title: Energy harvesting using thermoelectric generators

abstract: This paper presents an energy harvesting system for adaptation with thermoelectric generators (TEG). The TEG system is a dc power source based on Seebeck effect, which utilizes the temperature gradient between hot and cold sides of the TEG module. The output voltage is highly dependent on temperature difference. A DC-DC converter is needed to regulate the output power of the TEG and to actively track the maximum power point that can be achieved from the TEG. The power electronic interface of the TEG will be designed using boost converter. A modified maximum power point tracking algorithm (MPPT) is adapted for enhanced generation efficiency at enviroments with heat change dynamics. Indirect detection method of the open circuit voltage and short circuit current are key features for the adapted technique. Design considerations for the power electronic interface and the control algorithm will be covered as well in this manuscript.


title: Ferromagnetism in freestanding MoS2 nanosheets

abstract: Freestanding MoS2 nanosheets with different sizes were prepared through a simple exfoliated method by tuning the ultrasonic time in the organic solvent. Magnetic measurement results reveal the clear room-temperature ferromagnetism for all the MoS2 nanosheets, in contrast to the pristine MoS2 in its bulk form which shows diamagnetism only. Furthermore, results indicate that the saturation magnetizations of the nanosheets increase as the size decreases. Combining the X-ray photoelectron spectroscopy, transmission electron microscopy, and electron spin resonance results, it is suggested that the observed magnetization is related to the presence of edge spins on the edges of the nanosheets. These MoS2 nanosheets may find applications in nanodevices and spintronics by controlling the edge structures.


title: A Gentle Tutorial of Recurrent Neural Network with Error Backpropagation

abstract: We describe recurrent neural networks (RNNs), which have attracted great attention on sequential tasks, such as handwriting recognition, speech recognition and image to text. However, compared to general feedforward neural networks, RNNs have feedback loops, which makes it a little hard to understand the backpropagation step. Thus, we focus on basics, especially the error backpropagation to compute gradients with respect to model parameters. Further, we go into detail on how error backpropagation algorithm is applied on long short-term memory (LSTM) by unfolding the memory unit.


title: A comprehensive model for software rejuvenation

abstract: Recently, the phenomenon of software aging, one in which the state of the software system degrades with time, has been reported. This phenomenon, which may eventually lead to system performance degradation and/or crash/hang failure, is the result of exhaustion of operating system resources, data corruption, and numerical error accumulation. To counteract software aging, a technique called software rejuvenation has been proposed, which essentially involves occasionally terminating an application or a system, cleaning its internal state and/or its environment, and restarting it. Since rejuvenation incurs an overhead, an important research issue is to determine optimal times to initiate this action. In this paper, we first describe how to include faults attributed to software aging in the framework of Gray's software fault classification (deterministic and transient), and study the treatment and recovery strategies for each of the fault classes. We then construct a semi-Markov reward model based on workload and resource usage data collected from the UNIX operating system. We identify different workload states using statistical cluster analysis, estimate transition probabilities, and sojourn time distributions from the data. Corresponding to each resource, a reward function is then defined for the model based on the rate of resource depletion in each state. The model is then solved to obtain estimated times to exhaustion for each resource. The result from the semi-Markov reward model are then fed into a higher-level availability model that accounts for failure followed by reactive recovery, as well as proactive recovery. This comprehensive model is then used to derive optimal rejuvenation schedules that maximize availability or minimize downtime cost.


title: ANN-based forecasting of foreign currency exchange rates

abstract: In this paper, we have investigated artificial neural networks based prediction modeling of foreign currency rates using three learning algorithms, namely, Standard Backpropagation (SBP), Scaled Conjugate Gradient (SCG) and Backpropagation with Bayesian Regularization (BPR). The models were trained from historical data using five technical indicators to predict six currency rates against Australian dollar. The forecasting performance of the models was evaluated using a number of widely used statistical metrics and compared. Results show that significantly close prediction can be made using simple technical indicators without extensive knowledge of market data. Among the three models, SCG based model outperforms other models when measured on two commonly used metrics and attains comparable results with BPR based model on other three metrics. The effect of network architecture on the performance of the forecasting model is also presented. Future research direction outlining further improvement of the model is discussed. Keywords-Neural network, ARIMA, financial forecasting, foreign exchange


title: Fast Image-Based Obstacle Detection From Unmanned Surface Vehicles

abstract: Obstacle detection plays an important role in unmanned surface vehicles (USVs). The USVs operate in a highly diverse environments in which an obstacle may be a floating piece of wood, a scuba diver, a pier, or a part of a shoreline, which presents a significant challenge to continuous detection from images taken on board. This paper addresses the problem of online detection by constrained, unsupervised segmentation. To this end, a new graphical model is proposed that affords a fast and continuous obstacle image-map estimation from a single video stream captured on board a USV. The model accounts for the semantic structure of marine environment as observed from USV by imposing weak structural constraints. A Markov random field framework is adopted and a highly efficient algorithm for simultaneous optimization of model parameters and segmentation mask estimation is derived. Our approach does not require computationally intensive extraction of texture features and comfortably runs in real time. The algorithm is tested on a new, challenging, dataset for segmentation, and obstacle detection in marine environments, which is the largest annotated dataset of its kind. Results on this dataset show that our model outperforms the related approaches, while requiring a fraction of computational effort.


title: Processing of noisy and small-detailed gray-scale images using cellular neural networks

abstract: Algorithms of filtering, edge detection, and extraction of details and their implementation using cellular neural networks (CNN) are developed in this paper. The theory of CNN based on universal binary neurons (UBN) is also developed. A new learning algorithm for this type of neurons is carried out. Implementation of low-pass filtering algorithms using CNN is considered. Separate processing of the binary planes of gray-scale images is proposed. Algorithms of edge detection and impulsive noise filtering based on this approach and their implementation using CNN-UBN are presented. Algorithms of frequency correction reduced to filtering in the spatial domain are considered. These algorithms make it possible to extract details of given sizes. Implementation of such algorithms using CNN is presented. Finally, a general strategy of gray-scale image processing using CNN is considered.


title: Neurovascular bundle dissection for Nesbit procedure in congenital penile curvature patients: medial or lateral?

abstract: The objective of this study was to compare the outcomes of the modified Nesbit procedure using different techniques for dissecting the neurovascular bundle (NVB) to correct ventral congenital penile curvatures (CPCs). The bundle was mobilized using the medial and lateral dissection technique in 21 (Group 1) and 13 (Group 2) patients, respectively. In the medial technique, Buck's fascia is opened at the dorsal side of the penis, the deep dorsal vein is removed at the most prominent site of the curvature and a diamond-shaped tunica albuginea (TA) is excised from the midline of the penis. In the lateral technique, the bundle is mobilized using a longitudinal lateral incision of the Buck's fascia above the urethra at the 5 and 7 o'clock positions via a bilateral approach. The localization and degree of curvature was evaluated using the combined intracavernous injection stimulation test or from the patients' photographs. The mean patient age and degree of curvature were similar between groups. The mean operation time was longer for Group 2 ( P = 0.01). In Group 1, nine patients (42.8%) required one diamond excision, 10 (47.6%) required two diamond excisions and two (9.5%) required more than two excisions; in Group 2, six patients (46.2%) required two diamond excisions and seven patients (53.8%) required more than two diamond excisions ( P = 0.019). The differences in penile shortening, penile straightening and numbness of the glans penis were not statistically significant. Medial dissection of the bundle for the modified Nesbit procedure reduces the number of diamond-shaped removals of TA and thus shortens operation time in comparison with its lateral counterpart.


title: Logarithmic regret algorithms for online convex optimization

abstract: In an online convex optimization problem a decision-maker makes a sequence of decisions, i.e., chooses a sequence of points in Euclidean space, from a fixed feasible set. After each point is chosen, it encounters a sequence of (possibly unrelated) convex cost functions. Zinkevich (ICML 2003) introduced this framework, which models many natural repeated decision-making problems and generalizes many existing problems such as Prediction from Expert Advice and Cover's Universal Portfolios. Zinkevich showed that a simple online gradient descent algorithm achieves additive regret $O(\sqrt{T})$ , for an arbitrary sequence of T convex cost functions (of bounded gradients), with respect to the best single decision in hindsight. ::: ::: In this paper, we give algorithms that achieve regret O(log?(T)) for an arbitrary sequence of strictly convex functions (with bounded first and second derivatives). This mirrors what has been done for the special cases of prediction from expert advice by Kivinen and Warmuth (EuroCOLT 1999), and Universal Portfolios by Cover (Math. Finance 1:1---19, 1991). We propose several algorithms achieving logarithmic regret, which besides being more general are also much more efficient to implement. ::: ::: The main new ideas give rise to an efficient algorithm based on the Newton method for optimization, a new tool in the field. Our analysis shows a surprising connection between the natural follow-the-leader approach and the Newton method. We also analyze other algorithms, which tie together several different previous approaches including follow-the-leader, exponential weighting, Cover's algorithm and gradient descent.


title: A comprehensive inspection of cross site scripting attack

abstract: Cross Site Scripting attack (XSS) is the computer security threat which allows the attacker to get access over the sensitive information, when the javaScript, VBScript, ActiveX, Flash or HTML which is embedded in the malicious XSS link gets executed. In this paper, we authors have discussed about various impacts of XSS, types of XSS, checked whether the site is vulnerable towards the XSS or not, discussed about various tools for examining the XSS vulnerability and summarizes the preventive measures against XSS.


title: A novel resident-as-teacher training program to improve and evaluate obstetrics and gynecology resident teaching skills.

