title: Challenges at the interface of control engineering and synthetic biology

abstract: Synthetic biology is a rapidly expanding field at the interface of the engineering and biological sciences which aims to apply rational design principles in biological contexts. Many natural processes utilise regulatory architectures that parallel those found in control and electrical engineering, which has motivated their implementation as part of synthetic biological constructs. Tools based upon control theoretical concepts can be used to design such systems, as well as to guide their experimental realisation. In this paper we provide examples of biological implementations of negative feedback systems, and discuss progress made toward realisation of other feedback and control architectures. We then outline major challenges posed by the design of such systems, particularly focusing on those which are specific to biological contexts and on which feedback control can have a significant impact. We explore future directions for work in the field, including new approaches for theoretical design of biological control systems, the utilisation of novel components for their implementation, and the potential for application of automation and machine-learning approaches to accelerate synthetic biological research.


title: Interrelations between respiration and dormancy in buds of three hardwood species with different chilling requirements for dormancy release

abstract: Respiration in vegetative buds of mature Betula pendula, Alnus glutinosa and Prunus padus trees was measured monthly at 15°C from mid-October 1996 to natural outdoor budburst in April 1997. In B. pendula the effect of bud water content on respiration was also estimated (December–April) by artificial imbibition of buds for 24 h prior to measurement of respiration. For estimation of corresponding bud dormancy status, batches of twigs were forced at identical monthly intervals at 15°C in long days (24 h), and budburst recorded. In all species dormancy was deepest when the leaves were shed in October, and dormancy was first alleviated in P. padus followed by B. pendula and A. glutinosa. However, bud respiration capacity was not related to dormancy release as it decreased in all species from October to November and displayed no notable increase until February in P. padus, March in B. pendula and April in A. glutinosa, after completion of dormancy release. Rather, increase in respiration coincided with growth resumption prior to budburst. Artificial imbibition of B. pendula buds increased the water content by approximately 10% (FW) and induced a doubling of the respiration rate (December–February). Moreover, the seasonal variation in bud water content (October–April) explained 94% of the variation in respiration in B. pendula and P. padus, and 84% in A. glutinosa. These observations suggest an important role of water content for respiration. During a cold period from mid-December to mid-January with mean temperature of –9.7°C dormancy release was arrested in P. padus, and to some degree in A. glutinosa, whereas dormancy release progressed normally in B. pendula. This indicates species differences in lower critical temperatures for dormancy release.


title: Lyric Text Mining in Music Mood Classification

abstract: This research examines the role lyric text can play in improving audio music mood classification. A new method is proposed to build a large ground truth set of 5,585 songs and 18 mood categories based on social tags so as to reflect a realistic, user-centered perspective. A relatively complete set of lyric features and representation models were investigated. The best performing lyric feature set was also compared to a leading audio-based system. In combining lyric and audio sources, hybrid feature sets built with three different feature selection methods were also examined. The results show patterns at odds with findings in previous studies: audio features do not always outperform lyrics features, and combining lyrics and audio features can improve performance in many mood categories, but not all of them.


title: Applications and Benefits of Elliptic Curve Cryptography

abstract: This paper covers relatively new and emerging subject of the elliptic curve crypto systems whose fundamental security is based on the algorithmically hard discrete logarithm problem. Work includes the study of the following issues: mathematical background of the elliptic curve crypto systems, discrete logarithm problem, practical use cases in the industry, common implementation mistakes, performance comparison of elliptic curve and RSA crypto systems etc. The conclusion contains a brief summary of the elliptic curve cryptosystem practical applications, the potential practical benefits and disadvantages with respect to the widely used RSA crypto system.


title: An improved approach for model-based detection and pose estimation of texture-less objects

abstract: Detection and pose estimation of texture-less objects still faces several challenges such as foreground occlusions, background clutter, multi-instance objects, large scale and pose changes to name but a few. In this paper, we present an improved approach for model based detection and pose estimation of texture-less objects, LINEMOD [4], in order to improve the robustness of pose estimation with partial foreground occlusions. For template creation, we modify Gradient Response Maps and propose Gradient Orientation Maps, where Non-Maximum Suppression and Dual Threshold Algorithm are applied. And we adopt image pyramid searching method for fast template matching. Next, the approximate object pose associated with each detected template is used as a starting point for fine pose estimation with Iterative Closest Point algorithm. Thirdly, we improve the accuracy of fine pose estimation by using point cloud filter. Experimental results show that our approach is more robust to estimate the pose of texture-less objects with partial foreground occlusions.


title: Analysis of long queries in a large scale search log

abstract: We propose to use the search log to study long queries, in order to understand the types of information needs that are behind them, and to design techniques to improve search effectiveness when they are used. Long queries arise in many different applications, such as CQA (community-based question answering) and literature search, and they have been studied to some extent using TREC data. They are also, however, quite common in web search, as can be seen by looking at the distribution of query lengths in a large scale search log. In this paper we analyze the long queries in the search log with the aim of identifying the characteristics of the most commonly occurring types of queries, and the issues involved with using them effectively in a search engine. In addition, we propose a simple yet effective method for evaluating the performance of the queries in the search log using a combination of the click data in the search log with the existing TREC corpora.


title: Software-Defined Architectures and Technologies for Underwater Wireless Sensor Networks: A Survey

abstract: The ocean covers nearly two-thirds of the surface on the Earth, and there has been great interest in developing underwater wireless sensor networks (UWSNs) to help us explore the ocean realm. A great deal of efforts have been devoted to it, and significant progress has been made since the beginning of 2000s. However, most of the networks are isolatedly developed currently, inherently hardware-based and application-oriented with inflexible closed-form architectures, which are difficult to reconfigure, reprogram and evolve. They also lack the capability in sharing resources, and are far from service-oriented networks. These limitations impair their capacity for wide range of applications. To further propel the development of UWSNs, next-generation UWSNs have been proposed recently, which are robust, flexible, adaptive, programmable, support resource-sharing feature and are easy to manage and evolve. Moreover, a number of novel software-defined techniques and paradigms, such as software-defined radio, cognitive acoustic radio, network function virtualization, software-defined networking, Internet of Underwater Things, and sensor-cloud, have been emerging. These software-defined technologies have the capability of softwarizing network resources, and then redefining them to satisfy diverse application requirements, improve resource utilization efficiency and simplify network management. Consequently, these evolving technologies are envisioned as critical building blocks and major driving forces, which will transform conventional UWSNs toward software-based, programmable, user-customizable, and service-oriented next-generation UWSNs. In this paper, we provide a comprehensive review of existing works on implementing these techniques, and also present discussions for future research. We hope to inspire more active research on these areas and take a step further toward realizing next-generation UWSNs.


A Qualitative and Quantitative Analysis of Selected Ethnic and Racial Terminology Present in Assorted Public English Corpora


title: A Pseudo Random Numbers Generator Based on Chaotic Iterations. Application to Watermarking

abstract: In this paper, a new chaotic pseudo-random number generator (PRNG) is proposed. It combines the well-known ISAAC and XORshift generators with chaotic iterations. This PRNG possesses important properties of topological chaos and can successfully pass NIST and TestU01 batteries of tests. This makes our generator suitable for information security applications like cryptography. As an illustrative example, an application in the field of watermarking is presented.


title: Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval

abstract: The 2-Poisson model for term frequencies is used to suggest ways of incorporating certain variables in probabilistic models for information retrieval. The variables concerned are within-document term frequency, document length, and within-query term frequency. Simple weighting functions are developed, and tested on the TREC test collection. Considerable performance improvements (over simple inverse collection frequency weighting) are demonstrated.


title: Evolving Deep LSTM-based Memory Networks using an Information Maximization Objective

abstract: Reinforcement Learning agents with memory are constructed in this paper by extending neuroevolutionary algorithm NEAT to incorporate LSTM cells, i.e. special memory units with gating logic. Initial evaluation on POMDP tasks indicated that memory solutions obtained by evolving LSTMs outperform traditional RNNs. Scaling neuroevolution of LSTM to deep memory problems is challenging because: (1) the fitness landscape is deceptive, and (2) a large number of associated parameters need to be optimized. To overcome these challenges, a new secondary optimization objective is introduced that maximizes the information (Info-max) stored in the LSTM network. The network training is split into two phases. In the first phase (unsupervised phase), independent memory modules are evolved by optimizing for the info-max objective. In the second phase, the networks are trained by optimizing the task fitness. Results on two different memory tasks indicate that neuroevolution can discover powerful LSTM-based memory solution that outperform traditional RNNs.


title: The Social Explanatory Styles Questionnaire: Assessing Moderators of Basic Social-Cognitive Phenomena Including Spontaneous Trait Inference, the Fundamental Attribution Error, and Moral Blame

abstract: Why is he poor? Why is she failing academically? Why is he so generous? Why is she so conscientious? Answers to such everyday questions—social explanations—have powerful effects on relationships at the interpersonal and societal levels. How do people select an explanation in particular cases? We suggest that, often, explanations are selected based on the individual's pre-existing general theories of social causality. More specifically, we suggest that over time individuals develop general beliefs regarding the causes of social events. We refer to these beliefs as social explanatory styles. Our goal in the present article is to offer and validate a measure of individual differences in social explanatory styles. Accordingly, we offer the Social Explanatory Styles Questionnaire (SESQ), which measures three independent dimensions of social explanatory style: Dispositionism, historicism, and controllability. Studies 1–3 examine basic psychometric properties of the SESQ and provide positive evidence regarding internal consistency, factor structure, and both convergent and divergent validity. Studies 4–6 examine predictive validity for each subscale: Does each explanatory dimension moderate an important phenomenon of social cognition? Results suggest that they do. In Study 4, we show that SESQ dispositionism moderates the tendency to make spontaneous trait inferences. In Study 5, we show that SESQ historicism moderates the tendency to commit the Fundamental Attribution Error. Finally, in Study 6 we show that SESQ controllability predicts polarization of moral blame judgments: Heightened blaming toward controllable stigmas (assimilation), and attenuated blaming toward uncontrollable stigmas (contrast). Decades of research suggest that explanatory style regarding the self is a powerful predictor of self-functioning. We think it is likely that social explanatory styles—perhaps comprising interactive combinations of the basic dimensions tapped by the SESQ—will be similarly potent predictors of social functioning. We hope the SESQ will be a useful tool for exploring that possibility.


