Query: A Semi-automatized Modular Annotation Tool for Ancient Manuscript Annotation

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WebGT: An Interactive Web-Based System for Historical Document Ground Truth Generation <=SEP=> We present WebGT, the first web-based system to help users produce ground truth data for document images. This user-friendly software system helps historians and computer scientists collectively annotate historical documents. It supports real time collaboration among remote sites independent of the local operating system and also provides several novel semi-automatic tools that have proven effective for annotating degraded documents.

The OCRopus open source OCR system <=SEP=> OCRopus is a new, open source OCR system emphasizing modularity, easy extensibility, and reuse, aimed at both the research community and large scale commercial document conversions. This paper describes the current status of the system, its general architecture, as well as the major algorithms currently being used for layout analysis and text line recognition.

Creating Ground Truth for Historical Manuscripts with Document Graphs and Scribbling Interaction <=SEP=> Ground truth is both - indispensable for training and evaluating document analysis methods, and yet very tedious to create manually. This especially holds true for complex historical manuscripts that exhibit challenging layouts with interfering and overlapping handwriting. In this paper, we propose a novel semi-automatic system to support layout annotations in such a scenario based on document graphs and a pen-based scribbling interaction. On the one hand, document graphs provide a sparse page representation that is already close to the desired ground truth and on the other hand, scribbling facilitates an efficient and convenient pen-based interaction with the graph. The performance of the system is demonstrated in the context of a newly introduced database of historical manuscripts with complex layouts.

Aletheia - An Advanced Document Layout and Text Ground-Truthing System for Production Environments <=SEP=> Large-scale digitisation has led to a number of new possibilities with regard to adaptive and learning based methods in the field of Document Image Analysis and OCR. For ground truth production of large corpora, however, there is still a gap in terms of productivity. Ground truth is not only crucial for training and evaluation at the development stage of tools but also for quality assurance in the scope of production workflows for digital libraries. This paper describes Aletheia, an advanced system for accurate and yet cost-effective ground truthing of large amounts of documents. It aids the user with a number of automated and semi-automated tools which were partly developed and improved based on feedback from major libraries across Europe and from their digitisation service providers which are using the tool in a production environment. Novel features are, among others, the support of top-down ground truthing with sophisticated split and shrink tools as well as bottom-up ground truthing supporting the aggregation of lower-level elements to more complex structures. Special features have been developed to support working with the complexities of historical documents. The integrated rules and guidelines validator, in combination with powerful correction tools, enable efficient production of highly accurate ground truth.

LabelMe: A Database and Web-Based Tool for Image Annotation <=SEP=> We seek to build a large collection of images with ground truth labels to be used for object detection and recognition research. Such data is useful for supervised learning and quantitative evaluation. To achieve this, we developed a web-based tool that allows easy image annotation and instant sharing of such annotations. Using this annotation tool, we have collected a large dataset that spans many object categories, often containing multiple instances over a wide variety of images. We quantify the contents of the dataset and compare against existing state of the art datasets used for object recognition and detection. Also, we show how to extend the dataset to automatically enhance object labels with WordNet, discover object parts, recover a depth ordering of objects in a scene, and increase the number of labels using minimal user supervision and images from the web.

Decoronation - a conservative method to treat ankylosed teeth for preservation of alveolar ridge prior to permanent prosthetic reconstruction: literature review and case presentation. <=SEP=> Avulsed teeth that are stored extraorally in a dry environment for >60 min generally develop replacement root resorption or ankylosis following their replantation due to the absence of a vital periodontal ligament on their root surface. One negative sequelae of such ankylosis is tooth infra-positioning and the local arrest of alveolar bone growth. Removal of an ankylosed tooth may be difficult and traumatic leading to esthetic bony ridge deformities and optimal prosthetic treatment interferences. Recently a treatment option for ankylosed teeth named 'de-coronation' gained interest, particularly in pediatric dentistry that concentrated in dental traumatology. This article reviews the up-to-date literature that has been published on decoronation with respect to its importance for future prosthetic rehabilitation followed by a case presentation that demonstrates its clinical benefits.

