title: Managing technical debt: An industrial case study

abstract: Technical debt is the consequence of trade-offs made during software development to ensure speedy releases. The research community lacks rigorously evaluated guidelines to help practitioners characterize, manage and prioritize debt. This paper describes a study conducted with an industrial partner during their implementation of Agile development practices for a large software development division within the company. The report contains our initial findings based on ethnographic observations and semi-structured interviews. The goal is to identify the best practices regarding managing technical debt so that the researchers and the practitioners can further evaluate these practices to extend their knowledge of the technical debt metaphor. We determined that the developers considered their own taxonomy of technical debt based on the type of work they were assigned and their personal understanding of the term. Despite management's high-level categories, the developers mostly considered design debt, testing debt and defect debt. In addition to developers having their own taxonomy, assigning dedicated teams for technical debt reduction and allowing other teams about 20% of time per sprint for debt reduction are good initiatives towards lowering technical debt. While technical debt has become a well-regarded concept in the Agile community, further empirical evaluation is needed to assess how to properly apply the concept for various development organizations.


title: Cooperation in a multi-stage game for modeling distributed task delegation in a supply chain procurement problem

abstract: We develop an evolutionary method that combines reinforcement learning and fictitious playing to seek equilibrium solution for a multi-agent and multi-stage game in the context of supply chain procurement. The game is designed to model task delegation among a group of self-interested transportation companies which serve logistic shipment. The game involves more than two agents and multiple stages of matrix games. The integration of reinforcement learning and fictitious play overcomes the weaknesses of each approach and exploits their strengths. This innovative approach performs extraordinarily well on a game with three players, unknown number of stages, and large gaps of payoff values.


title: QR-code based Localization for Indoor Mobile Robot with validation using a 3D optical tracking instrument

abstract: This paper addresses effective artificial landmark based indoor localization technique for mobile robotics applications. In the proposed approach, QR-codes are strategically placed in the operating environment, attached to the ceiling; they are used as artificial landmarks for providing reference points to aid the robot localization. The vision processing, robot localization and navigation tasks are all carried out on an Android Smartphone mounted on the mobile robotic platform; by taking advantage of the computational power, the built-in sensing, and the communication capabilities of the Smartphone. The Smartphone detects and recognizes the QR-codes to calculate the approximate global location of the robot. The pixel coordinate information in the image of QR-code is also used to enhance the position estimate in the real-world environment. Furthermore, the QR-code orientation in the image view is used to calculate the mobile robot's heading direction. An NDI 3D optical tracking instrument is used for validation of the effectiveness and feasibility of the proposed method. This paper demonstrates successful implementation of the QR-code based localization strategy and the experimental evaluation of its performance in actual environment.


title: Online Contextual Influence Maximization in social networks

abstract: In this paper, we propose the Online Contextual Influence Maximization Problem (OCIMP). In OCIMP, the learner faces a series of epochs in each of which a different influence campaign is run to promote a certain product in a given social network. In each epoch, the learner first distributes a limited number of free-samples of the product among a set of seed nodes in the social network. Then, the influence spread process takes place over the network, other users get influenced and purchase the product. The goal of the learner is to maximize the expected total number of influenced users over all epochs. We depart from the prior work in two aspects: (i) the learner does not know how the influence spreads over the network, i.e., it is unaware of the influence probabilities; (ii) influence probabilities depend on the context. We develop a learning algorithm for OCIMP, called Contextual Online INfluence maximization (COIN). COIN can use any approximation algorithm that solves the offline influence maximization problem as a subroutine to obtain the set of seed nodes in each epoch. When the influence probabilities are Holder continuous functions of the context, we prove that COIN achieves sublinear regret with respect to an approximation oracle that knows the influence probabilities for all contexts. Moreover, our regret bound holds for any sequence of contexts. We also test the performance of COIN on several social networks, and show that it performs better than other methods.


title: Two Gaussian Approaches to Black-Box Optomization

abstract: Outline of several strategies for using Gaussian processes as surrogate models for the covariance matrix adaptation evolution strategy (CMA-ES).


title: Cannabidiol: An Overview of Some Pharmacological Aspects

abstract: Over the past few years, considerable attention has focused on cannabidiol (CBD), a major nonpsychotropic constituent of cannabis. The authors present a review on the chemistry of CBD and discuss the anticonvulsive, antianxiety, antipsychotic, antinausea, and antirheumatoid arthritic properties of CBD. CBD does not bind to the known cannabinoid receptors, and its mechanism of action is yet unknown. It is possible that, in part at least, its effects are due to its recently discovered inhibition of anandamide uptake and hydrolysis and to its antioxidative effect.


title: Exploring the Impact of Online Reviews with Brand Equity for Online Software Purchasing Behavior

abstract: Online purchasing is a booming business and is studied extensively in marketing and information systems research. Online product reviews are one factor that influence a consumer’s purchasing behavior and attitudes. Brand equity is a popular phenomenon that influences purchasing behaviors and attitudes of consumers. We seek to answer two questions in this research. First, do online consumer reviews contain elements of brand equity? Second, do these elements of brand equity in the online consumer reviews affect software purchasing? We develop an integrative research model which test these constructs on software downloads. We use data collected from CNET’s Download.com. We then analyze the effect of these reviews on software downloads using panel data week by week over a 42 week span. All four dimensions of brand equity (brand awareness, brand associations, perceived quality, and brand loyalty) show significant impacts on software downloads with perceived quality being the most influential.


title: WAKE-up: A wearable ankle knee exoskeleton

abstract: In this paper we present the alpha-prototype of the WAKE-up, a wearable robotic device for the rehabilitation of locomotion of pediatric subjects with neurological diseases such as Cerebral Palsy. The WAKE-up is an active knee-ankle orthosis. It is composed of two robotic modules for the rehabilitation of knee and ankle, respectively. Each module can be utilized either alone or together with the other one. The working principle is based on series elastic actuators (SEA), i.e., dc motors equipped with a torsional spring mounted in series to avoid the direct connection of the actuator with the patient's limb. A SEA permits the control of the force and the emulation of different orthoses with given value of stiffness. The torque transmission is achieved by a timing belt and it is mediated by a torsional spring. The experimental tests conducted on each modules confirmed a good precision of the spring deflection control (position error < 2°) and good overall performances of the force control obtained with the spring stiffness chosen at the design phase.


title: Home automation design using 6LoWPAN wireless sensor networks

abstract: Wireless sensor and actuator networks (WS&ANs) are a new technology based on networks of small radio-enabled embedded devices that are being deployed in areas such as environmental monitoring, vehicle tracking, building management, body monitoring and other applications. Power sources for network nodes are often limited, which imposes restrictions on hardware resources and their use by the underlying embedded software. We propose a new wireless sensor network architecture that is especially designed for the task of home automation. Our system relies on a low power WS&AN that employs energy harvesting techniques to maximize node lifetime and an embedded residential gateway that offers user interaction and secure connectivity to the outside world. The advantages of our system are its scalability, low power, self sufficiency and versatility.


title: Performance in computer-mediated work: the moderating role of level of automation

abstract: Organizations require good performance from individuals to achieve their objectives. In view of the growing presence of technology, it becomes necessary to understand performance in the context of information systems. Previous research shows that knowledge and perceived usefulness factors have direct effects on performance. However, the literature also recognizes that there may be different man–machine arrangements to carry out the tasks (level of automation). This study, using a multi-disciplinary approach, evaluates empirically whether the level of intervention moderates the effects of knowledge and perceived usefulness on performance. A questionnaire was used to collect data from 201 users in different organizations and different functional areas. The structural equations model was used for analysis. The results show that the degree of automation moderates the direct relationships. Thus, in structured and proceduralized environments, at high levels of automation, the relevance of knowledge of the task may decrease, and at low levels of intervention, the relevance of perceived usefulness may fall.


title: Hip-Muscle Activation during the Lunge, Single-Leg Squat, and Step-Up-and-Over Exercises

abstract: Context: Functional exercises are often used in strengthening programs after lower extremity injury. Activation levels of the stabilizing hip muscles have not been documented. Objective: To document the progression of hip-muscle activation levels during 3 lower extremity functional exercises. Design: Cross-sectional. Setting: Laboratory. Participants: 44 healthy individuals, 22 women and 22 men. Intervention: Subjects, in 1 testing session, completed 3 trials each of the lunge (LUN), single-leg squat (SLSQ), and step-up-and-over (SUO) exercise. Main Outcome Measures: Root-mean-square muscle amplitude (% reference voluntary muscle contraction) was measured for 5 muscles during the 3 exercises: rectus femoris (RF), dominant and nondominant gluteus medius (GMed_D and GMed_ND), adductor longus (ADD), and gluteus maximus (GMX). Results: The RF, GMAX, and GMed_D were activated in a progression from least to greatest during the SUO, LUN, and SLSQ. The progression for the GMed_ND activation was from least to greatest during the SLSQ, SUO, and then LUN. Activation levels of the ADD showed no progression. Conclusion: Progressive activation levels were documented for muscles acting on the hip joint during 3 functional lower extremity exercises. The authors recommend using this exercise progression when targeting the hip muscles during lower extremity strengthening. Keywords: electromyography, gluteus medius, exercise progression Activation levels of the muscles surrounding the hip joint have become an area of increasing interest in reference to lower extremity injuries. 1–4 Muscle weakness, specifically of the hip-abductor muscle group, has been observed in patients with patellofemoral pain syndrome, 3,5 iliotibial band syndrome, 6,7 anterior cruciate ligament injuries, 8 and ankle instability. 1,2 Previous studies have reported activation levels of the gluteus medius during rehabilitation exercises. 9–14 Less attention, however, has been paid to the activation levels of other muscles that help stabilize the hip joint during functional exercises. To progress a patient back to full activity, it is necessary to understand the complete muscular contribu


The Role of Visual Art in Improving Quality-Of-Life Related Outcomes for Older Adults


title: BRE large compartment fire tests—Characterising post-flashover fires for model validation

abstract: Reliable and comprehensive measurement data from large-scale fire tests are needed for validation of computer fire models, but is subject to various uncertainties, including radiation errors in temperature measurement. Here, a simple method for post-processing thermocouple data is demonstrated, within the scope of a series of large-scale fire tests, in order to establish a well characterised dataset of physical parameter values which can be used with confidence in model validation. Sensitivity analyses reveal the relationship of the correction uncertainty to the assumed optical properties and the thermocouple distribution. The analysis also facilitates the generation of maps of an equivalent radiative flux within the fire compartment, a quantity which usefully characterises the thermal exposures of structural components. Large spatial and temporal variations are found, with regions of most severe exposures not being collocated with the peak gas temperatures; this picture is at variance with the assumption of uniform heating conditions often adopted for post-flashover fires.