Workplace Bullying , Mobbing and General Harassment :


title: Solving General Arithmetic Word Problems

abstract: This paper presents a novel approach to automatically solving arithmetic word problems. This is the first algorithmic approach that can handle arithmetic problems with multiple steps and operations, without depending on additional annotations or predefined templates. We develop a theory for expression trees that can be used to represent and evaluate the target arithmetic expressions; we use it to uniquely decompose the target arithmetic problem to multiple classification problems; we then compose an expression tree, combining these with world knowledge through a constrained inference framework. Our classifiers gain from the use of quantity schemas that supports better extraction of features. Experimental results show that our method outperforms existing systems, achieving state of the art performance on benchmark datasets of arithmetic word problems.


title: Mother knows best? Comparing primiparous parturients’ expectations and predictions with actual birth outcomes

abstract: OBJECTIVE ::: The literature provides no clear evidence linking maternal optimism/pessimism to birth outcomes. Our objective was to determine whether maternal expectations and predictions regarding mode of delivery and epidural anesthesia aligned with birth outcomes. ::: ::: ::: METHODS ::: Primiparous, low-risk pregnant women at term filled in questionnaires in which they rated their chances of a vaginal delivery (VD) or a cesarean section (CS), and their intention to receive epidural anesthesia. Their responses were compared to actual outcomes. ::: ::: ::: RESULTS ::: Pre-birth perceptions of odds of delivery by a CS were significantly higher (P=0.04) among women who eventually had a vacuum extraction (VE) or CS as a result of an arrest disorder, compared with women who had a VD and those who had a CS or a VE due to non-reassuring fetal heart monitor. Intention to receive epidural anesthesia was significantly lower (P<0.001) among women who gave birth without it. ::: ::: ::: CONCLUSION ::: The pessimistic pre-birth perceptions of women of high odds for a CS may be related to an increased risk of arrest disorders of labor. However, the prenatal assessments of primiparous women are poor predictors of their demand for epidural anesthesia during labor. Larger-scale studies to determine whether pre-partum psychological interventions may contribute to the process of labor and improve obstetric outcomes are warranted.


title: Prenatal Diagnosis of Isolated Fetal Hydrocolpos Secondary to Congenital Imperforate Hymen

abstract: A 32-year-old primigravida was referred to our hospital at 36 weeks of gestation with a fetal pelvic mass. Ultrasonography showed the fluid-filled area to be a 9 x 4 x 5-cm pear-shaped retrovesical mass with a funnel-shaped blind pouch at the distal end of the fetal vagina. Marked left hydronephrosis resulting from mass compression was also detected. Fetal magnetic resonance imaging further defined a pelvic lesion extending cephalically into the abdomen and caudally into the vagina. Membranal protrusion of the introitus was clearly identified. Therefore, the diagnosis of congenital imperforate hymen with hydrocolpos was established. At 38 weeks of gestation, a 2,966-g female infant was delivered vaginally with good Apgar scores. Physical examination of the neonate revealed a bulging membrane covering the vaginal opening. The presence of syndromic disorders (McKusick-Kaufman, Ellis-van Creveld or Bardet-Biedl syndromes), genitourinary and anorectal anomalies were excluded. The karyotype was 46,XX. A hymenotomy was performed on the second day of life. The infant recovered fully after hymenotomy.


title: Intrinsically motivated reinforcement learning for human-robot interaction in the real-world

abstract: For a natural social human-robot interaction, it is essential for a robot to learn the human-like social skills. However, learning such skills is notoriously hard due to the limited availability of direct instructions from people to teach a robot. In this paper, we propose an intrinsically motivated reinforcement learning framework in which an agent gets the intrinsic motivation-based rewards through the action-conditional predictive model. By using the proposed method, the robot learned the social skills from the human-robot interaction experiences gathered in the real uncontrolled environments. The results indicate that the robot not only acquired human-like social skills but also took more human-like decisions, on a test dataset, than a robot which received direct rewards for the task achievement.


title: Polychronization: Computation with Spikes

abstract: We present a minimal spiking network that can polychronize, that is, exhibit reproducible time-locked but not synchronous firing patterns with millisecond precision, as in synfire braids. The network consists of cortical spiking neurons with axonal conduction delays and spike-timing-dependent plasticity (STDP); a ready-to-use MATLAB code is included. It exhibits sleeplike oscillations, gamma (40 Hz) rhythms, conversion of firing rates to spike timings, and other interesting regimes. Due to the interplay between the delays and STDP, the spiking neurons spontaneously self-organize into groups and generate patterns of stereotypical polychronous activity. To our surprise, the number of coexisting polychronous groups far exceeds the number of neurons in the network, resulting in an unprecedented memory capacity of the system. We speculate on the significance of polychrony to the theory of neuronal group selection (TNGS, neural Darwinism), cognitive neural computations, binding and gamma rhythm, mechanisms of attention, and consciousness as "attention to memories."


title: Automatic Generation of Efficient Domain-Optimized Planners from Generic Parametrized Planners

abstract: When designing state-of-the-art, domain-independent planning systems, many decisions have to be made with respect to the domain analysis or compilation performed during preprocessing, the heuristic functions used during search, and other features of the search algorithm. These design decisions can have a large impact on the performance of the resulting planner. By providing many alternatives for these choices and exposing them as parameters, planning systems can in principle be configured to work well on different domains. However, planners are typically used in default configurations that have been chosen because of their good average performance over a set of benchmark domains, with limited experimentation over the potentially huge range of possible configurations. In this work, we propose a general framework for automatically configuring a parameterized planner, and show that substantial performance gains can be achieved. We apply the framework to the well-known LPG planner, which in the context of this work was expanded to 62 parameters and over 6.5 x 10^17 possible configurations. By using this highly parameterized planning system in combination with the state-of-the-art automatic algorithm configuration procedure ParamILS, excellent performance on a broad range of well-known benchmark domains was achieved, as also witnessed by the results of the learning track of the 7th International Planning Competition.


title: The Effects and Antecedents of Conflict in Free and Open Source Software Development

abstract: Conflict is an important group process, and more so in self-organizing teams with fluid boundaries and high possibility for turnover. We empirically investigate different types of conflict in Free and Open Source Software development teams, their antecedents and impact on developers&' sustained participation. Following a survey of 222 FOSS developers, we find conflict to have an overall negative effect on developer retention. Furthermore, different types of conflict have varying impact on outcomes. In particular, only normative conflict levels negatively impact intention to remain in a FOSS project. Both normative and process conflict negatively affect perceptions of team performance, while the co-occurrence of task and process conflict exacerbate negative effects on outcomes. Though we find structural factors like task interdependence and geographical distribution increase overall conflict levels in FOSS teams, participatory decision-making and a transformational leadership style have an ameliorating effect. Implications for theory and practice are discussed.


title: Accelerating the local outlier factor algorithm on a GPU for intrusion detection systems

abstract: The Local Outlier Factor (LOF) is a very powerful anomaly detection method available in machine learning and classification. The algorithm defines the notion of local outlier in which the degree to which an object is outlying is dependent on the density of its local neighborhood, and each object can be assigned an LOF which represents the likelihood of that object being an outlier. Although this concept of a local outlier is a useful one, the computation of LOF values for every data object requires a large number of k-nearest neighbor queries -- this overhead can limit the use of LOF due to the computational overhead involved. Due to the growing popularity of Graphics Processing Units (GPU) in general-purpose computing domains, and equipped with a high-level programming language designed specifically for general-purpose applications (e.g., CUDA), we look to apply this parallel computing approach to accelerate LOF. In this paper we explore how to utilize a CUDA-based GPU implementation of the k-nearest neighbor algorithm to accelerate LOF classification. We achieve more than a 100X speedup over a multi-threaded dual-core CPU implementation. We also consider the impact of input data set size, the neighborhood size (i.e., the value of k) and the feature space dimension, and report on their impact on execution time.


title: WCL2R: A Benchmark Collection for Learning to Rank Research with Clickthrough Data

abstract: In this paper we present WCL2R, a benchmark collection for supporting research in learning to rank (L2R) algorithms which exploit clickthrough features. Differently from other L2R benchmark collections, such as LETOR and the recently released Yahoo!'s collection for a L2R competition, in WCL2R we focus on defining a significant (and new) set of features over clickthrough data extracted from the logs of a real-world search engine. In this paper, we describe the WCL2R collection by providing details about how the corpora, queries and relevance judgments were obtained, how the learning features were constructed and how the process of splitting the collection in folds for representative learning was performed. We also analyze the discriminative power of the WCL2R collection using traditional feature selection algorithms and show that the most discriminative features are, in fact, those based on clickthrough data. We then compare several L2R algorithms on WCL2R, showing that all of them obtain significant gains by exploiting clickthrough information over using traditional ranking approaches.


