title: Self-awareness in Active Music Systems

abstract: Self-aware and self-expressive technologies may be used to improve user experience in interactive music systems. This chapter presents how the concepts and techniques from the first parts of the book may be exploited to develop better technologies for active music. Nature-inspired and socially-inspired methods, as introduced in Chapter 7, are used to allow music listeners to influence high-level parameters of the music, such as mood or tempo, without requiring the skill of a professional musician. Several of the examples presented in this chapter utilise the same algorithms as presented for the multi-camera networks in Chapter 13, thus demonstrating the broad application domain of these algorithms. The chapter is organised as a discussion of three example systems. First, a mechanism for conflict resolution in a distributed active music system is presented. The second and third example presented take inspiration from the pheromone mechanism used in Ant Colony Optimisation. The approach is first used for continuous classification of the movement patterns of listeners to incorporate adaptive mapping between sensor data and musical output. In the third example the same mechanism enables a system to remember the preferences of a user when navigating in a musical space.


title: Using Constraint Programming to Reason on Feature Models

abstract: Feature models have been cited as one of the main contributions to model software product families. However, there is still a gap in product family engineering which is the automated reasoning on feature models. In this paper we describe how to reason on feature models using constraint programming. Although, there are a few attempts to reason on feature models there are two main drawbacks in these proposals: none of them associate parameters to features none of them use constraint programming as the reasoning base. Using constraint programming endows our proposal with a more powerful reasoning capacity and greater expressiveness than others.


title: Brainstorming under constraints: why software developers brainstorm in groups

abstract: Group brainstorming is widely adopted as a design method in the domain of software development. However, existing brainstorming literature has consistently proven group brainstorming to be ineffective under the controlled laboratory settings. Yet, electronic brainstorming systems informed by the results of these prior laboratory studies have failed to gain adoption in the field because of the lack of support for group well-being and member support. Therefore, there is a need to better understand brainstorming in the field. In this work, we seek to understand why and how brainstorming is actually practiced, rather than how brainstorming practices deviate from formal brainstorming rules, by observing brainstorming meetings at Microsoft. The results of this work show that, contrary to the conventional brainstorming practices, software teams at Microsoft engage heavily in the constraint discovery process in their brainstorming meetings. We identified two types of constraints that occur in brainstorming meetings. Functional constraints are requirements and criteria that define the idea space, whereas practical constraints are limitations that prioritize the proposed solutions.


title: Forecasting time series of inhomogeneous Poisson processes with application to call center workforce management

abstract: We consider forecasting the latent rate profiles of a time series of inhomogeneous Poisson processes. The work is motivated by operations management of queueing systems, in particular, telephone call centers, where accurate forecasting of call arrival rates is a crucial primitive for efficient staffing of such centers. Our forecasting approach utilizes dimension reduction through a factor analysis of Poisson variables, followed by time series modeling of factor score series. Time series forecasts of factor scores are combined with factor loadings to yield forecasts of future Poisson rate profiles. Penalized Poisson regressions on factor loadings guided by time series forecasts of factor scores are used to generate dynamic within-process rate updating. Methods are also developed to obtain distributional forecasts. Our methods are illustrated using simulation and real data. The empirical results demonstrate how forecasting and dynamic updating of call arrival rates can affect the accuracy of call center staffing.


98 PC-417 Development of a Collision Avoidance System


title: Dynamic Chemical Communication between Plants and Bacteria through Airborne Signals: Induced Resistance by Bacterial Volatiles

abstract: Certain plant growth-promoting rhizobacteria (PGPR) elicit induced systemic resistance (ISR) and plant growth promotion in the absence of physical contact with plants via volatile organic compound (VOC) emissions. In this article, we review the recent progess made by research into the interactions between PGPR VOCs and plants, focusing on VOC emission by PGPR strains in plants. Particular attention is given to the mechanisms by which these bacterial VOCs elicit ISR. We provide an overview of recent progress in the elucidation of PGPR VOC interactions from studies utilizing transcriptome, metabolome, and proteome analyses. By monitoring defense gene expression patterns, performing 2-dimensional electrophoresis, and studying defense signaling null mutants, salicylic acid and ethylene have been found to be key players in plant signaling pathways involved in the ISR response. Bacterial VOCs also confer induced systemic tolerance to abiotic stresses, such as drought and heavy metals. A review of current analytical approaches for PGPR volatile profiling is also provided with needed future developments emphasized. To assess potential utilization of PGPR VOCs for crop plants, volatile suspensions have been applied to pepper and cucumber roots and found to be effective at protecting plants against plant pathogens and insect pests in the field. Taken together, these studies provide further insight into the biological and ecological potential of PGPR VOCs for enhancing plant self-immunity and/or adaptation to biotic and abiotic stresses in modern agriculture.


title: Partially overlapped channels not considered harmful

abstract: Many wireless channels in different technologies are known to have partial overlap. However, due to the interference effects among such partially overlapped channels, their simultaneous use has typically been avoided. In this paper, we present a first attempt to model partial overlap between channels in a systematic manner. Through the model, we illustrate that the use of partially overlapped channels is not always harmful. In fact, a careful use of some partially overlapped channels can often lead to significant improvements in spectrum utilization and application performance. We demonstrate this through analysis as well as through detailed application-level and MAC-level measurements. Additionally, we illustrate the benefits of our developed model by using it to directly enhance the performance of two previously proposed channel assignment algorithms --- one in the context of wireless LANs and the other in the context of multi-hop wireless mesh networks. Through detailed simulations, we show that use of partially overlapped channels in both these cases can improve end-to-end application throughput by factors between 1.6 and 2.7 in different scenarios, depending on wireless node density. We conclude by observing that the notion of partial overlap can be the right model of flexibility to design efficient channel access mechanisms in the emerging software radio platforms.


title: Computing Personalized PageRank Quickly by Exploiting Graph Structures

abstract: We propose a new scalable algorithm that can compute Personalized PageRank (PPR) very quickly. The Power method is a state-of-the-art algorithm for computing exact PPR; however, it requires many iterations. Thus reducing the number of iterations is the main challenge. ::: ::: We achieve this by exploiting graph structures of web graphs and social networks. The convergence of our algorithm is very fast. In fact, it requires up to 7.5 times fewer iterations than the Power method and is up to five times faster in actual computation time. ::: ::: To the best of our knowledge, this is the first time to use graph structures explicitly to solve PPR quickly. Our contributions can be summarized as follows. ::: ::: 1. We provide an algorithm for computing a tree decomposition, which is more efficient and scalable than any previous algorithm. ::: ::: 2. Using the above algorithm, we can obtain a core-tree decomposition of any web graph and social network. This allows us to decompose a web graph and a social network into (1) the core, which behaves like an expander graph, and (2) a small tree-width graph, which behaves like a tree in an algorithmic sense. ::: ::: 3. We apply a direct method to the small tree-width graph to construct an LU decomposition. ::: ::: 4. Building on the LU decomposition and using it as pre-conditoner, we apply GMRES method (a state-of-the-art advanced iterative method) to compute PPR for whole web graphs and social networks.


title: And they shall walk: ideal versus reality in polio rehabilitation in the United States

abstract: This essay explores the significance that rehabilitation physicians and polio patients in the United States put on recovering the ability to walk. Polio often paralyzed or severely weakened the legs of those who contracted the disease. Regaining the ability to walk was thus a significant measure of recovery from the disease. However, walking meant more than the physical act itself. Regaining the ability to walk meant, in a symbolic sense, that one was no longer disabled, that one had again become normal. This attitude was shared by rehabilitation specialists and patients alike. This essay examines this attitude and the cultural values it embodied through a study of the efforts of selected polio survivors to learn to walk again and of the rehabilitation literature that held walking as an ideal. It also explores what happened when polio patients were unable to walk again because of the severity of their paralysis.


title: High Order Regularization for Semi-Supervised Learning of Structured Output Problems

abstract: Semi-supervised learning, which uses unlabeled data to help learn a discriminative model, is especially important for structured output problems, as considerably more effort is needed to label its multi-dimensional outputs versus standard single output problems. We propose a new max-margin framework for semi-supervised structured output learning, that allows the use of powerful discrete optimization algorithms and high order regularizers defined directly on model predictions for the unlabeled examples. We show that our framework is closely related to Posterior Regularization, and the two frameworks optimize special cases of the same objective. The new framework is instantiated on two image segmentation tasks, using both a graph regularizer and a cardinality regularizer. Experiments also demonstrate that this framework can utilize unlabeled data from a different source than the labeled data to significantly improve performance while saving labeling effort.


title: Computational and Experimental Approaches to Visual Aesthetics

abstract: Aesthetics has been the subject of long-standing debates by philosophers and psychologists alike. In psychology, it is generally agreed that aesthetic experience results from an interaction between perception, cognition, and emotion. By experimental means, this triad has been studied in the field of experimental aesthetics, which aims to gain a better understanding of how aesthetic experience relates to fundamental principles of human visual perception and brain processes. Recently, researchers in computer vision have also gained interest in the topic, giving rise to the field of computational aesthetics. With computing hardware and methodology developing at a high pace, the modelling of perceptually relevant aspect of aesthetic stimuli has a huge potential. In this review, we present an overview of recent developments in computational aesthetics and how they relate to experimental studies. In the first part, we cover topics such as the prediction of ratings, style and artist identification as well as computational methods in art history, such as the detection of influences among artists or forgeries. We also describe currently used computational algorithms, such as classifiers and deep neural networks. In the second part, we summarize results from the field of experimental aesthetics and cover several isolated image properties that are believed to have a effect on the aesthetic appeal of visual stimuli. Their relation to each other and to findings from computational aesthetics are discussed. Moreover, we compare the strategies in the two fields of research and suggest that both fields would greatly profit from a joined research effort. We hope to encourage researchers from both disciplines to work more closely together in order to understand visual aesthetics from an integrated point of view.


