title: An average current modulation method for single stage LED drivers with high power factor and zero low frequency current ripple

abstract: Conventional single-stage light-emitting diode (LED) drivers with a high power factor (PF) contain a significant LED current ripple at twice the ac line frequency, and would require large energy storage capacitors to limit the effect on LED light. Conventional designs and novel control techniques aim to power LED loads with a dc voltage to ensure a limited low-frequency LED current ripple. This paper proposes an average current modulation method that is designed to operate in conjunction with single-stage PF correction (PFC) circuits that contain significant ac voltage ripple, while maintaining zero low-frequency current ripple. This allows the energy storage capacitance of the PFC stage to be reduced, avoiding the need for electrolytic-type capacitors and prolonging the life of the LED driver. The average current modulation circuit requires a single low-voltage MOSFET, a current sense resistor, and a simple control circuit. By requiring no additional magnetic components, the cost of the current modulation circuit is very low and has minimal impact on the efficiency of the overall LED driver. Two experimental prototypes, an 8.75-W system with a buck–boost PFC converter and a 25-W system with a flyback PFC converter, have been built to verify the capability and excellent performance of the proposed driving technique.


Interaction of physical activity, mental health, health locus of control and quality of life : a study on university students in Pakistan


title: Light on horizontal interactive surfaces: Input space for tabletop computing

abstract: In the last 25 years we have witnessed the rise and growth of interactive tabletop research, both in academic and in industrial settings. The rising demand for the digital support of human activities motivated the need to bring computational power to table surfaces. In this article, we review the state of the art of tabletop computing, highlighting core aspects that frame the input space of interactive tabletops: (a) developments in hardware technologies that have caused the proliferation of interactive horizontal surfaces and (b) issues related to new classes of interaction modalities (multitouch, tangible, and touchless). A classification is presented that aims to give a detailed view of the current development of this research area and define opportunities and challenges for novel touch- and gesture-based interactions between the human and the surrounding computational environment.


title: The smartvision navigation prototype for blind users

abstract: The goal of the Portuguese project "SmartVision: active vision for the blind" is to develop a small, portable and cheap yet intelligent and reliable system for assisting the blind and visually impaired while navigating autonomously, both in- and outdoor. In this article we present an overview of the prototype, design issues, and its different modules which integrate GPS and Wi-Fi localisation with a GIS, passive RFID tags, and computer vision. The prototype addresses global navigation for going to some destiny, by following known landmarks stored in the GIS in combination with path optimisation, and local navigation with path and obstacle detection just beyond the reach of the white cane. The system does not replace the white cane but complements it, in order to alert the user to looming hazards. In addition, computer vision is used to identify objects on shelves, for example in a pantry or refrigerator. The user-friendly interface consists of a four-button hand-held box, a vibration actuator in the handle of the white cane, and speech synthesis. In the near future, passive RFID tags will be complemented by active tags for marking navigation landmarks, and speech recognition may complement or substitute the vibration actuator.


Feature Extraction and Classification of Brain Tumor using MRI


title: Highly Sensitive Piezocapacitive Sensor for Detecting Static and Dynamic Pressure Using Ion-Gel Thin Films and Conductive Elastomeric Composites

abstract: A new class of simple and highly sensitive piezocapacitive sensors that are capable of detecting static and dynamic pressure changes is reported. The pressure sensor structure is formed by vertically sandwiching a sandpaper-molded carbon nanotube/poly(dimethylsiloxane) composite (CPC) dielectric layer between two ion-gel thin film electrodes. Such a capacitive sensor system enables the distinguishable detection of directional movement of applied pressure as well as static pressure variation by modulating ion distribution in the ion-gel thin films. The resulting capacitive pressure sensors exhibit high sensitivity (9.55 kPa–1), high durability, and low operating voltage (0.1 V). Our proposed pressure sensors are successfully applied as potential platforms for monitoring human physiological signals and finger sliding motions in order to demonstrate their capability for practical usage. The outstanding sensor performance of the pressure sensors can permit applications in wearable electronic devices for human...


title: Endometrial leukocytes and menstruation

abstract: This review examines evidence supporting the concept that menstruation occurs as a result of an inflammatory process. In the endometrium, leukocyte numbers rise in the late secretory phase following the fall in serum progesterone concentrations. It is postulated that products released following activation of these leukocytes are critically important for menstruation. Mast cells, eosinophils, neutrophils and macrophages in particular are involved. Endometrial granular lymphocytes may also play a role, although their increase in numbers is somewhat earlier during the menstrual cycle than that of the others, suggesting perhaps a primary role in embryo implantation. Leukocyte products include a range of proteases, chemokines and cytokines which in concert result in focal production and activation of matrix metalloproteinases by endometrial cells and the subsequent breakdown of tissue that characterizes menstruation. Regulation of leukocyte entry, proliferation, differentiation and activation within the endometrium is not yet well understood, although both chemokines and cytokines produced locally by endometrial cells are clearly implicated. The role of progesterone in regulating these events is still not understood although the lack of progesterone receptors on endometrial leukocytes suggests indirect actions.


title: A Deep Learning-Based Approach to Progressive Vehicle Re-identification for Urban Surveillance

abstract: While re-identification (Re-Id) of persons has attracted intensive attention, vehicle, which is a significant object class in urban video surveillance, is often overlooked by vision community. Most existing methods for vehicle Re-Id only achieve limited performance, as they predominantly focus on the generic appearance of vehicle while neglecting some unique identities of vehicle (e.g., license plate). In this paper, we propose a novel deep learning-based approach to PROgressive Vehicle re-ID, called “PROVID”. Our approach treats vehicle Re-Id as two specific progressive search processes: coarse-to-fine search in the feature space, and near-to-distant search in the real world surveillance environment. The first search process employs the appearance attributes of vehicle for a coarse filtering, and then exploits the Siamese Neural Network for license plate verification to accurately identify vehicles. The near-to-distant search process retrieves vehicles in a manner like human beings, by searching from near to faraway cameras and from close to distant time. Moreover, to facilitate progressive vehicle Re-Id research, we collect to-date the largest dataset named VeRi-776 from large-scale urban surveillance videos, which contains not only massive vehicles with diverse attributes and high recurrence rate, but also sufficient license plates and spatiotemporal labels. A comprehensive evaluation on the VeRi-776 shows that our approach outperforms the state-of-the-art methods by 9.28 % improvements in term of mAP.


GIS Based Multicriteria Approaches to Housing Site Suitability Assessment


title: Advances in forecasting with neural networks ? Empirical evidence from the NN 3 competition on time series prediction

abstract: This paper reports the results of the NN3 competition, which is a replication of the M3 competition with an extension of the competition towards neural network (NN) and computational intelligence (CI) methods, in order to assess what progress has been made in the 10 years since the M3 competition. Two masked subsets of the M3 monthly industry data, containing 111 and 11 empirical time series respectively, were chosen, controlling for multiple data conditions of time series length (short/long), data patterns (seasonal/non-seasonal) and forecasting horizons (short/medium/long). The relative forecasting accuracy was assessed using the metrics from the M3, together with later extensions of scaled measures, and non-parametric statistical tests. The NN3 competition attracted 59 submissions from NN, CI and statistics, making it the largest CI competition on time series data. Its main findings include: (a) only one NN outperformed the damped trend using the sMAPE, but more contenders outperformed the AutomatANN of the M3; (b) ensembles of CI approaches performed very well, better than combinations of statistical methods; (c) a novel, complex statistical method outperformed all statistical and CI benchmarks; and (d) for the most difficult subset of short and seasonal series, a methodology employing echo state neural networks outperformed all others. The NN3 results highlight the ability of NN to handle complex data, including short and seasonal time series, beyond prior expectations, and thus identify multiple avenues for future research.


title: Emotion Cause Events: Corpus Construction and Analysis

abstract: Emotion processing has always been a great challenge. Given the fact that an emotion is triggered by cause events and that cause events are an integral part of emotion, this paper constructs a Chinese emotion cause corpus as a first step towards automatic inference of cause-emotion correlation. The corpus focuses on five primary emotions, namely happiness, sadness, fear, anger, and surprise. It is annotated with emotion cause events based on our proposed annotation scheme. Corpus data shows that most emotions are expressed with causes, and that causes mostly occur before the corresponding emotion verbs. We also examine the correlations between emotions and cause events in terms of linguistic cues: causative verbs, perception verbs, epistemic markers, conjunctions, prepositions, and others. Results show that each group of linguistic cues serves as an indicator marking the cause events in different structures of emotional constructions. We believe that the emotion cause corpus will be the useful resource for automatic emotion cause detection as well as emotion detection and classification.


title: PBit - a pattern-based testing framework for iptables

abstract: The most recent entry in a series of Linux firewall services is iptables. Because iptables is a security product in widespread use, software faults are a serious concern and firewall testing is an important activity. Firewall testing is hard because there are many parameters, resulting in a huge number of possible parameter combinations. We present a methodology for iptables regression testing based on test templates - parameterized test cases. Templates have been developed for the main iptables rules along with algorithms for generating combinations of template parameters. A GUI has also been developed, integrating the templates and the tuple generation algorithms.


title: ResearchOps: The case for DevOps in scientific applications

abstract: DevOps (a portmanteau of “development” and “operations”) is a software development method that extends the agile philosophy to rapidly produce software products and services and to improve operations performance and quality assurance. It was born to accelerate the delivery of web-based systems and quickly bring new value to users. Many web-based systems evolve according to usage trends without a clear long-term goal. Before the widespread use of web services, most software with a clear goal were delivered as packages that users installed on their own system. New versions were delivered with a much lower frequency, with periods in between versions ranging from months to years. Development cycles were divided into large design, coding and testing phases culminating in the release of a new stable version. In software development in the context of applied science, even when the goal is clear, the process to attain it is not. Hence, working releases that capture the current software state must be released frequently in order to reduce the risks for all stakeholders and to make it possible to assess the current state of a project and steer it in the right direction. This paper explores the usefulness of DevOps concepts to improve the development of software that supports scientific projects. We establish the similarities and differences between scientific projects and web applications development, and discuss where the related methodologies need to be extended. Unique challenges are discussed herewith developed solutions, and still open questions. Lessons learned are highlighted as best practices to be followed in research projects. This discussion is rooted in our experience in real-life projects at the IBM Research Brazil Lab, which just as well apply to other research institutions.