A Component-Based Dynamic Link Support for Safety-Critical Embedded Systems <=SEP=> Safety-critical embedded systems have to undergo rigorous development process in order to ensure that their function will not compromise humans or environment where they operate. Therefore, they rely on simple and proven-in-use design. However, with growing software complexity, maintenance becomes very important aspect in safety domain. Recent approaches for managing maintenance allow to perform changes on software at design-time, which implies that the whole system has to be rebuilt when the application software changes. In this paper, we describe more flexible solution for updating the application software. We apply the component-based paradigm to construct the application software, i.e. we define a model of a software function that can be dynamically linked with the entire operating system (OS). In order to avoid the usage of the OS-provided support for dynamic linking, we design software functions as position-independent and relocation-free binaries with well-defined interfaces. With the help of component-based paradigm we show how to simplify the link support and make it suitable for safety domain.

NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study <=SEP=> This paper introduces a novel large dataset for example-based single image super-resolution and studies the state-of-the-art as emerged from the NTIRE 2017 challenge. The challenge is the first challenge of its kind, with 6 competitions, hundreds of participants and tens of proposed solutions. Our newly collected DIVerse 2K resolution image dataset (DIV2K) was employed by the challenge. In our study we compare the solutions from the challenge to a set of representative methods from the literature and evaluate them using diverse measures on our proposed DIV2K dataset. Moreover, we conduct a number of experiments and draw conclusions on several topics of interest. We conclude that the NTIRE 2017 challenge pushes the state-of-the-art in single-image super-resolution, reaching the best results to date on the popular Set5, Set14, B100, Urban100 datasets and on our newly proposed DIV2K.

Parameter Estimation for Probabilistic Finite-State Transducers <=SEP=> Weighted finite-state transducers suffer from the lack of a training algorithm. Training is even harder for transducers that have been assembled via finite-state operations such as composition, minimization, union, concatenation, and closure, as this yields tricky parameter tying. We formulate a “parameterized FST” paradigm and give training algorithms for it, including a general bookkeeping trick (“expectation semirings”) that cleanly and efficiently computes expectations and gradients. 1 Background and Motivation Rational relations on strings have become widespread in language and speech engineering (Roche and Schabes, 1997). Despite bounded memory they are well-suited to describe many linguistic and textual processes, either exactly or approximately. A relation is a set of (input, output) pairs. Relations are more general than functions because they may pair a given input string with more or fewer than one output string. The class of so-called rational relations admits a nice declarative programming paradigm. Source code describing the relation (a regular expression) is compiled into efficient object code (in the form of a 2-tape automaton called a finite-state transducer). The object code can even be optimized for runtime and code size (via algorithms such as determinization and minimization of transducers). This programming paradigm supports efficient nondeterminism, including parallel processing over infinite sets of input strings, and even allows “reverse” computation from output to input. Its unusual flexibility for the practiced programmer stems from the many operations under which rational relations are closed. It is common to define further useful operations (as macros), which modify existing relations not by editing their source code but simply by operating on them “from outside.” ∗A brief version of this work, with some additional material, first appeared as (Eisner, 2001a). A leisurely journal-length version with more details has been prepared and is available. The entire paradigm has been generalized to weighted relations, which assign a weight to each (input, output) pair rather than simply including or excluding it. If these weights represent probabilities P (input, output) or P (output | input), the weighted relation is called a joint or conditional (probabilistic) relation and constitutes a statistical model. Such models can be efficiently restricted, manipulated or combined using rational operations as before. An artificial example will appear in §2. The availability of toolkits for this weighted case (Mohri et al., 1998; van Noord and Gerdemann, 2001) promises to unify much of statistical NLP. Such tools make it easy to run most current approaches to statistical markup, chunking, normalization, segmentation, alignment, and noisy-channel decoding,1 including classic models for speech recognition (Pereira and Riley, 1997) and machine translation (Knight and Al-Onaizan, 1998). Moreover, once the models are expressed in the finitestate framework, it is easy to use operators to tweak them, to apply them to speech lattices or other sets, and to combine them with linguistic resources. Unfortunately, there is a stumbling block: Where do the weights come from? After all, statistical models require supervised or unsupervised training. Currently, finite-state practitioners derive weights using exogenous training methods, then patch them onto transducer arcs. Not only do these methods require additional programming outside the toolkit, but they are limited to particular kinds of models and training regimens. For example, the forward-backward algorithm (Baum, 1972) trains only Hidden Markov Models, while (Ristad and Yianilos, 1996) trains only stochastic edit distance. In short, current finite-state toolkits include no training algorithms, because none exist for the large space of statistical models that the toolkits can in principle describe and run. Given output, find input to maximize P (input, output).

