title: An Implementation of EURORADIO Protocol for ERTMS Systems

abstract: European Rail Traffic Management System (ERTMS) is the European reference for interoperable and safer signaling systems to efficiently manage trains running. If implemented, it allows trains cross seamlessly intra-European national borders. ERTMS has defined a secure communication protocol, EURORADIO, based on open communication networks. Its RadioInfill function can improve the reaction of the signaling system to changes in line conditions, avoiding unnecessary braking: its advantages in terms of power saving and travel time has been analyzed. In this paper a software implementation of the EURORADIO protocol with RadioInfill for ERTMS Level 1 using GSM-R is illustrated as part of the SR-Secure Italian project. In this building-blocks architecture the EURORADIO layers communicates together through modular Application Programm Interfaces. Security coding rules and railway industry requirements specified by EN 50128 standard have been respected. The proposed implementation has successfully passed conformity tests and has been tested on a computer-based simulator.


title: GaAs heteroepitaxial growth on Si substrates with thin Si interlayers in situ annealed at high temperatures

abstract: The structural quality of GaAs/Si films can be improved by incorporating a Si interlayer. In this paper, we discuss the effect on the GaAs structural quality of a high‐temperature anneal, either in situ between the deposition of the Si layer and subsequent GaAs, or ex situ after the film has been fully grown. The Si interlayer has been found to act as both a cap layer for an in situ high‐temperature anneal and a blocking layer of dislocations. The full width at half maximum (FWHM) of the x‐ray rocking curve of (400)GaAs diffraction in 1‐μm‐thick GaAs/Si films decreased from 625 to 388 s upon the incorporation of a Si interlayer. Furthermore, the FWHM was decreased to 200 s by adding an ex situ anneal at 900 °C for 1 min after growth. However, it was found in GaAs/Si films with Si interlayers that a 78 meV acceptor level due to a Ga antisite defect was observed from photoluminescence (PL) at 77 K, and that oval defects were generated from secondary electron‐microscope observations. These phenomena suggeste...


title: A 200–225 GHz SiGe Power Amplifier with peak Psat of 9.6 dBm using wideband power combination

abstract: A 200–225 GHz SiGe combiner Power Amplifier (PA) based on a wideband 4-way power combiner architecture is presented in this paper. The circuit is implemented in a 130 nm SiGe BiCMOS technology with f T /f max of 250/370 GHz. A parallel power combining architecture based on the low-loss transmission line based zero-degree combiner is used to combine the power from 4 PA cores. At 215 GHz, the P sat is 9.6 dBm and from 200–225 GHz the average P sat is 9 dBm. From 200–225 GHz, the combiner enhances the P sat from the unit PA cores by 3.5–4 dB. For this circuit, the peak small signal gain is 25 dB at 213 GHz. To the best of the authors knowledge, this is the highest reported output power for silicon PAs above 200 GHz.


title: A FRACTAL-BASED ALGORITHM OF EMOTION RECOGNITION FROM EEG USING AROUSAL-VALENCE MODEL

abstract: Emotion recognition from EEG could be used in many applications as it allows us to know the “inner” emotion regardless of the human facial expression, behaviour, or verbal communication. In this paper, we proposed and described a novel fractal dimension (FD) based emotion recognition algorithm using an Arousal-Valence emotion model. FD values calculated from the EEG signal recorded from the corresponding brain lobes are mapped to the 2D emotion model. The proposed algorithm allows us to recognize emotions that could be defined by arousal and valence levels. Only 3 electrodes are needed for the emotions recognition. Higuchi and box-counting algorithms were used for the EEG analysis and comparison. Support Vector Machine classifier was applied for arousal and valence levels recognition. The proposed method is a subject dependent one. Experiments with music and sound stimuli to induce human emotions were realized. Sound clips from the International Affective Digitized Sounds (IADS) database were used in the experiments.


title: Robust Visual Tracking with Deep Convolutional Neural Network Based Object Proposals on PETS

abstract: Tracking by detection based object tracking methods encounter numerous complications including object appearance changes, size and shape deformations, partial and full occlusions, which make online adaptation of classifiers and object models a substantial challenge. In this paper, we employ an object proposal network that generates a small yet refined set of bounding box candidates to mitigate the this object model refitting problem by concentrating on hard negatives when we update the classifier. This helps improving the discriminative power as hard negatives are likely to be due to background and other distractions. Another intuition is that, in each frame, applying the classifier only on the refined set of object-like candidates would be sufficient to eliminate most of the false positives. Incorporating an object proposal makes the tracker robust against shape deformations since they are handled naturally by the proposal stage. We demonstrate evaluations on the PETS 2016 dataset and compare with the state-of-theart trackers. Our method provides the superior results.


title: HapticSerpent: A Wearable Haptic Feedback Robot for VR

abstract: Haptic feedback is an important part of virtual reality (VR), where it can increase the immersion and enjoyment. Within VR, research literature and products are mainly limited to vibrotactile feedback for the torso. We believe that additional types of haptic feedback around other areas of the body could potentially yield interesting VR experiences. Thus, we present HapticSerpent, which is a waist-worn robot capable of various haptic feedback on the torso, neck, face, arms and hands. We present our implementation specifications, followed by an initial evaluation to measure the distinguishability of taps applied to the torso. Also, we surveyed the acceptability of receiving feedback in different locations on the body. Participants noted an overall higher accuracy on the upper and sides of the torso and they generally disfavored haptic feedback in sensitive areas due to potential harm. Lastly, we discuss various research opportunities and challenges and present our future direction.


title: An object detection system based on YOLO in traffic scene

abstract: We build an object detection system for images in traffic scene. It is fast, accurate and robust. Traditional object detectors first generate proposals. After that the features are extracted. Then a classifier on these proposals is executed. But the speed is slow and the accuracy is not satisfying. YOLO an excellent object detection approach based on deep learning presents a single convolutional neural network for location and classification. All the fully-connected layers of YOLO's network are replaced with an average pool layer for the purpose of reproducing a new network. The loss function is optimized after the proportion of bounding coordinates error is increased. A new object detection method, OYOLO (Optimized YOLO), is produced, which is 1.18 times faster than YOLO, while outperforming other region-based approaches like R-CNN in accuracy. To improve accuracy further, we add the combination of OYOLO and R-FCN to our system. For challenging images in nights, pre-processing is presented using the histogram equalization approach. We have got more than 6% improvement in mAP on our testing set.


title: Using NLP techniques for file fragment classification

abstract: Abstract The classification of file fragments is an important problem in digital forensics. The literature does not include comprehensive work on applying machine learning techniques to this problem. In this work, we explore the use of techniques from natural language processing to classify file fragments. We take a supervised learning approach, based on the use of support vector machines combined with the bag-of-words model, where text documents are represented as unordered bags of words. This technique has been repeatedly shown to be effective and robust in classifying text documents (e.g., in distinguishing positive movie reviews from negative ones). In our approach, we represent file fragments as “bags of bytes” with feature vectors consisting of unigram and bigram counts, as well as other statistical measurements (including entropy and others). We made use of the publicly available Garfinkel data corpus to generate file fragments for training and testing. We ran a series of experiments, and found that this approach is effective in this domain as well.


title: A Fully Integrated Digital Low-Dropout Regulator Based on Event-Driven Explicit Time-Coding Architecture

abstract: This paper presents a fully integrated digital low-dropout (LDO) voltage regulator based on event-driven control architecture. The focus of the paper is to scale the off-chip output capacitor of an LDO conventionally used to compensate fast load current change. To shrink the output capacitor size, it is paramount to shorten control latency. This can be done by employing a high-speed error amplifier in analog LDO designs and by using fast clock in digital synchronous LDO designs. However, those approaches become less suitable for sub-1-V supply voltage due to the headroom problem of analog circuits and/or increase power dissipation due to the high-frequency clock. In this paper, we tackle the tradeoff between power consumption and control latency by introducing an event-driven approach. Our event-driven approach enables to perform regulation tasks only when the output voltage deviates significantly from a set point, simultaneously achieving short latency and low-power dissipation. We prototyped an event-driven digital LDO that supports 400- $\mu \text{A}$ load current at 0.5-V input and 0.45-V output voltage. The measurements show 40-mV droop voltage and 96.3% peak current efficiency with an on-chip integrated 0.4-nF output capacitor.


title: Large Scale Classification with Support Vector Machine Algorithms

abstract: Boosting of least-squares support vector machine (LS-SVM) algorithms can classify large datasets on standard personal computers (PCs). We extend the LS-SVM proposed by Suykens and Vandewalle in several ways to efficiently classify large datasets. We developed a row-incremental version for datasets with billions of data points and up to 10,000 dimensions. By adding a Tikhonov regularization term and using the Sherman-Morrison-Woodbury formula, we developed a column-incremental LS-SVM to process datasets with a small number of data points but very high dimensionality. Finally, by applying boosting to these incremental LS-SVM algorithms, we developed classification algorithms for massive, very-high-dimensional datasets, and we also applied these ideas to build boosting of other efficient SVM algorithms proposed by Mangasarian, including Lagrange SVM (LSVM), proximal SVM (PSVM) and Newton SVM (NSVM). Numerical test results on UCI, RCV1- binary, Reuters-21578, Forest cover type and KDD cup 1999 datasets showed that our algorithms are often significantly faster and/or more accurate than state-of- the-art algorithms LibSVM, SVM-perf and CB-SVM.