Faces InThe-Wild Challenge : database and results


title: A dynamic self-structuring neural network model to combat phishing

abstract: Creating a neural network based classification model is commonly accomplished using the trial and error technique. However, this technique has several difficulties in terms of time wasted and the availability of experts. In this article, an algorithm that simplifies structuring neural network classification models is proposed. The algorithm aims at creating a large enough structure to learn models from the training dataset that can be generalised on the testing dataset. Our algorithm dynamically tunes the structure parameters during the training phase aiming to derive accurate non-overfitting classifiers. The proposed algorithm has been applied to phishing website classification problem and it shows competitive results with respect to various evaluation measures such as harmonic mean (F1-score), precision, and classification accuracy.


title: Neural-network-based motor rolling bearing fault diagnosis

abstract: Motor systems are very important in modern society. They convert almost 60% of the electricity produced in the US into other forms of energy to provide power to other equipment. In the performance of all motor systems, bearings play an important role. Many problems arising in motor operations are linked to bearing faults. In many cases, the accuracy of the instruments and devices used to monitor and control the motor system is highly dependent on the dynamic performance of the motor bearings. Thus, fault diagnosis of a motor system is inseparably related to the diagnosis of the bearing assembly. In this paper, bearing vibration frequency features are discussed for motor bearing fault diagnosis. This paper then presents an approach for motor rolling bearing fault diagnosis using neural networks and time/frequency-domain bearing vibration analysis. Vibration simulation is used to assist in the design of various motor rolling bearing fault diagnosis strategies. Both simulation and real-world testing results obtained indicate that neural networks can be effective agents in the diagnosis of various motor bearing faults through the measurement and interpretation of motor bearing vibration signatures.


title: Video copy detection by conducting fast searching of inverted files

abstract: Fast content-based video copy detection is challenging because video databases have become extremely large. Conventional video-fingerprint-based copy detection systems that use the inverted-file approach involve many similarity computations based on the Hamming distance. To overcome this problem, a novel fast searching strategy for inverted files is proposed in this paper. The strategy involves simple table look-up and word counting operations for the fingerprint matching process. The similarity of video fragments is based on the number of matched fingerprints among all video candidates. In this method, the offset time is used, and fingerprints are ordered to further select the matched fingerprints from the video candidates. Moreover, a novel regional average fingerprint that is compatible with the proposed fast searching strategy is proposed. An experimental video copy detection system was used with the proposed algorithms, and the proposed algorithms were compared with other state-of-the-art fingerprinting algorithms on TRECVID 2011 dataset for different types of video distortions. In addition, VCDB dataset was also used to demonstrate the accuracy and efficiency of the proposed fast searching strategy while using inverted files to demonstrate the practicality of the method for a large database. The proposed system achieved higher accuracy on VCDB dataset with considerably higher operation speed compared with conventional inverted-file-based searching methods.


title: Low-level laser therapy in the management of plantar fasciitis: a randomized controlled trial

abstract: This study aimed at estimating the extent to which a combination therapy of low-level laser therapy (LLLT) with exercise and orthotic support (usual care) affects functional ability in the patient with plantar fasciitis (PF) when compared to usual care alone. Participants with PF were randomly allocated into two groups: LLLT (n = 27) and control (n = 22). All the participants received home exercise program with orthotic support. In addition, the LLLT group received a gallium-aluminum-arsenide laser with a 850-nm wavelength for ten sessions, three times a week. Functional outcomes were measured by function subscale of American Orthopedic Foot and Ankle Society Score (AOFAS-F) and 12-min walking test including walking speed, cadence, and activity-related pain using visual analog scale (VAS).The scores were recorded at baseline, third week, and third month after the treatment. Analysis was performed using repeated measures ANOVA and an intention to treat approach using multiple imputations. There was a significant improvement in AOFAS-F total score at 3 weeks in both groups (LLLT, p < 0.001; control, p = 0.002), but the improvements were seen only for the LLLT group for AOFAS-F total score (p = 0.04) and two individual items of AOFAS-F (walking distance (p < 0.001) and walking surface (p = 0.01)) at 3 months. The groups were comparable with each other for both walking speed and cadence at all assessment times (p > 0.05). Both groups showed significant reduction in pain over 3 months (LLLT, p < 0.001; control, p = 0.01); however, the LLLT group had lower pain than the control group at 3 months (p = 0.03). The combination therapy of LLLT with usual care is more effective to improve functional outcomes and activity-related pain when compared to usual care alone.


title: Micrometre-scale silicon electro-optic modulator

abstract: Metal interconnections are expected to become the limiting factor for the performance of electronic systems as transistors continue to shrink in size. Replacing them by optical interconnections, at different levels ranging from rack-to-rack down to chip-to-chip and intra-chip interconnections, could provide the low power dissipation, low latencies and high bandwidths that are needed. The implementation of optical interconnections relies on the development of micro-optical devices that are integrated with the microelectronics on chips. Recent demonstrations of silicon low-loss waveguides, light emitters, amplifiers and lasers approach this goal, but a small silicon electro-optic modulator with a size small enough for chip-scale integration has not yet been demonstrated. Here we experimentally demonstrate a high-speed electro-optical modulator in compact silicon structures. The modulator is based on a resonant light-confining structure that enhances the sensitivity of light to small changes in refractive index of the silicon and also enables high-speed operation. The modulator is 12 micrometres in diameter, three orders of magnitude smaller than previously demonstrated. Electro-optic modulators are one of the most critical components in optoelectronic integration, and decreasing their size may enable novel chip architectures.


eights: BS 8888:2011 first angle projection drawings from FreeCAD 3D model


title: Fungiculture or Termite Husbandry? The Ruminant Hypothesi

abstract: We present a new perspective for the role of Termitomyces fungi in the mutualism with fungus-growing termites. According to the predominant view, this mutualism is as an example of agriculture with termites as farmers of a domesticated fungus crop, which is used for degradation of plant-material and production of fungal biomass. However, a detailed study of the literature indicates that the termites might as well be envisioned as domesticates of the fungus. According to the “ruminant hypothesis” proposed here, termite workers, by consuming asexual fruiting bodies not only harvest asexual spores, but also lignocellulolytic enzymes, which they mix with foraged plant material and enzymes of termite and possibly bacterial origin. This mixture is the building material of the fungus garden and facilitates efficient degradation of plant material. The fungus garden thus functions as an external rumen for termites and primarily the fungi themselves benefit from their own, and gut-derived, lignocellulolytic enzymes, using the termites to efficiently mix these with their growth substrate. Only secondarily the termites benefit, when they consume the degraded, nitrogen-enriched plant-fungus mixture a second time. We propose that the details of substrate use, and the degree of complementarity and redundancy among enzymes in food processing, determine selection of horizontally transmitted fungal symbionts at the start of a colony: by testing spores on a specific, mechanically and enzymatically pre-treated growth substrate, the termite host has the opportunity to select specific fungal symbionts. Potentially, the gut-microbiota thus influence host-fungus specificity, and the selection of specific fungal strains at the start of a new colony. We argue that we need to expand the current bipartite insect-biased view of the mutualism of fungus-growing termites and include the possible role of bacteria and the benefit for the fungi to fully understand the division of labor among partners in substrate degradation.


title: Identification of milestone papers through time-balanced network centrality

abstract: Citations between scientific papers and related bibliometric indices, such as the h-index for authors and the impact factor for journals, are being increasingly used – often in controversial ways – as quantitative tools for research evaluation. Yet, a fundamental research question remains still open: to which extent do quantitative metrics capture the significance of scientific works? We analyze the network of citations among the 449,935 papers published by the American Physical Society (APS) journals between 1893 and 2009, and focus on the comparison of metrics built on the citation count with network-based metrics. We contrast five article-level metrics with respect to the rankings that they assign to a set of fundamental papers, called Milestone Letters, carefully selected by the APS editors for “making long-lived contributions to physics, either by announcing significant discoveries, or by initiating new areas of research”. A new metric, which combines PageRank centrality with the explicit requirement that paper score is not biased by paper age, is the best-performing metric overall in identifying the Milestone Letters. The lack of time bias in the new metric makes it also possible to use it to compare papers of different age on the same scale. We find that network-based metrics identify the Milestone Letters better than metrics based on the citation count, which suggests that the structure of the citation network contains information that can be used to improve the ranking of scientific publications. The methods and results presented here are relevant for all evolving systems where network centrality metrics are applied, for example the World Wide Web and online social networks. An interactive Web platform where it is possible to view the ranking of the APS papers by rescaled PageRank is available at the address http://www.sciencenow.info.