AUTOMATIC HEART DISEASE PREDICTION USING FEATURE SELECTION AND DATA MINING TECHNIQUE


title: Enhancing auditors' performance: The importance of motivational factors and the mediation effect of effort

abstract: Purpose - The purpose of this study is to examine the mediating effect of effort on the relationship between both accountability pressure and self-efficacy, and auditors' performance. Design/methodology/approach - The paper uses a between-subjects experimental research design with accountability pressure manipulated randomly to two groups, accountable and non-accountable. Each participant is required to perform internal control tasks. Findings - Based on partial least square (PLS) analysis, results indicate that both variables, i.e. accountability pressure and self-efficacy, are positively related to audit judgment performance through the process of high level of effort. High self-efficacious participants who received accountability pressure would have high levels of effort, which in turn increase audit judgment performance. Research implications/limitations - This study provides further evidence on the effect of motivational factors on auditors' performance. Understanding the mediating role of some motivational variables is crucial in designing a continuous development program to enhance auditors' performance. The proposed framework of effort as a mediating variable is consistent with Libby and Lipe and Chang Originality/value - The paper contributes to the literature on motivational factors that would explain the variability in audit judgments in coping with complex audit tasks.


title: Inferring Population Structure and Admixture Proportions in Low Depth NGS Data

abstract: We here present two methods for inferring population structure and admixture proportions in low depth next generation sequencing data. Inference of population structure is essential in both population genetics and association studies and is often performed using principal component analysis or clustering-based approaches. Next-generation sequencing methods provide large amounts of genetic data but are associated with statistical uncertainty for especially low depth sequencing data. Models can account for this uncertainty by working directly on genotype likelihoods of the unobserved genotypes. We propose a method for inferring population structure through principal component analysis in an iterative approach of estimating individual allele frequencies, where we demonstrate improved accuracy in samples with low and variable sequencing depth for both simulated and real datasets. We also use the estimated individual allele frequencies in a fast non-negative matrix factorization method to estimate admixture proportions. Both methods have been implemented in the PCAngsd framework available at http://www.popgen.dk/software/.


title: Recent developments on signal processing for smart grids

abstract: With the growing complexity of the electric grid, signal processing has a fundamental importance in characterization, diagnoses and protection. In this paper a brief review of digital signal processing techniques will be done. These techniques are focused in power systems applications, such as: signal compression, harmonic estimation and characterization of load and generation profiles.


title: Violent Video Games, Delinquency, and Youth Violence: New Evidence

abstract: Violent video game playing is correlated with aggression, but its relation to antisocial behavior in correctional and juvenile justice samples is largely unknown. Based on a data from a sample of institutionalized juvenile delinquents, behavioral and attitudinal measures relating to violent video game playing were associated with a composite measure of delinquency and a more specific measure of violent delinquency after controlling for the effects of screen time, years playing video games, age, sex, race, delinquency history, and psychopathic personality traits. Violent video games are asso- ciated with antisociality even in a clinical sample, and these effects withstand the robust influences of multiple correlates of juvenile delinquency and youth violence most notably psychopathy.


title: Automatic identification of best entry points for focused structured document retrieval

abstract: Focussed structured document retrieval employs the concept of best entry points (BEPs), which are intended to provide optimal starting-points from which users can browse to relevant document components. This paper describes two small-scale studies, using experimental data from the Shakespeare user study, which developed and evaluated different approaches to the problem of automatic identification of BEPs.


title: A Practical Network-Based Intrusion Detection and Prevention System

abstract: While Internet and network technology have been growing rapidly, cyber attack incidents also increase accordingly. The increasing occurrence of network attacks is an important problem to network services. In this paper, we present a network based Intrusion Detection and Prevention System DPS), which can efficiently detect many well-known attack types and can immediately prevent the network system from network attacks. Our approach is simple and efficient and can be used with several machine learning algorithms. We actually implement the IDPS using different machine learning algorithms and test in an online network environment. The experimental results show that our IDPS can distinguish normal network activities from main attack types (Probe and Denial of Service) with high accuracy of detection rate in a few seconds and automatically prevent the victim's computer network from the attacks. In addition, we apply a well-known machine learning technique called C4.5 Decision Tree in our approach to consider unknown or new network attack types. Surprisingly, the supervised Decision Tree technique can work very well, when experiencing with untrained or unknown network attack types.


title: Influence of automobile seat lumbar support prominence on spine and pelvic postures: a radiological investigation.

abstract: BACKGROUND ::: The use of lumbar supports has been associated with decreased reports of low back pain during driving exposures. However, there has been limited work investigating whether lumbar supports actually change spine and pelvic postures at the level of the vertebrae. ::: ::: ::: PURPOSE ::: To investigate the effectiveness of a lumbar support in changing radiological measures of lumbar spine and pelvic postures and to examine the impact of support excursion magnitudes on these postures. ::: ::: ::: METHODS ::: Eight male subjects were recruited with no history of back injury, pathologies or low back pain within the past 6 months. Radiographs were taken in four postures: standing, and sitting with 0 cm, 2 cm and 4 cm lumbar support prominence (LSP). ::: ::: ::: RESULTS ::: Lumbar lordosis angle increased from 20° with no support to 25° with 2 cm support and 30° with 4 cm support. Lumbar lordosis angles were significantly different between 0 cm support and 4 cm support (p < 0.0001) and between 2 cm support and 4 cm support (p = 0.0256). Increasing lumbar support reduced the flexion at intervertebral disc joints throughout the lumbar spine, however, these remained significantly different from upright standing (p > 0.001) with the exception of L1/L2 in 4 cm support (p = 0.1381) and L5/S1 for all seated postures (p = 0.0687). All measures of pelvic posture were significantly different in sitting compared to standing (p < 0.0001), however, the lumbar support had no significant impact on seated pelvic posture. ::: ::: ::: CONCLUSIONS ::: Lumbar supports were shown to impact the vertebral rotations of the lumbar spine yet had no effect on pelvis postures. Increasing support from the current maximum of 2 cm-4 cm resulted in increased lumbar lordosis. The changes were mostly imparted at the upper lumbar spine joints with the most marked change being exhibited at the approximate level of the lumbar support apex: in the L2/L3 joint.


title: An Electronic Nose for Detecting Hazardous Chemicals and Explosives

abstract: This study describes a portable system based on mimicking the mammalian olfactory mechanism (electronic nose), that can sense and identify chemical vapors by automated odor recognition. The electronic nose developed in this research consists of an array of tin oxide sensors, with each sensor in the array giving a different electrical response for a particular target vapor introduced into the sensing chamber. The combined output from the sensor array forms a fingerprint, or signature, that is unique for a particular odor. Pattern recognition techniques based on principal component analysis and artificial neural networks were developed for learning different chemical signatures. The electronic nose was successfully trained and tested in the laboratory to recognize various chemicals such as benzene, toluene, ethyl benzene, xylene, and gasoline. This study successfully demonstrates the feasibility of an electronic nose for detecting and identifying chemical and explosive vapors.


title: Finger-gesture Recognition Glove using Velostat (ICCAS 2011)

abstract: There are many researches into sign language translation by finger-gesture recognition. Recognition technic of finger alphabet can be a substitute for the deaf that are unable to use sound recognition devices instead of computer input devices. This paper is about the development of the finger-gesture recognition glove that recognizes sign language with a use of inexpensive and conductive materials. This material is called by ‘Velostat’, made of a film surfaced with carbon particles, used for anti-static package of electronic parts. Its resistance varies according to the strain of the film. So, we made a glove using Velostat and designed electronic circuits that have voltage on circuit, A/D converter, microcomputer, Bluetooth module and 5-DoF IMU (Inertial Measurement Unit). It can measure the five fingers' bending angle from voltage divided circuit with Velostat glove and calculate the hand motion angle from 5-DoF IMU.


title: Modeling and Design of Multilevel Bang–Bang CDRs in the Presence of ISI and Noise

abstract: Multilevel clock-and-data recovery (CDR) systems are analyzed, modeled, and designed. A stochastic analysis provides probability density functions that are used to estimate the effect of intersymbol interference (ISI) and additive white noise on the characteristics of the phase detector (PD) in the CDR. A slope detector based novel multilevel bang-bang CDR architecture is proposed and modeled using the stochastic analysis and its performance compared with a typical multilevel Alexander PD-based CDR for equal-loop bandwidths. The rms jitter of the CDRs are predicted using a linear jitter model and a Markov chain and verified using behavioral simulations. Jitter tolerance simulations are also employed to compare the two CDRs. Both analytical calculations and behavioral simulations predict that at equal-loop bandwidths, the proposed architecture is superior to the Alexander type CDR at large ISI and low signal-to-noise ratios.