Data mining in software engineering <=SEP=> The increased availability of data created as part of the sof tware development process allows us to apply novel analysis techniques on the data and use the results to guide t he process’s optimization. In this paper we describe variou s data sources and discuss the principles and techniques of data mi ning as applied on software engineering data. Data that can b e mined is generated by most parts of the development process: requi rements elicitation, development analysis, testing, debu gging, and maintenance. Based on this classification we survey the mini ng approaches that have been used and categorize them accord ing to the corresponding parts of the development process and th e task they assist. Thus the survey provides researchers wit h a concise overview of data mining techniques applied to softw are engineering data, and aids practitioners on the selecti on of appropriate data mining techniques for their work.

Signal Processing for FMCW SAR <=SEP=> The combination of frequency-modulated continuous-wave (FMCW) technology and synthetic aperture radar (SAR) techniques leads to lightweight cost-effective imaging sensors of high resolution. One limiting factor to the use of FMCW sensors is the well-known presence of nonlinearities in the transmitted signal. This results in contrast- and range-resolution degradation, particularly when the system is intended for high-resolution long-range applications, as it is the case for SAR. This paper presents a novel processing solution, which solves the nonlinearity problem for the whole range profile. Additionally, the conventional stop-and-go approximation used in pulse-radar algorithms is not valid in FMCW SAR applications under certain circumstances. Therefore, the motion within the sweep needs to be taken into account. Analytical development of the FMCW SAR signal model, starting from the deramped signal and without using the stop-and-go approximation, is presented in this paper. The model is then applied to stripmap, spotlight, and single-transmitter/multiple-receiver digital-beamforming SAR operational mode. The proposed algorithms are verified by processing real FMCW SAR data collected with the demonstrator system built at the Delft University of Technology.

Air-Gap Convection in Rotating Electrical Machines <=SEP=> This paper reviews the convective heat transfer within the air gap of both cylindrical and disk geometry rotating electrical machines, including worked examples relevant to fractional horsepower electrical machines. Thermal analysis of electrical machines is important because torque density is limited by maximum temperature. Knowledge of surface convective heat transfer coefficients is necessary for accurate thermal modeling, for example, using lumped parameter models. There exists a wide body of relevant literature, but much of it has traditionally been in other application areas, dominated by mechanical engineers, such as gas turbine design. Particular attention is therefore given to the explanation of the relevant nondimensional parameters and to the presentation of measured convective heat transfer correlations for a wide variety of situations from laminar to turbulent flow at small and large gap sizes for both radial-flux and axial-flux electrical machines.

CAPTCHA: Using Hard AI Problems for Security <=SEP=> We introduce captcha, an automated test that humans can pass, but current computer programs can’t pass: any program that has high success over a captcha can be used to solve an unsolved Artificial Intelligence (AI) problem. We provide several novel constructions of captchas. Since captchas have many applications in practical security, our approach introduces a new class of hard problems that can be exploited for security purposes. Much like research in cryptography has had a positive impact on algorithms for factoring and discrete log, we hope that the use of hard AI problems for security purposes allows us to advance the field of Artificial Intelligence. We introduce two families of AI problems that can be used to construct captchas and we show that solutions to such problems can be used for steganographic communication. captchas based on these AI problem families, then, imply a win-win situation: either the problems remain unsolved and there is a way to differentiate humans from computers, or the problems are solved and there is a way to communicate covertly on some channels.

Multiple social identities and stereotype threat: imbalance, accessibility, and working memory. <=SEP=> In 4 experiments, the authors showed that concurrently making positive and negative self-relevant stereotypes available about performance in the same ability domain can eliminate stereotype threat effects. Replicating past work, the authors demonstrated that introducing negative stereotypes about women's math performance activated participants' female social identity and hurt their math performance (i.e., stereotype threat) by reducing working memory. Moving beyond past work, it was also demonstrated that concomitantly presenting a positive self-relevant stereotype (e.g., college students are good at math) increased the relative accessibility of females' college student identity and inhibited their gender identity, eliminating attendant working memory deficits and contingent math performance decrements. Furthermore, subtle manipulations in questions presented in the demographic section of a math test eliminated stereotype threat effects that result from women reporting their gender before completing the test. This work identifies the motivated processes through which people's social identities became active in situations in which self-relevant stereotypes about a stigmatized group membership and a nonstigmatized group membership were available. In addition, it demonstrates the downstream consequences of this pattern of activation on working memory and performance.