abstract: BACKGROUND ::: Residents play a significant role in teaching, but formal training, feedback, and evaluation are needed. ::: ::: ::: AIMS ::: Our aims were to assess resident teaching skills in the resident-as-teacher program, quantify correlations of faculty evaluations with resident self-evaluations, compare resident-as-teacher evaluations with clinical evaluations, and evaluate the resident-as-teacher program. ::: ::: ::: METHOD ::: The resident-as-teacher training program is a simulated, videotaped teaching encounter with a trained medical student and standardized teaching evaluation tool. Evaluations from the resident-as-teacher training program were compared to evaluations of resident teaching done by faculty, residents, and medical students from the clinical setting. ::: ::: ::: RESULTS ::: Faculty evaluation of resident teaching skills in the resident-as-teacher program showed a mean total score of 4.5 ± 0.5 with statistically significant correlations between faculty assessment and resident self-evaluations (r = 0.47; p < 0.001). However, resident self-evaluation of teaching skill was lower than faculty evaluation (mean difference: 0.4; 95% CI 0.3-0.6). When compared to the clinical setting, resident-as-teacher evaluations were significantly correlated with faculty and resident evaluations, but not medical student evaluations. Evaluations from both the resident-as-teacher program and the clinical setting improved with duration of residency. ::: ::: ::: CONCLUSIONS ::: The resident-as-teacher program provides a method to train, give feedback, and evaluate resident teaching.


title: Scalable Classification of Repetitive Time Series Through Frequencies of Local Polynomials

abstract: Time-series classification has attracted considerable research attention due to the various domains where time-series data are observed, ranging from medicine to econometrics. Traditionally, the focus of time-series classification has been on short time-series data composed of a few patterns exhibiting variabilities, while recently there have been attempts to focus on longer series composed of multiple local patrepeating with an arbitrary irregularity. The primary contribution of this paper relies on presenting a method which can detect local patterns in repetitive time-series via fitting local polynomial functions of a specified degree. We capture the repetitiveness degrees of time-series datasets via a new measure. Furthermore, our method approximates local polynomials in linear time and ensures an overall linear running time complexity. The coefficients of the polynomial functions are converted to symbolic words via equi-area discretizations of the coefficients’ distributions. The symbolic polynomial words enable the detection of similar local patterns by assigning the same word to similar polynomials. Moreover, a histogram of the frequencies of the words is constructed from each time-series’ bag of words. Each row of the histogram enables a new representation for the series and symbolizes the occurrence of local patterns and their frequencies. In an experimental comparison against state-of-the-art baselines on repetitive datasets, our method demonstrates significant improvements in terms of prediction accuracy.


title: Sequential Feature Explanations for Anomaly Detection

abstract: In many applications, an anomaly detection system presents the most anomalous data instance to a human analyst, who then must determine whether the instance is truly of interest (e.g., a threat in a security setting). Unfortunately, most anomaly detectors provide no explanation about why an instance was considered anomalous, leaving the analyst with no guidance about where to begin the investigation. To address this issue, we study the problems of computing and evaluating sequential feature explanations (SFEs) for anomaly detectors. An SFE of an anomaly is a sequence of features, which are presented to the analyst one at a time (in order) until the information contained in the highlighted features is enough for the analyst to make a confident judgement about the anomaly. Since analyst effort is related to the amount of information that they consider in an investigation, an explanation’s quality is related to the number of features that must be revealed to attain confidence. In this article, we first formulate the problem of optimizing SFEs for a particular density-based anomaly detector. We then present both greedy algorithms and an optimal algorithm, based on branch-and-bound search, for optimizing SFEs. Finally, we provide a large scale quantitative evaluation of these algorithms using a novel framework for evaluating explanations. The results show that our algorithms are quite effective and that our best greedy algorithm is competitive with optimal solutions.


title: FPGA-oriented moving target defense against security threats from malicious FPGA tools

abstract: The imbalance relationship between FPGA hardware/software providers and FPGA users challenges the assurance of secure design on FPGAs. Existing efforts on FPGA security primarily focus on reverse engineering the downloaded FPGA configuration, retrieving the authentication code or crypto key stored on the embedded memory in FPGAs, and countermeasures for the security threats above. In this work, we investigate new security threats from malicious FPGA tools, and identify stealthy attacks that could occur during FPGA deployment. To address those attacks, we exploit the principles of moving target defense (MTD) and propose a FPGA-oriented MTD (FOMTD) method. Our method is composed of three defense lines, which are formed by an improved user constraint file, random selection of design replicas, and runtime submodule assembling, respectively. The FPGA emulation results show that the proposed FOMTD method reduces the hardware Trojan hit rate by 60% over the baseline, at the cost of 10.76% more power consumption.


title: Universal Semantic Communication II: A Theory of Goal-Oriented Communication. ECCC

abstract: We continue the investigation of the task of meaningful communication among intelligent entities (players, agents) without any prior common language. Our generic thesis is that such communication is feasible provided the goals of the communicating players are verifiable and compatible. In a previous work we gave supporting evidence for one specific goal — where one of the players wished to solve a hard computational problem and communicated with the other in the hope of finding a solution. In this work we initiate a “generic” study of the goals of communication. We present two definitions: one of a “generic” meta-goal, which captures the (potentially unrealizable) wishes of communicating agents, and the other being a “generic” syntactic goal, which captures effects that can observed by an agent. We then show, under some technical conditions, that those metagoals that have corresponding syntactic versions are also universally achievable, i.e., achievable when the two communicators do not (necessarily) share a common language. We also show how our formalism captures a variety of commonplace examples of goals of communication, including simple control-oriented goals that aim to effect a remote physical action by communication, as well as more subtle intellectual goals where the communicator’s intent is mostly to gain knowledge. Our examples from the latter class include a variety of settings where meta-goals differ significantly from syntactic goals.


title: Interactive Overlays: A New Method for Generating Global Journal Maps from Web-of-Science Data

abstract: Recent advances in methods and techniques enable us to develop interactive overlays to a global map of science based on aggregated citation relations among the 9162 journals contained in the Science Citation Index and Social Science Citation Index 2009. We first discuss the pros and cons of the various options: cited versus citing, multidimensional scaling versus spring-embedded algorithms, VOSViewer versus Gephi, and the various clustering algorithms and similarity criteria. Our approach focuses on the positions of journals in the multidimensional space spanned by the aggregated journal–journal citations. Using VOSViewer for the resulting mapping, a number of choices can be left to the user; we provide default options reflecting our preferences. Some examples are also provided; for example, the potential of using this technique to assess the interdisciplinarity of organizations and/or document sets.


title: A deep information based transfer learning method to detect annual urban dynamics of Beijing and Newyork from 1984–2016

abstract: Mapping activities of urban land change is important for human activity to Earth's dynamic change. To get the detailed information on urban development maps in large area, dense training samples are needed in different area and specific season, which is cost-consuming. To overcome this issue, we provide a transfer learning method based on deep information to extract urban areas in all season and different areas by only parts of training samples from Beijing in 1999. The proposed method, which is based on an improved recurrent neural network model, aims at: 1) learning a novel model to extract urban features with transfer ability in different areas; 2) overcoming the seasonal, annual and spatial variance to extract urban areas in all seasons; 3) learning the annual urban dynamics in two cities over 30 years simultaneously. Experiments are performed on Beijing and New York over the period from 1984 to 2016, and training samples are only used with a part of Beijing images in 1999. The results show good performances on annual urban detection results.


title: Tears and fears: modeling emotions and emotional behaviors in synthetic agents

abstract: Emotions play a critical role in creating engaging and believable characters to populate virtual worlds. Our goal is to create general computational models to support characters that act in virtual environments, make decisions, but whose behavior also suggests an underlying emotional current. In service of this goal, we integrate two complementary approaches to emotional modeling into a single unified system. Gratch's Emile system focuses on the problem of emotional appraisal: how emotions arise from an evaluation of how environmental events relate to an agent's plans and goals. Marsella et al. 's IPD system focuses more on the impact of emotions on behavior, including the impact on the physical expressions of emotional state through suitable choice of gestures and body language. This integrated model is layered atop Steve, a pedagogical agent architecture, and exercised within the context of the Mission Rehearsal Exercise, a prototype system designed to teach decision- making skills in highly evocative situations.


title: An enhanced automatic speech recognition system for Arabic

abstract: Automatic speech recognition for Arabic is a very challenging task. Despite all the classical techniques for Automatic Speech Recognition (ASR), which can be efficiently applied to Arabic speech recognition , it is essential to take into consideration the language specificities to improve the system performance. In this article, we focus on Modern Standard Arabic (MSA) speech recognition. We introduce the challenges related to Arabic language, namely the complex morphology nature of the language and the absence of the short vowels in written text, which leads to several potential vowelization for each graphemes, which is often conflicting. We develop an ASR system for MSA by using Kaldi toolkit. Several acoustic and language models are trained. We obtain a Word Error Rate (WER) of 14.42 for the baseline system and 12.2 relative improvement by rescoring the lattice and by rewriting the output with the right hamoza above or below Alif.