Logistic Matrix Factorization for Implicit Feedback Data


title: Approximating the spanning star forest problem and its applications to genomic sequence alignment

abstract: This paper studies the algorithmic issues of the spanning star forest problem. We prove the following results: (1) There is a polynomial-time approximation scheme for planar graphs; (2) there is a polynomial-time $\frac{3}{5}$-approximation algorithm for graphs; (3) it is NP-hard to approximate the problem within ratio $\frac{259}{260} + \epsilon$ for graphs; (4) there is a linear-time algorithm to compute the maximum star forest of a weighted tree; (5) there is a polynomial-time $\frac{1}{2}$-approximation algorithm for weighted graphs. We also show how to apply this spanning star forest model to aligning multiple genomic sequences over a tandem duplication region.


title: Semantic integration of UML class diagram with semantic validation on segments of mappings

abstract: Recently, attention has focused on the software development, specially by differ-ent teams that are geographically distant to support collaborative work. Manage-ment, description and modeling in such collaborative approach are through sever-al tools and techniques based on UML models. It is now supported by a large number of tools. Most of these systems have the ability to compare different UML models, assist developers, designers and also provide operations for the merging and integration, to produce a coherent model. The contribution in this ar-ticle is both to integrate a set of UML class diagrams using mappings that are re-sult of alignment and assist designers and developers in the integration. In addi-tion, we will present a detail integration of UML models with the validation of mappings between them. Such validation helps to achieve correct, consistent and coherent integrated model.


title: A Time Series Classification Method for Behaviour-Based Dropout Prediction

abstract: Students' dropout rate is a key metric in online and open distance learning courses. We propose a time-series classification method to construct data based on students' behaviour and activities on a number of online distance learning modules. Further, we propose a dropout prediction model based on the time series forest (TSF) classification algorithm. The proposed predictive model is based on interaction data and is independent of learning objectives and subject domains. The model enables prediction of dropout rates without the requirement for pedagogical experts. Results show that the prediction accuracy on two selected datasets increases as the portion of data used in the model grows. However, a reasonable prediction accuracy of 0.84 is possible with only 5% of the dataset processed. As a result, early prediction can help instructors design interventions to encourage course completion before a student falls too far behind.


title: A3Net: Adversarial-and-Attention Network for Machine Reading Comprehension

abstract: In this paper, we introduce Adversarial-and-attention Network (A3Net) for Machine Reading Comprehension. This model extends existing approaches from two perspectives. First, adversarial training is applied to several target variables within the model, rather than only to the inputs or embeddings. We control the norm of adversarial perturbations according to the norm of original target variables, so that we can jointly add perturbations to several target variables during training. As an effective regularization method, adversarial training improves robustness and generalization of our model. Second, we propose a multi-layer attention network utilizing three kinds of high-efficiency attention mechanisms. Multi-layer attention conducts interaction between question and passage within each layer, which contributes to reasonable representation and understanding of the model. Combining these two contributions, we enhance the diversity of dataset and the information extracting ability of the model at the same time. Meanwhile, we construct A3Net for the WebQA dataset. Results show that our model outperforms the state-of-the-art models (improving Fuzzy Score from 73.50% to 77.0%).


iTaxi : Context-Aware Taxi Demand Hotspots Prediction Using Ontology and Data Mining Approaches


title: A Semantic web: Intelligence in information retrieval

abstract: The rapid growth of web resources lead to a need of enhanced Search scheme for information retrieval. Every single user contributes a part of new information to be added to the web every day. This huge data supplied are of diverse area in origin being added, without a mere relation. Hence, a novel search scheme must be applied for bringing out the relevant results on querying web for data. The current web search scheme could bring out only relevant pages to be as results. But, a Semantic web is a solution to this issue through providing a suitable result on understanding the appropriate need of information. It can be acquired through extending the support for databases in machine readable form. It leads to redefinition of current web into semantic web by adding semantic annotations. This paper gives an overview of Semantic mapping approaches. The main goal of this paper is to propose the steps for bringing out a new Semantic web discovery algorithm with an efficient Semantic mapping and a novel Classification Scheme for categorization of concepts.


Deep Learning Approach to Planogram Compliance in Retail Stores


title: Robust map optimization using dynamic covariance scaling

abstract: Developing the perfect SLAM front-end that produces graphs which are free of outliers is generally impossible due to perceptual aliasing. Therefore, optimization back-ends need to be able to deal with outliers resulting from an imperfect front-end. In this paper, we introduce dynamic covariance scaling, a novel approach for effective optimization of constraint networks under the presence of outliers. The key idea is to use a robust function that generalizes classical gating and dynamically rejects outliers without compromising convergence speed. We implemented and thoroughly evaluated our method on publicly available datasets. Compared to recently published state-of-the-art methods, we obtain a substantial speed up without increasing the number of variables in the optimization process. Our method can be easily integrated in almost any SLAM back-end.


title: Exogenous Approach to Grid Cost Allocation in Peer-to-Peer Electricity Markets

abstract: The deployment of distributed energy resources, combined with a more proactive demand side, is inducing a new paradigm in power system operation and electricity markets. Within a consumer-centric market framework, peer-to-peer approaches have gained substantial interest. Peer-to-peer markets rely on multi-bilateral direct negotiation among all players to match supply and demand, and with product differentiation. These markets can yield a complete mapping of exchanges onto the grid, hence allowing to rethink our approach to sharing costs related to usage of common infrastructure and services. We propose here to attribute such costs in a number of alternative ways that reflects different views on usage of the grid and on cost allocation, i.e., uniformly and based on the electrical distance between players. Since attribution mechanisms are defined in an exogenous manner and made transparent they eventually affect the trades of the market participants and related grid usage. The interest of our approach is illustrated on a test case using the IEEE 39 bus test system, underlying the impact of attribution mechanisms on trades and grid usage.


title: Applying Deep Learning to the Newsvendor Problem

abstract: The newsvendor problem is one of the most basic and widely applied inventory models. There are numerous extensions of this problem. If the probability distribution of the demand is known, the problem can be solved analytically. ::: However, approximating the probability distribution is not easy and is prone to error; therefore, the resulting solution to the newsvendor problem may be not optimal. To address this issue, we propose an algorithm based on deep learning that optimizes the order quantities for all products based on features of the demand data. Our algorithm integrates the forecasting and inventory-optimization steps, rather than solving them separately, as is typically done, and does not require knowledge of the probability distributions of the demand. Numerical experiments on real-world data suggest that our algorithm outperforms other approaches, including data-driven and machine learning approaches, especially for demands with high volatility. Finally, in order to show how this approach can be used for other inventory optimization problems, we provide an extension for (r,Q) policies.


A Competency Model for "Industrie 4.0" Employees


The Role of Education On Economic Growth in Pakistan


title: SIMD Compression and the Intersection of Sorted Integers

abstract: Sorted lists of integers are commonly used in inverted indexes and database systems. They are often compressed in memory. We can use the SIMD instructions available in common processors to boost the speed of integer compression schemes. Our S4-BP128-D4 scheme uses as little as 0.7 CPU cycles per decoded integer while still providing state-of-the-art compression. ::: However, if the subsequent processing of the integers is slow, the effort spent on optimizing decoding speed can be wasted. To show that it does not have to be so, we (1) vectorize and optimize the intersection of posting lists; (2) introduce the SIMD Galloping algorithm. We exploit the fact that one SIMD instruction can compare 4 pairs of integers at once. ::: We experiment with two TREC text collections, GOV2 and ClueWeb09 (Category B), using logs from the TREC million-query track. We show that using only the SIMD instructions ubiquitous in all modern CPUs, our techniques for conjunctive queries can double the speed of a state-of-the-art approach.


title: Aspirin Induced Asthma- a Review

abstract: Asthma is currently a worldwide problem. Worldwide deaths from this condition have reached over 250,000 annually. Aspirin or Acetylsalicylic acid (ASA), has remained one of the world's safest, least expensive and most consumed analgesics. Aspirin (other Nonsteroidal anti-inflammatory drugs; NSAIDs) is contra-indicated for asthmatics because aspirin (other NSAIDs) precipitate asthmatic attacks in patients with bronchial asthma. Aspirin-induced asthma (AIA) refers to the development of acute bronchoconstriction, profuse rhinorrhea and skin flushing in asthmatic individuals following the ingestion of aspirin. The prevalence of AIA is 4.3, 8.8 and 10.5% in Poland, Finland and Australia, respectively. AIA is more common in women. The biochemical pathways involved in aspirin-sensitive asthma are not fully established. In this review, we try to provide an overview of pathogenesis, clinical symptoms, diagnosis & treatment of AIA.