Multiuser Resource Allocation for Mobile-Edge Computation Offloading <=SEP=> Mobile-edge computation offloading (MECO) offloads intensive mobile computation to clouds located at the edges of cellular networks. Thereby, MECO is envisioned as a promising technique for prolonging the battery lives and enhancing the computation capacities of mobiles. In this paper, we consider resource allocation in a MECO system comprising multiple users that time share a single edge cloud and have different computation loads. The optimal resource allocation is formulated as a convex optimization problem for minimizing the weighted sum mobile energy consumption under constraint on computation latency and for both the cases of infinite and finite edge cloud computation capacities. The optimal policy is proved to have a threshold-based structure with respect to a derived offloading priority function, which yields priorities for users according to their channel gains and local computing energy consumption. As a result, users with priorities above and below a given threshold perform complete and minimum offloading, respectively. Computing the threshold requires iterative computation. To reduce the complexity, a sub-optimal resource-allocation algorithm is proposed and shown by simulation to have close-to-optimal performance.

Trimming and Improving Skip-thought Vectors <=SEP=> The skip-thought model has been proven to be effective at learning sentence representations and capturing sentence semantics. In this paper, we propose a suite of techniques to trim and improve it. First, we validate a hypothesis that, given a current sentence, inferring the previous and inferring the next sentence provide similar supervision power, therefore only one decoder for predicting the next sentence is preserved in our trimmed skip-thought model. Second, we present a connection layer between encoder and decoder to help the model to generalize better on semantic relatedness tasks. Third, we found that a good word embedding initialization is also essential for learning better sentence representations. We train our model unsupervised on a large corpus with contiguous sentences, and then evaluate the trained model on 7 supervised tasks, which includes semantic relatedness, paraphrase detection, and text classification benchmarks. We empirically show that, our proposed model is a faster, lighter-weight and equally powerful alternative to the original skip-thought model.

Self-tuning fuzzy PID controller for electro-hydraulic cylinder <=SEP=> Hydraulic systems are widely used in industrial applications. This is due to its high speed of response with fast start, stop and speed reversal possible. The torque to inertia ratio is also large with resulting high acceleration capability. The nonlinear properties of hydraulic cylinder make the position tracking control design challenging. This paper presents the development and implementation of self-tuning fuzzy PID controller in controlling the position variation of electro-hydraulic actuator. The hydraulic system mathematical model is approximated using system identification technique. The simulation studies were done using Matlab Simulink environment. The output performance was compared with the design using pole-placement controller. The roots mean squared error for both techniques showed that self-tuning Fuzzy PID produced better result compared to using pole-placement controller.

Formula Derivation and Verification of Characteristic Impedance for Offset Double-Sided Parallel Strip Line (DSPSL) <=SEP=> Double-sided parallel strip line (DSPSL) is a versatile microwave transmission line. With an offset, it allows for large variations of the characteristic impedance and therefore flexibility for the design of novel microwave devices. In this letter, a formula for the characteristic impedance of DSPSL is derived by the fuzzy EM method and verified by commercial software. Very good agreement is observed.