title: Symbol-Based Control of a Ball-on-Plate Mechanical System

abstract: Title of Thesis: SYMBOL-BASED CONTROL OF A BALL-ON-PLATE MECHANICAL SYSTEM Degree candidate: Phillip Yip Degree and year: Master of Science, 2004 Thesis directed by: Assistant Professor Dimitrios Hristu-Varsakelis Department of Mechanical Engineering Modern control systems often consist of networks of components that must share a common communication channel. Not all components of the networked control system can communicate with one another simultaneously at any given time. The “attention” that each component receives is an important factor that affects the system’s overall performance. An effective controller should ensure that sensors and actuators receive sufficient attention. This thesis describes a “ball-on-plate” dynamical system that includes a digital controller, which communicates with a pair of language-driven actuators, and an overhead camera. A control algorithm was developed to restrict the ball to a small region on the plate using a quantized set of language-based commands. The size of this containment region was analytically determined as a function of the communication constraints and other control system parameters. The effectiveness of the proposed control law was evaluated in experiments and mathematical simulations. SYMBOL-BASED CONTROL OF A BALL-ON-PLATE MECHANICAL SYSTEM by Phillip Yip Thesis submitted to the Faculty of the Graduate School of the University of Maryland, College Park in partial fulfillment of the requirements for the degree of Master of Science 2004 Advisory Commmittee: Assistant Professor Dimitrios Hristu-Varsakelis, Chair/Advisor Professor Balakumar Balachandran Professor Amr Baz c ©Copyright by Phillip T. Yip 2004 DEDICATION: To my family


title: What is left to be understood in ATIS?

abstract: One of the main data resources used in many studies over the past two decades for spoken language understanding (SLU) research in spoken dialog systems is the airline travel information system (ATIS) corpus. Two primary tasks in SLU are intent determination (ID) and slot filling (SF). Recent studies reported error rates below 5% for both of these tasks employing discriminative machine learning techniques with the ATIS test set. While these low error rates may suggest that this task is close to being solved, further analysis reveals the continued utility of ATIS as a research corpus. In this paper, our goal is not experimenting with domain specific techniques or features which can help with the remaining SLU errors, but instead exploring methods to realize this utility via extensive error analysis. We conclude that even with such low error rates, ATIS test set still includes many unseen example categories and sequences, hence requires more data. Better yet, new annotated larger data sets from more complex tasks with realistic utterances can avoid over-tuning in terms of modeling and feature design. We believe that advancements in SLU can be achieved by having more naturally spoken data sets and employing more linguistically motivated features while preserving robustness due to speech recognition noise and variance due to natural language.


title: Design and simulation of a waveguide slot antenna

abstract: Due to low efficiency in waveguide slot antenna design, this paper proposes a method which combines numerical calculation with numerical simulation. We apply slot self-admittance extracted from the software to the active admittance formula. Then the normalized slot active admittance is obtained. This method completely considers the mutual coupling between slots. The normalized slot active admittance is accurate and can be directly used in antenna design. The simulated results of waveguide slot antenna prove that this method has high efficiency and can be used in waveguide slot antenna design.


title: The promises of big data and small data for travel behavior (aka human mobility) analysis

abstract: Abstract The last decade has witnessed very active development in two broad, but separate fields, both involving understanding and modeling of how individuals move in time and space (hereafter called “travel behavior analysis” or “human mobility analysis”). One field comprises transportation researchers who have been working in the field for decades and the other involves new comers from a wide range of disciplines, but primarily computer scientists and physicists. Researchers in these two fields work with different datasets, apply different methodologies, and answer different but overlapping questions. It is our view that there is much, hidden synergy between the two fields that needs to be brought out. It is thus the purpose of this paper to introduce datasets, concepts, knowledge and methods used in these two fields, and most importantly raise cross-discipline ideas for conversations and collaborations between the two. It is our hope that this paper will stimulate many future cross-cutting studies that involve researchers from both fields.


title: Stasis: flexible transactional storage

abstract: An increasing range of applications requires robust support for atomic, durable and concurrent transactions. Databases provide the default solution, but force applications to interact via SQL and to forfeit control over data layout and access mechanisms. We argue there is a gap between DBMSs and file systems that limits designers of data-oriented applications. Stasis is a storage framework that incorporates ideas from traditional write-ahead logging algorithms and file systems. It provides applications with flexible control over data structures, data layout, robustness, and performance. Stasis enables the development of unforeseen variants on transactional storage by generalizing write-ahead logging algorithms. Our partial implementation of these ideas already provides specialized (and cleaner) semantics to applications. We evaluate the performance of a traditional transactional storage system based on Stasis, and show that it performs favorably relative to existing systems. We present examples that make use of custom access methods, modified buffer manager semantics, direct log file manipulation, and LSN-free pages. These examples facilitate sophisticated performance optimizations such as zero-copy I/O. These extensions are composable, easy to implement and significantly improve performance.


COMPARISON OF THE CHARACTERISTICS BETWEEN SERIAL AND PARALLEL ROBOTS


title: fLDA: matrix factorization through latent dirichlet allocation

abstract: We propose fLDA, a novel matrix factorization method to predict ratings in recommender system applications where a "bag-of-words" representation for item meta-data is natural. Such scenarios are commonplace in web applications like content recommendation, ad targeting and web search where items are articles, ads and web pages respectively. Because of data sparseness, regularization is key to good predictive accuracy. Our method works by regularizing both user and item factors simultaneously through user features and the bag of words associated with each item. Specifically, each word in an item is associated with a discrete latent factor often referred to as the topic of the word; item topics are obtained by averaging topics across all words in an item. Then, user rating on an item is modeled as user's affinity to the item's topics where user affinity to topics (user factors) and topic assignments to words in items (item factors) are learned jointly in a supervised fashion. To avoid overfitting, user and item factors are regularized through Gaussian linear regression and Latent Dirichlet Allocation (LDA) priors respectively. We show our model is accurate, interpretable and handles both cold-start and warm-start scenarios seamlessly through a single model. The efficacy of our method is illustrated on benchmark datasets and a new dataset from Yahoo! Buzz where fLDA provides superior predictive accuracy in cold-start scenarios and is comparable to state-of-the-art methods in warm-start scenarios. As a by-product, fLDA also identifies interesting topics that explains user-item interactions. Our method also generalizes a recently proposed technique called supervised LDA (sLDA) to collaborative filtering applications. While sLDA estimates item topic vectors in a supervised fashion for a single regression, fLDA incorporates multiple regressions (one for each user) in estimating the item factors.


title: Cloud Broker: Bringing Intelligence into the Cloud

abstract: Handling changes of business processes, and making sure systems are up and running after a change in the business process with minimum downtime is something which has been of interest to researchers for long and there have been several approaches proposed for it. With Cloud computing becoming increasingly popular businesses require a strong system for cloud based implementations which can handle change management of processes. In this paper we propose a change management approach for cloud backed business process models.


title: An Evaluation of Vectorizing Compilers

abstract: Most of today's processors include vector units that have been designed to speedup single threaded programs. Although vector instructions can deliver high performance, writing vector code in assembly language or using intrinsics in high level languages is a time consuming and error-prone task. The alternative is to automate the process of vectorization by using vectorizing compilers. This paper evaluates how well compilers vectorize a synthetic benchmark consisting of 151 loops, two application from Petascale Application Collaboration Teams (PACT), and eight applications from Media Bench II. We evaluated three compilers: GCC (version 4.7.0), ICC (version 12.0) and XLC (version 11.01). Our results show that despite all the work done in vectorization in the last 40 years 45-71% of the loops in the synthetic benchmark and only a few loops from the real applications are vectorized by the compilers we evaluated.


title: Time-Frequency Processing of Nonstationary Signals: Advanced TFD Design to Aid Diagnosis with Highlights from Medical Applications

abstract: This article presents a methodical approach for improving quadratic time-frequency distribution (QTFD) methods by designing adapted time-frequency (T-F) kernels for diagnosis applications with illustrations on three selected medical applications using the electroencephalogram (EEG), heart rate variability (HRV), and pathological speech signals. Manual and visual inspection of such nonstationary multicomponent signals is laborious especially for long recordings, requiring skilled interpreters with possible subjective judgments and errors. Automated assessment is therefore preferred for objective diagnosis by using T-F distributions (TFDs) to extract more information. This requires designing advanced high-resolution TFDs for automating classification and interpretation. As QTFD methods are general and their coverage is very broad, this article concentrates on methodologies using only a few selected medical problems studied by the authors.


title: Anatomy of drive-by download attack

abstract: Drive-by download attacks where web browsers are subverted by malicious content delivered by web servers have become a common attack vector in recent years. Several methods for the detection of malicious content on web pages using data mining techniques to classify web pages as malicious or benign have been proposed in the literature. However, each proposed method uses different content features in order to do the classification and there is a lack of a high-level frameworks for comparing these methods based upon their choice of detection features. The lack of a framework makes it problematic to develop experiments to compare the effectiveness of methods based upon different selections of features. This paper presents such a framework derived from an analysis of of drive-by download attacks that focus upon potential state changes seen when Internet browsers render HTML documents. This framework can be used to identify potential features that have not yet been exploited and to reason about the challenges for using those features in detection drive-by download attack.


title: Capture Dense: Markerless Motion Capture Meets Dense Pose Estimation

abstract: We present a method to combine markerless motion capture and dense pose feature estimation into a single framework. We demonstrate that dense pose information can help for multiview/single-view motion capture, and multiview motion capture can help the collection of a high-quality dataset for training the dense pose detector. Specifically, we first introduce a novel markerless motion capture method that can take advantage of dense parsing capability provided by the dense pose detector. Thanks to the introduced dense human parsing ability, our method is demonstrated much more efficient, and accurate compared with the available state-of-the-art markerless motion capture approach. Second, we improve the performance of available dense pose detector by using multiview markerless motion capture data. Such dataset is beneficial to dense pose training because they are more dense and accurate and consistent, and can compensate for the corner cases such as unusual viewpoints. We quantitatively demonstrate the improved performance of our dense pose detector over the available DensePose. Our dense pose dataset and detector will be made public.


