large perturbations in the starting set of URLs. It discovers largely overlapping sets of resources in spite of these perturbations. It is also capable of exploring out and discovering valuable resources that are dozens of links away from the start set, while carefully pruning the millions of pages that may lie within this same radius. Our anecdotes suggest that focused crawling is very effective for building high-quality collections of Web documents on specific topics, using modest desktop hardware.  1999 Published by Elsevier Science B.V. All rights reserved.


A Training Algorithm for Optimal Margin Classifiers

A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjusted automatically to match the complexity of the problem. The solution is expressed as a linear combination of supporting patterns. These are the subset of training patterns that are closest to the decision boundary. Bounds on the generalization performance based on the leave-one-out method and the VC-dimension are given. Experimental results on optical character recognition problems demonstrate the good generalization obtained when compared with other learning algorithms.


Searching the world wide Web

The coverage and recency of the major World Wide Web search engines was analyzed, yielding some surprising results. The coverage of any one engine is significantly limited: No single engine indexes more than about one-third of the "indexable Web," the coverage of the six engines investigated varies by an order of magnitude, and combining the results of the six engines yields about 3.5 times as many documents on average as compared with the results from only one engine. Analysis of the overlap between pairs of engines gives an estimated lower bound on the size of the indexable Web of 320 million pages.


WebWatcher : A Tour Guide for the World Wide Web

1 We explore the notion of a tour guide software agent for assisting users browsing the World Wide Web. A Web tour guide agent provides assistance similar to that provided by a human tour guide in a museum { it guides the user along an appropriate path through the collection, based on its knowledge of the user's interests, of the location and relevance of various items in the collection, and of the way in which others have interacted with the collection in the past. This paper describes a simple but operational tour guide, called WebWatcher, which has given over 5000 tours to people browsing CMU's School of Computer Science Web pages. WebWatcher accompanies users from page to page, suggests appropriate hyperlinks, and learns from experience to improve its advice-giving skills. We describe the learning algorithms used by WebWatcher, experimental results showing their e ectiveness, and lessons learned from this case study in Web tour guide agents.


Application-specific slicing for MVNO and traffic characterization [Invited]

In this paper, we apply the concept of a software-defined data plane to defining new services for mobile virtual network operators (MVNOs) to empower them with the capability of tailoring fine-grained subscription plans that can meet users' demands. For this purpose, we have recently proposed the concept of application-and/ or device-specific slicing that classifies application-and/or device-specific traffic into slices and introduced various applications of our proposed system [Optical Fiber Communication Conf. (OFC), 2016, paper Th3I.4]. This paper reports the prototype implementation of the proposal into a real MVNO connecting customized smartphones so we can identify applications/devices from the given traffic with 100% accuracy. We first characterize the traffic patterns of popular applications to build a detailed understanding of how network resources are utilized for real users and applications and what their demands on different network resources are. Then, we classify traffic according to the devices and find that the flow characteristics of LTE RAN slices are very similar to those of 3G. We discover that most of the bandwidth is consumed by those flows with the duration of several hundreds of seconds, and the flow size is larger than 5 Mbytes in both LTE and 3G MVNO networks.


A Solution to Single Point of Failure Using Voter Replication and Disagreement Detection

This paper suggests a method, called distributed voting, to overcome the problem of the single point of failure in a TMR system used in robotics and industrial control applications. It uses time redundancy and is based on TMR with disagreement detector feature. This method masks faults occurring in the voter where the TMR system can continue its function properly. The method has been evaluated by injecting faults into Vertex2Pro and Vertex4 Xilinx FPGAs An analytical evolution is also performed. The results of both evaluation approaches show that the proposed method can improve the reliability and the mean time to failure (MTTF) of a TMR system by at least a factor of (2-RV (t)) where RV(t) is the reliability of the voter


Error detecting and error correcting codes

When a message is transmitted, it has the potential to get scrambled by noise. This is certainly true of voice messages, and is also true of the digital messages that are sent to and from computers. Now even sound and video are being transmitted in this manner. A digital message is a sequence of 0’s and 1’s which encodes a given message. More data will be added to a given binary message that will help to detect if an error has been made in the transmission of the message; adding such data is called an error-detecting code. More data may also be added to the original message so that errors made in transmission may be detected, and also to figure out what the original message was from the possibly corrupt message that was received. This type of code is an error-correcting code.


Millibot Trains for Enhanced Mobility

The objective of this work is to enhance the mobility of small mobile robots by enabling them to link into a train configuration capable of crossing relatively large obstacles. In particular, we are building on Millibots, semiautonomous, tracked mobile sensing/communication platforms at the 5-cm scale previously developed at Carnegie Mellon University. TheMillibot Train concept provides couplers that allow the Millibot modules to engage/disengage under computer control and joint actuators that allow lifting of one module by another and control of the whole train shape in two dimensions. A manually configurable train prototype demonstrated the ability to climb standard stairs and vertical steps nearly half the train length. A fully functional module with powered joints has been developed and several have been built and tested. Construction of a set of six modules is well underway and will allow testing of the complete train in the near future. This paper focuses on the development, design, and construction of the electromechanical hardware for the Millibot Train.