title: DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset

abstract: We develop a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects. The language is human-written and less noisy. The dialogues in the dataset reflect our daily communication way and cover various topics about our daily life. We also manually label the developed dataset with communication intention and emotion information. Then, we evaluate existing approaches on DailyDialog dataset and hope it benefit the research field of dialog systems.


title: Mining communities and their relationships in blogs: A study of online hate groups

abstract: Blogs, often treated as the equivalence of online personal diaries, have become one of the fastest growing types of Web-based media. Everyone is free to express their opinions and emotions very easily through blogs. In the blogosphere, many communities have emerged, which include hate groups and racists that are trying to share their ideology, express their views, or recruit new group members. It is important to analyze these virtual communities, defined based on membership and subscription linkages, in order to monitor for activities that are potentially harmful to society. While many Web mining and network analysis techniques have been used to analyze the content and structure of the Web sites of hate groups on the Internet, these techniques have not been applied to the study of hate groups in blogs. To address this issue, we have proposed a semi-automated approach in this research. The proposed approach consists of four modules, namely blog spider, information extraction, network analysis, and visualization. We applied this approach to identify and analyze a selected set of 28 anti-Blacks hate groups (820 bloggers) on Xanga, one of the most popular blog hosting sites. Our analysis results revealed some interesting demographical and topological characteristics in these groups, and identified at least two large communities on top of the smaller ones. The study also demonstrated the feasibility in applying the proposed approach in the study of hate groups and other related communities in blogs.


title: Calcium stores regulate the polarity and input specificity of synaptic modification

abstract: Activity-induced synaptic modification is essential for the development and plasticity of the nervous system1,2,3. Repetitive correlated activation of pre- and postsynaptic neurons can induce persistent enhancement or decrement of synaptic efficacy, commonly referred to as long-term potentiation or depression2,3(LTP or LTD). An important unresolved issue is whether and to what extent LTP and LTD are restricted to the activated synapses4,5,6,7,8. Here we show that, in the CA1 region of the hippocampus, reduction of postsynaptic calcium influx by partial blockade of NMDA (N-methyl-d-aspartate) receptors results in a conversion of LTP to LTD and a loss of input specificity normally associated with LTP, with LTD appearing at heterosynaptic inputs. The induction of LTD at homo- and heterosynaptic sites requires functional ryanodine receptors and inositol triphosphate (InsP3) receptors, respectively. Functional blockade or genetic deletion of type 1 InsP3 receptors led to a conversion of LTD to LTP and elimination of heterosynaptic LTD, whereas blocking ryanodine receptors eliminated only homosynaptic LTD. Thus, postsynaptic Ca2+, deriving from Ca2+ influx and differential release of Ca2+ from internal stores through ryanodine and InsP3 receptors, regulates both the polarity and input specificity of activity-induced synaptic modification.


title: The functional significance of delta oscillations in cognitive processing

abstract: Ample evidence suggests that electroencephalographic (EEG) oscillatory activity is linked to a broad variety of perceptual, sensorimotor, and cognitive operations. However, few studies have investigated the delta band (0.5–3.5 Hz) during different cognitive processes. The aim of this review is to present data and propose the hypothesis that sustained delta oscillations inhibit interferences that may affect the performance of mental tasks, possibly by modulating the activity of those networks that should be inactive to accomplish the task. It is clear that two functionally distinct and potentially competing brain networks can be broadly distinguished by their contrasting roles in attention to the external world vs. the internally directed mentation or concentration. During concentration, EEG delta (1–3.5 Hz) activity increases mainly in frontal leads in different tasks: mental calculation, semantic tasks, and the Sternberg paradigm. This last task is considered a working memory task, but in neural, as well as phenomenological, terms, working memory can be best understood as attention focused on an internal representation. In the Sternberg task, increases in power in the frequencies from 1 to 3.90 Hz in frontal regions are reported. In a Go/No-Go task, power increases at 1 Hz in both conditions were observed during 100–300 ms in central, parietal and temporal regions. However, in the No-Go condition, power increases were also observed in frontal regions, suggesting its participation in the inhibition of the motor response. Increases in delta power were also reported during semantic tasks in children. In conclusion, the results suggest that power increases of delta frequencies during mental tasks are associated with functional cortical deafferentation, or inhibition of the sensory afferences that interfere with internal concentration. These inhibitory oscillations would modulate the activity of those networks that should be inactive to accomplish the task.


title: Suicide and suicidal behavior.

abstract: Suicidal behavior is a leading cause of injury and death worldwide. Information about the epidemiology of such behavior is important for policy-making and prevention. The authors reviewed government data on suicide and suicidal behavior and conducted a systematic review of studies on the epidemiology of suicide published from 1997 to 2007. The authors' aims were to examine the prevalence of, trends in, and risk and protective factors for suicidal behavior in the United States and cross-nationally. The data revealed significant cross-national variability in the prevalence of suicidal behavior but consistency in age of onset, transition probabilities, and key risk factors. Suicide is more prevalent among men, whereas nonfatal suicidal behaviors are more prevalent among women and persons who are young, are unmarried, or have a psychiatric disorder. Despite an increase in the treatment of suicidal persons over the past decade, incidence rates of suicidal behavior have remained largely unchanged. Most epidemiologic research on suicidal behavior has focused on patterns and correlates of prevalence. The next generation of studies must examine synergistic effects among modifiable risk and protective factors. New studies must incorporate recent advances in survey methods and clinical assessment. Results should be used in ongoing efforts to decrease the significant loss of life caused by suicidal behavior.


title: AutoEncoder by Forest

abstract: Auto-encoding is an important task which is typically realized by deep neural networks (DNNs) such as convolutional neural networks (CNN). In this paper, we propose EncoderForest (abbrv. eForest), the first tree ensemble based auto-encoder. We present a procedure for enabling forests to do backward reconstruction by utilizing the equivalent classes defined by decision paths of the trees, and demonstrate its usage in both supervised and unsupervised setting. Experiments show that, compared with DNN autoencoders, eForest is able to obtain lower reconstruction error with fast training speed, while the model itself is reusable and damage-tolerable.


title: A vision-based robotic grasping system using deep learning for 3D object recognition and pose estimation

abstract: Pose estimation of object is one of the key problems for the automatic-grasping task of robotics. In this paper, we present a new vision-based robotic grasping system, which can not only recognize different objects but also estimate their poses by using a deep learning model, finally grasp them and move to a predefined destination. The deep learning model demonstrates strong power in learning hierarchical features which greatly facilitates the recognition mission. We apply the Max-pooling Convolutional Neural Network (MPCNN), one of the most popular deep learning models, in this system, and assign different poses of objects as different classes in MPCNN. Besides, a new object detection method is also presented to overcome the disadvantage of the deep learning model. We have built a database comprised of 5 objects with different poses and illuminations for experimental performance evaluation. The experimental results demonstrate that our system can achieve high accuracy on object recognition as well as pose estimation. And the vision-based robotic system can grasp objects successfully regardless of different poses and illuminations.


title: Actions in the Eye: Dynamic Gaze Datasets and Learnt Saliency Models for Visual Recognition

abstract: Systems based on bag-of-words models from image features collected at maxima of sparse interest point operators have been used successfully for both computer visual object and action recognition tasks. While the sparse, interest-point based approach to recognition is not inconsistent with visual processing in biological systems that operate in ‘saccade and fixate’ regimes, the methodology and emphasis in the human and the computer vision communities remains sharply distinct. Here, we make three contributions aiming to bridge this gap. First, we complement existing state-of-the art large scale dynamic computer vision annotated datasets like Hollywood-2 [1] and UCF Sports [2] with human eye movements collected under the ecological constraints of visual action and scene context recognition tasks. To our knowledge these are the first large human eye tracking datasets to be collected and made publicly available for video, vision.imar.ro/eyetracking (497,107 frames, each viewed by 19 subjects), unique in terms of their (a) large scale and computer vision relevance, (b) dynamic, video stimuli, (c) task control, as well as free-viewing . Second, we introduce novel dynamic consistency and alignment measures , which underline the remarkable stability of patterns of visual search among subjects. Third, we leverage the significant amount of collected data in order to pursue studies and build automatic, end-to-end trainable computer vision systems based on human eye movements. Our studies not only shed light on the differences between computer vision spatio-temporal interest point image sampling strategies and the human fixations, as well as their impact for visual recognition performance, but also demonstrate that human fixations can be accurately predicted, and when used in an end-to-end automatic system, leveraging some of the advanced computer vision practice, can lead to state of the art results.


title: A plagiarism detection system

abstract: The problem of plagiarism in programming assignments by students in computer science courses has caused considerable concern among both faculty and students. There are a number of methods which instructors use in an effort to control the plagiarism problem. In this paper we describe a plagiarism detection system which was recently implemented in our department. This system is being used to detect similarities in student programs.


title: IMPROVING SAFETY WITH OBSTACLE DETECTION AND TRACK FOLLOWING CAR USING SENSOR, GPS AND GSM

abstract: Through this Project our expectation is that the car should start when authorized persons send predefined message on the External SIM. Otherwise the car is locked. Smart vehicle sends current locations on the different predefined mobile numbers. In this situation owner calls on External SIM for the first time, then the car should stop & when the user calls second time, then the vehicle will start automatically. Also, When the car is in motion and suddenly Obstacle occurring in front of the vehicle, then the sensor should detect barrier, (may be small or big) car should break automatic. And try to search alternate path in case of an accident. For immediate help and send that message to an owner and care center. In that Short Message Service it is should send current place latitude and longitude, Time, Speed in knots, Date, obtained from Global Positioning, Therefore, using that latitude and longitude, we find location on earth and we may provide fast help in an emergency. The Car should also break if the car is on wrong driving track and automatically re-track car. The Micro Controller PIC18F26K22 interfaces with Infrared sensors and Ultrasonic sensor that continuously detect, track, and the vehicle is in motion. If an Infrared sensor output detects off road path, then Micro controller immediately slows down the vehicle speed and try to move the vehicle back on path depending on track sensor output. In this project Micro controller is programmed using embedded C language and built using MPLAP.