title: Efficient Graph Similarity Search Over Large Graph Databases

abstract: Since many graph data are often noisy and incomplete in real applications, it has become increasingly important to retrieve graphs $g$ in the graph database $D$ that approximately match the query graph $q$ , rather than exact graph matching. In this paper, we study the problem of graph similarity search, which retrieves graphs that are similar to a given query graph under the constraint of graph edit distance. We propose a systematic method for edit-distance based similarity search problem. Specifically, we derive two lower bounds, i.e., partition-based and branch-based bounds, from different perspectives. More importantly, a hybrid lower bound incorporating both ideas of the two lower bounds is proposed, which is theoretically proved to have higher (at least not lower) pruning power than using the two lower bounds together. We also present a uniform index structure, namely u-tree, to facilitate effective pruning and efficient query processing. Extensive experiments confirm that our proposed approach outperforms the existing approaches significantly, in terms of both the pruning power and query response time.


title: Data mining for meteorological applications: Decision trees for modeling rainfall prediction

abstract: Prediction is a challenging task and that too for weather is even more complex, dynamic and mind-boggling. Weather prediction poses right from the ancient times as a big herculean task, because it depends on various parameters to predict the dependent variables like temperature, rainfall, humidity, wind speed and direction, which are changing from time to time and weather calculation varies with the geographical location along with its atmospheric variables. There are many data mining techniques employed for weather prediction, but decision tree evaluation can be quantified. This paper highlights a model using decision tree to predict weather phenomena like fog, rainfall, cyclones and thunderstorms, which can be a life saving information and used by peoples of all walks of life in making wise and intelligent decisions. This model may be used in machine learning and further promises the scope for improvement as more and more relevant attributes can be used in predicting the dependent variables. The proposed model is implemented using the open source data mining tool Rapidminer.


title: AudioDAQ: turning the mobile phone's ubiquitous headset port into a universal data acquisition interface

abstract: We present AudioDAQ, a new platform for continuous data acquisition using the headset port of a mobile phone. AudioDAQ differs from existing phone peripheral interfaces by drawing all necessary power from the microphone bias voltage, encoding all data as analog audio, and leveraging the phone's built-in voice memo application (or a custom application) for continuous data collection. These properties make the AudioDAQ design more universal, so it works across a broad range of phones including sophisticated smart phones and simpler feature phones, enables simple analog peripherals without requiring a microcontroller, requires no hardware or software modifications on the phone itself, uses significantly less power than prior approaches, and allows continuous data capture over an extended period of time. The AudioDAQ design is efficient because it draws all necessary power from the microphone bias voltage, and it is general because this voltage and a voice memo application are present on most mobile phones in use today. We show the viability of our architecture by evaluating an end-to-end system that can capture EKG signals continuously for hours and send the data to the cloud for storage, processing, and visualization.


title: A Procedure for Measuring Microplastics using Pressurized Fluid Extraction.

abstract: A method based on pressurized fluid extraction (PFE) was developed for measuring microplastics in environmental samples. This method can address some limitations of the current microplastic methods and provide laboratories with a simple analytical method for quantifying common microplastics in a range of environmental samples. The method was initially developed by recovering 101% to 111% of spiked plastics on glass beads and was then applied to a composted municipal waste sample with spike recoveries ranging from 85% to 94%. The results from municipal waste samples and soil samples collected from an industrial area demonstrated that the method is a promising alternative for determining the concentration and identity of microplastics in environmental samples.


title: Augmented Reality Driving Using Semantic Geo-Registration

abstract: We propose a new approach that utilizes semantic information to register 2D monocular video frames to the world using 3D georeferenced data, for augmented reality driving applications. The geo-registration process uses our predicted vehicle pose to generate a rendered depth map for each frame, allowing 3D graphics to be convincingly blended with the real world view. We also estimate absolute depth values for dynamic objects, up to 120 meters, based on the rendered depth map and update the rendered depth map to reflect scene changes over time. This process also creates opportunistic global heading measurements, which are fused with other sensors, to improve estimates of the 6 degrees-of- freedom global pose of the vehicle over state-of-the-art outdoor augmented reality systems [5]–, [19]. We evaluate the navigation accuracy and depth map quality of our system on a driving vehicle within various large-scale environments for producing realistic augmentations.


title: Ceramics for the Future

abstract: The advantages of higher-frequency operation, such as wider bandwidth and finer spatial and temporal resolution, have led to increased interest in the use of millimeter-waves (mmWs) in both commercial and military applications-covering, in particular, areas ranging from high-speed wireless communication (including wireless local area networking, wireless gigabit communication, sensor networks, and fifthgeneration systems) to space science to security [1]-[5]. For biological and health applications, mmW imaging offers a superior, safer, and lower-cost alternative to conventional techniques [6], [7].


title: Metabolites from algae with economical impact.

abstract: In order to survive in a highly competitive environment, freshwater or marine algae have to develop defense strategies that result in a tremendous diversity of compounds from different metabolic pathways. Recent trends in drug research from natural sources have shown that algae are promising organisms to furnish novel biochemically active compounds. The current review describes the main substances biosynthesized by algae with potential economic impact in food science, pharmaceutical industry and public health. Emphasis is given to fatty acids, steroids, carotenoids, polysaccharides, lectins, mycosporine-like amino acids, halogenated compounds, polyketides and toxins.


title: Effects of rapeseed oil replacement in fish feed on lipid composition and self-selection by rainbow trout (Oncorhynchus mykiss)

abstract: Increased use of plant oils with different origins and quality in fish feed needs to be approached from a food safety and fish welfare point of view. Plant oils contain a number of bioactive minor lipid compounds that may affect the fish’s metabolism and taste perception. This study focuses on the effect of replacing fish oil (FO) with different levels of cold-pressed rapeseed oil (RO) on the lipid composition in muscle and liver as well as on the preference by the fish. Rainbow trout (Oncorhynchus mykiss) were fed diets with a FO : RO ratio of 100 : 0, 75 : 25, 50 : 50 and 25 : 75 until twofold weight increase. In self-selecting feed trials of single rainbow trout, fish preferred the diet composed of only FO compared with the diets with RO but did not discriminate between different levels of RO. Plant sterols and their metabolites were found in liver of the fish fed RO diets, suggesting an effect on the sterol metabolism different from fish fed a 100% FO diet. The largest effects were seen in the fatty acid composition of the edible tissue of the fish with a decrease in 22:6n-3 and 20:5n-3 and an increase in 18:2n-6 and 18:1n-9.


Predicting sports events from past results Towards effective betting on football matches


title: Video retrieval using speech and text in video

abstract: This paper presents an analysis process of lecture video retrieval with automated video indexing and video search for lecture video databases. The video retrieval extracts the relevant metadata from the two main parts of lecture videos, namely the visual screen and audio tracks. From the visual screen, we firstly detect the slide transition and extract each unique slide frame with its temporal transition considered as the video segment. The textual metadata from slide frames is extracted and then recognized using video OCR technique. Based on OCR results, the corresponding text in video information is saved. Secondly, the speech-to-text analysis is carried out from the audio signal. Sphinx speech recognition models are used for recognition of speech-to-text conversion process. In the proposed work, the combined text information from the visual slide frame and audio signal were used to retrieve the video from the lecture video database. The performance of this combined video retrieval system shows a significant improvement in performance when compared with an individual system built using audio and text in video systems respectively.


title: Phospholipase C mimics insulin action on pyruvate dehydrogenase and insulin mediator generation but not glucose transport or utilization

abstract: This study investigated the extent to which a purified phosphatidylinositol-specific and a commercial non-specific phospholipase C mimicked acute insulin action in rat adipocytes. The enzymes mimicked insulin stimulation of pyruvate dehydrogenase (PDH) and breakdown of a glycophospholipid proposed as a precursor for an intracellular mediator of insulin action, but were much less effective in stimulating glucose transport and utilization. These observations corroborate recent suggestions that insulin may activate a phospholipase C to generate a mediator that can account for insulin activation of PDH from a mediator precursor with a phosphatidylinositol anchor. This mediator precursor is probably an outer membrane component since effects were obtained with intact cells. It is unlikely that this mechanism accounts fully for insulin action since phosphatidylinositol-specific and commercial phospholipase C stimulation of glucose transport was significantly less than that elicited by insulin.


title: Fixation of buried and surface mounted magnets in high-speed permanent magnet synchronous motors

abstract: High-speed applications involve technical and economical advantages, because as direct drives they avoid the gear as an additional mechanical drive component Permanent magnet synchronous machines are attracting growing attention for high-speed drives. Surface mounted permanent magnet synchronous machines request a glass or carbon fibre bandage to fasten the magnets to the rotor surface at high speed. At rotors with "buried" magnets the rotor iron itself fixes the magnets. The paper presents simple calculation strategies and discusses their limits for the mechanical design of high-speed machines with either surface mounted or buried magnets. The results of the calculations are compared with FE-calculations.