title: Hotel Branding Strategy: Its Relationship to Guest Satisfaction and Room Revenue

abstract: U.S. hotel brands and international hotel brands headquartered in the United States have increasingly evolved away from being hotel operating companies to being brand management and franchise administration organizations. This trend has allowed for the accelerated growth and development of many major hotel brands and the increasing growth of franchised hotels. There are numerous strategic implications related to this trend. This study seeks to analyze some of these strategic implications by evaluating longitudinal data regarding the performance of major hotel brands in the marketplace, both in terms of guest satisfaction and revenue indicators. Specifically, the authors test whether guest satisfaction at variousU.S. and international brands influences both brand occupancy percentage and average daily room rate 3 years later. In addition, the authors investigate whether the percentage of franchised hotel properties influences both guest satisfaction and occupancy 3 years later. Also, they test whether over...


title: LTCC Packages With Embedded Phased-Array Antennas for 60 GHz Communications

abstract: A low-cost, fully-integrated antenna-in-package solution for 60 GHz phased-array systems is demonstrated. Sixteen patch antennas are integrated into a 28 mm × 28 mm ball grid array together with a flip-chip attached transmitter or receiver IC. The packages have been implemented using low temperature co-fired ceramic technology. 60 GHz interconnects, including flip-chip transitions and via structures, are optimized using full-wave simulation. Anechoic chamber measurement has shown ~ 5 dBi unit antenna gain across all four IEEE 802.15.3c channels, achieving excellent model-to-hardware correlation. The packaged transmitter and receiver ICs, mounted on evaluation boards, have demonstrated beam-steered, non-line-of-sight links with data rates up to 5.3 Gb/s.


title: Detection of Cyber-Aggressive Comments on Social Media Networks: A Machine Learning and Text mining approach

abstract: The spread of aggressive tweets, status and comments on social network are increasing gradually. People are using social media networks as a virtual platform to troll, objurgate, blaspheme and revile one another. These activities are spreading animosity in race-to-race, religion to religion etc. So, these comments should be identified and blocked on social networks. This work focuses on extracting comments from social networks and analyzes those comments whether they convey any blaspheme or revile in meaning. Comments are classified into three distinct classes; offensive, hate speech and neither. Document similarity analyses are done to identify the correlations among the documents. A well defined text pre-processing analysis is done to create an optimized word vector to train the classification model. Finally, the proposed model categorizes the comments into their respective classes with more than 93% accuracy.


title: Learning Purposeful Behaviour in the Absence of Rewards

abstract: Artificial intelligence is commonly defined as the ability to achieve goals in the world. In the reinforcement learning framework, goals are encoded as reward functions that guide agent behaviour, and the sum of observed rewards provide a notion of progress. However, some domains have no such reward signal, or have a reward signal so sparse as to appear absent. Without reward feedback, agent behaviour is typically random, often dithering aimlessly and lacking intentionality. In this paper we present an algorithm capable of learning purposeful behaviour in the absence of rewards. The algorithm proceeds by constructing temporally extended actions (options), through the identification of purposes that are "just out of reach" of the agent's current behaviour. These purposes establish intrinsic goals for the agent to learn, ultimately resulting in a suite of behaviours that encourage the agent to visit different parts of the state space. Moreover, the approach is particularly suited for settings where rewards are very sparse, and such behaviours can help in the exploration of the environment until reward is observed.


title: Hundreds kW Charging Stations for e-Buses Operating Under Regular Ultra-Fast Charging

abstract: A natural evolution in public transportation vehicles is the development toward electric-driven buses (e-buses). Battery weight and volume is still a challenge in the electrification of general-purpose vehicles, but the urban bus systems have some characteristics that can be explored to address such issues. In particular, the predefined routes and stops create opportunities for reduction of onboard storage capacity through regular ultra-fast charging (R-UFC), based on chargers installed next to the boarding stations. This paper proposes a power architecture for UFC stations using the true unity power factor (TUPF) rectifier as the charger front end and interleaved dc–dc converters as the charger output. Both technologies contribute significantly to the reduction of filter components and assure high power quality standards. The proposed TUPF rectifier eliminates all harmonic orders up to the 50th, operates with low switching frequency, and does not require sinusoidal filters. The UFC stations are presented, and their investigation is carried on using computer simulations of a 400-kW application. The proposed concept is verified experimentally using a laboratory test setup.


title: Ubiquitous Mobile Health Monitoring System for Elderly (UMHMSE)

abstract: Recent research in ubiquitous computing uses technologies of Body Area Networks (BANs) to monitor the person's kinematics and physiological parameters. In this paper we propose a real time mobile health system for monitoring elderly patients from indoor or outdoor environments. The system uses a bio- signal sensor worn by the patient and a Smartphone as a central node. The sensor data is collected and transmitted to the intelligent server through GPRS/UMTS to be analyzed. The prototype (UMHMSE) monitors the elderly mobility, location and vital signs such as Sp02 and Heart Rate. Remote users (family and medical personnel) might have a real time access to the collected information through a web application.


title: A Cloud-based Intrusion Detection Service framework

abstract: Intrusion Detection System (IDS) have become increasingly popular over the past years as an important network security technology to detect cyber attacks in a wide variety of network communication. IDS monitors' network or host system activities by collecting network information, and analyze this information for malicious activities. Cloud computing, with the concept of Software as a Service (SaaS) presents an exciting benefit when it enables providers to rent their services to users in perform complex tasks over the Internet. In addition, Cloud based services reduce a cost in investing new infrastructure, training new personnel, or licensing new software. In this paper, we introduce a novel framework based on Cloud computing called Cloud-based Intrusion Detection Service (CBIDS). This model enables the identification of malicious activities from different points of network and overcome the deficiency of classical intrusion detection. CBIDS can be implemented to detect variety of attacks in private and public Clouds.


title: Network attack detection at flow level

abstract: In this paper, we propose a new method for detecting unauthorized network intrusions, based on a traffic flow model and Cisco NetFlow protocol application. The method developed allows us not only to detect the most common types of network attack (DDoS and port scanning), but also to make a list of trespassers' IP-addresses. Therefore, this method can be applied in intrusion detection systems, and in those systems which lock these IP-addresses.


title: What do the Teachers Think?: Introducing Computational Thinking in the Primary School Curriculum

abstract: This paper reports on the preliminary results of an ongoing study examining the teaching of new primary school topics based on Computational Thinking in New Zealand. We analyse detailed feedback from 13 teachers participating in the study, who had little or no previous experience teaching Computer Science or related topics. From this we extract key themes identified by the teachers that are likely to be encountered when deploying a new curriculum, including unexpected opportunities for cross-curricula learning, development of students' social skills, and engaging a wide range of students. From here we articulate key concepts and issues that arise in the primary school context, based on feedback during professional development for the study, and direct feedback from teachers on the experience of delivering the new material in the classroom.


title: The Role of Classification in Knowledge Representation and Discovery

abstract: THELINK BETWEEN CLASSIFICATION AND KNOWLEDGE is explored. Classification schemes have properties that enable the representation of entities and relationships in structures that reflect knowledge of the domain being classified. The strengths and limitations of four classificatory approaches are described in terms of their ability to reflect, discover, and create new knowledge. These approaches are hierarchies, trees, paradigms, and faceted analysis. Examples are provided of the way in which knowledge and the classification process affect each other.


title: Extreme 3D Face Reconstruction: Seeing Through Occlusions

abstract: Existing single view, 3D face reconstruction methods can produce beautifully detailed 3D results, but typically only for near frontal, unobstructed viewpoints. We describe a system designed to provide detailed 3D reconstructions of faces viewed under extreme conditions, out of plane rotations, and occlusions. Motivated by the concept of bump mapping, we propose a layered approach which decouples estimation of a global shape from its mid-level details (e.g., wrinkles). We estimate a coarse 3D face shape which acts as a foundation and then separately layer this foundation with details represented by a bump map. We show how a deep convolutional encoder-decoder can be used to estimate such bump maps. We further show how this approach naturally extends to generate plausible details for occluded facial regions. We test our approach and its components extensively, quantitatively demonstrating the invariance of our estimated facial details. We further provide numerous qualitative examples showing that our method produces detailed 3D face shapes in viewing conditions where existing state of the art often break down.


title: The Cross-Entropy Method Optimizes for Quantiles

abstract: Cross-entropy optimization (CE) has proven to be a powerful tool for search in control environments. In the basic scheme, a distribution over proposed solutions is repeatedly adapted by evaluating a sample of solutions and refocusing the distribution on a percentage of those with the highest scores. We show that, in the kind of noisy evaluation environments that are common in decision-making domains, this percentage-based refocusing does not optimize the expected utility of solutions, but instead a quantile metric. We provide a variant of CE (Proportional CE) that effectively optimizes the expected value. We show using variants of established noisy environments that Proportional CE can be used in place of CE and can improve solution quality.