title: Scheduling tasks with precedence constraints on multiple servers

abstract: We consider the problem of scheduling jobs which are modeled by directed acyclic graphs (DAG). In such graphs, nodes represent tasks of a job and edges represent precedence constraints in processing these tasks. The DAG scheduling problem, also known as scheduling in fork-join processing networks, is motivated by examples such as job scheduling in data centers and cloud computing, patient flow scheduling in health systems and many other applications. We consider a flexible system, in which servers may process different, possibly overlapping, sets of task types. In this paper, we first discuss the difficulties in designing provably efficient policies for DAG scheduling, which arise due to interactions between the flexibility of the processing environment and the precedence constraints in the system. A major difficulty is the classical synchronization issue, which is further complicated in the presence of system flexibility. Then, we propose two queueing networks to model the scheduling problem that overcome this difficulty. These are virtual queues that enable us to design provably efficient scheduling policies. We show that the well-known Max-Weight policy for these queueing networks is throughput-optimal. Finally, to compare the delay performance of the two queueing networks, we consider a simplified model in which tasks and servers are identical. We characterize their delay performances under a simple first-come-first-serve policy, via a novel coupling argument.


title: Learning from the memory of Atari 2600

abstract: We train a number of neural networks to play games Bowling, Breakout and Seaquest using information stored in the memory of a video game console Atari 2600. We consider four models of neural networks which differ in size and architecture: two networks which use only information contained in the RAM and two mixed networks which use both information in the RAM and information from the screen. As the benchmark we used the convolutional model proposed in NIPS and received comparable results in all considered games. Quite surprisingly, in the case of Seaquest we were able to train RAM-only agents which behave better than the benchmark screen-only agent. Mixing screen and RAM did not lead to an improved performance comparing to screen-only and RAM-only agents.


title: A Novel Low-Cost, Large Curvature Bend Sensor Based on a Bowden-Cable

abstract: Bend sensors have been developed based on conductive ink, optical fiber, and electronic textiles. Each type has advantages and disadvantages in terms of performance, ease of use, and cost. This study proposes a new and low-cost bend sensor that can measure a wide range of accumulated bend angles with large curvatures. This bend sensor utilizes a Bowden-cable, which consists of a coil sheath and an inner wire. Displacement changes of the Bowden-cable’s inner wire, when the shape of the sheath changes, have been considered to be a position error in previous studies. However, this study takes advantage of this position error to detect the bend angle of the sheath. The bend angle of the sensor can be calculated from the displacement measurement of the sensing wire using a Hall-effect sensor or a potentiometer. Simulations and experiments have shown that the accumulated bend angle of the sensor is linearly related to the sensor signal, with an R-square value up to 0.9969 and a root mean square error of 2% of the full sensing range. The proposed sensor is not affected by a bend curvature of up to 80.0 m−1, unlike previous bend sensors. The proposed sensor is expected to be useful for various applications, including motion capture devices, wearable robots, surgical devices, or generally any device that requires an affordable and low-cost bend sensor.


Neural Knowledge Acquisition via Mutual Attention between Knowledge Graph and Text


title: Specification-Based Intrusion Detection for Advanced Metering Infrastructures

abstract: It is critical to develop an effective way to monitor advanced metering infrastructures (AMI). To ensure the security and reliability of a modernized power grid, the current deployment of millions of smart meters requires the development of innovative situational awareness solutions to prevent compromised devices from impacting the stability of the grid and the reliability of the energy distribution infrastructure. To address this issue, we introduce a specification-based intrusion detection sensor that can be deployed in the field to identify security threats in real time. This sensor monitors the traffic among meters and access points at the network, transport, and application layers to ensure that devices are running in a secure state and their operations respect a specified security policy. It does this by implementing a set of constraints on transmissions made using the C12.22 standard protocol that ensure that all violations of the specified security policy will be detected. The soundness of these constraints was verified using a formal framework, and a prototype implementation of the sensor was evaluated with realistic AMI network traffic.


title: OPT: a new framework for overlapped and parallel triangulation in large-scale graphs

abstract: Graph triangulation, which finds all triangles in a graph, has been actively studied due to its wide range of applications in the network analysis and data mining. With the rapid growth of graph data size, disk-based triangulation methods are in demand but little researched. To handle a large-scale graph which does not fit in memory, we must iteratively load small parts of the graph. In the existing literature, achieving the ideal cost has been considered to be impossible for billion-scale graphs due to the memory size constraint. In this paper, we propose an overlapped and parallel disk-based triangulation framework for billion-scale graphs, OPT, which achieves the ideal cost by (1) full overlap of the CPU and I/O operations and (2) full parallelism of multi-core CPU and FlashSSD I/O. In OPT, triangles in memory are called the internal triangles while triangles constituting vertices in memory and vertices in external memory are called the external triangles. At the macro level, OPT overlaps the internal triangulation and the external triangulation, while it overlaps the CPU and I/O operations at the micro level. Thereby, the cost of OPT is close to the ideal cost. Moreover, OPT instantiates both vertex-iterator and edge-iterator models and benefits from multi-thread parallelism on both types of triangulation. Extensive experiments conducted on large-scale datasets showed that (1) OPT achieved the elapsed time close to that of the ideal method with less than 7% of overhead under the limited memory budget, (2) OPT achieved linear speed-up with an increasing number of CPU cores, (3) OPT outperforms the state-of-the-art parallel method by up to an order of magnitude with 6 CPU cores, and (4) for the first time in the literature, the triangulation results are reported for a billion-vertex scale real-world graph.


title: Using the patient-centered method to achieve excellence in care for women with breast cancer.

abstract: The diagnosis of breast cancer remains one of the most threatening and emotionally debilitating diagnoses given to patients. Breast cancer elicits fears of loss of ability, control, hope, and at worst--loss of life. Patients and their families, devastated by the diagnosis of cancer, often feel unsupported or dismissed by their health care providers. Using the example of breast cancer, this paper describes how the application of the patient-centred clinical method can assist physicians in caring for patients diagnosed with breast cancer. Composite cases and quotes drawn from qualitative research studies with breast cancer patients are used to illustrate the application of the patient-centred clinical method.


Survey of Sensor Technology for Aircraft Cabin Environment Sensing


title: Active learning for relation type extension with local and global data views

abstract: Relation extraction is the process of identifying instances of specified types of semantic relations in text; relation type extension involves extending a relation extraction system to recognize a new type of relation. We present LGCo-Testing, an active learning system for relation type extension based on local and global views of relation instances. Locally, we extract features from the sentence that contains the instance. Globally, we measure the distributional similarity between instances from a 2 billion token corpus. Evaluation on the ACE 2004 corpus shows that LGCo-Testing can reduce annotation cost by 97% while maintaining the performance level of supervised learning.


title: Controversy Corner: What do software architects really do?

abstract: To be successful, a software architect-or a software architecture team, collectively-must strike a delicate balance between an external focus-both outwards: Listening to customers, users, watching technology, developing a long-term vision, and inwards: driving the development teams-and an internal, reflective focus: spending time to make the right design choices, validating them, and documenting them. Teams that stray too far away from this metastable equilibrium fall into some traps that we describe as antipatterns of software architecture teams.


title: Large scale instance matching via multiple indexes and candidate selection

abstract: Instance matching aims to discover the linkage between different descriptions of real objects across heterogeneous data sources. With the rapid development of Semantic Web, especially of the linked data, automatically instance matching has been become the fundamental issue for ontological data sharing and integration. Instances in the ontologies are often in large scale, which contains millions of, or even hundreds of millions objects. Directly applying previous schema level ontology matching methods is infeasible. In this paper, we systematically investigate the characteristics of instance matching, and then propose a scalable and efficient instance matching approach named VMI. VMI generates multiple vectors for different kinds of intained in the ontology instances, and uses a set of inverted indexes based rules to get the primary matching candidates. Then it employs user customized property values to further eliminate the incorrect matchings. Finally the similarities of matching candidates are computed as the integrated vector distances and the matching results are extracted. Experiments on instance track from OAEI 2009 and OAEI 2010 show that the proposed method achieves better effectiveness and efficiency (a speedup of more than 100 times and a bit better performance (+3.0% to 5.0% in terms of F1-score) than top performer RiMOM on most of the datasets). Experiments on Linked MDB and DBpedia show that VMI can obtain comparable results with the SILK system (about 26,000 results with good quality).


title: Explicit Reasoning over End-to-End Neural Architectures for Visual Question Answering

abstract: Many vision and language tasks require commonsense reasoning beyond data-driven image and natural language processing. Here we adopt Visual Question Answering (VQA) as an example task, where a system is expected to answer a question in natural language about an image. Current state-of-the-art systems attempted to solve the task using deep neural architectures and achieved promising performance. However, the resulting systems are generally opaque and they struggle in understanding questions for which extra knowledge is required. In this paper, we present an explicit reasoning layer on top of a set of penultimate neural network based systems. The reasoning layer enables reasoning and answering questions where additional knowledge is required, and at the same time provides an interpretable interface to the end users. Specifically, the reasoning layer adopts a Probabilistic Soft Logic (PSL) based engine to reason over a basket of inputs: visual relations, the semantic parse of the question, and background ontological knowledge from word2vec and ConceptNet. Experimental analysis of the answers and the key evidential predicates generated on the VQA dataset validate our approach.