title: Discriminative feature learning for efficient RGB-D object recognition

abstract: This paper presents an efficient approach to recognize objects captured with an RGB-D sensor. The proposed approach uses a Bag-of-Words (BOW) model to learn feature representations from raw RGB-D point clouds in a weakly supervised manner. To this end, we introduce a novel method based on randomized clustering trees to learn visual vocabularies which are fast to compute and more discriminative compared to the vocabularies generated by classical methods such as k-means. We show that, when combined with standard spatial pooling strategies, our proposed approach yields a powerful feature representation for RGB-D object recognition. Our extensive experimental evaluation on two challenging RGB-D object datasets and live video streams from Kinect shows that our learned features result in superior object recognition accuracies compared with the state-of-the-art methods.


title: A review of solar photovoltaic technologies

abstract: Global environmental concerns and the escalating demand for energy, coupled with steady progress in renewable energy technologies, are opening up new opportunities for utilization of renewable energy resources. Solar energy is the most abundant, inexhaustible and clean of all the renewable energy resources till date. The power from sun intercepted by the earth is about 1.8Â xÂ 1011Â MW, which is many times larger than the present rate of all the energy consumption. Photovoltaic technology is one of the finest ways to harness the solar power. This paper reviews the photovoltaic technology, its power generating capability, the different existing light absorbing materials used, its environmental aspect coupled with a variety of its applications. The different existing performance and reliability evaluation models, sizing and control, grid connection and distribution have also been discussed.


title: The Wisdom of MaSSeS: Majority, Subjectivity, and Semantic Similarity in the Evaluation of VQA

abstract: We introduce MASSES, a simple evaluation metric for the task of Visual Question Answering (VQA). In its standard form, the VQA task is operationalized as follows: Given an image and an open-ended question in natural language, systems are required to provide a suitable answer. Currently, model performance is evaluated by means of a somehow simplistic metric: If the predicted answer is chosen by at least 3 human annotators out of 10, then it is 100% correct. Though intuitively valuable, this metric has some important limitations. First, it ignores whether the predicted answer is the one selected by the Majority (MA) of annotators. Second, it does not account for the quantitative Subjectivity (S) of the answers in the sample (and dataset). Third, information about the Semantic Similarity (SES) of the responses is completely neglected. Based on such limitations, we propose a multi-component metric that accounts for all these issues. We show that our metric is effective in providing a more fine-grained evaluation both on the quantitative and qualitative level.


title: The lexical nature of syntactic ambiguity resolution

abstract: Ambiguity resolution is a central problem in language comprehension. Lexical and syntactic ambiguities are standardly assumed to involve different types of knowledge representations and be resolved by different mechanisms . An alternative account is provided in which both types of ambiguity derive from aspects of lexical representation and are resolved by the same processing mechanisms . Reinterpreting syntactic ambiguity resolution as a form of lexical ambiguity resolution obviates the need for special parsing principles to account for syntactic interpretation preferences, reconciles a number of apparently conflicting results concerning the roles of lexical and contextual information in sentence processing, explains differences among ambiguities in terms of ease of resolution, and provides a more unified account of language comprehension than was previously available .


title: A smart-device news recommendation technology based on the user click behavior

abstract: Studies have been conducted in regard to personalized news recommendation using collaborative filtering mechanisms based on users' click behaviors. However, few existing studies have focused on news recommendations depending on the rates of news-category interests. In this paper, we present a personalized news-recommendation system that builds profiles of users' news-category interests, determines the number of news articles to be recommended for each news category in proportion to news-category interests and ranks news articles according to the user's news interests. In order to find the news categories that are interesting to read, the smart device collects the web pages viewed by the user and classifies the contents. We use machine learning techniques in order to classify web pages into different categories, and the results of our experimentation show that Naive Bayes achieved the highest F-measure. The news preference is automatically calculated by the news reading time and the length of the news text. News articles with high preference generated by the collaborative filtering technique are recommended based on the rate of each category. The recommendation system we have implemented is based on collaborative filtering using Mahout Recommender API on Hadoop. Through user testing, we also assess whether the proposed system is useful.


title: p-Cycle-based node failure protection for survivable virtual network embedding

abstract: Network Virtualization offers to Physical Infrastructure Provider (PIP) the possibility to smoothly roll out multiple isolated networks on top of his infrastructure. A major challenge in this respect is the embedding problem which deals with the mapping of Virtual Networks (VNs) resources on PIP network. In literature, a number of research proposals have been focused on providing heuristic approaches to solve this NP-hard problem while most often assuming that PIP infrastructure is operational at all time. In virtualization environment a single physical node failure can result in single/multi logical link failure(s) as it effects all VNs with a mapping that spans over. Setup a dedicated backup for each VN embedding, i.e., no sharing, is inefficient in terms of resource usage. To address these concerns, we propose in this work an approach for VN mapping with combined physical node and multiple logical links protection mechanism (VNM-CNLP) that: (a) uses a large scale optimization technique, developed in our previous work, to calculate in a first stage a cost-efficient VN mapping while minimizing the effects of a single node failure in VN layer, and (b) proposes in a second stage link p-Cycle based protection techniques that minimize the backup resources while providing a full VN protection scheme against a single physical node failure and a multiple logical links failure. Our simulations show a clear advantage of VNM-CNLP approach over benchmark in terms of VN backup cost and resources utilization.


title: GPU-accelerated high-accuracy molecular docking using guided differential evolution: real world applications

abstract: The objective in molecular docking is to determine the best binding mode of two molecules in silico. A common application of molecular docking is in drug discovery where a large number of ligands are docked against a protein to identify potential drug candidates. This is a computationally intensive problem especially if flexibility of the molecules are taken into account. In this paper we show how MolDock, which is a high accuracy method for flexible molecular docking using a variant of differential evolution, can be parallelised on both CPU and GPU. The methods presented for parallelising the workload result in an average speedup of 3.9x on a 4-core CPU and 27.4x on a comparable CUDA enabled GPU when docking 133 ligands of different sizes. Furthermore, the presented parallelisation schemes are generally applicable and can easily be adapted to other common flexible docking methods.


title: An Optimised Deep Neural Network Approach for Forest Trail Navigation for UAV Operation within the Forest Canopy

abstract: Autonomous flight within a forest canopy represents a key challenge for generalised scene understanding ::: on-board a future Unmanned Aerial Vehicle (UAV) platform. Here we present an approach for automatic ::: trail navigation within such an environment that successfully generalises across differing image resolutions - ::: allowing UAV with varying sensor payload capabilities to operate equally in such challenging environmental ::: conditions. Specifically, this work presents an optimised deep neural network architecture, capable of stateof-the-art ::: performance across varying resolution aerial UAV imagery, that improves forest trail detection for ::: UAV guidance even when using significantly low resolution images that are representative of low-cost search ::: and rescue capable UAV platforms.


title: Programmable Automotive Headlights

abstract: The primary goal of an automotive headlight is to improve safety in low light and poor weather conditions. But, despite decades of innovation on light sources, more than half of accidents occur at night even with less traffic on the road. Recent developments in adaptive lighting have addressed some limitations of standard headlights, however, they have limited flexibility - switching between high and low beams, turning off beams toward the opposing lane, or rotating the beam as the vehicle turns - and are not designed for all driving environments. This paper introduces an ultra-low latency reactive visual system that can sense, react, and adapt quickly to any environment while moving at highway speeds. Our single hardware design can be programmed to perform a variety of tasks. Anti-glare high beams, improved driver visibility during snowstorms, increased contrast of lanes, markings, and sidewalks, and early visual warning of obstacles are demonstrated.


title: Semantic Interaction for Sensemaking: Inferring Analytical Reasoning for Model Steering

abstract: Visual analytic tools aim to support the cognitively demanding task of sensemaking. Their success often depends on the ability to leverage capabilities of mathematical models, visualization, and human intuition through flexible, usable, and expressive interactions. Spatially clustering data is one effective metaphor for users to explore similarity and relationships between information, adjusting the weighting of dimensions or characteristics of the dataset to observe the change in the spatial layout. Semantic interaction is an approach to user interaction in such spatializations that couples these parametric modifications of the clustering model with users' analytic operations on the data (e.g., direct document movement in the spatialization, highlighting text, search, etc.). In this paper, we present results of a user study exploring the ability of semantic interaction in a visual analytic prototype, ForceSPIRE, to support sensemaking. We found that semantic interaction captures the analytical reasoning of the user through keyword weighting, and aids the user in co-creating a spatialization based on the user's reasoning and intuition.


title: Colony nutritional status modulates worker responses to foraging recruitment pheromone in the bumblebee Bombus terrestris

abstract: Foraging activity in social insects should be regulated by colony nutritional status and food availability, such that both the emission of, and response to, recruitment signals depend on current conditions. Using fully automatic radio-frequency identification (RFID) technology to follow the foraging activity of tagged bumblebees (Bombus terrestris) during 16,000 foraging bouts, we tested whether the cue provided by stored food (the number of full honeypots) could modulate the response of workers to the recruitment pheromone signal. Artificial foraging phero- mones were applied to colonies with varied levels of food reserves. The response to recruitment pheromones was stronger in colonies with low food, resulting in more workers becoming active and more foraging bouts being performed. In addition to previous reports showing that in colonies with low food successful foragers perform more excited runs during which they release recruitment phero- mone and inactive workers are more prone to leave the nest following nectar influx, our results indicate that evolution has shaped a third pathway that modulates bumblebee foraging activity, thus preventing needless energy expendi- ture and exposure to risk when food stores are already high. This new feedback loop is intriguing since it involves context-dependent response to a signal. It highlights the integration of information from both forager-released pheromones (signal) and nutritional status (cue) that occurs within individual workers before making the decision to start foraging. Our results support the emerging view that responses to pheromones may be less hardwired than commonly acknowledged.