title: Escape the waterfall: Agile for aerospace

abstract: Agile is an umbrella software methodology that incorporates many of the best practices of the last couple of decades. In this paper, we will examine some of those key techniques for possible application in the aerospace domain, starting with a brief literature review to identify the key Agile publications and the germane DO-178B work. Virtually all Agile practices can be mapped to a DO-178B software development process. We provide a detailed analysis of the key practices, with a preliminary assessment of the ease of implementation for each. An analysis of a number of the difficulties involving transitioning from a traditional waterfall software development process to Agile practices will show that, though difficult, a transition is possible. The transition to Agile development does not require sudden, sweeping change, but instead can be accomplished through incorporating Agile methods into an existing process. We will document successful integration of test-driven development, pair programming, refactoring, an iterative approach, and other Agile methods into a traditional DO-178B software development process. We conclude with a call for a collaborative effort to further explore Agile as an answer to the urgent need for new approaches to complex systems that have become increasingly difficult to verify and validate.


title: A New Unified Scheme for Controlled Power System Separation Using Synchronized Phasor Measurements

abstract: Controlled power system separation, which separates the transmission system into islands in a controlled manner, is considered the final resort against a blackout under severe disturbances, e.g. cascading events. Three critical problems of controlled separation are where and when to separate and what to do after separation, which are rarely studied together. They are addressed in this paper by a proposed unified controlled separation scheme based on synchrophasors. The scheme decouples the three problems by partitioning them into sub-problems handled strategically in three time stages: the Offline Analysis stage determines elementary generator groups, optimizes potential separation points in between and designs post-separation control strategies; the Online Monitoring stage predicts separation boundaries by modal analysis on synchrophasor data; the Real-time Control stage calculates a synchrophasor-based separation risk index for each boundary to predict the time to perform separation. The proposed scheme is demonstrated on a 179-bus power system by case studies.


title: On the scalability of source/drain current enhancement in thin film sSOI

abstract: This paper demonstrates for the first time the scalability of source/drain current enhancement on low-doped thin film strained silicon on insulator (sSOI) substrate. Current improvement is maintained in narrow channel NFETs despite the relaxation from biaxial to uniaxial tensile strain after mesa patterning. Using strained contact etch-stop layers (sCESL), additional boost is achieved in short devices, resulting in 50% improvement in the drive current of 50 nm gate length devices with respect to conventional reference SOI process.


title: Eight-port dual-polarized MIMO antenna for 5G smartphone applications

abstract: An 8-port dual-polarized antenna array operating in 2.6-GHz band (2550–2650 MHz) for 5G communication is proposed. The proposed array is composed of four pairs of orthogonally dual-polarized loop type antenna element, and each antenna pair is coupling fed by two independent feeding structures to obtain dual-polarized radiation. The antenna array is mounted on four corners of the main board (a 68 × 136 mm2 FR4 substrate) to get good port isolations. The antenna is simulated and optimized, good port parameters and radiating performances show the proposed array a good candidate for 5G smartphone applications.


title: Preparation and Biodegradation of Thermosensitive Chitosan Hydrogel as a Function of pH and Temperature

abstract: We have developed an injectable thermosensitive hydrogel for local drug delivery to treat cancers clinically. We selected chitosan as a polymer matrix because of its biocompatibility and biodegradability. Glycerol 2-phosphate disodium salt hydrate (β-GP) was used to neutralize the chitosan solution to physiological pH. The chitosan solution displayed a sol-gel phase transition in a pH- and temperature-dependent manner and formed an endothermic hydrogel after subcutaneous injection into mouse in the presence of β-GP. Additionally, we evaluated the biodegradation of chitosan hydrogel in mice by measuring the volume of injected chitosan hydrogel after subcutaneous injection. The injected chitosan hydrogel in mice was sected and stained with hematoxylin-eosin reagent for histological observation to confirm biodegradation of the hydrogel by the infiltrated cells. Chitosan hydrogel systems that possess biocompatibility and biodegradability could be promising thermosensitive injectable materials useful as depot systems for local anti-cancer drug delivery.


title: The Jump Tracker: Nonlinear Bayesian Tracking with Adaptive Meshes and a Markov Jump Process Model

abstract: Many tracking problems lie outside of the traditional situation of linear (or linearizable) measurements and dynamics addressed by the Kalman filter and its variants. This is especially true in applications where target measurements are highly ambiguous and visibility is affected by unpredictable phenomena such as intermittent interference and low signal-to-noise ratios. Bayesian tracking provides the general solution to this more general class of problems. Conceptually, Bayesian tracking is straightforward: given the target measurements, apply Bayes' rule to compute the probability density of the target location at any given time, all the while assuming a target motion model. Bayesian trackers are computationally expensive; there are two basic approaches to their implementation: sequential Monte Carlo methods such as particle filtering, and deterministic methods that compute the target density directly. This paper presents a new approach to the direct method that is theoretically and computationally novel in several ways. First, recent results from the theory of adaptive moving meshes are modified and applied, an approach that is distinctly different from previously published direct methods that use fixed meshes to solve the Fokker-Planck equation. Second, a straight-line motion model based on a Markov jump process for the velocity is assumed. Straight-line motion punctuated by jumps in target velocity may be a more suitable assumption for some target dynamics than the traditional random walk assumed in the Kalman filter and many Bayesian trackers. The resulting linear partial differential equation that describes the target position density is relatively easy to solve numerically, especially compared to the Fokker- Planck equation that results from the random walk motion assumption. The proposed Bayesian tracking algorithm is a promising alternative to competing methods. It is also shown that, like particle filters, the adaptive mesh approach necessarily suffers from the curse of dimensionality. Simulation results are shown using the example of bistatic radar.


title: Streaming the Web : Reasoning over Dynamic Data

abstract: In the last few years a new research area, called stream reasoning, emerged to bridge the gap between reasoning and stream processing. While current reasoning approaches are designed to work on mainly static data, the Web is, on the other hand, extremely dynamic: information is frequently changed and updated, and new data is continuously generated from a huge number of sources, often at high rate. In other words, fresh information is constantly made available in the form of streams of new data and updates.Despite some promising investigations in the area, stream reasoning is still in its infancy, both from the perspective of models and theories development, and from the perspective of systems and tools design and implementation.The aim of this paper is threefold: (i)?we identify the requirements coming from different application scenarios, and we isolate the problems they pose; (ii)?we survey existing approaches and proposals in the area of stream reasoning, highlighting their strengths and limitations; (iii)?we draw a research agenda to guide the future research and development of stream reasoning. In doing so, we also analyze related research fields to extract algorithms, models, techniques, and solutions that could be useful in the area of stream reasoning.


title: On (Commercial) Benefits of Automatic Text Summarization Systems in the News Domain: A Case of Media Monitoring and Media Response Analysis

abstract: In this work, we present the results of a systematic study to investigate the (commercial) benefits of automatic text summarization systems in a real world scenario. More specifically, we define a use case in the context of media monitoring and media response analysis and claim that even using a simple query-based extractive approach can dramatically save the processing time of the employees without significantly reducing the quality of their work.


title: Keyframe extraction in endoscopic video

abstract: In medical endoscopy more and more surgeons archive the recorded video streams in a long-term storage. One reason for this development, which is enforced by law in some countries, is to have evidence in case of lawsuits from patients. Another more practical reason is to allow later inspection of previous procedures and also to use parts of such videos for research and for training. However, due to the dramatic amount of video data recorded in a hospital on a daily basis, it is very important to have good preview images for these videos in order to allow for quick filtering of undesired content and for easier browsing through such a video archive. Unfortunately, common shot detection and keyframe extraction methods cannot be used for that video data, because these videos contain unedited and highly similar content, especially in terms of color and texture, and no shot boundaries at all. We propose a new keyframe extraction approach for this special video domain and show that our method is significantly better than a previously proposed approach.


The Memory Laws of Jost


title: HSV and RGB color histograms comparing for objects tracking among non overlapping FOVs, using CBTF

abstract: Object tracking over wide-areas, such as an airport, the downtown of a large city or any large public area, is done by multiple cameras. Especially in realistic application, those cameras have non overlapping Field of Views (FOVs). Multiple camera tracking is very important to establish correspondence among detected objects across different cameras. In this paper we investigate color histogram techniques to evaluate inter-camera tracking algorithm based on object appearances. We compute HSV and RGB color histograms in order to evaluate their performance in establishing correspondence between object appearances in different FOVs before and after Cumulative Brightness Transfer Function (CBTF).


title: P.: Parsing combinatory categorial grammar with answer set programming: Preliminary report

abstract: Combinatory categorial grammar (CCG) is a grammar formalism used for natural language parsing. CCG assigns structured lexical categories to words and uses combinatory rules to combine these categories to parse a sentence. In this work we propose and implement a new approach to CCG parsing that relies on a prominent knowledge representation formalism, answer set programming (ASP) - a declarative programming paradigm. We formulate the task of CCG parsing as a planning problem and use an ASP computational tool to compute solutions that correspond to valid parses. Compared to other approaches, there is no need to implement a specific parsing algorithm using such a declarative method. Our approach aims at producing all semantically distinct parse trees for a given sentence. From this goal, normalization and efficiency issues arise, and we deal with them by combining and extending existing strategies.We have implemented a CCG parsing tool kit-AspCcgTk-that uses ASP as its main computational means. The C&C supertagger can be used as a preprocessor within AspCcgTk, which allows us to achieve wide-coverage natural language parsing.


