title: Ultrasound tightening of facial and neck skin: a rater-blinded prospective cohort study.

abstract: BACKGROUND ::: Nonablative skin tightening technologies offer the prospect of reduction of wrinkles and skin sagging with minimal downtime, discomfort, and risk of adverse events. The excellent safety profile is mitigated by the limited efficacy of such procedures. ::: ::: ::: OBJECTIVE ::: We sought to assess the efficacy of ultrasound skin tightening for brow-lift in the context of a procedure treating the full face and neck. ::: ::: ::: METHODS ::: This was a rater-blinded, prospective cohort study at a dermatology clinic in an urban academic medical center. Subjects were medicated with topical anesthetic and then treated with an investigational focused intense ultrasound tightening device to the forehead, temples, cheeks, submental region, and side of neck using the following probes: 4 MHz, 4.5-mm focal depth; 7 MHz, 4.5-mm focal depth; and 7 MHz, 3.0-mm focal depth. Standardized photographs of front and side views were obtained at 2, 7, 28, 60, and 90 days; rating scales of pain, adverse events, physical findings, and patient satisfaction were also completed. Primary outcome measure was detection of improvement in paired comparison of pretreatment and posttreatment (day 90) photographs by 3 masked expert physician assessors, cosmetic and laser dermatologists, and plastic surgeons who were not authors. Second primary outcome measure was objective brow elevation as quantitated by a standard procedure using fixed landmarks. Secondary outcomes measure was patient satisfaction as measured by a questionnaire. ::: ::: ::: RESULTS ::: A total of 36 subjects (34 female) were enrolled, one subject dropped out, and 35 subjects were evaluated. Median age was 44 years (range 32-62). On the first primary outcome measure, 30 of 35 subjects (86%) were judged by the 3 masked experienced clinician raters to show clinically significant brow-lift 90 days after treatment (P = .00001). On the second primary outcome measure, mean value of average change in eyebrow height as assessed by measurement of photographs at 90 days was 1.7 mm. ::: ::: ::: LIMITATIONS ::: Limitations of this study include the inability to quantitatively measure lower face tightening because of the lack of fixed anatomic landmarks in this area. ::: ::: ::: CONCLUSION ::: Ultrasound appears to be a safe and effective modality for facial skin tightening. A single ultrasound treatment of the forehead produced on average brow height elevation of slightly less than 2 mm. Most treated individuals responded, commonly with accompanying transitory mild erythema and edema.


Effects of Avatar and Background Types on Users ’ Co-presence and Trust for Mixed Reality-Based Teleconference Systems


title: Long-distance liquid transport in plants

abstract: A brief review of the thermodynamic and fluid dynamic problems related to long-distance liquid flow and signalling in plants is presented. Geometrical parameters of the plant leaf venation are measured and the general relationships between the diameters and lengths of the veins, branching angles at the vein bifurcations, and the corresponding drainage areas are obtained. The same relationships had been obtained before for the bifurcations of the pathways in the arterial and bronchial systems of mammals and humans; tree trunks, branches and roots; and river basins. The identity of the principle of design of the transportation systems in the nature can be understood on the concept of optimal networks that provide liquid delivery at total minimal energy costs. The corresponding models of the optimal vessels and branching systems of vessels with impermeable and permeable walls are presented and discussed.


title: AutoPart: Parameter-Free Graph Partitioning and Outlier Detection

abstract: Graphs arise in numerous applications, such as the analysis of the Web, router networks, social networks, co-citation graphs, etc. Virtually all the popular methods for analyzing such graphs, for example, k-means clustering, METIS graph partitioning and SVD/PCA, require the user to specify various parameters such as the number of clusters, number of partitions and number of principal components. We propose a novel way to group nodes, using information-theoretic principles to choose both the number of such groups and the mapping from nodes to groups. Our algorithm is completely parameter-free, and also scales practically linearly with the problem size. Further, we propose novel algorithms which use this node group structure to get further insights into the data, by finding outliers and computing distances between groups. Finally, we present experiments on multiple synthetic and real-life datasets, where our methods give excellent, intuitive results.


title: Rear end planar slot antenna of a substrate integrated waveguide cavity fed by grounded coplanar waveguide

abstract: A design of simple profile planar slot antenna which is placed at the rear end of the substrate integrated waveguide is been studied in this paper. The substrate Integrated Waveguide (SIW) adopts the mm wave technology and it facilitates the feeding elemen and the antennato be built over the same substrate. The return loss is obtained as −30dB at 10GHz. Also the performance is analysed by varying the height of the substrate, the length and width of the slot. The simulated antenna has good performance when compared to conventional slot antenna along with miniaturisation by the use of SIW technology. The merits of the antenna include simple profile, low weight, easy fabrication and simple integration with planar circuits.


title: Comparison of AOTF, grating, and FTS imaging spectrometers for hyperspectral remote sensing applications

abstract: In this paper we compare the performance for effluent gas detection of three types of imaging spectrometers. The spectrometers compared are the grating and Fourier Transform (FTS) for their wide implementation and the Acousto-Optic Tunable Filter (AOTF) for its unique feature of spectral tunability. The analysis is performed in the thermal emission region of spectra using Raytheon developed software simulation and modeling tools. The paper concludes with a proposed system design and architecture considerations for an AOTF-based hyperspectral and polarimetric imaging sensor for airborne and spaceborne platforms.


title: Control system design of multi-gimbal Electro-Optical platform by the technique of μ-synthesis

abstract: To extend target tracking range, configuration of two-axis four-gimbal is used for high precision Electro-Optical stabilized platform, but coupling among gimbals and complexity of system are augmented. The significant target of control system design is to improve the stabilized performance of optical sensors' line-of-sight. Based on dynamic equations of the platform system, state space model is derived. Because of coupling characteristic, the inner gimbal subsystem and the outer gimbal subsystem are regarded as a multi-input multi-output plant so that multivariable control is employed. With parameter variation considered as perturbation, which is associated with elevation channel coupling information, a controller for azimuth channel based on μ-synthesis is designed according to respective performance specification of stabilizing and angle position servo. Simulations show that the system has achieved robust performance with changing of elevation working conditions, and that under the circumstances of ensuring appropriate servo precision, there is improvement in evidence about stabilizing performance comparing with system using decoupling controllers for inner gimbal subsystem and outer. Owing to multivariable μ-synthesis control method, system performance is improved effectively.


title: Design, Fabrication and Performances of a Biomimetic Robotic Earthworm

abstract: This paper presents the design and development of a microrobot which aims to replicate the locomotion principle of earthworms. By investigating the biological field, the authors developed artificial earthworms by mimicking the structures and locomotion principles of real ones. Prototypes with or without micro-legs (which affect the locomotion performance) have been developed. Each prototype has four modules which can be driven independently according to defined undulatory patterns with a typical frequency of 0.5 Hz. Each module is actuated by one or more SMA springs whose configuration has been designed in order to limit the wiring problems and optimizing working frequency. The robots are covered by shaped silicone material which can be used as a platform to insert tiny legs for obtaining differential friction conditions. Preliminary tests demonstrate that the maximum speed of earthworm prototypes can reach 0.22 mm/s without micro-legs and 2.5 mm/s with micro-legs, thus approximating the behavior of biological earthworms


title: Wireless fingerprint attendance management system

abstract: This paper presents the design methodology of a simple and high real time Zigbee - biometric system for easy and time saving attendance management using the finger prints of the employees at any organization along with the employee incoming and outgoing log maintenance. Firstly employee's fingerprints are scanned by software and an identity number is allotted as their enrollment. During the attendance time when employees impress their fingerprints, against the scanner, the system compares the new fingerprint patterns and the connection between various points in the fingerprint with the enrollment database. A match is recorded as a knock exercising acquisition, processing, transmission, matching. Through this automatic system, time and manpower is reduced to the great extent.


title: Towards a Cognitive Assistant System for Emergency Response

abstract: This abstract presents our preliminary results on development of a cognitive assistant system for emergency response that aims to improve situational awareness and safety of first responders. This system integrates a suite of smart wearable sensors, devices, and analytics for real-time collection and analysis of in-situ data from incident scene and providing dynamic data-driven insights to responders on the most effective response actions to take.