title: Fairness in Criminal Justice Risk Assessments: The State of the Art

abstract: Objectives: Discussions of fairness in criminal justice risk assessments typically lack conceptual precision. Rhetoric too often substitutes for careful analysis. In this paper, we seek to clarify the tradeoffs between different kinds of fairness and between fairness and accuracy. ::: Methods: We draw on the existing literatures in criminology, computer science and statistics to provide an integrated examination of fairness and accuracy in criminal justice risk assessments. We also provide an empirical illustration using data from arraignments. ::: Results: We show that there are at least six kinds of fairness, some of which are incompatible with one another and with accuracy. ::: Conclusions: Except in trivial cases, it is impossible to maximize accuracy and fairness at the same time, and impossible simultaneously to satisfy all kinds of fairness. In practice, a major complication is different base rates across different legally protected groups. There is a need to consider challenging tradeoffs.


title: Personalized recommendation engine using HADOOP

abstract: More and more E-commerce Websites provide products with different prices which made it hard for consumers to find the products and services they want. In order to overcome this data overload, personalized recommendation engines are used to suggest products and to provide consumers with relevant data to help them decide which products to purchase. Recommendation engines are highly computational and hence ideal for the Hadoop Platform. This system aims at building a book recommendation engine which uses item or user based recommendation from Mahout for recommending books. It will analyze the data and give suggestions based on what similar users did and on the past transaction history of the user.


title: Neural network adaptive control of systems with input saturation

abstract: In the application of adaptive flight control, significant issues arise due to limitations on the plant inputs, such as actuator displacement limits. The concept of utilizing a modified reference model to prevent an adaptation law from "seeing" this system-input characteristic is described. The method allows correct adaptation while the plant input is saturated. To apply the method, estimates of actuator positions must be found. However, the adaptation law can correct for errors in these estimates. A theorem of boundedness for all system signals is included for a single hidden layer neural network adaptive law. The domain of attraction is also discussed.


title: JAutomate: A Tool for System- and Acceptance-test Automation

abstract: System- and acceptance-testing are primarily performed with manual practices in current software industry. However, these practices have several issues, e.g. they are tedious, error prone and time consuming with costs up towards 40 percent of the total development cost. Automated test techniques have been proposed as a solution to mitigate these issues, but they generally approach testing from a lower level of system abstraction, leaving a gap for a flexible, high system-level test automation technique/tool. In this paper we present JAutomate, a Visual GUI Testing (VGT) tool that fills this gap by combining image recognition with record and replay functionality for high system-level test automation performed through the system under test's graphical user interface. We present the tool, its benefits compared to other similar techniques and manual testing. In addition, we compare JAutomate with two other VGT tools based on their static properties. Finally, we present the results from a survey with industrial practitioners that identifies test-related problems that industry is currently facing and discuss how JAutomate can solve or mitigate these problems.


Perception , Planning , and Execution for Mobile Manipulation in Unstructured Environments


title: Increased cerebral response during a divided attention task following sleep deprivation.

abstract: We recently reported that the brain showed greater responsiveness to some cognitive demands following total sleep deprivation (TSD). Specifically, verbal learning led to increased cerebral activation following TSD while arithmetic resulted in decreased activation. Here we report data from a divided attention task that combined verbal learning and arithmetic. Thirteen normal control subjects performed the task while undergoing functional magnetic resonance imaging (FMRI) scans after a normal night of sleep and following 35 h TSD. Behaviourally, subjects showed only modest impairments following TSD. With respect to cerebral activation, the results showed (a) increased activation in the prefrontal cortex and parietal lobes, particularly in the right hemisphere, following TSD, (b) activation in left inferior frontal gyrus correlated with increased subjective sleepiness after TSD, and (c) activation in bilateral parietal lobes correlated with the extent of intact memory performance after TSD. Many of the brain regions showing a greater response after TSD compared with normal sleep are thought to be involved in control of attention. These data imply that the divided attention task required more attentional resources (specifically, performance monitoring and sustained attention) following TSD than after normal sleep. Other neuroimaging results may relate to the verbal learning and/or arithmetic demands of the task. This is the first study to examine divided attention performance after TSD with neuroimaging and supports our previous suggestion that the brain may be more plastic during cognitive performance following TSD than previously thought.


title: Image segmentation using local variation

abstract: We present a new graph-theoretic approach to the problem of image segmentation. Our method uses local criteria and yet produces results that reflect global properties of the image. We develop a framework that provides specific definitions of what it means for an image to be under- or over-segmented. We then present an efficient algorithm for computing a segmentation that is neither under- nor over-segmented according to these definitions. Our segmentation criterion is based on intensity differences between neighboring pixels. An important characteristic of the approach is that it is able to preserve detail in low-variability regions while ignoring detail in high-variability regions, which we illustrate with several examples on both real and synthetic images.


title: Contextual Motifs: Increasing the Utility of Motifs using Contextual Data

abstract: Motifs are a powerful tool for analyzing physiological waveform data. Standard motif methods, however, ignore important contextual information (e.g., what the patient was doing at the time the data were collected). We hypothesize that these additional contextual data could increase the utility of motifs. Thus, we propose an extension to motifs, contextual motifs, that incorporates context. Recognizing that, oftentimes, context may be unobserved or unavailable, we focus on methods to jointly infer motifs and context. Applied to both simulated and real physiological data, our proposed approach improves upon existing motif methods in terms of the discriminative utility of the discovered motifs. In particular, we discovered contextual motifs in continuous glucose monitor (CGM) data collected from patients with type 1 diabetes. Compared to their contextless counterparts, these contextual motifs led to better predictions of hypo- and hyperglycemic events. Our results suggest that even when inferred, context is useful in both a long- and short-term prediction horizon when processing and interpreting physiological waveform data.


title: Orientation invariant gait matching algorithm based on the Kabsch alignment

abstract: Accelerometer and gyroscope sensors in smart phones capture the dynamics of human gait that can be matched to arrive at identity authentication measures of the person carrying the phone. Any such matching method has to take into account the reality that the phone may be placed at uncontrolled orientations with respect to the human body. In this paper, we present a novel orientation invariant gaitmatching algorithm based on the Kabsch alignment. The algorithm consists of simple, intuitive, yet robust methods for cycle splitting, aligning orientation, and comparing gait signals. We demonstrate the effectiveness of the method using a dataset from 101 subjects, with the phone placed in uncontrolled orientations in the holster and in the pocket, and collected on different days. We find that the orientation invariant gait algorithm results in a significant reduction in error: up to a 9% reduction in equal error rate, from 30.4% to 21.5% when comparing data captured on different days. On the McGill dataset from 20 subjects, which is the other dataset with orientation variation, we find a more pronounced effect; the identification rate increased from 67.5% to 96.5%. On the OU-ISIR data, which has data from 745 subjects, the equal error rates are as low as 6.3%, which is among the best reported in the literature.


title: Mutation based test case generation via a path selection strategy

abstract: Context: Generally, mutation analysis has been identified as a powerful testing method. Researchers have shown that its use as a testing criterion exercises quite thoroughly the system under test while it achieves to reveal more faults than standard structural testing criteria. Despite its potential, mutation fails to be adopted in a widespread practical use and its popularity falls significantly short when compared with other structural methods. This can be attributed to the lack of thorough studies dealing with the practical problems introduced by mutation and the assessment of the effort needed when applying it. Such an incident, masks the real cost involved preventing the development of easy and effective to use strategies to circumvent this problem. Objective: In this paper, a path selection strategy for selecting test cases able to effectively kill mutants when performing weak mutation testing is presented and analysed. Method: The testing effort is highly correlated with the number of attempts the tester makes in order to generate adequate test cases. Therefore, a significant influence on the efficiency associated with a test case generation strategy greatly depends on the number of candidate paths selected in order to achieve a predefined coverage goal. The effort can thus be related to the number of infeasible paths encountered during the test case generation process. Results: An experiment, investigating well over 55 million of program paths is conducted based on a strategy that alleviates the effects of infeasible paths. Strategy details, along with a prototype implementation are reported and analysed through the experimental results obtained by its employment to a set of program units. Conclusion: The results obtained suggest that the strategy used can play an important role in making the mutation testing method more appealing and practical.


title: Learning style and learning strategies

abstract: Two important factors need attention when contemplating how and where to use the new computer-based learning technologies. Firstly, in order to select the right kind of software, it is necessary to have a clear picture of the desired outcomes of a particular learning situation. There are many varieties of interactive multimedia available, and matching the tool to the job should be an early consideration. Of equal, if not greater importance, is the question of whether, for a particular learner, interactive multimedia is the most appropriate learning tool. If there is too great a mismatch between the learning styles and strategies of a particular learner and the given learning environment, learning may be inhibited rather than enhanced. DOI: 10.1080/0968776950030103