Automatic Drum Transcription Using Bi-Directional Recurrent Neural Networks


1 Exploration and Mapping with Autonomous Robot Teams Results from the MAGIC 2010 Competition


title: 3D HMM-based Facial Expression Recognition using Histogram of Oriented Optical Flow

abstract: In this paper, we propose a 3D HMM (Three-dimensional Hidden Markov Models) approach to recognizing human facial expressions and associated emotions. Human emotion is usually classified by psychologists into six categories: Happiness, Sadness, Anger, Fear, Disgust and Surprise. Further, psychologists categorize facial movements based on the muscles that produce those movements using a Facial Action Coding System (FACS). We look beyond pure muscle movements and investigate facial features – brow, mouth, nose, eye height and facial shape – as a means of determining associated emotions. Histogram of Optical Flow is used as the descriptor for extracting and describing the key features, while training and testing are performed on 3D Hidden Markov Models. Experiments on datasets show our approach is promising and robust.


Content-based Analysis and Visualization of Story Complexity


title: Design and preliminary assessment of Vanderbilt hand exoskeleton

abstract: This paper presents the design of a hand exoskeleton intended to enable or facilitate bimanual activities of daily living (ADLs) for individuals with chronic upper extremity hemiparesis resulting from stroke. The paper describes design of the battery-powered, self-contained exoskeleton and presents the results of initial testing with a single subject with hemiparesis from stroke. Specifically, an experiment was conducted requiring the subject to repeatedly remove the lid from a water bottle both with and without the hand exoskeleton. The relative times required to remove the lid from the bottles was considerably lower when using the exoskeleton. Specifically, the average amount of time required to grasp the bottle with the paretic hand without the exoskeleton was 25.9 s, with a standard deviation of 33.5 s, while the corresponding average amount of time required to grasp the bottle with the exoskeleton was 5.1 s, with a standard deviation of 1.9 s. Thus, the task time involving the paretic hand was reduced by a factor of five, while the standard deviation was reduced by a factor of 16.


title: A survey of postoperative pain management in fourteen hospitals in the UK

abstract: Summary The effectiveness of acute pain services in 14 hospitals in one English region was audited. We collected data on analgesia used, its efficacy and patient satisfaction for 522 patients after four commonly performed procedures: abdominal hysterectomy, total knee replacement, mastectomy and major abdominal surgery. Pain scores were measured on a verbal numeric rating scale in the recovery room and both pain scores and patient satisfaction were assessed at 24 h and 7 days postoperatively. Pain was managed well in recovery rooms but less well on the wards. Epidural analgesia gave significantly better pain scores than other therapies. Better pain relief was obtained if opioids were combined with NSAIDs than when given alone. There were marked differences between hospitals in the delivery of postoperative analgesia. Pain scores in most hospitals left room for improvement, particularly following abdominal hysterectomy, but patient satisfaction was good. The better efficacy of multimodal analgesia was confirmed. Evidence from this data for the effectiveness of multidisciplinary acute pain services was equivocal.


title: Equation of state calculations by fast computing machines

abstract: A general method, suitable for fast computing machines, for investigating such properties as equations of state for substances consisting of interacting individual molecules is described. The method consists of a modified Monte Carlo integration over configuration space. Results for the two‐dimensional rigid‐sphere system have been obtained on the Los Alamos MANIAC and are presented here. These results are compared to the free volume equation of state and to a four‐term virial coefficient expansion.


title: Modeling Time Lags in Citation Networks

abstract: The extant work on network analyses has thus far paid little attention to the heterogeneity in time lags and speed of information propagation along edges. In this paper, we study this novel problem, modeling the time dimension and lags on network edges, in the context of paper and patent citation networks where the variation in the speed of knowledge flows between connected nodes is apparent. We propose to model time lags in knowledge diffusions in citation networks in one of the two ways: deterministic lags and probabilistic lags. Then, we discuss two approaches of computationally working with time lags in edges of citation networks. Experimentally, we study two different applications to demonstrate the importance of the time dimension and lags in citations: (1) HITS algorithm and (2) patent citation recommendation. We conduct experiments on millions of U. S. patent data and Web of Science (WOS) paper data. Our experiments show that incorporating time dimension and lags in edges significantly improve network modeling and analyses.


title: Probiotics and the gut microbiota in intestinal health and disease

abstract: The use of probiotics is increasing in popularity for both the prevention and treatment of a variety of diseases. While a growing number of well-conducted, prospective, randomized, controlled, clinical trials are emerging and investigations of underlying mechanisms of action are being undertaken, questions remain with respect to the specific immune and physiological effects of probiotics in health and disease. This Review considers recent advances in clinical trials of probiotics for intestinal disorders in both adult and pediatric populations. An overview of recent in vitro and in vivo research related to potential mechanisms of action of various probiotic formulations is also considered.


title: Consistency-Driven Alternating Optimization for Multigraph Matching: A Unified Approach

abstract: The problem of graph matching (GM) in general is nondeterministic polynomial-complete and many approximate pairwise matching techniques have been proposed. For a general setting in real applications, it typically requires to find the consistent matching across a batch of graphs. Sequentially performing pairwise matching is prone to error propagation along the pairwise matching sequence, and the sequences generated in different pairwise matching orders can lead to contradictory solutions. Motivated by devising a robust and consistent multiple-GM model, we propose a unified alternating optimization framework for multi-GM. In addition, we define and use two metrics related to graphwise and pairwise consistencies. The former is used to find an appropriate reference graph, which induces a set of basis variables and launches the iteration procedure. The latter defines the order in which the considered graphs in the iterations are manipulated. We show two embodiments under the proposed framework that can cope with the nonfactorized and factorized affinity matrix, respectively. Our multi-GM model has two major characters: 1) the affinity information across multiple graphs are explored in each iteration by fixing part of the matching variables via a consistency-driven mechanism and 2) the framework is flexible to incorporate various existing pairwise GM solvers in an out-of-box fashion, and also can proceed with the output of other multi-GM methods. The experimental results on both synthetic data and real images empirically show that the proposed framework performs competitively with the state-of-the-art.


title: The Retention and Fracture Resistance of Two All-ceramic Post and Core Systems in Anterior Endodontically Treated Teeth

abstract: Background : In anterior endodontically treated teeth a better esthetic can be achieved by using all-ceramic post -core restorations instead of metal post systems. Maximum retention and fracture resistance of the post are the criteria upon which selection of a post system is based. The aim of this study was to evaluate the fracture resistance and retention of two all-ceramic post and core systems including IPS Empress 2 post-core system and IPS Empress 2 core connected to Zirconium Cosmopost, and comparing these features among the two groups Material and methods : Eighty extracted maxillary central incisors and canines were divided into two groups of 40 including IPS Empress 2 and zirconium Cosmopost. To evaluate retention, shear force was applied to the cement at the crosshead speed of 0.5 mm/min; force was applied until the post was detached from the canal. To measure the fracture resistance, the samples were loaded at 45-degrees to the horizon at the crosshead speed of 5 mm/min until fracture occurred. Result : The mean fracture strength and standard deviation of zirconium Cosmopost and IPS Empress 2 were 302.10 N (69.922), 151.63 N (48.661), respectively. The difference between groups was significant statistically (P<0.001). The zirconium Cosmoposts had lower retention than IPS Empress2 dowel-core, but the difference between groups was insignificant (p =0.134). Conclusion : Within the limitations of the study, it could be concluded that zirconium Cosmopost system may be a candidate for the restoration of anterior endodontically treated teeth, as the high fracture toughness and propitious esthetic feature.


title: The Role of Social Networks in Information Diffusion

abstract: Online social networking technologies enable individuals to simultaneously share information with any number of peers. Quantifying the causal effect of these mediums on the dissemination of information requires not only identification of who influences whom, but also of whether individuals would still propagate information in the absence of social signals about that information. We examine the role of social networks in online information diffusion with a large-scale field experiment that randomizes exposure to signals about friends' information sharing among 253 million subjects in situ. Those who are exposed are significantly more likely to spread information, and do so sooner than those who are not exposed. We further examine the relative role of strong and weak ties in information propagation. We show that, although stronger ties are individually more influential, it is the more abundant weak ties who are responsible for the propagation of novel information. This suggests that weak ties may play a more dominant role in the dissemination of information online than currently believed.


title: Configurable Structure Tree as a Means to Manage Configurable Business Processes

abstract: A configurable Business Process (BP) is an abstract BP that engineers customize with respect to specific requirements. To keep track of the multiple and recurrent customizations that lead to a set of derived BPs, this paper proposes a knowledge-based approach that uses a new Process Structure Tree called configurable PST (cPST). A cPST abstracts a separate variability option of the configurable BP. All cPSTs should be equivalent to the set of all PSTs associated with the derived BPs that could originate from the same configurable BP. This paper also proposes a logic-based configuration model for capturing configuration details on the cBP and describing the cPST computing.


title: Taxonomic Classification for Living Organisms Using Convolutional Neural Networks

abstract: Taxonomic classification has a wide-range of applications such as finding out more about evolutionary history. Compared to the estimated number of organisms that nature harbors, humanity does not have a thorough comprehension of to which specific classes they belong. The classification of living organisms can be done in many machine learning techniques. However, in this study, this is performed using convolutional neural networks. Moreover, a DNA encoding technique is incorporated in the algorithm to increase performance and avoid misclassifications. The algorithm proposed outperformed the state of the art algorithms in terms of accuracy and sensitivity, which illustrates a high potential for using it in many other applications in genome analysis.