title: Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets

abstract: Imitation learning has traditionally been applied to learn a single task from demonstrations thereof. The requirement of structured and isolated demonstrations limits the scalability of imitation learning approaches as they are difficult to apply to real-world scenarios, where robots have to be able to execute a multitude of tasks. In this paper, we propose a multi-modal imitation learning framework that is able to segment and imitate skills from unlabelled and unstructured demonstrations by learning skill segmentation and imitation learning jointly. The extensive simulation results indicate that our method can efficiently separate the demonstrations into individual skills and learn to imitate them using a single multi-modal policy. The video of our experiments is available at this http URL


title: Civility 2.0: a comparative analysis of incivility in online political discussion

abstract: In an effort to clean up user comment sections, news organizations have turned to Facebook, the world's largest social network site, as a way to make users more identifiable and accountable for the content they produce. It is hypothesized that users leaving comments via their Facebook profile will be less likely to engage in uncivil and impolite discussion, even when it comes to discussing politically sensitive and potentially divisive issues. By analysing the content of discussion as it occurs in response to political news content on the Washington Post Facebook, and comparing it to that which occurs on the Washington Post website where users are afforded a relatively high level of anonymity, the present study determines the extent to which Facebook increases the level of civility and impoliteness in an area of political discussion renowned for uncivil and impolite communicative behaviour. In line with earlier theories of social interaction, the paper finds that political discussion on The Washington Post website is significantly more likely to be uncivil than discussion of the same content on the Washington Post Facebook page. Moreover, the incivility and impoliteness on the Washington Post website are significantly more likely to be directed towards other participants in the discussion compared to The Washington Post Facebook page.


title: Evolutionary programming based recommendation system for online shopping

abstract: In this paper, we propose an interactive evolutionary programming based recommendation system for online shopping that estimates the human preference based on eye movement analysis. Given a set of images of different clothes, the eye movement patterns of the human subjects while looking at the clothes they like differ from clothes they do not like. Therefore, in the proposed system, human preference is measured from the way the human subjects look at the images of different clothes. In other words, the human preference can be measured by using the fixation count and the fixation length using an eye tracking system. Based on the level of human preference, the evolutionary programming suggests new clothes that close the human preference by operations such as selection and mutation. The proposed recommendation is tested with several human subjects and the experimental results are demonstrated.


title: Continuous Head Movement Estimator for Driver Assistance: Issues, Algorithms, and On-Road Evaluations

abstract: Analysis of a driver's head behavior is an integral part of a driver monitoring system. In particular, the head pose and dynamics are strong indicators of a driver's focus of attention. Many existing state-of-the-art head dynamic analyzers are, however, limited to single-camera perspectives, which are susceptible to occlusion of facial features from spatially large head movements away from the frontal pose. Nonfrontal glances away from the road ahead, however, are of special interest since interesting events, which are critical to driver safety, occur during those times. In this paper, we present a distributed camera framework for head movement analysis, with emphasis on the ability to robustly and continuously operate even during large head movements. The proposed system tracks facial features and analyzes their geometric configuration to estimate the head pose using a 3-D model. We present two such solutions that additionally exploit the constraints that are present in a driving context and video data to improve tracking accuracy and computation time. Furthermore, we conduct a thorough comparative study with different camera configurations. For experimental evaluations, we collected a novel head pose data set from naturalistic on-road driving in urban streets and freeways, with particular emphasis on events inducing spatially large head movements (e.g., merge and lane change). Our analyses show promising results.


title: An analysis of eye-tracking and electroencephalography data for cognitive load measurement during arithmetic tasks

abstract: The paper presents multiple features analysis of cognitive load case study. The set of features applied in the research covers response times, committed errors, EEG spectral data as well as pupillometry and eye-tracking (ET) data including fixations, saccades and blinks. The experiment took the form of eleven intervals: six containing arithmetic tasks and five breaks. Two correlation analyses were performed. The first one aimed in finding correlation between cognitive measure, EEG and ET features in each interval. The second analysis was performed to find correlation of cognitive workload and EEG and ET features. The results proved that the best cognitive workload measures are selected eye movement and pupil dilation measures.


title: Acute diffuse and total alopecia: A new subtype of alopecia areata with a favorable prognosis.

abstract: BACKGROUND ::: Alopecia areata (AA) appears in several clinical forms, all having different clinical courses and different prognoses. Acute diffuse and total alopecia (ADTA) has been reported to have a short clinical course ranging from acute hair loss to total baldness, followed by rapid recovery. ::: ::: ::: OBJECTIVE ::: To determine the clinical course and prognosis of ADTA through precise clinical observations. ::: ::: ::: METHODS ::: Thirty Korean patients who showed ADTA of the scalp within an average of 10 weeks after the onset of hair loss were studied. ::: ::: ::: RESULTS ::: Most patients were women who were older than 20 years of age. The histopathology of the lesion revealed infiltration of mononuclear cells around the hair follicles and prominent pigment incontinence. The patients experienced hair regrowth within about 6 months, without regard to the method of treatment. ::: ::: ::: LIMITATIONS ::: The duration of follow-up after remission ranged from 3 to 49 months, with a mean of 24 months. ::: ::: ::: CONCLUSIONS ::: These cases can be categorized as having "acute diffuse and total alopecia," a new subtype of AA that is associated with a favorable prognosis and rapid and spontaneous recovery even without treatment.


title: Sex differences in tuberculosis

abstract: Tuberculosis is the most prevalent bacterial infectious disease in humans and the leading cause of death from a single infectious agent, ranking above HIV/AIDS. The causative agent, Mycobacterium tuberculosis, is carried by an estimated two billion people globally and claims more than 1.5 million lives each year. Tuberculosis rates are significantly higher in men than in women, reflected by a male-to-female ratio for worldwide case notifications of 1.7. This phenomenon is not new and has been reported in various countries and settings over the last century. However, the reasons for the observed gender bias are not clear, potentially highly complex and discussed controversially in the literature. Both gender- (referring to sociocultural roles and behavior) and sex-related factors (referring to biological aspects) likely contribute to higher tuberculosis rates in men and will be discussed.


title: Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells

abstract: Automated design of neural network architectures tailored for a specific task is an extremely promising, albeit inherently difficult, avenue to explore. While most results in this domain have been achieved on image classification and language modelling problems, here we concentrate on dense per-pixel tasks, in particular, semantic image segmentation using fully convolutional networks. In contrast to the aforementioned areas, the design choices of a fully convolutional network require several changes, ranging from the sort of operations that need to be used - e.g., dilated convolutions - to a solving of a more difficult optimisation problem. In this work, we are particularly interested in searching for high-performance compact segmentation architectures, able to run in real-time using limited resources. To achieve that, we intentionally over-parameterise the architecture during the training time via a set of auxiliary cells that provide an intermediate supervisory signal and can be omitted during the evaluation phase. The design of the auxiliary cell is emitted by a controller, a neural network with the fixed structure trained using reinforcement learning. More crucially, we demonstrate how to efficiently search for these architectures within limited time and computational budgets. In particular, we rely on a progressive strategy that terminates non-promising architectures from being further trained, and on Polyak averaging coupled with knowledge distillation to speed-up the convergence. Quantitatively, in 8 GPU-days our approach discovers a set of architectures performing on-par with state-of-the-art among compact models on the semantic segmentation, pose estimation and depth prediction tasks. Code will be made available here: https://github.com/drsleep/nas-segm-pytorch


title: Effective uses of social media in public health and medicine: a systematic review of systematic reviews

abstract: Introduction: Research examining the effective uses of social media (SM) in public health and medicine, especially in the form of systematic r e views (SRs), has grown considerably in the past decade. To our knowledge, no comprehensive synthesis of this literature has been conducted to date. Aims and methods : To conduct a systematic review of systematic reviews of the benefits and harms (“effects”) of SM tools and platforms (such as Twitter and Facebook) in public health and medicine. To perform a synthesis of this literature and create a ‘living systematic review’. Results: Forty-two (42) high-quality SRs were examined. Overall, evidence of SM’s effectiveness in public health and medicine was judged to be minimal. However, qualitative benefits for patients are seen in improved psychosocial support and psychological functioning. Health professionals benefited from better peer-to-peer communication and lifelong learning. Harms on all groups include the impact of SM on mental health, privacy, confidentiality and information reliability. Conclusions: A range of negatives and positives of SM in public health and medicine are seen in the SR literature but definitive conclusions cannot be made at this time. Clearly better research designs are needed to measure the effectiveness of social technologies. For ongoing updates, see the wiki “Effective uses of social media in health: a living systematic review of systematic reviews”. http://hlwiki.slais.ubc.ca/index.php/Effective_uses_of_social_media_in_healthcare:_a_living_systematic_review_of_reviews


title: Unsupervised learning of object semantic parts from internal states of CNNs by population encoding

abstract: We address the key question of how object part representations can be found from the internal states of CNNs that are trained for high-level tasks, such as object classification. This work provides a new unsupervised method to learn semantic parts and gives new understanding of the internal representations of CNNs. Our technique is based on the hypothesis that semantic parts are represented by populations of neurons rather than by single filters. We propose a clustering technique to extract part representations, which we call Visual Concepts. We show that visual concepts are semantically coherent in that they represent semantic parts, and visually coherent in that corresponding image patches appear very similar. Also, visual concepts provide full spatial coverage of the parts of an object, rather than a few sparse parts as is typically found in keypoint annotations. Furthermore, We treat single visual concept as part detector and evaluate it for keypoint detection using the PASCAL3D+ dataset and for part detection using our newly annotated ImageNetPart dataset. The experiments demonstrate that visual concepts can be used to detect parts. We also show that some visual concepts respond to several semantic parts, provided these parts are visually similar. Thus visual concepts have the essential properties: semantic meaning and detection capability. Note that our ImageNetPart dataset gives rich part annotations which cover the whole object, making it useful for other part-related applications.