Two Conceptions of the Physical <=SEP=> The debate over physicalism in philosophy of mind can be seen as concerning an inconsistent tetrad of theses: (1) if physicalism is true, a priori physicalism is true; (2) a priori physicalism is false; (3) if physicalism is false, epiphenomenalism is true; (4) epiphenomenalism is false. This paper argues that one may resolve the debate by distinguishing two conceptions of the physical: on the theory-based conception, it is plausible that (2) is true and (3) is false; on the object-based conception, it is plausible that (3) is true and (2) is false. The paper also defends and explores the version of physicalism that results from this strategy. 1. One way to view the contemporary debate in philosophy of mind over physicalism is to see it as being organized around an inconsistent tetrad of theses. These are: (1) If physicalism is true, a priori physicalism is true. (2) A priori physicalism is false. (3) If physicalism is false, epiphenomenalism is true. (4) Epiphenomenalism is false. It is obvious of course that these theses are inconsistent: (1) and (2) entail that physicalism is false, while (3) and (4) entail that it is true. Barring ambiguity, therefore, one thing we know is that one of the theses is false. On the other hand, each of the theses has powerful considerations, or at least what seem initially to be powerful considerations, in its favor. (1) In support of (1) are considerations of supervenience, articulated most clearly in recent times by Frank Jackson and David Chalmers. A priori physicalism is a thesis with two parts. The first part-the physicalist part-is that the mental supervenes with metaphysical necessity on the physical. The second part-the a priori part-is that mental truths are a priori entailed by physical truths. Many philosophers hold that supervenience stands in need of justification or explanation; Jackson and Chalmers argue that the project of justifying or explaining supervenience just is the project of making it plausible that there is an a priori entailment of the mental by the physical. This suggests that the first part of

A modular architecture for building automation systems <=SEP=> The deployment of building automation systems (BAS) allows to increase comfort, safety and security and to reduce operational cost. Today such systems typically follow a two-layered hierarchical approach. While control networks interconnect distributed sensors, actuators and controllers, a backbone provides the necessary infrastructure for management tasks hosted by configuration and management devices. In addition, devices interconnecting the control network with the backbone and the backbone with further networks (e.g., the Internet) play a strategic role. All BAS devices contributing to a particular functionality differ in their requirements for hardware. This paper discusses requirements for devices used in the building automation domain and presents our work in progress to assemble platforms with different purposes relying on a modular architecture.

Exploring actor-object relationships for query-focused multi-document summarization <=SEP=> Most research on multi-document summarization explores methods that generate summaries based on queries regardless of the users’ preferences. We note that, different users can generate somewhat different summaries on the basis of the same source data and query. This paper presents our study on how to exploit the information regards how users summarized their texts. Models of different users can be used either separately, or in an ensemble-like fashion. Machine learning methods are explored in the construction of the individual models. However, we explore yet another hypothesis. We believe that the sentences selected into the summary should be coherent and supplement each other in their meaning. One method to model this relationship between sentences is by detecting actor–object relationship (AOR). The sentences that satisfy this relationship have their importance value enhanced. This paper combines ensemble summarizing system and AOR to generate summaries. We have evaluated this method on DUC 2006 and DUC 2007 using ROUGE measure. Experimental results show the supervised method that exploits the ensemble summarizing system combined with AOR outperforms previous models when considering performance in query-based multidocument summarization tasks. Keyword User-based summarization · Actor–object relationship · Multi-document summarization · Ensemble summarizing system · Training data construction Communicated by V. Loia. M. Valizadeh (B) · P. Brazdil LIAAD INESC Tec, University of Porto, Porto, Portugal e-mail: valizadehmr@gmail.com P. Brazdil FEP, University of Porto, Porto, Portugal e-mail: pbrazdil@inescporto.pt

WhyCon : An Efficent , Marker-based Localization System <=SEP=> We present an open-source marker-based localization system intended as a low-cost easy-to-deploy solution for aerial and swarm robotics. The main advantage of the presented method is its high computational efficiency, which allows its deployment on small robots with limited computational resources. Even on low-end computers, the core component of the system can detect and estimate 3D positions of hundreds of black and white markers at the maximum frame-rate of standard cameras. The method is robust to changing lighting conditions and achieves accuracy in the order of millimeters to centimeters. Due to its reliability, simplicity of use and availability as an open-source ROS module (http://purl.org/robotics/whycon), the system is now used in a number of aerial robotics projects where fast and precise relative localization is required.

A Novel Feature Selection Based on One-Way ANOVA F-Test for E-Mail Spam Classification <=SEP=> Spam is commonly defined as unwanted e-mails and it became a global threat against e-mail users. Although, Support Vector Machine (SVM) has been commonly used in e-mail spam classification, yet the problem of high data dimensionality of the feature space due to the massive number of e-mail dataset and features still exist. To improve the limitation of SVM, reduce the computational complexity (efficiency) and enhancing the classification accuracy (effectiveness). In this study, feature selection based on one-way ANOVA F-test statistics scheme was applied to determine the most important features contributing to e-mail spam classification. This feature selection based on one-way ANOVA F-test is used to reduce the high data dimensionality of the feature space before the classification process. The experiment of the proposed scheme was carried out using spam base wellknown benchmarking dataset to evaluate the feasibility of the proposed method. The comparison is achieved for different datasets, categorization algorithm and success measures. In addition, experimental results on spam base English datasets showed that the enhanced SVM (FSSVM) significantly outperforms SVM and many other recent spam classification methods for English dataset in terms of computational complexity and dimension reduction.