title: Male circumcision: pain, trauma, and psychosexual sequelae

abstract: Infant male circumcision continues despite growing questions about its medical justification. As usually performed without analgesia or anaesthetic, circumcision is observably painful. It is likely that genital cutting has physical, sexual and psychological consequences too. Some studies link involuntary male circumcision with a range of negative emotions and even post-traumatic stress disorder (PTSD). Some circumcised men have described their current feelings in the language of violation, torture, mutilation and sexual assault. In view of the acute as well as long-term risks from circumcision and the legal liabilities that might arise, it is timely for health professionals and scientists to re-examine the evidence on this issue and participate in the debate about the advisability of this surgical procedure on unconsenting minors.


title: XiaoIce Band: A Melody and Arrangement Generation Framework for Pop Music

abstract: With the development of knowledge of music composition and the recent increase in demand, an increasing number of companies and research institutes have begun to study the automatic generation of music. However, previous models have limitations when applying to song generation, which requires both the melody and arrangement. Besides, many critical factors related to the quality of a song such as chord progression and rhythm patterns are not well addressed. In particular, the problem of how to ensure the harmony of multi-track music is still underexplored. To this end, we present a focused study on pop music generation, in which we take both chord and rhythm influence of melody generation and the harmony of music arrangement into consideration. We propose an end-to-end melody and arrangement generation framework, called XiaoIce Band, which generates a melody track with several accompany tracks played by several types of instruments. Specifically, we devise a Chord based Rhythm and Melody Cross-Generation Model (CRMCG) to generate melody with chord progressions. Then, we propose a Multi-Instrument Co-Arrangement Model (MICA) using multi-task learning for multi-track music arrangement. Finally, we conduct extensive experiments on a real-world dataset, where the results demonstrate the effectiveness of XiaoIce Band.


title: Comparison of time delay processing methods in control system

abstract: Time delay exists in the actual control system and presents some difficulties in processing of transfer function for process control. Approximation for time delay is commonly used in control system. This paper analyses the effects of different approximation including Taylor, Pade and all-pole approximation for different orders closed loop system. Second order Pade approximation can fit the time delay in first-order closed-loop system. Taylor approximation cannot apply to the system with large time delay. First order all-pole approximation gets the smallest over shoot, adjusting time both in first and second-order large delay system.


title: Performance analysis of application layer firewall

abstract: Nowadays organizations are likely to have Internet presence and this has led to a significant increase in all kinds of network attacks. Network security became one of the highest priorities to protect valuable data from unauthorized access. One of the solutions to protect against illegal access is to use a firewall. A firewall is a device designed to permit or deny network traffic. This paper will investigate the performance of firewall based on two main factors, namely response time and link utilization. Various scenarios were considered to show the impact of firewall in network performance using the OPNET simulation program.


title: Prediction of Financial Performance Using Genetic Algorithm and Associative Rule Mining

abstract: The proposed system introduces a new genetic algorithm for prediction of financial performance with input data sets from a financial domain. The goal is to produce a GA-based methodology for prediction of stock market performance along with an associative classifier from numerical data. This work restricts the numerical data to stock trading data. Stock trading data contains the quotes of stock market. From this information, many technical indicators can be extracted, and by investigating the relations between these indicators trading signals can discovered. Genetic algorithm is being used to generate all the optimized relations among the technical indicator and its value. Along with genetic algorithm association rule mining algorithm is used for generation of association rules among the various Technical Indicators. Associative rules are generated whose left side contains a set of trading signals, expressed by relations among the technical indicators, and whose right side indicates whether there is a positive ,negative or no change. The rules are being further given to the classification process which will be able to classify the new data making use of the previously generated rules. The proposed idea in the paper is to offer an efficient genetic algorithm in combination with the association rule mining algorithm which predicts stock market performance.


title: Author Disambiguation in PubMed: Evidence on the Precision and Recall of Author-ity among NIH-Funded Scientists

abstract: We examined the usefulness (precision) and completeness (recall) of the Author-ity author disambiguation for PubMed articles by associating articles with scientists funded by the National Institutes of Health (NIH). In doing so, we exploited established unique identifiers—Principal Investigator (PI) IDs—that the NIH assigns to funded scientists. Analyzing a set of 36,987 NIH scientists who received their first R01 grant between 1985 and 2009, we identified 355,921 articles appearing in PubMed that would allow us to evaluate the precision and recall of the Author-ity disambiguation. We found that Author-ity identified the NIH scientists with 99.51% precision across the articles. It had a corresponding recall of 99.64%. Precision and recall, moreover, appeared stable across common and uncommon last names, across ethnic backgrounds, and across levels of scientist productivity.


title: Sleep for cognitive enhancement

abstract: Sleep is essential for effective cognitive functioning. Loosing even a few hours of sleep can have detrimental effects on a wide variety of cognitive processes such as attention, language, reasoning, decision making, learning and memory. While sleep is necessary to ensure normal healthy cognitive functioning, it can also enhance performance beyond the boundaries of the normal condition. This article discusses the enhancing potential of sleep, mainly focusing on the domain of learning and memory. Sleep is known to facilitate the consolidation of memories learned before sleep as well as the acquisition of new memories to be learned after sleep. According to a widely held model this beneficial effect of sleep relies on the neuronal reactivation of memories during sleep that is associated with sleep-specific brain oscillations (slow oscillations, spindles, ripples) as well as a characteristic neurotransmitter milieu. Recent research indicates that memory processing during sleep can be boosted by (i) cueing memory reactivation during sleep, (ii) stimulating sleep-specific brain oscillations, and (iii) targeting specific neurotransmitter systems pharmacologically. Olfactory and auditory cues can be used, for example, to increase reactivation of associated memories during post-learning sleep. Intensifying neocortical slow oscillations (the hallmark of slow wave sleep) by electrical or auditory stimulation and modulating specific neurotransmitters such as noradrenaline and glutamate likewise facilitates memory processing during sleep. With this evidence in mind, this article concludes by discussing different methodological caveats and ethical issues that should be considered when thinking about using sleep for cognitive enhancement in everyday applications.


title: The effect of online reviews on product sales: A joint sentiment-topic analysis

abstract: Abstract This research examines the business impact of online reviews. It empirically investigates the influence of numerical and textual reviews on product sales performance. We use a Joint Sentiment-Topic model to extract the topics and associated sentiments in review texts. We further propose that numerical rating mediates the effects of textual sentiments. Findings not only contribute to the knowledge of how eWOM impacts product sales, but also illustrate how numerical rating and textual reviews interplay while shaping product sales. In practice, the findings help online vendors strategize business analytics operations by focusing on more relevant aspects that ultimately drive sales.


title: Operating system fingerprinting via automated network traffic analysis

abstract: Operating System (OS) detection significantly impacts network management and security. Current OS classification systems used by administrators use human-expert generated network signatures for classification. In this study, we investigate an automated approach for classifying host OS by analyzing the network packets generated by them without relying on human experts. While earlier approaches look for certain packets such as SYN packets, our approach is able to use any TCP/IP packet to determine the host systems' OS. We use genetic algorithms for feature subset selection in three machine learning algorithms (i.e., OneR, Random Forest and Decision Trees) to classify host OS by analyzing network packets. With the help of feature subset selection and machine learning, we can automatically detect the difference in network behaviors of OSs and also adapt to new OSs. Results show that the genetic algorithm significantly reduces the number of packet features to be analyzed while increasing the classification performance.


title: The Third PASCAL Recognizing Textual Entailment Challenge

abstract: This paper describes the PASCAL Network of Excellence first Recognising Textual Entailment (RTE-1) Challenge benchmark 1 . The RTE task is defined as recognizing, given two text fragments, whether the meaning of one text can be inferred (entailed) from the other. This application-independent task is suggested as capturing major inferences about the variability of semantic expression which are commonly needed across multiple applications. The Challenge has raised noticeable attention in the research community, attracting 17 submissions from diverse groups, suggesting the generic relevance of the task.


title: Visual road following using intrinsic images

abstract: We present a real-time visual-based road following method for mobile robots in outdoor environments. The approach combines an image processing method, that allows to retrieve illumination invariant images, with an efficient path following algorithm. The method allows a mobile robot to autonomously navigate along pathways of different types in adverse lighting conditions using monocular vision. To validate the proposed method, we have evaluated its ability to correctly determine boundaries of pathways in a challenging outdoor dataset. Moreover, the method's performance was tested on a mobile robotic platform that autonomously navigated long paths in urban parks. The experiments demonstrated that the mobile robot was able to identify outdoor pathways of different types and navigate through them despite the presence of shadows that significantly influenced the paths' appearance.