title: Fisher and VLAD with FLAIR

abstract: A major computational bottleneck in many current algorithms is the evaluation of arbitrary boxes. Dense local analysis and powerful bag-of-word encodings, such as Fisher vectors and VLAD, lead to improved accuracy at the expense of increased computation time. Where a simplification in the representation is tempting, we exploit novel representations while maintaining accuracy. We start from state-of-the-art, fast selective search, but our method will apply to any initial box-partitioning. By representing the picture as sparse integral images, one per codeword, we achieve a Fast Local Area Independent Representation. FLAIR allows for very fast evaluation of any box encoding and still enables spatial pooling. In FLAIR we achieve exact VLAD's difference coding, even with L2 and power-norms. Finally, by multiple codeword assignments, we achieve exact and approximate Fisher vectors with FLAIR. The results are a 18x speedup, which enables us to set a new state-of-the-art on the challenging 2010 PASCAL VOC objects and the fine-grained categorization of the CUB-2011 200 bird species. Plus, we rank number one in the official ImageNet 2013 detection challenge.


title: A Dynamic Method for Discovering Density Varied Clusters

abstract: Density-based spatial clustering of applications with noise (DBSCAN) is a base algorithm for density based clustering. It can find out the clusters of different shapes and sizes from a large amount of data, which is containing noise and outliers. However, it fails to handle the local density variation that exists within the cluster. Thus, a good clustering method should allow a significant density variation within the cluster because, if we go for homogeneous clustering, a large number of smaller unimportant clusters may be generated. In this paper an enhancement of DBSCAN algorithm is proposed, which detects the clusters of different shapes, sizes that differ in local density. We introduce new algorithm Dynamic Method DBSCAN (DMDBSCAN). It selects several values of the radius of a number of objects (Eps) for different densities according to a k-dist plot. For each value of Eps, DBSCAN algorithm is adopted in order to make sure that all the clusters with respect to the corresponding density are clustered. For the next process, the points that have been clustered are ignored, which avoids marking both denser areas and sparser ones as one cluster. Experimental results are obtained from artificial data sets and UCI real data sets. The final results show that our algorithm get a good results with respect to the original DBSCAN and DVBSCAN algorithms.


title: Preparation of Microwave Absorbing Coatings and Its Compatibility with Infrared Camouflage Coatings

abstract: A triple-layer microwave absorbing coatings has been developed, which composed of carbonyl iron particles microwave absorbing layer,phenolic aldehyde hollow microsphere intermediate layer and carbonyl iron particles microwave absorbing layer. The microwave absorbing coating was further coated with an infrared and visible light camouflage coatings. The results show that when the thickness of coating was about 2.6mm, the reflectivity was lower than -8dB within the frequency of 3-18 GHz. The absorbing proper- ties were hardly changed when further coated with the infrared camouflage coatings, and the infrared and visible light camouflage properties kept in effect.


title: On positivity-preserving high order discontinuous Galerkin schemes for compressible Euler equations on rectangular meshes

abstract: We construct uniformly high order accurate discontinuous Galerkin (DG) schemes which preserve positivity of density and pressure for Euler equations of compressible gas dynamics. The same framework also applies to high order accurate finite volume (e.g. essentially non-oscillatory (ENO) or weighted ENO (WENO)) schemes. Motivated by Perthame and Shu (1996) [20] and Zhang and Shu (2010) [26], a general framework, for arbitrary order of accuracy, is established to construct a positivity preserving limiter for the finite volume and DG methods with first order Euler forward time discretization solving one-dimensional compressible Euler equations. The limiter can be proven to maintain high order accuracy and is easy to implement. Strong stability preserving (SSP) high order time discretizations will keep the positivity property. Following the idea in Zhang and Shu (2010) [26], we extend this framework to higher dimensions on rectangular meshes in a straightforward way. Numerical tests for the third order DG method are reported to demonstrate the effectiveness of the methods.


title: Principles, techniques, and applications of biocatalyst immobilization for industrial application

abstract: Immobilization is one of the most effective and powerful tools used in industry, which has been studied and improved since the last century. Various immobilization techniques and support materials have been used on both laboratory and industrial scale. Each immobilization technique is applicable for a specific production mostly depending on the cost and sensibility of process. Compared to free biocatalyst systems, immobilization techniques often offer better stability, increased activity and selectivity, higher resistance, improved separation and purification, reuse of enzymes, and consequently more efficient process. Recently, many reviews have been published about immobilization systems; however, most of them have focused on a specific application or not emphasized in details. This review focuses on most commonly used techniques in industry with many recent applications including using bioreactor systems for industrial production. It is also aimed to emphasize the advantages and disadvantages of the immobilization techniques and how these systems improve process productivity compared to non-immobilized systems.


title: FPGA Implementation of a Switching Frequency Modulation Circuit for EMI Reduction in Resonant Inverters for Induction Heating Appliances

abstract: This paper presents the use of frequency modulation as a spread spectrum technique to reduce conducted electromagnetic interference (EMI) in the A frequency band (9-150 kHz) caused by resonant inverters used in induction heating home appliances. For sinusoidal, triangular, and sawtooth modulation profiles, the influence of peak period deviation in EMI reduction and in the power delivered to the load is analyzed. A digital circuit that generates the best of the analyzed modulation profiles is implemented in a field programmable gate array. The design is modeled in a very-high-speed integrated circuits hardware description language (VHDL). The digital circuit, the power converter, and the spectrum analyzer are simulated all together using a mixed-signal simulation tool to verify the functionality of the VHDL description. The spectrum analyzer is modeled in VHDL-analog and mixed-signal extension language (VHDL-AMS) and takes into account the resolution bandwidth stipulated by the EMI measurement standard. Finally, the simulations are experimentally verified on a 3.5 kW resonant inverter operating at 35 kHz.


title: Mining and analyzing the enterprise knowledge graph

abstract: Today's enterprises hold ever-growing amounts of public data, stemming from different organizational systems, such as development environments, CRM systems, business intelligence systems, and enterprise social media. This data unlocks rich and diverse information about entities, people, terms, and the relationships among them. A lot of insight can be gained through analyzing this knowledge graph, both by individual employees and by the organization as a whole. In this talk, I will review recent work done by the Social Technologies & Analytics group at IBM Research-Haifa to mine these relationships, represent them in a generalized model, and use the model for different aims within the enterprise, including social search [5], expertise location [1], social recommendation [2, 3], and network analysis [4].


title: A Counterexample to the Generalized Linial-Nisan Conjecture

abstract: In earlier work, we gave an oracle separating the relational versions of BQP and the polynomial hierarchy, and showed that an oracle separating the decision versions would follow from what we called the Generalized Linial-Nisan (GLN) Conjecture: that "almost k-wise independent" distributions are indistinguishable from the uniform distribution by constant-depth circuits. The original Linial-Nisan Conjecture was recently proved by Braverman; we offered a $200 prize for the generalized version. In this paper, we save ourselves $200 by showing that the GLN Conjecture is false, at least for circuits of depth 3 and higher. As a byproduct, our counterexample also implies that Pi2P is not contained in P^NP relative to a random oracle with probability 1. It has been conjectured since the 1980s that PH is infinite relative to a random oracle, but the highest levels of PH previously proved separate were NP and coNP. Finally, our counterexample implies that the famous results of Linial, Mansour, and Nisan, on the structure of AC0 functions, cannot be improved in several interesting respects.


title: A New EEG Acquisition Protocol for Biometric Identification Using Eye Blinking Signals

abstract: In this paper, a new acquisition protocol is adopted for identifying individuals from electroencephalogram signals based on eye blinking waveforms. For this purpose, a database of 10 subjects is collected using Neurosky Mindwave headset. Then, the eye blinking signal is extracted from brain wave recordings and used for the identification task. The feature extraction stage includes fitting the extracted eye blinks to auto- regressive model. Two algorithms are implemented for auto- regressive modeling namely; Levinson-Durbin and Burg algorithms. Then, discriminant analysis is adopted for classification scheme. Linear and quadratic discriminant functions are tested and compared in this paper. Using Burg algorithm with linear discriminant analysis, the proposed system can identify subjects with best accuracy of 99.8%. The obtained results in this paper confirm that eye blinking waveform carries discriminant information and is therefore appropriate as a basis for person identification methods.


title: A Neural Probabilistic Structured-Prediction Method for Transition-Based Natural Language Processing

abstract: We propose a neural probabilistic structured-prediction method for transition-based natural language processing, which integrates beam search and contrastive learning. The method uses a global optimization model, which can leverage arbitrary features over nonlocal context. Beam search is used for efficient heuristic decoding, and contrastive learning is performed for adjusting the model according to search errors. When evaluated on both chunking and dependency parsing tasks, the proposed method achieves significant accuracy improvements over the locally normalized greedy baseline on the two tasks, respectively.


title: Bidirectional Radio-over-Fiber architecture based on frequency up- and down-conversion with lightsource and LO-source free BS

abstract: Radio-over-Fiber (RoF) is a promising technology to fulfill future broadband access requirements with reliability, transparency and flexibility. We propose bi-directional RoF system with high RF carrier support. Downlink scheme utilizes optical frequency up-conversion while uplink scheme is based on electrical frequency down-conversion. For downlink, an un-modulated micro-wave band signal, fLO electrically combined with DPSK encoded digital data stream sub-carrier modulated at intermediate frequency (IF) is intensity modulated at an optical carrier, fIF in control station. An un-modulated high frequency carrier, local oscillator (LO) is also modulated over separate wavelength, fLO at CS. At remote node (RN), semiconductor optical amplifier Mach-Zehnder interferometer (SOA-MZI) is used for photonic mm-wave mixing (between fIF and fLO) and frequency up-conversion. After photo-detection at BS of optical channel modulated with up-converted RF signal, fRF up-converted downlink data signal at high RF (fRF) is extracted, down-converted to IF, de-modulated and tested for BER. During mixing process at RN, fLO also gets up-converted and modulated over wavelength fRF. For uplink, data at high RF is down-converted to IF at BS by extracting and using up-converted fLO from downlink electrical signal. Further wavelength re-use concept was employed for uplink where down-converted uplink signal is modulated over extracted optical carrier (from downlink signal) and transmitted to CS. Hence, it does not require sending separate optical LO, fLO to each BS. Cost-effective bi-directional solution is proposed with acceptable performance measures by employing wavelength reuse and remote LO delivery concepts.