title: Development of ceftazidime resistance in an acute Burkholderia pseudomallei infection

abstract: Burkholderia pseudomallei, a bacterium that causes the disease melioidosis, is intrinsically resistant to many antibiotics. First-line antibiotic therapy for treating melioidosis is usually the synthetic β-lactam, ceftazidime (CAZ), as almost all B. pseudomallei strains are susceptible to this drug. However, acquired CAZ resistance can develop in vivo during treatment with CAZ, which can lead to mortality if therapy is not switched to a different drug in a timely manner. Serial B. pseudomallei isolates obtained from an acute Thai melioidosis patient infected by a CAZ susceptible strain, who ultimately succumbed to infection despite being on CAZ therapy for the duration of their infection, were analyzed. Isolates that developed CAZ resistance due to a proline to serine change at position 167 in the β-lactamase PenA were identified. Importantly, these CAZ resistant isolates remained sensitive to the alternative melioidosis treatments; namely, amoxicillin-clavulanate, imipenem, and meropenem. Lastly, real-time polymerase chain reaction-based assays capable of rapidly identifying CAZ resistance in B. pseudomallei isolates at the position 167 mutation site were developed. The ability to rapidly identify the emergence of CAZ resistant B. pseudomallei populations in melioidosis patients will allow timely alterations in treatment strategies, thereby improving patient outcomes for this serious disease.


Failure in the Proximal Femur in Simulated Sideways Fall Impact


An Analysis of the Fraud Triangle Incentive / Pressure Fraud Risk Opportunity


title: Limitations of the Pub/Sub pattern for cloud based IoT and their implications

abstract: The current approach to roll out large scale IoT systems is to outsource the crucial parts of the system to cloud based services, such as message brokerage, devices management, or sensor data storage and processing. One core protocol for messaging in those settings is the widely adopted publish/subscribe protocol MQTT. Pub/sub protocols, however, were not designed for this particular scenario and have decoupling properties that make some common task in IoT settings more challenging to achieve. It is, for instance, not straightforward to discover potential publishers of sensor data or to give guarantees that all, a certain number or at least one subscriber of a certain set of possible subscribers will received a message. Because they are missing in the standard, different approaches and implementations tackling those challenges will lead to incompatibilities between users of different systems. In this work, we therefore give an overview of the challenges with discovery and guaranteed delivery to a certain number of subscribers over pub/sub networks in IoT settings and present different possible solutions. We give advice on which implementation is useful under which circumstances and provide a proof-of-concept that can be used with little adaption for enabling discovery and reliability in MQTT.


title: Social Media Use and Perceived Emotional Support Among US Young Adults

abstract: Low emotional support is associated with poor health outcomes. Engagement with face-to-face social networks is one way of increasing emotional support. However, it is not yet known whether engagement with proliferating electronic social networks is similarly associated with increased emotional support. Thus, the purpose of this study was to assess associations between social media use and perceived emotional support in a large, nationally-representative sample. In October 2014, we collected data from 1796 U.S. adults ages 19-32. We assessed social media use using both total time spent and frequency of visits to each of the 11 most popular social media platforms. Our dependent variable was perceived emotional support as measured by the brief Patient-Reported Outcomes Measurement Information System (PROMIS) emotional support scale. A multivariable model including all sociodemographic covariates and accounting for survey weights demonstrated that, compared with the lowest quartile of time on social media, being in the highest quartile (spending two or more hours per day) was significantly associated with decreased odds of having higher perceived emotional support (AOR 0.62, 95 % CI 0.40, 0.94). However, compared with those in the lowest quartile, being in the highest quartile regarding frequency of social media use was not significantly associated with perceived emotional support (AOR 0.70, 95 % CI 0.45, 1.09). In conclusion, while the cross-sectional nature of these data hinder inference regarding directionality, it seems that heavy users of social media may actually feel less and not more emotional support.


title: Efficient vectorization of SIMD programs with non-aligned and irregular data access hardware

abstract: Automatic vectorization of programs for partitioned-ALU SIMD (Single Instruction Multiple Data) processors has been difficult because of not only data dependency issues but also non-aligned and irregular data access problems. A non-aligned or irregular data access operation incurs many overhead cycles for data alignment. Moreover, this causes difficulty in efficient code generation and hinders automatic vectorization. In this paper, we employ special memory access hardware for improving the performance of SIMD processors; one is the split line buffer and the other is the packing buffer. The former solves the non-aligned memory access problem, while the latter simplifies irregular and stride data access. The addition of these hardware units not only requires very small changes to the instruction set architecture but also contributes to the significant performance improvement by vectorizing more loops and reducing the overhead cycles. We have also developed an auto-vectorization compiler which utilizes these special hardware units. Experiments have been conducted to compare the proposed method with the conventional one, which show 50% increase in the number of vectorized loops and 77% increase in the total performance of an MPEG2 encoder program.


A Matlab / Simulink Framework for PLC Controlled Processes


title: An actuated transfemoral prosthesis with optimized polycentric knee joint

abstract: Powered transfemoral prostheses can give above-knee amputees more flexibility than passive devices, for example allowing them to ascend and descend stairs more easily, or allowing a more natural and symmetrical gait pattern. To achieve the high torques required, most devices employ an electric motor with a ball-screw transmission, as is done in this work. The geometry of such a design determines how the peak torque is modulated as a function of joint angle. Therefore, it is important that this geometry is optimized to fulfill the requirements of the application. In this paper, we optimize this geometry to approximate a physiological peak torque versus joint angle versus joint velocity profile. Other powered knee prostheses commonly employ a single-axis joint. We investigate four different joint types: a single-axis joint, a biomimetic polycentric joint, a polycentric joint used in conventional passive prostheses, and an optimized polycentric joint. Our simulations suggest that employing an optimized polycentric joint can generate a uniform torque profile over the whole range of motion. An optimized geometry using a single-axis joint, however, can be used to obtain a peak torque versus angle profile that is similar to a physiological profile, and should, hence, be suitable for our application.


title: Revising the evolutionary computation abstraction: minimal criteria novelty search

abstract: Though based on abstractions of nature, current evolutionary algorithms and artificial life models lack the drive to complexity characteristic of natural evolution. Thus this paper argues that the prevalent fitness-pressure-based abstraction does not capture how natural evolution discovers complexity. Alternatively, this paper proposes that natural evolution can be abstracted as a process that discovers many ways to express the same functionality. That is, all successful organisms must meet the same minimal criteria of survival and reproduction. This abstraction leads to the key idea in this paper: Searching for novel ways of meeting the same minimal criteria, which is an accelerated model of this new abstraction, may be an effective search algorithm. Thus the existing novelty search method, which rewards any new behavior, is extended to enforce minimal criteria. Such minimal criteria novelty search prunes the space of viable behaviors and may often be more efficient than the search for novelty alone. In fact, when compared to the raw search for novelty and traditional fitness-based search in the two maze navigation experiments in this paper, minimal criteria novelty search evolves solutions more consistently. It is possible that refining the evolutionary computation abstraction in this way may lead to solving more ambitious problems and evolving more complex artificial organisms.


title: Segmentation and reconstruction of vascular structures for 3D real-time simulation

abstract: We propose a technique to obtain accurate and smooth surfaces of patient specific vascular structures, using two steps: segmentation and reconstruction. The first step provides accurate and smooth centerlines of the vessels, together with cross section orientations and cross section fitting. The initial centerlines are obtained from a homotopic thinning of the vessels segmented using a level set method. In addition to circle fitting, an iterative scheme fitting ellipses to the cross sections and correcting the centerline positions is proposed, leading to a strong improvement of the cross section orientations and of the location of the centerlines. The second step consists of reconstructing the surface based on this data, by generating a set of topologically preserved quadrilateral patches of branching tubular structures. It improves Felkel's meshing method (Felkel et al., 2004) by: allowing a vessel to have multiple parents and children, reducing undersampling artifacts, and adapting the cross section distribution. Experiments, on phantom and real datasets, show that the proposed technique reaches a good balance in terms of smoothness, number of triangles, and distance error. This technique can be applied in interventional radiology simulations, virtual endoscopy and in reconstruction of smooth and accurate three-dimensional models for use in simulation.


title: RECOGNIZING BEHAVIOR IN HAND-EYE COORDINATION PATTERNS

abstract: Modeling human behavior is important for the design of robots as well as human-computer interfaces that use humanoid avatars. Constructive models have been built, but they have not captured all of the detailed structure of human behavior such as the moment-to-moment deployment and coordination of hand, head and eye gaze used in complex tasks. We show how this data from human subjects performing a task can be used to program a dynamic Bayes network (DBN) which in turn can be used to recognize new performance instances. As a specific demonstration we show that the steps in a complex activity such as sandwich making can be recognized by a DBN in real time.