title: Efficacy of in-feed-administered ivermectin on Elaphostrongylus cervi first-stage excretion in red deer (Cervus elaphus)

abstract: Elaphostrongylus cervi (Nematoda, Metastrongyloidea) is an extrapulmonary lungworm of red deer (Cervus elaphus), which has a wide natural distribution in Eurasia. The presence and seasonal pattern of excretion of large spiny-tailed protostrongylid larvae comparable to the descriptions of first-stage E. cervi in fecal samples (prevalence of 66.47%) from southern and central Spain were recently reported for the first time (Vicente and Gortazar 2001; Vicente et al. 2005). Although individual anthelmintic treatment allows administrating precise drug dosage, such a manipulation would be highly stressful for wild animals. Moreover, it would require the involvement of many people. Management problems could be overcome by using in-feed drug formulations. Only drugs known to be safe and having a broad therapeutic spectrum should be used (Rajkovic-Janje et al. 2004). Ivermectin is a semisynthetic derivative of avermectin characterized by an exceptional potency and wide spectrum of activity against many parasitic nematodes and arthropods. This compound has stimulated interest in its application in the treatment and prevention of parasitic problems common to free-ranging species of wildlife (Kocan 1985 ; Fernandez de Mera et al. 2004). The control of diseases in hunting-managed animals is of concern (Peterson 1991). In particular, E. cervi has considerable pathological consequences for other wild and domestic ungulates, thus being of importance for wildlife managers, livestock breeders and sanitary authorities (Gajadhar and Tessaro 1995; Pusterla 1998). The aim of this study was to assess the efficacy of a treatment usually employed in sanitary programs of semi-extensive red deer hunting ranges: the use of an in-feed 0.6% ivermectin premix formulation under field conditions. A simple case-control study was designed. Naturally infected adult hinds (n=132) fromMontes de Toledo (Ciudad Real province, Spain) were captured from the wild. During May–June 2004, the control group (n=20) and the treated group (n=112) were separately kept in two fenced grounds of similar characteristics. Oral in-feed ivermectin (Ivomec Premix 0.6% Merial Sharp & Dohme, The Netherlands) was mixed with commercial deer feed at a concentration of 7.5 ppm and administrated ad libitum during 7 days. The control group was fed with untreated deer feed. We employed fecal examination as a useful indicator of relative parasite abundance (Todd et al. 1970; Mason 1989). We collected fresh fecal pellet groups (11–22) on day 0 (prior to treatment) and then fortnightly until the end of the study. Protostrongylid larvae were extracted by coprological analysis in less than 24 h from 8–10 g of feces collected using the beaker extraction method as described by Forrester and Lankester (1997). The larvae were quantified in a Favatti counting chamber and expressed as the number of L1 per gram of feces. The terms prevalence and intensity of excretion are used as in Margolis et al. (1982). L1 counts were log10+1-transformed and Mann–Whitney’s nonparametric test (Siegel 1990) was employed to compare treatment effectiveness on L1 excretion intensity between (1) treatment groups throughout the different sampling dates and between (2) pretreatment and post-treatment prevalence separately for each group. Similarly, chi-square tests were employed to test for differences in prevalence. The significance level was established at P 0.05; ZM-W=0.75, P>0.05, respectively, I. G. Fernandez de Mera . J. Vicente (*) . C. Gortazar Instituto de Investigacion en Recursos Cinegeticos IREC (CSIC-UCLM-JCCM), Ronda de Toledo s/n, 13005 Ciudad Real, Spain e-mail: jvicente@irec.uclm.es Tel.: +34-926-225450 Fax: +34-926-225451


title: ANTIOXIDANT ACTIVITY, TOTAL PHENOLIC AND FLAVONOID CONTENTS OF ARTOCARPUS HETEROPHYLLUS AND MANILKARA ZAPOTA SEEDS AND ITS REDUCTION POTENTIAL Research Article

abstract: The four different extracts of Artocarpus heterophyllus and Manilkara zapota seeds were undertaken and studied for their total phenolics and flavonoids contents, reducing power and antioxidant activity. Total phenolic contents were determined spectrometrically by using Folin-Coicalteu method and were calculated as Gallic acid equivalents. Flavonoids and reducing power were also determined spectrometrically using standards methods. The antioxidant capacity was studied using DPPH radical scavenging assay and ABTS radical scavenging assay methods. Plants containing phenols and flavonoids have been reported to possess strong antioxidant properties. The results of antioxidant activity showed high effect in both extracts. From the present study, it can be concluded that Artocarpus heterophyllus and Manilkara zapota seed extract has effective antioxidant activity due to the presence of high amounts of phenolic compounds. The results revealed that the extracts showed high


title: On hierarchical brain tumor segmentation in MRI using fully convolutional neural networks: A preliminary study

abstract: Magnetic Resonance Imaging is the preferred imaging modality for assessing brain tumors, and segmentation is necessary for diagnosis and treatment planning. Thus, robust automatic segmentation methods are required. Machine learning proposals where the model is learned from data are quite successful. Hierarchical segmentation approaches firstly segment the whole tumor, followed by intra-tumor tissue identification. However, results comparing it with single stages approaches are needed, as state of the art results are also achieved by all-at-once strategies. Currently, fully convolutional networks approaches for segmentation are very efficient. In this paper, a hierarchical approach for brain tumor segmentation using a fully convolutional network is studied. The evaluation is performed on the Brain Tumor Segmentation Challenge 2013 dataset, and we report the metrics Dice Score Coefficient, Positive Predictive Value, and Sensitivity. Results show benefits from segmenting the complete tumor first, over all tissues in one stage. Moreover, the tumor core also benefits from such approach. This behavior may be justified by the high data imbalance observed between tumor and normal tissues, which is mitigated by considering the tumor as a whole.


title: A Distributed Fault-Tolerant Topology Control Algorithm for Heterogeneous Wireless Sensor Networks

abstract: This paper introduces a distributed fault-tolerant topology control algorithm, called the Disjoint Path Vector (DPV), for heterogeneous wireless sensor networks composed of a large number of sensor nodes with limited energy and computing capability and several supernodes with unlimited energy resources. The DPV algorithm addresses the $k$ -degree Anycast Topology Control problem where the main objective is to assign each sensor’s transmission range such that each has at least $k$ -vertex-disjoint paths to supernodes and the total power consumption is minimum. The resulting topologies are tolerant to $k-1$ node failures in the worst case. We prove the correctness of our approach by showing that topologies generated by DPV are guaranteed to satisfy $k$ -vertex supernode connectivity. Our simulations show that the DPV algorithm achieves up to 4-fold reduction in total transmission power required in the network and 2-fold reduction in maximum transmission power required in a node compared to existing solutions.


title: Automatic Detection and Classification of Glioma Tumors using Statistical Features

abstract: The characterization and grading of glioma tumors, via image derived features, for diagnosis, prognosis, and treatment response has been an active research area in medical image computing. This paper presents a novel method for automatic detection and classification of glioma from conventional T2 weighted MR images. Automatic detection of the tumor was established using newly developed method called Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA).Statistical Features were extracted from the detected tumor texture using first order statistics and gray level co-occurrence matrix (GLCM) based second order statistical methods. Statistical significance of the features was determined by t-test and its corresponding p- value. A decision system was developed for the grade detection of glioma using these selected features and its p- value. The detection performance of the decision system was validated using the receiver operating characteristic (ROC) curve. The diagnosis and grading of glioma using this non-invasive method can contribute promising results in medical image computing.


title: A synthetic research on the multimedia data encryption based mobile computing security enhancement model and multi-channel mobile human computer interaction framework

abstract: With the development of computer network, people could obtain information through the network with stronger impulsion, the dependence of the requirements also becomes higher, this is not only reflected in the increase of information, but to obtain and submit more reflected in real-time and easy to access to information on the pressing needs of the. Therefore, people devoted all aspects from the terminal, network and software platforms to make unremitting efforts. Under this basis, we conduct synthetic research on the data encryption based mobile computing security enhancement model and multi-channel mobile human computer interaction framework in this paper. We firstly introduce the discrete-time Hopfield neural network based data encryption algorithm beyond the analysis on the information flow security and type based model, data security review and attack model under mobile computing environment. We improve learning algorithm of the MD method to avoid the sample mobile and cross interference problems of Hebb rule. Later, we integrate the multi-channel concept to propose the new multi-channel man–machine interaction. In our framework, the 3D interactive technology, speech recognition and synthesis technology, natural language understanding and processing technology, eye tracking technology, posture, input, tactile, force display basic technology are taken into consideration for the synthetic analysis. The result from the experimental simulation proves that our methodology obtains better effectiveness and feasibility from both the angels of data security and interface experience.