Developing a Teacher Dashboard For Use with Intelligent Tutoring Systems


title: A Framework for Coordinated Surface Operations Planning at Dallas-Fort Worth International Airport

abstract: An Integer Programming formulation is developed for optimizing surface operations at Dallas-Fort Worth airport, with the goal of assessing the potential benefits of taxi route planning. The model is based on operations in the eastern half of the airport under the most frequently used configuration. The focus is on operational concepts that optimize taxi routes by utilizing dierent control points on the airport surface. The benefits of two dierent concepts for optimizing taxiway operations, namely controlled pushback and taxi reroutes are analyzed, for both current data and a projected data set with approximately twice the trac density. The analysis estimates that: (1) for current trac densities, controlled pushback would reduce the average departure taxi time by 17% without altering runway schedule conformance, while the benefits of taxi reroutes would be minimal; and (2) for high-density operations, controlled pushback would reduce the average departure taxi time by 18%, while incorporating taxi reroutes would reduce the average arrival taxi time by 14%. Other benefits analyzed for these control strategies include a decrease in the average time spent in runway crossing queues.


title: Peristaltic locomotion with antagonistic actuators in soft robotics

abstract: This paper presents a soft robotic platform that exhibits peristaltic locomotion. The design principle is based on the unique antagonistic arrangement of radial/circular and longitudinal muscle groups of Oligochaeta. Sequential antagonistic motion is achieved in a flexible braided mesh-tube structure with NiTi coil actuators. A numerical model for the mesh structure describes how peristaltic motion induces robust locomotion and details the deformation by the contraction of NiTi actuators. Several peristaltic locomotion modes are modeled, tested, and compared on the basis of locomotion speed. The entire mechanical structure is made of flexible mesh materials and can withstand significant external impacts during locomotion. This approach can enable a completely soft robotic platform by employing a flexible control unit and energy sources.


title: Dynamic Capacity Networks

abstract: We introduce the Dynamic Capacity Network (DCN), a neural network that can adaptively assign its capacity across different portions of the input data. This is achieved by combining modules of two types: low-capacity sub-networks and high-capacity sub-networks. The low-capacity sub-networks are applied across most of the input, but also provide a guide to select a few portions of the input on which to apply the high-capacity sub-networks. The selection is made using a novel gradient-based attention mechanism, that efficiently identifies input regions for which the DCN's output is most sensitive and to which we should devote more capacity. We focus our empirical evaluation on the Cluttered MNIST and SVHN image datasets. Our findings indicate that DCNs are able to drastically reduce the number of computations, compared to traditional convolutional neural networks, while maintaining similar or even better performance.


title: Developing Language-tagged Corpora for Code-switching Tweets

abstract: Code-switching, where a speaker switches between languages mid-utterance, is frequently used by multilingual populations worldwide. Despite its prevalence, limited effort has been devoted to develop computational approaches or even basic linguistic resources to support research into the processing of such mixedlanguage data. We present a user-centric approach to collecting code-switched utterances from social media posts, and develop language universal guidelines for the annotation of codeswitched data. We also present results for several baseline language identification models on our corpora and demonstrate that language identification in code-switched text is a difficult task that calls for deeper investigation.


title: NoFTL for Real: Databases on Real Native Flash Storage

abstract: Flash SSDs are omnipresent as database storage. HDD replacement is seamless since Flash SSDs implement the same legacy hardware and software interfaces to enable backward compatibility. Yet, the price paid is high as backward compatibility masks the native behaviour, incurs significant complexity and decreases I/O performance, making it non-robust and unpredictable. Flash SSDs are black-boxes. Although DBMS have ample mechanisms to control hardware directly and utilize the performance potential of Flash memory, the legacy interfaces and black-box architecture of Flash devices prevent them from doing so. In this paper we demonstrate NoFTL, an approach that enables native Flash access and integrates parts of the Flashmanagement functionality into the DBMS yielding significant performance increase and simplification of the I/O stack. NoFTL is implemented on real hardware based on the OpenSSD research platform. The contributions of this paper include: (i) a description of the NoFTL native Flash storage architecture; (ii) its integration in Shore-MT and (iii) performance evaluation of NoFTL on a real Flash SSD and on an on-line data-driven Flash emulator under TPCB,C,E and H workloads. The performance evaluation results indicate an improvement of at least 2.4x on real hardware over conventional Flash storage; as well as better utilisation of native Flash parallelism.


title: Using Smart Sensor Strings for Continuous Monitoring of Temperature Stratification in Large Water Bodies

abstract: A "smart" thermistor string for continuous long-term temperature profiling in large water bodies is described allowing highly matched yet low-cost spatial and temporal temperature measurements. The sensor uses the three-wire SDI-12 communications standard to enable a low-powered radio or data logger on supporting buoys to command measurements and retrieve high-resolution temperature data in digital form. Each "smart" temperature sensor integrates a thermistor element, measurement circuitry, power control, calibration coefficient storage, temperature computation, and data communications. Multiple addressable sensors at discrete vertical depths are deployed along a three-wire cable that provides power and allows data transfer at regular intervals. Circuit, manufacturing, and automated calibration techniques allow temperature measurements with a resolution of plusmn0.003degC, and with intersensor matching of plusmn0.006degC. The low cost of each sensor is achieved by using poor tolerance thermistor and circuit components in conjunction with a 15-bit charge-balance analog-to-digital converter. Sensor inaccuracies and temperature coefficients are corrected by a two-point calibration procedure made possible by a standard-curve generator within the sensor, based upon the method of finite differences. This two-point calibration process allows in-field sensor string calibration in stratified water bodies and provides a means to correct for long-term calibration drift without having to return the string to a laboratory


title: Academic abilities in children and adolescents with a history of autism spectrum disorders who have achieved optimal outcomes

abstract: This study examines the academic abilities of children and adolescents who were once diagnosed with an autism spectrum disorder, but who no longer meet diagnostic criteria for this disorder. These individuals have achieved social and language skills within the average range for their ages, receive little or no school support, and are referred to as having achieved “optimal outcomes.” Performance of 32 individuals who achieved optimal outcomes, 41 high-functioning individuals with a current autism spectrum disorder diagnosis (high-functioning autism), and 34 typically developing peers was compared on measures of decoding, reading comprehension, mathematical problem solving, and written expression. Groups were matched on age, sex, and nonverbal IQ; however, the high-functioning autism group scored significantly lower than the optimal outcome and typically developing groups on verbal IQ. All three groups performed in the average range on all subtests measured, and no significant differences were found in per...


title: Review of Group Prediction Model for Counter Terrorism Using CLOPE Algorithm

abstract: In the present scenario terrorist attacks are biggest problem for the mankind and whole world is under constant threat from these well-planned, sophisticated and coordinated terrorist operations. Now every country is focusing on counter-terrorism. Counter-terrorism is the practices, tactics, strategies and techniques that governments, militaries, police and security agency uses to prevent or in response to terrorist threats. This paper will focus on terrorist group prediction techniques for counter terrorism. Also analysis of predicting the responsible group using CLOPE algorithm.


City of Las Vegas human resources promotional selection process program evaluation


title: A competitive Texas Hold’em poker player via automated abstraction and realtime equilibrium computation

abstract: We present a game theory-based heads-up Texas Hold'em poker player, GS1. To overcome the computational obstacles stemming from Texas Hold'em's gigantic game tree, the player employs our automated abstraction techniques to reduce the complexity of the strategy computations. Texas Hold'em consists of four betting rounds. Our player solves a large linear program (offline) to compute strategies for the abstracted first and second rounds. After the second betting round, our player updates the probability of each possible hand based on the observed betting actions in the first two rounds as well as the revealed cards. Using these updated probabilities, our player computes in real-time an equilibrium approximation for the last two abstracted rounds. We demonstrate that our player, which incorporates very little poker-specific knowledge, is competitive with leading poker-playing programs which incorporate extensive domain knowledge, as well as with advanced human players.


title: Development of a new graphical technique for dissolved gas analysis in power transformers based on the five combustible gases

abstract: Several methods have been proposed for dissolved gas analysis (DGA) of mineral oil in power transformers. One of the simple and most widely used methods is Duval triangle. However, Duval triangle does not consider the concentrations of two combustible gases, namely ethane (C2H6) and hydrogen (H2). As a result, Duval triangle exhibits a lower accuracy in diagnosing certain fault types, for which these gases are the key gases, such as low overheating and corona discharge. Accordingly, this paper proposes a novel graphical technique for DGA based on all the five combustible gases. The proposed graphical technique is developed in the form of a pentagon shape. The pentagon heads represents the percentage concentration of each individual gas to the total combustible gases. The corresponding point for a certain faulty case is determined by the center of mass of all pentagon heads. The knowledge extracted from previous DGA methods and field experiences are used to estimate the preliminary fault regions within the pentagon. The exact boundaries between fault regions are then specified using actual DGA data with corresponding fault types collected from the Egyptian Electricity Network and published cases. The overall performance of the proposed pentagon has been evaluated using a set of fault cases and it is revealed that the proposed pentagon has higher diagnostic accuracy compared to other methods including Duval triangles and IEC standard 60599.


title: Underlying Dynamics of Child Birth Registration in Zimbabwe

abstract: By accentuating the potential adverse impacts of the absence of a birth certificate on a child’s well-being across their life cycle, human rights organisations have made a strong case for universal child birth registration. Consequently, in most developing countries, people’s awareness of the importance of birth registration has dramatically increased. Yet in Zimbabwe, universal birth registration remains a pipe dream. Drawing on a mixed-method study of birth registration in Zimbabwe’s Bindura district, the article addresses this paradox by closely examining the underlying dynamics of birth registration success. It finds that birth registration outcomes emerge from a dynamic interplay of economic, non-economic, personal and structural factors. Moreover, whereas existing research tends to accentuate economic determinants of birth registration, social and cultural factors are equally important influences of birth registration success.


