title: Information quality assessment for user perception on Indonesia Kreatif web portal

abstract: Web portal's capability in information quality (IQ) will determine its success; especially in complying with user's need. On Indonesia Kreatif web portal, poor IQ can be seen by the low number of visitor penetration and engagement. The aims of this research are to identify users' perception of information quality on Indonesia Kreatif web portal and analyze their causative factors. This research was conducted in three phases: First, the dimensions of IQ was synthesized from numerous previous literatures, followed by an IQ assessment, and finalized with a thorough analysis of their causative factors. The results of this research yielded fourteen IQ dimensions. Additionally, the main causative problems include the low quantity and incomplete published information, coupled with a rigid editor team. For those problems, this research proposed the following recommendations: (1) expand information sources (2) complete the information with mandatory fields, and (3) arrange a routine time schedule from drafting to publishing.


title: PEER INFLUENCE IN RELATION TO ACADEMIC PERFORMANCE AND SOCIALIZATION AMONG ADOLESCENTS: A LITERATURE REVIEW by

abstract: ________Howard__________Nicole_______M._________________________ (Writer) (Last Name) (First) (Initial) Peer Influence In Relation To Academic Performance and Socialization Among__ (Title) Adolescents: A Literature Review______________________________________ School Psychology Dr. Helen Swanson May/2004_____ 30__________ (Graduate Major) (Research Advisor) (Month/Year) (No. of Pages) Publication Manual of the American Psychological Association, Fifth Edition___ (Name of Style Manual Used in This Study) Adolescents have always been exposed to peer influence, but the kinds of peer influence that they encounter have changed tremendously in the past years. Peers can influence everything from what an adolescent chooses to wear to whether or not an adolescent engages in drug related or other delinquent behavior. This is an important topic because if society and education related professionals understand the issues surrounding negative peer influence, they are more likely to prevent it and be more adequately prepared to help a teenager facing negative aspects of peer pressure. This research is a review of the existing literature on the positive and negative aspects of peer influence among adolescents in relation to academic performance and socialization. Directions for future research and implications for professional practice are also included.


title: Massive MIMO channel estimation based on block iterative support detection

abstract: Massive MIMO has become a promising key technology for future 5G wireless communications to increase the channel capacity and link reliability. However, with greatly increased number of transmit antennas at the base station (BS) in massive MIMO systems, the pilot overhead for accurate acquisition of channel state information (CSI) will be prohibitively high. To address this issue, we propose a block iterative support detection (block-ISD) based algorithm for channel estimation to reduce the pilot overhead. The proposed block-ISD algorithm fully exploits the block sparsity inherent in the block-sparse equivalent channel impulse response (CIR) generated by considering the spatial correlations of MIMO channels. Furthermore, unlike conventional greedy compressive sensing (CS) algorithms that rely on prior knowledge of the channel sparsity level, block-ISD relaxes this demanding requirement and is thus more practically appealing. Simulation results demonstrate that block-ISD yields better normalized mean square error (NMSE) performance than classical CS algorithms, and achieve a reduction of 87.5% pilot overhead than conventional channel estimation techniques.


title: Implementation of ECG signal processing and analysis techniques in digital signal processor based system

abstract: This work describes the implementation of wavelet-based de noising algorithm on electrocardiogram (ECG) signal and detection of important parameter such as heart rate, amplitude, timings of the ECG, etc. The algorithm is implemented in DSP (TI TMS320C67x) based starter kit (DSK) with a two-electrode ECG preamplifier. The signal from the ECG preamplifier is acquired through the Codec input of the DSP starter kit. The acquired data is subjected to signal processing techniques such as removal of power line frequencies and high frequency component removal using wavelet-denoising technique. ECG component analysis such as QRS peak detection, heart rate calculation, etc is performed using nonlinear filter technique called first order derivative and moving average filter. The performance of the algorithm (such as speed, calculation efficiency, accuracy, etc) is studied in the DSP environment as well as MATLAB environment for comparison. The results of this study reveal the potentiality of the DSP system for routine clinical use.


title: Automotive Radome Design - Reflection Reduction of Stratified Media

abstract: This letter summarizes the electromagnetic field theory of stratified media and provides an approach for designing antireflection structures. The theory of stratified media has been well known for over half a century. Nevertheless, the calculation of reflection degradation is often done using the assumption of load matching. This assumption turns out to be not accurate enough for efficient design of antireflection media for radomes in the millimeter-wave regime. We introduce an approach for optimized matching in this case and show a tolerance analysis of an example structure (rib profile).


Anonymity for Bitcoin with accountable mixes ( Full version )


title: New SMARCA2 mutation in a patient with Nicolaides–Baraitser syndrome and myoclonic astatic epilepsy

abstract: We report a de novo SMARCA2 missense mutation discovered on exome sequencing in a patient with myoclonic astatic epilepsy, leading to reassessment and identification of Nicolaides-Baraitser syndrome. This de novo SMARCA2 missense mutation c.3721C>G, p.Gln1241Glu is the only reported mutation on exon 26 outside the ATPase domain of SMARCA2 to be associated with Nicolaides-Baraitser syndrome and adds to chromatin remodeling as a pathway for epileptogenesis. © 2016 The Authors. American Journal of Medical Genetics Part A published by Wiley Periodicals, Inc.


title: Electrical performance of high bandwidth memory (HBM) interposer channel in terabyte/s bandwidth graphics module

abstract: Recently, IT trends such as big data, cloud computing, internet of things (IoT), 3D visualization, network, and so on demand terabyte/s bandwidth computer performance in a graphics card. In order to meet these performance, terabyte/s bandwidth graphics module using 2.5D-IC with high bandwidth memory (HBM) technology has been emerged. Due to the difference in scale of interconnect pitch between GPU or HBM and package substrate, the HBM interposer is certainly required for terabyte/s bandwidth graphics module. In this paper, the electrical performance of the HBM interposer channel in consideration of the manufacturing capabilities is analyzed by simulation both the frequency- and time-domain. Furthermore, although the silicon substrate is most widely employed for the HBM interposer fabrication, the organic and glass substrate are also proposed to replace the high cost and high loss silicon substrate. Therefore, comparison and analysis of the electrical performance of the HBM interposer channel using silicon, organic, and glass substrate are conducted.


title: Many apples a day keep the blues away--daily experiences of negative and positive affect and food consumption in young adults.

abstract: Objectives ::: Prior research has focused on the association between negative affect and eating behaviour, often utilizing laboratory or cross-sectional study designs. These studies have inherent limitations, and the association between positive affect and eating behaviour remains relatively unexplored. Therefore, the objective of this study was to investigate the bidirectional relationships between daily negative and positive affective experiences and food consumption in a naturalistic setting among healthy young adults. ::: ::: Design ::: Daily diary study across 21 days (microlongitudinal, correlational design). ::: ::: Methods ::: A total of 281 young adults with a mean age of 19.9 (±1.2) years completed an Internet-based daily diary for 21 consecutive days. Each day they reported their negative and positive affect, and their consumption of five specific foods. Hierarchical linear modelling was used to test same-day associations between daily affect and food consumption, and next-day (lagged) associations to determine directionality. Moderating effects of BMI and gender were also examined in exploratory analyses. ::: ::: Results ::: Analyses of same-day within-person associations revealed that on days when young adults experienced greater positive affect, they reported eating more servings of fruit (p = .002) and vegetables (p < .001). Results of lagged analysis showed that fruits and vegetables predicted improvements in positive affect the next day, suggesting that healthy foods were driving affective experiences and not vice versa. Meaningful changes in positive affect were observed with the daily consumption of approximately 7–8 servings of fruit or vegetables. ::: ::: Conclusions ::: Eating fruit and vegetables may promote emotional well-being among healthy young adults. ::: ::: Statement of contribution ::: What is already known on this subject? Laboratory and cross-sectional studies have found a strong link between experiences of negative affect and food consumption. These studies generally show that people eat more food and less healthy food when experiencing negative affect; however, there is less evidence of this association in a natural setting. Moreover, the association between positive affect and eating remains relatively unexplored. Some studies have found stronger links between negative affect and unhealthy food consumption among women and individuals with higher BMI. Conversely, the foods people eat may influence their affective experiences. Cross-sectional research has shown that a diet high in fruits and vegetables is associated with a lower lifetime prevalence of depression and anxiety, but it is not known whether healthy food consumption may also influence affective experiences on a day-to-day basis. ::: ::: What does this study add? Using online daily diaries for three weeks, we found strong relationships between daily positive affect and fruit and vegetable consumption. Lagged analyses showed that fruit and vegetable consumption predicted improvements in positive affect the next day, and not vice versa. Gender and BMI were not major factors in these associations. Fruit and vegetable consumption may promote feelings of well-being among healthy young adults.


title: Taxi Dispatch with Real-Time Sensing Data in Metropolitan Areas: A Receding Horizon Control Approach

abstract: Traditional transportation systems in metropolitan areas often suffer from inefficiencies due to uncoordinated actions as system capacity and traffic demand change. With the pervasive deployment of networked sensors in modern vehicles, large amounts of information regarding traffic demand and system status can be collected in real-time. This information provides opportunities to perform various types of control and coordination for large scale intelligent transportation systems. In this paper, we present a novel receding horizon control (RHC) framework to dispatch taxis, which combines highly spatiotemporally correlated demand/supply models and real-time GPS location and occupancy information. The objectives include reducing taxi idle driving distance and matching spatiotemporal ratio between demand and supply for service quality. Moreover, our RHC framework is compatible with different predictive models and optimization problem formulations. This compatibility property allows us to model disruptive passenger demands and traffic conditions into a robust optimization problem. Extensive trace driven analysis with a real taxi data set from San Francisco shows that our solution reduces the average total idle distance by 52%, and reduces the total supply demand ratio error across the city by up to 45%.