title: Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering

abstract: We propose the Gaussian attention model for content-based neural memory ::: access. With the proposed attention model, a neural network has the ::: additional degree of freedom to control the focus of its attention from ::: a laser sharp attention to a broad attention. It is applicable whenever ::: we can assume that the distance in the latent space reflects some notion ::: of semantics. We use the proposed attention model as a scoring function ::: for the embedding of a knowledge base into a continuous vector space and ::: then train a model that performs question answering about the entities ::: in the knowledge base. The proposed attention model can handle both the ::: propagation of uncertainty when following a series of relations and also ::: the conjunction of conditions in a natural way. On a dataset of soccer ::: players who participated in the FIFA World Cup 2014, we demonstrate that ::: our model can handle both path queries and conjunctive queries well.


title: Creating efficient codebooks for visual recognition

abstract: Visual codebook based quantization of robust appearance descriptors extracted from local image patches is an effective means of capturing image statistics for texture analysis and scene classification. Codebooks are usually constructed by using a method such as k-means to cluster the descriptor vectors of patches sampled either densely ('textons') or sparsely ('bags of features' based on key-points or salience measures) from a set of training images. This works well for texture analysis in homogeneous images, but the images that arise in natural object recognition tasks have far less uniform statistics. We show that for dense sampling, k-means over-adapts to this, clustering centres almost exclusively around the densest few regions in descriptor space and thus failing to code other informative regions. This gives suboptimal codes that are no better than using randomly selected centres. We describe a scalable acceptance-radius based clusterer that generates better codebooks and study its performance on several image classification tasks. We also show that dense representations outperform equivalent keypoint based ones on these tasks and that SVM or mutual information based feature selection starting from a dense codebook further improves the performance.


title: Effects of early maternal distress and parenting on the development of children's self-regulation and externalizing behavior

abstract: Emotional distress experienced by mothers increases young children's risk of externalizing problems through suboptimal parenting and child self-regulation. An integrative structural equation model tested hypotheses that mothers’ parenting (i.e., low levels of inductive discipline and maternal warmth) would mediate adverse effects of early maternal distress on child effortful control, which in turn would mediate effects of maternal parenting on child externalizing behavior. This longitudinal study spanning ages 3, 6, and 10 included 241 children, mothers, and a subset of teachers. The hypothesized model was partially supported. Elevated maternal distress was associated with less inductive discipline and maternal warmth, which in turn were associated with less effortful control at age 3 but not at age 6. Inductive discipline and maternal warmth mediated adverse effects of maternal distress on children's effortful control. Less effortful control at ages 3 and 6 predicted smaller relative decreases in externalizing behavior at 6 and 10, respectively. Effortful control mediated effects of inductive discipline, but not maternal warmth, on externalizing behavior. Findings suggest elevated maternal distress increases children's risk of externalizing problems by compromising early parenting and child self-regulation.


title: Collaborative Metric Learning

abstract: Metric learning algorithms produce distance metrics that capture the important relationships among data. In this work, we study the connection between metric learning and collaborative filtering. We propose Collaborative Metric Learning (CML) which learns a joint metric space to encode not only users' preferences but also the user-user and item-item similarity. The proposed algorithm outperforms state-of-the-art collaborative filtering algorithms on a wide range of recommendation tasks and uncovers the underlying spectrum of users' fine-grained preferences. CML also achieves significant speedup for Top-K recommendation tasks using off-the-shelf, approximate nearest-neighbor search, with negligible accuracy reduction.


title: WS4A: a Biomedical Question and Answering System based on public Web Services and Ontologies

abstract: This paper describes our system, dubbed WS4A (Web Services for All), that participated in the fourth edition of the BioASQ challenge (2016). We used WS4A to perform the Question and Answering (QA) task 4b, which consisted on the retrieval of relevant concepts, documents, snippets, RDF triples, exact answers and ideal answers for each given question. The novelty in our approach consists on the maximum exploitation of existing web services in each step of WS4A, such as the annotation of text, and the retrieval of metadata for each annotation. The information retrieved included concept identifiers, ontologies, ancestors, and most importantly, PubMed identifiers. The paper describes the WS4A pipeline and also presents the precision, recall and f-measure values obtained in task 4b. Our system achieved two second places in two subtasks on one of the five batches.


title: Optimum inductive loading of short whip antennas

abstract: Short whips (monopoles) are often used on vehicles when a quarter wavelength is too long. It is common knowledge that a loading coil located in series with the whip improves narrow band performance. This series inductive loading of monopole (dipole) antennas is analyzed by a piecewise sinusoidal moment method. When loaded to resonance, the current rises from the feed value to a peak slightly beyond the load point with decay to zero at the end. Radiation resistance improvement factors as large as 5 have been calculated. The resonant loading reactance is shown to vary inversely with the antenna length for most lengths. Efficiency varies slowly with load point and peaks at roughly 0.4 (of the whip length) from the feed in contrast to earlier approximate theory that predicted higher efficiency for loads farther out toward the end. The curves allow a tradeoff between slightly reduced efficiency and larger input resistance (to allow easier matching) to be made. For this case, a 2/3 loading point may be a good compromise. The length for which efficiency is 50 percent is a useful design guide. This length varies slowly with Q and h/a and is in the range 0.06 to 0.09λ. Thus shorter monopoles will have low efficiency. Extensive graphical data are presented.


title: The Virtuality and Reality of Augmented Reality

abstract: This thesis explores the creative possibilities and implications of Augmented Reality, not just as a specific technology of computer science, but as one of high technologies that influence people’s perception and even the concepts of the real and the virtual. For a broader definition of Augmented Reality which has a unique status between virtual environment and real environment, the virtuality and the reality of Augmented Reality are discussed in an interdisciplinary context such as arts, philosophy, and film and animation studies. This approach is rooted in the belief that theory of new media ought to traverse and encompass all realms of academic categories.


title: Pythagorean fuzzy subsets

abstract: We introduce a new class of non-standard fuzzy subsets called Pythagorean fuzzy subsets and the related idea of Pythagorean membership grades. We focus on the negation operation and its relationship to the Pythagorean theorem. We compare Pythagorean fuzzy subsets with intuitionistic fuzzy subsets. We look at the basic set operations for the Pythagorean fuzzy subsets.


title: HYDRA: hybrid design for remote attestation (using a formally verified microkernel)

abstract: Remote Attestation (RA) allows a trusted entity (verifier) to securely measure internal state of a remote untrusted hardware platform (prover). RA can be used to establish a static or dynamic root of trust in embedded and cyber-physical systems. It can also be used as a building block for other security services and primitives, such as software updates and patches, verifiable deletion and memory resetting. There are three major types of RA designs: hardware-based, software-based, and hybrid, each with its own set of benefits and drawbacks. This paper presents the first hybrid RA design - called HYDRA - that builds upon formally verified software components that ensure memory isolation and protection, as well as enforce access control to memory and other resources. HYDRA obtains these properties by using the formally verified seL4 microkernel. (Until now, this was only attainable with purely hardware-based designs.) Using seL4 imposes fewer hardware requirements on the underlying microprocessor. Also, building upon a formally verified software component increases confidence in security of the overall design of HYDRA and its implementation. We instantiate HYDRA on two commodity hardware platforms and assess the performance and overhead of performing RA on such platforms via experimentation; we show that HYDRA can attest 10MB of memory in less than 250msec when using a Speck-based cryptographic checksum.


title: Benchmarking Spatial Join Operations with Spatial Output

abstract: The spatial join operation with spatial output is benchmarked using the R-tree, W-tree, R+-tree, and the PMR quadtree. The studied quantities are the time to build the data structure and the time to do the spatial join in an application domain consisting of planar line segment data. Experiments reveal that spatial data structures based on a disjoint decomposition of space and bounding boxes (i.e., the R+tree and the PMR quadtree with bounding boxes) outperform the other structures that are based upon a non-disjoint decomposition (i.e., the R-tree and R*-tree). As the size of the output of the spatial join increases with respect to the larger of the two inputs, methods based on a disjoint regular decomposition (i.e., the PMR quadtree regardless of the presence of bounding boxes) perform significantly better. *This work was supportedin part by the National Science Foundation under Grant IRI-92-16970 and ASC-93-18183, and by a grant from the Computer Research and Applications Group at Los Alamos National Laboratory. tAuthor’s current address is Graphic Data Systems Corp., 6200 South Syracuse Way, Englewood CO 80111 Permission to copy without fee all OT part of this material is granted pToviaea that the copies are not made OT ai5tTihtd jOT diTeCt commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, OT to republish, requires a fee and/or special permission from the Endowment. Proceedings of the 21st VLDB Conference Ziirich, Switzerland, 1995


A fine-grained corpus annotation schema of German nephrology records.


title: A Sensitivity Analysis Approach for Some Deterministic Multi-Criteria Decision-Making Methods

abstract: Often data in multi-criteria decision making (MCDM) problems are imprecise and changeable. Therefore, an important step in many applications of MCDM is to perform a sensitivity analysis on the input data. This paper presents a methodology for performing a sensitivity analysis on the weights of the decision criteria and the performance values of the alternatives expressed in terms of the decision criteria. The proposed methodology is demonstrated on three widely used decision methods. These are the weighted sum model (WSM), the weighted product model (WPM), and the analytic hierarchy process (AHP). This paper formalizes a number of important issues on sensitivity analysis and derives some critical theoretical results. Also, a number of illustrative examples and computational experiments further illustrate the application of the proposed methodology.


title: SimpleDS: A Simple Deep Reinforcement Learning Dialogue System

abstract: This article presents SimpleDS, a simple and publicly available dialogue system trained with deep reinforcement learning. In contrast to previous reinforcement learning dialogue systems, this system avoids manual feature engineering by performing action selection directly from raw text of the last system and (noisy) user responses. Our initial results, in the restaurant domain, report that it is indeed possible to induce reasonable behaviours with such an approach that aims for higher levels of automation in dialogue control for intelligent interactive systems and robots.


