title: Policy search for learning robot control using sparse data

abstract: © 2014 IEEE. In many complex robot applications, such as grasping and manipulation, it is difficult to program desired task solutions beforehand, as robots are within an uncertain and dynamic environment. In such cases, learning tasks from experience can be a useful alternative. To obtain a sound learning and generalization performance, machine learning, especially, reinforcement learning, usually requires sufficient data. However, in cases where only little data is available for learning, due to system constraints and practical issues, reinforcement learning can act suboptimally. In this paper, we investigate how model-based reinforcement learning, in particular the probabilistic inference for learning control method (Pilco), can be tailored to cope with the case of sparse data to speed up learning. The basic idea is to include further prior knowledge into the learning process. As Pilco is built on the probabilistic Gaussian processes framework, additional system knowledge can be incorporated by defining appropriate prior distributions, e.g. A linear mean Gaussian prior. The resulting Pilco formulation remains in closed form and analytically tractable. The proposed approach is evaluated in simulation as well as on a physical robot, the Festo Robotino XT. For the robot evaluation, we employ the approach for learning an object pick-up task. The results show that by including prior knowledge, policy learning can be sped up in presence of sparse data.


title: Is the rostro-caudal axis of the frontal lobe hierarchical?

abstract: The frontal lobes in the brain are a component of the cerebral system that supports goal-directed behaviour. However, their functional organization remains controversial. Recent studies have reported rostro-caudal distinctions in frontal cortex activity based on the abstractness of action representations. In addition, some have proposed that these differences reflect a hierarchical organization, whereby anterior frontal regions influence processing by posterior frontal regions during the realization of abstract action goals as motor acts. However, few have considered whether the anatomy and physiology of the frontal lobes support such a scheme. To address this gap, this Review surveys anatomical, neuroimaging, electrophysiological and developmental findings, and considers the question: could the organization of the frontal cortex be hierarchical?


Utilization of papermill sludges as binders for iron ore concentrate


title: Novel Microwave Microfluidic Sensor Using a Microstrip Split-Ring Resonator

abstract: In this paper, a new type of microwave microfluidic sensor is proposed to detect and determine the dielectric properties of common liquids. The technique is based on perturbation theory, in which the resonant frequency and quality factor of the microwave resonator depend on the dielectric properties of the resonator. A microstrip split-ring resonator with two gaps is adopted for the design of the sensors (i.e., a double split-ring resonator). This resonator is both compact and planar, making it suitable for a lab-on-a-chip approach. Several types of solvents are tested with two types of capillaries to verify sensor performance. At 3 GHz, very good agreement is demonstrated between simulated and measured results.


title: Semantic reconciliation of knowledge extracted from text through a novel machine reader

abstract: This paper describes a novel method for generating and integrating knowledge graphs extracted from multiple natural language sources by FRED, a machine reading tool for generating abstract representations of text documents. This is a key problem in human-robot spoken dialogue interaction, issue which arises from a current research project related to active and healthy ageing using caring service robots where we are involved. The problem is also relevant in many application scenarios requiring the creation and dynamic evolution of a knowledge base, such as automatic news summarisation. Solving this problem requires solving sub-tasks that have only been studied individually, so far. We propose a holistic approach to handle FRED's graphs related to different input texts and output a knowledge graph representing the reconciled knowledge.


title: Reliability of Silver Sintering on DBC and DBA Substrates for Power Electronic Applications

abstract: In terms of reliability standard power electronic modules are at their limit regarding robustness aspects. As a high reliable alternative for assembly processes such as Sn-based soldering and aluminum wire bonding, the assembly with nano silver sintering technology on different substrate materials has been investigated in this work. The reliability of silver sintered top and bottom side interconnects has been determined by end-of-life active power cycling testing with 50 mm(exp 2) commercial diodes from Infineon sintered on DBC (direct bonded copper) and DBA (direct bonded aluminum) substrates with a sintered silver ribbon for the top side connect. The setup with DBC substrate showed a 17 times, the one with DBA substrates a 2.7 times higher statistical lifetime than the soldered and wire bonded reference. Delamination of the sintered layers and with a progressing aging in active temperature cycles also showed oxidized cracks in the Cu metallization of the substrate as new failure mechanisms of the DBC samples with double sided sinter technology. For DBA assemblies a formation of hillocks on the Al surface underneath the diode could be detected next to delamination of the sinter layer.


title: SCOPIGNO R.: A Low-Memory, Straightforward and Fast Bilateral Filter Through Subsampling

abstract: In this work we present a new algorithm for accelerating the colour bilateral filter based on a subsampling strategy working in the spatial domain. The base idea is to use a suitable subset of samples of the entire kernel in order to obtain a good estimation of the exact filter values. The main advantages of the proposed approach are that it has an excellent trade-off between visual quality and speed-up, a very low memory overhead is required and it is straightforward to implement on the GPU allowing real-time filtering. We show different applications of the proposed filter, in particular efficient cross-bilateral filtering, real-time edge-aware image editing and fast video denoising. We compare our method against the state of the art in terms of image quality, time performance and memory usage. © 2012 Wiley Periodicals, Inc.


title: A 2.4GHz Planar Printed Antenna with Omni-directional Horizontally Polarized Pattern for WLAN Applications

abstract: This paper presents the design simulation, implement, and measurement of a 2.4 GHz omni-directional horizontally polarized planar printed antenna for WLAN application. The HFSS 3-D EM simulator is used for design simulation. The antenna is fabricated on a double-sided FR-4 printed circuit board. The antenna pattern measurements are performed for the antenna is alone in free space, printed on a PCMCIA card, and that inserted inside a notebook PC. In addition to be used alone for a horizontally polarized antenna, it can be also a part of a diversity antenna.


title: Behavior Query Discovery in System-Generated Temporal Graphs

abstract: Computer system monitoring generates huge amounts of logs that record the interaction of system entities. How to query such data to better understand system behaviors and identify potential system risks and malicious behaviors becomes a challenging task for system administrators due to the dynamics and heterogeneity of the data. System monitoring data are essentially heterogeneous temporal graphs with nodes being system entities and edges being their interactions over time. Given the complexity of such graphs, it becomes time-consuming for system administrators to manually formulate useful queries in order to examine abnormal activities, attacks, and vulnerabilities in computer systems. ::: In this work, we investigate how to query temporal graphs and treat query formulation as a discriminative temporal graph pattern mining problem. We introduce TGMiner to mine discriminative patterns from system logs, and these patterns can be taken as templates for building more complex queries. TGMiner leverages temporal information in graphs to prune graph patterns that share similar growth trend without compromising pattern quality. Experimental results on real system data show that TGMiner is 6-32 times faster than baseline methods. The discovered patterns were verified by system experts; they achieved high precision (97%) and recall (91%).


title: Security Analysis of Cordova Applications in Google Play

abstract: Mobile Cross-Platform Tools (CPTs) provide an alternative to native application development that allows mobile app developers to drastically reduce the development time and cost when targeting multiple platforms. They allow sharing a significant part of the application codebase between the implementations for the targeted platforms (e.g. Android, iOS, Windows Phone). Although CPTs provide significant benefits for developers, there can introduce several disadvantages. The CPT software layers and translation steps can impact the security of the produced applications. One of the most well-known and often-used CPTs is Cordova, formerly known as PhoneGap. Cordova has, over the years, taken several steps to reduce the attack surface and introduced several mechanisms that allow developers to increase the security of Cordova applications. This paper gives a statistical overview of the adoption of Cordova security best practices and mechanisms in Cordova applications downloaded from the Google Play Store. For the analysis, over a thousand Cordova application were downloaded. The research shows that the poor adoption of these mechanisms leads to a significant number of insecure Cordova applications.


title: The Essential Role of Medical Ethics Education in Achieving Professionalism

abstract: This article—the Romanell Report— offers an analysis of the current state of medical ethics education in the United States, focusing in particular on its essential role in cultivating professionalism among medical learners. Education in ethics has become an integral part of medical education and training over the past three decades and has received particular attention in recent years because of the increasing emphasis placed on professional formation by accrediting bodies such as the Liaison Committee on Medical Education and the Accreditation Council for Graduate Medical Education. Yet, despite the development of standards, milestones, and competencies related to professionalism, there is no consensus about the specific goals of medical ethics education, the essential knowledge and skills expected of learners, the best pedagogical methods and processes for implementation, and optimal strategies for assessment. Moreover, the quality, extent, and focus of medical ethics instruction vary, particularly at the graduate medical education level. Although variation in methods of instruction and assessment may be appropriate, ultimately medical ethics education must address the overarching articulated expectations of the major accrediting organizations. With the aim of aiding medical ethics educators in meeting these expectations, the Romanell Report describes current practices in ethics education and offers guidance in several areas: educational goals and objectives, teaching methods, assessment strategies, and other challenges and opportunities (including course structure and faculty development). The report concludes by proposing an agenda for future research.


title: Emotion recognition from EEG signals by using multivariate empirical mode decomposition

abstract: This is the artifact for the paper titled "POKER: Permutation-based SIMD Execution of Intensive Tree Search by Path Encoding" accepted at CGO 2018. This artifact helps reproduce the results presented in Figures 7 - 9 and Tables 2 - 3 in Section 4. For more information on how to use it, please refer to our paper and the README.txt file in this package. Please note that POKER is a work in progress. This artifact is a snapshot of this work and thus is only applicable under the experimental settings described in this paper. Please feel free to contact the authors if you have any questions.