title: Perpendicular Strut Injection of Hyaluronic Acid Filler for Deep Wrinkles

abstract: Various techniques of hyaluronic acid (HA) filler injection have been developed, such as the serial puncture, linear threading, fanning, cross-hatching, and tower technique.1,2 An optimal injection technique depends on the filling agent, the area and target to be corrected, and preference of the surgeon. The differential use of these techniques is a key to maximizing aesthetic results. Intradermal injection is a basic approach to treat deep wrinkles; however, occasionally, unfavorable results are encountered, such as conspicuous ridging or beading on or adjacent to the target wrinkle3 and negligible effects on wrinkles. Deep wrinkles/grooves accompanied with ptosis of the soft tissue are often not fully corrected by a single procedure and may need to be treated by a combination of intradermal and subdermal injections. To provide more consistent intradermal structural support, we tested an intradermal injection technique to place “multiple perpendicular struts” of HA. Herein, we report the clinical benefit suggested by this study.


title: Team Teaching Buddies: Student Teaching in the Era of edTPA

abstract: Student teachers are typically placed in a classroom with a single mentor teacher. Although there have been many changes in education within the past decades, including new teacher certification requirements, there has been little modification in this student teaching structure. It is time to review student teaching programs to find the best way to insure that student teacher candidates have an optimal experience. This study examines a buddy approach wherein two student teachers work as a team in a classroom with a single mentor teacher to determine if this is a useful paradigm for helping students through the certification process.


title: Automatic Face Replacement in Video Based on 2D Morphable Model

abstract: This paper presents an automatic face replacement approach in video based on 2D morphable model. Our approach includes three main modules: face alignment, face morph, and face fusion. Given a source image and target video, the Active Shape Models (ASM) is adopted to source image and target frames for face alignment. Then the source face shape is warped to match the target face shape by a 2D morphable model. The color and lighting of source face are adjusted to keep consistent with those of target face, and seamlessly blended in the target face. Our approach is fully automatic without user interference, and generates natural and realistic results.


title: Bayesian statistical methods for genetic association studies

abstract: Bayesian statistical methods have recently made great inroads into many areas of science, and this advance is now extending to the assessment of association between genetic variants and disease or other phenotypes. We review these methods, focusing on single-SNP tests in genome-wide association studies. We discuss the advantages of the Bayesian approach over classical (frequentist) approaches in this setting and provide a tutorial on basic analysis steps, including practical guidelines for appropriate prior specification. We demonstrate the use of Bayesian methods for fine mapping in candidate regions, discuss meta-analyses and provide guidance for refereeing manuscripts that contain Bayesian analyses.


Cooking up Food Embeddings Understanding Flavors in the Recipe-Ingredient Graph


title: Prevalence, Patterns, and Predictors of Yoga Use: Results of a U.S. Nationally Representative Survey.

abstract: INTRODUCTION ::: The purpose of this study was to investigate the prevalence, patterns, and predictors of yoga use in the U.S. general population. ::: ::: ::: METHODS ::: Using cross-sectional data from the 2012 National Health Interview Survey Family Core, Sample Adult Core, and Adult Complementary and Alternative Medicine questionnaires (N=34,525), weighted frequencies for lifetime and 12-month prevalence of yoga use and patterns of yoga practice were analyzed. Using logistic regression analyses, sociodemographic predictors of lifetime yoga use were analyzed. Analyses were conducted in 2015. ::: ::: ::: RESULTS ::: Lifetime and 12-month prevalence of yoga use were 13.2% and 8.9%, respectively. Compared with nonpractitioners, lifetime yoga practitioners were more likely female, younger, non-Hispanic white, college educated, higher earners, living in the West, and of better health status. Among those who had practiced in the past 12 months, 51.2% attended yoga classes, 89.9% used breathing exercises, and 54.9% used meditation. Yoga was practiced for general wellness or disease prevention (78.4%), to improve energy (66.1%), or to improve immune function (49.7%). Back pain (19.7%), stress (6.4%), and arthritis (6.4%) were the main specific health problems for which people practiced yoga. ::: ::: ::: CONCLUSIONS ::: About 31 million U.S. adults have ever used yoga, and about 21 million practiced yoga in the past 12 months. Disease prevention and back pain relief were the most important health reasons for yoga practice. Yoga practice is associated with age, gender, ethnicity, SES, and health status.


title: A 10Gb/s two dimensional scanning eye opening monitor in 0.18um CMOS process

abstract: An eye-opening monitor (EOM) capable of providing qualitative two-dimensional (2D) map of the eye opening of a 10Gbps signal is designed. The EOM is designed in 0.18 um CMOS process. The 2D map of the eye opening is obtained by scanning the eye opening with a phase-offset clock to obtain the different best-fit mask sizes. In the present design the clock is provided from an external source. The design operates with a 1.8V supply and consumes 95 mA of current.


title: Personalized Faceted Navigation in the Semantic Web

abstract: This paper presents the prototype of an adaptive faceted semantic browser - Factic. Factic implements our novel method of navigation in open information spaces represented by ontologies based on an enhanced faceted browser with support for dynamic facet generation and adaptation based on user characteristics. It is developed as part of a modular framework that supports personalization based on an automatically acquired ontological user model. We describe software tool design and implementation together with discussion on several problems mostly related to the general immaturity of current Semantic Web solutions.


title: Durable write cache in flash memory SSD for relational and NoSQL databases

abstract: In order to meet the stringent requirements of low latency as well as high throughput, web service providers with large data centers have been replacing magnetic disk drives with flash memory solid-state drives (SSDs). They commonly use relational and NoSQL database engines to manage OLTP workloads in the warehouse-scale computing environments. These modern database engines rely heavily on redundant writes and frequent cache flushes to guarantee the atomicity and durability of transactional updates. This has become a serious bottleneck of performance in both relational and NoSQL database engines. This paper presents a new SSD prototype called DuraSSD equipped with tantalum capacitors. The tantalum capacitors make the device cache inside DuraSSD durable, and additional firmware features of DuraSSD take advantage of the durable cache to support the atomicity and durability of page writes. It is the first time that a flash memory SSD with durable cache has been used to achieve an order of magnitude improvement in transaction throughput without compromising the atomicity and durability. Considering that the simple capacitors increase the total cost of an SSD no more than one percent, DuraSSD clearly provides a cost-effective means for transactional support. DuraSSD is also expected to alleviate the problem of high tail latency by minimizing write stalls.


title: Machine learning inspired energy-efficient hybrid precoding for mmWave massive MIMO systems

abstract: Hybrid precoding is a promising technique for mmWave massive MIMO systems, as it can considerably reduce the number of required radio-frequency (RF) chains without obvious performance loss. However, most of the existing hybrid precoding schemes require a complicated phase shifter network, which still involves high energy consumption. In this paper, we propose an energy-efficient hybrid precoding architecture, where the analog part is realized by a small number of switches and inverters instead of a large number of high-resolution phase shifters. Our analysis proves that the performance gap between the proposed hybrid precoding architecture and the traditional one is small and keeps constant when the number of antennas goes to infinity. Then, inspired by the cross-entropy (CE) optimization developed in machine learning, we propose an adaptive CE (ACE)-based hybrid precoding scheme for this new architecture. It aims to adaptively update the probability distributions of the elements in hybrid precoder by minimizing the CE, which can generate a solution close to the optimal one with a sufficiently high probability. Simulation results verify that our scheme can achieve the near-optimal sum-rate performance and much higher energy efficiency than traditional schemes.


title: Framing-effects approach: A theoretical and methodological critique

abstract: Abstract The article deals with research on framing effects. First, I will start with classifying different approaches on framing. Subsequently, I will provide a definition of the concepts of frame, schema and framing, expand on framing research conducted so far – both theoretically and operationally. Having this equipment at hand, I will initiate a discussion on studies of framing-effects in terms of theory, methods and empirical results. This discussion leads to the conclusion that studies on framing effects are insufficiently concerned with the more recent psychological constructs and theories. In merely focusing on the activation of schemata, most studies ignore the more elaborate types of framing-effects. Therefore, several empirical questions remain unanswered and some methodical chances seem to be wasted.


title: Solving Markov Decision Processes via Simulation

abstract: This chapter presents an overview of simulation-based techniques useful for solving Markov decision processes (MDPs). MDPs model problems of sequential decision-making under uncertainty, in which decisions made in each state collectively affect the trajectory of the states visited by the system over a time horizon of interest. Traditionally, MDPs have been solved via dynamic programming (DP), which requires the transition probability model that is difficult to derive in many realistic settings. The use of simulation for solving MDPs allows us to bypass the transition probability model and solve large-scale MDPs considered intractable to solve by traditional DP. The simulation-based methodology for solving MDPs, which like DP is also rooted in the Bellman equations, goes by names such as reinforcement learning, neuro-DP, and approximate or adaptive DP. We begin with a description of algorithms for infinite-horizon discounted reward MDPs, followed by the same for infinite-horizon average reward MDPs. Then we present a discussion on finite-horizon MDPs. For each problem considered, we present a step-by-step description of a selected group of algorithms. In making this selection, we have attempted to blend the old and the classical with more recent developments. Finally, after touching upon extensions and convergence theory, we conclude with a brief summary of some applications and directions for future research.