Planning and Designing for Students with Disabilities National Clearinghouse for Educational Fa c i l i t i e s


title: Switching bilateral filter with a texture/noise detector for universal noise removal

abstract: In this paper, we propose a switching bilateral filter (SBF) with a texture and noise detector for universal noise removal. Operation was carried out in two stages: detection followed by filtering. For detection, we propose the sorted quadrant median vector (SQMV) scheme, which includes important features such as edge or texture information. This information is utilized to allocate a reference median from SQMV, which is in turn compared with a current pixel to classify it as impulse noise, Gaussian noise, or noise-free. The SBF removes both Gaussian and impulse noise without adding another weighting function. The range filter inside the bilateral filter switches between the Gaussian and impulse modes depending upon the noise classification result. Simulation results show that our noise detector has a high noise detection rate as well as a high classification rate for salt-and-pepper, uniform impulse noise and mixed impulse noise. Unlike most other impulse noise filters, the proposed SBF achieves high peak signal-to-noise ratio and great image quality by efficiently removing both types of mixed noise, salt-and-pepper with uniform noise and salt-and-pepper with Gaussian noise. In addition, the computational complexity of SBF is significantly less than that of other mixed noise filters.


title: Predictive Control of Autonomous Ground Vehicles With Obstacle Avoidance on Slippery Roads

abstract: Two frameworks based on Model Predictive Control (MPC) for obstacle avoidance with autonomous vehicles are presented. A given trajectory represents the driver intent. An MPC has to safely avoid obstacles on the road while trying to track the desired trajectory by controlling front steering angle and differential braking. We present two different approaches to this problem. The first approach solves a single nonlinear MPC problem. The second approach uses a hierarchical scheme. At the high-level, a trajectory is computed on-line, in a receding horizon fashion, based on a simplified point-mass vehicle model in order to avoid an obstacle. At the low-level an MPC controller computes the vehicle inputs in order to best follow the high level trajectory based on a nonlinear vehicle model. This article presents the design and comparison of both approaches, the method for implementing them, and successful experimental results on icy roads.Copyright © 2010 by ASME


title: Learning Topics in Short Texts by Non-negative Matrix Factorization on Term Correlation Matrix

abstract: Nowadays, short texts are very prevalent in various web applications, such as microblogs, instant messages. The severe sparsity of short texts hinders existing topic models to learn reliable topics. In this paper, we propose a novel way to tackle this problem. The key idea is to learn topics by exploring term correlation data, rather than the high-dimensional and sparse term occurrence information in documents. Such term correlation data is less sparse and more stable with the increase of the collection size, and can well capture the necessary information for topic learning. To obtain reliable topics from term correlation data, we first introduce a novel way to compute term correlation in short texts by representing each term with its co-occurred terms. Then we formulated the topic learning problem as symmetric non-negative matrix factorization on the term correlation matrix. After learning the topics, we can easily infer the topics of documents. Experimental results on three data sets show that our method provides substantially better performance than the baseline methods.


title: Cahit Arf: Exploring his scientific influence using social network analysis, author co-citation maps and single publication h index1

abstract: Cahit Arf (1910‑1997), a famous Turkish scientist whose picture is depicted in one of the Turkish banknotes, is a well‑known figure in mathematics with his discoveries named after him (e.g., Arf invariant, Arf rings, the Hasse‑Arf theorem). Although Arf may not be considered as a prolific scientist in terms of number of papers (he authored a total of 23 papers), his influence on mathematics and related disciplines was profound. As he was active before, during, and after World War II, Arf’s contributions are not properly listed in citation indexes, and thus did not generate many citations even though several papers with “Arf” in their titles appeared in literature. This paper traces influence of Arf in scientific world using citation analysis techniques first. It reviews scientific impact of Arf by analyzing both; the papers authored by Arf and papers whose titles or keywords containing various combinations of “Arf invariant,” “Arf rings,” and so on. The paper then goes on to study Arf’s contributions using social network analysis (SNA) and author co‑citation analysis (ACA) techniques. CiteSpace and pennant diagrams are used to explore scientific impact of Arf by mapping his cited references derived from Thomson Reuters’ Web of Science (WoS) database. The direct and indirect influences of Arf’s highly cited paper on Arf invariant are assessed through ACA and single publication h index, respectively. The paper ends with a discussion of whether data analysis techniques used in this study can be useful to study scientific impact of researchers retrospectively.


title: Engaged Brains: Strategies for Mastering Learner Engagement

abstract: In an age of constant distraction, how do we rein in student focus? Neuroscience offers insights into what it takes to create an optimal learning environment. It all starts with learner engagement. In this session we’ll explore the neurobiological underpinnings of attention and address five key strategies you can immediately apply in your eLearning course design. ::: Presentation Objectives: ::: ::: Review a model defining what triggers learner engagement ::: Discuss brain sticky qualities we can employ in course design ::: Explore five key engagement strategies applied to case examples ::: ::: http://www.inspired-ed.com/


title: Photorealistic Video Super Resolution

abstract: With the advent of perceptual loss functions, new possibilities in super-resolution have emerged, and we currently have models that successfully generate near-photorealistic high-resolution images from their low-resolution observations. Up to now, however, such approaches have been exclusively limited to single image super-resolution. The application of perceptual loss functions on video processing still entails several challenges, mostly related to the lack of temporal consistency of the generated images, i.e., flickering artifacts. In this work, we present a novel adversarial recurrent network for video upscaling that is able to produce realistic textures in a temporally consistent way. The proposed architecture naturally leverages information from previous frames due to its recurrent architecture, i.e. the input to the generator is composed of the low-resolution image and, additionally, the warped output of the network at the previous step. We also propose an additional loss function to further reinforce temporal consistency in the generated sequences. The experimental validation of our algorithm shows the effectiveness of our approach which obtains competitive samples in terms of perceptual quality with improved temporal consistency.


title: Baidu Apollo EM Motion Planner

abstract: In this manuscript, we introduce a real-time motion planning system based on the Baidu Apollo (open source) autonomous driving platform. The developed system aims to address the industrial level-4 motion planning problem while considering safety, comfort and scalability. The system covers multilane and single-lane autonomous driving in a hierarchical manner: (1) The top layer of the system is a multilane strategy that handles lane-change scenarios by comparing lane-level trajectories computed in parallel. (2) Inside the lane-level trajectory generator, it iteratively solves path and speed optimization based on a Frenet frame. (3) For path and speed optimization, a combination of dynamic programming and spline-based quadratic programming is proposed to construct a scalable and easy-to-tune framework to handle traffic rules, obstacle decisions and smoothness simultaneously. The planner is scalable to both highway and lower-speed city driving scenarios. We also demonstrate the algorithm through scenario illustrations and on-road test results. The system described in this manuscript has been deployed to dozens of Baidu Apollo autonomous driving vehicles since Apollo v1.5 was announced in September 2017. As of May 16th, 2018, the system has been tested under 3,380 hours and approximately 68,000 kilometers (42,253 miles) of closed-loop autonomous driving under various urban scenarios. The algorithm described in this manuscript is available at https://github.com/ApolloAuto/apollo/tree/master/modules/planning.


title: Lightweight Resource Scaling for Cloud Applications

abstract: Elastic resource provisioning is a key feature of cloud computing, allowing users to scale up or down resource allocation for their applications at run-time. To date, most practical approaches to managing elasticity are based on allocation/de-allocation of the virtual machine (VM) instances to the application. This VM-level elasticity typically incurs both considerable overhead and extra costs, especially for applications with rapidly fluctuating demands. In this paper, we propose a lightweight approach to enable cost-effective elasticity for cloud applications. Our approach operates fine-grained scaling at the resource level itself (CPUs, memory, I/O, etc) in addition to VM-level scaling. We also present the design and implementation of an intelligent platform for light-weight resource management of cloud applications. We describe our algorithms for light-weight scaling and VM-level scaling and show their interaction. We then use an industry standard benchmark to evaluate the effectiveness of our approach and compare its performance against traditional approaches.


title: Sketch based face recognition: Forensic vs. composite sketches

abstract: Facial sketches are widely used by law enforcement agencies to assist in the identification and apprehension of suspects involved in criminal activities. Sketches used in forensic investigations are either drawn by forensic artists (forensic sketches) or created with computer software (composite sketches) following the verbal description provided by an eyewitness or the victim. These sketches are posted in public places and in media in hopes that some viewers will provide tips about the identity of the suspect. This method of identifying suspects is slow and tedious and may not lead to apprehension of the suspect. Hence, there is a need for a method that can automatically and quickly match facial sketches to large police mugshot databases. We address the problem of automatic facial sketch to mugshot matching and, for the first time, compare the effectiveness of forensic sketches and composite sketches. The contributions of this paper include: (i) a database containing mugshots and corresponding forensic and composite sketches that will be made available to interested researchers; (ii) a comparison of holistic facial representations versus component based representations for sketch to mugshot matching; and (iii) an analysis of the effect of filtering a mugshot gallery using three sources of demographic information (age, gender and race/ethnicity). Our experimental results show that composite sketches are matched with higher accuracy than forensic sketches to the corresponding mugshots. Both of the face representations studied here yield higher sketch to photo matching accuracy compared to a commercial face matcher.


title: Adaptability and accountability of information architectures in interorganizational networks

abstract: Governmental service provisioning is increasingly organized around networks of agencies. These agencies have various objectives and a variety of heterogeneous types of information systems. Each agency can independently design (a set of) subsystems that should function together to provide a service. Often the collaborating organizations have various degrees of technology-readiness and different objectives and interests. The service executing depends on the weakest subsystem and total network performance need to be accounted for. Furthermore, network arrangements are likely to change and evolve over time and need to adapt to changing circumstances, whereas, the entire network's performance is dependent on the performance of the individual departments. In this paper we investigate adaptability and accountability aspects of information systems in networks of organizations. Adaptability is the ability to deal with new environmental conditions. Accountability is the answerability for one's actions or inactions and to be responsible for their consequences. Adaptability requires a loosely coupled structure, which can seriously hamper accountability. Accountability requires tight integration and clear processes. We analyzed the adaptability and accountability aspects in two case studies. The case studies demonstrate that both requirements are not necessarily conflicting and can be complimentary. More research is necessary on designing architectures which are able to meet the adaptability and accountability requirements at the same time.