title: Permission Re-Delegation: Attacks and Defenses

abstract: Modern browsers and smartphone operating systems treat applications as mutually untrusting, potentially malicious principals. Applications are (1) isolated except for explicit IPC or inter-application communication channels and (2) unprivileged by default, requiring user permission for additional privileges. Although inter-application communication supports useful collaboration, it also introduces the risk of permission redelegation. Permission re-delegation occurs when an application with permissions performs a privileged task for an application without permissions. This undermines the requirement that the user approve each application's access to privileged devices and data. We discuss permission re-delegation and demonstrate its risk by launching real-world attacks on Android system applications; several of the vulnerabilities have been confirmed as bugs. ::: ::: We discuss possible ways to address permission redelegation and present IPC Inspection, a new OS mechanism for defending against permission re-delegation. IPC Inspection prevents opportunities for permission redelegation by reducing an application's permissions after it receives communication from a less privileged application. We have implemented IPC Inspection for a browser and Android, and we show that it prevents the attacks we found in the Android system applications.


title: Shyness and Locus of Control as Predictors of Internet Addiction and Internet Use

abstract: The new psychological disorder of Internet addiction is fast accruing both popular and professional recognition. Past studies have indicated that some patterns of Internet use are associated with loneliness, shyness, anxiety, depression, and self-consciousness, but there appears to be little consensus about Internet addiction disorder. This exploratory study attempted to examine the potential influences of personality variables, such as shyness and locus of control, online experiences, and demographics on Internet addiction. Data were gathered from a convenient sample using a combination of online and offline methods. The respondents comprised 722 Internet users mostly from the Net-generation. Results indicated that the higher the tendency of one being addicted to the Internet, the shyer the person is, the less faith the person has, the firmer belief the person holds in the irresistible power of others, and the higher trust the person places on chance in determining his or her own course of life. People w...


title: Pelvic instability - Painful pelvic girdle in pregnancy.

abstract: BACKGROUND ::: Pelvic instability is a term describing a group of conditions causing pelvic girdle pain in pregnancy. Although a common problem in pregnancy, it is easily overlooked and sometimes dismissed as 'normal' by primary care professionals. ::: ::: ::: OBJECTIVE ::: This article outlines the clinical features, diagnosis and management of pelvic instability in pregnancy. ::: ::: ::: DISCUSSION ::: General practitioners are in an ideal position to diagnose pelvic instability, implement treatment, and refer for further care to a physiotherapist with an interest in women's health. Use of a supportive belt or taping, appropriate analgesia, core stability and pelvic floor exercises, and avoiding heavy lifting and activities that exacerbate the pain are the mainstays of treatment.


title: A Taxonomy of Blockchain-Based Systems for Architecture Design

abstract: Blockchain is an emerging technology for decentralised and transactional data sharing across a large network of untrusted participants. It enables new forms of distributed software architectures, where agreement on shared states can be established without trusting a central integration point. A major difficulty for architects designing applications based on blockchain is that thetechnology has many configurations and variants. Since blockchains are at an early stage, there is little product data or reliable technology evaluation available to compare different blockchains. In this paper, we propose how to classify and compare blockchains and blockchain-based systems to assist with the design and assessment of their impact on software architectures. Our taxonomy captures major architectural characteristics of blockchains and the impact of their principal design decisions. This taxonomy is intended to help with important architectural considerations about the performance and quality attributes of blockchain-based systems.


title: In vivo effects of Escherichia coli lipopolysaccharide on regulation of immune response and protein expression in striped catfish (Pangasianodon hypophthalmus)

abstract: Abstract Lipolysaccharide (LPS), a component of outer membrane protein of gram-negative bacteria, reportedly stimulates fish immune system. However, mechanisms driving this immunomodulatory effect are yet unknown. To determine effects of Escherichia coli lipopolysaccharide on regulation of immune response and protein expression of striped catfish ( Pangasianodon hypophthalmus ), juvenile fish (20–25 g) were injected with 3, 15 or 45 mg E.coli LPS/kg and challenged with Edwardsiella ictaluri. Plasma cortisol and glucose were rather low and did not differ ( p E.coli LPS enhanced innate immune responses.


title: A High Repetition Rate CMOS Driver for High-Energy Sub-ns Laser Pulse Generation in SPAD-Based Time-of-Flight Range Finding

abstract: An integrated 0.35-μm HV-CMOS driver has been designed for a gain-switched quantum well laser diode to generate short, energetic (100 ps/~0.5 nJ) optical pulses for a pulsed time-of-flight (TOF) laser radar, operating on the single-photon detection principle. The driver can produce a current pulse with a peak amplitude of ~2 A, a pulsewidth of 1 ns, and a pulsing rate of 500 kHz. The peak optical power and pulsewidth of the transmitter output with these parameters are 3 W and 100 ps, respectively. A single-shot precision of 2-3 cm is achieved in the TOF measurements of a non-cooperative target at 25 m with a reflectivity of 8% and a receiver aperture of 18 mm using a CMOS SPAD as the receiver.


title: SAVI: Synthetic apertures for long-range, subdiffraction-limited visible imaging using Fourier ptychography

abstract: Synthetic aperture radar is a well-known technique for improving resolution in radio imaging. Extending these synthetic aperture techniques to the visible light domain is not straightforward because optical receivers cannot measure phase information. We propose to use macroscopic Fourier ptychography (FP) as a practical means of creating a synthetic aperture for visible imaging to achieve subdiffraction-limited resolution. We demonstrate the first working prototype for macroscopic FP in a reflection imaging geometry that is capable of imaging optically rough objects. In addition, a novel image space denoising regularization is introduced during phase retrieval to reduce the effects of speckle and improve perceptual quality of the recovered high-resolution image. Our approach is validated experimentally where the resolution of various diffuse objects is improved sixfold.


title: DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers

abstract: In this paper we evaluate the quality of the activation layers of a convolutional neural network (CNN) for the generation of object proposals. We generate hypotheses in a sliding-window fashion over different activation layers and show that the final convolutional layers can find the object of interest with high recall but poor localization due to the coarseness of the feature maps. Instead, the first layers of the network can better localize the object of interest but with a reduced recall. Based on this observation we design a method for proposing object locations that is based on CNN features and that combines the best of both worlds. We build an inverse cascade that, going from the final to the initial convolutional layers of the CNN, selects the most promising object locations and refines their boxes in a coarse-to-fine manner. The method is efficient, because i) it uses the same features extracted for detection, ii) it aggregates features using integral images, and iii) it avoids a dense evaluation of the proposals due to the inverse coarse-to-fine cascade. The method is also accurate, it outperforms most of the previously proposed object proposals approaches and when plugged into a CNN-based detector produces state-of-the-art detection performance.


title: The SAPHO syndrome revisited with an emphasis on spinal manifestations

abstract: The synovitis, acne, pustulosis, hyperostosis, and osteitis (SAPHO) syndrome includes a group of chronic, relapsing, inflammatory musculoskeletal disorders with similar manifestations, in particular synovitis, hyperostosis, and osteitis, which may or may not be associated with neutrophilic skin eruptions such as palmoplantar pustulosis and acne conglobata. The syndrome occurs at any age, can involve any skeletal site, and its imaging appearances are variable, depending on the stage/age of the lesion and imaging method. The diagnosis is difficult if there is no skin disease. Awareness of the imaging appearances, especially in the spine, may help the radiologist in avoiding misdiagnosis (e.g., infection, tumor) and unnecessary invasive procedures, while facilitating early diagnosis and selection of an effective treatment. In this article, we provide an overview of the radiological appearances of SAPHO syndrome, focusing on the magnetic resonance imaging findings of vertebral involvement, and present relevant clinical and pathological features that assist early diagnosis.


title: Localization, Grasping, and Transportation of Magnetic Objects by a Team of MAVs in Challenging Desert-Like Environments

abstract: Autonomous Micro Aerial Vehicles have the potential to assist in real life tasks involving grasping and transportation, but not before solving several difficult research challenges. In this work, we address the design, control, estimation, and planning problems for cooperative localization, grasping, and transportation of objects in challenging outdoor scenarios. We demonstrate an autonomous team of MAVs able to plan safe trajectories for manipulation of ferrous objects, while guaranteeing inter-robot collision avoidance and automatically creating a map of the objects in the environment. Our solution is predominantly distributed, allowing the team to pick and transport ferrous disks to a final destination without collisions. This result is achieved using a new magnetic gripper with a novel feedback approach, enabling the detection of successful grasping. The gripper design and all the components to build a platform are clearly provided as open-source hardware for reuse by the community. Finally, the proposed solution is validated through experimental results where difficulties include inconsistent wind, uneven terrain, and sandy conditions.