title: POP-TRAFFIC: a novel fuzzy neural approach to road traffic analysis and prediction

abstract: Although much research has been done over the decades on the formulation of statistical regression models for road traffic relationships, they have been largely unsuitable due to the complexity of traffic characteristics. Traffic engineers have resorted to alternative methods such as neural networks, but despite some promising results, the difficulties in their design and implementation remain unresolved. In addition, the opaqueness of trained networks prevents understanding the underlying models. Fuzzy neural networks, which combine the complementary capabilities of both neural networks and fuzzy logic, thus constitute a more promising technique for modeling traffic flow. This paper describes the application of a specific class of fuzzy neural network known as the pseudo outer-product fuzzy neural network using the truth-value-restriction method (POPFNN-TVR) for short-term traffic flow prediction. The obtained results highlight the capability of POPFNN-TVR in fuzzy knowledge extraction and generalization from input data as well its high degree of prediction capability as compared to traditional feedforward neural networks using backpropagation learning.


title: The Social Nature of Agile Teams

abstract: Agile methodologies represent a 'people' centered approach to delivering software. This paper investigates the social processes that contribute to their success. Qualitative grounded theory was used to explore socio-psychological experiences in agile teams, where agile teams were viewed as complex adaptive socio-technical systems. Advances in systems theory suggest that human agency changes the nature of a system and how it should be studied. In particular, end-goals and positive sources of motivation, such as pride, become important. Research included the questions: How do agile practices structure and mediate the experience of individuals developing software? And in particular, how do agile practices mediate the interaction between individuals and the team as a whole? Results support an understanding of how social identity and collective effort are supported by agile methods.


title: Trade Theory with Numbers: Quantifying the Consequences of Globalization

abstract: We review a recent body of theoretical work that aims to put numbers on the consequences of globalization. A unifying theme of our survey is methodological. We rely on gravity models and demonstrate how they can be used for counterfactual analysis. We highlight how various economic considerations--market structure, firm-level heterogeneity, multiple sectors, intermediate goods, and multiple factors of production--affect the magnitude of the gains from trade liberalization. We conclude by discussing a number of outstanding issues in the literature as well as alternative approaches for quantifying the consequences of globalization.


title: Lattice-Based Recurrent Neural Network Encoders for Neural Machine Translation

abstract: Neural machine translation (NMT) heavily relies on word-level modelling to learn semantic representations of input sentences. However, for languages without natural word delimiters (e.g., Chinese) where input sentences have to be tokenized first, conventional NMT is confronted with two issues: 1) it is difficult to find an optimal tokenization granularity for source sentence modelling, and 2) errors in 1-best tokenizations may propagate to the encoder of NMT. To handle these issues, we propose word-lattice based Recurrent Neural Network (RNN) encoders for NMT, which generalize the standard RNN to word lattice topology. The proposed encoders take as input a word lattice that compactly encodes multiple tokenizations, and learn to generate new hidden states from arbitrarily many inputs and hidden states in preceding time steps. As such, the word-lattice based encoders not only alleviate the negative impact of tokenization errors but also are more expressive and flexible to embed input sentences. Experiment results on Chinese-English translation demonstrate the superiorities of the proposed encoders over the conventional encoder.


An Economic Framework of Microbial Trade


title: Estimating Android applications' CPU energy usage via bytecode profiling

abstract: Optimizing the energy efficiency of mobile applications can greatly increase user satisfaction. However, developers lack easily applied tools for estimating the energy consumption of their applications. This paper proposes a new approach, eCalc, that is lightweight in terms of its developer requirements and provides code-level estimates of energy consumption. The approach achieves this using estimation techniques based on program analysis of the mobile application. In evaluation, eCalc is able to estimate energy consumption within 9.5% of the ground truth for a set of mobile applications. Additionally, eCalc provides useful and meaningful feedback to the developer that helps to characterize energy consumption of the application.


title: Edge detection using adaptive thresholding and Ant Colony Optimization

abstract: In this paper, we present an approach for edge detection using adaptive thresholding and Ant Colony Optimization (ACO) algorithm to obtain a well-connected image edge map. Initially, the edge map of the image is obtained using adaptive thresholding. The end points obtained using adaptive threshoding are calculated and the ants are placed at these points. The movement of the ants is guided by the local variation in the pixel intensity values. The probability factor of only undetected neighboring pixels is taken into consideration while moving an ant to the next probable edge pixel. The two stopping rules are implemented to prevent the movement of ants through the pixel already detected using the adoptive thresholding. The results are qualitative analyze using Shanon's Entropy function.


title: Design and analysis of a simple D flip-flop based sequential logic circuits for QCA implementation

abstract: Quantum-dot Cellular Automata (QCA) is one of the emerging computing paradigms. Its advantages such as smaller size, lower power consumption and faster speed are very attractive. QCA performs highly dense computing that could be realized in a variety of material systems. It is presently being investigated as an alternative to CMOS VLSI. In conventional digital systems the information is transferred from one place to another by means of electrical current, while as QCA cells transfer information by propagating a polarization state. This paper proposes a detailed design and simulation of a simple D flip-flop based sequential logic circuits like shift register, ring counter and modulo n counter circuits for quantum-dot cellular automata. The proposed designs are based on the D-type flip-flop (DFF) device. A QCA binary wire with four clocking zones can be used to implement a DFF. The aim is to maximize the circuit density and focus on a layout that is minimal in its use of cells.


title: An X11 Toolkit Based on the Tcl Language

abstract: This paper describes a new toolkit for X11 called Tk. The overall functions provided by Tk are similar to those of the standard toolkit Xt. However, Tk is implemented using Tcl, a lightweight interpretive command language. This means that Tk’s functions are available not just from C code compiled into the application but also via Tcl commands issued dynamically while the application runs. Tcl commands are used for binding keystrokes and other events to application-specific actions, for creating and configuring widgets, and for dealing with geometry managers and the selection. The use of an interpretive language means that any aspect of the user interface may be changed dynamically while an application executes. It also means that many interesting applications can be created without writing any new C code, simply by writing Tcl scripts for existing applications. Furthermore, Tk provides a special send command that allows any Tk-based application to invoke Tcl commands in any other Tk-based application. Send allows applications to communicate in more powerful ways than a selection mechanism and makes it possible to replace monolithic applications with collections of reusable tools.


title: Assessing muscle fatigue by measuring the EMG of biceps brachii

abstract: The purpose of this study is to measure the surface electromyography (sEMG) to assess the muscle fatigue endurance when doing the forearm contraction exercise. There were six subjects (24.4±1.35 years, height 176.8±7.41cm and weight 72.2 ± 2.48 kg) who completed endurance repetitions forearm exercise of lifting 12 lb dumbbell and the sEMG signal of each subject's biceps brachii were recorded. Signal of maximal voluntary contraction (MVC) were measured before and after exercise. Median (MedF) and mean (MF) spectral frequency of MVC were calculated for each percentage of repetition. The calculated results indicated that the later exercise (endurance concentric contraction) indicated the muscle fatigue between 90% and 100% (muscle fatigue phase) of time percentage and the linear regression of spectral frequency (MedF, MF) indicated that the line equations can be used to assess the tendencies of muscle fatigue.


title: Translating Algorithms to Handle Fully Homomorphic Encrypted Data on the Cloud

abstract: Cloud provides large shared resources where users (or foundations) can enjoy the facility of storing data or executing applications. In spite of gaining convenience of large resources, storing critical data in cloud is not secured. Hence, cloud security is an important issue to make cloud useful at the enterprise level. Data encryption is a primary solution for providing confidentiality to sensitive data. However, processing of encrypted data requires extra overhead, since repeated encryption-decryption need to be performed for every simple processing on encrypted data. Hence, direct processing on encrypted cloud data is advantageous, which is supported by homomorphic encryption schemes. Fully Homomorphic Encryption (FHE) provides a method of performing arbitrary operations directly on encrypted data. This seemingly magical idea is a welcome to cloud computing. However, there are several challenges to overcome for making the technology viable in practical applications. In this paper, we make an initial effort to highlight the problem of translating algorithms that can run on unencrypted or normal data to those which operate on encrypted data. Here, we show that although FHE provides the ability to perform arbitrary computations, its complete benefit can only be obtained if they also allow to execute arbitrary algorithms on encrypted data. In this pursuit, we provide techniques to translate basic operators (like bitwise, arithmetic and relational operators), which are used for implementation of algorithms in any high level language like C. Subsequently, we address decision making and loop handling and related data structures which are vital to realize when the controlling variables are encrypted. Since, termination is a major challenge while handling encrypted data, we propose a method of handling termination by message passing between server and client.


title: Object Detection for Cattle Gait Tracking

abstract: Lameness in cattle is a health issue where gait is modified to minimise pain. Cattle are currently visually assessed for locomotion score, which provides the degree of lameness for individual animals. This subjective method is costly in terms of labour, and its level of accuracy and ability to detect small changes in locomotion that is critical for early detection of lameness and associated intervention. Current automatic lameness detection systems found in literature have not yet met the ultimate goal of widespread commercial adoption. We present a sensor configuration to record cattle kinematics towards automatic lameness detection. This configuration features four Time of Flight sensors to view cattle from above and from one side as they exit an automatic rotary milking dairy. Two dimensional near infrared images sampled from 223 cows passing through the system were used to train a Faster R-CNN to detect hooves (F1-score = 0.90) and carpal/tarsal joints (Fl-score = 0.85). The depth images were used to project these detected key points into Cartesian space where they were tracked to obtain individual trajectories per limb. The results show that kinematic gait features can be successfully obtained as a first and important step towards objective, accurate, automatic lameness detection.