A Simple Scalable Neural Networks based Model for Geolocation Prediction in Twitter.


title: MODEC: Multimodal Decomposable Models for Human Pose Estimation

abstract: We propose a multimodal, decomposable model for articulated human pose estimation in monocular images. A typical approach to this problem is to use a linear structured model, which struggles to capture the wide range of appearance present in realistic, unconstrained images. In this paper, we instead propose a model of human pose that explicitly captures a variety of pose modes. Unlike other multimodal models, our approach includes both global and local pose cues and uses a convex objective and joint training for mode selection and pose estimation. We also employ a cascaded mode selection step which controls the trade-off between speed and accuracy, yielding a 5x speedup in inference and learning. Our model outperforms state-of-the-art approaches across the accuracy-speed trade-off curve for several pose datasets. This includes our newly-collected dataset of people in movies, FLIC, which contains an order of magnitude more labeled data for training and testing than existing datasets.


title: Low Profile, Miniaturized, Inductively Coupled Capacitively Loaded Monopole Antenna

abstract: A novel high-gain low-profile miniaturized antenna with omnidirectional vertically polarized radiation, similar to a short dipole is presented. The proposed design focus is on increasing the gain and improving the polarization purity of the radiated field in the horizontal plane. The gain and polarization improvement are achieved by isolating the feed structure from a miniaturized resonant radiating structure composed of an in-plane capacitor and a structurally embedded transformer. The antenna topology is developed, based on circuit model and through full-wave simulations the equivalence is established. The equivalent circuit model assists in the initial design, and then minor modifications are required to achieve the desired frequency of operation. The initial topology of the proposed antenna, the so-called Inductively Coupled Capacitively Loaded Monopole Antenna (ICCLMA), consists of two metal layers, a feeding pin and a shorting pin. The performance of the proposed antenna is compared to that of an ordinary inverted F antenna and a more recent low profile vertically polarized antenna (LMMMA) . It is shown that the gain of ICCLMA is 9 dB and 4 dB higher than that of the conventional inverted-F antenna and the LMMMA, respectively. To simplify the fabrication process a modified single-layer ICCLMA topology is presented and optimized. Finally, a design procedure to further reduce the lateral dimension of ICCLMA is presented. A procedure for accurate measurement of antennas with small ground planes is also presented.


title: The neuropsychiatry of the cerebellum — insights from the clinic

abstract: A central aspect of the cerebellar cognitive affective syndrome is the dysregulation of affect that occurs when lesions involve the ‘limbic cerebellum’ (vermis and fastigial nucleus). In this case series we describe neuropsychiatric disturbances in adults and children with congenital lesions including cerebellar agenesis, dysplasia, and hypoplasia, and acquired conditions including cerebellar stroke, tumor, cerebellitis, trauma, and neurodegenerative disorders. The behaviors that we witnessed and that were described by patients and families included distractibility and hyperactivity, impulsiveness, disinhibition, anxiety, ritualistic and stereotypical behaviors, illogical thought and lack of empathy, as well as aggression and irritability. Ruminative and obsessive behaviors, dysphoria and depression, tactile defensiveness and sensory overload, apathy, childlike behavior, and inability to appreciate social boundaries and assign ulterior motives were also evident. We grouped these disparate neurobehavioral profiles into five major domains, characterized broadly as disorders of attentional control, emotional control, and social skill set as well as autism spectrum disorders, and psychosis spectrum disorders. Drawing on our dysmetria of thought hypothesis, we conceptualized the symptom complexes within each putative domain as reflecting either exaggeration (overshoot, hypermetria) or diminution (hypotonia, or hypometria) of responses to the internal or external environment. Some patients fluctuated between these two states. We consider the implications of these neurobehavioral observations for the care of patients with ataxia, discuss the broader role of the cerebellum in the pathogenesis of these neuropsychiatric symptoms, and revisit the possibility of using cerebellar stimulation to treat psychiatric disorders by enhancing cerebellar modulation of cognition and emotion.


title: Luck is Hard to Beat: The Difficulty of Sports Prediction

abstract: Predicting the outcome of sports events is a hard task. We quantify this difficulty with a coefficient that measures the distance between the observed final results of sports leagues and idealized perfectly balanced competitions in terms of skill. This indicates the relative presence of luck and skill. We collected and analyzed all games from 198 sports leagues comprising 1503 seasons from 84 countries of 4 different sports: basketball, soccer, volleyball and handball. We measured the competitiveness by countries and sports. We also identify in each season which teams, if removed from its league, result in a completely random tournament. Surprisingly, not many of them are needed. As another contribution of this paper, we propose a probabilistic graphical model to learn about the teams' skills and to decompose the relative weights of luck and skill in each game. We break down the skill component into factors associated with the teams' characteristics. The model also allows to estimate as 0.36 the probability that an underdog team wins in the NBA league, with a home advantage adding 0.09 to this probability. As shown in the first part of the paper, luck is substantially present even in the most competitive championships, which partially explains why sophisticated and complex feature-based models hardly beat simple models in the task of forecasting sports' outcomes.


title: Boosted Mixture of Experts: An Ensemble Learning Scheme

abstract: We present a new supervised learning procedure for ensemble machines, in which outputs of predictors, trained on different distributions, are combined by a dynamic classifier combination model. This procedure may be viewed as either a version of mixture of experts (Jacobs, Jordan, Nowlan, & Hinton, 1991), applied to classification, or a variant of the boosting algorithm (Schapire, 1990). As a variant of the mixture of experts, it can be made appropriate for general classification and regression problems by initializing the partition of the data set to different experts in a boostlike manner. If viewed as a variant of the boosting algorithm, its main gain is the use of a dynamic combination model for the outputs of the networks. Results are demonstrated on a synthetic example and a digit recognition task from the NIST database and compared with classical ensemble approaches.


title: Identifying Equivalent URLs Using URL Signatures

abstract: In the standard URL normalization mechanism, URLs are normalized syntactically by a set of predefined steps. In this paper, we propose to enhance the standard URL normalization by incorporating the semantically meaningful metadata of the Web pages. The metadata taken into account are the body texts of the Web pages, which can be extracted during HTML parsing. Given a URL which has undergone the standard normalization mechanism, we construct its URL signature by hashing or fingerprinting the body text of the associated Web page using Message-Digest algorithm 5. URLs which share identical signatures are considered to be equivalent in our scheme. The experimental results show that our proposed method helps to further reduce redundant Web information retrieval by 34.57% in comparison with the standard URL normalization mechanism.


title: Greenhouse temperature and humidity intelligent control system

abstract: The article based on embedded database of greenhouse temperature and humidity control system intelligent. Put forward by embedded database system set up in an ideal environment for data greenhouse temperature and humidity control, greenhouse crops in the process of growth under control. To address the growth of greenhouse crops in the process of temperature and humidity controlled environment is not ideal, at the same time improve the efficiency of control and cost-effective system. The article focuses on the structure of the control system, hardware, software design and system control strategy. The control system has a simple hardware structure, cost-effective, easy to use and maintenance, temperature and humidity data, and other advantages of good stability.


title: Equivalence of the primal and dual simplex algorithms for the maximum flow problem

abstract: In this paper, we study the primal and dual simplex algorithms for the maximum flow problem. We show that any primal simplex algorithm for the maximum flow problem can be converted into a dual simplex algorithm that performs the same number of pivots and runs in the same time. The converse result is also true though in a somewhat weaker form.


title: Compact microstrip diplexer based on a novel coupling topology

abstract: A compact microstrip diplexer with second-order bandpass response has been reported in this paper. It is composed of two quarter-wavelength resonators and two folded half-wavelength resonators. By taking advantages of the new coupling topology proposed, two transmission zeros are generated around each passband to improve frequency selectivity and isolation performance. In addition, no extra matching circuits are needed for the designed diplexer, which greatly simplifies the design procedures and reduces the circuit size. For the demonstration, a diplexer sample occupying a compact size of 0.29 λ g by 0.23 λ g , has been designed and fabricated using single-layer printed circuit board (PCB) technology. Figure of merits such as low loss, high frequency selectivity, fairly good isolation and stopband rejection (>20 dB) have been achieved, which exhibits a satisfactory agreement with the EM simulations.


title: The Overview of Research on Microgrid Protection Development

abstract: In general, the microgrid can operate in both grid-connection mode and islanded mode that challenges the traditional over-current protection scheme in the distribution network. The novel research and development of protection, including both the microgrid and distribution network are comprehensively analyzed in this paper, which mainly focuses on two aspects. The first one analyses the impact on the network protection from the fault current feature while microgrid-connected. Secondly, several innovative protection schemes for network and microgrid are discussed. It is essential to improve traditional protection schemes by using communication techniques. In order to protect the microgrid in islanded mode, two innovative voltage protection schemes are discussed. However, the differential current protection scheme is possibly still the most effective solution for both network and microgrid.