title: Cross-view and multi-view gait recognitions based on view transformation model using multi-layer perceptron

abstract: Highlights? One-view to one-view VTM solves across-view gait recognition. ?Multi-view to one-view VTM solves multi-view gait recognition. ?VTM is built up from regressions of local motion using multi-layer perceptron. ?Multi-view to one-view VTM can be claimed as implicit 3D gait model based method. ?Accuracy of 98% is achieved for multi-view gait recognition using 4 cameras. Gait has been shown to be an efficient biometric feature for human identification at a distance. However, performance of gait recognition can be affected by view variation. This leads to a consequent difficulty of cross-view gait recognition. A novel method is proposed to solve the above difficulty by using view transformation model (VTM). VTM is constructed based on regression processes by adopting multi-layer perceptron (MLP) as a regression tool. VTM estimates gait feature from one view using a well selected region of interest (ROI) on gait feature from another view. Thus, trained VTMs can normalize gait features from across views into the same view before gait similarity is measured. Moreover, this paper proposes a new multi-view gait recognition which estimates gait feature on one view using selected gait features from several other views. Extensive experimental results demonstrate that the proposed method significantly outperforms other baseline methods in literature for both cross-view and multi-view gait recognitions. In our experiments, particularly, average accuracies of 99%, 98% and 93% are achieved for multiple views gait recognition by using 5 cameras, 4 cameras and 3 cameras respectively.


title: Sex Differences in the Effects of Inhaled Corticosteroids on Weight Gain among Patients with Asthma

abstract: Background. Studies have shown that asthma and asthma exacerbations are related to body weight and that this relationship might be sex-specific. While oral corticosteroids have been associated with weight gain, little is known about the effect of inhaled corticosteroid (ICS) use on short-term weight gain. The purpose of the present study was to examine whether ICSs would be associated with weight gain among asthmatic patients. Methods. A total of 180 adult patients with physician-diagnosed asthma provided details of their medical history and demographic information, along with height and weight at baseline and at one year. Weight change was defined as follow-up minus baseline weight. General linear models were used to assess the relationship between ICS dose (fluticasone propionate equivalent) and sex. Results. Significant main effects of sex (𝑃=.005) and ICS dose (𝑃=.036) and an interaction effect of sex and ICS dose (𝑃=.003) on weight change were observed. Further analysis of the interaction indicated that women had greater weight gain, while men had decreased weight with increased ICS dose. ::: Conclusions. Findings suggest that ICSs may trigger weight gain in females and highlight the need for studies to confirm this relationship and examine the potential underlying mechanisms.


title: CEFLS: A Cost-Effective File Lookup Service in a Distributed Metadata File System

abstract: As large file systems increasingly grow in size, metadata operations become one of the major performance bottlenecks that constrain the overall I/O performance. Previous analysis on I/O workloads shows the file lookup operation makes up a large proportion of metadata operations. Existing optimizations for lookup operations such as MHS method employ the directory lookup table (DLT) to avoid directory traversal. However, the inefficient design of DLT produces large amount of storage cost and rename overhead, not suitable for large file systems. In this paper, we present a cost-effective file lookup service (CEFLS) for a distributed metadata file system. Our method benefits from efficient partition method and structures to increase the cache efficiency for DLT. Extensive simulations show that the percentages of cached directories with CELFS can be increased by factors of up to 305 and 279 percent compared with MHS when the cache size on each metadata server is configured as 1GB and 2GB, respectively. Meanwhile, CELFS can also significantly reduce the average latency for both file lookup and directory rename operations.


title: Go Climb a Dependency Tree and Correct the Grammatical Errors

abstract: State-of-art systems for grammar error correction often correct errors based on word sequences or phrases. In this paper, we describe a grammar error correction system which corrects grammatical errors at tree level directly. We cluster all error into two groups and divide our system into two modules correspondingly: the general module and the special module. In the general module, we propose a TreeNode Language Model to correct errors related to verbs and nouns. The TreeNode Language Model is easy to train and the decoding is efficient. In the special module, two extra classification models are trained to correct errors related to determiners and prepositions. Experiments show that our system outperforms the state-of-art systems and improves theF1 score.


title: Which Predictability Measures Affect Content Word Durations?

abstract: The invention relates to a process for the production of small moulded components by die-casting of metals. In this process, the metal to be melted in a crucible is limited to the charge weight for casting in a single die. Heat is supplied electrically and interrupted during casting. A metal heel is retained in the crucible so that its heat content allows compensation of a large part of the heat loss during said interruption of heat supply.


title: Mitochondrial Metabolism and Cancer

abstract: Historically, it has been assumed that glycolytic metabolism, not mitochondrial metabolism, is essential for tumor cell proliferation. However, most tumor cells have functional mitochondria, and recent studies suggest that the citric acid cycle (TCA) cycle intermediates are precursors for synthesis of nucleotides, lipids, and amino acids. Here we review the accumulating evidence that mitochondrial metabolism plays an essential role in tumor cell proliferation.


title: ACRE: selecting methods for requirements acquisition

abstract: A framework is presented that assists requirements engineers in choosing methods for requirements acquisition. Practitioners are often unaware of the range of methods available. Even when practitioners are aware, most do not foresee the need to use several methods to acquire complete and accurate requirements. One reason for this is the lack of guidelines for method selection. The ACRE framework sets out to overcome these limitations. Method selection is achieved using questions driven from a set of facets which define the strengths and weaknesses of each method. The framework provides guidelines for requirements engineering practitioners. It has undergone evaluation through its presentation to highly experienced requirements engineers. Some results from this evaluation have been incorporated into the presented version of ACRE.


title: School performance, social networking effects, and learning of school children: Evidence of reciprocal relationships in Abu Dhabi

abstract: Abstract This study uses structural equations modeling to test a hypothetical social network model with applications to a sample of 34,896 school children in Abu Dhabi. The main independent constructs in the model are related to children’s attitude with regard to social networking, reasons for using social networks, things done on social networks, and topics used. The dependent constructs cover perceived school performance and social effects of social networking. The study will describe the relations among the various constructs. The effect of other variables, such as parental knowhow, is also investigated. Our work has improved our insight in the social networking model. Results support the idea of reciprocal relations among perceived performance, learning from social networking, and the effect of social networking. Evidence for a model that includes opposite pathways implies that the problem of social networking constructs, its antecedents, and possible consequences should be examined with caution.


Financial flexibility: a measure of financial position for hospital managers.


title: Optimal selection of wavelet basis function applied to ECG signal denoising

abstract: Over the years ElectroCardioGram (ECG) signal has been used to assess the cardiovascular condition of humans. In practice, real time acquisition and transmission of the ECG may contain noise signals superimposed on it. In general, the signal processing algorithms employed for denoising provide optimal performance and eliminate the high frequency noise between any two beats contained in a continuous ECG signal. Despite their optimal performance, the signal processing algorithms significantly attenuate the peaks of characteristics wave of the ECG signal. This paper presents a selection procedure of mother wavelet basis functions applied for denoising of the ECG signal in wavelet domain while retaining the signal peaks close to their full amplitude. The obtained wavelet based denoised ECG signals retain the necessary diagnostics information contained in the original ECG signal.


title: Semantic Language Models For Topic Detection And Tracking

abstract: In this work, we present a new semantic language modeling approach to model news stories in the Topic Detection and Tracking (TDT) task. In the new approach, we build a unigram language model for each semantic class in a news story. We also cast the link detection subtask of TDT as a two-class classification problem in which the features of each sample consist of the generative log-likelihood ratios from each semantic class. We then compute a linear discriminant classifier using the perceptron learning algorithm on the training set. Results on the test set show a marginal improvement over the unigram performance, but are not very encouraging on the whole.


title: Grounded Language Learning from Video Described with Sentences

abstract: We present a method that learns representations for word meanings from short video clips paired with sentences. Unlike prior work on learning language from symbolic input, our input consists of video of people interacting with multiple complex objects in outdoor environments. Unlike prior computer-vision approaches that learn from videos with verb labels or images with noun labels, our labels are sentences containing nouns, verbs, prepositions, adjectives, and adverbs. The correspondence between words and concepts in the video is learned in an unsupervised fashion, even when the video depicts simultaneous events described by multiple sentences or when different aspects of a single event are described with multiple sentences. The learned word meanings can be subsequently used to automatically generate description of new video.


title: Effectiveness of kinesiotaping on diaphragm thickness, diaphragmatic thickening fraction, and intercostal muscle thickness in patients undergoing mechanical ventilation: a pilot study

abstract: Objective: We aimed to evaluate the effects of kinesiotaping applied on the chest wall on the diaphragmatic muscle and intercostal muscles in patients receiving mechanical ventilation. Methods: In this prospective, randomized, controlled, double-blind study, 24 patients who underwent mechanical ventilation in the intensive care unit were included the study. Randomization was used to identify the side of patients that the kinesiotaping was applied to. Group 1: side that kinesiotaping was applied (n = 24) and Group 2 (control group): side that kinesiotaping was not applied (n = 24). Kinesiotaping was changed in every 3 day. Thicknesses of diaphragmatic muscle, seventh intercostal muscle and ninth intercostal muscle were evaluated by using ultrasound in pretreatment, third day, sixth day and ninth day . Results: In both groups; a statistically significant decrease was found in all the evaluation parameters at all the measurement times when compared to the pretreatment values ( p 0.05). Conclusion: In conclusion, our study results showed that kinesiotaping decelerated the decrease in the diaphragmatic muscle and the intercostal muscles thickness until sixth day.