title: Active Tactile Transfer Learning for Object Discrimination in an Unstructured Environment Using Multimodal Robotic Skin

abstract: In this paper, we propose a probabilistic active tactile transfer learning (ATTL) method to enable robotic systems to exploit their prior tactile knowledge while discriminating among objects via their physical properties (surface texture, stiffness, and thermal conductivity). Using the proposed method, the robot autonomously selects and exploits its most relevant prior tactile knowledge to efficiently learn about new unknown objects with a few training samples or even one. The experimental results show that using our proposed method, the robot successfully discriminated among new objects with 72% discrimination accuracy using only one training sample (on-shot-tactile-learning). Furthermore, the results demonstrate that our method is robust against transferring irrelevant prior tactile knowledge (negative tactile knowledge transfer).


title: Deep learning for topology optimization design.

abstract: Generative modeling techniques are being rapidly developed in the field of deep learning, and they have been applied to topology optimization. The variational autoencoder (VAE) is a generative modeling technology that extends the autoencoder to generate new images with a limited latent space. We modified the basic VAE structure to encode optimization conditions and decode latent variables for topology optimization design. The modified VAE could efficiently predict the optimized structure after topology optimization. However, it was difficult to train the neural network to predict a very detailed structure. The generative adversarial network (GAN) is another generative modeling technique for generating images and is implemented by a system of two neural networks competing with each other to make realistic synthetic data. We applied GAN to obtain more detailed optimized results from the original design. The proposed methodology of using two generative modeling techniques for deep learning is expected to significantly increase the efficiency of topology optimization.


title: AskNow: A Framework for Natural Language Query Formalization in SPARQL

abstract: Natural Language Query Formalization involves semantically parsing queries in natural language and translating them into their corresponding formal representations. It is a key component for developing question-answering QA systems on RDF data. The chosen formal representation language in this case is often SPARQL. In this paper, we propose a framework, called AskNow, where users can pose queries in English to a target RDF knowledge base e.g.i?źDBpedia, which are first normalized into an intermediary canonical syntactic form, called Normalized Query Structure NQS, and then translated into SPARQL queries. NQS facilitates the identification of the desire or expected output information and the user-provided input information, and establishing their mutual semantic relationship. At the same time, it is sufficiently adaptive to query paraphrasing. We have empirically evaluated the framework with respect to the syntactic robustness of NQS and semantic accuracy of the SPARQL translator on standard benchmark datasets.


title: COBWEB: a robust map update system using GPS trajectories

abstract: The accuracy and completeness of a digital map plays a critical role in determining the quality of most location-based services. Unfortunately, road networks change frequently. Consequently, we study the issue of automatic map update in this paper. We propose a system called COBWEB which takes all the unmatched trajectories as input and generates the missing road segments with both the geometry properties and topology features well preserved. We conduct a comprehensive experimental study via real trajectory data generated by roughly 15,000 taxis in Singapore within a 5-month period. Compared with existing work, COBWEB demonstrates a better and more stable performance and a stronger resilience to various sampling rates and data sizes.


title: TAXONOMY OF CLOUD SECURITY

abstract: Cloud computing is envisioned as the next generation architecture for IT Enterprises, and has proliferated itself due to the advantages it provides.Cloud computing provides solutions for carrying out efficient, scalable and low cost computing. The pay per usage concept of Cloud computing increases the resource utilisation of a vendor’s computing power and resources; at the same time,it results in reduced hardware costs forits users. It also provides access mobility, easier maintenance, scalabilityand operability in terms of its management and usage. Because of the facilities and solutions it provides to the industry for the next generation computing, it is vulnerable to a variety of known and unknown attacks from attackers.Hence, securinga Cloud environment is a critical problem that needs urgent attention.This articlefocuses on a taxonomy of possible attacks on a Cloud environment and a taxonomy of the defence .The attack taxonomy describes existing threats on Cloud security, and the defence taxonomy gives a classification of the various counter measures that can be taken to protect the Cloud environment from such attacks. The aim of this article is to provideresearchers, academicians and industry with a better understanding of existing attacks and defence mechanisms on Cloud security.This is to provide a clearvision of the challenges that should be worked onto ensure next generation security forCloud computing.


title: ANFIS Approach for Navigation of Mobile Robots

abstract: This paper, discusses about navigation control of mobile robot using adaptive neuro-fuzzy inference system (ANFIS) in a real word dynamic environment. In the ANFIS controller after the input layer there is a fuzzy layer and rest of the layers are neural network layers. The adaptive neuro-fuzzy hybrid system combines the advantages of fuzzy logic system, which deal with explicit knowledge that can be explained and understood, and neural network, which deal with implicit knowledge, which can be acquired by learning. The inputs to fuzzy logic layer are front obstacle distance, left obstacle distance, right obstacle distance and target steering. A learning algorithm based on neural network technique has been developed to tune the parameters of fuzzy membership functions, which smooth the trajectory generated by the fuzzy logic system. Using the developed ANFIS controller, the mobile robots are able to avoid static and dynamic obstacles, and reach the target successfully in cluttered environments. The experimental results agree well with the simulation results, proves the authenticity of the theory developed.


title: Electrical flows, laplacian systems, and faster approximation of maximum flow in undirected graphs

abstract: We introduce a new approach to computing an approximately maximum s-t flow in a capacitated, undirected graph. This flow is computed by solving a sequence of electrical flow problems. Each electrical flow is given by the solution of a system of linear equations in a Laplacian matrix, and thus may be approximately computed in nearly-linear time. ::: Using this approach, we develop the fastest known algorithm for computing approximately maximum s-t flows. For a graph having n vertices and m edges, our algorithm computes a (1-\epsilon)-approximately maximum s-t flow in time \tilde{O}(mn^{1/3} \epsilon^{-11/3}). A dual version of our approach computes a (1+\epsilon)-approximately minimum s-t cut in time \tilde{O}(m+n^{4/3}\eps^{-8/3}), which is the fastest known algorithm for this problem as well. Previously, the best dependence on m and n was achieved by the algorithm of Goldberg and Rao (J. ACM 1998), which can be used to compute approximately maximum s-t flows in time \tilde{O}(m\sqrt{n}\epsilon^{-1}), and approximately minimum s-t cuts in time \tilde{O}(m+n^{3/2}\epsilon^{-3}).


title: Hybrid prediction model with missing value imputation for medical data

abstract: Proposed novel hybrid prediction model with missing value imputation.HPM-MI has improved accuracy, sensitivity, specificity, kappa and ROC on 3 datasets.The best accuracy is achieved for diabetes, hepatitis, and breast cancer datasets.MVI is one of the important step of proposed model. Accurate prediction in the presence of large number of missing values in the data set has always been a challenging problem. Most of hybrid models to address this challenge have either deleted the missing instances from the data set (popularly known as case deletion) or have used some default way to fill the missing values. This paper, presents a novel hybrid prediction model with missing value imputation (HPM-MI) that analyze various imputation techniques using simple K-means clustering and apply the best one to a data set. The proposed hybrid model is the first one to use combination of K-means clustering with Multilayer Perceptron. K-means clustering is also used to validate class labels of given data (incorrectly classified instances are deleted i.e. pattern extracted from original data) before applying classifier. The proposed system has significantly improved data quality by use of best imputation technique after quantitative analysis of eleven imputation approaches. The efficiency of proposed model as predictive classification system is investigated on three benchmark medical data sets namely Pima Indians Diabetes, Wisconsin Breast Cancer, and Hepatitis from the UCI Repository of Machine Learning. In addition to accuracy, sensitivity, specificity; kappa statistics and the area under ROC are also computed. The experimental results show HPM-MI has produced accuracy, sensitivity, specificity, kappa and ROC as 99.82%, 100%, 99.74%, 0.996 and 1.0 respectively for Pima Indian Diabetes data set, 99.39%, 99.31%, 99.54%, 0.986, and 1.0 respectively for breast cancer data set and 99.08%, 100%, 96.55%, 0.978 and 0.99 respectively for Hepatitis data set. Results are best in comparison with existing methods. Further, the performance of our model is measured and analyzed as function of missing rate and train-test ratio using 2D synthetic data set and Wisconsin Diagnostics Breast Cancer Data Sets. Results are promising and therefore the proposed model will be very useful in prediction for medical domain especially when numbers of missing value are large in the data set.


title: What monetary rewards can and cannot do: How to show employees the money

abstract: Abstract Monetary rewards can be a very powerful determinant of employee motivation and performance which, in turn, can lead to important returns in terms of firm-level performance. However, monetary rewards do not always lead to these desirable outcomes. We discuss in this installation of Human Performance what monetary rewards can and cannot do, and reasons why, in terms of improving employee performance. Also, we offer research-based recommendations including the following five general principles to guide the design of successful monetary reward systems: (1) define and measure performance accurately, (2) make rewards contingent on performance, (3) reward employees in a timely manner, (4) maintain justice in the reward system, and (5) use monetary and nonmonetary rewards. In addition, we offer specific research-based guidelines for implementing each of the five principles. In short, our article summarizes research-based findings and offers recommendations that will allow managers and other organizational decision makers to understand when and why monetary reward systems are likely to be successful in terms of enhancing employee motivation and performance.


title: A Survey of Monte Carlo Tree Search Methods

abstract: Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work.


title: Decision tree supported substructure prediction of metabolites from GC-MS profiles

abstract: Gas chromatography coupled to mass spectrometry (GC-MS) is one of the most widespread routine technologies applied to the large scale screening and discovery of novel metabolic biomarkers. However, currently the majority of mass spectral tags (MSTs) remains unidentified due to the lack of authenticated pure reference substances required for compound identification by GC-MS. Here, we accessed the information on reference compounds stored in the Golm Metabolome Database (GMD) to apply supervised machine learning approaches to the classification and identification of unidentified MSTs without relying on library searches. Non-annotated MSTs with mass spectral and retention index (RI) information together with data of already identified metabolites and reference substances have been archived in the GMD. Structural feature extraction was applied to sub-divide the metabolite space contained in the GMD and to define the prediction target classes. Decision tree (DT)-based prediction of the most frequent substructures based on mass spectral features and RI information is demonstrated to result in highly sensitive and specific detections of sub-structures contained in the compounds. The underlying set of DTs can be inspected by the user and are made available for batch processing via SOAP (Simple Object Access Protocol)-based web services. The GMD mass spectral library with the integrated DTs is freely accessible for non-commercial use at http://gmd.mpimp-golm.mpg.de/. All matching and structure search functionalities are available as SOAP-based web services. A XML + HTTP interface, which follows Representational State Transfer (REST) principles, facilitates read-only access to data base entities.