title: MIST: Fog-based data analytics scheme with cost-efficient resource provisioning for IoT crowdsensing applications

abstract: Abstract Development of Internet of things (IoT) has revitalized the feature scale of wearables and smart home/city applications. The landscape of these applications encountering big data needs to be replotted on cloud instead of solely relying on limited storage and computational resources of small devices. However, with the rapid increase in the number of Internet-connected devices, the increased demand of real-time, low-latency services is proving to be challenging for the traditional cloud computing framework. Although, fog computing, by providing elastic resources and services to end users at the edge of network, emerges as a promising solution, but the upsurge of novel social applications such as crowd sensing has fostered the need for scalable cost-efficient platforms that can enable distributed data analytics, while optimizing the allocation of resources and minimizing the response time. Following the existing trends, we are motivated to propose a fog computing based scheme, called MIST (i.e. a cloud near the earth's surface with lesser density than fog), to support crowd sensing applications in the context of IoT. For cost-efficient provisioning limited resources, we also jointly investigate data consumer association, task distribution, and virtual machine placement towards MIST. We first formulate the problem into a mixed-integer non-linear program (MINLP) and then linearise it into a mixed integer linear program (MILP). A comprehensive evaluation of MIST is performed by consideration of real world parameters of the Tehran province, the capital of Iran. Results show that as the number of applications demanding real-time service increases, the MIST fog-based scheme outperforms traditional cloud computing.


title: Defining Insight for Visual Analytics

abstract: Many have argued that providing insight is the main goal of information visualization. Stuart Card, Jock Mackinlay, and Ben Shneiderman declare that "the purpose of visualization is insight, while Jim Thomas and Kris Cook propose in Illuminating the Path that the purpose of visual analytics is to enable and discover insight. The idea that visualization should lead to insight seems logical, but researchers in the community have been slow to build on the concept because insight is difficult to define. As Ji Soo Yi and his colleagues point out, although a few definitions of insight exist, no commonly accepted definition has emerged in the community.


title: Authenticated encryption in SSH: provably fixing the SSH binary packet protocol

abstract: The Secure Shell (SSH) protocol is one of the most popular cryptographic protocols on the Internet. Unfortunately, the current SSH authenticated encryption mechanism is insecure. In this paper we propose several fixes to the SSH protocol and, using techniques from modern cryptography, we prove that our modified versions of SSH meet strong new chosen-ciphertext privacy and integrity requirements. Furthermore, our proposed fixes will require relatively little modification to the SSH protocol or to SSH implementations. We believe that our new notions of privacy and integrity for encryption schemes with stateful decryption algorithms will be of independent interest.


title: Vertical Relationships: An Introduction

abstract: This short essay introduces a special issue of The Journal of Industrial Economics on vertical relationships, including both vertical integration and vertical restraints. In the last few years, there has been a resurgence of theoretical and empirical work on vertical relationships, and they are central to major current issues in industrial policy. In this introduction, the authors briefly describe the contribution by the papers that make up this special issue. Copyright 1991 by Blackwell Publishing Ltd.


title: Inside dropbox: understanding personal cloud storage services

abstract: Personal cloud storage services are gaining popularity. With a rush of providers to enter the market and an increasing offer of cheap storage space, it is to be expected that cloud storage will soon generate a high amount of Internet traffic. Very little is known about the architecture and the performance of such systems, and the workload they have to face. This understanding is essential for designing efficient cloud storage systems and predicting their impact on the network. This paper presents a characterization of Dropbox, the leading solution in personal cloud storage in our datasets. By means of passive measurements, we analyze data from four vantage points in Europe, collected during 42 consecutive days. Our contributions are threefold: Firstly, we are the first to study Dropbox, which we show to be the most widely-used cloud storage system, already accounting for a volume equivalent to around one third of the YouTube traffic at campus networks on some days. Secondly, we characterize the workload users in different environments generate to the system, highlighting how this reflects on network traffic. Lastly, our results show possible performance bottlenecks caused by both the current system architecture and the storage protocol. This is exacerbated for users connected far from storage data-centers. All measurements used in our analyses are publicly available in anonymized form at the SimpleWeb trace repository: http://traces.simpleweb.org/dropbox/


title: ACED: A Hardware Library for Generating DSP Systems

abstract: Designers translate DSP algorithms into application-specific hardware via primitives composed in various ways for different architectural realizations. Despite sharing underlying algorithms and hardware constructs, designs are often difficult to reuse, leading to redeveloping/reverifying conceptually similar instances. Hardware generators are attractive solutions for effectively balancing fine-grained control of implementation details with simple, retargetable hardware descriptions. This work presents ACED, a hardware library for generating DSP systems. It extends the Chisel hardware construction language and FIRRTL compiler and operates on three principles: zero-cost abstraction, unobtrusive downstream optimization/specialization promoting generator reusability, and unified, portable systems modeling and verification.


Can Cryptography Secure Next Generation Air Traffic Surveillance?


title: The theory of signal detectability

abstract: The problem of signal detectability treated in this paper is the following: Suppose an observer is given a voltage varying with time during a prescribed observation interval and is asked to decide whether its source is noise or is signal plus noise. What method should the observer use to make this decision, and what receiver is a realization of that method? After giving a discussion of theoretical aspects of this problem, the paper presents specific derivations of the optimum receiver for a number of cases of practical interest. The receiver whose output is the value of the likelihood ratio of the input voltage over the observation interval is the answer to the second question no matter which of the various optimum methods current in the literature is employed including the Neyman - Pearson observer, Siegert's ideal observer, and Woodward and Davies' "observer." An optimum observer required to give a yes or no answer simply chooses an operating level and concludes that the receiver input arose from signal plus noise only when this level is exceeded by the output of his likelihood ratio receiver. Associated with each such operating level are conditional probabilities that the answer is a false alarm and the conditional probability of detection. Graphs of these quantities called receiver operating characteristic, or ROC, curves are convenient for evaluating a receiver. If the detection problem is changed by varying, for example, the signal power, then a family of ROC curves is generated. Such things as betting curves can easily be obtained from such a family. The operating level to be used in a particular situation must be chosen by the observer. His choice will depend on such factors as the permissable false alarm rate, a priori probabilities, and relative importance of errors. With these theoretical aspects serving as an introduction, attention is devoted to the derivation of explicit formulas for likelihood ratio, and for probability of detection and probability of false alarm, for a number of particular cases. Stationary, band-limited, white Gaussian noise is assumed. The seven special cases which are presented were chosen from the simplest problems in signal detection which closely represent practical situations. Two of the cases form a basis for the best available approximation to the important problem of finding probability of detection when the starting time of the signal, signal frequency, or both, are unknown. Furthermore, in these two cases uncertainty in the signal can be varied, and a quantitative relationship between uncertainty and ability to detect signals is presented for these two rather general cases. The variety of examples presented should serve to suggest methods for attacking other simple signal detection problems and to give insight into problems too complicated to allow a direct solution.


title: Deep Convolutional Neural Networks Based on Semi-Discrete Frames

abstract: Deep convolutional neural networks have led to breakthrough results in practical feature extraction applications. The mathematical analysis of these networks was pioneered by Mallat, 2012. Specifically, Mallat considered so-called scattering networks based on identical semi-discrete wavelet frames in each network layer, and proved translation-invariance as well as deformation stability of the resulting feature extractor. The purpose of this paper is to develop Mallat's theory further by allowing for different and, most importantly, general semi-discrete frames (such as, e.g., Gabor frames, wavelets, curvelets, shearlets, ridgelets) in distinct network layers. This allows to extract wider classes of features than point singularities resolved by the wavelet transform. Our generalized feature extractor is proven to be translation-invariant, and we develop deformation stability results for a larger class of deformations than those considered by Mallat. For Mallat's wavelet-based feature extractor, we get rid of a number of technical conditions. The mathematical engine behind our results is continuous frame theory, which allows us to completely detach the invariance and deformation stability proofs from the particular algebraic structure of the underlying frames.


title: Situation-based contingencies underlying trait-content manifestation in behavior.

abstract: ABSTRACT Two studies investigated whether situations are associated with the manifestation of Big Five trait contents in behavior. Several times per day for 2 or 5 weeks, participants reported their current Extraversion, Agreeableness, Conscientiousness, and Emotional Stability states and rated the concurrent situation on several characteristics. Multilevel models tested for the average individual's contingency of each Big Five state on each situation characteristic and for whether individuals differed from each other reliably in those contingencies. Results showed that (1) there are psychologically active characteristics of situations on which trait-manifesting behavior is contingent; (2) contingencies on psychologically active characteristics of varying situations are part of the explanation for the sizeable within-person variability in behavior; (3) individuals differ reliably in their contingencies, and such individual differences may partially explain individual differences in amount of variability; and (4) the situation characteristics that are psychologically active differ by trait. These findings suggest that within-person variability in personality states is meaningful and is related to situations, that personality psychology should characterize situations in terms of their relevance to personality states, and that process and individual-difference structure approaches can be integrated in personality psychology.


title: Characterizing storage workloads with counter stacks

abstract: Existing techniques for identifying working set sizes based on miss ratio curves (MRCs) have large memory overheads which make them impractical for storage workloads. We present a novel data structure, the counter stack, which can produce approximate MRCs while using sublinear space. We show how counter stacks can be checkpointed to produce workload representations that are many orders of magnitude smaller than full traces, and we describe techniques for estimating MRCs of arbitrary workload combinations over arbitrary windows in time. Finally, we show how online analysis using counter stacks can provide valuable insight into live workloads.