title: Getting to Know Each Other: The Role of Social Dialogue in Recovery from Errors in Social Robots

abstract: This work explores the extent to which social dialogue can mitigate (or exacerbate) the loss of trust caused when robots make conversational errors. Our study uses a NAO robot programmed to persuade users to agree with its rankings on two tasks. We perform two manipulations: (1) The timing of conversational errors - the robot exhibited errors either in the first task, the second task, or neither; (2) The presence of social dialogue - between the two tasks, users either engaged in a social dialogue with the robot or completed a control task. We found that the timing of the errors matters: replicating previous research, conversational errors reduce the robot's influence in the second task, but not on the first task. Social dialogue interacts with the timing of errors, acting as an intensifier: social dialogue helps the robot recover from prior errors, and actually boosts subsequent influence; but social dialogue backfires if it is followed by errors, because it extends the period of good performance, creating a stronger contrast effect with the subsequent errors. The design of social robots should therefore be more careful to avoid errors after periods of good performance than early on in a dialogue.


title: OFDM With Interleaved Subcarrier-Index Modulation

abstract: Orthogonal frequency division multiplexing with index modulation (OFDM-IM) is a recently developed technique which modulates part of the information bits using the indices of OFDM subcarriers. In this letter, a simple and efficient subcarrier-level interleaving scheme is introduced to improve the performance of conventional OFDM-IM through enlarging the Euclidean distances among the modulated symbols. Both theoretical analysis and simulation results are presented to show that the proposed OFDM with interleaved sucarrier-index modulation (OFDM-ISIM) can achieve better system performance than conventional OFDM-IM and OFDM with low-order modulation schemes such as binary phase shift keying, quadrature phase shift keying and 16 quadrature amplitude modulation.


title: Reflective Practice as a Means for Preparing to Teach Outdoors in an Ecological Garden

abstract: Although teachers are engaged in many field-trips, they seldom have the pedagogical knowledge and experience to enact them. This article presents an effort to support reflective practice of teachers in the outdoors. The teaching experience of five pre- and in-service teachers included preparation for teaching in the outdoors, designing learning materials, teaching elementary and junior-high school students in an ecogarden, and reflecting upon these teaching experiences. The data collected by teachers and researchers highlighted challenges such as lack of confidence, class management and inadequate student motivation. The group and the instructors’ support and collaboration and careful preparation yielded a positive outdoor teaching experience. We suggest that supported field experience followed by individual and group reflection are promising in encouraging teachers to carry out outdoor learning activities.


title: Fit-for-purpose biomarker method validation in anticancer drug development.

abstract: The introduction of new anticancer drugs into the clinic is often hampered by a lack of qualified biomarkers. Method validation is indispensable to successful biomarker qualification and is also a regulatory requirement. Recently, the fit-for-purpose approach has been developed to promote flexible yet rigorous biomarker method validation, although its full implications are often overlooked. This review aims to clarify many of the scientific and regulatory issues surrounding biomarker method validation and the analysis of samples collected from clinical trial subjects. It also strives to provide clear guidance on validation strategies for each of the five categories that define the majority of biomarker assays, citing specific examples.


title: The limits of trust-free systems: A literature review on blockchain technology and trust in the sharing economy

abstract: Abstract At the tip of the hype cycle, trust-free systems based on blockchain technology promise to revolutionize interactions between peers that require high degrees of trust, usually facilitated by third party providers. Peer-to-peer platforms for resource sharing represent a frequently discussed field of application for “trust-free” blockchain technology. However, trust between peers plays a crucial and complex role in virtually all sharing economy interactions. In this article, we hence shed light on how these conflicting notions may be resolved and explore the potential of blockchain technology for dissolving the issue of trust in the sharing economy. By means of a dual literature review we find that 1) the conceptualization of trust differs substantially between the contexts of blockchain and the sharing economy, 2) blockchain technology is to some degree suitable to replace trust in platform providers, and that 3) trust-free systems are hardly transferable to sharing economy interactions and will crucially depend on the development of trusted interfaces for blockchain-based sharing economy ecosystems.


title: Chatbots in education : A passing trend or a valuable pedagogical tool?

abstract: Digitalizing education and reinventing the learning experience is one of the big challenges in this age of information. In the eld of E-learning, the application of a chatbot as part of the educati ...


title: Multithread Content Based File Chunking System in CPU-GPGPU Heterogeneous Architecture

abstract: the fast development of Graphics Processing Unit (GPU) leads to the popularity of General-purpose usage of GPU (GPGPU). So far, most modern computers are CPU-GPGPU heterogeneous architecture and CPU is used as host processor. In this work, we promote a multithread file chunking prototype system, which is able to exploit the hardware organization of the CPU-GPGPU heterogeneous computer and determine which device should be used to chunk the file to accelerate the content based file chunking operation of deduplication. We built rules for the system to choose which device should be used to chunk file and also found the optimal choice of other related parameters of both CPU and GPGPU subsystem like segment size and block dimension. This prototype was implemented and tested. The result of using GTX460(336 cores) and Intel i5 (four cores) shows that this system can increase the chunking speed 63% compared to using GPGPU alone and 80% compared to using CPU alone.


title: Micro-Coaxial Impedance Transformers

abstract: This paper demonstrates two broadband air-filled micro-coaxial 4:1 (2-24 GHz) and 2.25:1 (2-22 GHz) impedance transformers. The 4:1 transformer converts 50 to 12.5 Ω and the 2.25:1 device transforms 50 to 22.22 Ω. The circuits are fabricated on silicon with PolyStrata technology, and are implemented with 650 μm × 400 μm air-filled micro-coaxial lines. Back-to-back circuits and single structures with geometrical tapers are designed for systematic characterization. Simulation and measurement results are in excellent agreement. The return loss for both transformers is better than 15 dB over the design bandwidth.


title: Principles of cosmetic dentistry in orthodontics: Part 1. Shape and proportionality of anterior teeth.

abstract: In the past decade, there has been a remarkable upswing in interdisciplinary collaboration between dentists, orthodontists, and periodontists in smile enhancement, and now an entire field of “cosmetic periodontics” has evolved in collaboration with cosmetic dentistry. Contemporary orthodontic smile analysis is generally defined in terms of (1) vertical placement of the anterior teeth to the upper lip at rest and on smile (adequate incisor display but not too gummy), (2) transverse smile dimension (buccal corridors), (3) smile arc characteristics, and (4) the vertical relationship of gingival margins to each other. Through the interaction with these other disciplines and the knowledge gained, we have expanded our diagnosis of the smile to further refine the finishing of anterior esthetics for our patients. As our interaction with cosmetic dentistry has increased, we have become very aware of what standards guide the dentist who strives for an excellent smile. Through cosmetic bonding and laminate veneers, the dentist can control tooth shape by adding or taking away from the tooth, crown, or laminate. As orthodontists, we have generally limited our toothreshaping efforts to incisal edge “dressing.” The purpose of this article is to examine some cosmetic ideas and present new ways in which we can improve the smiles of our patients. In Part 1, I will define and illustrate how these principles are applied to improve the cosmetics of orthodontic patients. In Part 2, my coauthor and I will review the new laser technology available for reshaping soft tissues, and, in Part 3, we will discuss the clinical use of those lasers.


title: A High-Density Scalable Twin Transistor RAM (TTRAM) With Verify Control for SOI Platform Memory IPs

abstract: Several high-density SOI memory technologies utilizing the body floating effects have been proposed. Conditions needed for SoC memory IPs for many kinds of applications are not only performance but also suitability for platform technologies. We had reported TTRAM (Twin Transistor RAM) and (Enhanced TTRAM) which are high-density capacitorless SOI-CMOS compatible memory IPs. A platform design methodology becomes the mainstream, providing QTAT and low-cost design. Now, we have upgraded the with application-required functions called scalable TTRAM. This memory IP can be applied to many kinds of applications using the verify control technique with compact actively body-bias controlled (ABC) sense amplifier, and the unique test mode functions have also been proposed for practical usage. The test chip of 4 Mbit macro fabricated with 90 nm standard SOI CMOS achieves performance of 263 MHz high-speed random access, 79 mW/4 Mb lower active power dissipation, 453 MHz data transfer of page/burst mode and lower stand-by current mode of 5 s data retention time. The scalable TTRAM can play the role of on-chip SoC memory IPs, for example, in consumer, mobile, and MPU/game applications.


title: Simplifying Lexical Simplification: Do We Need Simplified Corpora?

abstract: Simplification of lexically complex texts, by replacing complex words with their simpler synonyms, helps non-native speakers, children, and language-impaired people understand text better. Recent lexical simplification methods rely on manually simplified corpora, which are expensive and time-consuming to build. We present an unsupervised approach to lexical simplification that makes use of the most recent word vector representations and requires only regular corpora. Results of both automated and human evaluation show that our simple method is as effective as systems that rely on simplified corpora.


title: Vehicle speed control through fuzzy logic

abstract: Fuzzy logic can be used to construct a nonlinear controllers by heuristic information which can replace the human intelligence working in a chain to complete a process. So it is a smart option to use the fuzzy logic to construct a controller to control a vehicle. Different DC motors like the shunt motors are prominent and flexible for electric vehicles. To control the vehicle dynamics like acceleration, braking, gear and terrain need to control the motor speed in proper way. So this paper advocates a novel approach to design and analyze a controller using fuzzy logic to control the speed of the vehicle. Traditional control sometimes take long time to determine optimum control parameters. But the designed fuzzy logic controller has the self tuning algorithm which can adjust the control parameters to gain the optimum control characteristic automatically.


title: The role of emotional variables in the classification and prediction of collective social dynamics

abstract: We demonstrate the power of data mining techniques for the analysis of collective social dynamics within British Tweets during the Olympic Games 2012. The classification accuracy of online activities related to the successes of British athletes significantly improved when emotional components of tweets were taken into account, but employing emotional variables for activity prediction decreased the classifiers' quality. The approach could be easily adopted for any prediction or classification study with a set of problem-specific variables.