Soccer Event Detection


title: Individual differences in need for cognitive closure.

abstract: This article introduces an individual-difference measure of the need for cognitive closure. As a dis-positional construct, the need for cognitive closure is presently treated as a latent variable manifestedthrough several different aspects, namely, desire for predictability, preference for order and structure,discomfort with ambiguity, decisiveness, and close-mindedness. This article presents psychometricwork on the measure as well as several validation studies including (a) a "known-groups" discrimi-nation between populations assumed to differ in their need for closure, (b) discriminant and con-vergent validation with respect to related personality measures, and (c) replication of effects obtainedwith situational inductions of the need for closure. The present findings suggest that the Need forClosure Scale is a reliable and valid instrument of considerable potential utility in future "motivatedsocial cognition" research.


title: Modelling and simulation of complex sociotechnical systems: envisioning and analysing work environments

abstract: Accurate comprehension and analysis of complex sociotechnical systems is a daunting task. Empirically examining, or simply envisioning the structure and behaviour of such systems challenges traditional analytic and experimental approaches as well as our everyday cognitive capabilities. Computer-based models and simulations afford potentially useful means of accomplishing sociotechnical system design and analysis objectives. From a design perspective, they can provide a basis for a common mental model among stakeholders, thereby facilitating accurate comprehension of factors impacting system performance and potential effects of system modifications. From a research perspective, models and simulations afford the means to study aspects of sociotechnical system design and operation, including the potential impact of modifications to structural and dynamic system properties, in ways not feasible with traditional experimental approaches. This paper describes issues involved in the design and use of such models and simulations and describes a proposed path forward to their development and implementation. ::: ::: Practitioner Summary: The size and complexity of real-world sociotechnical systems can present significant barriers to their design, comprehension and empirical analysis. This article describes the potential advantages of computer-based models and simulations for understanding factors that impact sociotechnical system design and operation, particularly with respect to process and occupational safety.


The Effect of Immediate Environment on Children's Tendency to Reflect While Solving Problems.


Enhancement of Underwater Images by Image Fusion Using Wavelet Decomposition


title: Adaptive scale based entropy-like estimator for robust fitting

abstract: In this paper, we propose a novel robust estimator, called ASEE (Adaptive Scale based Entropy-like Estimator) which minimizes the entropy of inliers. This estimator is based on IKOSE (Iterative Kth Ordered Scale Estimator) and LEL (Least Entropy-Like Estimator). Unlike LEL, ASEE only considers inliers' entropy while excluding outliers, which makes it very robust in parametric model estimation. Compared with other robust estimators, ASEE is simple and computationally efficient. From the experiments on both synthetic and real-image data, ASEE is more robust than several state-of-the-art robust estimators, especially in handling extreme outliers.


title: A systematic review of predictors of maintenance of normotension after withdrawal of antihypertensive drugs

abstract: Abstract Background: The identification and treatment of hypertension in the general community has contributed to the reduction in strokes and coronary heart disease observed during the past 30 years. However, concerns have arisen that some patients may be receiving unnecessary antihypertensive drug therapy leading to wasted resources and the potential for adverse drug effects. Once therapy has been started, treating physicians have difficulty in selecting patients for withdrawal and have concerns regarding patient safety and their own legal liability. Procedures: This study reviews and consolidates information from published studies to identify known predictors of the successful maintenance of normotension after antihypertensive drug withdrawal. The predictors were identified by determining the proportion of subjects with various baseline characteristics who remained normotensive while off medication for at least 12 months. From these data we have developed a clinical algorithm to help identify patients in whom antihypertensive drug withdrawal might be considered. This may assist primary care physicians in achieving successful withdrawal of antihypertensive therapy among selected hypertensive patients. Results: The most consistent predictors identified were blood pressure (BP) (lower pretreatment, on treatment, and after withdrawal), nature of pharmacotherapy (fewer agents and lower dose), and preparedness to accept dietary intervention (weight and sodium reduction). Conclusions: On the basis of this information, a trial of withdrawal of antihypertensive medication might be recommended for patients who have mildly elevated, uncomplicated BP that is well controlled on a single agent, and who are motivated and likely to accept lifestyle changes. Withdrawal, antihypertensive drugs, predictors.


title: Social Recommendation With Multiple Influence From Direct User Interactions

abstract: With the sustained development of social networks, increasing attention has been paid to social recommender systems. Current studies usually focus on indirect factors such as the similarity between users, but multiple direct interactions, such as mentions, reposts, and comments, are seldom considered. This paper addresses direct connections between users in social recommender systems. We analyze direct interactions to investigate the connection strength between users, and then, user preferences and item characteristics can be better described. Based on the analysis of social influence between users and users’ influence over the whole social network, we propose a recommendation method with social influence, which makes full use of information among users in social networks and introduces the mechanisms of macroscopic and microscopic influences. Direct interactions between users are incorporated into a matrix factorization objective function. Real-world microblog data are applied to verify our model, and the results show that the proposed recommendation method outperforms other state-of-the-art recommendation algorithms.


Ethics and Style: The Lessons of Literature for Law


The Evolutionary Significance of Pongid Sign Language Acquisition.


title: 5G in rural and low-income areas: Are we ready?

abstract: Current trends in telecommunication networks foresee the adoption of the fifth generation (5G) of wireless networks in the near future. However, a large number of people are living without coverage and connectivity. To face this issue, we consider the possibility of deploying 5G networks in rural and low-income zones. After detailing the current state-of-the-art, we consider the main challenges that need to be faced. Moreover, we define the main pillars to follow in order to deploy 5G networks in such zones, as well as a proposal of a future network architecture.


title: Filter optimization for human-machine interaction in aviation

abstract: Pilot's responses to a sudden step change in flight altitude were measured on a flight simulator. The measured data was then mathematically analyzed in the MATLAB® environment, providing the input and output data for the filter optimization. The aim of such an optimal filter design is to obtain the best transfer function parameters (pilot gain and time constants) of human behavior while controlling an aircraft. Due to the measurements and the filter optimization, the pilot behavior model can be more realistic in identifying any potential imperfections in the aircraft's flight control systems at an early stage of its development.


title: Survey of smart metering communication technologies

abstract: The smart metering system allows continuous reading and recording of many measurands, e.g. power quality and load curve profile, as well as the early-stage failure detection, monitoring of the meter lid opening alarm (detection of unlicensed actions), detection of the opposite energy flow and many other useful signals. The capabilities and information provided by a meter reading system are important for a variety of purposes, including (but not limited to) interval data, time-based demand data, time-based energy data (usage and production), outage management, service interruption, service restoration, quality of service monitoring, distribution network analysis, distribution planning, demand reduction, customer billing and work management. This paper provides an overview of communication technologies used in smart metering, summary of the recommended technologies for specific areas of smart measurement and a short description of the communication technology used in the Croatian distribution system operator (DSO).


How I Do It Adjunctive Tenotomy During Middle Ear Surgery


title: Why would online gamers share their innovation-conducive knowledge in the online game user community? Integrating individual motivations and social capital perspectives

abstract: The user community has been an important external source of a firm's product or service innovation. Users' innovation-conducive knowledge sharing enables the community to work as a vital source of innovation. But, traditional economic theories of innovation seem to provide few explanations about why such knowledge sharing takes place for free in the user community. Therefore, this study investigates what drives community users to freely share their innovation-conducive knowledge, using the theory of planned behavior. Based on an empirical analysis of the data from 1244 members of a South Korean online game user community, it reveals that intrinsic motivation, shared goals, and social trust are salient factors in promoting users' innovation-conducive knowledge sharing. Extrinsic motivation and social tie, however, were found to affect such sharing adversely, contingent upon whether a user is an innovator or a non-innovator. The study illustrates how social capital, in addition to individual motivations, forms and influences users' innovation-conducive knowledge sharing in the online gaming context.


title: A High-Efficiency Step-Up Current-Fed Push–Pull Quasi-Resonant Converter With Fewer Components for Fuel Cell Application

abstract: In this paper, a new high-efficiency step-up current-fed push–pull quasi-resonant converter is proposed, which is suitable for low-voltage fuel cell power conditioning system. The proposed converter conserves inherent advantages of low-input-current stress and high voltage conversion ratio of the conventional current-fed push–pull converter. All of power devices can achieve soft switching at light load improving the overall efficiency. Moreover, similar features have been obtained with fewer components in comparison with the active-clamped current-fed push–pull converter [37] and current-fed push–pull resonant converter [40] , that enabling to reduce the cost and improve system reliability. In addition, the voltage-doubler rectifier is adopted to eliminate the reverse-recovery problem of secondary diodes and provides much higher voltage conversion ratio resulting in small turn ratio of the high-frequency transformer. Detailed operation, analysis, design, comparative study, experimental results, and loss breakdown for the proposed converter are presented in the paper. A 510-W prototype verifies the theoretical analysis and the effectiveness of the proposed concept.