title: BRACHYURY and CDX2 Mediate BMP-Induced Differentiation of Human and Mouse Pluripotent Stem Cells into Embryonic and Extraembryonic Lineages

abstract: Summary BMP is thought to induce hESC differentiation toward multiple lineages including mesoderm and trophoblast. The BMP-induced trophoblast phenotype is a long-standing paradox in stem cell biology. Here we readdressed BMP function in hESCs and mouse epiblast-derived cells. We found that BMP4 cooperates with FGF2 (via ERK) to induce mesoderm and to inhibit endoderm differentiation. These conditions induced cells with high levels of BRACHYURY (BRA) that coexpressed CDX2. BRA was necessary for and preceded CDX2 expression; both genes were essential for expression not only of mesodermal genes but also of trophoblast-associated genes. Maximal expression of the latter was seen in the absence of FGF but these cells coexpressed mesodermal genes and moreover they differed in cell surface and epigenetic properties from placental trophoblast. We conclude that BMP induces human and mouse pluripotent stem cells primarily to form mesoderm, rather than trophoblast, acting through BRA and CDX2.


title: Chapter 8 The Architecture of Human-Like General Intelligence

abstract: By exploring the relationships between different AGI architectures, one can work toward a holistic cognitive model of human-level intelligence. In this vein, here an integrative architecture diagram for human-like general intelligence is proposed, via merging of lightly modified version of prior diagrams including Aaron Sloman’s high-level cognitive model, Stan Franklin and the LIDA group’s model of working memory and the cognitive cycle, Joscha Bach and Dietrich Dorner’s Psi model of motivated action and cognition, James Albus’s three-hierarchy intelligent robotics model, and the author’s prior work on cognitive synergy in deliberative thought and metacognition, along with ideas from deep learning and computational linguistics.


title: A field trial of privacy nudges for facebook

abstract: Anecdotal evidence and scholarly research have shown that Internet users may regret some of their online disclosures. To help individuals avoid such regrets, we designed two modifications to the Facebook web interface that nudge users to consider the content and audience of their online disclosures more carefully. We implemented and evaluated these two nudges in a 6-week field trial with 28 Facebook users. We analyzed participants' interactions with the nudges, the content of their posts, and opinions collected through surveys. We found that reminders about the audience of posts can prevent unintended disclosures without major burden; however, introducing a time delay before publishing users' posts can be perceived as both beneficial and annoying. On balance, some participants found the nudges helpful while others found them unnecessary or overly intrusive. We discuss implications and challenges for designing and evaluating systems to assist users with online disclosures.


title: MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network

abstract: In this paper, we present MultiPoseNet, a novel bottom-up multi-person pose estimation architecture that combines a multi-task model with a novel assignment method. MultiPoseNet can jointly handle person detection, keypoint detection, person segmentation and pose estimation problems. The novel assignment method is implemented by the Pose Residual Network (PRN) which receives keypoint and person detections, and produces accurate poses by assigning keypoints to person instances. On the COCO keypoints dataset, our pose estimation method outperforms all previous bottom-up methods both in accuracy (+4-point mAP over previous best result) and speed; it also performs on par with the best top-down methods while being at least 4x faster. Our method is the fastest real time system with 23 frames/sec. Source code is available at: https://github.com/mkocabas/pose-residual-network


title: Leveraging external information in topic modelling

abstract: Besides the text content, documents usually come with rich sets of meta-information, such as categories of documents and semantic/syntactic features of words, like those encoded in word embeddings. Incorporating such meta-information directly into the generative process of topic models can improve modelling accuracy and topic quality, especially in the case where the word-occurrence information in the training data is insufficient. In this article, we present a topic model called MetaLDA, which is able to leverage either document or word meta-information, or both of them jointly, in the generative process. With two data augmentation techniques, we can derive an efficient Gibbs sampling algorithm, which benefits from the fully local conjugacy of the model. Moreover, the algorithm is favoured by the sparsity of the meta-information. Extensive experiments on several real-world datasets demonstrate that our model achieves superior performance in terms of both perplexity and topic quality, particularly in handling sparse texts. In addition, our model runs significantly faster than other models using meta-information.


title: Efficient processing of SPARQL joins in memory by dynamically restricting triple patterns

abstract: Since there are a lot of similar or common properties between RDF and relational databases and between SPARQL and SQL, many efforts focus on leveraging the research results of optimizing relational query languages for optimizing SPARQL queries. However, SPARQL has its own characteristics different from SQL, which are not fully exploited by existing work. Therefore, there is still much space for research on optimizing SPARQL queries. Based on the triple nature of RDF data, we create 7 indices to retrieve RDF data quickly; based on the SPARQL-specific properties and the 7 indices, we develop a new, efficient approach to computing join by dynamically restricting triple patterns. Our experimental results show the efficiency of our approach.


title: Causal inference for statistical fault localization

abstract: This paper investigates the application of causal inference methodology for observational studies to software fault localization based on test outcomes and profiles. This methodology combines statistical techniques for counterfactual inference with causal graphical models to obtain causal-effect estimates that are not subject to severe confounding bias. The methodology applies Pearl's Back-Door Criterion to program dependence graphs to justify a linear model for estimating the causal effect of covering a given statement on the occurrence of failures. The paper also presents the analysis of several proposed-fault localization metrics and their relationships to our causal estimator. Finally, the paper presents empirical results demonstrating that our model significantly improves the effectiveness of fault localization.


title: Fuzzy logic control of a two-output high-frequency series-resonant induction heater

abstract: This paper presents the design and concept of fuzzy logic controller analysis of two-output high-frequency series-resonant induction heater. The fuzzy logic controller is applied for non-linear load and unknown mathematical model as well as this prototype, which is used to control the duty-cycle of power switch to maintain current output. The power of the prototype can be controlled by using 3 branches of full bridge series resonant inverter: one branch for common controlled and others controlled for each independently two output. The power for others can be controlled by the asymmetrical voltage cancellation methodology.


title: Factors Influencing Employee Knowledge Sharing Capabilities in Electronic Government Agencies in Malaysia

abstract: Effective managing and sharing of knowledge has the power to improve individual’s lives and society. However, research has shown that people are reluctant to share. Knowledge sharing (KS) involve not only our knowledge, but a process of giving and receiving of knowledge with others. Knowledge sharing capabilities (KSC) is an individual’s capability to share experience, expertise and know-how with other employees in the organization. Previous studies identified many factors affecting KSC either in public or private sectors. Upon a critical review on factors affecting KS and factors affecting KSC, this paper attempts to examine the factors that have been cited as significant in influencing employees KSC within Electronic Government (EG) agencies in Malaysia. Two capable factors that are considered in this study are technical factor and non-technical factor. This paper proposes an integrated conceptual framework of employees KSC which can be used for research enhancement.


title: Perception of psychopathy and the Uncanny Valley in virtual characters

abstract: Virtual characters with a realistic, human-like appearance are increasingly being used in video games and animation. However, increased realism does not necessarily imply increased acceptance and factors such as aberrant facial expression may evoke the Uncanny Valley phenomenon. In humans, personality traits such as anger, callousness, coldness, dominance, being unconcerned, and untrustworthiness are associated with psychopathy; a visual facial marker of this condition being a lack of visible response in the eye region to emotive situations. As such, the present study investigated if inadequate upper facial animation in human-like virtual characters evoked the uncanny due to a perception of psychopathic traits within a character. The results revealed that virtual characters that showed a lack of a startle response to a scream sound were regarded as most uncanny and perceptions of personality traits associated with psychopathy were a strong predictor of reported uncanniness but, that other negative personality traits not associated with psychopathy were not. The study presents possible psychological drivers of uncanniness to inform designers why a lack of detail in a character's upper face when portraying a startle response may evoke perception of specific negative personality traits in a character, to help control the uncanny in character design.


title: Backing Up a Truck and Trailer Using Variable Universe Based Fuzzy Controller

abstract: This paper presents an efficient and practical variable universe based fuzzy controller (VUBFC) for the control problem of backing up a truck and trailer. The design of VUBFC essentially amounts to (1) designing a typical fuzzy controller, and (2) choosing contraction-expansion factors on input-output universes. To design a VUBFC for truck backer-upper problem, we propose a new contraction-expansion factor on the universe of the fuzzy controller's output. Being with such a contraction-expansion factor, a VUBFC is able to vary the membership functions of its input-output valuables in response to the varying of system errors, thus to achieve a precise, efficient and energy-economic control. Moreover, such a VUBFC is easy to design because it is not difficult to construct a typical fuzzy controller from data or the experience of human experts, and we do not need to optimize the typical fuzzy controller integrated inside the VUBFC. Compared to the typical fuzzy controller, the newly designed VUBFC exhibits good performance in many ways in the simulation experiments


title: Software risk management barriers: An empirical study

abstract: This paper reports results from a survey of experienced project managers on their perception of software risk management. From a sample of 18 experienced project managers, we have found good awareness of risk management, but low tool usage. We offer evidence that the main barriers to performing risk management are related to its perceived high cost and comparative low value. Psychological issues are also important, but less so. Risk identification and monitoring, in particular, are perceived as effort intensive and costly. The perception is that risk management is not prioritised highly enough. Our conclusion is that more must be done to visibly prove the value : cost ratio for risk management activities.


title: Semi-supervised Multi-label k-Nearest Neighbors Classification Algorithms

abstract: Classification is one of the most traditional tasks in machine learning. In supervised learning for classification, the goal is to learn a classifier function using a completely labeled dataset. Semi-supervised learning modifies the learning algorithm function allowing the use of partially labeled data. Single-label classification assigns only one label to each instance in the dataset, while multi-label classification can assign multiple labels for each instance. It would be relevant to develop techniques that are both multi-label and semi-supervised. However, few previous work has been devoted to semi-supervised multi-label classification. In the current work, we propose two new algorithms by extending the Multi-label k-Nearest Neighbors (MLkNN) algorithm to semi-supervised learning. The original MLkNN is a graph-based supervised algorithm. In our proposal, we augmented the graph structure and adapted two semi-supervised algorithms, label propagation and label spreading, for performing the label expansion in the augmented graph. We compare the proposed algorithms with a group of baseline supervised multi-label algorithms. The results for the metrics analyzed showed that the new algorithms were suitable for the multi-label semi-supervised scenarios.