title: A Supervised Approach for Text Illustration

abstract: In this paper we propose a novel method to illustrate text articles with pictures from a tagged repository. Certain types of documents, like news articles, are often accompanied by a few pictures only. Prior works leverage topics or key phrases from the text to suggest relevant pictures. We propose a supervised model based on features like readability, picturability, sentiment polarity, and presence of important phrases, to identify and rank key sentences. The proposed method then suggests some relevant pictures based on the top ranked sentences thus identified.


title: Browsing-oriented semantic faceted search

abstract: Faceted search enables users to browse and discover relevant items from a large collection such as the Web of data. Existing faceted search solutions assume a precise information need, and thus optimise relevance, interestingness, and costs of fulfilling an information need. In this paper, we propose a complementary solution. Instead of assuming a search scenario (i.e., a user has a precise information need), our solution targets a browsing scenario (i.e., a user has a fuzzy need). We aimto support users in exploring an unknown collection of items, thereby allowing them to discover new or unfamiliar items of interest. Our approach comprises mechanisms for grouping facets and facet values and facet ranking. Via a task-based evaluation, we demonstrate that the proposed solution enables more effective browsing compared to the state-of-the-art, given fuzzy information needs.


title: QBF Resolution Systems and Their Proof Complexities

abstract: Quantified Boolean formula (QBF) evaluation has a broad range of applications in computer science and is gaining increasing attention. Recent progress has shown that for a certain family of formulas, Q-resolution, which forms the foundation of learning in modern search-based QBF solvers, is exponentially inferior in proof size to two of its extensions: Q-resolution with resolution over universal literals (QU-resolution) and long-distance Q-resolution (LQ-resolution). The relative proof power between LQ-resolution and QU-resolution, however, remains unknown. In this paper, we show their incomparability by exponential separations on two families of QBFs, and further propose a combination of the two resolution methods to achieve an even more powerful proof system. These results may shed light on solver development with enhanced learning mechanisms. In addition, we show how QBF Skolem/Herbrand certificate extraction can benefit from polynomial LQ-resolution proofs in contrast to their exponential Q-resolution counterparts.


title: Networked Drone Cameras for Sports Streaming

abstract: A network of drone cameras can be deployed to cover live events, such as high-action sports game played on a large field, but managing networked drone cameras in real-time is challenging. Distributed approaches yield suboptimal solutions from lack of coordination but coordination with a centralized controller incurs round-trip latencies of several hundreds of milliseconds over a wireless channel. We propose a fog-networking based system architecture to automatically coordinate a network of drones equipped with cameras to capture and broadcast the dynamically changing scenes of interest in a sports game. We design both optimal and practical algorithms to balance the tradeoff between two metrics: coverage of the most important scenes and streamed video bitrate. To compensate for network round-trip latencies, the centralized controller uses a predictive approach to predict which locations the drones should cover next. The controller maximizes video bitrate by associating each drone to an optimally matched server and dynamically re-assigns drones as relay nodes to boost the throughput in low-throughput scenarios. This dynamic assignment at centralized controller occurs at slower time-scale permitted by round-trip latencies, while the predictive approach and drones’ local decision ensures that the system works in real-time. Experimental results over tens of flights on the field suggest our system can achieve really good performance, for example, 8 drones can achieve a tradeoff of 94% coverage and (on average) 2K video support at 20 Mbps by optimizing between coverage and throughput. By dynamically allocating drones to cover the game or act as relays, our system also demonstrates a 2x gain over systems maximizing static coverage alone that achieves only 9 Mbps video throughput.


title: Permutation importance: a corrected feature importance measure

abstract: Motivation: In life sciences, interpretability of machine learning models is as important as their prediction accuracy. Linear models are probably the most frequently used methods for assessing feature relevance, despite their relative inflexibility. However, in the past years effective estimators of feature relevance have been derived for highly complex or non-parametric models such as support vector machines and RandomForest (RF) models. Recently, it has been observed that RF models are biased in such a way that categorical variables with a large number of categories are preferred. Results: In this work, we introduce a heuristic for normalizing feature importance measures that can correct the feature importance bias. The method is based on repeated permutations of the outcome vector for estimating the distribution of measured importance for each variable in a non-informative setting. The P-value of the observed importance provides a corrected measure of feature importance. We apply our method to simulated data and demonstrate that (i) non-informative predictors do not receive significant P-values, (ii) informative variables can successfully be recovered among non-informative variables and (iii) P-values computed with permutation importance (PIMP) are very helpful for deciding the significance of variables, and therefore improve model interpretability. Furthermore, PIMP was used to correct RF-based importance measures for two real-world case studies. We propose an improved RF model that uses the significant variables with respect to the PIMP measure and show that its prediction accuracy is superior to that of other existing models. Availability: R code for the method presented in this article is available at http://www.mpi-inf.mpg.de/~altmann/download/PIMP.R Contact: altmann@mpi-inf.mpg.de, laura.tolosi@mpi-inf.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online.


title: Frequent subgraph discovery

abstract: As data mining techniques are being increasingly applied to non-traditional domains, existing approaches for finding frequent itemsets cannot be used as they cannot model the requirement of these domains. An alternate way of modeling the objects in these data sets is to use graphs. Within that model, the problem of finding frequent patterns becomes that of discovering subgraphs that occur frequently over the entire set of graphs.The authors present a computationally efficient algorithm for finding all frequent subgraphs in large graph databases. We evaluated the performance of the algorithm by experiments with synthetic datasets as well as a chemical compound dataset. The empirical results show that our algorithm scales linearly with the number of input transactions and it is able to discover frequent subgraphs from a set of graph transactions reasonably fast, even though we have to deal with computationally hard problems such as canonical labeling of graphs and subgraph isomorphism which are not necessary for traditional frequent itemset discovery.


title: Design of Ternary Logic Combinational Circuits Based on Quantum Dot Gate FETs

abstract: In this paper, we discuss logic circuit designs using the circuit model of three-state quantum dot gate field effect transistors (QDGFETs). QDGFETs produce one intermediate state between the two normal stable ON and OFF states due to a change in the threshold voltage over this range. We have developed a simplified circuit model that accounts for this intermediate state. Interesting logic can be implemented using QDGFETs. In this paper, we discuss the designs of various two-input three-state QDGFET gates, including NAND- and NOR-like operations and their application in different combinational circuits like decoder, multiplier, adder, and so on. Increased number of states in three-state QDGFETs will increase the number of bit-handling capability of this device and will help us to handle more number of bits at a time with less circuit elements.


title: History checking of temporal fuzzy logic formulas for monitoring behavior-based mobile robots

abstract: Behavior-based robot control systems have shown remarkable success for controlling robots evolving in real world environments. However, they can fail in different manners due to their distributed control and their local decision making. In this case, monitoring can be used to detect failures and help to recover from them. In this work, we present an approach for specifying monitoring knowledge and a method for using this knowledge to detect failures. In particular we show how temporal fuzzy logic can be used to represent monitoring knowledge and then utilized to effectively detect runtime failures. New semantics are introduced to take into consideration uncertainty and noisy information. There are numbers of advantages to our approach including a declarative semantics for the monitoring knowledge and an independence of this knowledge from the implementation details of the control system. Moreover we show how our system can deal effectively with noisy information and sensor readings. Experiments with two real world robots and the simulator are used to illustrate failure examples and the benefits of failure detection and noise elimination.


title: ANALYZING AVIATION SAFETY REPORTS: FROM TOPIC MODELING TO SCALABLE MULTI-LABEL CLASSIFICATION

abstract: The Aviation Safety Reporting System (ASRS) is used to collect voluntarily submitted aviation safety reports from pilots, controllers and others. As such it is particularly useful in researching aviation safety deficiencies. In this paper we address two challenges related to the analysis of ASRS data: (1) the unsupervised extraction of meaningful and interpretable topics from ASRS reports and (2) multi-label classification of ASRS data based on a set of predefined categories. For topic modeling we investigate the practical usefulness of Latent Dirichlet Allocation (LDA) when it comes to modeling ASRS reports in terms of interpretable topics. We also utilize LDA to generate a more compact representation of ASRS reports to be used in multi-label classification. For multi-label classification we propose a novel and highly scalable multi-label classification algorithm based on multi-variate regression. Empirical results indicate that our approach is superior to several baseline and state-of-the-art approaches.


title: Robust camera calibration of soccer video using genetic algorithm

abstract: This paper proposes an exact and robust genetic algorithm-based method for the calibration of soccer camera. According to the FIFA official soccer field layout we defined a field model for the soccer court. Camera calibration is done through finding the homography transform between the field model and the input frame which is followed with DLT decomposition. The intersection of lines in the field model and input frame form feature points and by means of a genetic algorithm, we found the correspondence between those points. Our algorithm was applied to a couple of soccer video frames and the achieved results demonstrate its robustness, accuracy and high performance.


title: Dynamics and control of a biomimetic single-wing nano air vehicle

abstract: This paper describes the Samarai - a rotating mono-wing nano air vehicle (NAV) designed at Lockheed Martin Advanced Technology Laboratories. Challenges uniquely associated with developing this type of vehicle are identified and a dynamic modeling and control synthesis procedure is described for tackling them. It is shown that, by modeling the rotating wing as a lower-complexity spinning disk, and using the disk states for feedback control, a very simple control design results. This approach is validated in simulation of flight maneuvers that include vertical, hovering and translational motion segments.