Magnetic Resonance Contrast Prediction Using Deep Learning


title: Towards a Fully Automated Diagnostic System for Orthodontic Treatment in Dentistry

abstract: A deep learning technique has emerged as a successful approach for diagnostic imaging. Along with the increasing demands for dental healthcare, the automation of diagnostic imaging is increasingly desired in the field of orthodontics for many reasons (e.g., remote assessment, cost reduction, etc.). However, orthodontic diagnoses generally require dental and medical scientists to diagnose a patient from a comprehensive perspective, by looking at the mouth and face from different angles and assessing various features. This assessment process takes a great deal of time even for a single patient, and tends to generate variation in the diagnosis among dental and medical scientists. In this paper, the authors propose a deep learning model to automate diagnostic imaging, which provides an objective morphological assessment of facial features for orthodontic treatment. The automated diagnostic imaging system dramatically reduces the time needed for the assessment process. It also helps provide objective diagnosis that is important for dental and medical scientists as well as their patients because the diagnosis directly affects to the treatment plan, treatment priorities, and even insurance coverage. The proposed deep learning model outperforms a conventional convolutional neural network model in its assessment accuracy. Additionally, the authors present a work-in-progress development of a data science platform with a secure data staging mechanism, which supports computation for training our proposed deep learning model. The platform is expected to allow users (e.g., dental and medical scientists) to securely share data and flexibly conduct their data analytics by running advanced machine learning algorithms (e.g., deep learning) on high performance computing resources (e.g., a GPU cluster).


title: Recent developments in human gait research: parameters, approaches, applications, machine learning techniques, datasets and challenges

abstract: Human gait provides a way of locomotion by combined efforts of the brain, nerves, and muscles. Conventionally, the human gait has been considered subjectively through visual observations but now with advanced technology, human gait analysis can be done objectively and empirically for the better quality of life. In this paper, the literature of the past survey on gait analysis has been discussed. This is followed by discussion on gait analysis methods. Vision-based human motion analysis has the potential to provide an inexpensive, non-obtrusive solution for the estimation of body poses. Data parameters for gait analysis have been discussed followed by preprocessing steps. Then the implemented machine learning techniques have been discussed in detail. The objective of this survey paper is to present a comprehensive analysis of contemporary gait analysis. This paper presents a framework (parameters, techniques, available database, machine learning techniques, etc.) for researchers in identifying the infertile areas of gait analysis. The authors expect that the overview presented in this paper will help advance the research in the field of gait analysis. Introduction to basic taxonomies of human gait is presented. Applications in clinical diagnosis, geriatric care, sports, biometrics, rehabilitation, and industrial area are summarized separately. Available machine learning techniques are also presented with available datasets for gait analysis. Future prospective in gait analysis are discussed in the end.


title: Web search personalization with ontological user profiles

abstract: Every user has a distinct background and a specific goal when searching for information on the Web. The goal of Web search personalization is to tailor search results to a particular user based on that user's interests and preferences. Effective personalization of information access involves two important challenges: accurately identifying the user context and organizing the information in such a way that matches the particular context. We present an approach to personalized search that involves building models of user context as ontological profiles by assigning implicitly derived interest scores to existing concepts in a domain ontology. A spreading activation algorithm is used to maintain the interest scores based on the user's ongoing behavior. Our experiments show that re-ranking the search results based on the interest scores and the semantic evidence in an ontological user profile is effective in presenting the most relevant results to the user.


title: Learning to Detect Visual Grasp Affordance

abstract: Appearance-based estimation of grasp affordances is desirable when 3-D scans become unreliable due to clutter or material properties. We develop a general framework for estimating grasp affordances from 2-D sources, including local texture-like measures as well as object-category measures that capture previously learned grasp strategies. Local approaches to estimating grasp positions have been shown to be effective in real-world scenarios, but are unable to impart object-level biases and can be prone to false positives. We describe how global cues can be used to compute continuous pose estimates and corresponding grasp point locations, using a max-margin optimization for category-level continuous pose regression. We provide a novel dataset to evaluate visual grasp affordance estimation; on this dataset we show that a fused method outperforms either local or global methods alone, and that continuous pose estimation improves over discrete output models. Finally, we demonstrate our autonomous object detection and grasping system on the Willow Garage PR2 robot.


title: Fully Dynamic Effective Resistances

abstract: In this paper we consider the \emph{fully-dynamic} All-Pairs Effective Resistance problem, where the goal is to maintain effective resistances on a graph $G$ among any pair of query vertices under an intermixed sequence of edge insertions and deletions in $G$. The effective resistance between a pair of vertices is a physics-motivated quantity that encapsulates both the congestion and the dilation of a flow. It is directly related to random walks, and it has been instrumental in the recent works for designing fast algorithms for combinatorial optimization problems, graph sparsification, and network science. ::: We give a data-structure that maintains $(1+\epsilon)$-approximations to all-pair effective resistances of a fully-dynamic unweighted, undirected multi-graph $G$ with $\tilde{O}(m^{4/5}\epsilon^{-4})$ expected amortized update and query time, against an oblivious adversary. Key to our result is the maintenance of a dynamic \emph{Schur complement}~(also known as vertex resistance sparsifier) onto a set of terminal vertices of our choice. ::: This maintenance is obtained (1) by interpreting the Schur complement as a sum of random walks and (2) by randomly picking the vertex subset into which the sparsifier is constructed. We can then show that each update in the graph affects a small number of such walks, which in turn leads to our sub-linear update time. We believe that this local representation of vertex sparsifiers may be of independent interest.


title: SDN-Assisted Slow HTTP DDoS Attack Defense Method

abstract: A Slow HTTP distributed denial of service (DDoS) attack causes a Web server to be unavailable, but it is difficult to detect in a network, because its traffic patterns are similar to those of legitimate clients. In this letter, we propose a network-based Slow HTTP DDoS attack defense method, which is assisted by a software-defined network that can detect and mitigate Slow HTTP DDoS attacks in the network. Simulation results show that the proposed Slow HTTP DDoS attack defense method successfully protects Web servers against Slow HTTP DDoS attacks.


title: Lower Lateral Cartilage Cephalic Malposition: An Over-Diagnosed Entity

abstract: Lower lateral cartilage malposition is represented by anterior convexity of the lower lateral cartilage (LLC) dome with posterior pinch, as defined by Sheen and Constantian. This anatomic variation consists of cephalic, or upward and inward, rotation of lateral crura, particularly in bulbous tip patients. In most cases, “bulbous pinch” LLC is positioned toward the medial canthus, not laterally, so it is referred to as cephalic displacement. Accordingly, it is recommended to caudally displace cartilage in the majority of rhinoplasty cases in which variation is seen. The purpose of this paper is to measure the exact angle of lateral crura with fixed reference points on the face. We drew and marked LLC contours and vertical/horizontal lines in 40 consecutive rhinoplasty cases. We then divided them into two groups: (1) bulbous pinch and (2) flat LLCs. The right- and left-sided LLC angles to midline and horizontal lines were measured and compared to assess whether there was any significant difference between the two subgroups. There was no significant difference between the angles of LLC rotation in the bulbous and flat LLCs groups, measured both vertically and horizontally. Based on our findings, although cephalic malposition of LLCs may be present in some patients but in the majority of cases the etiology of nasal lateral wall pinching is not cephalic displacement of lateral crura but most probably is due, rather, to severe convexity of the posterior and lateral crura. According to our findings, cephalic malposition is an uncommon anatomic variation of LLCs that has been reported at high frequency (60–70% of their rhinoplasty cases). This finding may help to correct this deformity into a normal anatomic configuration. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.


Using Patterns in Knowledge Graphs for Targeted Information Extraction


title: Resilience and the course of daily parenting stress in families of young children with intellectual disabilities

abstract: Background Parenting stresses have consistently been found to be higher in parents of children with intellectual disabilities (ID); yet, some families are able to be resilient and thrive in the face of these challenges. Despite the considerable research on stress in families of ID, there is still little known about the stability and compensatory factors associated with everyday parenting stresses. ::: ::: Methods Trajectories of daily parenting stress were studied for both mothers and fathers of children with ID across child ages 36–60 months, as were specific familial risk and resilience factors that affect these trajectories, including psychological well-being of each parent, marital adjustment and positive parent–child relationships. ::: ::: Results Mothers' daily parenting stress significantly increased over time, while fathers' daily parenting stress remained more constant. Decreases in mothers' daily parenting stress trajectory were associated with both mother and father's well-being and perceived marital adjustment, as well as a positive father–child relationship. However, decreases in fathers' daily parenting stress trajectory were only affected by mother's well-being and both parents' perceived marital adjustment. ::: ::: Conclusions Parenting stress processes are not shared entirely across the preschool period in parents of children with ID. Although individual parent characteristics and high-quality dyadic relationships contribute to emerging resilience in parents of children with ID, parents also affect each others' more resilient adaptations in ways that have not been previously considered.


title: A model for predicting customer value from perspectives of product attractiveness and marketing strategy

abstract: This paper proposes a model for customer relationship management (CRM) using iThink^(R), which incorporates the concept of system dynamics. The proposed CRM model consists of module 1: a customer purchasing behavior model, module 2: a Markov chain model, and module 3: a financial returns model. By considering the marketing activities and product attractiveness to the customer, the probability that a customer will (re)purchase can be modeled in module 1. The probabilities are then fitted into module 2 for the calculation of customer lifetime value (CLV). The estimated CLV for each customer is inputted into module 3 to predict the firm's return on investment in the long term. By defining the parameters on the attractiveness of a product and on user responses from historical marketing campaigns, a firm can easily evaluate its business strategy from both marketing and product development perspectives, thereby refining those parameters and adopting the best strategy for creating customer value and yielding the maximum profit. A case study of a listed firm in Hong Kong is employed to illustrate our model, which not only gives insights into the product development, but can also support the decisions related to marketing activities.