Investigation of Bubble Breakup in Laminar, Transient, and Turbulent Regime Behind Micronozzles


title: OntoCrawler: A focused crawler with ontology-supported website models for information agents

abstract: This paper proposed the use of ontology-supported website models to provide a semantic level solution for an information agent so that it can provide fast, precise, and stable query results. We have based on the technique to develop a focused crawler, namely, OntoCrawler which can benefit both user requests and domain semantics. The technique in this research has practically applied on Google and Yahoo searching engines to actively search for webpages of related information, and the experiment outcomes indicated that this technique could definitely up-rise precision rate and recall rate of webpage query. Equipped with this technique, we have developed an ontology-supported information agent shell in Scholar domain which manifests the following interesting features: ontology-supported construction of website models, website models-supported website model expansion, website models-supported webpage retrieval, high-level outcomes of information recommendation, and accordingly proved the feasibility of the related techniques proposed in this paper.


title: Predicting Process Behaviour using Deep Learning

abstract: Abstract Predicting business process behaviour is an important aspect of business process management. Motivated by research in natural language processing, this paper describes an application of deep learning with recurrent neural networks to the problem of predicting the next event in a business process. This is both a novel method in process prediction, which has largely relied on explicit process models, and also a novel application of deep learning methods. The approach is evaluated on two real datasets and our results surpass the state-of-the-art in prediction precision.


title: Solar/battery electric auto rickshaw three-wheeler

abstract: Auto rickshaws are three-wheeled vehicles used extensively in many Asian countries as taxis of people and goods. Although the vehicle design is well-suited to the environment in which it operates, it is a crude, inefficient design. Due to poor vehicle maintenance and the use of inefficient 2 or 4 stroke engines with very little pollution control, auto rickshaws present a huge pollution problem in major Indian cities. Illinois Institute of Technology's (IIT) rickshaw project is aimed at developing an advanced solar-based electric auto rickshaw. This paper presents research on the conventional auto rickshaw, future conceptual infrastructure designs for electric rickshaws, and the recent design research and simulations of the next auto rickshaw. IIT's solar/battery electric three-wheeler is meant to match and exceed the conventional vehicle's performance, but with a more intelligent and efficient design. We introduce the next overall design of the rickshaw in this paper as the Auto Rickshaw 2.0, where the conventional vehicle is version 1.0. IIT's technical development aim for Auto Rickshaw 2.0 is to decrease the total electric power needed for propulsion with an optimized battery system and a more efficient motor. Four system drive train options are covered, and the selected configuration is simulated and analyzed in ADVISOR software. Additionally, conceptual infrastructure designs are modeled and optimized in HOMER software.


title: Managing Data Quality in Business Intelligence Applications

abstract: Business Intelligence (BI) solutions commonly aim at assisting decision-making processes by providing a comprehensive view over a company’s core business data and suitable abstractions thereof. Decision-making based on BI solutions therefore builds on the assumption that providing users with targeted, problemspecific fact data enables them to make informed and, hence, better decisions in their everyday businesses. In order to really provide users with all the necessary details to make informed decisions, we however believe that – in addition to conventional reports – it is essential to also provide users with information about the quality, i.e. with quality metadata, regarding the data from which reports are generated. Identifying a lack of support for quality metadata management in conventional BI solutions, in this paper we propose the idea of quality-aware reports and a possible architecture for quality-aware BI, able to involve the users themselves into the quality metadata management process, by explicitly soliciting and exploiting user feedback.


title: The clinical observation of umbilical cord blood stem cell transplantation on the treatment of type 2 diabetes

abstract: Objective To observe the umbilical cord blood stem cells to treat type 2 diabetes mellitus. Methods Select 18 type 2 diabetes patients in our hospital,according to the length of the course were divided into A,B groups,were given percutaneous pancreatic dorsal artery input cord stem cells.Observed the glucose, insulin and C-peptide release test,HbA1C,body mass index and medication changes of patients before and after infusion. Results The blood glucose levels of two groups after transplantation and islet function improved. 10 patients of A group transplanted in 4 cases out within 3 months of oral medication,4 patients in the case of drugs does not increase to the desired level of glycemic control,blood glucose of 2 patients were not satisfactory,the need to add in another oral medication.8 patients in group B,blood glucose drugs of 6 patients does not increase to a good level,only 2 patients required to increase the amount of drug treatment. Conclusion Cord blood stem cell transplantation in patients with type 2 diabetes recovery of β cell function and glucose control have a certain effect.However,clinical studies need large samples to further assess the efficacy and safety of the method.


title: Design & performance analysis of Fuzzy based MPPT control using two-switch non inverting Buck-Boost converter

abstract: The Solar energy conversion system is a very important substitute of energy among other renewable sources. The photovoltaic array shows a non-linear behavior pertaining to insolation and cell temperature. It is vital to elicit maximum power from the solar cell under irregular climatic conditions to elevate the efficiency of the solar energy conversion system. The maximum power point techniques (MPPT) are employed to maximize the output power of PV array by ceaselessly tracking maximum power point. This paper investigates the design of a two switch non inverting Buck-Boost converter using MPPT controller to store charge in a battery. The proposed system utilizes Fuzzy Logic based MPPT controller. The modeling of a Photovoltaic system is implemented using MATLAB/Simulink to inculcate the effectiveness of MPPT controller in terms of quick response time and improved efficiency of the system.


title: A Real-Time Wall Detection Method for Indoor Environments

abstract: This paper presents an effective and real-time approach for detecting walls in indoor environment. This approach relies on the fact that the rear of the opaque walls is not visible. Thus, to detect the walls in an indoor environment a set of hypothetical walls, based on the ceiling edges or ground level edges, are considered; and their validity is checked using point cloud, generated by a sensor. A certainty factor is calculated for each detected wall, which is updated continuously based on the newly gathered sensory information. Furthermore, the certainty of the walls can be updated using other source of information for better and more reliable wall detection. The novelty of this approach is in its capability to handle environments, with texture-less walls, in real-time. The algorithm has been implemented in simulation, and tested in real environment and has shown effective, reliable and real-time performance


title: Optimal Processing of Polarimetric Synthetic-Aperture Radar Imagery

abstract: The Advanced Detection Technology Sensor can detect, discriminate, and classify stationary ground targets—during the day or night—even through cloud cover, fog, smoke, dust, or rain. The sensor is a coherent, fully polarimetric, 35-GHz syntheticaperture radar (SAR) with a resolution of 1 ft × 1 ft. And, to minimize SAR speckle while preserving image resolution, it uses the polarimetric whitening filter, our recently developed method for processing fully polarimetric data into SAR imagery. The Advanced Detection Technology Sensor (ADTS) is a fully polarimetric, 35-GHz synthetic-aperture radar. The polarimetric capability of the radar is used to enhance the quality of the imagery taken from a small aircraft; the synthetic aperture permits data to be processed to a resolution of 1 ft by 1 ft at a slant range of 7 km. The sensor was developed to provide a high-quality database of clutter and targetin-clutter imagery, and to perform stationary-target detection, discrimination, and


title: Emotional Intelligence and Academic Success among Low Income College Students

abstract: Emotional intelligence is the ability to understand and to manage the relationship between emotion, cognition and behavior. It was hypothesized that levels of emotional intelligence are associated with levels of income. Few studies have examined the relationship between emotional intelligence and academic success among low income college students. The purpose of this correlational and comparative research study was to test a hypothesized model about students' income level, emotional intelligence skills, and academic success. Relations between variables associated to academic success were explored using multivariate analysis techniques. The results showed significant direct and/or indirect effects of income level on emotional intelligence and academic success.


title: Increasing The Performance Of Mobile Smartphones Using Partition And Migration Of Mobile Applications To Cloud Computing

abstract: With the increasing use of smartphones devices, mobile applications with richer functionalities are becoming ubiquitous but mobile devices are limited by their resources for computing and power consumption. Cloud the place for abundant resources. Clouds provide opportunity to do huge computations quickly and accurately so we can use cloud for mobile computations. Mobile Cloud Computing (MCC) which combines mobile computing and cloud computing, has become one of a major discussion thread in the IT world in the recent few years. We developed an architecture that uses cloud to do computations that consume resources badly on mobiles. It aims at finding the right spots in an application automatically where the execution can be partitioned and migrated to the cloud. Thus, an elastic application can augment the capabilities of a mobile device including computation power, storage, and network bandwidth, with the light of dynamic execution configuration according to device"s status including CPU load, memory, and battery level. We demonstrate results of the proposed application model using data collected from one of our elastic application.


title: Design of Rectangular Microstrip 4x2 Patch Array Antenna at 2.4 GHz for WLAN Application

abstract: An array of Rectangular Micro strip Antenna (RMSA) is designed and compared with single patch RMSA and optimized for observing an increase in directivity along with achieving expected radiating parameters such as S11 < 10dB and Side Lobe Level (SLL) < -20dB using IE3D simulator. Maximum directivity of 13.058dBi is obtained by implementing four by two (4x2) array of eight patches as compared to 6.74dBi for single patch for the application of WLAN at 2.4 GHz centre frequency. The parametric variations are done for obtaining desired results.