title: EyeSwipe: Dwell-free Text Entry Using Gaze Paths

abstract: Text entry using gaze-based interaction is a vital communication tool for people with motor impairments. Most solutions require the user to fixate on a key for a given dwell time to select it, thus limiting the typing speed. In this paper we introduce EyeSwipe, a dwell-time-free gaze-typing method. With EyeSwipe, the user gaze-types the first and last characters of a word using the novel selection mechanism "reverse crossing." To gaze-type the characters in the middle of the word, the user only needs to glance at the vicinity of the respective keys. We compared the performance of EyeSwipe with that of a dwell-time-based virtual keyboard. EyeSwipe afforded statistically significantly higher typing rates and more comfortable interaction in experiments with ten participants who reached 11.7 words per minute (wpm) after 30 min typing with EyeSwipe.


title: Toward an emergent theory of broadening participation in computer science education

abstract: A fundamental challenge to computer science education is the difficulty of broadening participation of women and underserved communities. The idea of game design and game programming as an activity to introduce children at an early age to computational thinking in a motivational way is quickly gaining momentum. A pedagogical approach called Project First has allowed the Scalable Game Design project to reach a large group of middle schools students including a large percentage of female (45%) and underrepresented (48%) students. With over 4000 students in inner city, remote rural, and Native American communities Scalable Game Design has investigated the impact on students' interest level of pedagogical approaches employed by teachers such as mediation and scaffolding. Findings suggest strong gender effects based on classroom scaffolding approaches. For instance, girls are substantially less likely to be motivated through scaffolding based on direct instruction. Conversely, guided discovery scaffolding approaches are highly motivating to the point where they can even overcome other negative predictors such as small girls to boys class participation ratios. This paper introduces the project, discusses different scaffolding approaches and presents data connecting gender specific motivational levels with scaffolding approaches.


title: Clash of the Titans: MapReduce vs. Spark for Large Scale Data Analytics

abstract: MapReduce and Spark are two very popular open source cluster computing frameworks for large scale data analytics. These frameworks hide the complexity of task parallelism and fault-tolerance, by exposing a simple programming API to users. In this paper, we evaluate the major architectural components in MapReduce and Spark frameworks including: shuffle, execution model, and caching, by using a set of important analytic workloads. To conduct a detailed analysis, we developed two profiling tools: (1) We correlate the task execution plan with the resource utilization for both MapReduce and Spark, and visually present this correlation; (2) We provide a break-down of the task execution time for in-depth analysis. Through detailed experiments, we quantify the performance differences between MapReduce and Spark. Furthermore, we attribute these performance differences to different components which are architected differently in the two frameworks. We further expose the source of these performance differences by using a set of micro-benchmark experiments. Overall, our experiments show that Spark is about 2.5x, 5x, and 5x faster than MapReduce, for Word Count, k-means, and PageRank, respectively. The main causes of these speedups are the efficiency of the hash-based aggregation component for combine, as well as reduced CPU and disk overheads due to RDD caching in Spark. An exception to this is the Sort workload, for which MapReduce is 2x faster than Spark. We show that MapReduce's execution model is more efficient for shuffling data than Spark, thus making Sort run faster on MapReduce.


Forecasting economic time series using targeted predictors


title: Medical diagnosis with the aid of using fuzzy logic and intuitionistic fuzzy logic

abstract: The objective of the present study is to develop/establish a web-based medical diagnostic support system (MDSS) by which health care support can be provided for people living in rural areas of a country. In this respect, this research provides a novel approach for medical diagnosis driven by integrating fuzzy and intuitionistic fuzzy (IF) frameworks. Subsequently, based on the proposed approach a web-based MDSS is developed. The proposed MDSS comprises of a knowledge base (KB) and intuitionistic fuzzy inference system (IFIS). Based on the observation that medical data cannot be described with both precision and certainty, a medical KB is constructed in the form of a set of if-then decision rules by employing both fuzzy and IF logics. After constructing the medical KB, a new set of patients is considered for diagnosing the diseases. For each patient, linguistic values of the patients' symptoms are considered as inputs of the proposed IFIS and modeled by using the generalized triangular membership functions. Subsequently, integrated fuzzy and IF rule-based inference system is used to find a valid conclusion for the new set of patients. In a nutshell, in this paper fuzzy rule-based and IFS based inference systems are combined for better and more realistic representation of uncertainty of the medical diagnosis problem and for more accurate diagnostic result. The method is composed of following four steps: (1) the modeling of antecedent part of the rules, which consist of linguistic assessments of the patients' symptoms provided by the doctors/medical experts with their corresponding confidence levels, by using generalized fuzzy numbers; (2) the modeling of consequent part, which reveals the degree of association and the degree of non-association of diseases into the patient, by using IFSs; (3) the use of IF aggregation operator in inference process; (4) the application of relative closeness function to find the final crisp output for a given diagnosis. Finally, the applicability of the proposed approach is illustrated with a suitable case study. This article has also justified the proposed approach by using similarity measurement.


title: G-CNN: An Iterative Grid Based Object Detector

abstract: We introduce G-CNN, an object detection technique based on CNNs which works without proposal algorithms. G-CNN starts with a multi-scale grid of fixed bounding boxes. We train a regressor to move and scale elements of the grid towards objects iteratively. G-CNN models the problem of object detection as finding a path from a fixed grid to boxes tightly surrounding the objects. G-CNN with around 180 boxes in a multi-scale grid performs comparably to Fast R-CNN which uses around 2K bounding boxes generated with a proposal technique. This strategy makes detection faster by removing the object proposal stage as well as reducing the number of boxes to be processed.


title: Bankruptcy prediction modeling with hybrid case-based reasoning and genetic algorithms approach

abstract: One of the most important research issues in finance is building effective corporate bankruptcy prediction models because they are essential for the risk management of financial institutions. Researchers have applied various data-driven approaches to enhance prediction performance including statistical and artificial intelligence techniques, and many of them have been proved to be useful. Case-based reasoning (CBR) is one of the most popular data-driven approaches because it is easy to apply, has no possibility of overfitting, and provides good explanation for the output. However, it has a critical limitation-its prediction performance is generally low. In this study, we propose a novel approach to enhance the prediction performance of CBR for the prediction of corporate bankruptcies. Our suggestion is the simultaneous optimization of feature weighting and the instance selection for CBR by using genetic algorithms (GAs). Our model can improve the prediction performance by referencing more relevant cases and eliminating noises. We apply our model to a real-world case. Experimental results show that the prediction accuracy of conventional CBR may be improved significantly by using our model. Our study suggests ways for financial institutions to build a bankruptcy prediction model which produces accurate results as well as good explanations for these results.


title: Privacy in the digital age: a review of information privacy research in information systems

abstract: Information privacy refers to the desire of individuals to control or have some influence over data about themselves. Advances in information technology have raised concerns about information privacy and its impacts, and have motivated Information Systems researchers to explore information privacy issues, including technical solutions to address these concerns. In this paper, we inform researchers about the current state of information privacy research in IS through a critical analysis of the IS literature that considers information privacy as a key construct. The review of the literature reveals that information privacy is a multilevel concept, but rarely studied as such. We also find that information privacy research has been heavily reliant on studentbased and USA-centric samples, which results in findings of limited generalizability. Information privacy research focuses on explaining and predicting theoretical contributions, with few studies in journal articles focusing on design and action contributions. We recommend that future research should consider different levels of analysis as well as multilevel effects of information privacy. We illustrate this with a multilevel framework for information privacy concerns. We call for research on information privacy to use a broader diversity of sampling populations, and for more design and action information privacy research to be published in journal articles that can result in IT artifacts for protection or control of information privacy.


title: Monopoly money: The effect of payment coupling and form on spending behavior

abstract: This article examines consumer spending as a function of payment mode both when the modes differ in terms of payment coupling (association between purchase decision and actual parting of money) and physical form as well as when the modes differ only in terms of form. Study 1 demonstrates that consumers are willing to spend more when a credit card logo is present versus absent. Study 2 shows that the credit card effect can be attenuated when people estimate their expenses using a decomposition strategy (vs. a holistic one). Noting that credit card and cash payments differ in terms of payment coupling and form, Studies 3 and 4 examine consumer spending when the payment mode differs only in physical form. Study 3 demonstrates that consumers spend more when they are spending scrip (a form of stored value certificate) versus cash of the same face value. Study 4 shows that the difference in spending across payment modes (cash and gift certificates) is attenuated by altering the salience of parting with money through contextual manipulations of the differences between cash and gift certificates.


title: Long period hash functions for procedural texturing

abstract: Procedural texturing is commonly used to increase visual complexity and realism in rendered scenes. Procedural texture basis functions, such as Perlin's noise function, are often built on hash functions defined over the integer lattice. These hash functions are usually based on permutation tables and have a short period. This causes procedural textures to repeat. In this paper, we present a method for constructing hash functions with long periods. Our method is based on techniques for constructing long-period pseudo-random number generators. The hash functions we propose are almost as efficient as the traditional ones, but allow to generate procedural textures with very large periods. This increases the visual complexity and realism of procedural textures, and enables robust implementations of texture basis functions.


title: A low profile 10:1 (200–2000 MHz) wide band long slot array

abstract: A low profile 10:1 band (200-2000 MHz) long slot array was developed for radar and EW applications. For performance demonstration, a prototype array of 11times11 elements (1.2 m times 1.2 m) was built and tested. It is backed by a ferrite loaded ground plane, separated from the aperture by 5-cm air gap. The antenna is ultra thin, with a total thickness of 8 cm including the ferrite tiles, about 5% of the wave length at 200 MHz. On average, the array has 2 dB loss over the 10:1 band. The antenna is designed for airborne missions that require very wide band and ultra-thin profile.