title: STRUCTURAL HIGH-RESOLUTION SATELLITE IMAGE INDEXING

abstract: Satellite images with high spatial resolution raise many challenging issues in image understanding and pattern recognition. First, they allow measurement of small objects maybe up to 0.5 m, and both texture and geometrical structures emerge simultaneously. Second, objects in the same type of scenes might appear at different scales and orientations. Consequently, image indexing methods should combine the structure and texture information of images and comply with some invariant properties. This paper contributes to the indexing of high-resolution satellite images. We suggest a satellite image indexing method relying on topographic maps (Caselles et al., 1999) and a shape-based image indexing scheme (Xia et al., 2009). The proposed approach contains both the textural and structural information of satellite images and is also robust to changes in scale, orientation and contrast. Experimental analysis on a real satellite image database confirms the efficiency of the approach.


title: Sentiment analysis of twitter data using machine learning approaches and semantic analysis

abstract: The wide spread of World Wide Web has brought a new way of expressing the sentiments of individuals. It is also a medium with a huge amount of information where users can view the opinion of other users that are classified into different sentiment classes and are increasingly growing as a key factor in decision making. This paper contributes to the sentiment analysis for customers' review classification which is helpful to analyze the information in the form of the number of tweets where opinions are highly unstructured and are either positive or negative, or somewhere in between of these two. For this we first pre-processed the dataset, after that extracted the adjective from the dataset that have some meaning which is called feature vector, then selected the feature vector list and thereafter applied machine learning based classification algorithms namely: Naive Bayes, Maximum entropy and SVM along with the Semantic Orientation based WordNet which extracts synonyms and similarity for the content feature. Finally we measured the performance of classifier in terms of recall, precision and accuracy.


title: A rule based bengali stemmer

abstract: One of the biggest challenges in doing word lookups is to derive the appropriate base word for any given word in Bengali. The basic concept to the solution of the problem is to eliminate inflections from a given word to derive its stem word. Stemmers attempt to reduce a word to its root form using stemming process, which reduces an inflected or derived word to its stem or root form. Existing works in the literature use lookup tables either for stem words or suffixes, increasing the overheads in terms of memory and time. This paper develops a rule-based algorithm that eliminates inflections stepwise without continuously searching for the desired root in the dictionary. To the best of our knowledge, this paper first investigates that, in Bengali morphology, for a large set of inflections, the stems can be computed algorithmically cutting down the inflections step by step. The proposed algorithm is independent of inflected word lengths and our evaluation shows around 88% accuracy.


title: Smart antennas for satellite communications on the move

abstract: Smart antennas are important for SATCOM on-the-move (SOTM) because they can increase the channel capacity and coverage range of the communication systems. Firstly, this paper briefly reviews the state-of-art on the smart antenna design for SOTM applications. Different types of antenna for SOTM are discussed. Then, two design examples from the recent collaborated research work from the University of Kent are presented. One is a Ka-band low-cost beam scanning folded reflectarray. This reflectarray uses the dual-polarized slot antenna with multi-layered PCB structure as the unit cell and scans its beam within large angle range. Another design example is a Ku-band low cost dual polarized reconfigurable reflectarray with single bit phase control by using PIN diodes. For both design examples, measurement results are given to show their good beam scanning performance.


title: XDoG: advanced image stylization with eXtended Difference-of-Gaussians

abstract: Recent extensions to the standard Difference-of-Gaussians (DoG) edge detection operator have rendered it less susceptible to noise and increased its aesthetic appeal for stylistic depiction applications. Despite these advances, the technical subtleties and stylistic potential of the DoG operator are often overlooked. This paper reviews the DoG operator, including recent improvements, and offers many new results spanning a variety of styles, including pencil-shading, pastel, hatching, and binary black-and-white images. Additionally, we demonstrate a range of subtle artistic effects, such as ghosting, speed-lines, negative edges, indication, and abstraction, and we explain how all of these are obtained without, or only with slight modifications to an extended DoG formulation. In all cases, the visual quality achieved by the extended DoG operator is comparable to or better than those of systems dedicated to a single style.


title: MULTISCALE IMAGE REGISTRATION

abstract: A multiscale image registration technique is presented for the registration of medical images that contain significant levels of noise. An overview of the medical image registration problem is presented, and various registration techniques are discussed. Experiments using mean squares, normalized correlation, and mutual information optimal linear registration are presented that determine the noise levels at which registration using these techniques fails. Further experiments in which classical denoising algorithms are applied prior to registration are presented, and it is shown that registration fails in this case for significantly high levels of noise, as well. The hierarchical multiscale image decomposition of E. Tadmor, S. Nezzar, and L. Vese [20] is presented, and accurate registration of noisy images is achieved by obtaining a hierarchical multiscale decomposition of the images and registering the resulting components. This approach enables successful registration of images that contain noise levels well beyond the level at which ordinary optimal linear registration fails. Image registration experiments demonstrate the accuracy and efficiency of the multiscale registration technique, and for all noise levels, the multiscale technique is as accurate as or more accurate than ordinary registration techniques.


title: A Review on the Applications of Petri Nets in Modeling, Analysis, and Control of Urban Traffic

abstract: Urban traffic systems that possess system states that are distributed, parallel, deterministic, stochastic, discrete, and continuous are well suited for a Petri net (PN) approach. The literature survey conducted in this paper shows the vast applications of PNs in modeling and simulation, analyzing and evaluating performances, intelligent control and optimization, and congestion management in urban traffic systems. This paper outlines the related works conducted using PNs and discusses its viability, such as its contributions and limitations. Extendibility and future research potential to further the successful applications of PNs in traffic systems are discussed and proposed in this paper.


title: Learning to Make Inferences in a Semantic Parsing Task

abstract: We introduce a new approach to training a semantic parser that uses textual entailment judgements as supervision. These judgements are based on high-level inferences about whether the meaning of one sentence follows from another. When applied to an existing semantic parsing task, they prove to be a useful tool for revealing semantic distinctions and background knowledge not captured in the target representations. This information is used to improve the quality of the semantic representations being learned and to acquire generic knowledge for reasoning. Experiments are done on the benchmark Sportscaster corpus (Chen and Mooney, 2008), and a novel RTE-inspired inference dataset is introduced. On this new dataset our method strongly outperforms several strong baselines. Separately, we obtain state-of-the-art results on the original Sportscaster semantic parsing task.


title: Self-Directed Learning in Problem-Based Learning and its Relationships with Self-Regulated Learning

abstract: This study investigated the role of self-directed learning (SDL) in problem-based learning (PBL) and examined how SDL relates to self-regulated learning (SRL). First, it is explained how SDL is implemented in PBL environments. Similarities between SDL and SRL are highlighted. However, both concepts differ on important aspects. SDL includes an additional premise of giving students a broader role in the selection and evaluation of learning materials. SDL can encompass SRL, but the opposite does not hold. Further, a review of empirical studies on SDL and SRL in PBL was conducted. Results suggested that SDL and SRL are developmental processes, that the “self” aspect is crucial, and that PBL can foster SDL. It is concluded that conceptual clarity of what SDL entails and guidance for both teachers and students can help PBL to bring forth self-directed learners.


title: Acquiring distributed representations for verb-object pairs by using word2vec

abstract: We propose three methods for obtaining distributed representations for verb-object pairs in predicated argument structures by using word2vec. Word2vec is a method for acquiring distributed representations for a word by retrieving a weight matrix in neural networks. First, we analyze a large amount of text with an HPSG parser; then, we obtain distributed representations for the verb-object pairs by learning neural networks from the analyzed text. We evaluated our methods by measuring the MRR score for verb-object pairs and the Spearman’s rank correlation coefficient for verb-object pairs in experiments.


title: Incremental Coordination: Attention-Centric Speech Production in a Physically Situated Conversational Agent

abstract: Inspired by studies of human-human conversations, we present methods for incrementally coordinating speech production with listeners’ visual foci of attention. We introduce a model that considers the demands and availability of listeners’ attention at the onset and throughout the production of system utterances, and that incrementally coordinates speech synthesis with the listener’s gaze. We present an implementation and deployment of the model in a physically situated dialog system and discuss lessons learned.


title: SLocX: Predicting Subcellular Localization of Arabidopsis Proteins Leveraging Gene Expression Data

abstract: Despite the growing volume of experimentally validated knowledge about the subcellular localization of plant proteins, a well performing in-silico prediction tool is still a necessity. Existing tools, which employ information derived from protein sequence alone, offer limited accuracy and/or rely on full sequence availability. We explored whether gene expression profiling data can be harnessed to enhance prediction performance. To achieve this, we trained several support vector machines to predict the subcellular localization of Arabidopsis thaliana proteins using sequence derived information, expression behavior or a combination of these data and compared their predictive performance through a cross-validation test. We show that gene expression carries information about the subcellular localization not available in sequence information, yielding dramatic benefits for plastid localization prediction, and some notable improvements for other compartments such as the mitochondrion, the Golgi and the plasma membrane. Based on these results, we constructed a novel subcellular localization prediction engine, SLocX, combining gene expression profiling data with protein sequence based information. We then validated the results of this engine using an independent test set of annotated proteins and a transient expression of GFP-fusion proteins. Here, we present the prediction framework and a website of predicted localizations for Arabidopsis. The relatively good accuracy of our prediction engine, even in cases where only partial protein sequence is available (e.g. in sequences lacking the N-terminal region), offers a promising opportunity for similar application to non-sequenced or poorly annotated plant species. Although the prediction scope of our method is currently limited by the availability of expression information on the ATH1 array, we believe that the advances in measuring gene expression technology will make our method applicable for all Arabidopsis proteins.