title: Non-Classical Monocytes and Monocyte Chemoattractant Protein-1 (MCP-1) Correlate with Coronary Artery Calcium Progression in Chronically HIV-1 Infected Adults on Stable Antiretroviral Therapy

abstract: BACKGROUND ::: Persistent inflammation and immune activation has been hypothesized to contribute to increased prevalence of subclinical atherosclerosis and cardiovascular disease (CVD) risk in patients with chronic HIV infection. In this study, we examined the correlation of peripheral monocyte subsets and soluble biomarkers of inflammation to coronary artery calcium (CAC) progression, as measured by cardiac computed tomography scan. ::: ::: ::: METHODS ::: We conducted a longitudinal analysis utilizing baseline data of 78 participants with HIV infection on stable antiretroviral therapy (ART) in the Hawaii Aging with HIV-Cardiovascular study who had available baseline monocyte subset analysis as well as CAC measurement at baseline and at 2-year follow up. Monocyte phenotypes were assessed from cryopreserved blood by flow cytometry and plasma was assayed for soluble biomarkers using antibody-coated beads in a high sensitivity Milliplex Luminex platform. Change in CAC over 2 years was analyzed as the primary outcome variable. ::: ::: ::: RESULTS ::: Of all monocyte subsets and biomarkers tested, higher non-classical monocyte percentage (ρ = 0.259, p = 0.022), interleukin (IL)-6 (ρ = 0.311, p = 0.012), and monocyte chemoattractant protein (MCP)-1 (ρ = 0.524, p = <0.001) were significantly correlated to higher 2-year CAC progression in unadjusted Spearman's correlation. Non-classical monocyte percentage (ρ = 0.247, p = 0.039), and MCP-1 (ρ = 0.487, p = <0.001), remained significantly correlated to 2-year CAC progression, while IL-6 was not (ρ = 0.209, p = 0.120) after adjustment for age, hypertension, diabetes mellitus, total/HDL cholesterol ratio, smoking history, and BMI. ::: ::: ::: CONCLUSION ::: The percentage of non-classical monocytes and plasma MCP-1 levels were independently associated with CAC progression and may be related to the progression of atherosclerosis and increased CVD risk associated with chronic HIV infection on stable ART.


title: Partial discharge detection and localization of defected power cable using HFCT and UHF sensors

abstract: This paper proposes two alternative methods for online partial discharge (PD) detection and localization of a 6.2/7 kV XLPE insulated medium voltage (MV) power cable. The artificial defect as surface discharge was made in the middle of the cable at the joint connecting between two of 10 m length cables with 50 sq.mm diameter. Therefore, the total length of the cable is 20 m. In addition, the corona discharge was also made at the end of cable termination. The PD measurement was performed under uncontrollable noises with limitations of cable characteristics. UHF and HFCT sensors together with their supplemented techniques as Trending method for UHF sensor as well as Time Domain Reflectometry (TDR) technique for HFCT sensor are applied in the experiment in order to identify and locate the PD types. The results for PD identification and localization of both UHF and HFCT sensors are satisfied.


title: Measurement of the Elastic Properties and Intrinsic Strength of Monolayer Graphene

abstract: We measured the elastic properties and intrinsic breaking strength of free-standing monolayer graphene membranes by nanoindentation in an atomic force microscope. The force-displacement behavior is interpreted within a framework of nonlinear elastic stress-strain response, and yields second- and third-order elastic stiffnesses of 340 newtons per meter (N m –1 ) and –690 Nm –1 , respectively. The breaking strength is 42 N m –1 and represents the intrinsic strength of a defect-free sheet. These quantities correspond to a Young9s modulus of E = 1.0 terapascals, third-order elastic stiffness of D = –2.0 terapascals, and intrinsic strength of σ int = 130 gigapascals for bulk graphite. These experiments establish graphene as the strongest material ever measured, and show that atomically perfect nanoscale materials can be mechanically tested to deformations well beyond the linear regime.


title: Channel capacity of fast fading channels

abstract: The capacity of a fast fading channel with a Gaussian signal is analyzed using a simple block-wise fading model. We conclude that, the capacity degradation due to the high fading rate is small, and the fading degradation compared with a static channel is also very small, so in most cases of mobile communications, we need only consider the path loss and the shadowing effect.


title: To pass or not to pass: More a question of body orientation than visual cues

abstract: This study investigated the influence of pitch body tilt on judging the possibility of passing under high obstacles in the presence of an illusory horizontal self-motion. Seated subjects tilted at various body orientations were asked to estimate the possibility of passing under a projected bar (i.e., a parking barrier), while imagining a forward whole-body displacement normal to gravity. This task was performed under two visual conditions, providing either no visual surroundings or a translational horizontal optic flow that stopped just before the barrier appeared. The results showed a main overestimation of the possibility of passing under the bar in both cases and most importantly revealed a strong influence of body orientation despite the visual specification of horizontal self-motion by optic flow (i.e., both visual conditions yielded a comparable body tilt effect). Specifically, the subjective passability was proportionally deviated towards the body tilt by 46% of its magnitude when facing a horizontal optic flow and 43% without visual surroundings. This suggests that the egocentric attraction exerted by body tilt when referring the subjective passability to horizontal self-motion still persists even when anchoring horizontally related visual cues are displayed. These findings are discussed in terms of interaction between spatial references. The link between the reliability of available sensory inputs and the weight attributed to each reference is also addressed.


title: Functional MRI of the Brain During Orgasm In Women

abstract: Abstract Women diagnosed with complete spinal cord injury (SCI) at T10 or higher report sensations generated by vaginal-cervical mechanical self-stimulation (CSS). In this paper we review brain responses to sexual arousal and orgasm in such women, and further hypothesize that the afferent pathway for this unexpected perception is provided by the Vagus nerves, which bypass the spinal cord. Using functional magnetic resonance imaging (fMRI), we ascertained that the region of the medulla oblongata to which the Vagus nerves project (the Nucleus of the Solitary Tract or NTS) is activated by CSS. We also used an objective measure, CSS-induced analgesia response to experimentally induced finger pain, to ascertain the functionality of this pathway. During CSS, several women experienced orgasms. Brain regions activated during orgasm included the hypothalamic paraventricular nucleus, amygdala, accumbens-bed nucleus of the stria terminalis-preoptic area, hippocampus, basal ganglia (especially putamen), cerebellum, a...


title: An ultra thin metamaterial absorber using electric field driven LC resonator with meander lines

abstract: This paper describes the electric field driven LC (ELC) resonator structure using Meander lines in C-band. The proposed ELC driven metamaterial structure is simulated using Ansoft HFSS which shows an absorption peak at 6.26 GHz with absorbance of 99.1% and S11 of −20.55 dB. The reduction of frequency at which absorption occurs can be explained from the equivalent LC circuit combination of the proposed structure. The incorporation of Meander lines enhances the net inductance, and thus frequency is reduced. The absorbance is supported by the real and imaginary parts of the impedance value extracted from S-parameters. The absorption frequencies can be shifted by varying the gap as well as length-segment of the Meander Line structure.


title: What are the learning affordances of 3-D virtual environments?

abstract: Barney Dalgarno is a research fellow with the Centre for Research in Complex Systems (CRiCS), Charles Sturt University, and an associate professor with the School of Education at the same university. His research interests lie in desktop virtual reality learning environments, as well as constructivist computerassisted learning theories, techniques and tools. Dalgarno’s PhD work examined the characteristics of 3-D environments and their potential contributions to spatial learning, and he is now studying the application of brain imaging though Functional Magnetic Resonance Imaging (fMRI) to interactive multimedia research. Mark J. W. Lee is an adjunct senior lecturer with the School of Education, Charles Sturt University. Previously, he worked in a variety of teaching, instructional design, and managerial roles within the private vocational education and higher education sectors. He has published approximately 50 refereed book chapters, journal articles, and conference papers in the areas of educational technology, e-learning, and innovative pedagogy in tertiary education. Lee is the Chair of the New South Wales Chapter of the IEEE Education Society and serves as founding Editor-in-Chief of Impact:Journal of Applied Research in Workplace E-learning, in addition to being on the editorial boards of a number of international journals. Address for correspondence: Mark J. W. Lee, School of Education, Charles Sturt University, Locked Bag 588, Wagga Wagga, NSW 2678, Australia. Email: malee@csu.edu.au


Socratic Learning


Travel Recommender Systems


title: Component-Based Robotic Engineering (Part II)

abstract: This article discusses the role of software components as architectural units of large, possibly distributed, software-intensive robotic systems. The focus is on technologies to manage the heterogeneity of hardware, computational, and communication resources and on design techniques to assemble components into systems .A component-based system is a composition of components, and the way components interact with other components and with the computational environment greatly affects the flexibility of the entire system and the reusability of individual functionality.


title: Historical Document Image Segmentation with LDA-Initialized Deep Neural Networks

abstract: In this paper, we present a novel approach to perform deep neural networks layer-wise weight initialization using Linear Discriminant Analysis (LDA). Typically, the weights of a deep neural network are initialized with: random values, greedy layer-wise pre-training (usually as Deep Belief Network or as auto-encoder) or by re-using the layers from another network (transfer learning). Hence, many training epochs are needed before meaningful weights are learned, or a rather similar dataset is required for seeding a fine-tuning of transfer learning. In this paper, we describe how to turn an LDA into either a neural layer or a classification layer. We analyze the initialization technique on historical documents. First, we show that an LDA-based initialization is quick and leads to a very stable initialization. Furthermore, for the task of layout analysis at pixel level, we investigate the effectiveness of LDA-based initialization and show that it outperforms state-of-the-art random weight initialization methods.


title: Web-based 3D Reconstruction Service

abstract: The use of 3D information in the field of cultural heritage is increasing year by year. From this field comes a large demand for cheaper and more flexible ways of 3D reconstruction. This paper describes a web-based 3D reconstruction service, developed to relieve those needs of the cultural heritage field. This service consists of a pipeline that starts with the user uploading images of an object or scene(s) he wants to reconstruct in 3D. The automatic reconstruction process, running on a server connected to a cluster of computers, computes the camera calibration, as well as dense depth (or range-) maps for the images. This result can be downloaded from an ftp server and visualized with a specific tool running on the user’s PC.