SEISMIC BEHAVIOR OF INTERMEDIATE BEAMS IN STEEL PLATE SHEAR WALLS <=SEP=> This paper presents some preliminary results of an ongoing research whose objective is to investigate the seismic behavior of intermediate beams (i.e., the beams other than those at the roof and foundation levels) in multi-story steel plate shear walls (SPSWs). Of primary interest is the determination of the strength level needed to avoid the formation of in-span plastic hinges, a relevant practical issue that has not been considered in past investigations. To attain this objective, the seismic response of different SPSW models was analyzed by performing linear and nonlinear analysis. The intermediate beams of the SPSW models were designed to resist: (I) forces imposed by gravity loads only; (II) forces determined by the ASCE 7 load combinations; and (III) forces imposed by fully yielded plates. For comparison purposes, SPSW models designed according to the Canadian Standard CAN/CSA S16-01 were considered as well. It is concluded that intermediate beams designed according to criteria I and II are prone to inspan plastic hinges and to excessive plastic deformations. It was also found that, while in-span plastic hinges do not appear in the intermediate beams of the CAN/CSA S16-01 models, they do appear in the roof and foundation beams of these models when a collapse mechanism develops.

MBBA: A Multi-Bandwidth Bus Arbiter for Hard Real-Time <=SEP=> Multi-core architectures are being increasingly used in embedded systems as they offer several advantages: improved hardware integration, low thermal dissipation and reduced energy consumption, while they make it possible to improve the computing power. In order to run real-time software on a multicore architecture, computing the Worst-Case Execution Time of every thread should be achievable. This notably involves bounding memory latencies by employing a predictable bus arbiter. However, state-of-the-art techniques prove to be irrelevant to schedule unbalanced workloads in which some threads require more bus bandwidth than the other ones. This paper proposes a new bus arbitration scheme that ensures that the shared bus latencies can be upper bounded. Compared to other schemes that make the bus latencies predictable, like the Round-Robin protocol, our approach defines several levels of bandwidth to meet requirements that may vary from one thread to another. Experimental results (WCET estimates) show that the worst-case bus latency is noticeably shortened, compared to Round-Robin, for the cores with highest priority that get the largest bandwidth. The relevance of the scheme is shown through an example workload composed of various benchmarks.

LoRa Scalability: A Simulation Model Based on Interference Measurements <=SEP=> LoRa is a long-range, low power, low bit rate and single-hop wireless communication technology. It is intended to be used in Internet of Things (IoT) applications involving battery-powered devices with low throughput requirements. A LoRaWAN network consists of multiple end nodes that communicate with one or more gateways. These gateways act like a transparent bridge towards a common network server. The amount of end devices and their throughput requirements will have an impact on the performance of the LoRaWAN network. This study investigates the scalability in terms of the number of end devices per gateway of single-gateway LoRaWAN deployments. First, we determine the intra-technology interference behavior with two physical end nodes, by checking the impact of an interfering node on a transmitting node. Measurements show that even under concurrent transmission, one of the packets can be received under certain conditions. Based on these measurements, we create a simulation model for assessing the scalability of a single gateway LoRaWAN network. We show that when the number of nodes increases up to 1000 per gateway, the losses will be up to 32%. In such a case, pure Aloha will have around 90% losses. However, when the duty cycle of the application layer becomes lower than the allowed radio duty cycle of 1%, losses will be even lower. We also show network scalability simulation results for some IoT use cases based on real data.

Improvements in muscle symmetry in children with cerebral palsy after equine-assisted therapy (hippotherapy). <=SEP=> OBJECTIVE
To evaluate the effect of hippotherapy (physical therapy utilizing the movement of a horse) on muscle activity in children with spastic cerebral palsy.


DESIGN
Pretest/post-test control group.


SETTING/LOCATION
Therapeutic Riding of Tucson (TROT), Tucson, AZ.