title: A novel capacitive type torque sensor for robotic applications

abstract: This paper presents a novel capacitive type joint torque sensor for robotic applications. The proposed torque sensor enables to measure the rotational torque value while it decouples the external Force/Torque loads without any complicated computing procedure. To measure the joint torque, just two capacitive transducer cells are used. These two cells are located in the opposite sides of each other, which compensates the cross couplings of the torques when external Force/Torque loads are applied. To simply the manufacturing process, the proposed sensor is designed to be composed of three plate-shaped parts and a single printed circuit board (PCB). Lastly, the developed torque sensor is manufactured and its performances are experimentally demonstrated.


title: Precise Power Delay Profiling with Commodity WiFi

abstract: Power delay profiles characterize multipath channel features, which are widely used in motion- or localization-based applications. Recent studies show that the power delay profile may be derived from the CSI traces collected from commodity WiFi devices, but the performance is limited by two dominating factors. The resolution of the derived power delay profile is determined by the channel bandwidth, which is however limited on commodity WiFi. The collected CSI reflects the signal distortions due to both the channel attenuation and the hardware imperfection. A direct derivation of power delay profiles using raw CSI measures, as has been done in the literature, results in significant inaccuracy. In this paper, we present Splicer, a software-based system that derives high-resolution power delay profiles by splicing the CSI measurements from multiple WiFi frequency bands. We propose a set of key techniques to separate the mixed hardware errors from the collected CSI measurements. Splicer adapts its computations within stringent channel coherence time and thus can perform well in presence of mobility. Our experiments with commodity WiFi NICs show that Splicer substantially improves the accuracy in profiling multipath characteristics, reducing the errors of multipath distance estimation to be less than $2m$. Splicer can immediately benefit upper-layer applications. Our case study with recent single-AP localization achieves a median localization error of $0.95m$.


title: Enabling Reputation and Trust in Privacy-Preserving Mobile Sensing

abstract: Mobile sensing is becoming a popular paradigm to collect information from and outsource tasks to mobile users. These applications deal with lot of personal information, e.g., identity and location. Therefore, we need to pay a deeper attention to privacy and anonymity. However, the knowledge of the data source is desired to evaluate the trustworthiness of the sensing data. Anonymity and trust become two conflicting objectives in mobile sensing. In this paper, we propose ARTSense , a framework to solve the problem of “trust without identity” in mobile sensing. Our solution consists of a privacy-preserving provenance model, a data trust assessment scheme and an anonymous reputation management protocol. In contrast to other recent solutions, our scheme does not require a trusted third party and both positive and negative reputation updates can be enforced. In the trust assessment, we consider contextual factors that dynamically affects the trustworthiness of the sensing data as well as the mutual support and conflict among data from difference sources. Security analysis shows that ARTSense achieves our desired anonymity and security goals. Our prototype implementation on Android demonstrates that ARTSense incurs minimal computation overhead on mobile devices, and simulation results justify that ARTSense captures the trust of information and reputation of participants accurately.


title: Efficient Tagged Memory

abstract: We characterize the cache behavior of an in-memory tag table and demonstrate that an optimized implementation can typically achieve a near-zero memory traffic overhead. Both industry and academia have repeatedly demonstrated tagged memory as a key mechanism to enable enforcement of powerful security invariants, including capabilities, pointer integrity, watchpoints, and information-flow tracking. A single-bit tag shadowspace is the most commonly proposed requirement, as one bit is the minimum metadata needed to distinguish between an untyped data word and any number of new hardware-enforced types. We survey various tag shadowspace approaches and identify their common requirements and positive features of their implementations. To avoid non-standard memory widths, we identify the most practical implementation for tag storage to be an in-memory table managed next to the DRAM controller. We characterize the caching performance of such a tag table and demonstrate a DRAM traffic overhead below 5% for the vast majority of applications. We identify spatial locality on a page scale as the primary factor that enables surprisingly high table cache-ability. We then demonstrate tag-table compression for a set of common applications. A hierarchical structure with elegantly simple optimizations reduces DRAM traffic overhead to below 1% for most applications. These insights and optimizations pave the way for commercial applications making use of single-bit tags stored in commodity memory.


title: DeepEmo: Learning and Enriching Pattern-Based Emotion Representations

abstract: We propose a graph-based mechanism to extract rich-emotion bearing patterns, which fosters a deeper analysis of online emotional expressions, from a corpus. The patterns are then enriched with word embeddings and evaluated through several emotion recognition tasks. Moreover, we conduct analysis on the emotion-oriented patterns to demonstrate its applicability and to explore its properties. Our experimental results demonstrate that the proposed techniques outperform most state-of-the-art emotion recognition techniques.


title: Broadband Acoustic Cloak for Ultrasound Waves

abstract: Invisibility devices based on coordinate transformation have opened up a new field of considerable interest. We present here the first practical realization of a low-loss and broadband acoustic cloak for underwater ultrasound. This metamaterial cloak is constructed with a network of acoustic circuit elements, namely, serial inductors and shunt capacitors. Our experiment clearly shows that the acoustic cloak can effectively bend the ultrasound waves around the hidden object, with reduced scattering and shadow. Because of the nonresonant nature of the building elements, this low-loss (� 6d B=m) cylindrical cloak exhibits invisibility over a broad frequency range from 52 to 64 kHz. Furthermore, our experimental study indicates that this design approach should be scalable to different acoustic frequencies and offers the possibility for a variety of devices based on coordinate transformation.


title: Morin Temperature and Néel Temperature of Hematite Nanocrystals

abstract: Thermodynamic analytic models have been established first to describe the size dependences of Morin temperature TM and Neel temperature TN in hematite nanocrystals based on size-dependent cohesive energy model. The models indicate that both Morin temperature and Neel temperature reduce with decreasing size D or increasing magnetic proximity effect constant m at the interface, where the size effect is the principle factor while the proximity effect is secondary. Agreements between the model predictions and the corresponding experimental results are found to be reasonable, which enables us to determine the values of correlation length ξ0 and shift exponent λ in the finite size scaling theory thermodynamically for the first time. The calculated ξ0 and λ at infinite size respectively correspond to the result of the mean field theory and that obtained through fitting experimental results. Moreover, the shift exponent λ is found to be size-dependent. It decreases with increasing of the size and then reaches to ...


title: Dynamic switching-based reliable flooding in low-duty-cycle wireless sensor networks

abstract: Reliable flooding in wireless sensor networks (WSNs) is desirable for a broad range of applications and network operations. However, it is a challenging problem to ensure 100% flooding coverage efficiently considering the combined effects of low-duty-cycle operation and unreliable wireless transmission. In this work, we propose a novel dynamic switching-based reliable flooding (DSRF) framework, which is designed as an enhancement layer to provide efficient and reliable flooding over a variety of existing flooding tree structures in low-duty-cycle WSNs. Through comprehensive simulations, we demonstrate that DSRF can effectively improve both flooding energy efficiency and latency.


title: An effective algorithm for hyperparameter optimization of neural networks

abstract: A major challenge in designing neural network (NN) systems is to determine the best structure and parameters for the network given the data for the machine learning problem at hand. Examples of parameters are the number of layers and nodes, the learning rates, and the dropout rates. Typically, these parameters are chosen based on heuristic rules and manually fine-tuned, which may be very time-consuming, because evaluating the performance of a single parametrization of the NN may require several hours. This paper addresses the problem of choosing appropriate parameters for the NN by formulating it as a box-constrained mathematical optimization problem, and applying a derivative-free optimization tool that automatically and effectively searches the parameter space. The optimization tool employs a radial basis function model of the objective function (the prediction accuracy of the NN) to accelerate the discovery of configurations yielding high accuracy. Candidate configurations explored by the algorithm are trained to a small number of epochs, and only the most promising candidates receive full training. The performance of the proposed methodology is assessed on benchmark sets and in the context of predicting drug-drug interactions, showing promising results. The optimization tool used in this paper is open-source.


Software engineering for automotive systems: A roadmap


title: An Extended Classification and Comparison of NoSQL Big Data Models

abstract: In last few years, the volume of the data has grown manyfold. The data storages have been inundated by various disparate potential data outlets, leading by social media such as Facebook, Twitter, etc. The existing data models are largely unable to illuminate the full potential of Big Data; the information that may serve as the key solution to several complex problems is left unexplored. The existing computation capacity falls short for the increasingly expanded storage capacity. The fast-paced volume expansion of the unorganized data entails a complete paradigm shift in new age data computation and witnesses the evolution of new capable data engineering techniques such as capture, curation, visualization, analyses, etc. In this paper, we provide the first level classification for modern Big Data models. Some of the leading representatives of each classification that claim to best process the Big Data in reliable and efficient way are also discussed. Also, the classification is further strengthened by the intra-class and inter-class comparisons and discussions of the undertaken Big Data models.


title: Animal monitoring with unmanned aerial vehicle-aided wireless sensor networks

abstract: In this paper, we focus on an application of wireless sensor networks (WSNs) with unmanned aerial vehicle (UAV). The aim of the application is to detect the locations of endangered species in large-scale wildlife areas or monitor movement of animals without any attachment devices. We first define the mathematical model of the animal monitoring problem in terms of the value of information (VoI) and rewards. We design a network model including clusters of sensor nodes and a single UAV that acts as a mobile sink and visits the clusters. We propose a path planning approach based on a Markov decision process (MDP) model that maximizes the VoI while reducing message delays. We used real-world movement dataset of zebras. Simulation results show that our approach outperforms greedy and random heuristics as well as the path planning based on the solution of the traveling salesman problem.