Local Government Bargaining and Management Structure


title: Formal Verification of Probabilistic SystemC Models with Statistical Model Checking

abstract: Transaction-level modeling with SystemC has been very successful in describing the behavior of embedded systems by providing high-level executable models, in which many of them have inherent probabilistic behaviors, e.g., random data and unreliable components. It thus is crucial to have both quantitative and qualitative analysis of the probabilities of system properties. Such analysis can be conducted by constructing a formal model of the system under verification and using Probabilistic Model Checking (PMC). However, this method is infeasible for large systems, due to the state space explosion. In this article, we demonstrate the successful use of Statistical Model Checking (SMC) to carry out such analysis directly from large SystemC models and allow designers to express a wide range of useful properties. The first contribution of this work is a framework to verify properties expressed in Bounded Linear Temporal Logic (BLTL) for SystemC models with both timed and probabilistic characteristics. Second, the framework allows users to expose a rich set of user-code primitives as atomic propositions in BLTL. Moreover, users can define their own fine-grained time resolution rather than the boundary of clock cycles in the SystemC simulation. The third contribution is an implementation of a statistical model checker. It contains an automatic monitor generation for producing execution traces of the model-under-verification (MUV), the mechanism for automatically instrumenting the MUV, and the interaction with statistical model checking algorithms.


Improving Bird Identification using Multiresolution Template Matching and Feature Selection during Training


title: Single-Switch High-Power-Factor Inverter Driving Piezoelectric Ceramic Transducer for Ultrasonic Cleaner

abstract: A novel cost-effective and high-efficiency single-switch resonant inverter with a power-factor-correction (PFC) scheme is proposed to drive a piezoelectric ceramic transducer (PCT) for an ultrasonic cleaner. The circuit topology is derived by integrating a flyback converter that served as the PFC stage with a Class E resonant inverter for driving a thin PCT at its resonance frequency. A unity power factor is achieved by operating the flyback converter in discontinuous conduction mode. With tactfully designed circuit parameters, the Class E resonant inverter is operated at the suboptimum mode to minimize switching losses of the active switch, leading to a high circuit efficiency. The active switch is controlled by pulsewidth modulation at a fixed switching frequency and a constant duty ratio to adjust the output power of the PCT. The operation modes, design equations, and steps for the circuit parameters are proposed. A prototype circuit designed for a 42-W ultrasonic cleaner was built and tested to verify the analytical predictions. Satisfactory performances are obtained from the experimental results.


title: Quality of life in bipolar disorder: A review of the literature

abstract: A sizable body of research has now examined the complex relationship between quality of life (QoL) and depressive disorder. Uptake of QoL research in relation to bipolar disorder (BD) has been comparatively slow, although increasing numbers of QoL studies are now being conducted in bipolar populations. We aimed to perform a review of studies addressing the assessment of generic and health-related QoL in patients with bipolar disorder.


title: Efficient Edge Detection on Low-Cost FPGAs

abstract: Improving the efficiency of edge detection in embedded applications, such as UAV control, is critical for reducing system cost and power dissipation. Field programmable gate arrays (FPGA) are a good platform for making improvements because of their specialised internal structure. However, current FPGA edge detectors do not exploit this structure well. A new edge detection architecture is proposed that is better optimised for FPGAs. The basis of the architecture is the Sobel edge kernels that are shown to be the most suitable because of their separability and absence of multiplications. Edge intensities are calculated with a new 4:2 compressor that consists of two custom-designed 3:2 compressors. Addition speed is increased by breaking carry propagation chains with look-ahead logic. Testing of the design showed it gives a 28% increase in speed and 4.4% reduction in area over previous equivalent designs, which demonstrated that it will lower the cost of edge detection systems, dissipate less power and still maintain high-speed control.


title: Learning Verb Inference Rules from Linguistically-Motivated Evidence

abstract: Learning inference relations between verbs is at the heart of many semantic applications. However, most prior work on learning such rules focused on a rather narrow set of information sources: mainly distributional similarity, and to a lesser extent manually constructed verb co-occurrence patterns. In this paper, we claim that it is imperative to utilize information from various textual scopes: verb co-occurrence within a sentence, verb co-occurrence within a document, as well as overall corpus statistics. To this end, we propose a much richer novel set of linguistically motivated cues for detecting entailment between verbs and combine them as features in a supervised classification framework. We empirically demonstrate that our model significantly outperforms previous methods and that information from each textual scope contributes to the verb entailment learning task.


title: An RFID-enabled inkjet-printed soil moisture sensor on paper for “smart” agricultural applications

abstract: In this paper, an RFID-enabled inkjet-printed passive soil moisture sensor on paper for agricultural applications is presented and the design procedure, the operation principle and benchmarking experimental results are discussed in detail. A prototype made of a passive RFID tag integrated with a capacitive sensor, which consists of a printed interdigitated capacitor (IDC), has been printed on a low-cost paper substrate. The capacitance variation of the IDC shifts the resonant frequency of the RFID tag taking advantage of the soil-moisture based load matching variations since a matching condition changes due to the capacitive sensor. The proposed sensor can be easily integrated with conventional RFID systems for practical large-scale agricultural applications. Keywords—inkjet printing; RFID-enabled sensor; soil moisture sensor; paper electronics


title: Single and Four-Element $Ka$-Band Transmit/Receive Phased-Array Silicon RFICs With 5-bit Amplitude and Phase Control

abstract: Ka-band SiGe BiCMOS single- and four-element phased arrays capable of both transmit and receive operation with 5-bit phase and amplitude control are presented. The design is based on the All-RF architecture with RF phase shifters and attenuators, and a 4:1 passive power combining/dividing network. The four-element array results in an average gain of ~ 0 dB per channel and a noise figure of 9.0 dB, and is designed to be placed behind III-V T/R modules. The rms phase error is 5.6° (5-bit operation) and < 12.5° (4-bit operation) over a 2 or 5 GHz instantaneous bandwidth, respectively, centered at around 36.5 GHz. In the receive mode, the input P1dB is -16 dBm per channel (IIP3 of - 5.9 dBm), and in the transmit mode, the output P1dB is +4-5 dBm, all at 35-36 GHz. The measured isolation between the channels is better than 30 dB. The array maintained excellent phase characteristics up to 100°C with no change in the rms phase error. Also, ten different four-element phased arrays were tested (40 channels) and result in an rms gain variation of 0.5 dB at 34-39 GHz. The four-element array consumes 171 and 142 mW in the Tx and Rx modes from 1.8 V, and occupies an area of 2.0 × 2.02 mm2. To our knowledge, this is the smallest and lowest power consumption on-chip K ?-band phased-array to-date.


title: A Novel Approach for Detection of Microaneurysms in Diabetic Retinopathy Disease from Retinal Fundus Images

abstract: Diabetic Retinopathy (DR) is a leading cause of blindness in human beings aged between 20 to 74 years. It has a great influence on the patient and society because it normally influences humans in their most gainful years. Early detection in DR is very difficult which is not detected by human beings. Many algorithms and techniques were established to detect DR. These techniques faced the problems such as increasing sensitivity, specificity and accuracy. To overcome those problems we have to introduce an effective image processing algorithms for increasing performances and also easily identify the DR diseases. One of the most challenging tasks in screening is automatic detection of Microaneurysms (MAs). This paper presents a new approach to detect MAs. Our proposed work consists of preprocessing, blood vessel segmentation (FPCM), fovea localization, fovea elimination, feature extraction and classification (Neuro-Fuzzy). Neuro-Fuzzy is a combined version of neural networks and fuzzy logical models. Experiments are conducted using MATLAB simulation tool. Using MESSIDOR database for our experiments which provides efficient and effective results in sensitivity, specificity, correct classification and detection rate (accuracy) and precision.


title: Topic Augmented Neural Response Generation with a Joint Attention Mechanism

abstract: We consider incorporating topic information as prior knowledge into the sequence to sequence (Seq2Seq) network structure with attention mechanism for response generation in chatbots. To this end, we propose a topic augmented joint attention based Seq2Seq (TAJA-Seq2Seq) model. In TAJA-Seq2Seq, information from input posts and information from topics related to the posts are simultaneously embedded into vector spaces by a content encoder and a topic encoder respectively. The two kinds of information interact with each other and help calibrate weights of each other in the joint attention mechanism in TAJA2Seq2Seq, and jointly determine the generation of responses in decoding. The model simulates how people behave in conversation and can generate well-focused and informative responses with the help of topic information. Empirical study on large scale human judged generation results show that our model outperforms Seq2Seq with attention on both response quality and diversity.


title: The Therapist's Self in Dialogical Family Therapy: Some Ideas About Not-Knowing and the Therapist's Inner Conversation

abstract: In this article, the focus is on the therapist’s self, which will be in line with Bakhtin’s thinking, viewed as a dialogical self. First, the dialogical view of the self is situated in the context of psychology’s traditional focus on the individual self. Then, leaning on Bakhtin and Volosinov, the self is described as a dialogue of multiple inner voices. Some of the implications of this concept for family therapy practice are examined, focusing especially on the therapist’s participation in the therapeutic process and on the therapist’s inner conversation. The author argues that not-knowing does not only refer to the therapist’s receptivity and respect but also implies that the therapist is aware of his or her experience and reflects on how his or her inner conversation might inform and enrich the therapeutic conversation. Finally, these ideas are illustrated with a brief clinical vignette.


title: A UHF helical resonator filter

abstract: The 6-pole UHF helical resonators filter was designed, simulated, fabricated, and tested. The design factors, simulation results, filter performance characteristics are presented in this paper. The coupling of helical resonators was designed using a mode-matching technique. The design procedures are simple, and measured performance is excellent. The simulated and measured results show the validity of the proposed design method.


title: Full-mouth adhesive rehabilitation of a severely eroded dentition: the three-step technique. Part 1.

abstract: Traditionally, a full-mouth rehabilitation based on full-crown coverage has been the recommended treatment for patients affected by severe dental erosion. Nowadays, thanks to improved adhesive techniques, the indications for crowns have decreased and a more conservative approach may be proposed. Even though adhesive treatments simplify both the clinical and laboratory procedures, restoring such patients still remains a challenge due to the great amount of tooth destruction. To facilitate the clinician's task during the planning and execution of a full-mouth adhesive rehabilitation, an innovative concept has been developed: the three-step technique. Three laboratory steps are alternated with three clinical steps, allowing the clinician and the laboratory technician to constantly interact to achieve the most predictable esthetic and functional outcome. During the first step, an esthetic evaluation is performed to establish the position of the plane of occlusion. In the second step, the patient's posterior quadrants are restored at an increased vertical dimension. Finally, the third step reestablishes the anterior guidance. Using the three-step technique, the clinician can transform a full-mouth rehabilitation into a rehabilitation for individual quadrants. This article illustrates only the first step in detail, explaining all the clinical parameters that should be analyzed before initiating treatment.