title: Hybrid Propulsion System Design of a VTOL Tailsitter UAV

abstract: Tailsitter UAVs with their combined vertical take off and landing (VTOL) and fixed-wing aircraft with full flightspeed regime capability provides a distinct alternative to rotary-wing and ducted fan UAVs (OAVs). ITU-BYU Tailsitter concept aims to obtain the energy efficient regimes across the VTOL and the cruising flight regimes. This paper describes the hybrid propulsion system design approach to attain this. The preliminary design and the analysis indicate advantageous performance over other mini-class unmanned air vehicles.


title: A reinforcement learning approach for dynamic selection of virtual machines in cloud data centres

abstract: In recent years Machine Learning techniques have proven to reduce energy consumption when applied to cloud computing systems. Reinforcement Learning provides a promising solution for the reduction of energy consumption, while maintaining a high quality of service for customers. We present a novel single agent Reinforcement Learning approach for the selection of virtual machines, creating a new energy efficiency practice for data centres. Our dynamic Reinforcement Learning virtual machine selection policy learns to choose the optimal virtual machine to migrate from an over-utilised host. Our experiment results show that a learning agent has the abilities to reduce energy consumption and decrease the number of migrations when compared to a state-of-the-art approach.


title: An increase in theAkkermansiaspp. population induced by metformin treatment improves glucose homeostasis in diet-induced obese mice

abstract: Background Recent evidence indicates that the composition of the gut microbiota contributes to the development of metabolic disorders by affecting the physiology and metabolism of the host. Metformin is one of the most widely prescribed type 2 diabetes (T2D) therapeutic agents. Objective To determine whether the antidiabetic effect of metformin is related to alterations of intestinal microbial composition. Design C57BL/6 mice, fed either a normal-chow diet or a high-fat diet (HFD), were treated with metformin for 6 weeks. The effect of metformin on the composition of the gut microbiota was assessed by analysing 16S rRNA gene sequences with 454 pyrosequencing. Adipose tissue inflammation was examined by flow cytometric analysis of the immune cells present in visceral adipose tissue (VAT). Results Metformin treatment significantly improved the glycaemic profile of HFD-fed mice. HFD-fed mice treated with metformin showed a higher abundance of the mucin-degrading bacterium Akkermansia than HFD-fed control mice. In addition, the number of mucin-producing goblet cells was significantly increased by metformin treatment (p Akkermansia muciniphila to HFD-fed mice without metformin significantly enhanced glucose tolerance and attenuated adipose tissue inflammation by inducing Foxp3 regulatory T cells (Tregs) in the VAT. Conclusions Modulation of the gut microbiota (by an increase in the Akkermansia spp. population) may contribute to the antidiabetic effects of metformin, thereby providing a new mechanism for the therapeutic effect of metformin in patients with T2D. This suggests that pharmacological manipulation of the gut microbiota in favour of Akkermansia may be a potential treatment for T2D.


title: Cross-domain Effects of Music and Language Experience on the Representation of Pitch in the Human Auditory Brainstem

abstract: Neural encoding of pitch in the auditory brainstem is known to be shaped by long-term experience with language or music, implying that early sensory processing is subject to experience-dependent neural plasticity. In language, pitch patterns consist of sequences of continuous, curvilinear contours; in music, pitch patterns consist of relatively discrete, stair-stepped sequences of notes. The primary aim was to determine the influence of domain-specific experience (language vs. music) on the encoding of pitch in the brainstem. Frequency-following responses were recorded from the brainstem in native Chinese, English amateur musicians, and English nonmusicians in response to iterated rippled noise homologues of a musical pitch interval (major third; M3) and a lexical tone (Mandarin tone 2; T2) from the music and language domains, respectively. Pitch-tracking accuracy (whole contour) and pitch strength (50 msec sections) were computed from the brainstem responses using autocorrelation algorithms. Pitch-tracking accuracy was higher in the Chinese and musicians than in the nonmusicians across domains. Pitch strength was more robust across sections in musicians than in nonmusicians regardless of domain. In contrast, the Chinese showed larger pitch strength, relative to nonmusicians, only in those sections of T2 with rapid changes in pitch. Interestingly, musicians exhibited greater pitch strength than the Chinese in one section of M3, corresponding to the onset of the second musical note, and two sections within T2, corresponding to a note along the diatonic musical scale. We infer that experience-dependent plasticity of brainstem responses is shaped by the relative saliency of acoustic dimensions underlying the pitch patterns associated with a particular domain.


title: MAPO: mining API usages from open source repositories

abstract: To improve software productivity, when constructing new software systems, developers often reuse existing class libraries or frameworks by invoking their APIs. Those APIs, however, are often complex and not well documented, posing barriers for developers to use them in new client code. To get familiar with how those APIs are used, developers may search the Web using a general search engine to find relevant documents or code examples. Developers can also use a source code search engine to search open source repositories for source files that use the same APIs. Nevertheless, the number of returned source files is often large. It is difficult for developers to learn API usages from a large number of returned results. In order to help developers understand API usages and write API client code more effectively, we have developed an API usage mining framework and its supporting tool called MAPO (for M ining AP I usages from O pen source repositories). Given a query that describes a method, class, or package for an API, MAPO leverages the existing source code search engines to gather relevant source files and conducts data mining. The mining leads to a short list of frequent API usages for developers to inspect. MAPO currently consists of five components: a code search engine, a source code analyzer, a sequence preprocessor, a frequent sequence miner, and a frequent sequence post processor. We have examined the effectiveness of MAPO using a set of various queries. The preliminary results show that the framework is practical for providing informative and succinct API usage patterns.


Techniques in Valvular Heart Surgery


title: A New Deep Neighbor Residual Network for Protein Secondary Structure Prediction

abstract: A protein secondary structure defines the local conformation of the protein's polypeptide backbone, which provides important information for protein 3D structure prediction and protein functions. In this study, a new deep neural network, the deep neighbor residual network (DeepNRN), is proposed for protein secondary structure predictions. The network takes three types of inputs, namely protein sequence features, profile features generated by PSI-BLAST, and profile features generated by HHBlits, and predicts the protein secondary structure in either one of eight states (Q8) or one of three states (Q3). The basic building block of the network, the neighbor residual unit, is designed with two types of short-cut connections that are more general and expressive than residual units in existing residual deep neural networks, yet can still be computed efficiently. In addition, the prediction result of DeepNRN can be refined by a Struct2Struct network to make the result more protein-like. Extensive experimental results on multiple widely used benchmark data sets show that the new DeepNRN-based method outperformed existing methods and obtained the best results across multiple data sets.


title: Control of distributed generation systems - Part II: Load sharing control

abstract: This work is concerned with the control strategy for the parallel operation of distributed generation systems (DGS) in a standalone ac power supply. The proposed control method uses only low-bandwidth data communication signals between each generation system in addition to the locally measurable feedback signals. This is achieved by combining two control methods: droop control method and average power control method. The average power method with slow update rate is used in order to overcome the sensitivity about voltage and current measurement errors. In addition, a harmonic droop scheme for sharing harmonic content of the load currents is proposed based on the voltages and currents control algorithm. Experimental and simulation studies using two parallel three-phase pulsewidth modulation (PWM) inverters are presented to show the effectiveness of the proposed control.


title: Design of Complex Event-Processing IDS in Internet of Things

abstract: With the development of Internet of Things (IoT), there have been more and more services and applications deployed in physical spaces and information systems. Massive number of situation-aware sensors and devices are embedded in IoT environments, which produce huge amounts of data continuously for the IoT systems and platforms. Processing these data stream generated by the IoT networks with different patterns has raised new challenges for the real-time performance of intrusion detection system (IDS) in IoT environments, which has to react quickly to the hacking attacks and malicious activities to IoT. In recent years, Complex Event Processing (CEP) technology provides new solutions in the field of complex pattern identifications and real-time data processing, which can be used to improve the performance of traditional IDS in IoT environments. IDS integrated with CEP can be used to deal with patterns among events and process large volumes of messages with low latency. In this paper we proposed an event-processing IDS architecture in IoT environments on the basis of security requirements analysis for IDS. Then the implementation details for real-time event processing are also proposed, which is developed by Esper, a CEP engine for complex event processing and event series analysis.


title: Pneumatic muscle actuated continuum arms: Modelling and experimental assessment

abstract: This paper presents an improved mode shape function-based 3D dynamic model for pneumatic muscle actuated continuum arms, and validates the model and simulation results through experimental testing. The model also facilitates the direct control of pneumatic muscle actuated continuum arms through the use of input pressure. This is achieved without additional intermediary transformations and does not have singularity problems present in previous models. The proposed arm model uses a new pneumatic muscle actuator (PMA) dynamic model with hysteresis that is capable of modelling both extending and contracting PMAs. The proposed hysteric model is simple, easily adaptable, and validated experimentally. The PMA model can be applied to dynamically model any PMA based system as well as PMA actuated continuum arms utilizing different actuator configurations.