title: A Highly Efficient and Linear Power Amplifier for 28-GHz 5G Phased Array Radios in 28-nm CMOS

abstract: This paper presents the first linear bulk CMOS power amplifier (PA) targeting low-power fifth-generation (5G) mobile user equipment integrated phased array transceivers. The output stage of the PA is first optimized for power-added efficiency (PAE) at a desired error vector magnitude (EVM) and range given a challenging 5G uplink use case scenario. Then, inductive source degeneration in the optimized output stage is shown to enable its embedding into a two-stage transformer-coupled PA; by broadening interstage impedance matching bandwidth and helping to reduce distortion. Designed and fabricated in 1P7M 28 nm bulk CMOS and using a 1 V supply, the PA achieves +4.2 dBm/9% measured $P_{\text {out}}$ /PAE at −25 dBc EVM for a 250 MHz-wide 64-quadrature amplitude modulation orthogonal frequency division multiplexing signal with 9.6 dB peak-to-average power ratio. The PA also achieves 35.5%/10% PAE for continuous wave signals at saturation/9.6 dB back-off from saturation. To the best of the authors’ knowledge, these are the highest measured PAE values among published ${K}$ - and Ka -band CMOS PAs.


title: Intelligent haptic sensor system for robotic manipulation

abstract: Controlling robotic interventions on small devices creates important challenges on the sensing stage as resolution limitations of noncontact sensors are rapidly reached. The integration of haptic sensors to refine information provided by vision sensors appears as a very promising approach in the development of autonomous robotic systems because it reproduces the multiplicity of sensing sources used by humans. This paper discusses an intelligent multimodal sensor system developed to enhance the haptic control of robotic manipulations of small three-dimensional (3-D) objects. The proposed system combines a 16 /spl times/16 array of force sensing resistor (FSR) elements to refine 3-D shape measurements in selected areas previously monitored with a laser range finder. Using the integrated technologies, the sensor system is able to recognize small-size objects that cannot be accurately differentiated through range measurements and provides an estimate of the objects orientation. Characteristics of the system are demonstrated in the context of a robotic intervention that requires fine objects to be localized and identified for their shape and orientation.


title: On the selection of tags for tag clouds

abstract: We examine the creation of a tag cloud for exploring and understanding a set of objects (e.g., web pages, documents). In the first part of our work, we present a formal system model for reasoning about tag clouds. We then present metrics that capture the structural properties of a tag cloud, and we briefly present a set of tag selection algorithms that are used in current sites (e.g., del.icio.us, Flickr, Technorati) or that have been described in recent work. In order to evaluate the results of these algorithms, we devise a novel synthetic user model. This user model is specifically tailored for tag cloud evaluation and assumes an "ideal" user. We evaluate the algorithms under this user model, as well as the model itself, using two datasets: CourseRank (a Stanford social tool containing information about courses) and del.icio.us (a social bookmarking site). The results yield insights as to when and why certain selection schemes work best.


title: Approximate graph mining with label costs

abstract: Many real-world graphs have complex labels on the nodes and edges. Mining only exact patterns yields limited insights, since it may be hard to find exact matches. However, in many domains it is relatively easy to define a cost (or distance) between different labels. Using this information, it becomes possible to mine a much richer set of approximate subgraph patterns, which preserve the topology but allow bounded label mismatches. We present novel and scalable methods to efficiently solve the approximate isomorphism problem. We show that approximate mining yields interesting patterns in several real-world graphs ranging from IT and protein interaction networks to protein structures.


title: On the Value of Game Theory in Social Science

abstract: This article discusses the value and limitations of game theory's use in the social sciences. The role of game theory is discussed and contrasted with exaggerated expectations of the subject. The importance of a modeling dialogue between theorists and empiricists is reviewed. The basic limitations of game theory are discussed, including the rationality and intelligence assumptions and the problem of multiple equilibria. The appropriate interpretation of randomized equilibria is illustrated.


title: A Functional Model of Sensemaking in a Neurocognitive Architecture

abstract: Sensemaking is the active process of constructing a meaningful representation (i.e., making sense) of some complex aspect of the world. In relation to intelligence analysis, sensemaking is the act of finding and interpreting relevant facts amongst the sea of incoming reports, images, and intelligence. We present a cognitive model of core information-foraging and hypothesisupdating sensemaking processes applied to complex spatial probability estimation and decision-making tasks. While the model was developed in a hybrid symbolic-statistical cognitive architecture, its correspondence to neural frameworks in terms of both structure and mechanisms provided a direct bridge between rational and neural levels of description. Compared against data from two participant groups, the model correctly predicted both the presence and degree of four biases: confirmation, anchoring and adjustment, representativeness, and probability matching. It also favorably predicted human performance in generating probability distributions across categories, assigning resources based on these distributions, and selecting relevant features given a prior probability distribution. This model provides a constrained theoretical framework describing cognitive biases as arising from three interacting factors: the structure of the task environment, the mechanisms and limitations of the cognitive architecture, and the use of strategies to adapt to the dual constraints of cognition and the environment.


title: Power Electronics Converters for Wind Turbine Systems

abstract: The steady growth of installed wind power together with the upscaling of the single wind turbine power capability has pushed the research and development of power converters toward full-scale power conversion, lowered cost pr kW, increased power density, and also the need for higher reliability. In this paper, power converter technologies are reviewed with focus on existing ones and on those that have potential for higher power but which have not been yet adopted due to the important risk associated with the high-power industry. The power converters are classified into single- and multicell topologies, in the latter case with attention to series connection and parallel connection either electrical or magnetic ones (multiphase/windings machines/transformers). It is concluded that as the power level increases in wind turbines, medium-voltage power converters will be a dominant power converter configuration, but continuously cost and reliability are important issues to be addressed.


title: Predicting credit card customer churn in banks using data mining

abstract: In this paper, we solve the customer credit card churn prediction via data mining. We developed an ensemble system incorporating majority voting and involving Multilayer Perceptron (MLP), Logistic Regression (LR), decision trees (J48), Random Forest (RF), Radial Basis Function (RBF) network and Support Vector Machine (SVM) as the constituents. The dataset was taken from the Business Intelligence Cup organised by the University of Chile in 2004. Since it is a highly unbalanced dataset with 93% loyal and 7% churned customers, we employed (1) undersampling, (2) oversampling, (3) a combination of undersampling and oversampling and (4) the Synthetic Minority Oversampling Technique (SMOTE) for balancing it. Furthermore, tenfold cross-validation was employed. The results indicated that SMOTE achieved good overall accuracy. Also, SMOTE and a combination of undersampling and oversampling improved the sensitivity and overall accuracy in majority voting. In addition, the Classification and Regression Tree (CART) was used for the purpose of feature selection. The reduced feature set was fed to the classifiers mentioned above. Thus, this paper outlines the most important predictor variables in solving the credit card churn prediction problem. Moreover, the rules generated by decision tree J48 act as an early warning expert system.


title: Simulation and experiment on SIW slot array antennas

abstract: By etching longitudinal slots on the top metallic surface of the substrate integrated waveguide (SIW), an integrated slot-array antenna is proposed in this letter. The whole antenna and feeding system are fabricated on a single substrate, which takes the advantage of small size, low profile, and low cost, etc. The design process and experimental results of a four-by-four SIW slot array antenna at X-band are presented.


title: Stabilizing a Gimbal Platform using Self-Tuning Fuzzy PID Controller

abstract: The aim of the inertial stabilization system is to stabilize the sensor’s line of sight (LOS) toward a target while is isolating the sensor against the disturbances which are caused by the operating environment. The purpose which this paper is following is a model of control servo system for an inertially stabilized gimbal mechanism to improve the performance of system which is using only PID controller by finding a way to tune the PID gains online, for this purpose a self-tuning Fuzzy PID type controller is used. First the relationships that governs gimbals torque will be derived from Newton’s law under consideration of the dynamic mass unbalance and base angular motion. The next step is construction of stabilization loop through designing the proposed self-tuning Fuzzy-PID controller. For simulation and investigate the system performance in different cases of controller, the Matlab/Simulink is used and Based on different performance criteria a comparison study is made. Based on the results in different conditions on proposed controller the system performance is improved. The simulation result shows improvement on transient and the steady-state performance.


title: Using CoreSight PTM to Integrate CRA Monitoring IPs in an ARM-Based SoC

abstract: The ARM CoreSight Program Trace Macrocell (PTM) has been widely deployed in recent ARM processors for real-time debugging and tracing of software. Using PTM, the external debugger can extract execution behaviors of applications running on an ARM processor. Recently, some researchers have been using this feature for other purposes, such as fault-tolerant computation and security monitoring. This motivated us to develop an external security monitor that can detect control hijacking attacks, of which the goal is to maliciously manipulate the control flow of victim applications at an attacker’s disposal. This article focuses on detecting a special type of attack called code reuse attacks (CRA), which use a recently introduced technique that allows attackers to perform arbitrary computation without injecting their code by reusing only existing code fragments. Our external monitor is attached to the outside of the host system via the system bus and ARM CoreSight PTM, and is fed with execution traces of a victim application running on the host. As a majority of CRAs violates the normal execution behaviors of a program, our monitor constantly watches and analyzes the execution traces of the victim application and detects a symptom of attacks when the execution behaviors violate certain rules that normal applications are known to adhere. We present two different implementations for this purpose: a hardware-based solution in which all CRA detection components are implemented in hardware, and a hardware/software mixed solution that can be employed in a more resource-constrained environment where the deployment of full hardware-level CRA detection is burdensome.