title: Generative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combination

abstract: Systematic trading strategies are algorithmic procedures that allocate assets aiming to optimize a certain performance criterion. To obtain an edge in a highly competitive environment, the analyst needs to proper fine-tune its strategy, or discover how to combine weak signals in novel alpha creating manners. Both aspects, namely fine-tuning and combination, have been extensively researched using several methods, but emerging techniques such as Generative Adversarial Networks can have an impact into such aspects. Therefore, our work proposes the use of Conditional Generative Adversarial Networks (cGANs) for trading strategies calibration and aggregation. To this purpose, we provide a full methodology on: (i) the training and selection of a cGAN for time series data; (ii) how each sample is used for strategies calibration; and (iii) how all generated samples can be used for ensemble modelling. To provide evidence that our approach is well grounded, we have designed an experiment with multiple trading strategies, encompassing 579 assets. We compared cGAN with an ensemble scheme and model validation methods, both suited for time series. Our results suggest that cGANs are a suitable alternative for strategies calibration and combination, providing outperformance when the traditional techniques fail to generate any alpha.


title: New trends in dark silicon

abstract: This paper presents new trends in dark silicon reflecting, among others, the deployment of FinFETs in recent technology nodes and the impact of voltage/frquency scaling, which lead to new less-conservative predictions. The focus is on dark silicon from a thermal perspective: we show that it is not simply the chip's total power budget, e.g., the Thermal Design Power (TDP), that leads to the dark silicon problem, but instead it is the power density and related thermal effects. We therefore propose to use Thermal Safe Power (TSP) as a more efficient power budget. It is also shown that sophisticated spatio-temporal mapping decisions result in improved thermal profiles with reduced peak temperatures. Moreover, we discuss the implications of Near-Threshold Computing (NTC) and employment of Boosting techniques in dark silicon systems.


title: The self in action: Lessons from delusions of control

abstract: Patients with delusions of control are abnormally aware of the sensory consequences of their actions and have difficulty with on-line corrections of movement. As a result they do not feel in control of their movements. At the same time they are strongly aware of the action being intentional. This leads them to believe that their actions are being controlled by an external agent. In contrast, the normal mark of the self in action is that we have very little experience of it. Most of the time we are not aware of the sensory consequences of our actions or of the various subtle corrections that we make during the course of goal-directed actions. We know that we are agents and that we are successfully causing the world to change. But as actors we move through the world like shadows glimpsed only occasional from the corner of an eye.


title: Second harmonic reduction in broadband HF/VHF/UHF class E RF power amplifiers

abstract: Class E amplifier offers high efficiency approaching 100% for ideal case. This paper describes a topology to reduce second harmonic in a broadband parallel circuit class E power amplifier (PA). Drain efficiency > 60% and second harmonic < 40dBm across wide bandwidth (134–174MHz) is shown at power operation more than 5.5W.


title: Android mobile augmented reality application based on different learning theories for primary school children

abstract: Due to advancements in the mobile technology and the presence of strong mobile platforms, it is now possible to use the revolutionising augmented reality technology in mobiles. This research work is based on the understanding of different types of learning theories, concept of mobile learning and mobile augmented reality and discusses how applications using these advanced technologies can shape today's education systems.


title: Part-Of-Speech Induction From Scratch

abstract: This paper presents a method for inducing the parts of speech of a language and part-of-speech labels for individual words from a large text corpus. Vector representations for the part-of-speech of a word are formed from entries of its near lexical neighbors. A dimensionality reduction creates a space representing the syntactic categories of unambiguous words. A neural net trained on these spatial representations classifies individual contexts of occurrence of ambiguous words. The method classifies both ambiguous and unambiguous words correctly with high accuracy.


title: Smart parking solutions for urban areas

abstract: Finding a parking place in a busy city centre is often a frustrating task for many drivers; time and fuel are wasted in the quest for a vacant spot and traffic in the area increases due to the slow moving vehicles circling around. In this paper, we present the results of a survey on the needs of drivers from parking infrastructures from a smart services perspective. As smart parking systems are becoming a necessity in today's urban areas, we discuss the latest trends in parking availability monitoring, parking reservation and dynamic pricing schemes. We also examine how these schemes can be integrated forming technologically advanced parking infrastructures whose aim is to benefit both the drivers and the parking operators alike.


title: Single and Multipolarimetric SAR Tomography of Forested Areas: A Parametric Approach

abstract: In this paper, a technique is described for the tomographic characterization of forested areas through multiple synthetic aperture radar (SAR) observations, based on either single or multipolarimetric acquisitions. This technique is based on the idea of characterizing the Fourier spectrum of the multibaseline data as being constituted by two effective scattering centers displaced along the vertical direction, plus the associated decorrelation terms. As a result, SAR tomography will be formulated as the problem of detecting the number of scattering centers within the resolution cell, estimating the parameters that describe their spatial structure, and evaluating the associated backscattered powers. Parameter estimation is carried out through the covariance matching estimation technique, which provides an asymptotically optimal solution. The results of an experiment performed on a real P-band multibaseline fully polarimetric data set relative to the forested site of Remningstorp, Sweden, are reported.


title: Automated Storytelling in Sports: A Rich Domain to Be Explored

abstract: Sports broadcasting typically involves a play-by-play commentator and a color commentator. The color commentator's function is to entertain the viewer during the game. The prevalent way of doing so is by telling brief stories relevant to the game in progress. In this paper, we propose that storytelling in sports is a challenging and rich problem for Artificial Intelligence (AI) research for the following reasons. First, storytelling is considered to be a very "human" activity as it requires engaging the audience in a story and appealing to their factual as well as emotional sides. Thus, automating it will be an advance in AI. Second, while automated storytelling in general has received ample attention, storytelling in sports has seen much less research. Third, an AI storyteller can be used as an assistant to a human color commentator in a real game, or autonomously in the context of a sports video game. Fourth, the task is non-trivial as we demonstrate by applying off-the-shelf machine learning methods in an attempt to match baseball stories to baseball game states.


title: SemEval-2015 Task 1: Paraphrase and Semantic Similarity in Twitter (PIT)

abstract: In this shared task, we present evaluations on two related tasks Paraphrase Identification (PI) and Semantic Textual Similarity (SS) systems for the Twitter data. Given a pair of sentences, participants are asked to produce a binary yes/no judgement or a graded score to measure their semantic equivalence. The task features a newly constructed Twitter Paraphrase Corpus that contains 18,762 sentence pairs. A total of 19 teams participated, submitting 36 runs to the PI task and 26 runs to the SS task. The evaluation shows encouraging results and open challenges for future research. The best systems scored a F1-measure of 0.674 for the PI task and a Pearson correlation of 0.619 for the SS task respectively, comparing to a strong baseline using logistic regression model of 0.589 F1 and 0.511 Pearson; while the best SS systems can often reach >0.80 Pearson on well-formed text. This shared task also provides insights into the relation between the PI and SS tasks and suggests the importance to bringing these two research areas together. We make all the data, baseline systems and evaluation scripts publicly available. 1


title: Perfect Space–Time Codes for Any Number of Antennas

abstract: In a recent paper, perfect (n times n) space-time codes were introduced as the class of linear dispersion space-time (ST) codes having full rate, nonvanishing determinant, a signal constellation isomorphic to either the rectangular or hexagonal lattices in 2n 2 dimensions, and uniform average transmitted energy per antenna. Consequence of these conditions include optimality of perfect codes with respect to the Zheng-Tse diversity-multiplexing gain tradeoff (DMT), as well as excellent low signal-to-noise ratio (SNR) performance. Yet perfect space-time codes have been constructed only for two, three, four, and six transmit antennas. In this paper, we construct perfect codes for all channel dimensions, present some additional attributes of this class of ST codes, and extend the notion of a perfect code to the rectangular case.


title: Homotopy classification of director fields on polyhedral domains with tangent and periodic boundary conditions, with applications to bi-stable post-aligned liquid crystal displays

abstract: We obtain complete topological classification of states of nematic liquid crystal in the geometry of periodic array of rectangular posts between two parallel slabs, with tangent or normal boundary conditions. This classification has applications in bi-stable pos-aligned liquid crystal display design and have technological significance. Methods used in classification are those of algebraic topology and go beyond relative homotopy groups.


title: Gut Microbes and the Brain: Paradigm Shift in Neuroscience

abstract: The discovery of the size and complexity of the human microbiome has resulted in an ongoing reevaluation of many concepts of health and disease, including diseases affecting the CNS. A growing body of preclinical literature has demonstrated bidirectional signaling between the brain and the gut microbiome, involving multiple neurocrine and endocrine signaling mechanisms. While psychological and physical stressors can affect the composition and metabolic activity of the gut microbiota, experimental changes to the gut microbiome can affect emotional behavior and related brain systems. These findings have resulted in speculation that alterations in the gut microbiome may play a pathophysiological role in human brain diseases, including autism spectrum disorder, anxiety, depression, and chronic pain. Ongoing large-scale population-based studies of the gut microbiome and brain imaging studies looking at the effect of gut microbiome modulation on brain responses to emotion-related stimuli are seeking to validate these speculations. This article is a summary of emerging topics covered in a symposium and is not meant to be a comprehensive review of the subject.