title: The Five-Step Lower Blepharoplasty

abstract: BACKGROUND ::: Lower lid blepharoplasty has potential for significant long-lasting complications and marginal aesthetic outcomes if not performed correctly, or if one disregards the anatomical aspects of the orbicularis oculi muscle. This has detracted surgeons from performing the technical maneuvers necessary for optimal periorbital rejuvenation. A simplified, "five-step" clinical approach based on sound anatomical principles is presented. ::: ::: ::: METHODS ::: A review of 50 lower lid blepharoplasty patients (each bilateral) using the five-step technique was conducted to delineate the efficacy in improving lower eyelid aesthetics. Digital images from 50 consecutive primary lower blepharoplasty patients (100 lower lids: 37 women and 13 men) were measured using a computer program with standardized data points that were later converted to ratios. ::: ::: ::: RESULTS ::: Of the 100 lower eyelid five-step blepharoplasties analyzed, complication rates were low and data points measured demonstrated improvements in all aesthetic parameters. The width and position of the tear trough, position of the lower lid relative to the pupil, and the intercanthal angle were all improved. There were no cases of lower lid malposition. ::: ::: ::: CONCLUSIONS ::: Aesthetic outcomes in lower lid blepharoplasty can be improved using a five-step technical sequence that addresses all of the anatomical findings. Lower lid blepharoplasty results are improved when (1) the supportive deep malar fat compartment is augmented; (2) lower lid orbicularis oculi muscle is preserved with minimal fat removal (if at all); (3) the main retaining structure (orbicularis retaining ligament) is selectively released; (4) lateral canthal support is established or strengthened (lateral retinacular suspension); and (5) minimal skin is removed. ::: ::: ::: CLINICAL QUESTION/LEVEL OF EVIDENCE ::: Therapeutic, IV.


title: Mobile networking with UAVs: Opportunities and challenges

abstract: With the advances in computation, sensor, communication and networking technologies, utilization of Unmanned Aerial Vehicles (UAVs) for military and civilian areas has become extremely popular for the last two decades. Since small UAVs are relatively cheap, the focus is changing, and usage of several small UAVs is preferred rather than one large UAV. This change in orientation is dramatic, and it is resulting to develop new networking technologies between UAVs, which can constitute swarm UAV teams for executing specific tasks with different levels of intra and inter vehicle communication especially for coordination and control of the system. Setting up a UAV network not only extends operational scope and range but also enables quick and reliable response time. Because UAVs are highly mobile nodes for networking, setting up an ad-hoc network is a challenging issue, and this networking has some requirements, which differ from traditional networks, mobile ad-hoc networks (MANETs) and vehicular ad-hoc networks (VANETs) in terms of connectivity, routing process, services, applications, etc. In this paper, it is aimed to point out the challenges in the usage of UAVs as mobile nodes in an ad-hoc network and to depict open research issues with analyzing the opportunities and future works.


title: Fusion of facial expressions and EEG for implicit affective tagging

abstract: The explosion of user-generated, untagged multimedia data in recent years, generates a strong need for efficient search and retrieval of this data. The predominant method for content-based tagging is through slow, labor-intensive manual annotation. Consequently, automatic tagging is currently a subject of intensive research. However, it is clear that the process will not be fully automated in the foreseeable future. We propose to involve the user and investigate methods for implicit tagging, wherein users' responses to the interaction with the multimedia content are analyzed in order to generate descriptive tags. Here, we present a multi-modal approach that analyses both facial expressions and electroencephalography (EEG) signals for the generation of affective tags. We perform classification and regression in the valence-arousal space and present results for both feature-level and decision-level fusion. We demonstrate improvement in the results when using both modalities, suggesting the modalities contain complementary information.


title: Information Flow Management of Vendor- Managed Inventory System in Automobile Parts Inbound Logistics Based on Internet of Things

abstract: Reducing inventory levels is a major supply chain management challenge in automobile industries. With the development of information technology new cooperative supply chain contracts emerge such as Vendor-Managed Inventory (VMI). This research aims to look at the literature of information management of VMI and the Internet of Things, then analyzes information flow model of VMI system. The paper analyzes information flow management of VMI system in automobile parts inbound logistics based on the environment of Internet of Things.


title: See what's enBlogue: real-time emergent topic identification in social media

abstract: With the increasing popularity of Web 2.0 streams, people become overwhelmed by the available information. This is partly countered by tagging blog posts and tweets, so that users can filter messages according to their tags. However, this is insufficient for detecting newly emerging topics that are not reflected by a single tag but are rather expressed by unusual tag combinations. This paper presents enBlogue, an approach for automatically detecting such emergent topics. EnBlogue uses a time-sliding window to compute statistics about tags and tag-pairs. These statistics are then used to identify unusual shifts in correlations, most of the time caused by real-world events. We analyze the strength of these shifts and measure the degree of unpredictability they include, used to rank tag-pairs expressing emergent topics. Additionally, this "indicator of surprise" is carried over to subsequent time points, as user interests do not abruptly vanish from one moment to the other. To avoid monitoring all tag-pairs we can also select a subset of tags, e. g., the most popular or volatile of them, to be used as seed-tags for subsequent pair-wise correlation computations. The system is fully implemented and publicly available on the Web, processing live Twitter data. We present experimental studies based on real world datasets demonstrating both the prediction quality by means of a user study and the efficiency of enBlogue.


title: Treatment of Localized Vitiligo with 1% Pimecrolimus Cream versus 0.05% Clobetasol Propionate Cream—Single, Blinded, Comparative Therapeutic Trial

abstract: Background: Calcineurin inhibitors like tacrolimus and ::: pimecrolimus are new modality of treatment of localized vitiligo. Objective: To ::: compare the effectiveness and side effects of 1% pimecrolimus cream in ::: comparison with 0.05% clobetasol propionate cream as a treatment of localized ::: type of vitiligo. Patients and Methods: Fifty two patients with localized ::: vitiligo were included in this single, blind, comparative therapeutic trial. ::: Full history and examination for each patient was done. Wood’s light was used ::: when needed to confirm the diagnosis. Thirty (57.7%) were females and 22 ::: (42.3%) males, female to male ratio of 1.36:1. Their ages ranged between 3 - 40 ::: (13.15 ± 7.9) years, while disease duration ranged from 6 - 84 (29.62 ± 20.56) ::: months. Total numbers of lesions were 114 lesions with a mean of 2.2 lesions ::: per patient. The surface area of the lesions ranged between 1 - 31 (7.15 ± ::: 6.98) cm2. Vitiliginous patches were most commonly located on face 48 (42.1%) ::: lesions, and lower limbs 35 (30.7%) lesions. Patients were divided in to two ::: groups according to the type of therapeutic modality. Group A consisted of 25 patients (52 lesions) ::: receiving 1% pimecrolimus cream and Group B 27 ::: patients (62 lesions) treated with 0.05% clobetasol propionate cream, both used ::: twice daily. The amount of cream per area was applied according to rule of ::: fingertip unit. Measuring the surface area of the lesions and calculating the ::: reduction rate were done every month till the end of the 6th month. ::: Then patients were asked to stop the use of medication to be re-evaluated again ::: after 3 months without any treatment and to record any local or systemic side ::: effects. Results: After 6 months of treatment there was 79.67% reduction in the ::: surface area of lesions in Group A, while inGroup B there was 82.59% reduction in the ::: surface area with no statistically significant difference between the two ::: groups. The reduction rate was early as there was statistically significant ::: reduction for both groups after one month of treatment (p value < 0.01). The ::: reduction rates for facial lesions were more than other parts of the body. ::: There was no significant relapse after 3 months of stopping treatment for both ::: groups. Side effects were minimal and tolerable. Conclusions: Topical 1% ::: pimecrolimus is as effective as clobetasol propionate in treatment of localized ::: vitiligo affecting less than 5% of the body surface area but pimecrolimus was ::: more preferred as the side effects of topical steroid could be avoided.


title: Understanding Malvertising Through Ad-Injecting Browser Extensions

abstract: Malvertising is a malicious activity that leverages advertising to distribute various forms of malware. Because advertising is the key revenue generator for numerous Internet companies, large ad networks, such as Google, Yahoo and Microsoft, invest a lot of effort to mitigate malicious ads from their ad networks. This drives adversaries to look for alternative methods to deploy malvertising. In this paper, we show that browser extensions that use ads as their monetization strategy often facilitate the deployment of malvertising. Moreover, while some extensions simply serve ads from ad networks that support malvertising, other extensions maliciously alter the content of visited webpages to force users into installing malware. To measure the extent of these behaviors we developed Expector, a system that automatically inspects and identifies browser extensions that inject ads, and then classifies these ads as malicious or benign based on their landing pages. Using Expector, we automatically inspected over 18,000 Chrome browser extensions. We found 292 extensions that inject ads, and detected 56 extensions that participate in malvertising using 16 different ad networks and with a total user base of 602,417.


title: The Unified Accumulator Architecture: A Configurable, Portable, and Extensible Floating-Point Accumulator

abstract: Applications accelerated by field-programmable gate arrays (FPGAs) often require pipelined floating-point accumulators with a variety of different trade-offs. Although previous work has introduced numerous floating-point accumulation architectures, few cores are available for public use, which forces designers to use fixed-point implementations or vendor-provided cores that are not portable and are often not optimized for the desired set of trade-offs. In this article, we combine and extend previous floating-point accumulator architectures into a configurable, open-source core, referred to as the unified accumulator architecture (UAA), which enables designers to choose between different trade-offs for different applications. UAA is portable across FPGAs and allows designers to specialize the underlying adder core to take advantage of device-specific optimizations. By providing an extensible, open-source implementation, we hope for the research community to extend the provided core with new architectures and optimizations.