Comparing UCT versus CFR in Simultaneous Games


title: Malware detection via API calls, topic models and machine learning

abstract: Dissemination of malicious code, also known as malware, poses severe challenges to cyber security. Malware authors embed software in seemingly innocuous executables, unknown to a user. The malware subsequently interacts with security-critical OS resources on the host system or network, in order to destroy their information or to gather sensitive information such as passwords and credit card numbers. Malware authors typically use Application Programming Interface (API) calls to perpetrate these crimes. We present a model that uses text mining and topic modeling to detect malware, based on the types of API call sequences. We evaluated our technique on two publicly available datasets. We observed that Decision Tree and Support Vector Machine yielded significant results. We performed t-test with respect to sensitivity for the two models and found that statistically there is no significant difference between these models. We recommend Decision Tree as it yields ‘if-then’ rules, which could be used as an early warning expert system.


title: A Customized Processing-in-Memory Architecture for Biological Sequence Alignment

abstract: Sequence alignment is the most widely used operation in bioinformatics. With the exponential growth of the biological sequence databases, searching a database to find the optimal alignment for a query sequence (that can be at the order of hundreds of millions of characters long) would require excessive processing power and memory bandwidth. Sequence alignment algorithms can potentially benefit from the processing power of massive parallel processors due their simple arithmetic operations, coupled with the inherent fine-grained and coarse-grained parallelism that they exhibit. However, the limited memory bandwidth in conventional computing systems prevents exploiting the maximum achievable speedup. In this paper, we propose a processing-in-memory architecture as a viable solution for the excessive memory bandwidth demand of bioinformatics applications. The design is composed of a set of simple and lightweight processing elements, customized to the sequence alignment algorithm, integrated at the logic layer of an emerging 3D DRAM architecture. Experimental results show that the proposed architecture results in up to 2.4x speedup and 41% reduction in power consumption, compared to a processor-side parallel implementation.


title: Spectral State Compression of Markov Processes

abstract: Model reduction of the Markov process is a basic problem in modeling state-transition systems. Motivated by the state aggregation approach rooted in control theory, we study the statistical state compression of a finite-state Markov chain from empirical trajectories. Through the lens of spectral decomposition, we study the rank and features of Markov processes, as well as properties like representability, aggregatability, and lumpability. We develop a class of spectral state compression methods for three tasks: (1) estimate the transition matrix of a low-rank Markov model, (2) estimate the leading subspace spanned by Markov features, and (3) recover latent structures of the state space like state aggregation and lumpable partition. The proposed methods provide an unsupervised learning framework for identifying Markov features and clustering states. We provide upper bounds for the estimation errors and nearly matching minimax lower bounds. Numerical studies are performed on synthetic data and a dataset of New York City taxi trips.


title: A Deep Learning Approach for Modulation Recognition via Exploiting Temporal Correlations

abstract: We consider the problem of automatic modulation recognition for either digital or analogue modulation types. The receiver does not have any knowledge about the modulation type of the received signal, and the objective of this paper is to develop a deep learning approach that can automatically recognize the modulation type of the received signal. The performance of most existing automatic modulation recognition algorithms is highly dependent on the choice of key features and classifiers. In this paper, we resort to a deep learning approach to improve the robustness for modulation recognition. Specifically, to efficiently explore the temporal and spatial correlation, we construct a deep neural network consisting of a convolutional neural network followed by a long short-term memory as the classifier. Our experimental results show that the proposed network architecture achieves a higher classification accuracy than a convolutional neural network architecture that exploits only the spatial correlation of the signal.


title: M3L: Architecture for Multimedia Information Retrieval

abstract: Multimedia information retrieval systems operate on multimedia information. Multimedia information exists in text, audio, image and video forms. Existing multimedia information retrieval systems do not satisfy multimedia information needs. Information needs are only satisfied when information is searched within all information modalities of a multimedia based document and within a user specified subsets of multimedia object types. We propose a layered based architecture for multimedia information retrieval that is able to search multimedia information within multiple modalities and all subsets of multimedia object types. Architecture follows layered based architectural approach. Architecture contains three layers which are mode, model and modality. Architecture organizes system objects within three layers and also provides communication among them. We develop a prototypical multimedia information retrieval system based on proposed architecture.


title: Auf Wiedersehen , Rhineland Model: Embedding Neoliberalism in Germany

abstract: A Thatcherite revolution never occurred in Germany. Indeed, notwithstanding considerable pressures toward convergence on the liberal Anglo-American model (Harding and Patterson 2000), emanating from economic liberalization induced by the European Union (EU) and the global dissemination of neoliberal ideology and policies, the relative institutional stability of Rhineland capitalism has been interpreted as evidence of its continuing resilience. The highly organized neocorporatist Rhineland political economy of Germany has commonly been considered a distinct coordinated or ‘nonliberal’ variety of capitalism (Albert, 1991; Streeck, 1996; Coates, 2000; Schmidt, 2002; Streeck and Yamamura 2002).


title: Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening

abstract: BACKGROUND ::: The aggressive and heterogeneous nature of lung cancer has thwarted efforts to reduce mortality from this cancer through the use of screening. The advent of low-dose helical computed tomography (CT) altered the landscape of lung-cancer screening, with studies indicating that low-dose CT detects many tumors at early stages. The National Lung Screening Trial (NLST) was conducted to determine whether screening with low-dose CT could reduce mortality from lung cancer. ::: ::: ::: METHODS ::: From August 2002 through April 2004, we enrolled 53,454 persons at high risk for lung cancer at 33 U.S. medical centers. Participants were randomly assigned to undergo three annual screenings with either low-dose CT (26,722 participants) or single-view posteroanterior chest radiography (26,732). Data were collected on cases of lung cancer and deaths from lung cancer that occurred through December 31, 2009. ::: ::: ::: RESULTS ::: The rate of adherence to screening was more than 90%. The rate of positive screening tests was 24.2% with low-dose CT and 6.9% with radiography over all three rounds. A total of 96.4% of the positive screening results in the low-dose CT group and 94.5% in the radiography group were false positive results. The incidence of lung cancer was 645 cases per 100,000 person-years (1060 cancers) in the low-dose CT group, as compared with 572 cases per 100,000 person-years (941 cancers) in the radiography group (rate ratio, 1.13; 95% confidence interval [CI], 1.03 to 1.23). There were 247 deaths from lung cancer per 100,000 person-years in the low-dose CT group and 309 deaths per 100,000 person-years in the radiography group, representing a relative reduction in mortality from lung cancer with low-dose CT screening of 20.0% (95% CI, 6.8 to 26.7; P=0.004). The rate of death from any cause was reduced in the low-dose CT group, as compared with the radiography group, by 6.7% (95% CI, 1.2 to 13.6; P=0.02). ::: ::: ::: CONCLUSIONS ::: Screening with the use of low-dose CT reduces mortality from lung cancer. (Funded by the National Cancer Institute; National Lung Screening Trial ClinicalTrials.gov number, NCT00047385.).


title: The SEMA referential framework: Avoiding ambiguities in the terms "security" and "safety"

abstract: Abstract The meaning of the terms “security” and “safety” varies considerably from one context to another, leading to potential ambiguities. These ambiguities are very problematic in the critical infrastructure protection domain, which involves multiple actors and engineering disciplines. Avoiding misunderstandings caused by the ambiguities during the early stages of system design and risk assessment can save time and resources; it also helps ensure a more consistent and complete risk coverage. Based on a review of the existing definitions of security and safety, this paper identifies the main distinctions between the two notions. It proposes a referential framework called SEMA, which makes the latent differences underlying the use of the terms security and safety explicit. Three sectors are examined as use cases: The power grid, nuclear power generation, and telecommunications and data networks. Mapping the different sector definitions of security and safety in the SEMA framework makes their respective meanings explicit and reveals inconsistencies and overlaps.


title: Towards Better Understanding the Clothing Fashion Styles: A Multimodal Deep Learning Approach.

abstract: In this paper, we aim to better understand the clothing fashion styles. There remain two challenges for us: 1) how to quantitatively describe the fashion styles of various clothing, 2) how to model the subtle relationship between visual features and fashion styles, especially considering the clothing collocations. Using the words that people usually use to describe clothing fashion styles on shopping websites, we build a Fashion Semantic Space (FSS) based on Kobayashi's aesthetics theory to describe clothing fashion styles quantitatively and universally. Then we propose a novel fashion-oriented multimodal deep learning based model, Bimodal Correlative Deep Autoencoder (BCDA) , to capture the internal correlation in clothing collocations. Employing the benchmark dataset we build with 32133 full-body fashion show images, we use BCDA to map the visual features to the FSS. The experiment results indicate that our model outperforms (+13% in terms of MSE) several alternative baselines, confirming that our model can better understand the clothing fashion styles. To further demonstrate the advantages of our model, we conduct some interesting case studies, including fashion trends analyses of brands, clothing collocation recommendation, etc.