Extended Transconjunctival Lower Eyelid Blepharoplasty with Release of the Tear Trough Ligament and Fat Redistribution


title: Chinese Poetry Generation with a Salient-Clue Mechanism

abstract: As a precious part of the human cultural heritage, Chinese poetry has influenced people for generations. Automatic poetry composition is a challenge for AI. In recent years, significant progress has been made in this area benefiting from the development of neural networks. However, the coherence in meaning, theme or even artistic conception for a generated poem as a whole still remains a big problem. In this paper, we propose a novel Salient-Clue mechanism for Chinese poetry generation. Different from previous work which tried to exploit all the context information, our model selects the most salient characters automatically from each so-far generated line to gradually form a salient clue, which is utilized to guide successive poem generation process so as to eliminate interruptions and improve coherence. Besides, our model can be flexibly extended to control the generated poem in different aspects, for example, poetry style, which further enhances the coherence. Experimental results show that our model is very effective, outperforming three strong baselines.


Lorikeet: A Model-Driven Engineering Tool for Blockchain-Based Business Process Execution and Asset Management


title: Brain-Computer Interfacing [In the Spotlight]

abstract: Recently, CNN reported on the future of brain-computer interfaces (BCIs). BCIs are devices that process a user's brain signals to allow direct communication and interaction with the environment. BCIs bypass the normal neuromuscular output pathways and rely on digital signal processing and machine learning to translate brain signals to action (Figure 1). Historically, BCIs were developed with biomedical applications in mind, such as restoring communication in completely paralyzed individuals and replacing lost motor function. More recent applications have targeted nondisabled individuals by exploring the use of BCIs as a novel input device for entertainment and gaming. The task of the BCI is to identify and predict behaviorally induced changes or "cognitive states" in a user's brain signals. Brain signals are recorded either noninvasively from electrodes placed on the scalp [electroencephalogram (EEG)] or invasively from electrodes placed on the surface of or inside the brain. BCIs based on these recording techniques have allowed healthy and disabled individuals to control a variety of devices. In this article, we will describe different challenges and proposed solutions for noninvasive brain-computer interfacing.


title: Kronecker Graphs: An Approach to Modeling Networks

abstract: How can we generate realistic networks? In addition, how can we do so with a mathematically tractable model that allows for rigorous analysis of network properties? Real networks exhibit a long list of surprising properties: Heavy tails for the in- and out-degree distribution, heavy tails for the eigenvalues and eigenvectors, small diameters, and densification and shrinking diameters over time. Current network models and generators either fail to match several of the above properties, are complicated to analyze mathematically, or both. Here we propose a generative model for networks that is both mathematically tractable and can generate networks that have all the above mentioned structural properties. Our main idea here is to use a non-standard matrix operation, the Kronecker product, to generate graphs which we refer to as "Kronecker graphs". ::: ::: First, we show that Kronecker graphs naturally obey common network properties. In fact, we rigorously prove that they do so. We also provide empirical evidence showing that Kronecker graphs can effectively model the structure of real networks. ::: ::: We then present KRONFIT, a fast and scalable algorithm for fitting the Kronecker graph generation model to large real networks. A naive approach to fitting would take super-exponential time. In contrast, KRONFIT takes linear time, by exploiting the structure of Kronecker matrix multiplication and by using statistical simulation techniques. ::: ::: Experiments on a wide range of large real and synthetic networks show that KRONFIT finds accurate parameters that very well mimic the properties of target networks. In fact, using just four parameters we can accurately model several aspects of global network structure. Once fitted, the model parameters can be used to gain insights about the network structure, and the resulting synthetic graphs can be used for null-models, anonymization, extrapolations, and graph summarization.


title: A Column Generation Algorithm For Boosting

abstract: We examine linear program (LP) approaches to boosting and demonstrate their efficient solution using LPBoost, a column generation simplex method. We prove that minimizing the soft margin error function (equivalent to solving an LP) directly optimizes a generalization error bound. LPBoost can be used to solve any boosting LP by iteratively optimizing the dual classification costs in a restricted LP and dynamically generating weak learners to make new LP columns. Unlike gradient boosting algorithms, LPBoost converges finitely to a global solution using well defined stopping criteria. Computationally, LPBoost finds very sparse solutions as good as or better than those found by ADABoost using comparable computation.


title: Underwater Fish Tracking for Moving Cameras Based on Deformable Multiple Kernels

abstract: Fishery surveys that call for the use of single or multiple underwater cameras have been an emerging technology as a nonextractive mean to estimate the abundance of fish stocks. Tracking live fish in an open aquatic environment posts challenges that are different from general pedestrian or vehicle tracking in surveillance applications. In many rough habitats, fish are monitored by cameras installed on moving platforms, where tracking is even more challenging due to inapplicability of background models. In this paper, a novel tracking algorithm based on the deformable multiple kernels is proposed to address these challenges. Inspired by the deformable part model technique, a set of kernels is defined to represent the holistic object and several parts that are arranged in a deformable configuration. Color histogram, texture histogram, and the histogram of oriented gradients (HOGs) are extracted and serve as object features. Kernel motion is efficiently estimated by the mean-shift algorithm on color and texture features to realize tracking. Furthermore, the HOG-feature deformation costs are adopted as soft constraints on kernel positions to maintain the part configuration. Experimental results on practical video set from underwater moving cameras show the reliable performance of the proposed method with much less computational cost comparing with state-of-the-art techniques.


title: Trainable Generation of Big-Five Personality Styles through Data-Driven Parameter Estimation

abstract: Previous work on statistical language generation has primarily focused on grammaticality and naturalness, scoring generation possibilities according to a language model or user feedback. More recent work has investigated data-driven techniques for controlling linguistic style without overgeneration, by reproducing variation dimensions extracted from corpora. Another line of work has produced handcrafted rule-based systems to control specific stylistic dimensions, such as politeness and personality. This paper describes a novel approach that automatically learns to produce recognisable variation along a meaningful stylistic dimension— personality—without the computational cost incurred by overgeneration techniques. We present the first evaluation of a data-driven generation method that projects multiple personality traits simultaneously and on a continuous scale. We compare our performance to a rule-based generator in the same domain.


title: BIG-BIO: - big data hadoop-based analytic cluster framework for bioinformatics

abstract: Big volume of bioinformatics data needs high processing powers. BIG-BIO is one of the solutions for addressing these challenges. BIG-BIO is a big data analyst MapReduce Hadoop Cluster for Bioinformatics applications. BIG-BIO is tested by implementing the bioinformatics wordcount problem and its applications using MapReduce programming pattern. BIG-BIO counts the number of occurrence of each word in a text and extracts unique words in molecular sequences. The application is characterized by almost low-weight computation on big size data sets. Performance of BIG-BIO is tested. BIG-BIO framework could analyze bioinformatics big data faster and more efficient. It has many bioinformatics applications while maintaining good processing capabilities, scalability and ease of maintenance by using cheap commodity. BIG-BIO reduces the processing time for parallel bioinformatics algorithms compared to legacy serial and MPI based applications. Testing BIG-BIO stated that it scales automatically with size of data. BIG-BIO can be portable on many Hadoop infrastructures without modification, with accelerating data-intensive bioinformatics analysis.


title: Wideband Planar Log-Periodic Antenna

abstract: A compact planar wideband microstrip antenna is introduced. The antenna is a log-periodic antenna with 7 dipole elements. The antenna is designed to function in the frequency range of 500 to 700 MHz. It has a dimension of 268 mm by 145 mm by 3 mm. The matching is better than -10 dB in the working bandwidth. The proposed antenna generates linear polarization. Its maximum gain is 4.5 dB at the central frequency. It provides a low-cost alternative to the conventional Yagi-Uda TV antennas


Liner2 — a Generic Framework for Named Entity Recognition


title: Bitcoin Market Return and Volatility Forecasting Using Transaction Network Flow Properties

abstract: Bit coin, as the foundation for a secure electronic payment system, has drawn broad interests from researchers in recent years. In this paper, we analyze a comprehensive Bit coin transaction dataset and investigate the interrelationship between the flow of Bit coin transactions and its price movement. Using network theory, we examine a few complexity measures of the Bit coin transaction flow networks, and we model the joint dynamic relationship between these complexity measures and Bit coin market variables such as return and volatility. We find that a particular complexity measure of the Bit coin transaction network flow is significantly correlated with the Bit coin market return and volatility. More specifically we document that the residual diversity or freedom of Bit coin network flow scaled by the total system throughput can significantly improve the predictability of Bit coin market return and volatility.