title: Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures

abstract: Machine-learning (ML) algorithms are increasingly utilized in privacy-sensitive applications such as predicting lifestyle choices, making medical diagnoses, and facial recognition. In a model inversion attack, recently introduced in a case study of linear classifiers in personalized medicine by Fredrikson et al., adversarial access to an ML model is abused to learn sensitive genomic information about individuals. Whether model inversion attacks apply to settings outside theirs, however, is unknown. We develop a new class of model inversion attack that exploits confidence values revealed along with predictions. Our new attacks are applicable in a variety of settings, and we explore two in depth: decision trees for lifestyle surveys as used on machine-learning-as-a-service systems and neural networks for facial recognition. In both cases confidence values are revealed to those with the ability to make prediction queries to models. We experimentally show attacks that are able to estimate whether a respondent in a lifestyle survey admitted to cheating on their significant other and, in the other context, show how to recover recognizable images of people's faces given only their name and access to the ML model. We also initiate experimental exploration of natural countermeasures, investigating a privacy-aware decision tree training algorithm that is a simple variant of CART learning, as well as revealing only rounded confidence values. The lesson that emerges is that one can avoid these kinds of MI attacks with negligible degradation to utility.


title: Adjunctive non-pharmacological analgesia for invasive medical procedures: a randomised trial

abstract: BACKGROUND ::: Non-pharmacological behavioural adjuncts have been suggested as efficient safe means in reducing discomfort and adverse effects during medical procedures. We tested this assumption for patients undergoing percutaneous vascular and renal procedures in a prospective, randomised, single-centre study. ::: ::: ::: METHODS ::: 241 patients were randomised to receive intraoperatively standard care (n=79), structured attention (n=80), or self-hypnotic relaxation (n=82). All had access to patient-controlled intravenous analgesia with fentanyl and midazolam. Patients rated their pain and anxiety on 0-10 scales before, every 15 min during and after the procedures. ::: ::: ::: FINDINGS ::: Pain increased linearly with procedure time in the standard group (slope 0.09 in pain score/15 min, p<0.0001), and the attention group (slope 0.04/15 min; p=0.0425), but remained flat in the hypnosis group. Anxiety decreased over time in all three groups with slopes of -0.04 (standard), -0.07 (attention), and -0.11 (hypnosis). Drug use in the standard group (1.9 units) was significantly higher than in the attention and hypnosis groups (0.8 and 0.9 units, respectively). One hypnosis patient became haemodynamically unstable compared with ten attention patients (p=0.0041), and 12 standard patients (p=0.0009). Procedure times were significantly shorter in the hypnosis group (61 min) than in the standard group (78 min, p=0.0016) with procedure duration of the attention group in between (67 min). ::: ::: ::: INTERPRETATION ::: Structured attention and self-hypnotic relaxation proved beneficial during invasive medical procedures. Hypnosis had more pronounced effects on pain and anxiety reduction, and is superior, in that it also improves haemodynamic stability.


title: Web challenges faced by blind and vision impaired users in libraries of Delhi

abstract: – The purpose of this paper is to analyse the challenges faced by blind/vision-impaired users during the web access in the leading academic and special libraries of Delhi, India. , – Survey methodology has been used as the basic research tool for data collection with the help of questionnaire. A total of 125 users surveyed in all the five libraries are selected randomly on the basis of willingness of the users to participate in the survey with the experience of working in digital environment. The survey results were tabulated and analysed with descriptive statistics methods using Excel software and “Stata version 11”. , – There are various barriers faced by blind/vision-impaired users in the libraries of Delhi (India) during the “web access” with the help of assistive technologies as assistive technology like “Screen Readers” available today are “somewhat compatible” with the web. The facility of “web-based catalogue” is generally lacking in Delhi libraries. , – The paper does not offer a comprehensive set of web accessibility issues, concentrating instead on the most common problems as epitome of this study. , – The paper raises awareness of web accessibility issues in libraries of Delhi, India. , – The paper highlights the web accessibility issues faced by blind/vision-impaired users of leading academic and special libraries of India including the users’ recommendations for the accessible web page/website design for the first time.


title: Asymmetrical voltage-cancellation control for full-bridge series resonant inverters

abstract: This paper presents and analyzes the asymmetrical voltage-cancellation (AVC) control, a generalized control technique for resonant inverters. It is applied to the popular full-bridge series resonant inverter. The proposed control technique achieves better efficiency performances than conventional fixed-frequency control strategies, while considering zero-voltage-switching operation, output power and load variations. The theoretical results are verified experimentally, using a prototype for an induction-heating cooking appliance.


title: Density Modeling of Images using a Generalized Normalization Transformation

abstract: We introduce a parametric nonlinear transformation that is well-suited for Gaussianizing data from natural images. The data are linearly transformed, and each component is then normalized by a pooled activity measure, computed by exponentiating a weighted sum of rectified and exponentiated components and a constant. We optimize the parameters of the full transformation (linear transform, exponents, weights, constant) over a database of natural images, directly minimizing the negentropy of the responses. The optimized transformation substantially Gaussianizes the data, achieving a significantly smaller mutual information between transformed components than alternative methods including ICA and radial Gaussianization. The transformation is differentiable and can be efficiently inverted, and thus induces a density model on images. We show that samples of this model are visually similar to samples of natural image patches. We demonstrate the use of the model as a prior probability density that can be used to remove additive noise. Finally, we show that the transformation can be cascaded, with each layer optimized using the same Gaussianization objective, thus offering an unsupervised method of optimizing a deep network architecture.


title: Application of GPS and GIS in Sugarcane Agriculture

abstract: Technology certainly plays a significant part in the precision agriculture scheme, but understanding the appropriateness of the different forms of technology in response to a management problem is an essential element for successful precision agriculture implementation. Many of the enabling technologies discussed are at varying stages of development with many having restricted application and usefulness when applied at the within-paddock level. However, these technologies can still contribute to largerscale forms of precision agriculture at the region, mill, farm or paddock level. If the sugar industry decides that using global positioning system and geographic information system to chase the benefits of selective harvesting is potentially worthwhile, sorting out the spatial and temporal interactions between yield and CCS will be a critical research issue. Thus the adoption of theses technologies in a precision conservation philosophy could assist in managing the interactions between cane farming and environmental protection in the sensitive coastal floodplain ecosystems.


Visual Design Methods for Virtual Reality


title: Relationship between English Language, Learning Strategies, Attitudes, Motivation, and Students’ Academic Achievement

abstract: Background: A considerable amount of researches has been done to explore the key factors that affect learning a second language. Among these factors are students' motivation, attitude, learning strategies, and learning environment. Such issues have not been explored officially at the College of Applied Medical Sciences at the University of Dammam in Saudi Arabia, even though English is used as the medium of teaching and learning. Objectives: First, to explore the learning strategies used to study English as a second language. Second, to investigate students' attitude towards the speakers of English. Third, to assess students' perceptions toward their learning environment. Fourth, to identify students' motivations for studying English. Fifth, to explore any possible relationships among learning strategies, attitudes, motivation, learning environment, English language proficiency grades of students, and their grades for their general medical courses. Methods: A cross-sectional study was conducted in May, 2008. A total of 110 questionnaires were completed. Results: Students utilize all types of learning strategies. There were significant correlations between the achievement in English and performance in general medical courses, learning strategies, motivation, age, and formal level at which student started to learn English. Conclusion: The study showed that students utilize all types of language learning strategies. However, cognitive strategies were utilized more frequently than others. Students have relatively more positive than negative perception scores toward their learning environment, teacher, courses, and people spoke English. Achievement in English was associated positively with performance in the general medical courses, motivation, and learning strategies. Students were motivated more instrumentally than integratively.


title: MARKOV CHAIN MONTE CARLO METHODS: COMPUTATION AND INFERENCE

abstract: This chapter reviews the recent developments in Markov chain Monte Carlo simulation methods. These methods, which are concerned with the simulation of high dimensional probability distributions, have gained enormous prominence and revolutionized Bayesian statistics. The chapter provides background on the relevant Markov chain theory and provides detailed information on the theory and practice of Markov chain sampling based on the Metropolis-Hastings and Gibbs sampling algorithms. Convergence diagnostics and strategies for implementation are also discussed. A number of examples drawn from Bayesian statistics are used to illustrate the ideas. The chapter also covers in detail the application of MCMC methods to the problems of prediction and model choice.


title: A Fluxgate Current Sensor With an Amphitheater Busbar

abstract: Large dc and ac electric currents are often measured by open-loop sensors without a magnetic yoke. A widely used configuration uses a differential magnetic sensor inserted into a hole in a flat busbar. The use of a differential sensor offers the advantage of partial suppression of fields coming from external currents. Hall sensors and AMR sensors are currently used in this application. In this paper, we present a current sensor of this type that uses novel integrated fluxgate sensors, which offer a greater range than magnetoresistors and better stability than Hall sensors. The frequency response of this type of current sensor is limited due to the eddy currents in the solid busbar. We present a novel amphitheater geometry of the hole in the busbar of the sensor, which reduces the frequency dependence from 15% error at 1 kHz to 9%.


title: Fast Solving of Influence Diagrams for Multiagent Planning on GPU-enabled Architectures

abstract: Planning under uncertainty in multiagent settings is highly intractable because of history and plan space complexities. Probabilistic graphical models exploit the structure of the problem domain to mitigate the computational burden. In this paper, we introduce the first parallelization of planning in multiagent settings on a CPU-GPU heterogeneous system. In particular, we focus on the algorithm for exactly solving interactive dynamic influence diagrams, which is a recognized graphical models for multiagent planning. Beyond parallelizing the standard Bayesian inference, the computation of decisions' expected utilities are parallelized. The GPU-based approach provides significant speedup on two benchmark problems.