Cleaning the Null Space: A Privacy Mechanism for Predictors.


title: How to Make a BLT Sandwich? Learning to Reason towards Understanding Web Instructional Videos

abstract: Understanding web instructional videos is an essential branch of video understanding in two aspects. First, most existing video methods focus on short-term actions for a-few-second-long video clips; these methods are not directly applicable to long videos. Second, unlike unconstrained long videos, e.g., movies, instructional videos are more structured in that they have step-by-step procedure constraining the understanding task. In this paper, we study reasoning on instructional videos via question-answering (QA). Surprisingly, it has not been an emphasis in the video community despite its rich applications. We thereby introduce YouQuek, an annotated QA dataset for instructional videos based on the recent YouCook2. The questions in YouQuek are not limited to cues on one frame but related to logical reasoning in the temporal dimension. Observing the lack of effective representations for modeling long videos, we propose a set of carefully designed models including a novel Recurrent Graph Convolutional Network (RGCN) that captures both temporal order and relation information. Furthermore, we study multiple modalities including description and transcripts for the purpose of boosting video understanding. Extensive experiments on YouQuek suggest that RGCN performs the best in terms of QA accuracy and a better performance is gained by introducing human annotated description.


title: Open-ended triangular waveguide antenna

abstract: This communication introduces the open-ended (equilateral) triangular waveguide antenna as a possible elementary radiator for phase-array antenna systems. The proposed structure combines the collinear feeding geometry and the specific aperture shape that would allow for closely packed array architectures required for wide-angle scanning applications. The input matching and the radiation properties of the antenna have been investigated by means of numerical simulations. A possible array arrangement and a feeding strategy have been suggested.


title: Sensor based healthcare information system

abstract: Over the last few years, the convincing forward steps in the development of Internet of Things (IoT)-enabling solutions are spurring the advent of novel and fascinating applications. Today's healthcare system is the lack of security and real time monitoring. In the wake of this tendency, this paper proposes a novel, IoT-aware, smart architecture for automatic monitoring and tracking of patients from their home itself. Staying true to the IoT vision, we propose a Automation Healthcare System (AHS). The proposed AHS is to investigate advanced home health care services. Data produced in AHS shared with doctors and patients through IoT. The system utilizes IoT telemetry to transmit data from sensors to a remote monitor. This paper discusses recent advances in wearable sensors healthcare system to monitor temperature, heart rate and the energy efficient routing. The system provides the security and real time monitoring.


title: Designing metacognitive activities

abstract: Designing metacognitive activities that focus on both cognitive and social development is a theoretical and practical challenge. This balanced approach to metacognition concerns itself with many aspects of student development, ranging from academic competence to knowledge about the self-as-learner. In this article, I examine two basic approaches to supporting metacognition: (a) strategy training and (b) creation of a supprotive social environment for metacognition. There are also two kinds of content that are taught using these two approaches: (a) knowledge about a specific domain and (b) knowledge about the self-as-learner. These approaches and contents have been used frequently in metacognitive interventions over the past two decades. Each offers unique contributions to metacognitive development. However, programs that address these approaches and contents simultaneously are rare. Maintaining the coordination, on one hand, between strategy training and creating social supports, and on the other hand, between knowledge about the subject domain and knowledge about the self-as-learner, is a challenge for most design efforts in metacognition. Future design issues include: (a) developing a system approach to promote coordination among these approaches and contents; and (b) finding ways to build knowledge about the self-as-learner.


Internet Voting Using Zcash.


title: Behind the game: Exploring the twitch streaming platform

abstract: Twitch is a streaming platform that lets users broadcast their screen whilst playing games. People can share their game experience and interact with others in real time. Twitch has now become the fourth largest source of peak Internet traffic in the US. This paper explores the unique nature of this platform over a 11 month dataset. We find that Twitch is very different to existing video platforms, with a small number of games consistently achieving phenomenal dominance. We find a complex game ecosystem combining consistently popular games over years, newly released games enjoying bursts of popularity, and even old games appearing on the platform. Despite a strong skew of views across channels, the top ranked channels, although taking a significant share of the viewers, exhibit unexpectedly high churn. The reason behind this churn lies within another unique feature of this ecosystem, namely tournaments, live events that last for a limited amount of time but are capable of attracting a huge share of views when they take place, as well as dominate the views of the related games. Overall, our work reveals a complex and rich ecosystem, very different from existing user generated content platforms.


title: Predicting Commercial Activeness over Urban Big Data

abstract: This study aims at revealing how commercial hotness of urban commercial districts (UCDs) is shaped by social contexts of surrounding areas so as to render predictive business planning. We define social contexts for a given region as the number of visitors, the region functions, the population and buying power of local residents, the average price of services, and the rating scores of customers, which are computed from heterogeneous data including taxi GPS trajectories, point of interests, geographical data, and user-generated comments. Then, we apply sparse representation to discover the impactor factor of each variable of the social contexts in terms of predicting commercial activeness of UCDs under a linear predictive model. The experiments show that a linear correlation between social contexts and commercial activeness exists for Beijing and Shanghai based on an average prediction accuracy of 77.69% but the impact factors of social contexts vary from city to city, where the key factors are rich life services, diversity of restaurants, good shopping experience, large number of local residents with relatively high purchasing power, and convenient transportation. This study reveals the underlying mechanism of urban business ecosystems, and promise social context-aware business planning over heterogeneous urban big data.


title: Parasitology Meets Ecology on Its Own Terms: Margolis et al. Revisited.

abstract: We consider 27 population and community terms used frequently by parasitologists when describing the ecology of parasites. We provide suggestions for various terms in an attempt to foster consistent use and to make terms used in parasite ecology easier to interpret for those who study free-living organisms. We suggest strongly that authors, whether they agree or disagree with us, provide complete and unambiguous definitions for all parameters of their studies.


title: Integrated antenna with inkjet-printed compact artificial magnetic surface for UHF applications

abstract: A low-profile antenna on HIS/AMC/EBG surface was developed for UHF applications. The surface is inkjet-printed on paper which produces a low-cost, light-weight, and environment-friendly solution. More compact unit cells (and thus smaller overall size) and/or wider antenna bandwidth can be realized using alternative unit cells/antenna configurations as discussed in [5–6].


title: Robust object tracking via sparsity-based collaborative model

abstract: In this paper we propose a robust object tracking algorithm using a collaborative model. As the main challenge for object tracking is to account for drastic appearance change, we propose a robust appearance model that exploits both holistic templates and local representations. We develop a sparsity-based discriminative classifier (SD-C) and a sparsity-based generative model (SGM). In the S-DC module, we introduce an effective method to compute the confidence value that assigns more weights to the foreground than the background. In the SGM module, we propose a novel histogram-based method that takes the spatial information of each patch into consideration with an occlusion handing scheme. Furthermore, the update scheme considers both the latest observations and the original template, thereby enabling the tracker to deal with appearance change effectively and alleviate the drift problem. Numerous experiments on various challenging videos demonstrate that the proposed tracker performs favorably against several state-of-the-art algorithms.


A Novel Illumination Estimation for Face Recognition under Complex Illumination Conditions


title: Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application

abstract: We present the design, implementation, evaluation, and user ex periences of theCenceMe application, which represents the first system that combines the inference of the presence of individuals using off-the-shelf, sensor-enabled mobile phones with sharing of this information through social networking applications such as Facebook and MySpace. We discuss the system challenges for the development of software on the Nokia N95 mobile phone. We present the design and tradeoffs of split-level classification, whereby personal sensing presence (e.g., walking, in conversation, at the gym) is derived from classifiers which execute in part on the phones and in part on the backend servers to achieve scalable inference. We report performance measurements that characterize the computational requirements of the software and the energy consumption of the CenceMe phone client. We validate the system through a user study where twenty two people, including undergraduates, graduates and faculty, used CenceMe continuously over a three week period in a campus town. From this user study we learn how the system performs in a production environment and what uses people find for a personal sensing system.


title: Optical Flow as a Tool for Cardiac Motion Estimation

abstract: A sparse based algorithm for optical flow estimation is presented and compared with several classical optical flow estimation algorithms. The comparison is performed using several video sequences available from the Middlebury benchmark where the ground truth optical flow is known. The sparse algorithm attains competitive results with average angular errors as low as 2.09° and average magnitude errors as low as 0.100. The algorithms are also tested using a 4 -- D cardiac Magnetic Resonance Image (MRI) sequence. The sparse algorithm estimates an optical flow field that represents the motion of contraction during the systole interval.


title: Theia: A Fast and Scalable Structure-from-Motion Library

abstract: In this paper, we have presented a comprehensive multi-view geometry library, Theia, that focuses on large-scale SfM. In addition to state-of-the-art scalable SfM pipelines, the library provides numerous tools that are useful for students, researchers, and industry experts in the field of multi-view geometry. Theia contains clean code that is well documented (with code comments and the website) and easy to extend. The modular design allows for users to easily implement and experiment with new algorithms within our current pipeline without having to implement a full end-to-end SfM pipeline themselves. Theia has already gathered a large number of diverse users from universities, startups, and industry and we hope to continue to gather users and active contributors from the open-source community.


title: From the ephemeral to the enduring: how approach-oriented mindsets lead to greater status.

abstract: We propose that the psychological states individuals bring into newly formed groups can produce meaningful differences in status attainment. Three experiments explored whether experimentally created approach-oriented mindsets affected status attainment in groups, both immediately and over time. We predicted that approach-oriented states would lead to greater status attainment by increasing proactive behavior. Furthermore, we hypothesized that these status gains would persist longitudinally, days after the original mindsets had dissipated, due to the self-reinforcing behavioral cycles the approach-oriented states initiated. In Experiment 1, individuals primed with a promotion focus achieved higher status in their newly formed groups, and this was mediated by proactive behavior as rated by themselves and their teammates. Experiment 2 was a longitudinal experiment and revealed that individuals primed with power achieved higher status, both immediately following the prime and when the groups were reassembled 2 days later to work on new tasks. These effects were mediated by independent coders' ratings of proactive behavior during the first few minutes of group interaction. Experiment 3 was another longitudinal experiment and revealed that priming happiness led to greater status as well as greater acquisition of material resources. Importantly, these immediate and longitudinal effects were independent of the effects of a number of stable dispositional traits. Our results establish that approach-oriented psychological states affect status attainment, over and above the more stable characteristics emphasized in prior research, and provide the most direct test yet of the self-reinforcing nature of status hierarchies. These findings depict a dynamic view of status organization in which the same group may organize itself differently depending on members' incoming psychological states. (PsycINFO Database Record (c) 2013 APA, all rights reserved)