SUBJECTS
Fifteen (15) children ranging from 4 to 12 years of age diagnosed with spastic cerebral palsy.


INTERVENTIONS
Children meeting inclusion criteria were randomized to either 8 minutes of hippotherapy or 8 minutes astride a stationary barrel.


OUTCOME MEASURES
Remote surface electromyography (EMG) was used to measure muscle activity of the trunk and upper legs during sitting, standing, and walking tasks before and after each intervention.


RESULTS
After hippotherapy, significant improvement in symmetry of muscle activity was noted in those muscle groups displaying the highest asymmetry prior to hippotherapy. No significant change was noted after sitting astride a barrel.


CONCLUSIONS
Eight minutes of hippotherapy, but not stationary sitting astride a barrel, resulted in improved symmetry in muscle activity in children with spastic cerebral palsy. These results suggest that the movement of the horse rather than passive stretching accounts for the measured improvements.

Gain and bandwidth improvement of microstrip antenna using RIS and FPC resonator <=SEP=> Improvement in gain as well as bandwidth of microstrip antenna using reactive impedance surface and Fabry Perot cavity resonator is proposed. Suspended microstrip antenna (MSA) is designed on reactive impedance surface (RIS) backed FR4 substrate. RIS improves bandwidth, gain and efficiency of antenna due to reduced coupling between MSA and ground plane. This MSA backed RIS is placed in a FPC resonator for further improving the gain of MSA. 1×1 to 4×4 square patch arrays are designed on a dielectric layer which is energized by the radiations from RIS backed MSA. The PRS layer is placed at about λ0/2 from ground. The S11 is < −9.5 dB over 5.725–6.4 GHz. Maximum gain of 15.8 dBi with gain variation < 2 dB is obtained using 4×4 array. Beside this, cross polarization and Side lobe level are < −2ο dB. Antenna offers front to back lobe ratio (FBR) > 20 dB and > 70 % antenna efficiency. The measured results of prototype structure are in agreement with the simulation results.

Using Spoken Language Benchmarks to Characterize the Expressive Language Skills of Young Children With Autism Spectrum Disorders. <=SEP=> PURPOSE
Spoken language benchmarks proposed by Tager-Flusberg et al. (2009) were used to characterize communication profiles of toddlers with autism spectrum disorders and to investigate if there were differences in variables hypothesized to influence language development at different benchmark levels.


METHOD
The communication abilities of a large sample of toddlers with autism spectrum disorders (N = 105) were characterized in terms of spoken language benchmarks. The toddlers were grouped according to these benchmarks to investigate whether there were differences in selected variables across benchmark groups at a mean age of 2.5 years.


RESULTS
The majority of children in the sample presented with uneven communication profiles with relative strengths in phonology and significant weaknesses in pragmatics. When children were grouped according to one expressive language domain, across-group differences were observed in response to joint attention and gestures but not cognition or restricted and repetitive behaviors.


CONCLUSION
The spoken language benchmarks are useful for characterizing early communication profiles and investigating features that influence expressive language growth.

THE DEMOCRATIZATION OF PERSONAL CONSUMER LOANS? DETERMINANTS OF SUCCESS IN ONLINE PEER-TO-PEER LENDING COMMUNITIES <=SEP=> Online peer-to-peer (P2P) lending communities enable individual consumers to borrow from, and lend money to, one another directly. We study the borrowerand loan listing-related determinants of funding success in an online P2P lending community by conceptualizing loan decision variables (loan amount, interest rate offered, duration of loan listing) as mediators between borrower attributes such as demographic characteristics, financial strength, and effort prior to making the request, and the likelihood of funding success. Borrower attributes are also treated as moderators of the effects of loan decision variables on funding success. The results of our empirical study, conducted using a database of 5,370 completed P2P loan transactions, provide support for the proposed conceptual framework. Although demographic attributes such as race and gender do affect likelihood of funding success, their effects are very small in comparison to effects of borrowers’ financial strength and their effort when listing and publicizing the loan. These results are substantially different from the documented discriminatory practices of US financial institutions, suggesting that individual lenders lend more fairly when their own investment money is at stake in P2P loans. The paper concludes with specific suggestions to borrowers to increase their chances of receiving funding in P2P lending communities, and a discussion of future research opportunities.