title: Phytoplasmas in apricot, peach and sour cherry orchards in East Bohemia, Czech Republic

abstract: Symptoms of prematurely yellowing and leaf roll in the summer were observed in apricot, peach and sour cherry orchards in the East Bohemia (Czech Republic) during 2009-2010. Samples from selected symptomatic trees were tested by PCR assays followed by RFLP analyses. Detected phytoplasmas were classified in the apple proliferation group, subgroup 16SrX-B, which representative strains are apricot chlorotic leaf roll and European stone fruit yellows. Although the majority of symptomatic trees was observed in apricot orchards, out of 54 apricot trees examined by PCR/RFLP assays, only 10 plants were positive (18.5%) to phytoplasma presence. In the peach orchards, phytoplasmas were detected in 4 trees out of 14 plants tested (28.6%). Total number of trees examined from sour cherry orchards was 25, out of these 9 plants revealed positive reaction (36%). DNA sequencing of phytoplasma strain from one sour cherry tree confirmed the RFLP identification as ‘Candidatus Phytoplasma prunorum’. Experiments for transmission of ‘Ca. P. prunorum’ by double budding from positive trees to indicator plants were carried out in open field during 2009 and 2010. No symptoms typical for phytoplasma infection were observed to date.


title: Local sleep spindle modulations in relation to specific memory cues

abstract: Abstract Sleep spindles have been connected to memory processes in various ways. In addition, spindles appear to be modulated at the local cortical network level. We investigated whether cueing specific memories during sleep leads to localized spindle modulations in humans. During learning of word–location associations, words presented in the left and right visual hemifields were paired with different odors. By presenting a single odor during a subsequent nap, we aimed to selectively reactivate a subset of the studied material in sleeping subjects. During sleep, we observed topographically restricted spindle responses to memory cues, suggesting successful reactivation of specific memory traces. In particular, we found higher amplitude and greater incidence of fast spindles over posterior brain areas involved in visuospatial processing, contralateral to the visual field being cued. These results suggest that sleep spindles in different cortical areas reflect the reprocessing of specific memory traces.


title: Perceptual weighting deep neural networks for single-channel speech enhancement

abstract: Improving the perceptual quality of speech signals is a key yet challenging problem for many real world applications. In this paper, we propose a perceptually motivated approach based on deep neural networks (DNNs) for speech enhancement task. The proposed approach take into consider the masking properties of the human auditory system and reduces the perceptual effect of the residual noise. Given the good performance of deep learning in signal representation, a DNN is employed for accurately modeling the clean speech spectrum. In the DNN training stage, perceptual weighting matrix is used to adjust the weight of the error when using back propagation algorithm transfer the error from DNN output layer to the front layer. Evaluations on various real-world background noises show that the proposed method performs better than different competitive methods.


title: Plasmapheresis-Related Hypotension

abstract: : Hypotension is an uncommon complication of procedures involving extracorporeal circulation, including plasmapheresis. From November 1993 to March 1999, we treated 139 patients who underwent a total of 1,137 sessions of double filtration plasmapheresis (DFP). Hypotension was defined as a systolic blood pressure (BP) < 80 mm Hg or any decrease of systolic BP with systemic reactions. A total of 17 (1.5%) episodes of hypotension were documented in 15 patients during the study period. Hypotensive episodes occurred in 2.3% of patients with inflammatory neuropathy, 1.2% of patients with myasthenia gravis, and 1.2% of patients with all other medical diseases. Involvement of the autonomic nerve system (ANS) and a low baseline BP were associated with the occurrence of hypotension. Eight (47%) of 17 episodes were symptomatic and 2 were complicated with seizure. Patients with symptomatic hypotension had a higher level of systolic BP prior to DFP and a larger drop of systolic BP and pulse rate during hypotensive attacks compared to asymptomatic patients. Most hypotensive episodes were resolved briefly after intravenous infusion of saline within 30 min. Eight (47%) of the hypotensive episodes occurred during the first session of DFP treatment. Twelve (71%) of 17 episodes occurred during the last half period of treatment; 6 of them were noted during the terminating stage of DFP. In conclusion, in this series plasmapheresis-related hypotension occurred in 1.5% of DFP sessions and had a higher prevalence in patients with ANS instability and low BP. Extra caution in monitoring BP during DFP therapy is warranted in these vulnerable patients, especially during the termination phase of the first DFP session.


title: Consciousness: a neural capacity for objectivity, especially pronounced in humans

abstract: Consciousness tends to be viewed either as subjective experience of sensations and feelings, or as perception and internal representation of objects. This paper argues that neither view sufficiently acknowledges that consciousness may refer to the brain’s most adaptive property: its capacity to produce states of objectivity. It is proposed that this capacity relies on multiple sensorimotor networks for internally representing objects and their properties in terms of expectancies, as well as on motivational and motor mechanisms involved in exploration, play, and care for vulnerable living and non-living objects. States of objectivity are associated with a very special phenomenal aspect; the experience that subjective aspects are absent and one is “just looking” at the world as it really is and can be. However, these states are normally closely preceded and followed by (and tend to be combined or fused with) sensations and feelings which are caused by activation of sensory and motivational mechanisms. A capacity for objectivity may have evolved in different species and can be conceived as a common basis for other elusive psychological properties such as intelligence, conscience, and esthetic experience; all three linked to crucial behaviors in human evolution such as tool making, cooperation, and art. The brain’s pervasive tendency to objectify may be responsible for wrongly equating consciousness with feelings and wrongly opposing it to well-learned or habitual (“unconscious”) patterns of perception and behavior.


title: A 3D facial expression database for facial behavior research

abstract: Traditionally, human facial expressions have been studied using either 2D static images or 2D video sequences. The 2D-based analysis is incapable of handing large pose variations. Although 3D modeling techniques have been extensively used for 3D face recognition and 3D face animation, barely any research on 3D facial expression recognition using 3D range data has been reported. A primary factor for preventing such research is the lack of a publicly available 3D facial expression database. In this paper, we present a newly developed 3D facial expression database, which includes both prototypical 3D facial expression shapes and 2D facial textures of 2,500 models from 100 subjects. This is the first attempt at making a 3D facial expression database available for the research community, with the ultimate goal of fostering the research on affective computing and increasing the general understanding of facial behavior and the fine 3D structure inherent in human facial expressions. The new database can be a valuable resource for algorithm assessment, comparison and evaluation.


title: Multichannel texture analysis using localized spatial filters

abstract: A computational approach for analyzing visible textures is described. Textures are modeled as irradiance patterns containing a limited range of spatial frequencies, where mutually distinct textures differ significantly in their dominant characterizing frequencies. By encoding images into multiple narrow spatial frequency and orientation channels, the slowly varying channel envelopes (amplitude and phase) are used to segregate textural regions of different spatial frequency, orientation, or phase characteristics. Thus, an interpretation of image texture as a region code, or carrier of region information, is emphasized. The channel filters used, known as the two-dimensional Gabor functions, are useful for these purposes in several senses: they have tunable orientation and radial frequency bandwidths and tunable center frequencies, and they optimally achieve joint resolution in space and in spatial frequency. By comparing the channel amplitude responses, one can detect boundaries between textures. Locating large variations in the channel phase responses allows discontinuities in the texture phase to be detected. Examples are given of both types of texture processing using a variety of real and synthetic textures. >


title: The progression of realism in computer generated images

abstract: The synthesis of continuous tone raster scan images has been the subject of considerable research effort over the past decade or so. Many algorithms have been developed during this period, and many significant advances have been made. However, very few images have been produced which require more than a moment's study to reveal their synthetic origins. This paper reviews the major issues of relevance to the goal of realism in synthetic images. The major achievements towards this goal are reviewed, and the main outstanding problems are identified.


title: Development of attentional networks in childhood

abstract: Recent research in attention has involved three networks of anatomical areas that carry out the functions of orienting, alerting and executive control (including conflict monitoring). There have been extensive cognitive and neuroimaging studies of these networks in adults. We developed an integrated Attention Network Test (ANT) to measure the efficiency of the three networks with adults. We have now adapted this test to study the development of these networks during childhood. The test is a child-friendly version of the flanker task with alerting and orienting cues. We studied the development of the attentional networks in a cross-sectional experiment with four age groups ranging from 6 through 9 (Experiment 1). In a second experiment, we compared children (age 10 years) and adult performance in both child and adults versions of the ANT. Reaction time and accuracy improved at each age interval and positive values were found for the average efficiency of each of the networks. Alertness showed evidence of change up to and beyond age 10, while conflict scores appear stable after age seven and orienting scores do not change in the age range studied. A final experiment with forty 7-year-old children suggested that children like adults showed independence between the three networks under some conditions. © 2004 Elsevier Ltd. All rights reserved.


title: Energy-Aware Clustering for Wireless Sensor Networks using Particle Swarm Optimization

abstract: Wireless sensor networks (WSNs) are mainly characterized by their limited and non-replenishable energy supply. Hence, the need for energy efficient infrastructure is becoming increasingly more important since it impacts upon the network operational lifetime. Sensor node clustering is one of the techniques that can expand the lifespan of the whole network through data aggregation at the cluster head. In this paper, we present an energy-aware clustering for wireless sensor networks using particle swarm optimization (PSO) algorithm which is implemented at the base station. We define a new cost function, with the objective of simultaneously minimizing the intra-cluster distance and optimizing the energy consumption of the network. The performance of our protocol is compared with the well known cluster-based protocol developed for WSNs, LEACH (low-energy adaptive clustering hierarchy) and LEACH-C, the later being an improved version of LEACH. Simulation results demonstrate that our proposed protocol can achieve better network lifetime and data delivery at the base station over its comparatives.


title: Randomized Prior Functions for Deep Reinforcement Learning

abstract: Dealing with uncertainty is essential for efficient reinforcement learning. There is a growing literature on uncertainty estimation for deep learning from fixed datasets, but many of the most popular approaches are poorly-suited to sequential decision problems. Other methods, such as bootstrap sampling, have no mechanism for uncertainty that does not come from the observed data. We highlight why this can be a crucial shortcoming and propose a simple remedy through addition of a randomized untrainable `prior' network to each ensemble member. We prove that this approach is efficient with linear representations, provide simple illustrations of its efficacy with nonlinear representations and show that this approach scales to large-scale problems far better than previous attempts.