title: Isolation and Emotions in the Workplace: The Influence of Perceived Media Richness and Virtuality

abstract: Remote work and intensive use of Information Technologies (IT) are increasingly common in organizations. At the same time, professional stress seems to develop. However, IS research has paid little attention to the relationships between these two phenomena. The purpose of this research in progress is to present a framework that introduces the influence of (1) new spatial and temporal constraints and of (2) intensive use of IT on employee emotions at work. Specifically, this paper relies on virtuality (e.g. Chudoba et al. 2005) and media richness (Daft and Lengel 1984) theories to determine the emotional consequences of geographically distributed work.


title: Pedestrian detection at 100 frames per second

abstract: We present a new pedestrian detector that improves both in speed and quality over state-of-the-art. By efficiently handling different scales and transferring computation from test time to training time, detection speed is improved. When processing monocular images, our system provides high quality detections at 50 fps. We also propose a new method for exploiting geometric context extracted from stereo images. On a single CPU+GPU desktop machine, we reach 135 fps, when processing street scenes, from rectified input to detections output.


title: Android Power Management and Analyses of Power Consumption in an Android Smartphone

abstract: With the rapid development of Android phones, the users' requirements are increasingly demanding. But the huge power consumption has seriously affected the users' communication demands. In this article, we introduce the power management modules and Wake Lock mechanism of the Android system, and analyze the power consumption of it. And we used DC power sources to measure the power consumption of every hardware modules of Android phones. The analysis of measured power consumption data could produce possible Android power-saving modes or a low power consumption mechanism, or lead us to the design of a new power consumption monitoring and power-saving software for Android. Finally, we summarize Android power management and make a prospect on the uncompleted work of Android power consumption monitoring software models.


title: Data management in cloud environments: NoSQL and NewSQL data stores

abstract: Advances in Web technology and the proliferation of mobile devices and sensors connected to the Internet have resulted in immense processing and storage requirements. Cloud computing has emerged as a paradigm that promises to meet these requirements. This work focuses on the storage aspect of cloud computing, specifically on data management in cloud environments. Traditional relational databases were designed in a different hardware and software era and are facing challenges in meeting the performance and scale requirements of Big Data. NoSQL and NewSQL data stores present themselves as alternatives that can handle huge volume of data. Because of the large number and diversity of existing NoSQL and NewSQL solutions, it is difficult to comprehend the domain and even more challenging to choose an appropriate solution for a specific task. Therefore, this paper reviews NoSQL and NewSQL solutions with the objective of: (1) providing a perspective in the field, (2) providing guidance to practitioners and researchers to choose the appropriate data store, and (3) identifying challenges and opportunities in the field. Specifically, the most prominent solutions are compared focusing on data models, querying, scaling, and security related capabilities. Features driving the ability to scale read requests and write requests, or scaling data storage are investigated, in particular partitioning, replication, consistency, and concurrency control. Furthermore, use cases and scenarios in which NoSQL and NewSQL data stores have been used are discussed and the suitability of various solutions for different sets of applications is examined. Consequently, this study has identified challenges in the field, including the immense diversity and inconsistency of terminologies, limited documentation, sparse comparison and benchmarking criteria, and nonexistence of standardized query languages.


title: Review Twenty years of ‘‘hot hand’ ’ research: Review and critique

abstract: Abstract Objectives This article systematically reviews the state of the art of the “hot hand” research in sports. The belief that successive attempts of an individual player are positively related, as well as the behavior influenced by such a belief, will be investigated. Method The analysis of experiments, simulations, and archival data from actual sport competitions are structured in a way that evidence for or against the existence of the hot hand is presented. In addition, key issues that have been raised over this debate will be highlighted, including their merits and pitfalls. Results The empirical evidence for the existence of the hot hand is considerably limited. Methodological advancements as well as some experimental results indicate a shift in the debate from the adaptiveness of a potentially faulty belief to an adaptive behavior based partly on the hot hand belief. Conclusions The potential implications of this review for cognitive theories, empirical studies, and sport practice may provide a significant leverage point for future research and application.


title: A 27-mW 3.6-gb/s I/O transceiver

abstract: This paper describes a 3.6-Gb/s 27-mW transceiver for chip-to-chip applications. A voltage-mode transmitter is proposed that equalizes the channel while maintaining impedance matching. A comparator is proposed that achieves sampling bandwidth control and offset compensation. A novel timing recovery circuit controls the phase by mismatching the current in the charge pump. The architecture maintains high signal integrity while each port consumes only 7.5 mW/Gb/s. The entire design occupies 0.2 mm/sup 2/ in a 0.18-/spl mu/m 1.8-V CMOS technology.


title: The Relative Neighbourhood Graph of a Finite Planar Set

abstract: Abstract The relative neighbourhood graph (RNG) of a set of n points on the plane is defined. The ability of the RNG to extract a perceptually meaningful structure from the set of points is briefly discussed and compared to that of two other graph structures: the minimal spanning tree (MST) and the Delaunay (Voronoi) triangulation (DT). It is shown that the RNG is a superset of the MST and a subset of the DT. Two algorithms for obtaining the RNG of n points on the plane are presented. One algorithm runs in 0(n2) time and the other runs in 0(n3) time but works also for the d-dimensional case. Finally, several open problems concerning the RNG in several areas such as geometric complexity, computational perception, and geometric probability, are outlined.


title: Neural correlates of change detection and change blindness

abstract: Functional magnetic resonance imaging (fMRI) of subjects attempting to detect a visual change occurring during a screen flicker was used to distinguish the neural correlates of change detection from those of change blindness. Change detection resulted in enhanced activity in the parietal and right dorsolateral prefrontal cortex as well as category-selective regions of the extrastriate visual cortex (for example, fusiform gyrus for changing faces). Although change blindness resulted in some extrastriate activity, the dorsal activations were clearly absent. These results demonstrate the importance of parietal and dorsolateral frontal activations for conscious detection of changes in properties coded in the ventral visual pathway, and thus suggest a key involvement of dorsal‐ventral interactions in visual awareness. formance cost in the secondary task that varies with the difficulty of the primary task 3 . In this case, we varied the difficulty of the primary letter detection task per individual so that each subject produced approximately equal numbers of detections and misses in the secondary change detection task. Changes in two different image categories were investigated: faces and outdoor scenes (or ‘places’). These image categories were chosen because they selectively activate different regions of the ventral visual pathway 4‐6 . We could thus distinguish between activity related to change detection in general and activity related to the detection of the particular type of changing object. One quarter of all trials were no-change trials, in which the images remained the same between sequential displays. RESULTS


title: Synthesis of reactive(1) designs

abstract: We consider the problem of synthesizing digital designs from their ltl specification. In spite of the theoretical double exponential lower bound for the general case, we show that for many expressive specifications of hardware designs the problem can be solved in time N3, where N is the size of the state space of the design. We describe the context of the problem, as part of the Prosyd European Project which aims to provide a property-based development flow for hardware designs. Within this project, synthesis plays an important role, first in order to check whether a given specification is realizable, and then for synthesizing part of the developed system.


title: Another Advanced Test of Theory of Mind: Evidence from Very High Functioning Adults with Autism or Asperger Syndrome

abstract: Previous studies have found a subgroup of people with autism or Asperger Syndrome who pass second-order tests of theory of mind. However, such tests have a ceiling in developmental terms corresponding to a mental age of about 6 years. It is therefore impossible to say if such individuals are intact or impaired in their theory of mind skills. We report the performance of very high functioning adults with autism or Asperger Syndrome on an adult test of theory of mind ability. The task involved inferring the mental state of a person just from the information in photographs of a person's eyes. Relative to age-matched normal controls and a clinical control group (adults with Tourette Syndrome), the group with autism and Asperger Syndrome were significantly impaired on this task. The autism and Asperger Syndrome sample was also impaired on Happe's strange stories tasks. In contrast, they were unimpaired on two control tasks: recognising gender from the eye region of the face, and recognising basic emotions from the whole face. This provides evidence for subtle mindreading deficits in very high functioning individuals on the autistic continuum.


title: A zero-sum power allocation game in the parallel Gaussian wiretap channel with an unfriendly jammer

abstract: This paper investigates optimal power allocation strategies over a bank of independent parallel Gaussian wiretap channels where a legitimate transmitter and a legitimate receiver communicate in the presence of an eavesdropper and an unfriendly jammer. In particular, we formulate a zero-sum power allocation game between the transmitter and the jammer where the payoff function is the secrecy rate. We characterize the optimal power allocation strategies as well as the Nash equilibrium in some asymptotic regimes. We also provide a set of results that cast further insight into the problem. Our scenario, which is applicable to current OFDM communications systems, demonstrates that transmitters that adapt to jammer experience much higher secrecy rates than non-adaptive transmitters.


title: WordNet::Similarity - Measuring The Relatedness Of Concepts

abstract: WordNet: Similarity is a freely available software package that makes it possible to measure the semantic similarity or relatedness between a pair of concepts (or word senses). It provides six measures of similarity, and three measures of relatedness, all of which are based on the lexical database WordNet. These measures are implemented as Perl modules which take as input two concepts, and return a numeric value that represents the degree to which they are similar or related.