title: UAV exploitation: A new domain for cyber power

abstract: The risks of military unmanned aerial vehicles (UAVs) being subjected to electronic attack are well recognised, especially following high-profile incidents such as the interception of unencrypted video feeds from UAVs in Iraq and Israel, or the diversion and downing of a UAV in Iran. Protection of military UAV assets rightly focuses on defence against sophisticated cyber penetration or electronic attack, including data link intercepts and navigational spoofing. Offensive activity to counter adversary drone operations presumes a requirement for high-end electronic attack systems. However, combat operations in eastern Ukraine in 2014–16 have introduced an entirely new dimension to UAV and counter-UAV operations. In addition to drones with military-grade standards of electronic defence and encryption, a large number of civilian or amateur UAVs are in operation in the conflict. This presents both opportunities and challenges to future operations combating hybrid threats. Actual operations in eastern Ukraine, in combination with studies of potential criminal or terrorist use of UAV technologies, provide indicators for a range of aspects of UAV use in future conflict. However, apart from the direct link to military usage, UAVs are rapidly approaching ubiquity with a wide range of applications reaching from entertainment purposes to border patrol, surveillance, and research, which imposes an indirect security and safety threat. Issues associated with the unguarded use of drones by the general public range from potentially highly dangerous situations such as failing to avoid controlled airspace, to privacy violations. Specific questions include attribution of UAV activities to the individuals actually directing the drone; technical countermeasures against hacking, interception or electronic attack; and options for controlling and directing adversary UAVs. Lack of attribution and security measures protecting civilian UAVs against electronic attack, hacking or hijacking, with the consequent likelihood of unauthorised use or interception, greatly increases the complication of each of these concerns.


title: Hypermentalizing, Attachment, and Epistemic Trust in Adolescent BPD: Clinical Illustrations

abstract: Borderline personality disorder (BPD) has been shown to be a valid and reliable diagnosis in adolescents and associated with a decrease in both general and social functioning. With evidence linking BPD in adolescents to poor prognosis, it is important to develop a better understanding of factors and mechanisms contributing to the development of BPD. This could potentially enhance our knowledge and facilitate the design of novel treatment programs and interventions for this group. In this paper, we outline a theoretical model of BPD in adolescents linking the original mentalization-based theory of BPD, with recent extensions of the theory that focuses on hypermentalizing and epistemic trust. We then provide clinical case vignettes to illustrate this extended theoretical model of BPD. Furthermore, we suggest a treatment approach to BPD in adolescents that focuses on the reduction of hypermentalizing and epistemic mistrust. We conclude with an integration of theory and practice in the final section of the paper and make recommendations for future work in this area.


title: A Novel Pipeline Approach for Efficient Big Data Broadcasting

abstract: Big-data computing is a new critical challenge for the ICT industry. Engineers and researchers are dealing with data sets of petabyte scale in the cloud computing paradigm. Thus, the demand for building a service stack to distribute, manage, and process massive data sets has risen drastically. In this paper, we investigate the Big Data Broadcasting problem for a single source node to broadcast a big chunk of data to a set of nodes with the objective of minimizing the maximum completion time. These nodes may locate in the same datacenter or across geo-distributed datacenters. This problem is one of the fundamental problems in distributed computing and is known to be NP-hard in heterogeneous environments. We model the Big-data broadcasting problem into a LockStep Broadcast Tree (LSBT) problem. The main idea of the LSBT model is to define a basic unit of upload bandwidth, $r$ , such that a node with capacity $c$ broadcasts data to a set of $\lfloor c/r\rfloor$ children at the rate $r$ . Note that $r$ is a parameter to be optimized as part of the LSBT problem. We further divide the broadcast data into $m$ chunks. These data chunks can then be broadcast down the LSBT in a pipeline manner. In a homogeneous network environment in which each node has the same upload capacity $c$ , we show that the optimal uplink rate $r^*$ of LSBT is either $c/2$ or $c/3$ , whichever gives the smaller maximum completion time. For heterogeneous environments, we present an $O(n \text{log}^2 n)$ algorithm to select an optimal uplink rate $r^*$ and to construct an optimal LSBT. Numerical results show that our approach performs well with less maximum completion time and lower computational complexity than other efficient solutions in literature.


Human-Directed Optical Music Recognition


title: InFO_AiP Technology for High Performance and Compact 5G Millimeter Wave System Integration

abstract: InFO_AiP technology, with low loss chip-to-antenna interconnect and wideband slot-coupled patch antenna, is proposed for low power, high performance, and compact 5G millimeter wave (mmWave) system integration. The low loss chip-to-antenna interconnect is related to low metal surface roughness of redistribution layer (RDL) and smooth interconnect transition between chip and package in InFO technology. The InFO RDL with low metal roughness results in transmission loss of 0.3 dB/mm which is lower than the loss of Cu trace on substrate, and the smooth interconnect transition with low discontinuity structure reduces the interconnect loss, by 0.78 dB at 60 GHz. A wideband slot-coupled patch antenna is designed successfully with 22.8% FBW (56.6-71.2GHz) and over antenna gain of 3 dBi in the operating band. To verify the technology performance, an InFO_AiP sample at 60 GHz is first designed, fabricated, and measured. The measurement result has a good agreement with the simulation and shows S11 ? -10 dB bandwidth of 55-65 GHz. As a result, InFO_AiP is a leading technology for 5G mmWave system application from power and performance considerations.


title: The mitochondrial genomes of the mesozoans Intoshia linei, Dicyema sp. and Dicyema japonicum.

abstract: The Dicyemida and Orthonectida are two groups of tiny, simple, vermiform parasites that have historically been united in a group named the Mesozoa. Both Dicyemida and Orthonectida have just two cell layers and appear to lack any defined tissues. They were initially thought to be evolutionary intermediates between protozoans and metazoans but more recent analyses indicate that they are protostomian metazoans that have undergone secondary simplification from a complex ancestor. Here we describe the first almost complete mitochondrial genome sequence from an orthonectid, Intoshia linei, and describe nine and eight mitochondrial protein-coding genes from Dicyema sp. and Dicyema japonicum, respectively. The 14,247 base pair long I. linei sequence has typical metazoan gene content, but is exceptionally AT-rich, and has a unique gene order. The data we have analysed from the Dicyemida provide very limited support for the suggestion that dicyemid mitochondrial genes are found on discrete mini-circles, as opposed to the large circular mitochondrial genomes that are typical of the Metazoa. The cox1 gene from dicyemid species has a series of conserved, in-frame deletions that is unique to this lineage. Using cox1 genes from across the genus Dicyema, we report the first internal phylogeny of this group.


title: Smart Detection and Reporting of Potholes via Image-Processing using Raspberry-Pi Microcontroller

abstract: One of the causes of local road accidents in developing countries, such as the Philippines, is due to road damages such as potholes. In addition, there is no proper road maintenance in the local roads, and so the checking of pothole is done manually. Hence, in this paper we propose a simple and robust design of a portable and affordable device that will be suitable for local jeepney (cab) drivers here in the Philippines. A distinguishing feature of this proposal is that it does not need a sophisticated Smartphone to automatically send the reports, and was tested in an actual moving vehicle. Furthermore, the system can be installed in a moving vehicle to automatically detect and report potholes via image-processing of Raspberry-Pi microcontroller. Integration of several image-processing schemes has been used to produce an algorithm using Python Language from the OpenCV library that can detect and report potholes automatically from a moving vehicle. The reported image of the pothole and its location are stored and viewed through the use of the Internet, Dropbox, and web server. The system was tested on a Hyundai Eon city car with maximum speed of 10kph-40kph during daytime. With a rate of about 8 frames per second, images were processed per frame to detect potholes by analyzing its color, depth, and area. Overall, the whole system was successfully implemented using the Raspberry-Pi microcomputer and was able to detect and report potholes from a moving car with 100% reporting success rate.


abstract: Sampling in qualitative research. Purposeful and theoretical sampling; merging or clear boundaries? Sampling is a very complex issue in qualitative research as there are many variations of qualitative sampling described in the literature and much confusion and overlapping of types of sampling, particularly in the case of purposeful and theoretical sampling. The terms purposeful and theoretical are viewed synonomously and used interchangeably in the literature. Many of the most frequent misinterpretations relate to the disparate meanings and usage of the terminology. It is important that the terminology is examined so that underlying assumptions be made more explicit. Lack of shared meanings and terminology in the nursing discourse creates confusion for the neophyte researcher and increases the production of studies with weak methodologies. This paper analyses critically purposeful and theoretical sampling and oers clarification on the use of theoretical sampling for nursing research. The aim is not to make prescriptive statements on sampling; rather, to enhance understanding of the dierences between purposeful and theoretical sampling for nursing research.


title: Improved Audio Embeddings by Adjacency-Based Clustering with Applications in Spoken Term Detection

abstract: Embedding audio signal segments into vectors with fixed dimensionality is attractive because all following processing will be easier and more efficient, for example modeling, classifying or indexing. Audio Word2Vec previously proposed was shown to be able to represent audio segments for spoken words as such vectors carrying information about the phonetic structures of the signal segments. However, each linguistic unit (word, syllable, phoneme in text form) corresponds to unlimited number of audio segments with vector representations inevitably spread over the embedding space, which causes some confusion. It is therefore desired to better cluster the audio embeddings such that those corresponding to the same linguistic unit can be more compactly distributed. In this paper, inspired by Siamese networks, we propose some approaches to achieve the above goal. This includes identifying positive and negative pairs from unlabeled data for Siamese style training, disentangling acoustic factors such as speaker characteristics from the audio embedding, handling unbalanced data distribution, and having the embedding processes learn from the adjacency relationships among data points. All these can be done in an unsupervised way. Improved performance was obtained in preliminary experiments on the LibriSpeech data set, including clustering characteristics analysis and applications of spoken term detection.