title: Attachment from infancy to early adulthood in a high-risk sample: continuity, discontinuity, and their correlates.

abstract: This study explores the stability of attachment security and representations from infancy to early adulthood in a sample chosen originally for poverty and high risk for poor developmental outcomes. Participants for this study were 57 young adults who are part of an ongoing prospective study of development and adaptation in a high-risk sample. Attachment was assessed during infancy by using the Ainsworth Strange Situation (Ainsworth & Wittig) and at age 19 by using the Berkeley Adult Attachment Interview (George, Kaplan, & Main). Possible correlates of continuity and discontinuity in attachment were drawn from assessments of the participants and their mothers over the course of the study. Results provided no evidence for significant continuity between infant and adult attachment in this sample, with many participants transitioning to insecurity. The evidence, however, indicated that there might be lawful discontinuity. Analyses of correlates of continuity and discontinuity in attachment classification from infancy to adulthood indicated that the continuous and discontinuous groups were differentiated on the basis of child maltreatment, maternal depression, and family functioning in early adolescence. These results provide evidence that although attachment has been found to be stable over time in other samples, attachment representations are vulnerable to difficult and chaotic life experiences.


title: Ant Colony Optimization and Stochastic Gradient Descent

abstract: In this article, we study the relationship between the two techniques known as ant colony optimization (ACO) and stochastic gradient descent. More precisely, we show that some empirical ACO algorithms approximate stochastic gradient descent in the space of pheromones, and we propose an implementation of stochastic gradient descent that belongs to the family of ACO algorithms. We then use this insight to explore the mutual contributions of the two techniques.


Online Students ‘ Attendance Monitoring System in Classroom Using Radio Frequency Identification Technology: A Proposed System Framework


title: An information gain formulation for active volumetric 3D reconstruction

abstract: We consider the problem of next-best view selection for volumetric reconstruction of an object by a mobile robot equipped with a camera. Based on a probabilistic volumetric map that is built in real time, the robot can quantify the expected information gain from a set of discrete candidate views. We propose and evaluate several formulations to quantify this information gain for the volumetric reconstruction task, including visibility likelihood and the likelihood of seeing new parts of the object. These metrics are combined with the cost of robot movement in utility functions. The next best view is selected by optimizing these functions, aiming to maximize the likelihood of discovering new parts of the object. We evaluate the functions with simulated and real world experiments within a modular software system that is adaptable to other robotic platforms and reconstruction problems. We release our implementation open source.


title: Reliable Blast UDP : predictable high performance bulk data transfer

abstract: High speed bulk data transfer is an important part of many data-intensive scientific applications. This paper describes an aggressive bulk data transfer scheme, called Reliable Blast UDP (RBUDP), intended for extremely high bandwidth, dedicated- or Quality-of-Service-enabled networks, such as optically switched networks. This paper also provides an analytical model to predict RBUDP's performance and compares the results of our model against our implementation of RBUDP. Our results show that RBUDP performs extremely efficiently over high speed dedicated networks and our model is able to provide good estimates of its performance.


title: Functional limits of oriP, the Epstein-Barr virus plasmid origin of replication.

abstract: The Epstein-Barr virus (EBV) genome contains two cis-acting elements which are required for stable extrachromosomal plasmid maintenance in latently infected cells. The first consists of 20 30-base-pair (bp) repeats, each of which contains a DNA-binding site for EBV nuclear antigen 1 (EBNA-1), the trans-acting factor required for plasmid persistence. The second element is composed of a 65-bp dyad symmetry, containing four EBNA-1-binding sites. Deletion mutants were constructed which reduce the number of EBNA-1-binding sites in the 30-bp repeats, alter the number of EBNA-1-binding sites in the dyad region, or truncate the dyad element. The effect of the deletion mutations on plasmid maintenance was examined by transfecting recombinant plasmids, containing both the mutated EBV sequences and a drug resistance marker, into D98-Raji cells. The plasmids were tested for their ability to generate drug-resistant D98-Raji cell colonies and their capacity to be maintained in an extrachromosomal form without undergoing extensive rearrangements. EBV plasmids with 12 or 15 copies of the 30-bp repeats were wild type in both assays. Plasmids with just two or six copies of these repeated elements failed to generate drug-resistant colonies at a normal level, and normal episomal plasmids were not detected in the resulting colonies. Rare colonies of cells resulting from transfection of these two- or six-copy mutants contained rearranged, episomal forms of the input plasmids. The rearrangements most often produced head-to-tail oligomers containing a minimum of eight 30-bp repeated elements. The rearranged plasmids were shown to be revertant for plasmid maintenance in that they yielded wild-type or greater numbers of drug-resistant colonies and persisted at the wild-type or a greater episomal copy number. By use of an EBV plasmid that contained no 30-bp elements, no revertants could be isolated. One to five copies of a synthetic linker corresponding to a consensus 30-bp repeated element inserted into a plasmid with no 30-bp elements now permitted the generation of oligomeric, episomal forms of the mutant test plasmid. These experiments demonstrate a requirement for a minimal number (six to eight copies) of the 30-bp repeated element. Deletions in the 65-bp dyad region had little or no effect upon the ability to generate enhanced numbers of drug-resistant D98-Raji colonies, indicating that the 30-bp repeated element is predominantly required for this phenotype.(ABSTRACT TRUNCATED AT 400 WORDS)


title: Articulated Gaussian kernel correlation for human pose estimation

abstract: In this paper, we address the problem of human pose estimation through a novel articulated Gaussian kernel correlation function which is applied to human pose tracking from a single depth sensor. We first derive a unified Gaussian kernel correlation that can generalize the previous Sum-of-Gaussians (SoG)-based methods for the similarity measure between a template and the observation. Furthermore, we develop an articulated Gaussian kernel correlation by embedding a tree-structured skeleton model, which enables us to estimate the full-body pose parameters. Also, the new kernel correlation framework can easily penalize undesired body intersection which is more natural than the clamping function in previous methods. Our algorithm is general, simple yet effective and can achieve real-time performance. The experimental results on a public depth dataset are promising and competitive when compared with state-of-the-art algorithms.


title: High-Resolution Fiber Tract Reconstruction in the Human Brain by Means of Three-Dimensional Polarized Light Imaging

abstract: Functional interactions between different brain regions require connecting fiber tracts, the structural basis of the human connectome. To assemble a comprehensive structural understanding of neural network elements from the microscopic to the macroscopic dimensions, a multimodal and multiscale approach has to be envisaged. However, the integration of results from complementary neuroimaging techniques poses a particular challenge. In this paper, we describe a steadily evolving neuroimaging technique referred to as three-dimensional polarized light imaging (3D-PLI). It is based on the birefringence of the myelin sheaths surrounding axons, and enables the high-resolution analysis of myelinated axons constituting the fiber tracts. 3D-PLI provides the mapping of spatial fiber architecture in the postmortem human brain at a sub-millimeter resolution, i.e. at the mesoscale. The fundamental data structure gained by 3D-PLI is a comprehensive 3D vector field description of fibers and fiber tract orientations – the basis for subsequent tractography. To demonstrate how 3D-PLI can contribute to unravel and assemble the human connectome, a multiscale approach with the same technology was pursued. Two complementary state-of-the-art polarimeters providing different sampling grids (pixel sizes of 100 μm and 1.6 μm) were used. To exemplarily highlight the potential of this approach, fiber orientation maps and 3D fiber models were reconstructed in selected regions of the brain (e.g., Corpus callosum, Internal capsule, Pons). The results demonstrate that 3D-PLI is an ideal tool to serve as an interface between the microscopic and macroscopic levels of organization of the human connectome.


title: Simulation of Detecting Function object for AGV Using Computer Vision with Neural Network

abstract: The objective of this paper is to simulate the algorithm to detect object for Automated Guide Vehicle (AGV) guidance problem, namely to obstacle avoidance. Currently, the AGV is a transport vehicle widely used in manufacturing factories and plays an important role in the design of material handling system, moving goods to raw materials or finished product to rightful destination that work automatically. This method has been designed for use in security of internal transportation system in order to prevent a collision to the AGVs and the obstacles, which do not know the exact shape, size and color. Such real-time obstacle detection was crucial, we need to classify the obstacles that are real obstacles or fake obstacles. The computer vision with artificial neural networks (ANN) can help the AGV can see and recognize like a human by imitating the functions of the human brain. In this paper, we proposed an algorithm for detecting obstacle that work automatically with no human is required. The algorithm is divided in to two parts: First, a preliminary feature extraction stage by using the Histograms of Oriented Gradients (HOG) to extract height objects feature from the actual video images. Second, the recognition and classification of the obstacles that are real obstacles or fake obstacles such as a painting or text on the floor by a feed-forward time delay neural network (TDNN) in sequences of black and white video images taken by an on-board camera, then decide whether to hold or continue to be moved.As a result, this paper presents the simulate the algorithm to detect the obstacles for AGV, which can work to destination are defined and avoided obstacles by computer vision with TDNN as an alternative principle with high accuracy and reliability.


title: Fraud detection system

abstract: The increment of computer technology use and the continued growth of companies have enabled most financial transactions to be performed through the electronic commerce systems, such as using the credit card system, telecommunication system, healthcare insurance system, etc. Unfortunately, these systems are used by both legitimate users and fraudsters. In addition, fraudsters utilized different approaches to breach the electronic commerce systems. Fraud prevention systems (FPSs) are insufficient to provide adequate security to the electronic commerce systems. However, the collaboration of FDSs with FPSs might be effective to secure electronic commerce systems. Nevertheless, there are issues and challenges that hinder the performance of FDSs, such as concept drift, supports real time detection, skewed distribution, large amount of data etc. This survey paper aims to provide a systematic and comprehensive overview of these issues and challenges that obstruct the performance of FDSs. We have selected five electronic commerce systems; which are credit card, telecommunication, healthcare insurance, automobile insurance and online auction. The prevalent fraud types in those E-commerce systems are introduced closely. Further, state-of-the-art FDSs approaches in selected E-commerce systems are systematically introduced. Then a brief discussion on potential research trends in the near future and conclusion are presented.