title: TIGRESS: Trustful Inference of Gene REgulation using Stability Selection

abstract: Inferring the structure of gene regulatory networks (GRN) from gene expression data has many applications, from the elucidation of complex biological processes to the identification of potential drug targets. It is however a notoriously difficult problem, for which the many existing methods reach limited accuracy. In this paper, we formulate GRN inference as a sparse regression problem and investigate the performance of a popular feature selection method, least angle regression (LARS) combined with stability selection. We introduce a novel, robust and accurate scoring technique for stability selection, which improves the performance of feature selection with LARS. The resulting method, which we call TIGRESS (Trustful Inference of Gene REgulation using Stability Selection), was ranked among the top methods in the DREAM5 gene network reconstruction challenge. We investigate in depth the influence of the various parameters of the method and show that a fine parameter tuning can lead to significant improvements and state-of-the-art performance for GRN inference. TIGRESS reaches state-of-the-art performance on benchmark data. This study confirms the potential of feature selection techniques for GRN inference. Code and data are available on this http URL Running TIGRESS online is possible on GenePattern: this http URL


title: Utilisation of postnatal care among rural women in Nepal

abstract: BACKGROUND ::: Postnatal care is uncommon in Nepal, and where it is available the quality is often poor. Adequate utilisation of postnatal care can help reduce mortality and morbidity among mothers and their babies. Therefore, our study assessed the utilisation of postnatal care at a rural community level. ::: ::: ::: METHODS ::: A descriptive, cross-sectional study was carried out in two neighbouring villages in early 2006. A total of 150 women who had delivered in the previous 24 months were asked to participate in the study using a semi-structured questionnaire. ::: ::: ::: RESULTS ::: The proportion of women who had received postnatal care after delivery was low (34%). Less than one in five women (19%) received care within 48 hours of giving birth. Women in one village had less access to postnatal care than women in the neighbouring one. Lack of awareness was the main barrier to the utilisation of postnatal care. The woman's own occupation and ethnicity, the number of pregnancies and children and the husband's socio-economic status, occupation and education were significantly associated with the utilisation of postnatal care. Multivariate analysis showed that wealth as reflected in occupation and having attended antenatal are important factors associated with the uptake of postnatal care. In addition, women experiencing health problems appear strongly motivated to seek postnatal care. ::: ::: ::: CONCLUSION ::: The postnatal care has a low uptake and is often regarded as inadequate in Nepal. This is an important message to both service providers and health-policy makers. Therefore, there is an urgent need to assess the actual quality of postnatal care provided. Also there appears to be a need for awareness-raising programmes highlighting the availability of current postnatal care where this is of sufficient quality.


title: The Impact of Nonlinear Junction Capacitance on Switching Transient and Its Modeling for SiC MOSFET

abstract: The nonlinear junction capacitances of power devices are critical for the switching transient, which should be fully considered in the modeling and transient analysis, especially for high-frequency applications. The silicon carbide (SiC) MOSFET combined with SiC Schottky Barrier Diode (SBD) is recognized as the proposed choice for high-power and high-frequency converters. However, in the existing SiC MOSFET models only the nonlinearity of gate-drain capacitance is considered meticulously, but the drain–source capacitance, which affects the switching commutation process significantly, is generally regarded as constant. In addition, the nonlinearity of diode junction capacitance is neglected in some simplified analysis. Experiments show that without full consideration of nonlinear junction capacitances, some significant deviations between simulated and measured results will emerge in the switching waveforms. In this paper, the nonlinear characteristics of drain–source capacitance in SiC MOSFET are studied in detail, and the simplified modeling methods for engineering applications are presented. On this basis, the SiC MOSFET model is improved and the simulation results with improved model correspond with the measured results much better than before, which verify the analysis and modeling.


title: Visuo-Haptic Illusions for Improving the Perceived Performance of Shape Displays

abstract: In this work, we utilize visuo-haptic illusions to improve the perceived performance of encountered-type haptic devices, specifically shape displays, in virtual reality. Shape displays are matrices of actuated pins that travel vertically to render physical shapes; however, they have limitations such as low resolution, small display size, and low pin speed. To address these limitations, we employ illusions such as redirection, scaling, and retargeting that take advantage of the visual dominance effect, the idea that vision often dominates when senses conflict. Our evaluation of these techniques suggests that redirecting sloped lines with angles less than 40 degrees onto a horizontal line is an effective technique for increasing the perceived resolution of the display. Scaling up the virtual object onto the shape display by a factor less than 1.8x can also increase the perceived resolution. Finally, using vertical redirection a perceived 3x speed increase can be achieved.


title: Text mining and sentiment extraction in central bank documents

abstract: The deep transformation induced by the World Wide Web (WWW) revolution has thoroughly impacted a relevant part of the social interactions in our present global society. The huge amount of unstructured information available on blogs, forum and public institution web sites puts forward different challenges and opportunities. Starting from these considerations, in this paper we pursue a two-fold goal. Firstly we review some of the main methodologies employed in text mining and for the extraction of sentiment and emotions from textual sources. Secondly we provide an empirical application by considering the latest 20 issues of the Bank of Italy Governor's concluding remarks from 1996 to 2015. By taking advantage of the open source software package R, we show the following: 1) checking the word frequency distribution features of the documents; 2) extracting the evolution of the sentiment and the polarity orientation in the texts; 3) evaluating the evolution of an index for the readability and the formality level of the texts; 4) attempting to measure the popularity gained from the documents in the web. The results of the empirical analysis show the feasibility in extracting the main topics from the considered corpus. Moreover it is shown how to check for positive and negative terms in order to gauge the polarity of statements and whole documents. The evaluation of these synthetic indexes is quite relevant for increasing the transparency of the central banks' communications.


title: Driver Sleepiness Detection System Based on Eye Movements Variables

abstract: Driver sleepiness is a hazard state, which can easily lead to traffic accidents. To detect driver sleepiness in real time, a novel driver sleepiness detection system using support vector machine (SVM) based on eye movements is proposed. Eye movements data are collected using SmartEye system in a driving simulator experiment. Characteristic parameters, which include blinking frequency, gaze direction, fixation time, and PERCLOS, are extracted based on the data using a statistical method. 13 sleepiness detection models including 12 specific models and 1 general model are developed based on SVM. Experimental results demonstrate that eye movements can be used to detect driver sleepiness in real time. The detecting accuracy of the specific models significantly exceeds the general model (P < 0.001), suggesting that individual differences are an important consideration when building detection algorithms for different drivers.


title: Statistical Learning Theory: Models, Concepts, and Results

abstract: Publisher Summary Statistical learning theory is regarded as one of the most beautifully developed branches of artificial intelligence. It provides the theoretical basis for many of today's machine learning algorithms. The theory helps to explore what permits to draw valid conclusions from empirical data. This chapter provides an overview of the key ideas and insights of statistical learning theory. The statistical learning theory begins with a class of hypotheses and uses empirical data to select one hypothesis from the class. If the data generating mechanism is benign, then it is observed that the difference between the training error and test error of a hypothesis from the class is small. The statistical learning theory generally avoids metaphysical statements about aspects of the true underlying dependency, and thus is precise by referring to the difference between training and test error. The chapter also describes some other variants of machine learning.


title: A hybrid computational approach to derive new ground-motion prediction equations

abstract: A novel hybrid method coupling genetic programming and orthogonal least squares, called GP/OLS, was employed to derive new ground-motion prediction equations (GMPEs). The principal ground-motion parameters formulated were peak ground acceleration (PGA), peak ground velocity (PGV) and peak ground displacement (PGD). The proposed GMPEs relate PGA, PGV and PGD to different seismic parameters including earthquake magnitude, earthquake source to site distance, average shear-wave velocity, and faulting mechanisms. The equations were established based on an extensive database of strong ground-motion recordings released by Pacific Earthquake Engineering Research Center (PEER). For more validity verification, the developed equations were employed to predict the ground-motion parameters of the Iranian plateau earthquakes. A sensitivity analysis was carried out to determine the contributions of the parameters affecting PGA, PGV and PGD. The sensitivity of the models to the variations of the influencing parameters was further evaluated through a parametric analysis. The obtained GMPEs are effectively capable of estimating the site ground-motion parameters. The equations provide a prediction performance better than or comparable with the attenuation relationships found in the literature. The derived GMPEs are remarkably simple and straightforward and can reliably be used for the pre-design purposes.


title: Incorporating modeling uncertainties in the assessment of seismic collapse risk of buildings

abstract: The primary goal of seismic provisions in building codes is to protect life safety through the prevention of structural collapse. To evaluate the extent to which current and past building code provisions meet this objective, the authors have conducted detailed assessments of collapse risk of reinforced-concrete moment frame buildings, including both ‘ductile’ frames that conform to current building code requirements, and ‘non-ductile’ frames that are designed according to out-dated (pre-1975) building codes. Many aspects of the assessment process can have a significant impact on the evaluated collapse performance; this study focuses on methods of representing modeling parameter uncertainties in the collapse assessment process. Uncertainties in structural component strength, stiffness, deformation capacity, and cyclic deterioration are considered for non-ductile and ductile frame structures of varying heights. To practically incorporate these uncertainties in the face of the computationally intensive nonlinear response analyses needed to simulate collapse, the modeling uncertainties are assessed through a response surface, which describes the median collapse capacity as a function of the model random variables. The response surface is then used in conjunction with Monte Carlo methods to quantify the effect of these modeling uncertainties on the calculated collapse fragilities. Comparisons of the response surface based approach and a simpler approach, namely the first-order second-moment (FOSM) method, indicate that FOSM can lead to inaccurate results in some cases, particularly when the modeling uncertainties cause a shift in the prediction of the median collapse point. An alternate simplified procedure is proposed that combines aspects of the response surface and FOSM methods, providing an efficient yet accurate technique to characterize model uncertainties, accounting for the shift in median response. The methodology for incorporating uncertainties is presented here with emphasis on the collapse limit state, but is also appropriate for examining the effects of modeling uncertainties on other structural limit states.


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