title: Video captioning with recurrent networks based on frame- and video-level features and visual content classification

abstract: In this paper, we describe the system for generating textual descriptions of short video clips using recurrent neural networks (RNN), which we used while participating in the Large Scale Movie Description Challenge 2015 in ICCV 2015. Our work builds on static image captioning systems with RNN based language models and extends this framework to videos utilizing both static image features and video-specific features. In addition, we study the usefulness of visual content classifiers as a source of additional information for caption generation. With experimental results we show that utilizing keyframe based features, dense trajectory video features and content classifier outputs together gives better performance than any one of them individually.


title: Efficient validation input generation in RTL by hybridized source code analysis

abstract: We present HYBRO, an automatic methodology to generate high coverage input vectors for Register Transfer Level (RTL) designs based on branch-coverage directed approach. HYBRO uses dynamic simulation data and static analysis of RTL control flow graphs (CFGs). A concrete simulation is applied over a fixed number of cycles. Instrumented code records the branches covered. The corresponding symbolic trace is extracted from the CFG with an RTL symbolic execution engine. A guard in the symbolic expression is mutated. If the mutated guard has dependent branches that have not already been covered, it is mutated and passed to an SMT solver. A satisfiable assignment generates a valid input vector. We implement the Verilog RTL symbolic execution engine and show that the notion of branch-coverage directed exploration can avoid path explosion caused by previous path-based approach to input vector generation and achieve full branch and more than 90% functional(assertion) coverage quickly on ITC99 benchmark and several Openrisc designs. We also describe two types of optimizations a) dynamic UD chain slicing b)local conflict resolution to speed up HYBRO by 1.6–12 times on different benchmarks.


title: Automating commonsense reasoning using the event calculus

abstract: A process for the finishing of hard chromium plated products which consists of polishing followed by friction finishing with an organic compound which contains abrasive particles. The organic compound consists of fatty acids derived from either animal or vegetable sources. The abrasive particles consists of oxides of aluminum, silicon, chromium, titanium, magnesium, or silicon. Nitrides or carbides of these metals could also be used as abrasive agents. This friction finishing compound is applied using specific pressure and surface velocity under dry conditions, and the product which results from using this technique has improved corrosion resistance and improved surface finish.


title: Adult content consumption in online social networks

abstract: Users in online social networks naturally organize themselves into overlapping and interlinked communities that are formed around common identity or shared topical interests. Some communities gather people around specific deviant behaviors, conducts that are commonly considered inappropriate with respect to the society’s norms or moral standards such as drug use, eating disorders, and pornographic content consumption. From a network analysis perspective, the set of interactions between members of these communities form deviant networks that map how the deviant content is shared and consumed. It is commonly believed that deviant networks are small and isolated from the mainstream social media life; accordingly, most research studies have considered them in isolation. We focus on adult content consumption networks, which is one deviant network with a significant presence in online social media and in the Web in general. We investigate two large online social networks and discuss the following insights. Deviant networks are limited in size, tightly connected and structured in subgroups. Nevertheless, content originated in deviant networks spreads widely across the whole social graph possibly touching a large number of unintentionally exposed users, such that the average local perception is that neighboring users share more deviant content. Finally, we investigate how content production and consumption vary with age and show that the consumption rate is very similar between male and female users up to the age of 25. We conclude that deviant communities are deeply rooted into the relational fabric of a social network, and that a deeper understanding of how their activity impacts on every other user is required.


title: Classification of Opinion Mining Techniques

abstract: important part to gather the information is always seems as what the people think. The growing availability of opinion rich resources like online review sites and blogs arises as people can easily seek out and understand the opinions of others. Users express their views and opinions regarding products and services. These opinions are subjective information which represents user's sentiments, feelings or appraisal related to the same. The concept of opinion is very broad. In this paper we focus on the Classification of opinion mining techniques that conveys user's opinion i.e. positive or negative at various levels. The precise method for predicting opinions enable us, to extract sentiments from the web and foretell online customer's preferences, which could prove valuable for marketing research. Much of the research work had been done on the processing of opinions or sentiments recently because opinions are so important that whenever we need to make a decision we want to know others' opinions. This opinion is not only important for a user but is also useful for an organization. KeywordsMining; Machine learning; Sentiments; Polarity; semantic.


Distributed Multi-Agent Persistent Surveillance and Tracking With Health Management


title: A phenomenological scattering model for order-independent transparency

abstract: Translucent objects such as fog, smoke, glass, ice, and liquids are pervasive in cinematic environments because they frame scenes in depth and create visually compelling shots. Unfortunately, they are hard to simulate in real-time and have thus previously been rendered poorly compared to opaque surfaces in games. This paper introduces the first model for a real-time rasterization algorithm that can simultaneously approximate the following transparency phenomena: wavelength-varying ("colored") transmission, translucent colored shadows, caustics, partial coverage, diffusion, and refraction. All render efficiently on modern GPUs by using order-independent draw calls and low bandwidth. We include source code for the transparency and resolve shaders.


title: Programming algorithms for multilevel phase-change memory

abstract: Phase-change memory (PCM) has emerged as one among the most promising technologies for next-generation nonvolatile solid-state memory. Multilevel storage, namely storage of non-binary information in a memory cell, is a key factor for reducing the total cost-per-bit and thus increasing the competitiveness of PCM technology in the nonvolatile memory market. In this paper, we present a family of advanced programming schemes for multilevel storage in PCM. The proposed schemes are based on iterative write-and-verify algorithms that exploit the unique programming characteristics of PCM in order to achieve significant improvements in resistance-level packing density, robustness to cell variability, programming latency, energy-per-bit and cell storage capacity. Experimental results from PCM test-arrays are presented to validate the proposed programming schemes. In addition, the reliability issues of multilevel PCM in terms of resistance drift and read noise are discussed.


title: Search Mode and Purchase Intention in Online Shopping Behavior

abstract: This study focuses on the effect of website visitors' degree of goal-oriented search mode on purchase intention in online environments. In a study of 874 respondents recruited from 13 online shops representing a diversity of product categories and customer segments, the effect of visitors' degree of goal-oriented search mode on purchase intention is found to be moderated by product risk. Furthermore, product involvement, product risk and internet experience are found to have positive effects on the degree of goal-oriented search mode of the visitors. Also, product knowledge, product risk and internet experience are reported to have direct effects on purchase intention. The results point to the importance of understanding the characteristics of website visitors, and to customise the support and search services offered on the website to the characteristics and preferences of the individual visitor to increase purchase intention, and eventually online sales.


title: A vehicle detection algorithm based on wireless magnetic sensor networks

abstract: With the explosively increase of automobiles, the traffic congestion problem becomes more and more severe. This paper proposes a vehicle detection algorithm, named Peak-Valley Algorithm (PVA), based on the Wireless Sensor Networks (WSN). By measuring the disturbance of the Earth's magnetic field, when a vehicle passes through a Honeywell 3-axis sensor, the vehicle can be detected. A large number of experimental data have been collected and analyzed. The system can perform in real-time with detection accuracy as high as 98.68% for vehicles. Especially, it can effectively solve the fluctuation problem caused by temperature drift in the baseline tracking method.


title: Individual Differences and Fitting Methods for the Two-Choice Diffusion Model of Decision Making

abstract: Methods of fitting the diffusion model were examined with a focus on what the model can tell us about individual differences. Diffusion model parameters were obtained from the fits to data from 2 experiments and consistency of parameter values, individual differences, and practice effects were examined using different numbers of observations from each subject. Two issues were examined—first, what sizes of differences between groups can be obtained to distinguish between groups, and second, what sizes of differences would be needed to find individual subjects that had a deficit relative to a control group. The parameter values from the experiments provided ranges that were used in a simulation study to examine recovery of individual differences. This study used several diffusion model fitting programs, fitting methods, and published packages. In a second simulation study, 64 sets of simulated data from each of 48 sets of parameter values (spanning the range of typical values obtained from fits to data) were fit with the different methods, and biases and standard deviations in recovered model parameters were compared across methods. Finally, in a third simulation study, a comparison between a standard chi-square method and a hierarchical Bayesian method was performed. The results from these studies can be used as a starting point for selecting fitting methods, and as a basis for understanding the strengths and weaknesses of using diffusion model analyses to examine individual differences in clinical, neuropsychological, and educational testing.


title: Online calibration of a compact series elastic actuator

abstract: We present a method for the online calibration of a compact series elastic actuator installed in a modular snake robot. Calibration is achieved by using the measured motor current of the actuator's highly geared motor and a simple linear model for the spring's estimated torque. A heuristic is developed to identify operating conditions where motor current is an accurate estimator of output torque, even when the motor is heavily geared. This heuristic is incorporated into an unscented Kalman filter that estimates a spring constant in real-time. Using this method on a prototype module of a series elastic snake robot, we are able accurately estimate the module's output torque, even with a poor initial calibration.


title: Joining Jolie to Docker - Orchestration of Microservices on a Containers-as-a-Service Layer

abstract: Cloud computing is steadily growing and, as IaaS vendors have started to offer pay-as-you-go billing policies, it is fundamental to achieve as much elasticity as possible, avoiding over-provisioning that would imply higher costs. In this paper, we briefly analyse the orchestration characteristics of PaaSSOA, a proposed architecture already implemented for Jolie microservices, and Kubernetes, one of the various orchestration plugins for Docker; then, we outline similarities and differences of the two approaches, with respect to their own domain of application. Furthermore, we investigate some ideas to achieve a federation of the two technologies, proposing an architectural composition of Jolie microservices on Docker Container-as-a-Service layer.