title: Dermatoscopy of flat pigmented facial lesions.

abstract: BACKGROUND ::: The diagnosis of flat pigmented lesions on the face is challenging because of the morphologic overlap of biologically different lesions and the unknown significance of dermatoscopic patterns. ::: ::: ::: OBJECTIVE ::: To better characterize dermatoscopic patterns of various types of flat pigmented facial lesions and to analyse their significance by calculating their relative risks and diagnostic values. ::: ::: ::: METHODS ::: We prospectively analysed dermatoscopic images of 240 flat pigmented facial skin lesions collected consecutively from 195 patients (41.5% females, mean age: 61 ± 14 years) between 2007 and March 2012 in a primary skin cancer practice situated in Queensland, Australia. ::: ::: ::: RESULTS ::: Histopathologically 114 (47.5%) lesions were malignant (24 lentigo maligna, 21 basal cell carcinomas and 69 pigmented actinic keratoses). Compared with all other patterns the positive predictive value for lentigo maligna was highest for a pattern of circles (31.3%, 95% CI: 11.1-58.6%). A pattern of clods was associated with basal cell carcinoma. If grey structures were present the relative risk for malignancy was 2.2 (95%CI: 1.4-3.4). The best clues to differentiate pigmented actinic keratosis from other lesions were the presence of scale (positive predictive value: 72.2%, specificity: 94.2%), white circles (positive predictive value: 68.8%, specificity: 94.2%) and a sharply demarcated border (positive predictive value: 44.2%, specificity: 86.0%). ::: ::: ::: CONCLUSIONS ::: In flat lesions a pattern of circles bears the highest risk for facial melanoma but other patterns do not exclude it. Scale, white circles and a sharply demarcated border are clues to pigmented actinic keratoses. The presence of grey colour is a clue to malignancy regardless of pattern.


title: Event Detection in Twitter using Aggressive Filtering and Hierarchical Tweet Clustering

abstract: Twitter has become as much of a news media as a social network, and much research has turned to analyzing its content for tracking real-world events, from politics to sports and natural disasters. This paper describes the techniques we employed for the SNOW Data Challenge 2014, described in [Pap14]. We show that aggressive filtering of tweets based on length and structure, combined with hierarchical clustering of tweets and ranking of the resulting clusters, achieves encouraging results. We present empirical results and discussion for two different Twitter streams focusing on the US presidential elections in 2012 and the recent events about Ukraine, Syria and the Bitcoin, in February 2014.


title: Low-voltage-swing monolithic dc-dc conversion

abstract: A low-voltage-swing MOSFET gate drive technique is proposed in this paper for enhancing the efficiency characteristics of high-frequency-switching dc-dc converters. The parasitic power dissipation of a dc-dc converter is reduced by lowering the voltage swing of the power transistor gate drivers. A comprehensive circuit model of the parasitic impedances of a monolithic buck converter is presented. Closed-form expressions for the total power dissipation of a low-swing buck converter are proposed. The effect of reducing the MOSFET gate voltage swings is explored with the proposed circuit model. A range of design parameters is evaluated, permitting the development of a design space for full integration of active and passive devices of a low-swing buck converter on the same die, for a target CMOS technology. The optimum gate voltage swing of a power MOSFET that maximizes efficiency is lower than a standard full voltage swing. An efficiency of 88% at a switching frequency of 102 MHz is achieved for a voltage conversion from 1.8 to 0.9 V with a low-swing dc-dc converter based on a 0.18-/spl mu/m CMOS technology. The power dissipation of a low-swing dc-dc converter is reduced by 27.9% as compared to a standard full-swing dc-dc converter.


Service Management and IT Governance : Review , Comparative Analysis and their Impact on Utility Computing


title: Receptor tyrosine kinase alterations in AML - Biology and therapy

abstract: Acute myeloid leukemia (AML) is the most common form of leukemia in adults, and despite some recent progress in understanding the biology of the disease, AML remains the leading cause of leukemia-related deaths in adults and children. AML is a complex and heterogeneous disease, often involving multiple genetic defects that promote leukemic transformation and drug resistance. The cooperativity model suggests that an initial genetic event leads to maturational arrest in a myeloid progenitor cell, and subsequent genetic events induce proliferation and block apoptosis. Together, these genetic abnormalities lead to clonal expansion and frank leukemia. The purpose of this chapter is to review the biology of receptor tyrosine kinases (RTKs) in AML, exploring how RTKs are being used as novel prognostic factors and potential therapeutic targets.


title: Intravitreal ranibizumab (Lucentis) for branch retinal vein occlusion-induced macular edema: nine-month results of a prospective study.

abstract: PURPOSE ::: The purpose of this study was to evaluate the effect of individualized repeated intravitreal injections of ranibizumab (Lucentis, Genentech, South San Francisco, CA) on visual acuity and central foveal thickness (CFT) for branch retinal vein occlusion-induced macular edema. ::: ::: ::: METHODS ::: This study was a prospective interventional case series. Twenty-eight eyes of 28 consecutive patients diagnosed with branch retinal vein occlusion-related macular edema treated with repeated intravitreal injections of ranibizumab (when CFT was >225 microm) were evaluated. Optical coherence tomography and fluorescein angiography were performed monthly. ::: ::: ::: RESULTS ::: The mean best-corrected distance visual acuity improved from 62.67 Early Treatment of Diabetic Retinopathy Study letters (logarithm of the minimum angle of resolution = 0.74 +/- 0.28 [mean +/- standard deviation]) at baseline to 76.8 Early Treatment of Diabetic Retinopathy Study letters (logarithm of the minimum angle of resolution = 0.49 +/- 0.3; statistically significant, P < 0.001) at the end of the follow-up (9 months). The mean letter gain (including the patients with stable and worse visual acuities) was 14.3 letters (2.9 lines). During the same period, 22 of the 28 eyes (78.6%) showed improved visual acuity, 4 (14.2%) had stable visual acuity, and 2 (7.14%) had worse visual acuity compared with baseline. The mean CFT improved from 349 +/- 112 microm at baseline to 229 +/- 44 microm (significant, P < 0.001) at the end of follow-up. A mean of six injections was performed during the follow-up period. Our subgroup analysis indicated that patients with worse visual acuity at presentation (<or=50 letters in our series) showed greater visual benefit from treatment. "Rebound" macular edema was observed in 5 patients (17.85%) at the 3-month follow-up visit and in none at the 6- and 9-month follow-ups. In 18 of the 28 patients (53.6%), the CFT was <225 microm at the last follow-up visit, and therefore, further treatment was not instituted. No ocular or systemic side effects were noted. ::: ::: ::: CONCLUSION ::: Individualized repeated intravitreal injections of ranibizumab showed promising short-term results in visual acuity improvement and decrease in CFT in patients with macular edema associated with branch retinal vein occlusion. Further studies are needed to prove the long-term effect of ranibizumab treatment on patients with branch retinal vein occlusion.


title: Overtaking Maneuvers in Simulated Highway Driving using Deep Reinforcement Learning

abstract: Most methods that attempt to tackle the problem of Autonomous Driving and overtaking usually try to either directly minimize an objective function or iteratively in a Reinforcement Learning like framework to generate motor actions given a set of inputs. We follow a similar trend but train the agent in a way similar to a curriculum learning approach where the agent is first given an easier problem to solve, followed by a harder problem. We use Deep Deterministic Policy Gradients to learn overtaking maneuvers for a car, in presence of multiple other cars, in a simulated highway scenario. The novelty of our approach lies in the training strategy used where we teach the agent to drive in a manner similar to the way humans learn to drive and the fact that our reward function uses only the raw sensor data at the current time step. This method, which resembles a curriculum learning approach is able to learn smooth maneuvers, largely collision free, wherein the agent overtakes all other cars, independent of the track and number of cars in the scene.


title: Development of Wireless In-Wheel Motor Using Magnetic Resonance Coupling

abstract: In-wheel motors (IWMs) in electric vehicles are particularly important for motion control. A conventional IWM is powered from a battery aboard the vehicle via cables. Since power cables and signal cables of an IWM are exposed to harsh environments, they can possibly become disconnected by high acceleration or vibration. In order to overcome this problem, the wireless-in wheel motor (W-IWM) has been proposed. The risk of disconnection would disappear if the cables of the IWM are removed. One way to implement wireless power transfer is by utilizing the magnetic resonance coupling method. However, motion of the W-IWM, and thus, a misalignment between the wheel and the vehicle, leads to variations in the secondary-side voltage provided. To account for this, this paper discusses two new control methods. One proposed method maintains the secondary voltage using a hysteresis comparator. The other proposed method estimates the secondary inverter output power, applying it to a feedforward controller in order to keep the secondary dc-link voltage constant. Experimental results show that these methods can drive a W-IWM effectively with high efficiency.


title: FPGA-Based Sensorless PMSM Speed Control Using Reduced-Order Extended Kalman Filters

abstract: This paper presents the design and implementation of a field-programmable gate array (FPGA)-based architecture for the speed control of sensorless permanent-magnet synchronous motor (PMSM) drives. For the reduction of computation resources, as well as accuracy improvement in the rotor position estimation, a parallel reduced-order extended Kalman filter (EKF) is proposed in this work. Compared with an EKF, the system order is reduced and the iteration process is greatly simplified, resulting in significant savings of resource utility, while maintaining high estimation performance. The whole control system includes a current-control-and-coordinate-transformation unit, a proportional-integral (PI) speed controller, and other accessory modules, all implemented in a single FPGA chip. A hardware description language is adopted to describe advantageous features of the proposed control system. Moreover, the finite-state-machine method is applied with the purpose to reduce logic elements used in the FPGA chip. The validity of the approach is verified through simulation based on the Modelsim/Simulink cosimulation method. Finally, experimental results are obtained on an FPGA platform with an inverter-fed PMSM to show the feasibility and effectiveness of the proposed system-on-programmable-chip for PMSM drives.