title: Verification in the Age of Microservices

abstract: Many large applications are now built using collections of microservices, each of which is deployed in isolated containers and which interact with each other through the use of remote procedure calls (RPCs). The use of microservices improves scalability -- each component of an application can be scaled independently -- and deployability. However, such applications are inherently distributed and current tools do not provide mechanisms to reason about and ensure their global behavior. In this paper we argue that recent advances in formal methods and software packet processing pave the path towards building mechanisms that can ensure correctness for such systems, both when they are being built and at runtime. These techniques impose minimal runtime overheads and are amenable to production deployments.


title: Learning time-series shapelets

abstract: Shapelets are discriminative sub-sequences of time series that best predict the target variable. For this reason, shapelet discovery has recently attracted considerable interest within the time-series research community. Currently shapelets are found by evaluating the prediction qualities of numerous candidates extracted from the series segments. In contrast to the state-of-the-art, this paper proposes a novel perspective in terms of learning shapelets. A new mathematical formalization of the task via a classification objective function is proposed and a tailored stochastic gradient learning algorithm is applied. The proposed method enables learning near-to-optimal shapelets directly without the need to try out lots of candidates. Furthermore, our method can learn true top-K shapelets by capturing their interaction. Extensive experimentation demonstrates statistically significant improvement in terms of wins and ranks against 13 baselines over 28 time-series datasets.


title: BANK PERFORMANCE AND SUPERVISION IN NIGERIA: ANALYSING THE TRANSITION TO A DEREGULATED ECONOMY

abstract: Bank performance and supervision in Nigeria: Analysing the transition to a deregulated economy.


title: Ultra-Wideband Dual-Polarized Patch Antenna With Four Capacitively Coupled Feeds

abstract: A novel dual-polarized patch antenna for ultra-wideband (UWB) applications is presented. The antenna consists of a square patch and four capacitively coupled feeds to enhance the impedance bandwidth. Each feed is formed by a vertical isosceles trapezoidal patch and a horizontal isosceles triangular patch. The four feeds are connected to the microstrip lines that are printed on the bottom layer of the grounded FR4 substrate. Two tapered baluns are utilized to excite the antenna to achieve high isolation between the ports and reduce the cross-polarization levels. In order to increase the antenna gain and reduce the backward radiation, a compact surface mounted cavity is integrated with the antenna. The antenna prototype has achieved an impedance bandwidth of 112% at (|S11 | ≤ -10 dB) whereas the coupling between the two ports is below -28 dB across the operating frequency range. The measured antenna gain varies from 3.91 to 10.2 dBi for port 1 and from 3.38 to 9.21 dBi for port 2, with a 3-dB gain bandwidth of 107%.


title: The Extended iSelf: The Impact of iPhone Separation on Cognition, Emotion, and Physiology

abstract: This study uniquely examined the effects on self, cognition, anxiety, and physiology when iPhone users are unable to answer their iPhone while performing cognitive tasks. A 2 x 2 within-subjects experiment was conducted. Participants (N = 40 iPhone users) completed 2 word search puzzles. Among the key findings from this study were that when iPhone users were unable to answer their ringing iPhone during a word search puzzle, heart rate and blood pressure increased, self-reported feelings of anxiety and unpleasantness increased, and self-reported extended self and cognition decreased. These findings suggest that negative psychological and physiological outcomes are associated with iPhone separation and the inability to answer one's ringing iPhone during cognitive tasks. Implications of these findings are discussed.


title: Simulation Framework of Wireless Sensor Network (WSN) Using MATLAB/SIMULINK Software

abstract: A wireless sensor network consists of spatially distributed autonomous sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants. The development of wireless sensor networks was motivated by military applications such as battlefield surveillance. They are now used in many industrial and civilian application areas, including industrial process monitoring and control, machine health monitoring, environment and habitat monitoring, healthcare applications, home automation, and traffic control [1-2].


Moal processes in addiction Neurobiological mechanisms for opponent motivational


title: A CMOS Sub-l-V nanopower current and voltage reference with leakage compensation

abstract: In this paper, a CMOS sub-1-V nanopower reference is proposed, which is implemented without resistors and with only standard CMOS transistors. The proposed circuit has the most attractive merit that it can afford reference current and reference voltage simultaneously. Moreover, the leakage compensation technique is utilized, and thus it has very low temperature coefficient for a wide temperature range. The proposed circuit is verified by SPICE simulation with CMOS 0.18um process. The temperature coefficient of the reference voltage and reference current are 0.0037%/°C and 0.0091%/°C, respectively. Also, the power supply voltage can be as low as 0.85V and its power consumption is only 5.1nW.


title: Stimulation of pro-inflammatory responses by mebendazole in human monocytic THP-1 cells through an ERK signaling pathway

abstract: Oral helminthic mebendazole (MBZ) has been reported to cause liver injury with inflammatory responses. However, the underlying mechanism remains unknown. To examine the inflammatory reactions, we investigated whether MBZ and other helminthic drugs increase the release of pro-inflammatory cytokines and chemokines using human monocytic cells. The release of interleukin (IL)-8 and tumor necrosis factor (TNF) α from human monocytic THP-1 cells was significantly increased by treatment with MBZ, albendazole (ABZ), fenbendazole (FBZ), or oxibendazole (OBZ), but not by albendazole sulfoxide or praziquantel, suggesting that MBZ and structurally similar drugs can stimulate monocytes and increase the release of pro-inflammatory cytokines. MBZ also significantly increased the phosphorylation of extracellular signal-regulated kinase (ERK) 1/2 and c-Jun N-terminal kinase (JNK) 1/2 in THP-1 cells. Pretreatment with the MAP kinase/ERK kinase 1/2 inhibitor U0126 significantly suppressed the increase of IL-8 and TNFα levels by MBZ, ABZ, FBZ, or OBZ treatment in THP-1 cells, but the p38 mitogen-activated protein kinase inhibitor SB203580 or JNK1/2 inhibitor SP600125 did not. These results suggested that an ERK1/2 pathway plays an important role in the release of IL-8 and TNFα in THP-1 cells treated with MBZ and structurally similar drugs. In conclusion, the release of inflammatory mediators by MBZ might be one of the mechanisms underlying immune-mediated liver injury. This in vitro method may be useful to predict adverse inflammatory reactions that lead to hepatotoxicity.


title: Multiple-image super-resolution on mobile devices: an image warping approach

abstract: This paper discusses a super-resolution (SR) system implemented on a mobile device. We utilized an Android device’s camera to take successive shots and applied a classical multiple-image super-resolution (SR) technique that utilized a set of low-resolution (LR) images. Images taken from the mobile device are subjected to our proposed filtering scheme wherein images that have noticeable presence of blur are discarded to avoid outliers from affecting the produced high-resolution (HR) image. The remaining subset of images are subjected to non-local means denoising, then feature-matched against the first reference LR image. Successive images are then aligned with respect to the first image via affine and perspective warping transformations. The LR images are then upsampled using bicubic interpolation. An L ::: 2-norm minimization approach, which is essentially taking the pixel-wise mean of the aligned images, is performed to produce the final HR image. Our study shows that our proposed method performs better than the bicubic interpolation, which makes its implementation in a mobile device quite feasible. We have also proven in our experiments that there are substantial differences from images captured using burst mode that can be utilized by an SR algorithm to create an HR image.


title: Data Infrastructure at LinkedIn

abstract: Linked In is among the largest social networking sites in the world. As the company has grown, our core data sets and request processing requirements have grown as well. In this paper, we describe a few selected data infrastructure projects at Linked In that have helped us accommodate this increasing scale. Most of those projects build on existing open source projects and are themselves available as open source. The projects covered in this paper include: (1) Voldemort: a scalable and fault tolerant key-value store, (2) Data bus: a framework for delivering database changes to downstream applications, (3) Espresso: a distributed data store that supports flexible schemas and secondary indexing, (4) Kafka: a scalable and efficient messaging system for collecting various user activity events and log data.


title: On the k-NN performance in a challenging scenario of imbalance and overlapping

abstract: A two-class data set is said to be imbalanced when one (minority) class is heavily under-represented with respect to the other (majority) class. In the presence of a significant overlapping, the task of learning from imbalanced data can be a very difficult problem. Additionally, if the overall imbalance ratio is different from local imbalance ratios in overlap regions, the task can become in a major challenge. This paper explains the behaviour of the k-nearest neighbour (k-NN) rule when learning from such a complex scenario. This local model is compared to other machine learning algorithms, attending to how their behaviour depends on a number of data complexity features (global imbalance, size of overlap region, and its local imbalance). As a result, several conclusions useful for classifier design are inferred.


title: 65GHz Doppler Sensor with On-Chip Antenna in 0.18μm SiGe BiCMOS

abstract: A single-chip 65GHz Doppler radar transceiver with on-chip patch antenna is reported. Implemented in a production 0.18mum SiGe BiCMOS process, it features a differential output transmit power of 4.3dBm, 16.5dB single-ended down-conversion gain and a double-sideband noise figure of 12.8dB. The radar includes a 65GHz 2-stage cascode LNA with S 11<-15dB at 50-94GHz and 14dB gain at 65GHz, a double-balanced down-convert mixer, a SiGe HBT IF amplifier with low 1/f noise, a VCO and a 65GHz output buffer. The LO is provided by an integrated varactor-tuned 61-67GHz VCO optimized for low phase noise. The patch antenna is designed to be impedance-matched for dual-band operation at 62.8 and 64.6GHz. The use of lumped inductors in all blocks and a vertically-stacked transformer for single-ended to differential conversion in the receive path help reduce the transceiver area to 1mm times 1mm


title: Face Pose Discrimination Using Support Vector Machines (SVM)

abstract: This paper describes an approach for the problem of face pose discrimination using support vector machines (SVM). Face pose discrimination means that one can label the face image as one of several known poses. Face images are drawn from the standard FERET database. The training set consists of 150 images equally distributed among frontal, approximately 33.75/spl deg/ rotated left and right poses, respectively, and the test set consists of 450 images again equally distributed among the three different types of poses. SVM achieved perfect accuracy-100%-discriminating between the three possible face poses on unseen test data, using either polynomials of degree 3 or radial basis functions (RBF) as kernel approximation functions.