title: Operational Space Control of Constrained and Underactuated Systems

abstract: The operational space formulation (Khatib, 1987), applied to rigid-body manipulators, describes how to decouple task-space and null space dynamics, and write control equations that correspond only to forces at the end-effector or, alternatively, only to motion within the null space. We would like to apply this useful theory to modern humanoids and other legged systems, for manipulation or similar tasks, however these systems present additional challenges due to their underactuated floating bases and contact states that can dynamically change. In recent work, Sentis et al. derived controllers for such systems by implementing a task Jacobian projected into a space consistent with the supporting constraints and underactuation (the so called support consistent reduced Jacobian). Here, we take a new approach to derive operational space controllers for constrained underactuated systems, by first considering the operational space dynamics within projected inverse-dynamics (Aghili, 2005), and subsequently resolving underactuation through the addition of dynamically consistent control torques. Doing so results in a simplified control solution compared with previous results, and importantly yields several new insights into the underlying problem of operational space control in constrained environments: 1) Underactuated systems, such as humanoid robots, cannot in general completely decouple task and null space dynamics. However, 2) there may exist an infinite number of control solutions to realize desired task-space dynamics, and 3) these solutions involve the addition of dynamically consistent null space motion or constraint forces (or combinations of both). In light of these findings, we present several possible control solutions, with varying optimization criteria, and highlight some of their practical consequences.


title: Solving Power Supply Restoration Problems with Planning via Symbolic Model Checking

abstract: The past few years have seen a flurry of new approaches for planning under uncertainty, but their applicability to real-world problems is yet to be established since they have been tested only on toy benchmark problems. To fill this gap, the challenge of solving power supply restoration problems with existing planning tools has recently been issued. This requires the ability to deal with incompletely specified initial conditions, fault conditions, unpredictable action effects, and partial observability in real-time. This paper reports a first response to this nontrivial challenge, using the approach of planning via symbolic model-checking as implemented in the MBP planner. We show how the problem can be encoded in MBP's input language, and report very promising experimental results on a number of significant test cases.


title: Planar Affine Rectification from Change of Scale

abstract: A method for affine rectification of a plane exploiting knowledge of relative scale changes is presented. The rectifying transformation is fully specified by the relative scale change at three non-collinear points or by two pairs of points where the relative scale change is known; the relative scale change between the pairs is not required. The method also allows homography estimation between two views of a planar scene from three point-with-scale correspondences. ::: ::: The proposed method is simple to implement and without parameters; linear and thus supporting (algebraic) least squares solutions; and general, without restrictions on either the shape of the corresponding features or their mutual position. ::: ::: The wide applicability of the method is demonstrated on text rectification, detection of repetitive patterns, texture normalization and estimation of homography from three point-with-scale correspondences.


title: Human milk oligosaccharide consumption by intestinal microbiota

abstract: Human milk oligosaccharides (HMO) constitute the third most abundant class of molecules in breast milk. Since infants lack the enzymes required for milk glycan digestion, this group of carbohydrates passes undigested to the lower part of the intestinal tract, where they can be consumed by specific members of the infant gut microbiota. We review proposed mechanisms for the depletion and metabolism of HMO by two major bacterial genera within the infant intestinal microbiota, Bifidobacterium and Bacteroides


title: Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review

abstract: Convolutional neural networks CNNs have been applied to visual tasks since the late 1980s. However, despite a few scattered applications, they were dormant until the mid-2000s when developments in computing power and the advent of large amounts of labeled data, supplemented by improved algorithms, contributed to their advancement and brought them to the forefront of a neural network renaissance that has seen rapid progression since 2012. In this review, which focuses on the application of CNNs to image classification tasks, we cover their development, from their predecessors up to recent state-of-the-art deep learning systems. Along the way, we analyze 1 their early successes, 2 their role in the deep learning renaissance, 3 selected symbolic works that have contributed to their recent popularity, and 4 several improvement attempts by reviewing contributions and challenges of over 300 publications. We also introduce some of their current trends and remaining challenges.


title: The WDC Gold Standards for Product Feature Extraction and Product Matching

abstract: Finding out which e-shops offer a specific product is a central challenge for building integrated product catalogs and comparison shopping portals. Determining whether two offers refer to the same product involves extracting a set of features (product attributes) from the web pages containing the offers and comparing these features using a matching function. The existing gold standards for product matching have two shortcomings: (i) they only contain offers from a small number of e-shops and thus do not properly cover the heterogeneity that is found on the Web. (ii) they only provide a small number of generic product attributes and therefore cannot be used to evaluate whether detailed product attributes have been correctly extracted from textual product descriptions. To overcome these shortcomings, we have created two public gold standards: The WDC Product Feature Extraction Gold Standard consists of over 500 product web pages originating from 32 different websites on which we have annotated all product attributes (338 distinct attributes) which appear in product titles, product descriptions, as well as tables and lists. The WDC Product Matching Gold Standard consists of over \(75\,000\) correspondences between 150 products (mobile phones, TVs, and headphones) in a central catalog and offers for these products on the 32 web sites. To verify that the gold standards are challenging enough, we ran several baseline feature extraction and matching methods, resulting in F-score values in the range 0.39 to 0.67. In addition to the gold standards, we also provide a corpus consisting of 13 million product pages from the same websites which might be useful as background knowledge for training feature extraction and matching methods.


title: Road terrain detection: Avoiding common obstacle detection assumptions using sensor fusion

abstract: Obstacle detection is a fundamental task for Advanced Driver Assistance Systems (ADAS) and Self-driving cars. Several commercial systems like Adaptive Cruise Controls and Collision Warning Systems depend on them to notify the driver about a risky situation. Several approaches have been presented in the literature in the last years. However, most of them are limited to specific scenarios and restricted conditions. In this paper we propose a robust sensor fusion-based method capable of detecting obstacles in a wide variety of scenarios using a minimum number of parameters. Our approach is based on the spatial-relationship on perspective images provided by a single camera and a 3D LIDAR. Experimental tests have been carried out in different conditions using the standard ROAD-KITTI benchmark, obtaining positive results.


Overview of the Author Identification Task at PAN-2017: Style Breach Detection and Author Clustering


title: Introduction and initial exploration of an Active/Passive Exoskeleton framework for portable assistance

abstract: Assistive devices such as exoskeletons are capable of providing rehabilitative improvement and independence for individuals suffering from musculoskeletal conditions. Typical devices use either active assistance methods such as DC motors or passive methods such as springs. Active methods require a continuous power input, while passive methods are limited by user capability. This work introduces an Active/Passive EXoskeleton (APEX) framework. This device can passively provide continuous assistance, only requiring energy to change the dynamic properties of the passive state. The first prototype (APEX-α) is introduced and tested on six healthy subjects who performed hammer curls. It was found that changes in the passive state of the APEX-α affect the number of curls performed by an individual. By changing the passive state of the exoskeleton, increases in curl count of 65 – 92% were observed. This indicates the potential for such devices to provide assistance to an individual through the use of lightweight, energy efficient active/passive actuators.


title: Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data

abstract: We present a probabilistic framework for studying adversarial attacks on discrete data. Based on this framework, we derive a perturbation-based method, Greedy Attack, and a scalable learning-based method, Gumbel Attack, that illustrate various tradeoffs in the design of attacks. We demonstrate the effectiveness of these methods using both quantitative metrics and human evaluation on various state-of-the-art models for text classification, including a word-based CNN, a character-based CNN and an LSTM. As as example of our results, we show that the accuracy of character-based convolutional networks drops to the level of random selection by modifying only five characters through Greedy Attack.


title: Comparative biomechanical analysis of the rotational shot put technique.

abstract: The study aimed to establish the modalities of the rotational shot put technique of two elite shot putters with substantially different constitutional characteristics. A biomechanical analysis of the technique was carried out using the APAS 3-D kinematic system, whereby a 15-segment model of shot putters was defined by 18 reference points. To enable the calculation of the kinematic and dynamic parameters, independent routines were programmed by the Matlab software. Anthropometric characteristics were established on the basis of 15 variables measured by the International Biological Programme (IBP) procedure. The results of the study revealed some differences between the athletes in terms of their mesomorphic constitutional component, body mass index, circular measures of the lower and upper extremities and the muscular, fat and bone mass. The technique models of both shot putters differ mostly in terms of the following kinematic and dynamic parameters: absolute release velocity, height of release, maximum angular velocity of the elbow of the throwing arm, trajectory of the centre of gravity of the body and the shot, torsional rotation of the shoulder axis relative to the hip axis, maximum force applied to the shot, kinetic energy and the kinetic energy differential of the shot.


A Moderately Large Size Dataset to Learn Visual Affordances of Objects and Tools Using iCub Humanoid Robot


title: G3Di: A Gaming Interaction Dataset with a Real Time Detection and Evaluation Framework

abstract: This paper presents a new, realistic and challenging human interaction dataset for multiplayer gaming, containing synchronised colour, depth and skeleton data. In contrast to existing datasets where the interactions are scripted, G3Di was captured using a novel gamesourcing method so the movements are more realistic. Our detection framework decomposes interactions into the actions of each person to infer the interaction in real time. This modular approach is applicable to a virtual environment where the interaction between people occurs through a computer interface. We also propose an evaluation metric for real time applications, which assesses both the accuracy and latency of the interactions. Experimental results indicate higher complexity of the new dataset in comparison to existing gaming datasets.