title: The Perinatal-Neonatal Care Journey for Parents of Preterm Infants: What Is Working and What Can Be Improved

abstract: Preterm birth is traumatic for parents, but there are few reports of parents' views on how the healthcare journey can be improved. This secondary thematic analysis used focus group data from parent consultation on proposed neonatal services standards for Northern Ireland to discover parents' experiences and recommendations for the perinatal, neonatal, and home care phases. Parents of preterm infants (n = 40) described their healthcare journey as positive overall and were grateful for the caring and competent care providers they encountered. However, parents described experiences that varied in quality and family centeredness across the care journey from perinatal to home care. They noted inconsistencies in healthcare team communication and provider practices and reported receiving limited emotional and practical support at all phases. In the perinatal phase, parents described difficult situations of discovering medical problems leading to preterm birth. In the neonatal intensive care unit phase, they also experienced unmet needs for involvement in decision making, financial strain, and difficulty coping with transfers and discharge. Parents experienced emotional challenges and lack of support in the home care phase. Parents identified actions that health systems can take to improve the consistency of care and communication across all phases and settings to encourage better collaboration and transitions in care.


title: Spectral Clustering and Visualization: A Novel Clustering of Fisher's Iris Data Set

abstract: Clustering is the act of partitioning a set of elements into subsets, or clusters, so that elements in the same cluster are, in some sense, similar. Determining an appropriate number of clusters in a particular data set is an important issue in data mining and cluster analysis. Another important issue is visualizing the strength, or connectivity, of clusters. We begin by creating a consensus matrix using multiple runs of the clustering algorithm k-means. This consensus matrix can be interpreted as a graph, which we cluster using two spectral clustering methods: the Fiedler Method and the MinMaxCut Method. To determine if increasing the number of clusters from k to k + 1 is appropriate, we check whether an existing cluster can be split. Finally, we visualize the strength of clusters by using the consensus matrix and the clustering obtained through one of the aforementioned spectral clustering techniques. Using these methods, we then investigate Fisher’s Iris data set. Our methods support the existence of four clusters, instead of the generally accepted three clusters in this data.


title: Efficient multiclass maximum margin clustering

abstract: This paper presents a cutting plane algorithm for multiclass maximum margin clustering (MMC). The proposed algorithm constructs a nested sequence of successively tighter relaxations of the original MMC problem, and each optimization problem in this sequence could be efficiently solved using the constrained concave-convex procedure (CCCP). Experimental evaluations on several real world datasets show that our algorithm converges much faster than existing MMC methods with guaranteed accuracy, and can thus handle much larger datasets efficiently.


title: Carbon fiber reinforced polymer integrated antenna module

abstract: Rising demand for vehicular communication and cooperative movement have led to an increase of vehicle mounted antennas. A carbon fiber reinforced polymer cavity is presented, that can be integrated into the vehicle chassis. The concealed cavity is large enough to house the anticipated antennas for present and future vehicular communication. Influence of the cavity onto antennas is demonstrated with calibrated gain measurements on the example of a 5.9GHz monopole antenna for intelligent transportation systems. The cavities impact on antenna performance is found to be much smaller than influences of the car itself.


title: An Indepth Study of Mirror Adaptive Random Testing

abstract: Previous studies showed that adaptive random testing is an effective alternative to random testing method, but requires additional overheads to evenly spread test cases. Mirroring was introduced to reduce the overheads of adaptive random testing. This paper is the follow-up work to a previous study on the integration of mirroring and adaptive random testing, namely the mirror adaptive random testing. It studies characteristics and effectiveness of mirror adaptive random testing in depth, and provides guidelines on how to apply mirror adaptive random testing in practice.


title: Sparse Phase Retrieval via Truncated Amplitude Flow

abstract: This paper develops a novel algorithm, termed \emph{SPARse Truncated Amplitude flow} (SPARTA), to reconstruct a sparse signal from a small number of magnitude-only measurements. It deals with what is also known as sparse phase retrieval (PR), which is \emph{NP-hard} in general and emerges in many science and engineering applications. Upon formulating sparse PR as an amplitude-based nonconvex optimization task, SPARTA works iteratively in two stages: In stage one, the support of the underlying sparse signal is recovered using an analytically well-justified rule, and subsequently, a sparse orthogonality-promoting initialization is obtained via power iterations restricted on the support; and, in the second stage, the initialization is successively refined by means of hard thresholding based gradient-type iterations. SPARTA is a simple yet effective, scalable, and fast sparse PR solver. On the theoretical side, for any $n$-dimensional $k$-sparse ($k\ll n$) signal $\bm{x}$ with minimum (in modulus) nonzero entries on the order of $(1/\sqrt{k})\|\bm{x}\|_2$, SPARTA recovers the signal exactly (up to a global unimodular constant) from about $k^2\log n$ random Gaussian measurements with high probability. Furthermore, SPARTA incurs computational complexity on the order of $k^2n\log n$ with total runtime proportional to the time required to read the data, which improves upon the state-of-the-art by at least a factor of $k$. Finally, SPARTA is robust against additive noise of bounded support. Extensive numerical tests corroborate markedly improved recovery performance and speedups of SPARTA relative to existing alternatives.


title: Disinhibition, a Circuit Mechanism for Associative Learning and Memory

abstract: Although a wealth of data have elucidated the structure and physiology of neuronal circuits, we still only have a very limited understanding of how behavioral learning is implemented at the network level. An emerging crucial player in this implementation is disinhibition—a transient break in the balance of excitation and inhibition. In contrast to the widely held view that the excitation/inhibition balance is highly stereotyped in cortical circuits, recent findings from behaving animals demonstrate that salient events often elicit disinhibition of projection neurons that favors excitation and thereby enhances their activity. Behavioral functions ranging from auditory fear learning, for which most data are available to date, to spatial navigation are causally linked to disinhibition in different compartments of projection neurons, in diverse cortical areas and at timescales ranging from milliseconds to days, suggesting that disinhibition is a conserved circuit mechanism contributing to learning and memory expression.


title: When Loving Means Hurting: An Exploration of Attachment and Intimate Abuse In a Community Sample

abstract: Intimate relationship abuse can be understood by considering two critical tenets of attachment. First, attachment fulfills a basic need for survival. Thus, the tenacity of the attachment bond is independent of relationship quality. Second, individuals whose attachment needs have been frustrated may strike out violently to regain proximity to the perceived loss of an intimate partner. We examined how individual differences in attachment were associated with women's and men's relationship abuse. A telephone survey assessed levels of psychological and physical abuse in 1249 Vancouver residents. Of these, 128 completed an attachment interview exploring their interpersonal relationships. Hierarchical regressions revealed that attachment variables contributed significant variance to prediction of both receipt and perpetration of psychological and physical abuse, with preoccupied attachment acting as an independent predictor. There was no evidence that gender moderated these associations. The findings suggest that attachment preoccupation in either partner may increase likelihood of abuse in couples.


title: Blood Exosomes Endowed with Magnetic and Targeting Properties for Cancer Therapy

abstract: Exosomes are a class of naturally occurring nanoparticles that are secreted endogenously by mammalian cells. Clinical applications for exosomes remain a challenge because of their unsuitable donors, low scalability, and insufficient targeting ability. In this study, we developed a dual-functional exosome-based superparamagnetic nanoparticle cluster as a targeted drug delivery vehicle for cancer therapy. The resulting exosome-based drug delivery vehicle exhibits superparamagnetic behavior at room temperature, with a stronger response to an external magnetic field than individual superparamagnetic nanoparticles. These properties enable exosomes to be separated from the blood and to target diseased cells. In vivo studies using murine hepatoma 22 subcutaneous cancer cells showed that drug-loaded exosome-based vehicle delivery enhanced cancer targeting under an external magnetic field and suppressed tumor growth. Our developments overcome major barriers to the utility of exosomes for cancer application.


title: Multitasking across generations: Multitasking choices and difficulty ratings in three generations of Americans

abstract: This study investigated whether changes in the technological/social environment in the United States over time have resulted in concomitant changes in the multitasking skills of younger generations. One thousand, three hundred and nineteen Americans from three generations were queried to determine their at-home multitasking behaviors. An anonymous online questionnaire asked respondents to indicate which everyday and technology-based tasks they choose to combine for multitasking and to indicate how difficult it is to multitask when combining the tasks. Combining tasks occurred frequently, especially while listening to music or eating. Members of the ''Net Generation'' reported more multitasking than members of ''Generation X,'' who reported more multitasking than members of the ''Baby Boomer'' generation. The choices of which tasks to combine for multitasking were highly correlated across generations, as were difficulty ratings of specific multitasking combinations. The results are consistent with a greater amount of general multitasking resources in younger generations, but similar mental limitations in the types of tasks that can be multitasked.


title: Formalizing preference utilitarianism in physical world models

abstract: Most ethical work is done at a low level of formality. This makes practical moral questions inaccessible to formal and natural sciences and can lead to misunderstandings in ethical discussion. In this paper, we use Bayesian inference to introduce a formalization of preference utilitarianism in physical world models, specifically cellular automata. Even though our formalization is not immediately applicable, it is a first step in providing ethics and ultimately the question of how to “make the world better” with a formal basis.