title: Big Data Analysis Using Apache Hadoop

abstract: We live in on-demand, on-command Digital universe with data prolifering by Institutions, Individuals and Machines at a very high rate. This data is categories as "Big Data" due to its sheer Volume, Variety and Velocity. Most of this data is unstructured, quasi structured or semi structured and it is heterogeneous in nature. The volume and the heterogeneity of data with the speed it is generated, makes it difficult for the present computing infrastructure to manage Big Data. Traditional data management, warehousing and analysis systems fall short of tools to analyze this data. Due to its specific nature of Big Data, it is stored in distributed file system architectures. Hadoop and HDFS by Apache is widely used for storing and managing Big Data. Analyzing Big Data is a challenging task as it involves large distributed file systems which should be fault tolerant, flexible and scalable. Map Reduce is widely been used for the efficient analysis of Big Data. Traditional DBMS techniques like Joins and Indexing and other techniques like graph search is used for classification and clustering of Big Data. These techniques are being adopted to be used in Map Reduce. In this paper we suggest various methods for catering to the problems in hand through Map Reduce framework over Hadoop Distributed File System (HDFS). Map Reduce is a Minimization technique which makes use of file indexing with mapping, sorting, shuffling and finally reducing. Map Reduce techniques have been studied in this paper which is implemented for Big Data analysis using HDFS.


title: Innate immune recognition of the microbiota promotes host-microbial symbiosis

abstract: Pattern-recognition receptors (PRRs) are traditionally known to sense microbial molecules during infection to initiate inflammatory responses. However, ligands for PRRs are not exclusive to pathogens and are abundantly produced by the resident microbiota during normal colonization. Mechanism(s) that underlie this paradox have remained unclear. Recent studies reveal that gut bacterial ligands from the microbiota signal through PRRs to promote development of host tissue and the immune system, and protection from disease. Evidence from both invertebrate and vertebrate models reveals that innate immune receptors are required to promote long-term colonization by the microbiota. This emerging perspective challenges current models in immunology and suggests that PRRs may have evolved, in part, to mediate the bidirectional cross-talk between microbial symbionts and their hosts.


title: Representativeness revisited: Attribute substitution in intuitive judgment

abstract: The program of research now known as the heuristics and biases approach began with a survey of 84 participants at the 1969 meetings of the Mathematical Psychology Society and the American Psychological Association (Tversky & Kahneman, 1971). The respondents, including several authors of statistics texts, were asked realistic questions about the robustness of statistical estimates and the replicability of research results. The article commented tongue-in-heek on the prevalence of a belief that the law of large numbers applies to small numbers as well: Respondents placed too much confidence in the results of small samples, and their statistical judgments showed little sensitivity to sample size. The mathematical psychologists who participated in the survey not only should have known better – they did know better. Although their intuitive guesses were off the mark, most of them could have computed the correct answers on the back of an envelope. These sophisticated individuals apparently had access to two distinct approaches for answering statistical questions: one that is spontaneous, intuitive, effortless, and fast; and another that is deliberate, rule-governed, effortful, and slow. The persistence of large biases in the guesses of experts raised doubts about the educability of statistical intuitions. Moreover, it was known that the same biases affect choices in the real world, where researchers commonly select sample sizes that are too small to provide a fair test of their hypotheses (Cohen, 1969, 1992).


title: Functionality preserving shape style transfer

abstract: When geometric models with a desired combination of style and functionality are not available, they currently need to be created manually. We facilitate algorithmic synthesis of 3D models of man-made shapes which combines user-specified style, described via an exemplar shape, and functionality, encoded by a functionally different target shape. Our method automatically transfers the style of the exemplar to the target, creating the desired combination. The main challenge in performing cross-functional style transfer is to implicitly separate an object's style from its function: while stylistically the output shapes should be as close as possible to the exemplar, their original functionality and structure, as encoded by the target, should be strictly preserved. Recent literature point to the presence of similarly shaped, salient geometric elements as a main indicator of stylistic similarity between 3D shapes. We therefore transfer the exemplar style to the target via a sequence of element-level operations. We allow only compatible operations, ones that do not affect the target functionality. To this end, we introduce a cross-structural element compatibility metric that estimates the impact of each operation on the edited shape. Our metric is based on the global context and coarse geometry of evaluated elements, and is trained on databases of 3D objects. We use this metric to cast style transfer as a tabu search, which incrementally updates the target shape using compatible operations, progressively increasing its style similarity to the exemplar while strictly maintaining its functionality at each step. We evaluate our framework across a range of man-made objects including furniture, light fixtures, and tableware, and perform a number of user studies confirming that it produces convincing outputs combining the desired style and function.


title: Inguinal Hernia Repair: Incidence of Elective and Emergency Surgery, Readmission and Mortality

abstract: Background. Inguinal hernia repair is one of the most common operations undertaken in routine surgical practice. It generally carries a very low risk of major adverse sequelae. We analysed profiles, separately, for elective and emergency operations to report on the incidence and major adverse outcomes of inguinal hernia repair in a geographically defined population. Methods. Age- and sex-specific hospital admission rates, emergency readmission rates within 30 days of discharge, and mortality rates, separately for elective and emergency operations, were calculated for the period 1976-1986 in the Oxford Record Linkage Study (ORLS) area. Results. In all, 30 675 inguinal hernia repairs were performed in the area, an all-ages annual incidence of 13 per 10 000 population. Some 9% of patients underwent operation in an emergency admission. Elective operation rates remained constant over time. Emergency repairs decreased significantly over time in males. Patients who underwent emergency repair were older, had higher emergency readmission rates than those undergoing elective repair, and had significantly elevated postoperative mortality rates. In those who died it was uncommon for inguinal hernia to be recorded on their death certificates. Of the operations, 91% were undertaken on males ; age-specific rates were highest in infants and the elderly ; and emergency operation rates rose exponentially with age in people >50 years. Conclusions. The lifetime 'risk' of inguinal hernia repair is high : at currently prevailing rates we estimate it at 27% for men and 3% for women. There is significant elevation of mortality after emergency operations. Elective repair of inguinal hernia should be undertaken soon after the diagnosis is made to minimize the risk of adverse outcomes.


title: Upwind flight responses of tsetse flies ( Glossina spp.) (Diptera: Glossinidae) to acetone, octenol and phenols in nature: a video study

abstract: Video observations were made of tsetse flies (mainly Glossina pallidipes Austen) as they approached, responded to and left a vertical (I m square) black target in odour plumes of acetone, or a 4:1:8 mixture of octenol:propylphenol: methylphenol, or acetone plus this "4:1:8" mixture, or in no odour. No differences in mean flight speed or turn size in any of these situations were detected. With the odour source 5 m upwind of the target, the flight tracks of tsetse arriving at and leaving the target were significantly biased towards upwind, highly so when 4:1:8 was present, marginally so in acetone alone. With the source 10 m upwind, the same biases were still present but weaker. Circling flights around the black target were more frequent in acetone plus 4:1:8 than in no odour (26% vs 15%), but in either odour alone were only just significantly more than in no odour. Upwind turning at the target was more frequent (25% vs 17%) in acetone alone that in no odour (though not in 4:1:8 alone). It is concluded that 4:1:8 elicits an upwind anemotactic response comparable in strength to that in CO2, and that acetone elicits a similar response more weakly, but may be more involved in potentiating visual responses. Introd uction know whether they do in fact have this effect in the field, but that it has simply not been detected. We examined this question by recording the flight tracks of tsetse with video in Zimbabwe, using acetone and the combination of octenol and phenols now used commonly to bait targets and traps in fly control programmes. We report here on the analysis of these tracks.


title: Flexible and Stretchable Electronics for Biointegrated Devices

abstract: Advances in materials, mechanics, and manufacturing now allow construction of high-quality electronics and optoelectronics in forms that can readily integrate with the soft, curvilinear, and time-dynamic surfaces of the human body. The resulting capabilities create new opportunities for studying disease states, improving surgical procedures, monitoring health/wellness, establishing human-machine interfaces, and performing other functions. This review summarizes these technologies and illustrates their use in forms integrated with the brain, the heart, and the skin.


title: How Many Subjects Does It Take To Do A Regression Analysis

abstract: Numerous rules-of-thumb have been suggested for determining the minimum number of subjects required to conduct multiple regression analyses. These rules-of-thumb are evaluated by comparing their results against those based on power analyses for tests of hypotheses of multiple and partial correlations. The results did not support the use of rules-of-thumb that simply specify some constant (e.g., 100 subjects) as the minimum number of subjects or a minimum ratio of number of subjects (N) to number of predictors (m). Some support was obtained for a rule-of-thumb that N ≥ 50 + 8 m for the multiple correlation and N ≥104 + m for the partial correlation. However, the rule-of-thumb for the multiple correlation yields values too large for N when m ≥ 7, and both rules-of-thumb assume all studies have a medium-size relationship between criterion and predictors. Accordingly, a slightly more complex rule-of thumb is introduced that estimates minimum sample size as function of effect size as well as the number of pred...