title: Gender and Age Classification of Human Faces for Automatic Detection of Anomalous Human Behaviour

abstract: In this paper, we introduce an approach to classify gender and age from images of human faces which is an essential part of our method for autonomous detection of anomalous human behaviour. Human behaviour is often uncertain, and sometimes it is affected by emotion or environment. Automatic detection can help to recognise human behaviour which later can assist in investigating suspicious events. Central to our proposed approach is the recently introduced transfer learning. It was used on the basis of deep learning and successfully applied to image classification area. This paper is a continuous study from previous research on heterogeneous data in which we use images as supporting evidence. We present a method for image classification based on a pre-trained deep model for feature extraction and representation followed by a Support Vector Machine classifier. Because very few data sets with labels of gender and age exist of face images, we build one dataset named GAFace and applied our proposed method to this dataset achieving excellent results and robustness (gender classification: 90.33% and age classification: 80.17% accuracy) approaching human performance.


title: Multi-UAV monitoring with priorities and limited energy resources

abstract: This paper introduces a monitoring problem with limited energy resources and soft constraints on priorities employing a fleet of unmanned aerial vehicles (UAVs). This monitoring problem is a generalization of application cases ranging from surveillance of open-air events to monitoring crime scenes or disaster sites. In order to compute solutions, we propose an insertion heuristic with a negotiation mechanism for energy resources. The solution quality of the heuristic method is compared to cases where an optimal solution is known resulting in an average deviation of approximately 4%. By using this approach, we are able to plan routes for real-world scenarios which are currently unsolvable by general problem solvers in acceptable time. Moreover, the run-time is within seconds for such scenarios. Therefore, the method can be applied to dynamic environments, where re-planning during the mission is required.


title: How Many Words Is a Picture Worth? Automatic Caption Generation for News Images

abstract: In this paper we tackle the problem of automatic caption generation for news images. Our approach leverages the vast resource of pictures available on the web and the fact that many of them are captioned. Inspired by recent work in summarization, we propose extractive and abstractive caption generation models. They both operate over the output of a probabilistic image annotation model that pre-processes the pictures and suggests keywords to describe their content. Experimental results show that an abstractive model defined over phrases is superior to extractive methods.


title: Calibration of Action Cameras for Photogrammetric Purposes

abstract: The use of action cameras for photogrammetry purposes is not widespread due to the fact that until recently the images provided by the sensors, using either still or video capture mode, were not big enough to perform and provide the appropriate analysis with the necessary photogrammetric accuracy. However, several manufacturers have recently produced and released new lightweight devices which are: (a) easy to handle, (b) capable of performing under extreme conditions and more importantly (c) able to provide both still images and video sequences of high resolution. In order to be able to use the sensor of action cameras we must apply a careful and reliable self-calibration prior to the use of any photogrammetric procedure, a relatively difficult scenario because of the short focal length of the camera and its wide angle lens that is used to obtain the maximum possible resolution of images. Special software, using functions of the OpenCV library, has been created to perform both the calibration and the production of undistorted scenes for each one of the still and video image capturing mode of a novel action camera, the GoPro Hero 3 camera that can provide still images up to 12 Mp and video up 8 Mp resolution.


title: Challenges in introduction of artificial intelligence in medical practice - a review of clinical trials concerning adaptation of artificial intelligence in medicine

abstract: An interest in Artificial Intelligence [AI] as science is growing in the last years. It has become gradually more used in the medicine. Methodology of development and testing of AI algorithms is generally well established. Use of AI in medicine requires elaboration of standards of its validation in clinical settings. This paper is a review of literature concerning clinical trials on AI adaptation in medicine.


title: Construct3D: an augmented reality application for mathematics and geometry education

abstract: Construct3D is a three dimensional geometry construction tool specifically designed for mathematics and geometry education. It is based on the mobile collaborative augmented reality system "Studierstube". We describe our efforts in developing a system for the improvement of spatial abilities and maximization of transfer of learning. Means of application and integration in mathematics and geometry education at high school as well as university level are being discussed. Anecdotal evidence supports our claim that Construct3D is easy to learn, encourages experimentation with geometric constructions and improves spatial skills.


title: Mental health problems among university students and the impact of structural conditions

abstract: Aim ::: Mental health among university students represents an important and growing public health concern. International research has shown a high prevalence of depression and anxiety among university students. The aim of this study was to determine the prevalence of depression as well as anxiety among German university students and to assess gender differences and discrepancies between fields of study as well as class years. Additionally, an important public health issue—the impact of structural conditions at the university on depression and anxiety—was addressed.


title: Enabling mutation testing for Android apps

abstract: Mutation testing has been widely used to assess the fault-detection effectiveness of a test suite, as well as to guide test case generation or prioritization. Empirical studies have shown that, while mutants are generally representative of real faults, an effective application of mutation testing requires “traditional” operators designed for programming languages to be augmented with operators specific to an application domain and/or technology. This paper proposes MDroid+, a framework for effective mutation testing of Android apps. First, we systematically devise a taxonomy of 262 types of Android faults grouped in 14 categories by manually analyzing 2,023 so ware artifacts from different sources (e.g., bug reports, commits). Then, we identified a set of 38 mutation operators, and implemented an infrastructure to automatically seed mutations in Android apps with 35 of the identified operators. The taxonomy and the proposed operators have been evaluated in terms of stillborn/trivial mutants generated as compared to well know mutation tools, and their capacity to represent real faults in Android apps


title: R$^3$: Reinforced Reader-Ranker for Open-Domain Question Answering

abstract: In recent years researchers have achieved considerable success applying neural network methods to question answering (QA). These approaches have achieved state of the art results in simplified closed-domain settings such as the SQuAD (Rajpurkar et al., 2016) dataset, which provides a pre-selected passage, from which the answer to a given question may be extracted. More recently, researchers have begun to tackle open-domain QA, in which the model is given a question and access to a large corpus (e.g., wikipedia) instead of a pre-selected passage (Chen et al., 2017a). This setting is more complex as it requires large-scale search for relevant passages by an information retrieval component, combined with a reading comprehension model that "reads" the passages to generate an answer to the question. Performance in this setting lags considerably behind closed-domain performance. In this paper, we present a novel open-domain QA system called Reinforced Ranker-Reader $(R^3)$, based on two algorithmic innovations. First, we propose a new pipeline for open-domain QA with a Ranker component, which learns to rank retrieved passages in terms of likelihood of generating the ground-truth answer to a given question. Second, we propose a novel method that jointly trains the Ranker along with an answer-generation Reader model, based on reinforcement learning. We report extensive experimental results showing that our method significantly improves on the state of the art for multiple open-domain QA datasets.


title: Turn-on loss vs. free-wheel diode recovery dv/dt in IGBT modules

abstract: The latest 6th generation Carrier Stored Trench Gate Bipolar Transistor (CSTBT™) provides state of the art optimization of conduction and switching losses in IGBT modules. Use of low values of resistance in series with the IGBT gate produces low turn-on losses but increases stress on the recovery of the free-wheel diode resulting in higher dv/dt and increased EMI. The latest modules also incorporate new, improved recovery free-wheel diode chips which improve this situation but detailed evaluation of the trade-off between turn-on loss and dv/dt performance is required. This paper describes the evaluation, test results, and a comparative analysis of dv/dt versus turn-on loss as a function of gate drive conditions for the 6th generation IGBT compared to the standard 5th generation module.


title: Development of a Reinforcement Learning-based Evolutionary Fuzzy Rule-Based System for diabetes diagnosis

abstract: The early diagnosis of disease is critical to preventing the occurrence of severe complications. Diabetes is a serious health problem. A variety of methods have been developed for diagnosing diabetes. The majority of these methods have been developed in a black-box manner, which cannot be used to explain the inference and diagnosis procedure. Therefore, it is essential to develop methods with high accuracy and interpretability. In this study, a Reinforcement Learning-based Evolutionary Fuzzy Rule-Based System (RLEFRBS) is developed for diabetes diagnosis. The proposed model involves the building of a Rule Base (RB) and rule optimization. The initial RB is constructed using numerical data without initial rules; after learning the rules, redundant rules are eliminated based on the confidence measure. Next, redundant conditions in the antecedent parts are pruned to yield simpler rules with higher interpretability. Finally, an appropriate subset of the rules is selected using a Genetic Algorithm (GA), and the RB is constructed. Evolutionary tuning of the membership functions and weight adjusting using Reinforcement Learning (RL) are used to improve the performance of RLEFRBS. Moreover, to deal with uncovered instances, it makes use of an efficient rule stretching method. The performance of RLEFRBS was examined using two common datasets: Pima Indian Diabetes (PID) and BioSat Diabetes Dataset (BDD). The experimental results show that the proposed model provides a more compact, interpretable and accurate RB that can be considered to be a promising alternative for diagnosis of diabetes.


title: Student Engagement in Instructional Activity: Patterns in the Elementary, Middle, and High School Years

abstract: Although student engagement with the intellectual work of school is important to students' achievement and to their social and cognitive development, studies over a span of two decades have documented low levels of engagement, particularly in the classroom. Examining several theoretical perspectives that attempt to explain engagement through comprehensive frameworks, this study evaluates the effect on engagement of school reform initiatives that are consistent with the theories. The study also investigates whether patterns exist in students' engagement, whether the patterns are consistent across grade levels, and whether class subject matter (mathematics or social studies) differentially affects engagement. The sample includes 3,669 students representing 143 social studies and mathematics classrooms in a nationally selected sample of 24 restructuring elementary, middle, and high schools. Because of the nature of the nested data (students nested within classrooms nested within schools), the analysis is conducted using hierarchical linear modeling in its three-level application (HLM3L). The reform initiatives, which are consistent with the theories, eliminate personal background effects. Together with classroom subject matter, they substantially influence engagement. The results are generally consistent across grade levels.