title: Modeling Textures with Total Variation Minimization and Oscillating Patterns in Image Processing

abstract: This paper is devoted to the modeling of real textured images by functional minimization and partial differential equations. Following the ideas of Yves Meyer in a total variation minimization framework of L. Rudin, S. Osher, and E. Fatemi, we decompose a given (possible textured) image f into a sum of two functions u+v, where u∈BV is a function of bounded variation (a cartoon or sketchy approximation of f), while v is a function representing the texture or noise. To model v we use the space of oscillating functions introduced by Yves Meyer, which is in some sense the dual of the BV space. The new algorithm is very simple, making use of differential equations and is easily solved in practice. Finally, we implement the method by finite differences, and we present various numerical results on real textured images, showing the obtained decomposition u+v, but we also show how the method can be used for texture discrimination and texture segmentation.


title: Modeling and Control of Fuel Cell/Supercapacitor Hybrid Source Based on Differential Flatness Control

abstract: Fuel-cell vehicles (FCVs) with energy storage (ES) device(s) could result in improved lifetime, performance, fuel economy, and reduced cost. This paper presents the utilization of an ES device consisting of a supercapacitor bank for future electric vehicles with a hydrogen fuel cell (FC) as the main power source. The study mainly focuses on the innovative control law based on the flatness properties for a FC/supercapacitor hybrid power source. Utilizing the flatness principle, we propose simple solutions to the hybrid energy-management and stabilization problems. A supercapacitor module, as a high dynamic and high-power density device, functions to supply energy to regulate the dc-bus energy. The FC, as a slower dynamic source in this system, functions by supplying energy to keep the supercapacitor module charged. To ensure energy-efficient operation of the FC stack, the output current ripple of the FC stack is minimized by parallel boost converters with an interleaving switching technique for a high-frequency ripple by the supercapacitor for a low-frequency ripple. To authenticate the proposed control laws, a test bench is realized in the laboratory. The control algorithm (energy and current control loops) is digitally implemented by dSPACE controller DS1103. Experimental results with small-scale devices (a proton exchange membrane FC (PEMFC) of 500 W, 50 A, and 10 V and a supercapacitor bank of 250 F, 32 V, and 500 A) substantiate the excellent performance during load cycles.


title: Detection- and Trajectory-Level Exclusion in Multiple Object Tracking

abstract: When tracking multiple targets in crowded scenarios, modeling mutual exclusion between distinct targets becomes important at two levels: (1) in data association, each target observation should support at most one trajectory and each trajectory should be assigned at most one observation per frame, (2) in trajectory estimation, two trajectories should remain spatially separated at all times to avoid collisions. Yet, existing trackers often sidestep these important constraints. We address this using a mixed discrete-continuous conditional random field (CRF) that explicitly models both types of constraints: Exclusion between conflicting observations with super modular pairwise terms, and exclusion between trajectories by generalizing global label costs to suppress the co-occurrence of incompatible labels (trajectories). We develop an expansion move-based MAP estimation scheme that handles both non-sub modular constraints and pairwise global label costs. Furthermore, we perform a statistical analysis of ground-truth trajectories to derive appropriate CRF potentials for modeling data fidelity, target dynamics, and inter-target occlusion.


title: Detecting Fake Reviews Utilizing Semantic and Emotion Model

abstract: As people are spending more time to shop and view reviews on line, some reviewer write fake reviews to earn credit and to promote (demote) the sales of product and stores. Detecting fake reviews and spammers becomes more important when the spamming behavior is becoming damaging. This paper proposes three types of new features which include review density, semantic and emotion and gives the model and algorithm to construct each feature. Experiments show that the proposed model, algorithm and features are efficient in fake review detection task than traditional method based on content, reviewer info and behavior.


title: Decision-Based Transcription of Jazz Guitar Solos Using a Harmonic Bident Analysis Filter Bank and Spectral Distribution Weighting

abstract: Jazz guitar solos are improvised melody lines played on one instrument on top of a chordal accompaniment (comping). As the improvisation happens spontaneously, a reference score is non-existent, only a lead sheet. There are situations, however, when one would like to have the original melody lines in the form of notated music, see the Real Book. The motivation is either for the purpose of practice and imitation or for musical analysis. In this work, an automatic transcriber for jazz guitar solos is developed. It resorts to a very intuitive representation of tonal music signals: the pitchgram. No instrument-specific modeling is involved, so the transcriber should be applicable to other pitched instruments as well. Neither is there the need to learn any note profiles prior to or during the transcription. Essentially, the proposed transcriber is a decision tree, thus a classifier, with a depth of 3. It has a (very) low computational complexity and can be run on-line. The decision rules can be refined or extended with no or little musical education. The transcriber's performance is evaluated on a set of ten jazz solo excerpts and compared with a state-of-the-art transcription system for the guitar plus PYIN. We achieve an improvement of 34% w.r.t. the reference system and 19% w.r.t. PYIN in terms of the F-measure. Another measure of accuracy, the error score, attests that the number of erroneous pitch detections is reduced by more than 50% w.r.t. the reference system and by 45% w.r.t. PYIN.


title: Illinois-LH: A Denotational and Distributional Approach to Semantics

abstract: This paper describes and analyzes our SemEval 2014 Task 1 system. Its features are based on distributional and denotational similarities; word alignment; negation; and hypernym/hyponym, synonym, and antonym relations.


title: Building underwater ad-hoc networks and sensor networks for large scale real-time aquatic applications

abstract: Large-scale underwater ad-hoc networks (UANET) and underwater sensor networks (UWSN) are novel networking paradigms to explore the uninhabited oceans. However, the characteristics of these new networks, such as huge propagation delay, floating node mobility, and limited acoustic link capacity, are significantly different from ground-based mobile ad-hoc networks (MANET) and wireless sensor networks (WSN). In this paper we adopt a top-down approach to explore the new research subject. We at first show a new practical application scenario that cannot be addressed by existing technology and hence demands the advent of the UANET and UWSN. Then along the layered protocol stack, we go down from the top application layer to the bottom physical layer. At each layer we show a set of new design challenges. We conclude that UANET and UWSN are challenges that must be answered by inter-disciplinary efforts of acoustic communication, signal processing and mobile acoustic network protocol design


title: Feature Engineering for Drug Name Recognition in Biomedical Texts: Feature Conjunction and Feature Selection

abstract: Drug name recognition (DNR) is a critical step for drug information extraction. Machine learning-based methods have been widely used for DNR with various types of features such as part-of-speech, word shape, and dictionary feature. Features used in current machine learning-based methods are usually singleton features which may be due to explosive features and a large number of noisy features when singleton features are combined into conjunction features. However, singleton features that can only capture one linguistic characteristic of a word are not sufficient to describe the information for DNR when multiple characteristics should be considered. In this study, we explore feature conjunction and feature selection for DNR, which have never been reported. We intuitively select 8 types of singleton features and combine them into conjunction features in two ways. Then, Chi-square, mutual information, and information gain are used to mine effective features. Experimental results show that feature conjunction and feature selection can improve the performance of the DNR system with a moderate number of features and our DNR system significantly outperforms the best system in the DDIExtraction 2013 challenge.


title: Shape priors for real-time monocular object localization in dynamic environments

abstract: Reconstruction of dynamic objects in a scene is a highly challenging problem in the context of SLAM. In this paper, we present a real-time monocular object localization system that estimates the shape and pose of dynamic objects in real-time, using video frames captured from a moving monocular camera. Although the problem seems to be ill-posed, we demonstrate that, by incorporating prior knowledge of the object category, we can obtain more detailed instance-level reconstructions. As opposed to earlier object model specifications, the proposed shape-prior model leads to the formulation of a Bundle Adjustment-like optimization problem for simultaneous shape and pose estimation. Leveraging recent successes of Convolutional Neural Networks (CNNs) for object keypoint localization, we present a CNN architecture that performs precise keypoint localization. We then demonstrate how these keypoints can be used to recover 3D object properties, while accounting for any 2D localization errors and self-occlusion. We show significant performance improvements compared to state-of-the-art monocular competitors for 2D keypoint detection, as well as 3D localization and reconstruction of dynamic objects.


title: Beyond Success Rate: Utility as a Search Quality Metric for Online Experiments

abstract: User satisfaction metrics are an integral part of search engine development as they help system developers to understand and evaluate the quality of the user experience. Research to date has mostly focused on predicting success or frustration as a proxy for satisfaction. However, users' search experience is more complex than merely being either successful or not. As such, using success rate as a measure of satisfaction can be limiting. In this work, we propose the use of utility as a measure of searcher satisfaction. This concept represents the fulfillment a user receives from con-suming a service and explains how users aim to gain optimal overall satisfaction. Our utility metrics measure the user satisfac-tion by aggregating all their interaction with the search engine. These interactions are represented as a timeline of actions and their dwelltimes, where each action is classified as having a posi-tive or negative effect on the user. We examine sessions mined from Bing logs, with multi-point scale assessment of searcher satisfaction and show that utility is a better proxy for satisfaction compared to success. Leveraging that data, we design metrics of searcher satisfaction that assess the overall utility accumulated by a user during her search session. We use real user traffic from millions of users in an A/B setting to compare utility metrics to success rate metrics. We show that utility is a better metric for evaluating searcher satisfaction with the search engine, and a more sensitive and accurate metric when compared to predicting success. These metrics are currently adopted as the top-level met-ric for evaluating the thousands of A/B experiments that are run on Bing each year.


title: IT Security in Automotive Software Development

abstract: In the last years, automotive systems evolved to be more and more software-intensive systems. As a result, considerable attention has been paid to establish an efficient software development process of such systems, where reliability is an important criterion. Hence, model-driven development (MDD), software engineering and requirements engineering (amongst others) found their way into the systems engineering domain. However, one important aspect regarding the reliability of such systems, has been largely neglected on a holistic level: the IT security. In this paper, we introduce a potential approach for integrating IT security in the requirements engineering process of automotive software development using function net modeling.