title: Constructing attack scenarios through correlation of intrusion alerts

abstract: Traditional intrusion detection systems (IDSs) focus on low-level attacks or anomalies, and raise alerts independently, though there may be logical connections between them. In situations where there are intensive intrusions, not only will actual alerts be mixed with false alerts, but the amount of alerts will also become unmanageable. As a result, it is difficult for human users or intrusion response systems to understand the alerts and take appropriate actions. This paper presents a practical technique to address this issue. The proposed approach constructs attack scenarios by correlating alerts on the basis of prerequisites and consequences of intrusions. Intuitively, the prerequisite of an intrusion is the necessary condition for the intrusion to be successful, while the consequence of an intrusion is the possible outcome of the intrusion. Based on the prerequisites and consequences of different types of attacks, the proposed approach correlates alerts by (partially) matching the consequence of some previous alerts and the prerequisite of some later ones. The contribution of this paper includes a formal framework for alert correlation, the implementation of an off-line alert correlator based on the framework, and the evaluation of our method with the 2000 DARPA intrusion detection scenario specific datasets. Our experience and experimental results have demonstrated the potential of the proposed method and its advantage over alternative methods.


title: Feedback control for adaptive live video streaming

abstract: Multimedia content feeds an ever increasing fraction of the Internet traffic. Video streaming is one of the most important applications driving this trend. Adaptive video streaming is a relevant advancement with respect to classic progressive download streaming such as the one employed by YouTube. It consists in dynamically adapting the content bitrate in order to provide the maximum Quality of Experience, given the current available bandwidth, while ensuring a continuous reproduction. In this paper we propose a Quality Adaptation Controller (QAC) for live adaptive video streaming designed by employing feedback control theory. An experimental comparison with Akamai adaptive video streaming has been carried out. We have found the following main results: 1) QAC is able to throttle the video quality to match the available bandwidth with a transient of less than 30s while ensuring a continuous video reproduction; 2) QAC fairly shares the available bandwidth both in the cases of a concurrent TCP greedy connection or a concurrent video streaming flow; 3) Akamai underutilizes the available bandwidth due to the conservativeness of its heuristic algorithm; moreover, when abrupt available bandwidth reductions occur, the video reproduction is affected by interruptions.


Syntactic Dependency Based N-grams in Rule Based Automatic English as Second Language Grammar Correction


title: VEP-based brain-computer interfaces: time, frequency, and code modulations [Research Frontier]

abstract: A brain computer interface (BCI) translates human intentions into control signals to establish a direct communication channel between the human brain and external devices. Because a BCI does not depend on the brain's normal output pathways of peripheral nerves and muscles, it can provide a new communication channel to people with severe motor disabilities. Electroencephalograms (EEGs) recorded from the surface of the scalp are widely used in current BCIs for their non-invasive nature and easy applications. Among EEG based BCIs, systems based on visual evoked potentials (VEPs) have received widespread attention in recent decades. We described the three stimulus modulation approaches used in current VEP based BCIs: time modulation (t-VEP), frequency modulation (f-VEP), and pseudorandom code modulation (c-VEP). We then carried out a detailed comparison of system performance between an f-VEP BCI and a c-VEP BCI. The results show that an f-VEP BCI has the advantage of little or no training and simple system configuration, while the c-VEP based BCI has a higher communication speed. The stimulus modulation design is the crux of VEP based BCI systems.


title: The Cost of Concreteness: The Effect of Nonessential Information on Analogical Transfer

abstract: Most theories of analogical transfer focus on similarities between the learning and transfer domains, where transfer is more likely between domains that share common surface features, similar elements, or common interpretations of structure. We suggest that characteristics of the learning instantiation alone can give rise to different levels of transfer. We propose that concreteness of the learning instantiation can hinder analogical transfer of well-defined structured concepts, such as mathematical concepts. We operationalize the term concreteness as the amount of information communicated through a specific instantiation of a concept. The 5 reported experiments with undergraduate students tested the hypothesis by presenting participants with the concept of a commutative mathematical group of order 3. The experiments varied the level of concreteness of the training instantiation and measured transfer of learning to a new instantiation. The results support the hypothesis, demonstrating better transfer from more generic instantiations (i.e., ones that communicate minimal extraneous information) than from more concrete instantiations. Specifically, concreteness was found to create an obstacle to successful structural alignment across domains, whereas generic instantiations led to spontaneous structural alignment. These findings have important implications for the theory of learning and transfer and practical implications for the design of educational material. Although some concreteness may activate prior knowledge and perhaps offer a leg up in the learning process, this benefit may come at the cost of transfer.


HISTORY OF MEDICINE History of Violence as a Public Health Problem


title: A Soft Modularity Function For Detecting Fuzzy Communities in Social Networks

abstract: We discuss a new formulation of a fuzzy validity index that generalizes the Newman-Girvan (NG) modularity function. The NG function serves as a cluster validity functional in community detection studies. The input data is an undirected weighted graph that represents, e.g., a social network. Clusters correspond to socially similar substructures in the network. We compare our fuzzy modularity with two existing modularity functions using the well-studied Karate Club and American College Football datasets.


title: The self and social cognition: the role of cortical midline structures and mirror neurons

abstract: Recent evidence suggests that there are at least two large-scale neural networks that represent the self and others. Whereas frontoparietal mirror-neuron areas provide the basis for bridging the gap between the physical self and others through motor-simulation mechanisms, cortical midline structures engage in processing information about the self and others in more abstract, evaluative terms. This framework provides a basis for reconciling findings from two separate but related lines of research: self-related processing and social cognition. The neural systems of midline structures and mirror neurons show that self and other are two sides of the same coin, whether their physical interactions or their most internal mental processes are examined.


title: PipesFS: fast Linux I/O in the unix tradition

abstract: This paper presents PipesFS, an I/O architecture for Linux 2.6 that increases I/O throughput and adds support for heterogeneous parallel processors by (1) collapsing many I/O interfaces onto one: the Unix pipeline, (2) increasing pipe efficiency and (3) exploiting pipeline modularity to spread computation across all available processors. PipesFS extends the pipeline model to kernel I/O and communicates with applications through a Linux virtual filesystem (VFS), where directory nodes represent operations and pipe nodes export live kernel data. Users can thus interact with kernel I/O through existing calls like mkdir, tools like grep, most languages and even shell scripts. To support performance critical tasks, PipesFS improves pipe throughput through copy, context switch and cache miss avoidance. To integrate heterogeneous processors (e.g., the Cell) it transparently moves operations to the most efficient type of core.


title: Falls in young, middle-aged and older community dwelling adults: perceived cause, environmental factors and injury

abstract: BackgroundFalls in older people have been characterized extensively in the literature, however little has been reported regarding falls in middle-aged and younger adults. The objective of this paper is to describe the perceived cause, environmental influences and resultant injuries of falls in 1497 young (20–45 years), middle-aged (46–65 years) and older (> 65 years) men and women from the Baltimore Longitudinal Study on Aging.MethodsA descriptive study where participants completed a fall history questionnaire describing the circumstances surrounding falls in the previous two years.ResultsThe reporting of falls increased with age from 18% in young, to 21% in middle-aged and 35% in older adults, with higher rates in women than men. Ambulation was cited as the cause of the fall most frequently in all gender and age groups. Our population reported a higher percentage of injuries (70.5%) than previous studies. The young group reported injuries most frequently to wrist/hand, knees and ankles; the middle-aged to their knees and the older group to their head and knees. Women reported a higher percentage of injuries in all age groups.ConclusionThis is the first study to compare falls in young, middle and older aged men and women. Significant differences were found between the three age groups with respect to number of falls, activities engaged in prior to falling, perceived causes of the fall and where they fell.


title: Pes anserinus and anserine bursa: anatomical study

abstract: This study investigated the boundary of anserine bursa with the recommended injection site and shape on the insertion area of pes anserinus (PA), with the aim of improving clinical practice. Eighty six legs from 45 Korean cadavers were investigated. The mixed gelatin solution was injected to identify the shape of anserine bursa, and then the insertion site of the PA tendons was exposed completely and carefully dissected to identify the shape of the PA. The sartorius was inserted into the superficial layer and gracilis, and the semitendinosus was inserted into the deep layer on the medial surface of the tibia. The number of the semitendinosus tendons at the insertion site varied: 1 in 66% of specimens, 2 in 31%, and 3 in 3%. The gracilis and semitendinosus tendons were connected to the deep fascia of leg. Overall, the shape of the anserine bursa was irregularly circular. Most of the anserine bursa specimens reached the proximal line of the tibia, and some of the specimens reached above the proximal line of the tibia. In the medial view of the tibia, the anserine bursa was located posteriorly and superiorly from the tibia's midline, and it followed the lines of the sartorius muscle. The injection site for anserine bursa should be carried out at 20° from the vertical line medially and inferiorly, 15 or 20 mm deeply, and at the point of about 20 mm medial and 12 mm superior from inferomedial point of tibial tuberosity.


title: Characters Detection on Namecard with faster RCNN

abstract: We apply Faster R-CNN to the detection of characters in namecard, in order to solve the problem of a small amount of data and the inbalance between different class, we designed the data augmentation and the 'fake' data generalizer to generate more data for the training of network. Without using data augmentation, the average IoU in correct samples could be no less than 80% and the mAP result of 80% was also achieved with Faster R-CNN. By applying the data augmentation, the variance of mAP is decreased and both of the IoU and mAP score has increased a little.