Clarifying the Ambiguous Role of the CIO


title: Aerobic endurance exercise benefits memory and affect in young adults

abstract: Exercise seems a simple and widely practised behaviour that activates molecular and cellular signalling cascades involved in various central nervous system processes. Despite impressive results obtained in animal studies, fitness interventions have produced less reliable effects in humans, particularly in young adults. In the present study we tested the hypothesis that an individually adapted exercise training consisting of three running sessions of 30 minutes per week for 6 weeks, has the potential to improve visuospatial and verbal memory, concentration performance, and affect in young and healthy adults. Twenty-eight students participated and underwent a graded fitness test to assess individual fitness. The experimental group took part in an aerobic running programme, whereas the control group were asked not to vary their everyday activities. We found a significant increase in visuospatial memory performance and a significant increase in positive affect on a .05 alpha level of significance. However, we...


title: A security approach for health care information systems

abstract: From the perspective of services to the population, with vast social implications, in which the security, confidentiality, and access to personal data represents a critical region, the medical services and information systems that are on the base of the strategic management in health care systems, are a theme of maximum interest and rather less approached. In particular, the prospect of at-home health care systems for screening and rehabilitation has raised enormous interest and is seen as a new method to approach the disease more efficaciously. With the development of Information Technology and its major breakthroughs in health care information systems, bringing the rehabilitation and monitoring of the elderly patients from in-hospital to more advanced at-home health care systems, information security measures has been raised. A survey of security measures and data communication security involved in health care systems in order to ensure information protection is presented in this paper. The specific security issues involved in the development of a health care system that manages data to support monitoring and rehabilitation of patients with Parkinson's disease is subject of this paper.


title: Integrated transition of coplanar to rectangular waveguides

abstract: Usual transitions between planar circuit and rectangular waveguide make use of 3-D complex mounting structures. Such an integration requires costly high precision mechanical alignment, In this paper, a new planar platform is developed in which a coplanar waveguide (CPW) and a rectangular waveguide are fully integrated on the same substrate, and they are interconnected via a simple transition. They can be built with a standard PCB process. Our experiments at 28 GHz show that an effective bandwidth of 7% at 15 dB return loss can easily be achieved. The CPW-to-waveguide transition allows for a complete integration of waveguide components on substrate with active components such as MMIC.


title: Hierarchical Multilabel Classification with Minimum Bayes Risk

abstract: Hierarchical multilabel classification (HMC) allows an instance to have multiple labels residing in a hierarchy. A popular loss function used in HMC is the H-loss, which penalizes only the first classification mistake along each prediction path. However, the H-loss metric can only be used on tree-structured label hierarchies, but not on DAG hierarchies. Moreover, it may lead to misleading predictions as not all misclassifications in the hierarchy are penalized. In this paper, we overcome these deficiencies by proposing a hierarchy-aware loss function that is more appropriate for HMC. Using Bayesian decision theory, we then develop a Bayes-optimal classifier with respect to this loss function. Instead of requiring an exhaustive summation and search for the optimal multilabel, the proposed classification problem can be efficiently solved using a greedy algorithm on both tree-and DAG-structured label hierarchies. Experimental results on a large number of real-world data sets show that the proposed algorithm outperforms existing HMC methods.


title: Multicriteria high-speed rail route selection: application to Malaysia's high-speed rail corridor prioritization

abstract: This paper reviews the literature on multicriteria decision analysis in transportation and provides a case study of high-speed rail (HSR) corridor/route selection using multicriteria methods in the context of HSR corridor prioritization in Malaysia. Using the screening method proposed by Hagler and Todorovich and the ELECTRE I multicriteria method, it is found that the southbound corridor from Kuala Lumpur to Singapore has the highest priority, followed by the eastbound corridor to Kuantan, and northbound to Georgetown. HSR trains could potentially reduce the trip times to Singapore, Kuantan, and Georgetown from Kuala Lumpur, as compared to driving, by 65-73%. The results of this study can be used to assist in the planning of HSR and/or integrated transportation systems in Malaysia. The same methods can be used to evaluate potential HSR corridors/routes in other countries or regions.


title: Randomized Clinical Trial of Bright Light Therapy for Antepartum Depression

abstract: Background: Bright light therapy was shown to be a promising treatment for depression during pregnancy in a recent open-label study. In an extension of this work, we report findings from a double-blind placebo-controlled pilot study. Method: Ten pregnant women with DSM-IV major depressive disorder were randomly assigned from April 2000 to January 2002 to a 5-week clinical trial with either a 7000 lux (active) or 500 lux (placebo) light box. At the end of the randomized controlled trial, subjects had the option of continuing in a 5-week extension phase. The Structured Interview Guide for the Hamilton Depression Scale-Seasonal Affective Disorder Version was administered to assess changes in clinical status. Salivary melatonin was used to index circadian rhythm phase for comparison with antidepressant results. Results: Although there was a small mean group advantage of active treatment throughout the randomized controlled trial, it was not statistically significant. However, in the longer 10-week trial, the presence of active versus placebo light produced a clear treatment effect (p = .001) with an effect size (0.43) similar to that seen in antidepressant drug trials. Successful treatment with bright light was associated with phase advances of the melatonin rhythm. Conclusion: These findings provide additional evidence for an active effect of bright light therapy for antepartum depression and underscore the need for an expanded randomized clinical trial.


title: Detection of review spam

abstract: We have extracted all types of data that can be used in spam detection techniques.We have reviewed state of the art literature in the area of detection of spam reviews.In this research, we have categorized and classified spam detection methods and techniques.We have provided the strength and weaknesses for these methods and techniques. In recent years, online reviews have become the most important resource of customers' opinions. These reviews are used increasingly by individuals and organizations to make purchase and business decisions. Unfortunately, driven by the desire for profit or publicity, fraudsters have produced deceptive (spam) reviews. The fraudsters' activities mislead potential customers and organizations reshaping their businesses and prevent opinion-mining techniques from reaching accurate conclusions. The present research focuses on systematically analyzing and categorizing models that detect review spam. Next, the study proceeds to assess them in terms of accuracy and results. We find that studies can be categorized into three groups that focus on methods to detect spam reviews, individual spammers and group spam. Different detection techniques have different strengths and weaknesses and thus favor different detection contexts.


title: CubeSat Deployable Antenna Using Bistable Composite Tape-Springs

abstract: In this letter, a new conductive composite tape-spring is proposed for CubeSat deployable antennas that is constructed using a glass fiber reinforced epoxy with an embedded copper alloy conductor. The tape-spring is bistable enabling the antenna to be elastically stable in both the deployed and stowed states. A dipole antenna is designed, simulated, and tested to prove the viability of the electrical properties of this material.


title: Distinct contribution of the cortico-striatal and cortico-cerebellar systems to motor skill learning

abstract: This review paper focuses on studies in healthy human subjects that examined the functional neuroanatomy and cerebral plasticity associated with the learning, consolidation and retention phases of motor skilled behaviors using modern brain imaging techniques. Evidence in support of a recent model proposed by Doyon and Ungerleider [Functional Anatomy of Motor Skill Learning. In: Squire LR, Schacter DL, editors. Neuropsychology of Memory. New York: Guilford Press, 2002.] is also discussed. The latter suggests that experience-dependent changes in the brain depend not only on the stage of learning, but also on whether subjects are required to learn a new sequence of movements (motor sequence learning) or learn to adapt to environmental perturbations (motor adaptation). This model proposes that the cortico-striatal and cortico-cerebellar systems contribute differentially to motor sequence learning and motor adaptation, respectively, and that this is most apparent during the slow learning phase (i.e. automatization) when subjects achieve asymptotic performance, as well as during reactivation of the new skilled behavior in the retention phase.


title: Winged scapula incidence and upper limb morbidity after surgery for breast cancer with axillary dissection

abstract: PURPOSE ::: The aim of this study was to determine the incidence of winged scapula after breast cancer surgery, its impact on shoulder morbidity and difference in incidence according to surgery type. ::: ::: ::: METHODS ::: Patients with breast cancer and surgical indication for axillary dissection were included. A total of 112 patients were surveyed with one physical evaluation before the surgery and others 15, 30, 90, and 180 days after. Winged scapula was assessed with test proposed by Hoppenfeld. Shoulder range of motion (ROM) was assessed with goniometer for flexion, extension, adduction, abduction, internal rotation, and external rotation. A verbal scale from 0 to 10 was used to assess pain. ::: ::: ::: RESULTS ::: Winged scapula incidence was 8.0 % 15 days after surgery. Two patients recovered from winged scapula 90 days after surgery and four more 180 days after surgery, while three patients still had winged scapula at this time. The incidence after 15 days from surgery was 20.9 and 22.6 % among patients submitted to sentinel node biopsy or axillary lymphadenectomy (AL), respectively (p < 0.01). There was no statistical difference of incidence according to breast surgery type. Operated side shoulder flexion, adduction, and abduction ROM changes were statistically different in patients with or without winged scapula. The mean reduction was higher in patients with winged scapula. Both groups showed the same pattern over time in pain. ::: ::: ::: CONCLUSION ::: Winged scapula incidence was 8.0 % and was higher in AL, and prevalence decreased during 6 months after surgery. Patients who developed winged scapula had more shoulder flexion, adduction, and abduction limitation.